iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.

Neuropeptides are biomolecules with crucial physiological functions. Accurate identification of neuropeptides is essential for understanding nervous system regulatory mechanisms. However, traditional analysis methods are expensive and laborious, and the development of effective machine learning models continues to be a subject of current research. Hence, in this research, we constructed an SVM-based machine learning neuropeptide predictor, iNP_ESM, by integrating protein language models Evolutionary Scale Modeling (ESM) and Unified Representation (UniRep) for the first time. Our model utilized feature fusion and feature selection strategies to improve prediction accuracy during optimization. In addition, we validated the effectiveness of the optimization strategy with UMAP (Uniform Manifold Approximation and Projection) visualization. iNP_ESM outperforms existing models on a variety of machine learning evaluation metrics, with an accuracy of up to 0.937 in cross-validation and 0.928 in independent testing, demonstrating optimal neuropeptide recognition capabilities. We anticipate improved neuropeptide data in the future, and we believe that the iNP_ESM model will have broader applications in the research and clinical treatment of neurological diseases.

Regioselective O-acetylation of various glucosides catalyzed by Escherichia coli maltose O-acetyltransferase.

Escherichia coli, a well-known prokaryotic organism, has been widely employed as a versatile host for heterologous overexpression of proteins/biocatalysts and the production of pharmaceutically important intermediates/small molecules. However, some E. coli endogenous enzymes showing substrate promiscuity may disturb the heterologous metabolic flux, which will result in the reduction of substrates, intermediates, and target products. Here we reported an unexpected E. coli-catalyzed regioselective O-acetylation of various glucosides. The regioselectively O-acetylated products, 6'-O-acetyl-loganin and 6'-O-acetyl-loganic acid, were obtained and characterized from the enzymatic reaction in which the supernatants of E. coli expressing either CaCYP72A565 and CaCPR, the key enzymes involved in camptothecin biosynthesis, or empty vector were used as catalyst and loganin and loganic acid as independent substrate. An alkaloidal glucoside strictosamide was converted into the regioselectively O-acetylated product 6'-O-acetyl-strictosamide, implying substrate promiscuity of the E. coli-catalyzed O-acetylation reaction. Furthermore, 8 glucosides, including 5 iridoid glucosides and 3 flavonoid glucosides, were successfully converted into the regioselectively O-acetylated products by E. coli, indicating the wide substrate range for the unexpected E. coli-catalyzed O-acetylation. E. coli maltose O-acetyltransferase was demonstrated to be responsible for the mentioned regioselective O-acetylation at the 6-OH of the glucopyranosyl group of multiple classes of natural product glucosides through candidate acetyltransferase-encoding gene analysis, gene knock-out, gene complementation, and the relevant enzymatic reaction activity assays. The present study not only provides an efficient biocatalyst for regioselective O-acetylation but also notifies cautions for metabolic engineering and synthetic biology applications in E. coli. KEY POINTS: • 6-OH of glucosyl of multiple glucosides was regioselectively O-acetylated by E. coli. • Endogenous EcMAT is responsible for the regioselective O-acetylation reaction.

Methodological and reporting quality of systematic reviews on health effects of air pollutants were higher than extreme temperatures: a comparative study.

BACKGROUND: An increasing number of systematic reviews (SRs) in the environmental field have been published in recent years as a result of the global concern about the health impacts of air pollution and temperature. However, no study has assessed and compared the methodological and reporting quality of SRs on the health effects of air pollutants and extreme temperatures. This study aims to assess and compare the methodological and reporting quality of SRs on the health effects of ambient air pollutants and extreme temperatures. METHODS: PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Web of Science, and Epistemonikos databases were searched. Two researchers screened the literature and extracted information independently. The methodological quality of the SRs was assessed through A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2). The reporting quality was assessed through Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA). RESULTS: We identified 405 SRs (286 for air pollution, 108 for temperature, and 11 for the synergistic effects). The methodological and reporting quality of the included SRs were suboptimal, with major deficiencies in protocol registration. The methodological quality of SRs of air pollutants was better than that of temperature, especially in terms of satisfactory explanations for any heterogeneity (69.6% v. 45.4%). The reporting quality of SRs of air pollution was better than temperature, however, adherence to the reporting of the assessment results of risk of bias in all SRs (53.5% v. 34.3%) was inadequate. CONCLUSIONS: Methodological and reporting quality of SRs on the health effect of air pollutants were higher than those of temperatures. However, deficiencies in protocol registration and the assessment of risk of bias remain an issue for both pollutants and temperatures. In addition, developing a risk-of-bias assessment tool applicable to the temperature field may improve the quality of SRs.

