Enhancing insights into diseases through horizontal gene transfer event detection from gut microbiome.

Horizontal gene transfer (HGT) phenomena pervade the gut microbiome and significantly impact human health. Yet, no current method can accurately identify complete HGT events, including the transferred sequence and the associated deletion and insertion breakpoints from shotgun metagenomic data. Here, we develop LocalHGT, which facilitates the reliable and swift detection of complete HGT events from shotgun metagenomic data, delivering an accuracy of 99.4%-verified by Nanopore data-across 200 gut microbiome samples, and achieving an average F1 score of 0.99 on 100 simulated data. LocalHGT enables a systematic characterization of HGT events within the human gut microbiome across 2098 samples, revealing that multiple recipient genome sites can become targets of a transferred sequence, microhomology is enriched in HGT breakpoint junctions (P-value = 3.3e-58), and HGTs can function as host-specific fingerprints indicated by the significantly higher HGT similarity of intra-personal temporal samples than inter-personal samples (P-value = 4.3e-303). Crucially, HGTs showed potential contributions to colorectal cancer (CRC) and acute diarrhoea, as evidenced by the enrichment of the butyrate metabolism pathway (P-value = 3.8e-17) and the shigellosis pathway (P-value = 5.9e-13) in the respective associated HGTs. Furthermore, differential HGTs demonstrated promise as biomarkers for predicting various diseases. Integrating HGTs into a CRC prediction model achieved an AUC of 0.87.

Improving the production of carbamoyltobramycin by an industrial Streptoalloteichus tenebrarius through metabolic engineering.

Tobramycin is an essential and extensively used broad-spectrum aminoglycoside antibiotic obtained through alkaline hydrolysis of carbamoyltobramycin, one of the fermentation products of Streptoalloteichus tenebrarius. To simplify the composition of fermentation products from industrial strain, the main byproduct apramycin was blocked by gene disruption and constructed a mutant mainly producing carbamoyltobramycin. The generation of antibiotics is significantly affected by the secondary metabolism of actinomycetes which could be controlled by modifying the pathway-specific regulatory proteins within the cluster. Within the tobramycin biosynthesis cluster, a transcriptional regulatory factor TobR belonging to the Lrp/AsnC family was identified. Based on the sequence and structural characteristics, tobR might encode a pathway-specific transcriptional regulatory factor during biosynthesis. Knockout and overexpression strains of tobR were constructed to investigate its role in carbamoyltobramycin production. Results showed that knockout of TobR increased carbamoyltobramycin biosynthesis by 22.35%, whereas its overexpression decreased carbamoyltobramycin production by 10.23%. In vitro electrophoretic mobility shift assay (EMSA) experiments confirmed that TobR interacts with DNA at the adjacent tobO promoter position. Strains overexpressing tobO with ermEp* promoter exhibited 36.36% increase, and tobO with kasOp* promoter exhibited 22.84% increase in carbamoyltobramycin titer. When the overexpressing of tobO and the knockout of tobR were combined, the production of carbamoyltobramycin was further enhanced. In the shake-flask fermentation, the titer reached 3.76 g/L, which was 42.42% higher than that of starting strain. Understanding the role of Lrp/AsnC family transcription regulators would be useful for other antibiotic biosynthesis in other actinomycetes. KEY POINTS: • The transcriptional regulator TobR belonging to the Lrp/AsnC family was identified.  • An oxygenase TobO was identified within the tobramycin biosynthesis cluster. • TobO and TobR have significant effects on the synthesis of carbamoyltobramycin.

XylR Overexpression in Escherichia coli Alleviated Transcriptional Repression by Arabinose and Enhanced Xylitol Bioproduction from Xylose Mother Liquor.

Xylitol is a polyol widely used in food, pharmaceuticals, and light industries. It is currently produced through the chemical catalytic hydrogenation of xylose and generates xylose mother liquor as a substantial byproduct in the procedure of xylose extraction. If xylose mother liquor could also be efficiently bioconverted to xylitol, the greenness and atom economy of xylitol production would be largely improved. However, xylose mother liquor contains a mixture of glucose, xylose, and arabinose, raising the issue of carbon catabolic repression in its utilization by microbial conversion. Targeting this challenge, the transcriptional activator XylR was overexpressed in a previously constructed xylitol-producing E. coli strain CPH. The resulting strain CPHR produced 16.61 g/L of xylitol in shake-flask cultures from the mixture of corn cob hydrolysate and xylose mother liquor (1:1, v/v) with a xylose conversion rate of 90.1%, which were 2.23 and 2.15 times higher than the starting strain, respectively. Furthermore, XylR overexpression upregulated the expression levels of xylE, xylF, xylG, and xylH genes by 2.08-2.72 times in arabinose-containing medium, suggesting the alleviation of transcriptional repression of xylose transport genes by arabinose. This work lays the foundation for xylitol bioproduction from xylose mother liquor.

