LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer.

BACKGROUND: Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach. METHODS: In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30). The multivariate data analysis, determination of the fold change and the Mann-Whitney U test were used to screen out the differential metabolites. Additionally, machine learning methods including receiver operating curve analysis and logistic regression analysis were conducted to establish diagnostic biomarker panels. RESULTS: There were 36 metabolites found to be significantly different between BC and non-BC groups, and 12 metabolites discovered to be significantly different between TNBC and non-TNBC patients. Results also showed that four metabolites, including N-acetyl-D-tryptophan, 2-arachidonoylglycerol, pipecolic acid and oxoglutaric acid, were considered as vital biomarkers for the diagnosis of BC and non-BC with an area under the curve (AUC) of 0.995. Another two-metabolite panel of N-acetyl-D-tryptophan and 2-arachidonoylglycerol was discovered to discriminate TNBC from non-TNBC and produced an AUC of 0.965. CONCLUSION: This study demonstrated that serum metabolomics can be used to identify BC specifically and identified promising serum metabolic markers for TNBC diagnosis.

Machine learning-based classification of deubiquitinase USP26 and its cell proliferation inhibition through stabilizing KLF6 in cervical cancer.

OBJECTIVE: We aim to accurately distinguish ubiquitin-specific proteases (USPs) from other members within the deubiquitinating enzyme families based on protein sequences. Additionally, we seek to elucidate the specific regulatory mechanisms through which USP26 modulates Krüppel-like factor 6 (KLF6) and assess the subsequent effects of this regulation on both the proliferation and migration of cervical cancer cells. METHODS: All the deubiquitinase (DUB) sequences were classified into USPs and non-USPs. Feature vectors, including 188D, n-gram, and 400D dimensions, were extracted from these sequences and subjected to binary classification via the Weka software. Next, thirty human USPs were also analyzed to identify conserved motifs and ascertained evolutionary relationships. Experimentally, more than 90 unique DUB-encoding plasmids were transfected into HeLa cell lines to assess alterations in KLF6 protein levels and to isolate a specific DUB involved in KLF6 regulation. Subsequent experiments utilized both wild-type (WT) USP26 overexpression and shRNA-mediated USP26 knockdown to examine changes in KLF6 protein levels. The half-life experiment was performed to assess the influence of USP26 on KLF6 protein stability. Immunoprecipitation was applied to confirm the USP26-KLF6 interaction, and ubiquitination assays to explore the role of USP26 in KLF6 deubiquitination. Additional cellular assays were conducted to evaluate the effects of USP26 on HeLa cell proliferation and migration. RESULTS: 1. Among the extracted feature vectors of 188D, 400D, and n-gram, all 12 classifiers demonstrated excellent performance. The RandomForest classifier demonstrated superior performance in this assessment. Phylogenetic analysis of 30 human USPs revealed the presence of nine unique motifs, comprising zinc finger and ubiquitin-specific protease domains. 2. Through a systematic screening of the deubiquitinase library, USP26 was identified as the sole DUB associated with KLF6. 3. USP26 positively regulated the protein level of KLF6, as evidenced by the decrease in KLF6 protein expression upon shUSP26 knockdown in both 293T and Hela cell lines. Additionally, half-life experiments demonstrated that USP26 prolonged the stability of KLF6. 4. Immunoprecipitation experiments revealed a strong interaction between USP26 and KLF6. Notably, the functional interaction domain was mapped to amino acids 285-913 of USP26, as opposed to the 1-295 region. 5. WT USP26 was found to attenuate the ubiquitination levels of KLF6. However, the mutant USP26 abrogated its deubiquitination activity. 6. Functional biological assays demonstrated that overexpression of USP26 inhibited both proliferation and migration of HeLa cells. Conversely, knockdown of USP26 was shown to promote these oncogenic properties. CONCLUSIONS: 1. At the protein sequence level, members of the USP family can be effectively differentiated from non-USP proteins. Furthermore, specific functional motifs have been identified within the sequences of human USPs. 2. The deubiquitinating enzyme USP26 has been shown to target KLF6 for deubiquitination, thereby modulating its stability. Importantly, USP26 plays a pivotal role in the modulation of proliferation and migration in cervical cancer cells.

