Food-energy-water nexus optimization brings substantial reduction of urban resource consumption and greenhouse gas emissions.

Urban sustainability is a key to achieving the UN sustainable development goals (SDGs). Secure and efficient provision of food, energy, and water (FEW) resources is a critical strategy for urban sustainability. While there has been extensive discussion on the positive effects of the FEW nexus on resource efficiency and climate impacts, measuring the extent to which such synergy can benefit urban sustainability remains challenging. Here, we have developed a systematic and integrated optimization framework to explore the potential of the FEW nexus in reducing urban resource demand and greenhouse gas (GHG) emissions. Demonstrated using the Metropolis Beijing, we have identified that the optimized FEW nexus can reduce resource consumption and GHG emissions by 21.0 and 29.1%, respectively. These reductions come with increased costs compared to the siloed FEW management, but it still achieved a 16.8% reduction in economic cost compared to the business-as-usual scenario. These findings underscore the significant potential of FEW nexus management in enhancing urban resource efficiency and addressing climate impacts, while also identifying strategies to address trade-offs and increase synergies.

Systematic elucidation of independently modulated genes in Lactiplantibacillus plantarum reveals a trade-off between secondary and primary metabolism.

Lactiplantibacillus plantarum is a probiotic bacterium widely used in food and health industries, but its gene regulatory information is limited in existing databases, which impedes the research of its physiology and its applications. To obtain a better understanding of the transcriptional regulatory network of L. plantarum, independent component analysis of its transcriptomes was used to derive 45 sets of independently modulated genes (iModulons). Those iModulons were annotated for associated transcription factors and functional pathways, and active iModulons in response to different growth conditions were identified and characterized in detail. Eventually, the analysis of iModulon activities reveals a trade-off between regulatory activities of secondary and primary metabolism in L. plantarum.

Network Pharmacology and Experimental Validation of the Anti-Inflammatory Effect of Tingli Dazao Xiefei Decoction in Acute Lung Injury Treatment.

Purpose: Tingli Dazao Xiefei Decoction (TDXD) is a Traditional Chinese Medicine (TCM) formula used to treat acute lung injury (ALI). However, the precise mechanism of TDXD in treating ALI remains unclear. We investigated the therapeutic mechanism of TDXD against ALI using a complementary approach combining network pharmacology, molecular docking, and in vitro and in vivo experiments. Material and Methods: Potential drug targets of TDXD and relevant target genes associated with ALI were retrieved from Chinese medicines and disease genes databases. Bioinformatics technology was employed to screen potential active ingredients and core targets. Validation experiments were conducted using a lipopolysaccharide (LPS)-induced ALI mouse (C57BL/6J) model, LPS-induced inflammatory RAW264.7 cells, and molecular docking between active compounds of TDXD and potential targets. Results: Network pharmacology suggested that the mechanism of TDXD against ALI involved phosphoinositide 3-kinase (PI3K) / protein kinase B (AKT) / phosphatase and tensin homolog (PTEN) and Janus kinase 2 (JAK2) / signal transducer and activator of transcription 3 (STAT3) pathways. Quercetin, ß-sitosterol, kaempferol, isorhamnetin, and L-stepholidine were identified as the main active compounds of TDXD that exerted anti-ALI effects. Molecular docking indicated that these compounds exhibited good binding capabilities (≤ -5kcal/mol) to key targets in PI3K/AKT/PTEN and JAK2/STAT3 signaling pathways. In the animal model, TDXD alleviated injuries and inflammatory responses in lung tissues, accompanied by inhibition of expression of tumor necrosis factor-α (TNF-α), Interleukin-6 (IL-6), STAT3, and Suppressor of Cytokine Signaling 3 (SOCS3) mRNA, and key proteins in PI3K/AKT/PTEN and JAK2/STAT3 pathways (all P values < 0.05). Cell based experiments showed that TDXD dose-dependently inhibited the expression of essential proteins in PI3K/AKT/PTEN and JAK2/STAT3 pathways (P < 0.05). Conclusion: This study revealed that the mechanism of TDXD in ALI treatment might involve simultaneous regulation of PI3K/AKT/PTEN and JAK2/STAT3 pathways.

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook.

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

Dynamic metagenome-scale metabolic modeling of a yogurt bacterial community.