Prospects of cereal protein-derived bioactive peptides: Sources, bioactivities diversity, and production.

Cereals account for a large proportion of the human diet and are an important source of protein. The preparation of cereal protein peptides is a good way to utilize these proteins. Cereal protein peptides have good application potential as antioxidant, antibacterial, anti-inflammatory and anticancer compounds, in lowering blood pressure, controlling blood sugar, and inhibiting thrombosis. This article reviews the literature on the functional properties, mechanisms of action, and applications of cereal protein peptides in the food industry with two perspectives, and summarizes the methods for their preparation and identification. The biologically active peptides derived from different grain proteins have varied main functional properties, which may be related to the differences in the amino acid composition and protein types of different grains. On this basis, the structure-activity relationship of cereal protein peptides was discussed. The advancement of identification technology makes the integration of bioinformatics and bioactive peptide research closer. Bioinformatics by combination of online database, computer simulation and experimental verification is helpful to in-deep study the structure-activity relationship of biologically active peptides, and improve efficiency in the process of obtaining target peptides with less cost. In addition, the application of cereal protein peptides in the food industry is also discussed.

Identify Bitter Peptides by Using Deep Representation Learning Features.

A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too.

A homomeric geranyl diphosphate synthase-encoding gene from Camptotheca acuminata and its combinatorial optimization for production of geraniol in Escherichia coli.

Geranyl diphosphate (GPP), the unique precursor for all monoterpenoids, is biosynthesized from isopentenyl diphosphate and dimethylallyl diphosphate via the head-to-tail condensation reaction catalyzed by GPP synthase (GPPS). Herein a homomeric GPPS from Camptotheca acuminata, a camptothecin-producing plant, was obtained from 5'- and 3'-rapid amplification of cDNA ends and subsequent overlap extension and convenient PCR amplifications. The truncate CaGPPS was introduced to replace ispA of pBbA5c-MevT(CO)-MBIS(CO, ispA), a de novo biosynthetic construct for farnesyl diphosphate generation, and overexpressed in Escherichia coli, together with the truncate geraniol synthase-encoding gene from C. acuminata (tCaGES), to confirm CaGPPS-catalyzed reaction in vivo. A 24.0 ± 1.3 mg L-1 of geraniol was produced in the recombinant E. coli. The production of GPP was also validated by the direct UPLC-HRMSE analyses. The tCaGPPS and tCaGES genes with different copy numbers were introduced into E. coli to balance their catalytic potential for high-yield geraniol production. A 1.6-fold increase of geraniol production was obtained when four copies of tCaGPPS and one copy of tCaGES were introduced into E. coli. The following fermentation conditions optimization, including removal of organic layers and addition of new n-decane, led to a 74.6 ± 6.5 mg L-1 of geraniol production. The present study suggested that the gene copy number optimization, i.e., the ratio of tCaGPPS and tCaGES, plays an important role in geraniol production in the recombinant E. coli. The removal and addition of organic solvent are very useful for sustainable high-yield production of geraniol in the recombinant E. coli in view of that the solubility of geraniol is limited in the fermentation broth and/or n-decane.

Polo-like Kinase 1 (Plk1) Up-regulates Telomerase Activity by Affecting Human Telomerase Reverse Transcriptase (hTERT) Stability.