Prediction of LncRNA-Protein Interactions Based on Kernel Combinations and Graph Convolutional Networks.

The complexes of long non-coding RNAs bound to proteins can be involved in regulating life activities at various stages of organisms. However, in the face of the growing number of lncRNAs and proteins, verifying LncRNA-Protein Interactions (LPI) based on traditional biological experiments is time-consuming and laborious. Therefore, with the improvement of computing power, predicting LPI has met new development opportunity. In virtue of the state-of-the-art works, a framework called LncRNA-Protein Interactions based on Kernel Combinations and Graph Convolutional Networks (LPI-KCGCN) has been proposed in this article. We first construct kernel matrices by taking advantage of extracting both the lncRNAs and protein concerning the sequence features, sequence similarity features, expression features, and gene ontology. Then reconstruct the existent kernel matrices as the input of the next step. Combined with known LPI interactions, the reconstructed similarity matrices, which can be used as features of the topology map of the LPI network, are exploited in extracting potential representations in the lncRNA and protein space using a two-layer Graph Convolutional Network. The predicted matrix can be finally obtained by training the network to produce scoring matrices w.r.t. lncRNAs and proteins. Different LPI-KCGCN variants are ensemble to derive the final prediction results and testify on balanced and unbalanced datasets. The 5-fold cross-validation shows that the optimal feature information combination on a dataset with 15.5% positive samples has an AUC value of 0.9714 and an AUPR value of 0.9216. On another highly unbalanced dataset with only 5% positive samples, LPI-KCGCN also has outperformed the state-of-the-art works, which achieved an AUC value of 0.9907 and an AUPR value of 0.9267.

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method.

The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we designed a method to identify SH2 domain-containing proteins and non-SH2 domain-containing proteins through deep learning technology. Firstly, we collected SH2 and non-SH2 domain-containing protein sequences including multiple species. We built six deep learning models through DeepBIO after data preprocessing and compared their performance. Secondly, we selected the model with the strongest comprehensive ability to conduct training and test separately again, and analyze the results visually. It was found that 288-dimensional (288D) feature could effectively identify two types of proteins. Finally, motifs analysis discovered the specific motif YKIR and revealed its function in signal transduction. In summary, we successfully identified SH2 domain and non-SH2 domain proteins through deep learning method, and obtained 288D features that perform best. In addition, we found a new motif YKIR in SH2 domain, and analyzed its function which helps to further understand the signaling mechanisms within the organism.

AIEgens-Doped Photonic Crystals for High Sensitivity Fluorescence Detection of Tumor Markers.

Detection of tumor markers is of great significance to preliminarily judge whether patients have malignant tumors. Fluorescence detection (FD) is an effective means to achieve sensitive detection of tumor markers. Currently, the increased sensitivity of FD has attracted research interest worldwide. Here, we have proposed a method of doping luminogens with aggregation-induced emission (AIEgens) into photonic crystals (PCs), which can significantly enhance the fluorescence intensity to achieve high sensitivity in the detection of tumor markers. PCs are made by scraping and self-assembling, which has the special effect of fluorescence enhancement. The combination of AIEgens and PCs can enhance the fluorescence intensity 4-7 times. These characteristics make it extremely sensitive. The limit of detection (LOD) for the detection of alpha-fetoprotein (AFP) in the AIE10 (Tetraphenyl ethylene-Br) doped PCs with a reflection peak of 520 nm is 0.0377 ng/mL. LOD for the detection of carcinoembryonic antigen (CEA) in the AIE25 (Tetraphenyl ethylene-NH2) doped PCs with a reflection peak of 590 nm is 0.0337 ng/mL. Our concept offers a good solution for highly sensitive detection of tumor markers.

Electrochemical Microneedles: Innovative Instruments in Health Care.