Highly efficient biosynthesis of salidroside by a UDP-glucosyltransferase-catalyzed cascade reaction.

OBJECTIVE: Salidroside is an important plant-derived aromatic compound with diverse biological properties. The main objective of this study was to synthesize salidroside from tyrosol using UDP-glucosyltransferase (UGT) with in situ regeneration of UDP-glucose (UDPG). RESULTS: The UDP-glucosyltransferase 85A1 (UGT85A1) from Arabidopsis thaliana, which showed high activity and regioselectivity towards tyrosol, was selected for the production of salidroside. Then, an in vitro cascade reaction for in situ regeneration of UDPG was constructed by coupling UGT85A1 to sucrose synthase from Glycine max (GmSuSy). The optimal UGT85A1-GmSuSy activity ratio of 1:2 was determined to balance the efficiency of salidroside production and UDP-glucose regeneration. Different cascade reaction conditions for salidroside production were also determined. Under the optimized condition, salidroside was produced at a titer of 6.0 g/L with a corresponding molar conversion of 99.6% and a specific productivity of 199.1 mg/L/h in a continuous feeding reactor. CONCLUSION: This is the highest salidroside titer ever reported so far using biocatalytic approach.

The alleviating effect of ellagic acid on DSS-induced colitis via regulating gut microbiomes and gene expression of colonic epithelial cells.

The anti-inflammatory effect of ellagic acid (EA) and its possible underlying mechanism in dextran sulfate sodium (DSS)-induced mouse chronic colonic inflammation were studied. It was observed that EA administration significantly alleviated the colonic inflammation phenotypes, including decreasing the disease activity index (DAI), enhancing the body weight loss, and improving the shortened length of the colon and pathological damage of colon tissue. Additionally, EA reshaped the constitution of the gut microbiota by elevating the ratio of Bacteroidetes along with Bacteroides and Muribaculaceae, while decreasing the proportion of Firmicutes. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) revealed that the metabolic function of the gut microbiota was also changed. Furthermore, mouse colon transcriptome analysis showed that the tight junction and peroxisome proliferator-activated receptor (PPAR) signaling pathways were activated and the expressions of related genes were upregulated after EA intervention. These results showed that EA could remodel the gut bacterial composition, change the intestinal epithelial cell gene expressions in mice, and consequently improve the colonic inflammatory symptoms.

Compressible viscoelasticity of cell membranes determined by gigahertz-frequency acoustic vibrations.

Membrane viscosity is an important property of cell biology, which determines cellular function, development and disease progression. Various experimental and computational methods have been developed to investigate the mechanics of cells. However, there have been no experimental measurements of the membrane viscosity at high-frequencies in live cells. High frequency measurements are important because they can probe viscoelastic effects. Here, we investigate the membrane viscosity at gigahertz-frequencies through the damping of the acoustic vibrations of gold nanoplates. The experiments are modeled using a continuum mechanics theory which reveals that the membranes display viscoelasticity, with an estimated relaxation time of ca. 5.7 + 2.4 / - 2.7 ps. We further demonstrate that membrane viscoelasticity can be used to differentiate a cancerous cell line (the human glioblastoma cells LN-18) from a normal cell line (the mouse brain microvascular endothelial cells bEnd.3). The viscosity of cancerous cells LN-18 is lower than that of healthy cells bEnd.3 by a factor of three. The results indicate promising applications of characterizing membrane viscoelasticity at gigahertz-frequency in cell diagnosis.

Dextran-Sulfate-Sodium-Induced Colitis-Ameliorating Effect of Aqueous Phyllanthus emblica L. Extract through Regulating Colonic Cell Gene Expression and Gut Microbiomes.