Genome-scale metabolic models and flux balance analysis (FBA) have been extensively used for modeling and designing bacterial fermentation. However, FBA-based metabolic models that accurately simulate the dynamics of coculture are still rare, especially for lactic acid bacteria used in yogurt fermentation. To investigate metabolic interactions in yogurt starter culture of Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus, this study built a dynamic metagenome-scale metabolic model which integrated constrained proteome allocation. The accuracy of the model was evaluated by comparing predicted bacterial growth, consumption of lactose and production of lactic acid with reference experimental data. The model was then used to predict the impact of different initial bacterial inoculation ratios on acidification. The dynamic simulation demonstrated the mutual dependence of S. thermophilus and L. d. bulgaricus during the yogurt fermentation process. As the first dynamic metabolic model of the yogurt bacterial community, it provided a foundation for the computer-aided process design and control of the production of fermented dairy products.

Global green hydrogen-based steel opportunities surrounding high quality renewable energy and iron ore deposits.

The steel sector currently accounts for 7% of global energy-related CO2 emissions and requires deep reform to disconnect from fossil fuels. Here, we investigate the market competitiveness of one of the widely considered decarbonisation routes for primary steel production: green hydrogen-based direct reduction of iron ore followed by electric arc furnace steelmaking. Through analysing over 300 locations by combined use of optimisation and machine learning, we show that competitive renewables-based steel production is located nearby the tropic of Capricorn and Cancer, characterised by superior solar with supplementary onshore wind, in addition to high-quality iron ore and low steelworker wages. If coking coal prices remain high, fossil-free steel could attain competitiveness in favourable locations from 2030, further improving towards 2050. Large-scale implementation requires attention to the abundance of suitable iron ore and other resources such as land and water, technical challenges associated with direct reduction, and future supply chain configuration.

Quorum sensing-based interactions among drugs, microbes, and diseases.

Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

Development of a system model to predict flows and performance of regional waste management planning: A case study of England.

Significant loss of valuable resources and increasing burdens on landfills are often associated with a lack of proper planning in waste management and resource recovery strategy. A sustainable waste management model is thus urgently needed to improve resource efficiency and divert more waste from landfills. This paper proposes a comprehensive system model using stock-and-flow diagram to examine the current waste management performance and project the future waste generation, treatment and disposal scenarios, using England as a case study. The model comprises three integrated modules to represent household waste generation and collection; waste treatment and disposal; and energy recovery. A detailed mass and energy balance has been established and waste management performance has been evaluated using six upstream and downstream indicators. The base case scenario that assumes constant waste composition shows that waste to landfills can be reduced to less than 10% of the total amount, by 2035. However, it entails greater diversion of waste to energy-from-waste facilities, which is not sustainable and would incur higher capital investment and gate fees. Alternative case scenarios that promote recycling instead of energy recovery result in lower capital investment and gate fees. Complete elimination of the food and organic fraction from the residual waste stream will help meet the 65% recycling target by 2035. In light of the need for achieving a more circular economy in England, enhancing material recovery through reuse and recycling, reducing reliance on energy-from-waste and deploying more advanced waste valorisation technologies should be considered in future policy and planning for waste management.

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates.

The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict ${k}_{cat}$ and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on ${k}_{cat}$ values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.

Integrated network pharmacology and intestinal flora analysis to determine the protective effect of Xuanbai-Chengqi decoction on lung and gut injuries in influenza virus-infected mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Xuanbai-Chengqi decoction (XBCQ) is a traditional Chinese medicine (TCM) compound used in the treatment of pulmonary infection in China. Despite the popular usage of XBCQ, its underlying protective roles and the associated molecular mechanisms with the gut-lung axis in influenza remain unclear. AIM OF THE STUDY: We aimed to explore the protective effects and the underlying mechanism of XBCQ efficacy on lung and intestine injuries induced by influenza A virus as well as to identify the main active components through integrated network pharmacology, intestinal flora analysis and pathway validation. MATERIALS AND METHODS: The potential active components and therapeutic targets of XBCQ in the treatment of influenza were hypothesized through a series of network pharmacological strategies, including components screening, targets prediction and bioinformatics analysis. Inflammatory cytokines and pathway proteins were assayed to validate the results of network pharmacology. Then the mechanism of XBCQ alleviating lung and intestine injuries was further explored via intestinal flora analysis. The important role of Rhubarb in the formula was verified by removing Rhubarb. RESULTS: XBCQ could significantly improve the survival rate in IAV-infected mice. The network pharmacology results demonstrated that JUN, mitogen-activated protein kinase (MAPK), and tumor necrosis factor (TNF) are the key targets of XBCQ that can be useful in influenza treatment as it contains the core components luteolin, emodin, and aloe-emodin, which are related to the pathways of TNF, T-cell receptor (TCR), and NF-κB. Verification experiments demonstrated that XBCQ could significantly alleviate the immune injury of the lungs and the gut of the mice, which is attributable to the inhibition of the release of inflammatory cytokines (such as TNF-α, IL-6, and IL-1ß), the downregulation of the protein expression levels of Toll-like receptors-7 (TLR7), MyD88, and p-NF-κB65, and the reduction in the relative abundance of Enterobacteriaceae and Proteus, while an increase in that of Firmicutes and Lachnospiraceae. The overall protective role of XBCQ contributing to the treatment of the lungs and the gut was impaired when Rhubarb was removed from XBCQ. CONCLUSIONS: Our results suggest that the efficacy of XBCQ is related to the inhibition of the immune injury and remodeling of the intestinal flora, wherein Rhubarb plays an important role, which cumulatively provide the evidence applicable for the treatment of viral pneumonia induced by a different respiratory virus with XBCQ.