Maintenance of telomere is regulated by active telomerase complex, including telomerase holoenzyme and its associated proteins. The activity of telomerase is precisely controlled in cells, and its dysregulation is one of the hallmarks of cancer. The telomerase catalytic subunit human telomerase reverse transcriptase (hTERT) plays a central role for telomerase activity. In this study, we indentified that Polo-like kinase 1 (Plk1) is a novel telomerase-associated protein. Plk1 can interact with hTERT independently of its kinase activity. More importantly, we found that Plk1 is associated with active telomerase complex. In addition, we demonstrated that knockdown of Plk1 caused the reduction of telomerase activity, whereas overexpression of Plk1 increased telomerase activity. Further analysis showed that overexpression of Plk1 led to a significant increase of hTERT protein by prolonging its half-life but did not affect the level of hTERT mRNA. Furthermore, we found that Plk1 enhanced the chromatin loading of hTERT and inhibited its ubiquitination. This implied that Plk1 affected hTERT stability by inhibiting its ubiquitin-mediated degradation. Collectively, these observations suggested that Plk1 is a positive modulator of telomerase by enhancing the stability of hTERT.

MicroRNA-33a disturbs influenza A virus replication by targeting ARCN1 and inhibiting viral ribonucleoprotein activity.

In order to explore the roles of microRNA(s) [miRNA(s)] in the influenza A virus life cycle, we compared the miRNA profiles of 293T and HeLa cell lines, as influenza A virus can replicate efficiently in 293T cells but only poorly in HeLa cells. We analysed differentially expressed miRNAs and identified five, including miR-33a, that could disturb influenza A virus replication significantly. Using TargetScan analysis, we found that ARCN1 could be a potential target of miR-33a. To confirm whether miR-33a could truly target ARCN1, we generated a luciferase reporter for the ARCN1 3' untranslated region (UTR) and performed a luciferase assay. The data indicated that miR-33a could suppress the luciferase activity of the reporter for the ARCN1 3' UTR but not a reporter in which the predicted miR-33a targeting sites on ARCN1 3' UTR were mutated. We performed immunoblotting to confirm that miR-33a could downregulate the protein level of ARCN1. Consistently, the level of ARCN1 protein in HeLa cells was significantly lower than that in 293T cells. We also demonstrated that ectopic expression of ARCN1 could partially rescue the inhibitory effect of miR-33a on virus replication. Furthermore, we demonstrated that miR-33a could impede virus replication at the stage of virus internalization, which was similar to the pattern for knockdown of ARCN1, indicating that miR-33a inhibits influenza virus infection by suppressing ARCN1 expression. In addition, we found that miR-33a could also weaken the viral ribonucleoprotein activity in an ARCN1-independent manner. In conclusion, we found that miR-33a is a novel inhibitory factor for influenza A virus replication.

Functional characterization of a geraniol synthase-encoding gene from Camptotheca acuminata and its application in production of geraniol in Escherichia coli.

Geraniol synthase (GES) catalyzes the conversion of geranyl diphosphate (GPP) into geraniol, an acyclic monoterpene alcohol that has been widely used in many industries. Here we report the functional characterization of CaGES from Camptotheca acuminata, a camptothecin-producing plant, and its application in production of geraniol in Escherichia coli. The full-length cDNA of CaGES was obtained from overlap extension PCR amplification. The intact and N-terminus-truncated CaGESs were overexpressed in E. coli and purified to homogeneity. Recombinant CaGES showed the conversion activity from GPP to geraniol. To produce geraniol in E. coli using tCaGES, the biosynthetic precursor GPP should be supplied and transferred to the catalytic pocket of tCaGES. Thus, ispA(S80F), a mutant of farnesyl diphosphate (FPP) synthase, was prepared to produce GPP via the head-to-tail condensation of isoprenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). A slight increase of geraniol production was observed in the fermentation broth of the recombinant E. coli harboring tCaGES and ispA(S80F). To enhance the supply of IPP and DMAPP, the encoding genes involved in the whole mevalonic acid biosynthetic pathway were introduced to the E. coli harboring tCaGES and the ispA(S80F) and a significant increase of geraniol yield was observed. The geraniol production was enhanced to 5.85 ± 0.46 mg L(-1) when another copy of ispA(S80F) was introduced to the above recombinant strain. The following optimization of medium composition, fermentation time, and addition of metal ions led to the geraniol production of 48.5 ± 0.9 mg L(-1). The present study will be helpful to uncover the biosynthetic enigma of camptothecin and tCaGES will be an alternative to selectively produce geraniol in E. coli with other metabolic engineering approaches.