The anti-inflammation effect of aqueous Phyllanthus emblica L. extract (APE) and its possible underlying mechanism in dextran sulfate sodium (DSS)-induced mice chronic colonic inflammation were studied. APE treatment significantly improved the colitic symptoms, including ameliorating the shortening of the colon, increasing the DSS-induced body weight loss, reducing the disease activity index, and reversing the condition of colon tissue damage of mucus lost and goblet cell reduction. Overproduction of serum pro-inflammatory cytokines were suppressed by the treatment of APE. Gut microbiome analysis showed that APE remodeled the structure of gut bacteria in phylum and genus levels, upregulating the abundance of phylum Bacteroidetes, family Muribaculaceae, and genus Bacteroides and downregulating the abundance of phylum Firmicutes. The reshaped gut microbiome caused metabolic functions and pathway change with enhanced queuosine biosynthesis and reduced polyamine synthesis pathway. Colon tissue transcriptome analysis further elucidated APE-inhibited mitogen-activated protein kinase (MAPK), cytokine-cytokine receptor interaction, and tumor necrosis factor (TNF) signaling pathways and the expressions of the genes that promote the progress of colorectal cancer. It turned out that APE reshaped the gut microbiome and inhibited MAPK, cytokine-cytokine receptor interaction, and TNF signaling pathways as well as the colorectal-cancer-related genes to exert its colitis protective effect.

Manipulation of sterol homeostasis for the production of 24-epi-ergosterol in industrial yeast.

Brassinolide (BL) is the most biologically active compound among natural brassinosteroids. However, the agricultural applications are limited by the extremely low natural abundance and the scarcity of synthetic precursors. Here, we employ synthetic biology to construct a yeast cell factory for scalable production of 24-epi-ergosterol, an un-natural sterol, proposed as a precursor for BL semi-synthesis. First, we construct an artificial pathway by introducing a Δ24(28) sterol reductase from plants (DWF1), followed by enzyme directed evolution, to enable de novo biosynthesis of 24-epi-ergosterol in yeast. Subsequently, we manipulate the sterol homeostasis (overexpression of ARE2, YEH1, and YEH2 with intact ARE1), maintaining a balance between sterol acylation and sterol ester hydrolysis, for the production of 24-epi-ergosterol, whose titer reaches to 2.76 g L-1 using fed-batch fermentation. The sterol homeostasis engineering strategy can be applicable for bulk production of other economically important phytosterols.

Aqueous Extract of Phyllanthus emblica L. Alleviates Functional Dyspepsia through Regulating Gastrointestinal Hormones and Gut Microbiome In Vivo.

Phyllanthus emblica L. fruits were extracted by a hot water assistant with ultrasonication to obtain aqueous Phyllanthus emblica L. extract (APE). The ameliorating functional dyspepsia (FD) effect of a low dose (150 mg/kg) and a high dose (300 mg/kg) of APE was exhibited by determining the gastrointestinal motility, gastrointestinal hormones, and gut microbiome shifts in reserpine induced FD male balb/c mice. APE increased the gastrointestinal motility including the gastric emptying (GE) rate and small intestinal transit (SIT) rate. The level of serum gastrointestinal hormones such as motilin (MTL) and gastrin (GAS) increased, and the vasoactive intestinal peptide (VIP) level decreased after the administration of APE. Furthermore, the gut microbiome analysis demonstrated that APE could regulate the microbiome structure and restore homeostasis by elevating useful bacterial abundance, while simultaneously decreasing harmful bacterial abundance. This study demonstrated the ameliorating FD effect of APE and its potential efficacy in curing functional gastrointestinal disorders and maintaining a healthy digestive tract.

Recent progress in strategies for steroid production in yeasts.

As essential structural molecules of fungal cell membrane, ergosterol is not only an important component of fungal growth and stress-resistance but also a key precursor for manufacturing steroid drugs of pharmaceutical or agricultural significance. So far, ergosterol biosynthesis in yeast has been elucidated elaborately, and efforts have been made to increase ergosterol production through regulation of ergosterol metabolism and storage. Furthermore, the same intermediates shared by yeasts and animals or plants make the construction of heterologous sterol pathways in yeast a promising approach to synthesize valuable steroids, such as phytosteroids and animal steroid hormones. During these challenging processes, several obstacles have arisen and been combated with great endeavors. This paper reviews recent research progress of yeast metabolic engineering for improving the production of ergosterol and heterologous steroids. The remaining tactics are also discussed.