Machine learning aided construction of the quorum sensing communication network for human gut microbiota.

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database ( http://www.qshgm.lbci.net/ ) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.

Impact of fast-track regulatory designations on strategic commercialization decisions for autologous cell therapies.

Background: Regulatory authorities around the world have introduced incentives to improve the speed-to-market of innovative therapies. Aim & methods: To better understand the capacity and portfolio planning decisions of autologous cell therapies and particularly the impact of fast-tracking designations, this paper describes a mixed-integer linear programming approach for the optimization of capacity investment and portfolio selection decisions to maximize the net present value of a candidate portfolio of therapies under different regulatory programs. Results: The illustrative example shows that fast-track designations allow a 25% earlier breakeven, 42-86% higher net present value over a 20-year horizon with earlier upfront capital and reduce the portfolio's sensitivity to uncertainties. Conclusion: Fast-track designations are effective in providing commercialization incentives, but high capital risks given the compressed timeline should be better considered.

Kinetics-informed global assessment of mine tailings for CO2 removal.

Chemically reactive mine tailings are a potential resource for drawing down carbon dioxide out of the atmosphere in mineral weathering schemes. Such carbon dioxide removal (CDR) systems, applied on a large scale, could help to meet internationally agreed targets for minimising climate change, but crucially we need to identify what materials could react fast enough to provide CDR at relevant climate change mitigation timescales. This study focuses on a range of silicate-dominated tailings, calculating their CDR potential from their chemical composition (specific capacity), estimated global production rates, and the speed of weathering under different reaction conditions. Tailings containing high abundances of olivine, serpentine and diopside show the highest CDR potential due to their favourable kinetics. We conclude that the most suitable tailings for CDR purposes are those associated with olivine dunites, diamond kimberlites, asbestos and talc serpentinites, Ni sulphides, and PGM layered mafic intrusions. We estimate the average annual global CDR potential of tailings weathered over the 70-year period 2030-2100 to be ~93 (unimproved conditions) to 465 (improved conditions) Mt/year. Results indicate that at least 30 countries possess tailings materials that, under improved conditions, may offer a route for CDR which is not currently utilised within the mining industry. By 2100, the total cumulative CDR could reach some 33 GtCO2, of which more than 60% is contributed by PGM tailings produced in Southern Africa, Russia, and North America. The global CDR potential could be increased by utilization of historic tailings and implementing measures to further enhance chemical reaction rates. If practical considerations can be addressed and enhanced weathering rates can be achieved, then CDR from suitable tailings could contribute significantly to national offset goals and global targets. More research is needed to establish the potential and practicality of this technology, including measurements of the mineral weathering kinetics under various conditions.

Regional conditions shape the food-energy-land nexus of low-carbon indoor farming.

Modern greenhouses and vertical farming projects promise increased food output per unit area relative to open-field farming. However, their high energy consumption calls for a low-carbon power supply such as solar photovoltaic and wind, which adds to cost and overall land footprint. Here we use geospatial and mathematical modelling to compare open-field and two indoor farming methods for vegetable production in nine city-regions chosen globally with varying land availability, climatic conditions and population density. We find that renewable electricity supply is more costly for greenhouses per unit energy demand satisfied, which is due to the greater fluctuation in their energy demand profile. However, greenhouses have a lower energy demand per unit food output, which makes them the least land-intensive option in most of the analysed regions. Our results challenge the land-savings claims of vertical farming compared with open-field production. We also show that regionalizing vegetable supply is feasible in most regions and give recommendations based on the regional context.

Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis.

BACKGROUND: The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings. RESULTS: In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors' intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology. CONCLUSIONS: We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of 'protein costs' are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.

Combinational quorum sensing devices for dynamic control in cross-feeding cocultivation.

Quorum sensing (QS) offers cell density dependent dynamic regulations in cell culture through devices such as synchronized lysis circuit (SLC) and metabolic toggle switch (MTS). However, there is still a lack of studies on cocultivation with a combination of different QS-based devices. Taking the production of isopropanol and salidroside as case studies, we have mathematically modeled a comprehensive set of QS-regulated cocultivation schemes and constructed experimental combinations of QS devices, respectively, to evaluate their feasibility and optimality for regulating growth competition and corporative production. Glucose split ratio is proposed for the analysis of competition between cell growth and targeted production. Results show that the combination of different QS devices across multiple members offers a new tool with the potential to effectively coordinate synthetic microbial consortia for achieving high product titer in cross-feeding cocultivation. It is also evident that the performance of such systems is significantly affected by dynamic characteristics of chosen QS devices, carbon source control and the operational settings. This study offers insights for future applications of combinational QS devices in synthetic microbial consortia.

Mechanism of protective effect of xuan-bai-cheng-qi decoction on LPS-induced acute lung injury based on an integrated network pharmacology and RNA-sequencing approach.

Xuan-bai-cheng-qi decoction (XCD), a traditional Chinese medicine (TCM) prescription, has been widely used to treat a variety of respiratory diseases in China, especially to seriously infectious diseases such as acute lung injury (ALI). Due to the complexity of the chemical constituent, however, the underlying pharmacological mechanism of action of XCD is still unclear. To explore its protective mechanism on ALI, firstly, a network pharmacology experiment was conducted to construct a component-target network of XCD, which identified 46 active components and 280 predicted target genes. Then, RNA sequencing (RNA-seq) was used to screen differentially expressed genes (DEGs) between ALI model rats treated with and without XCD and 753 DEGs were found. By overlapping the target genes identified using network pharmacology and DEGs using RNA-seq, and subsequent protein-protein interaction (PPI) network analysis, 6 kernel targets such as vascular epidermal growth factor (VEGF), mammalian target of rapamycin (mTOR), AKT1, hypoxia-inducible factor-1α (HIF-1α), and phosphoinositide 3-kinase (PI3K) and gene of phosphate and tension homology deleted on chromsome ten (PTEN) were screened out to be closely relevant to ALI treatment. Verification experiments in the LPS-induced ALI model rats showed that XCD could alleviate lung tissue pathological injury through attenuating proinflammatory cytokines release such as tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1ß. Meanwhile, both the mRNA and protein expression levels of PI3K, mTOR, HIF-1α, and VEGF in the lung tissues were down-regulated with XCD treatment. Therefore, the regulations of XCD on PI3K/mTOR/HIF-1α/VEGF signaling pathway was probably a crucial mechanism involved in the protective mechanism of XCD on ALI treatment.

Comparison between centralized and decentralized supply chains of autologous chimeric antigen receptor T-cell therapies: a UK case study based on discrete event simulation.

BACKGROUND AIMS: Decentralized, or distributed, manufacturing that takes place close to the point of care has been a manufacturing paradigm of heightened interest within the cell therapy domain because of the product's being living cell material as well as the need for a highly monitored and temperature-controlled supply chain that has the potential to benefit from close proximity between manufacturing and application. METHODS: To compare the operational feasibility and cost implications of manufacturing autologous chimeric antigen receptor T (CAR T)-cell products between centralized and decentralized schemes, a discrete event simulation model was built using ExtendSIM 9 for simulating the patient-to-patient supply chain, from the collection of patient cells to the final administration of CAR T therapy in hospitals. Simulations were carried out for hypothetical systems in the UK using three demand levels-low (100 patients per annum), anticipated (200 patients per annum) and high (500 patients per annum)-to assess resource allocation, cost per treatment and system resilience to demand changes and to quantify the risks of mix-ups within the supply chain for the delivery of CAR T treatments. RESULTS: The simulation results show that although centralized manufacturing offers better economies of scale, individual facilities in a decentralized system can spread facility costs across a greater number of treatments and better utilize resources at high demand levels (annual demand of 500 patients), allowing for an overall more comparable cost per treatment. In general, raw material and consumable costs have been shown to be one of the greatest cost drivers, and genetic modification-associated costs have been shown to account for over one third of raw material and consumable costs. Turnaround time per treatment for the decentralized scheme is shown to be consistently lower than its centralized counterpart, as there is no need for product freeze-thaw, packaging and transportation, although the time savings is shown to be insignificant in the UK case study because of its rather compact geographical setting with well-established transportation networks. In both schemes, sterility testing lies on the critical path for treatment delivery and is shown to be critical for treatment turnaround time reduction. CONCLUSIONS: Considering both cost and treatment turnaround time, point-of-care manufacturing within the UK does not show great advantages over centralized manufacturing. However, further simulations using this model can be used to understand the feasibility of decentralized manufacturing in a larger geographical setting.