Human exposure risk assessment for infectious diseases due to temperature and air pollution: an overview of reviews.

Air pollution and global temperature change are expected to affect infectious diseases. Air pollution usually causes inflammatory response and disrupts immune defense system, while temperature mainly exacerbates the effect of vectors on humans. Yet to date overview of systematic reviews assessing the exposure risk of air pollutants and temperature on infectious diseases is unavailable. This article aims to fill this research gap. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature were searched. Systematic reviews and meta-analyses investigated the exposure risk of pollutants or temperature on infectious diseases were included. Two investigators screened literature, extracted data and performed the risk of bias assessments independently. A total of 23 articles met the inclusion criteria, which 3 (13%) were "low" quality and 20 (87%) were "critically low" quality. COVID-19 morbidity was associated with long-term exposure PM2.5 (RR = 1.056 per 1 [Formula: see text], 95% CI: 1.039-1.072) and NO2 (RR = 1.042 per 1 [Formula: see text], 95% CI: 1.017-1.068). In addition, for each 1 °C increase in temperature, the morbidity risk of dengue increased 13% (RR = 1.130 per 1 °C, 95% CI: 1.120-1.150), infectious diarrhea increased 8% (RR = 1.080 per 1 °C, 95% CI: 1.050-1.200), and hand, foot and mouth disease (HFMD) increased 5% (RR = 1.050 per 1 °C, 95% CI: 1.020-1.080). In conclusion, PM2.5 and NO2 increased the risk of COVID-19 and temperatures were associated with dengue, infectious diarrhoea and HFMD morbidity. Moreover, the exposure risk of temperature on COVID-19 was recommended to be further explored.

Study on morphological traits, nutrient compositions and comparative metabolomics of diploid and tetraploid Tartary buckwheat sprouts during sprouting.

Tartary buckwheat (TB) sprout is a kind of novel nutritional vegetable, but its consumption was limited by low biomass and thin hypocotyl. The tetraploid TB sprouts was considered to be able to solve this issue. However, the nutritional quality of tetraploid TB sprouts and differences between conventional (diploid) and tetraploid TB sprouts remain unclear. In this study, the morphological traits, nutrient compositions and metabolome changes of diploid and tetraploid TB sprouts were analyzed. The water, pigments and minerals contents of TB sprouts increased during sprouting, while the contents of total soluble protein, reducing sugar, cellulose, and total phenol decreased. Compared with diploid sprouts, tetraploid sprouts had higher biomass and thicker hypocotyl. Tetraploid sprouts had higher ash and carotenoid contents, but had lower phenol and flavonoid accumulation. 677 metabolites were identified in TB sprouts by UPLC-MS analysis, including 62 diseases-resistance metabolites and 43 key active ingredients. Some key bioactive metabolites, such as rimonabant, quinapril, 1-deoxynojirimycin and miglitol, were identified. 562 differential expressed metabolites (DEMs) were identified during sprouting with seven accumulation patterns, and five hormones were found to be involved in sprout development. Additionally, 209 DEMs between diploid and tetraploid sprouts were found, and some key bioactive metabolites were induced by chromosome doubling such as mesoridazine, amaralin, atractyloside A, rhamnetin and Qing Hau Sau. This work lays a basis for the development and utilization of TB sprouts and provides evidence for the selection of tetraploid varieties to produce sprouts with high biomass and quality.

Comparative transcriptome and genome analysis unravels the response of Tatary buckwheat root to nitrogen deficiency.