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks.

The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stability of sewage treatment. Therefore, timely prediction and early warning of heavy metal concentrations in industrial sewer networks is crucial. However, due to the complex sources of heavy metals in industrial sewer networks, traditional physical modeling and linear methods cannot establish an accurate prediction model. Herein, we developed a Gated Recurrent Unit (GRU) neural network model based on a deep learning algorithm for predicting the concentrations of heavy metals in industrial sewer networks. To train the GRU model, we used low-cost and easy-to-obtain urban multi-source data, including socio-environmental indicator data, air environmental indicator data, water quantity indicator data, and easily measurable water quality indicator data. The model was applied to predict the concentrations of heavy metals (Cu, Zn, Ni, and Cr) in the sewer networks of an industrial area in southern China. The results are compared with the commonly used Artificial Neural Network (ANN) model. In this study, it was shown that the GRU had better prediction performance for Cu, Zn, Ni, and Cr concentrations, with the average R2 significantly increased by 12.35%, 11.94%, 9.21%, and 8.13%, respectively, compared to ANN predictions. The sensitivity analysis based on Shapley (SHAP) values revealed that conductivity (σ), temperature (T), pH, and sewage flow (Flow) contributed significantly to the prediction results of the model. Furthermore, the three input variables including air pressure (AP), land area (A), and population (Pop.) were removed without affecting the prediction performance of the model, which maximized the modeling efficiency and reduced the operational cost. This study provides an economical and feasible technical method for early warning of abnormal heavy metal concentrations in urban industrial sewer networks.

Both simulation and sequencing data reveal coinfections with multiple SARS-CoV-2 variants in the COVID-19 pandemic.

SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerging SARS-CoV-2 variants could increase transmissibility and diminish vaccine protection. However, whether coinfection with multiple SARS-CoV-2 variants exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11), and Apr 1, 2021 (GISAID21Apr1), respectively. With single-nucleotide variant (SNV) and network clique analyses, we constructed single-nucleotide polymorphism (SNP) coexistence networks and discovered maximal SNP cliques of sizes 16 and 34 in the GISAID20May11 and GISAID21Apr1 datasets, respectively. Simulating the transmission routes and SNV accumulations, we discovered a linear relationship between the size of the maximal clique and the number of coinfected variants. We deduced that the COVID-19 cases in GISAID20May11 and GISAID21Apr1 were coinfections with 3.20 and 3.42 variants on average, respectively. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients and discovered recurrent heterozygous SNPs in twenty of the patients, including loci 8,782 and 28,144, which were crucial for SARS-CoV-2 lineage divergence. In conclusion, our findings reported SARS-CoV-2 variants coinfection in COVID-19 patients and demonstrated the increasing number of coinfected variants.

Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas.

The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models based on statistical methods to predict the content of FOG fail to provide satisfactory accuracy. Herein, a deep learning algorithm used a data-driven FOG content prediction model is proposed to achieve a more accurate prediction of FOG content. Meanwhile, global sensitivity analysis (GSA) is exploited to evaluate the contribution of input indicators to the output indicator (FOG) in the model, so that some input indicators that have less impact on the prediction performance can be screened out, the best combination of input indicators can be determined, and the operation cost of the model can be reduced. To evaluate the effectiveness of the proposed model, a case study was conducted in a city in southern China. The experimental results indicate that the prediction model obtains good FOG estimations and performs well from a single site to multiple sites with a mean R2 of 0.922, showing a good generalization performance. Through GSA, the key input indicators in the model were identified as pH, water temperature (T), relative humidity (RH), sewage flow (Flow), drinking water supply (DWS), velocity (V) and conductivity (σ), and the input indicators such as air pressure (AP), population (Pop.), and liquid level (LV) can be reduced without affecting the prediction accuracy of the model.

Polymer dependent acoustic mode coupling and Hooke's law spring constants in stacked gold nanoplates.