Emodin Inhibits Lipopolysaccharide-Induced Inflammation by Activating Autophagy in RAW 264.7 Cells.

OBJECTIVE: To investigate the effects of emodin on inflammation and autophagy in lipopolysaccharide (LPS)-induced RAW 264.7 macrophages and reveal its underlying mechanism. METHODS: 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay was conducted to find the appropriate dose for emodin. RAW264.7 cells pretreated with different concentrations (0-50 µmol/L) of emodin or vehicle for 2 h prior to exposure to LPS for 16 h. Cell morphology was examined and propidium iodide staining was used to examine cell cycle. Expressions of inflammation-related proteins [nuclear factor-kappaB (NF-κ B) and I-kappaB (I κ B)α] and autophagy-related proteins [light chain (LC)3, P62/sequestosome 1, mammalian target of rapamycin (mTOR), and p-mTOR] were examined using Western blot analysis. Expression of inflammation-related cytokines including tumor necrosis factor (TNF)-α, interleukin (IL)-1ß and IL-6 were detected by enzyme-linked immunosorbent assay. Autophagy was examined with LC3B fluorescence intensity and aggregation. The effect of emodin on autophagy was conducted with an autophagy inhibitor, 3-methyladenine (3-MA). RESULTS: The expression of NF-κ B in LPS-induced cells was significantly increased (P<0.01) and simultaneously I κ B α decreased compared with the normal cell (P<0.05). The expressions of TNF-α, IL-ß, and IL-6 proteins in the LPS-induced RAW264.7 cells were significantly higher than in the normal cell (P<0.05 or P<0.01). LPS increased the percentage of cells in the G0/G1 phase, which was recovered by emodin at different doses (12.5, 25, and 50µ mol/L, P<0.05 or P<0.01). The medium-dose (25 µ ml/L) emodin decreased the expressions of NF-κ B, P62 and p-mTOR (P<0.01) and increased I κ B α expression, LC3B II/I ratio as well as LC3B fluorescence intensity (P<0.05 or P<0.01). Meanwhile, the enhanced autophagic effects of emodin, such as the increment of LC3B II/ratio and the decrement of P62 expression, were suppressed by autophagy inhibitor 3-MA. CONCLUSION: Emodin could inhibit inflammation of mice RAW264.7 macrophages induced by LPS, possibly through activating autophagy.

QSIdb: quorum sensing interference molecules.

Quorum sensing interference (QSI), the disruption and manipulation of quorum sensing (QS) in the dynamic control of bacteria populations could be widely applied in synthetic biology to realize dynamic metabolic control and develop potential clinical therapies. Conventionally, limited QSI molecules (QSIMs) were developed based on molecular structures or for specific QS receptors, which are in short supply for various interferences and manipulations of QS systems. In this study, we developed QSIdb (http://qsidb.lbci.net/), a specialized repository of 633 reported QSIMs and 73 073 expanded QSIMs including both QS agonists and antagonists. We have collected all reported QSIMs in literatures focused on the modifications of N-acyl homoserine lactones, natural QSIMs and synthetic QS analogues. Moreover, we developed a pipeline with SMILES-based similarity assessment algorithms and docking-based validations to mine potential QSIMs from existing 138 805 608 compounds in the PubChem database. In addition, we proposed a new measure, pocketedit, for assessing the similarities of active protein pockets or QSIMs crosstalk, and obtained 273 possible potential broad-spectrum QSIMs. We provided user-friendly browsing and searching facilities for easy data retrieval and comparison. QSIdb could assist the scientific community in understanding QS-related therapeutics, manipulating QS-based genetic circuits in metabolic engineering, developing potential broad-spectrum QSIMs and expanding new ligands for other receptors.