As a significant part of drug therapy, the mode of drug transport has attracted worldwide attention. Efficient drug delivery methods not only markedly improve the drug absorption rate, but also reduce the risk of infection. Recently, microneedles have combined the advantages of subcutaneous injection administration and transdermal patch administration, which is not only painless, but also has high drug absorption efficiency. In addition, microneedle-based electrochemical sensors have unique capabilities for continuous health state monitoring, playing a crucial role in the real-time monitoring of various patient physiological indicators. Therefore, they are commonly applied in both laboratories and hospitals. There are a variety of reports regarding electrochemical microneedles; however, the comprehensive introduction of new electrochemical microneedles is still rare. Herein, significant work on electrochemical microneedles over the past two years is summarized, and the main challenges faced by electrochemical microneedles and future development directions are proposed.

BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism.

BACKGROUND: RNA Secondary Structure (RSS) has drawn growing concern, both for their pivotal roles in RNA tertiary structures prediction and critical effect in penetrating the mechanism of functional non-coding RNA. Computational techniques that can reduce the in vitro and in vivo experimental costs have become popular in RSS prediction. However, as an NP-hard problem, there is room for improvement that the validity of the prediction RSS with pseudoknots in traditional machine learning predictors. RESULTS: In this essay, by integrating the bidirectional GRU (Gated Recurrent Unit) with the attention, we propose a multilayered neural network called BAT-Net to predict RSS. Different from the state-of-the-art works, BAT-Net can not only make full use of the information about the direct predecessor and direct successor of the predicted base in the RNA sequence but also dynamically adjust the corresponding loss function. The experimental results on five representative datasets extracted from the RNA STRAND database show that the sensitivity, precision, accuracy, and MCC (Matthews Correlation Coefficient) of the BAT-Net have improved by 8.52%, 8.28%, 5.66% and 9.82%, respectively, compared with the benchmark approaches on the best averages. CONCLUSIONS: BAT-Net can provide users with more credible RSS results since it has further utilized the source information of the dataset. Comparative results show that the proposed BAT-Net is superior to the other existing methods on the relevant indicators.

Emerging 3D Printing Strategies for Enzyme Immobilization: Materials, Methods, and Applications.

As the strategies of enzyme immobilization possess attractive advantages that contribute to realizing recovery or reuse of enzymes and improving their stability, they have become one of the most desirable techniques in industrial catalysis, biosensing, and biomedicine. Among them, 3D printing is the emerging and most potential enzyme immobilization strategy. The main advantages of 3D printing strategies for enzyme immobilization are that they can directly produce complex channel structures at low cost, and the printed scaffolds with immobilized enzymes can be completely modified just by changing the original design graphics. In this review, a comprehensive set of developments in the fields of 3D printing techniques, materials, and strategies for enzyme immobilization and the potential applications in industry and biomedicine are summarized. In addition, we put forward some challenges and possible solutions for the development of this field and some possible development directions in the future.

Improvement of Rimocidin Biosynthesis by Increasing Supply of Precursor Malonyl-CoA via Over-expression of Acetyl-CoA Carboxylase in Streptomyces rimosus M527.

Precursor engineering is an effective strategy for the overproduction of secondary metabolites. The polyene macrolide rimocidin, which is produced by Streptomyces rimosus M527, exhibits a potent activity against a broad range of phytopathogenic fungi. It has been predicted that malonyl-CoA is used as extender units for rimocidin biosynthesis. Based on a systematic analysis of three sets of time-series transcriptome microarray data of S. rimosus M527 fermented in different conditions, the differentially expressed accsr gene that encodes acetyl-CoA carboxylase (ACC) was found. To understand how the formation of rimocidin is being influenced by the expression of the accsr gene and by the concentration of malonyl-CoA, the accsr gene was cloned and over-expressed in the wild-type strain S. rimosus M527 in this study. The recombinant strain S. rimosus M527-ACC harboring the over-expressed accsr gene exhibited better performances based on the enzymatic activity of ACC, intracellular malonyl-CoA concentrations, and rimocidin production compared to S. rimosus M527 throughout the fermentation process. The enzymatic activity of ACC and intracellular concentration of malonyl-CoA of S. rimosus M527-ACC were 1.0- and 1.5-fold higher than those of S. rimosus M527, respectively. Finally, the yield of rimocidin produced by S. rimosus M527-ACC reached 320.7 mg/L, which was 34.0% higher than that of S. rimosus M527. These results confirmed that malonyl-CoA is an important precursor for rimocidin biosynthesis and suggested that an adequate supply of malonyl-CoA caused by accsr gene over-expression led to the improvement in rimocidin production.