Metal nanoparticles are excellent acoustic resonators and their vibrational spectroscopy has been widely investigated. However, the coupling between vibrational modes of different nanoparticles is less explored. For example, how the intervening medium affects the coupling strength is not known. Here, we investigate how different polymers affect coupling in Au nanoplate-polymer-Au nanoplate sandwich structures. The coupling between the breathing modes of the Au nanoplates was measured using single-particle pump-probe spectroscopy, and the polymer dependent coupling strength was determined experimentally. Analysis of the acoustic mode coupling gives the effective spring constant for the polymers. A relative motion mode was also observed for the stacked Au nanoplates. The frequency of this mode is strongly correlated with the coupling constant for the breathing modes. The breathing mode coupling and relative motion mode were analyzed using a coupled oscillator model. This model shows that both these effects can be described using the same spring constant for the polymer. Finally, we present a new type of mass balance using the strongly coupled resonators. We show that the resonators have a mass detection limit of a few femtograms. We envision that further understanding of the vibrational coupling in acoustic resonators will improve the coupling strength and expand their potential applications.

Metagenomic analysis revealed the potential role of gut microbiome in gout.

Emerging evidence indicates an association between gut microbiome and arthritis diseases including gout. However, how and which gut bacteria affect host urate degradation and inflammation in gout remains unclear. Here we performed a metagenome analysis on 307 fecal samples from 102 gout patients and 86 healthy controls. Gout metagenomes significantly differed from those of healthy controls. The relative abundances of Prevotella, Fusobacterium, and Bacteroides were increased in gout, whereas those of Enterobacteriaceae and butyrate-producing species were decreased. Functionally, gout patients had greater abundances for genes in fructose, mannose metabolism and lipid A biosynthesis, and lower for genes in urate degradation and short chain fatty acid production. A three-pronged association between metagenomic species, functions and clinical parameters revealed that decreased abundances of species in Enterobacteriaceae were associated with reduced amino acid metabolism and environmental sensing, which together contribute to increased serum uric acid and C-reactive protein levels in gout. A random forest classifier based on three gut microbial genes showed high predictivity for gout in both discovery and validation cohorts (0.91 and 0.80 accuracy), with high specificity in the context of other chronic disorders. Longitudinal analysis showed that uric-acid-lowering and anti-inflammatory drugs partially restored gut microbiota after 24-week treatment. Comparative analysis with obesity, type 2 diabetes, ankylosing spondylitis and rheumatoid arthritis indicated that gout metagenomes were more similar to those of autoimmune than metabolic diseases. Our results suggest that gut dysbiosis was associated with dysregulated host urate degradation and systemic inflammation and may be used as non-invasive diagnostic markers for gout.

Combined Biosynthetic Pathway Engineering and Storage Pool Expansion for High-Level Production of Ergosterol in Industrial Saccharomyces cerevisiae.

Ergosterol, a terpenoid compound produced by fungi, is an economically important metabolite serving as the direct precursor of steroid drugs. Herein, ergsosterol biosynthetic pathway modification combined with storage capacity enhancement was proposed to synergistically improve the production of ergosterol in Saccharomyces cerevisiae. S. cerevisiae strain S1 accumulated the highest amount of ergosterol [7.8 mg/g dry cell weight (DCW)] among the wild-type yeast strains tested and was first selected as the host for subsequent metabolic engineering studies. Then, the push and pull of ergosterol biosynthesis were engineered to increase the metabolic flux, overexpression of the sterol acyltransferase gene ARE2 increased ergosterol content to 10 mg/g DCW and additional overexpression of a global regulatory factor allele (UPC2-1) increased the ergosterol content to 16.7 mg/g DCW. Furthermore, considering the hydrophobicity sterol esters and accumulation in lipid droplets, the fatty acid biosynthetic pathway was enhanced to expand the storage pool for ergosterol. Overexpression of ACC1 coding for the acetyl-CoA carboxylase increased ergosterol content from 16.7 to 20.7 mg/g DCW. To address growth inhibition resulted from premature accumulation of ergosterol, auto-inducible promoters were employed to dynamically control the expression of ARE2, UPC2-1, and ACC1. Consequently, better cell growth led to an increase of ergosterol content to 40.6 mg/g DCW, which is 4.2-fold higher than that of the starting strain. Finally, a two-stage feeding strategy was employed for high-density cell fermentation, with an ergosterol yield of 2986.7 mg/L and content of 29.5 mg/g DCW. This study provided an effective approach for the production of ergosterol and other related terpenoid molecules.

Metagenome-wide association study of gut microbiome revealed potential microbial marker set for diagnosis of pediatric myasthenia gravis.

