Microorganisms in wastewater treatment plants (WWTPs) are essential for water purification to protect public and environmental health. However, their diversity and the factors that control it are poorly understood. Using a systematic global-sampling effort, we analyzed the 16S rRNA gene sequences from ~1,200 activated sludge samples taken from 269 WWTPs in 23 countries on 6 continents. Our analyses revealed that the global activated sludge bacterial communities contain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution. Despite this high diversity, activated sludge has a small global core bacterial community (n = 28 OTUs) that is strongly linked to activated sludge performance. Meta-analyses with global datasets associate the activated sludge microbiomes most closely to freshwater populations. In contrast to macroorganism diversity, activated sludge bacterial communities show no latitudinal gradient. Furthermore, their spatial turnover is scale-dependent and appears to be largely driven by stochastic processes (dispersal, drift), although deterministic factors (temperature, organic input) also are important. Our findings enhance mechanistic understanding of the global diversity and biogeography of activated sludge bacterial communities within a theoretical ecology framework and have important implications for microbial ecology and wastewater treatment processes.

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In the version of this Article originally published, the name of the author 'Mathew Robert Brown' was incorrectly written as 'Mathew Brown' in the main author list and as 'Matthew Brown' in the Global Water Microbiome Consortium list. In addition, in the Global Water Microbiome Consortium list, the names of the authors 'Kevin F. Boehnke' , 'Janeth Sanabria' and ' Adalberto Noyola' were incorrectly written as 'Kevin Boehnke' , 'Janeth Sanabria Gómez' and ' Adalberto Noyola Robles' , respectively. The names have now been corrected and the author initials in the author contributions section updated accordingly.

High-throughput screening is a powerful tool for discovering strains in the natural environment that may be suitable for target production. Herein, a novel enzyme-based high-throughput screening method was developed for rapid screening of strains overproducing 2-keto-L-gulonic acid (2-KLG). The screening method detects changes in the fluorescence of reduced nicotinamide adenine dinucleotide (NADH) at 340 nm using a microplate reader when 2-KLG is degraded by 2-KLG reductase. In this research, three different 2-KLG reductases were expressed, purified, and studied. The 2-KLG reductase from Aspergillus niger were selected as the best appropriate reductase to establishment the method for its high activity below pH 7. Using the established method, and coupled with fluorescence-activated cell sorting, we achieved a high 2-KLG-producing strain of Gluconobacter oxydans WSH-004 from soil. When cultured with D-sorbitol as the substrate, the 2-KLG yield was 2.5 g/L from 50 g/L D-sorbitol without any side products. Compared with other reported screening methods, our enzyme-based method is more efficient and accurate for obtaining high-producing 2-KLG strains, and it is also convenient and cost-effective. The method is broadly applicable for screening keto acids and other products that can be oxidized via nicotinamide adenine dinucleotide (NAD + ) or nicotinamide adenine dinucleotide phosphate (NADP + ).

Background Hand-foot syndrome (HFS) is a side effect of skin related to pegylated liposomal doxorubicin (PLD) application. Moderate to severe hand-foot syndrome (MSHFS) might have a serious impact on patients’ quality of life and treatment. However, information on risk factors for the development of MSHFS is still limited. To analyze the risk factors for PLD-induced MSHFS in breast cancer patients and constructed a logistic regression prediction model. Methods We conducted a retrospective analysis of breast cancer patients who were treated with a PLD regimen in the Tumor Hospital of Harbin Medical University from January 2017 to August 2019. A total of 26 factors were collected from electronic medical records. Patients were divided into MSHFS (HFS > grade 1) and NMHFS (HFS ≤ grade 1) groups according to the NCI classification. Statistical analysis of these factors and the construction of a logistic regression prediction model based on risk factors. Results A total of 44.7% (206/461) of patients developed MSHFS. The BMI, dose intensity, and baseline Alanine aminotransferase (ALT) and Aspartate aminotransferase (AST) levels in the MSHFS group, as well as good peripheral blood circulation, excessive sweat excretion, history of gallstones, and tumour- and HER2-positive percentages, were all higher than those in the NMHFS group (P < 0.05). The model for predicting the occurrence of MSHFS was P = 1/1 + exp. (11.138–0.110*BMI-0.234*dose intensity-0.018*baseline ALT+ 0.025*baseline AST-1.225*gallstone history-0.681* peripheral blood circulation-1.073*sweat excretion-0.364*with or without tumor-0.680*HER-2). The accuracy of the model was 72.5%, AUC = 0.791, and Hosmer-Lemeshow fit test P = 0.114 > 0.05. Conclusions Nearly half of the patients developed MSHFS. The constructed prediction model may be valuable for predicting the occurrence of MSHFS in patients.

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