A Novel Nomogram for Predicting the Risk of Short-Term Recurrence After Surgery in Glioma Patients.

OBJECTIVE: The aim of this study was to establish a nomogram model for predicting the risk of short-term recurrence in glioma patients. METHODS: The clinical data of recurrent glioma patients were summarized and analyzed in this study. Univariate and multivariate logistic regression analyses were performed to analyze the correlation between clinical data and the risk of short-term recurrence after operation. A nomogram was established based on the multivariate logistic regression model results. RESULTS: A total of 175 patients with recurrent glioma were enrolled, with 53 patients in the short-term recurrence (STR) group (recurrent time ≤6 months) and 122 patients in the long-term recurrence (LTR) group (recurrent time ≥36 months). Univariate analysis revealed that age at diagnosis, Karnofsky performance scores (KPSs), tumor location, glioma grade, glioma type, extent of resection (EOR), adjuvant chemotherapy (ad-CT), concurrent chemotherapy (co-CT), and isocitrate dehydrogenase (IDH) status were significantly associated with the short-term glioma recurrence. Multivariate analyses revealed that age at diagnosis, KPS, glioma grade, EOR, and IDH were independent risk factors for short-term glioma recurrence. A risk nomogram for the short-term recurrence of glioma was established, with the concordance index (C-index) of 0.971. The findings of calibration and receiver operating characteristic (ROC) curves showed that our nomogram model had good performance and discrimination to estimate short-term recurrence probability. CONCLUSION: This nomogram model provides reliable information about the risk of short-term glioma recurrence for oncologists and neurosurgeons. This model can predict the short-term recurrence probability and give assistance to decide the interval of follow-up or formulate individualized treatment strategies based on the predicted results. A free online prediction risk tool for this nomogram is provided: https://rj2021.shinyapps.io/Nomogram_ recurrence-risk/.

Predicting subcellular location of protein with evolution information and sequence-based deep learning.

BACKGROUND: Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins. In order to improve the prediction accuracy, we present a deep learning-based method to predict protein subcellular locations. RESULTS: Our method utilizes not only amino acid compositions sequence but also evolution matrices of proteins. Our method uses a bidirectional long short-term memory network that processes the entire protein sequence and a convolutional neural network that extracts features from protein sequences. The position specific scoring matrix is used as a supplement to protein sequences. Our method was trained and tested on two benchmark datasets. The experiment results show that our method yields accurate results on the two datasets with an average precision of 0.7901, ranking loss of 0.0758 and coverage of 1.2848. CONCLUSION: The experiment results show that our method outperforms five methods currently available. According to those experiments, we can see that our method is an acceptable alternative to predict protein subcellular location.

Demographic Characteristics and Lifestyle Habits of Chinese Plasma Donors: A Multicenter Study.

BACKGROUND The demand for plasma and plasma products has increased in China, which has a short supply. Compared with whole blood donors, plasma donors and their donation behavior have received less attention. This study aimed to investigate the demographic characteristics and lifestyle habits of Chinese plasma donors. MATERIAL AND METHODS During 2018-2019, information on plasma donors was collected from blood product companies using a 25-item questionnaire, including sex, age, height, weight, blood group, donation frequency, occupation, smoking and drinking, and sleeping and dietary habits. RESULTS Among 15 497 plasma donors, 70.5% were women and 78.5% were aged 46-55 years. Among 4847 plasma donors, the average height of men was 169.5±6.2 cm and the average height of women was 157.0±4.6 cm. In addition, the average weight of men was 67.0±10.4 kg and the average weight of women was 60.0±8.3 kg. The prevalence of obesity (body mass index ≥30.0 kg/m²) of all donors was 14.8%; 14.7% of men were obese, and 15% of women were obese. Among all plasma donors, 88.8% were farmers and 60% were frequent donors with a donation history of at least 5 years. Among all donors, 84.0% did not smoke, 67.3% did not drink, and 95.1% reported good sleep quality. All respondents reported healthy dietary habits. CONCLUSIONS Healthy lifestyle habits considerably affect the health of plasma donors and the quality of source plasma. Chinese plasma donors in this study demonstrated imbalances in terms of characteristics, which became more marked with age.