Tartary buckwheat (Fagopyrum tataricum Garetn.), a dicotyledonous herbaceous crop, has good adaptation to low nitrogen (LN) condition. The plasticity of roots drives the adaption of Tartary buckwheat under LN, but the detailed mechanism behind the response of TB roots to LN remains unclear. In this study, the molecular mechanism of two Tartary buckwheat genotypes' roots with contrasting sensitivity in response to LN was investigated by integrating physiological, transcriptome and whole-genome re-sequencing analysis. LN improved primary and lateral root growth of LN-sensitive genotype, whereas the roots of LN-insensitive genotype showed no response to LN. 2, 661 LN-responsive differentially expressed genes (DEGs) were identified by transcriptome analysis. Of these genes, 17 N transport and assimilation-related and 29 hormone biosynthesis and signaling genes showed response to LN, and they may play important role in Tartary buckwheat root development under LN. The flavonoid biosynthetic genes' expression was improved by LN, and their transcriptional regulations mediated by MYB and bHLH were analyzed. 78 transcription factors, 124 small secreted peptides and 38 receptor-like protein kinases encoding genes involved in LN response. 438 genes were differentially expressed between LN-sensitive and LN-insensitive genotypes by comparing their transcriptome, including 176 LN-responsive DEGs. Furthermore, nine key LN-responsive genes with sequence variation were identified, including FtNRT2.4, FtNPF2.6 and FtMYB1R1. This paper provided useful information on the response and adaptation of Tartary buckwheat root to LN, and the candidate genes for breeding Tartary buckwheat with high N use efficiency were identified.

Interkingdom multi-omics analysis reveals the effects of nitrogen application on growth and rhizosphere microbial community of Tartary buckwheat.

Tartary buckwheat (Fagopyrum tataricum Gaertn.) is an important pseudocereal crop with excellent edible, nutritional and medicinal values. However, the yield of Tartary buckwheat (TB) is very low due to old-fashioned cultivation techniques, particularly unreasonable application of nitrogen fertilizer. To improve the understanding on the theories of nitrogen use in TB, the effects of nitrogen application on growth, as well as chemical properties and microbial community of rhizosphere soil were investigated in this study. Nitrogen application could promote the plant height, stem diameter, nitrogen accumulation and yield of TB. The relative abundance and diversity of bacteria and fungi in the rhizosphere soil of TB were improved by nitrogen fertilizer. Nitrogen application increased the abundance of beneficial bacteria such as Lysobacter and Sphingomonas in rhizosphere soil, and decreased the abundance of pathogenic fungi such as Fusarium and Plectosphaerella. The results indicated that nitrogen application changed the distribution of microbial communities in TB rhizosphere soil. Furthermore, the specific enriched or depleted microorganisms in the rhizosphere soil of four TB varieties were analyzed at OTU level. 87 specific nitrogen-responsive genes with sequence variation were identified in four varieties by integrating genomic re-sequencing and transcriptome analysis, and these genes may involve in the recruitment of specific rhizosphere microorganisms in different TB varieties. This study provided new insights into the effects of nitrogen application on TB growth and rhizosphere microbial community, and improved the understanding on the mechanisms of TB root-microbe interactions.

Dynamic transcriptome analysis suggests the key genes regulating seed development and filling in Tartary buckwheat (Fagopyrum tataricum Garetn.).

Tartary buckwheat is highly attractive for the richness of nutrients and quality, yet post-embryonic seed abortion greatly halts the yield. Seed development is crucial for determining grain yield, whereas the molecular basis and regulatory network of Tartary buckwheat seed development and filling is not well understood at present. Here, we assessed the transcriptional dynamics of filling stage Tartary buckwheat seeds at three developmental stages by RNA sequencing. Among the 4249 differentially expressed genes (DEGs), genes related to seed development were identified. Specifically, 88 phytohormone biosynthesis signaling genes, 309 TFs, and 16 expansin genes participating in cell enlargement, 37 structural genes involved in starch biosynthesis represented significant variation and were candidate key seed development genes. Cis-element enrichment analysis indicated that the promoters of differentially expressed expansin genes and starch biosynthesis genes are rich of hormone-responsive (ABA-, AUX-, ET-, and JA-), and seed growth-related (MYB, MYC and WRKY) binding sites. The expansin DEGs showed strong correlations with DEGs in phytohormone pathways and transcription factors (TFs). In total, phytohormone ABA, AUX, ET, BR and CTK, and related TFs could substantially regulate seed development in Tartary buckwheat through targeting downstream expansin genes and structural starch biosynthetic genes. This transcriptome data could provide a theoretical basis for improving yield of Tartary buckwheat.