BACKGROUND: Myasthenia gravis (MG) is an acquired immune-mediated disorder of the neuromuscular junction that causes fluctuating skeletal muscle weakness and fatigue. Pediatric MG and adult MG have many different characteristics, and current MG diagnostic methods for children are not quite fit. Previous studies indicate that alterations in the gut microbiota may be associated with adult MG. However, it has not been determined whether the gut microbiota are altered in pediatric MG patients. METHODS: Our study recruited 53 pediatric MG patients and 46 age- and gender-matched healthy controls (HC). We sequenced the fecal samples of recruited individuals using whole-genome shotgun sequencing and analyzed the data with in-house bioinformatics pipeline. RESULTS: We built an MG disease classifier based on the abundance of five species, Fusobacterium mortiferum, Prevotella stercorea, Prevotella copri, Megamonas funiformis, and Megamonas hypermegale. The classifier obtained 94% area under the curve (AUC) in cross-validation and 84% AUC in the independent validation cohort. Gut microbiome analysis revealed the presence of human adenovirus F/D in 10 MG patients. Significantly different pathways and gene families between MG patients and HC belonged to P. copri, Clostridium bartlettii, and Bacteroides massiliensis. Based on functional annotation, we found that the gut microbiome affects the production of short-chain fatty acids (SCFAs), and we confirmed the decrease in SCFA levels in pediatric MG patients via serum tests. CONCLUSIONS: The study indicated that altered fecal microbiota might play vital roles in pediatric MG's pathogenesis by reducing SCFAs. The microbial markers might serve as novel diagnostic methods for pediatric MG.

PStrain: an iterative microbial strains profiling algorithm for shotgun metagenomic sequencing data.

MOTIVATION: The microbial community plays an essential role in human diseases and physiological activities. The functions of microbes can differ due to strain-level differences in the genome sequences. Shotgun metagenomic sequencing allows us to profile the strains in microbial communities practically. However, current methods are underdeveloped due to the highly similar sequences among strains. We observe that strains genotypes at the same single nucleotide variant (SNV) locus can be speculated by the genotype frequencies. Also, the variants in different loci covered by the same reads can provide evidence that they reside on the same strain. RESULTS: These insights inspire us to design PStrain, an optimization method that utilizes genotype frequencies and the reads which cover multiple SNV loci to profile strains iteratively based on SNVs in a set of MetaPhlAn2 marker genes. Compared to the state-of-art methods, PStrain, on average, improved the performance of inferring strains abundances and genotypes by 87.75% and 59.45%, respectively. We have applied the PStrain package to the dataset with two cohorts of colorectal cancer (CRC) and found that the sequences of Bacteroides coprocola strains are significantly different between CRC and control samples, which is the first time to report the potential role of B.coprocola in the gut microbiota of CRC. AVAILABILITYAND IMPLEMENTATION: https://github.com/wshuai294/PStrain. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gas diffusion electrodes for H2O2 production and their applications for electrochemical degradation of organic pollutants in water: A review.

Nowadays, it is a great challenge to minimize the negative impact of hazardous organic compounds in the environment. Highly efficient hydrogen peroxide (H2O2) production through electrochemical methods with gas diffusion electrodes (GDEs) is greatly demand for degradation of organic pollutants that present in drinking water and industrial wastewater. The GDEs as cathodic electrocatalyst manifest more cost-effective, lower energy consumption and higher oxygen utilization efficiency for H2O2 production as compared to other carbonaceous cathodes due to its worthy chemical and physical characteristics. In recent years, the crucial research and practical application of GDE for degradation of organic pollutants have been well developed. In this review, we focus on the novel design, fundamental aspects, influence factors, and electrochemical properties of GDEs. Furthermore, we investigate the generation of H2O2 through electrocatalytic processes and degradation mechanisms of refractory organic pollutants on GDEs. We describe the advanced methodologies towards electrochemical kinetics, which include the enhancement of GDEs electrochemical catalytic activity and mass transfer process. More importantly, we also highlight the other technologies assisted electrochemical process with GDEs to enlarge the practical application for water treatment. In addition, the developmental prospective and the existing research challenges of GDE-based electrocatalytic materials for real applications in H2O2 production and wastewater treatment are forecasted. According to our best knowledge, only few review articles have discussed GDEs in detail for H2O2 production and their applications for degradation of organic pollutants in water.