A retrospective analysis of the risk factors affecting recurrence time in patients with recurrent glioblastoma.

BACKGROUND: This study explored the related factors that influence the recurrence time of glioblastomas (GBM). METHODS: A retrospective study of recurrent GBM patients with surgical resection was performed. Recurrence time was analyzed using Kaplan-Meier survival curves. The Cox regression model was used to investigate the possible factors associated with recurrence time. RESULTS: A total of 176 patients (113 males and 63 females) were enrolled in the study, with a median age of 57 years (range, 19-76 years). From this cohort, 18 patients (10.2%) had gross total resection (GTR), 53 patients (30.1%) had subtotal resection (STR), and 105 patients (59.7%) had partial resection (PR). Postoperatively, all patients received radiotherapy (RT), with 55.1% administered concurrent chemotherapy (CTh) and 59.7% administered adjuvant CTh. The median recurrence time was 10.0 months (range, 1.0-75.0 months). Patients with PR (P=0.004), gliomas that contacted the subventricular zone (SVZ) (P=0.004), isocitrate dehydrogenase 1 (IDH1) wild-type (P=0.048), telomerase reverse transcriptase (TERT) C228T wild-type (P=0.012), and positive glial fibrillary acidic protein (GFAP) expression (P=0.044) had a shortened time to recurrence. Cox regression analysis revealed that PR (P=0.036), SVZ contact (P=0.008), and TERT C228T wild type (P=0.023) were significantly associated with a shortened recurrence time. CONCLUSIONS: PR, tumor contacting the SVZ, and TERT C228T wild type were independent risk factors for tumor recurrence in patients with GBM.

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China.

To distinguish Methicillin-Resistant Staphylococcus aureus (MRSA) from Methicillin-Sensitive Staphylococcus aureus (MSSA) in the protein sequences level, test the susceptibility to antibiotic of all Staphylococcus aureus isolates from Quanzhou hospitals, define the virulence factor and molecular characteristics of the MRSA isolates. MRSA and MSSA Pfam protein sequences were used to extract feature vectors of 188D, n-gram and 400D. Weka software was applied to classify the two Staphylococcus aureus and performance effect was evaluated. Antibiotic susceptibility testing of the 81 Staphylococcus aureus was performed by the Mérieux Microbial Analysis Instrument. The 65 MRSA isolates were characterized by Panton-Valentine leukocidin (PVL), X polymorphic region of Protein A (spa), multilocus sequence typing test (MLST), staphylococcus chromosomal cassette mec (SCCmec) typing. After comparing the results of Weka six classifiers, the highest correctly classified rates were 91.94, 70.16, and 62.90% from 188D, n-gram and 400D, respectively. Antimicrobial susceptibility test of the 81 Staphylococcus aureus: Penicillin-resistant rate was 100%. No resistance to teicoplanin, linezolid, and vancomycin. The resistance rate of the MRSA isolates to clindamycin, erythromycin and tetracycline was higher than that of the MSSAs. Among the 65 MRSA isolates, the positive rate of PVL gene was 47.7% (31/65). Seventeen sequence types (STs) were identified among the 65 isolates, and ST59 was the most prevalent. SCCmec type III and IV were observed at 24.6 and 72.3%, respectively. Two isolates did not be typed. Twenty-one spa types were identified, spa t437 (34/65, 52.3%) was the most predominant type. MRSA major clone type of molecular typing was CC59-ST59-spa t437-IV (28/65, 43.1%). Overall, 188D feature vectors can be applied to successfully distinguish MRSA from MSSA. In Quanzhou, the detection rate of PVL virulence factor was high, suggesting a high pathogenic risk of MRSA infection. The cross-infection of CA-MRSA and HA-MRSA was presented, the molecular characteristics were increasingly blurred, HA-MRSA with typical CA-MRSA molecular characteristics has become an important cause of healthcare-related infections. CC59-ST59-spa t437-IV was the main clone type in Quanzhou, which was rare in other parts of mainland China.