IUP-BERT: Identification of Umami Peptides Based on BERT Features.

Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future.

Is short-term and long-term exposure to black carbon associated with cardiovascular and respiratory diseases? A systematic review and meta-analysis based on evidence reliability.

OBJECTIVE: Adverse health effects of fine particles (particulate matter2.5) have been well documented by a series of studies. However, evidences on the impacts of black carbon (BC) or elemental carbon (EC) on health are limited. The objectives were (1) to explored the effects of BC and EC on cardiovascular and respiratory morbidity and mortality, and (2) to verified the reliability of the meta-analysis by drawing p value plots. DESIGN: The systematic review and meta-analysis using adapted Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach and p value plots approach. DATA SOURCES: PubMed, Embase and Web of Science were searched from inception to 19 July 2021. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Time series, case cross-over and cohort studies that evaluated the associations between BC/EC on cardiovascular or respiratory morbidity or mortality were included. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcomes were analysed via a random effects model and reported as relative risk (RR) with 95% CI. The certainty of evidences was assessed by adapted GRADE. The reliabilities of meta-analyses were analysed by p value plots. RESULTS: Seventy studies met our inclusion criteria. (1) Short-term exposure to BC/EC was associated with 1.6% (95% CI 0.4% to 2.8%) increase in cardiovascular diseases per 1 µg/m3 in the elderly; (2) Long-term exposure to BC/EC was associated with 6.8% (95% CI 0.4% to 13.5%) increase in cardiovascular diseases and (3) The p value plot indicated that the association between BC/EC and respiratory diseases was consistent with randomness. CONCLUSIONS: Both short-term and long-term exposures to BC/EC were related with cardiovascular diseases. However, the impact of BC/EC on respiratory diseases did not present consistent evidence and further investigations are required. PROSPERO REGISTRATION NUMBER: CRD42020186244.

Engineering endogenous l-proline biosynthetic pathway to boost trans-4-hydroxy-l-proline production in Escherichia coli.

Non-proteinogenic trans-4-hydroxy-l-proline (t4HYP), a crucial naturally occurred amino acid, is present in most organisms. t4HYP is a regio- and stereo-selectively hydroxylated product of l-proline and a valuable building block for pharmaceutically important intermediates/ingredients synthesis. Microbial production of t4HYP has aroused extensive investigations because of its low-cost and environmentally benign features. Herein, we reported metabolic engineering of endogenous l-proline biosynthetic pathway to enhance t4HYP production in trace l-proline-producing Escherichia coli BL21(DE3) (21-S0). The genes responsible for by-product formation from l-proline, pyruvate, acetyl-CoA, and isocitrate in the biosynthetic network of 21-S0 were knocked out to channel the metabolic flux towards l-proline biosynthesis. PdhR was knocked out to remove its negative regulation and aceK was deleted to ensure isocitrate dehydrogenase's activity and to increase NADPH/NADP+ level. The other genes for l-proline biosynthesis were enhanced by integration of strong promoters and 5'-untranslated regions. The resulting engineered E. coli strains 21-S1 ∼ 21-S9 harboring a codon-optimized proline 4-hydroxylase-encoding gene (P4H) were grown and fermented. A titer of 4.82 g/L of t4HYP production in 21-S6 overexpressing P4H was obtained at conical flask level, comparing with the starting 21-S0 (26 mg/L). The present work paves an efficient metabolic engineering way for higher t4HYP production in E. coli.

Nitrate dose-responsive transcriptome analysis identifies transcription factors and small secreted peptides involved in nitrogen response in Tartary buckwheat.