Intranasal oxytocin decreases self-oriented learning.

RATIONALE: Oxytocin has been found to play an important role in human social cognition and social interaction. Over the last two decades, surge studies have been conducted to investigate how oxytocin impacts other-oriented processes, such as trust and generosity (Zak et al. in PLoS ONE 2(11):e1128, 2007); however, the examination of the effect of oxytocin on self-related processes was relatively inadequate. Appropriate and efficient social interactions require both self- and other-related information processing. Recent studies have found that intranasal oxytocin (IN-OT) influences the self-related process, although the results have been mixed. The computational process underlying the effects of IN-OT on self-processing remains unknown. OBJECTIVES: We aim to investigate the effect of IN-OT on self-oriented learning across different contexts (self-other independent vs. self-other dependent) and uncover the process by which IN-OT affects dynamic behavior changes. METHODS: We performed two double-blind, placebo-controlled studies and used reinforcement learning theory to integrate action and related feedback for participants' behaviors. RESULTS: In study 1, IN-OT decreased self-oriented reward learning when self-oriented learning and prosocial (other-oriented) learning were assessed separately. These effects were partially due to the OT-related increase in choice variability during self-oriented learning. In study 2, IN-OT also decreased learning performance during self-oriented reward learning when self-related and other-related rewards were present together. These effects occurred at an early stage of the learning process and could be moderated by the participants' social value orientation. Our findings show that OT attenuates the process of self-oriented learning and provides an underlying computational process. CONCLUSIONS: Our findings shed new light on the dynamics of IN-OT's effects on human self-oriented learning processes. For future studies on OT effects on self-oriented learning, individual factors such as social value orientation should be taken into consideration in research development and analysis.

Identification of a gene from Streptomyces rimosus M527 negatively affecting rimocidin biosynthesis and morphological differentiation.

The polyene macrolide rimocidin, produced by Streptomyces rimosus M527, was found to be highly effective against a broad range of fungal plant pathogens. Current understanding of the regulatory mechanism of rimocidin biosynthesis and morphological differentiation in S. rimosus M527 is limited. NsdA is considered a negative regulator involved in morphological differentiation and biosynthesis of secondary metabolites in some Streptomyces species. In this study, nsdAsr was cloned from S. rimosus M527. The role of nsdAsr in rimocidin biosynthesis and morphological differentiation was investigated by gene deletion, complementation, and over-expression. A ΔnsdAsr mutant was obtained using CRISPR/Cas9. The mutant produced more rimocidin (46%) and accelerated morphological differentiation than the wild-type strain. Over-expression of nsdAsr led to a decrease in rimocidin production and impairment of morphological differentiation. Quantitative RT-PCR analysis revealed that transcription of rim genes responsible for rimocidin biosynthesis was upregulated in the ΔnsdAsr mutant but downregulated in the nsdAsr over-expression strain. Similar effects have been described for Streptomyces coelicolor M145 and the industrial toyocamycin-producing strain Streptomyces diastatochromogenes 1628. KEY POINTS: • A negative regulator for sporulation and rimocidin production was identified. • The CRISPR/Cas9 system was used for gene deletion in S. rimosus M527.

Cloning and Overexpression of the Toy Cluster for Titer Improvement of Toyocamycin in Streptomyces diastatochromogenes.