Tartary buckwheat (Fagopyrum tataricum Gaertn.) is an economically important pseudocereal crop, which can adapt well to extreme environments, including low nitrogen (LN) stress. However, little is known regarding the associated molecular mechanisms. In this study, the molecular mechanism of Tartary buckwheat roots in response to different doses of nitrate was investigated by combining physiological changes with transcriptional regulatory network. LN improved elongation and branching of lateral roots, indicating that the plasticity of lateral roots drives the adaption of Tartary buckwheat under LN condition. The roots of the seedlings that were cultivated under four N conditions were selected for RNA-Seq analysis. In total 1686 nitrate dose-responsive genes were identified. Of these genes, 16 genes encoding N transporters showed response to N availability, and they may play important roles in N transport and root system architecture in Tartary buckwheat roots. 108 transcription factors (TFs) showed dose-response to N availability, and they may regulate N response and root growth under varied N conditions by modulating the expression of N transporters. A NIN-like protein, FtNLP7, was identified and it may contribute to the transcriptional regulation of N transporters. Furthermore, 81 N-responsive genes were identified as the small secreted peptides (SSPs). 48 N-responsive SSPs were annotated as hypothetical proteins and they may be the species-specific proteins of Tartary buckwheat. This paper provides useful information for further investigation of the mechanisms underlying the adaptation of Tartary buckwheat under N-deficient condition.

The impact of main air pollutants on respiratory emergency department visits and the modification effects of temperature in Beijing, China.

Research indicates that air pollution is a risk factor of an increased occurrence of diseases. However, evidence is limited on the effects of the pollution index on disease and whether temperature modifies the effects. The objectives were (i) to explore the effects of the Air Pollution Index (API) and specific indices for pollutants (PM10, NO2, and SO2) on respiratory emergency department (ED) visits in Beijing and (ii) to investigate whether temperature modified the effects of main air pollutants on respiratory ED visits. A quasi-Poisson generalized additive model was employed to examine the association of API and indices for pollutants with respiratory disease. Bivariate response surface model and stratification model (cold days, moderately cold days, moderately hot days, and hot days) were used to analyze the modification effects of temperature on air pollution and respiratory disease. The results showed that (i) the effects of API on respiratory diseases were similar to the index for PM10 in Beijing. (ii) API and PM10 were associated with increased respiratory ED visits on cold days and moderately cold days. Furthermore, the effects of PM10 on respiratory disease on moderately cold days [Relative risk (RR) = 1.006 per 10 µg/m3, 95% CI 1.002-1.009] were stronger than on cold days (RR = 1.004 per 10 µg/m3, 95% CI 1.000-1.008). (iii) PM10 (API) had a greater impact on children aged 10 to 17 years and females on moderately cold days, while the elderly had an increased risk of respiratory disease to PM10 (RR = 1.008 per 10 µg/m3, 95% CI 1.002-1.013) and API (RR = 1.013 per 10, 95% CI 1.004-1.022) on cold days. In conclusion, temperature can modify the association between API and respiratory morbidity. A stronger correlation existed between PM10 and respiratory diseases on moderately cold days, while the effects of cold days were less than that attributable to moderately cold days.

Impact of short-term exposure to extreme temperatures on diabetes mellitus morbidity and mortality? A systematic review and meta-analysis.

The relationship between diabetes mellitus and short-term exposure to extreme temperatures remains controversial. A systematic review and meta-analysis were performed to assess the association between extreme temperatures and diabetes mellitus morbidity and mortality. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched since inception to January 1, 2019, and updated on November 17, 2020. The results were combined using random effects model and reported as relative risk (RR) with 95% confidence interval (CI). In total, 32 studies met the inclusion criteria. (1) Both heat and cold exposures have impact on diabetes. (2) For heat exposure, the subgroup analysis revealed that the effect on diabetes mortality (RR=1.139, 95% CI: 1.089-1.192) was higher than morbidity (RR=1.012, 95% CI: 1.004-1.019). (3) With the increase of definition threshold, the impact of heat exposure on diabetes rose. (4) A stronger association between heat exposure and diabetes was observed in the elderly (≥ 60 years old) (RR=1.040, 95% CI: 1.017-1.064). In conclusion, short-term exposure to both heat and cold temperatures has impact on diabetes. The elderly is the vulnerable population of diabetes exposure to heat temperature. Developing definitions of heatwaves at the regional level are suggested.