The nucleoside antibiotic toyocamycin (TM) is a potential fungicide that can control plant diseases, and it has become an attractive target for research. Streptomyces diastatochromogenes 1628, a TM-producing strain, was isolated by our laboratory and was considered to be a potent industrial producer of TM. Recently, the putative TM biosynthetic gene cluster (toy cluster) in S. diastatochromogenes 1628 was found by genome sequencing. In this study, the role of toy cluster for TM biosynthesis in S. diastatochromogenes 1628 was investigated by heterologous expression, deletion, and complementation. The extract of the recombinant strain S. albusJ1074-TC harboring a copy of toy cluster produced TM as shown by HPLC analysis. The Δcluster mutant completely lost its ability to produce TM. TM production in the complemented strain was restored to a level comparable to that of the wild-type strain. These results confirmed that the toy cluster is responsible for TM biosynthesis. Moreover, the introduction of an extra copy of the toy cluster into S. diastatochromogenes 1628 led to onefold increase in TM production (312.9 mg/l vs. 152.1 mg/l) as well as the transcription of all toy genes. The toy gene cluster was engineered in which the native promoter of toyA gene, toyM gene, toyBD operon, and toyEI operon was, respectively, replaced by permE ∗ or SPL57. To further improve TM production, the engineered toy gene cluster was, respectively, introduced and overexpressed in S. diastatochromogenes 1628 to generate recombinant strains S. diastatochromogenes 1628-EC and 1628-SC. After 84 h, S. diastatochromogenes 1628-EC and 1628-SC produced 456.5 mg/l and 638.9 mg/l TM, respectively, which is an increase of 2- and 3.2-fold compared with the wild-type strain.

Chemoenzymatic conversion of Sorghum durra stalk into furoic acid by a sequential microwave-assisted solid acid conversion and immobilized whole-cells biocatalysis.

In this study, chemoenzymatic conversion of Sorghum durra stalk (SDS) into furoic acid was developed by a sequential microwave-assisted solid acid conversion and immobilized whole-cells biocatalysis method. Dry dewaxed SDS (75 g/L) was catalyzed into furfural at 57.8% yield with heterogeneous Sn-argil (2.0 wt% dosage) in n-ethyl butyrate-H2O (1:1, v:v) biphasic system using a microwave (600 W) for 10 min at 180 °C. In this biphasic media (pH 6.5), SDS-derived furfural (125.0 mM) was biologically oxidized to furoic acid by immobilized Brevibacterium lutescens cells harboring furfural-oxidizing activity at 30 °C, and furfural was wholly transformed to furoic acid within 24 h. Finally, the recovery and reuse of the Sn-argil catalyst and immobilized biocatalysts were conducted for synthesizing furoic acid from SDS in the biphasic system. This chemoenzymatic route can be attractive for furoic acid production.

Development and optimization of an intergeneric conjugation system and analysis of promoter activity in Streptomyces rimosus M527.

An efficient genetic transformation system and suitable promoters are essential prerequisites for gene expression studies and genetic engineering in streptomycetes. In this study, firstly, a genetic transformation system based on intergeneric conjugation was developed in Streptomyces rimosus M527, a bacterial strain which exhibits strong antagonistic activity against a broad range of plant-pathogenic fungi. Some experimental parameters involved in this procedure were optimized, including the conjugative media, ratio of donor to recipient, heat shock temperature, and incubation time of mixed culture. Under the optimal conditions, a maximal conjugation frequency of 3.05×10-5 per recipient was obtained. Subsequently, based on the above developed and optimized transformation system, the synthetic promoters SPL-21 and SPL-57, a native promoter potrB, and a constitutive promoter permE* commonly used for gene expression in streptomycetes were selected and their activity was analyzed using gusA as a reporter gene in S. rimosus M527. Among the four tested promoters, SPL-21 exhibited the strongest expression activity and gave rise to a 2.2-fold increase in ß-glucuronidase (GUS) activity compared with the control promoter permE*. Promoter SPL-57 showed activity comparable to that of permE*. Promoter potrB, which showed the lowest activity, showed a 50% decrease in GUS activity compared with the control permE*. The transformation system developed in this study and the tested promotors provide a basis for the further modification of S. rimosus M527.