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Background Hyperhomocysteinemia is associated with autoimmune diseases such as ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA). Current findings regarding plasma/serum homocysteine (HCY) levels in AS patients are inconsistent. This study aims to systematically evaluate the association between circulating HCY levels and AS. Methods Online electronic databases (PubMed, Web of Science, Embase, ScienceDirect, China National Knowledge Infrastructure (CNKI), and Wanfang data) were used to retrieve all relevant articles published up to May 7, 2020. The pooled standardized mean difference (SMD) with 95% confidence interval (CI) was calculated using the random-effect model, Stata16 software. Results Nine articles containing 778 AS patients and 522 controls were included in this meta-analysis. No significant differences in HCY levels were found between AS and control groups (pooled SMD = 0.46, 95% CI = − 0.30 to 1.23, P = 0.23). However, subgroup analysis suggested that HCY levels were significantly higher (P P Conclusion This meta-analysis indicates that HCY levels are similar between AS and controls, and do not correlate with disease activity. However, different medical treatments cause fluctuations of circulating HCY levels in AS patients. Further and larger-scale studies are needed to confirm these findings. Trial registration This study was registered at international prospective register of systematic reviews (PROSPERO), registration number: CRD42020184426.

Summary: Newborn neurons undergo inside-out migration to their final destinations during neocortical development. Reelin-induced tyrosine phosphorylation of disabled 1 (Dab1) is a critical mechanism controlling cortical neuron migration. However, the roles of Reelin-independent phosphorylation of Dab1 remain unclear. Here, we report that deleted in colorectal carcinoma (DCC) interacts with Dab1 via its P3 domain. Netrin 1, a DCC ligand, induces Dab1 phosphorylation at Y220 and Y232. Interestingly, knockdown of DCC or truncation of its P3 domain dramatically delays neuronal migration and impairs the multipolar-to-bipolar transition of migrating neurons. Notably, the migration delay and morphological transition defects are rescued by the expression of a phospho-mimetic Dab1 or a constitutively active form of Fyn proto-oncogene (Fyn), a member of the Src-family tyrosine kinases that effectively induces Dab1 phosphorylation. Collectively, these findings illustrate a DCC-Dab1 interaction that ensures proper neuronal migration during neocortical development. : Non-Reelin-induced Dab1 phosphorylation was previously identified, but not characterized. Zhang et al. find that DCC interacts with Dab1, and DCC-mediated signaling effectively induces Dab1 phosphorylation. The DCC/Dab1 signaling complex is essential for neuronal migration, especially during the multipolar-to-bipolar transition. Keywords: Dab1, DCC, Fyn, morphological transition, netrin 1, neocortex development, neuronal migration, Reelin, tyrosine phosphorylation

Agricultural application studies, including field experiments and acute toxicity tests, were conducted for lipopeptides secreted by marine-derived Bacillus marinus B-9987. Benefiting from commercially available scaled-up lipopeptide purification, the sample of impurities (isolated from target lipopeptides), raw extracted sample (purity: 9.08%), partially purified sample (purity: 20.86%), and highly purified sample (purity: 87.51%) were prepared from B. marinus B-9987 fermentation broth, and used in lab-scale antagonism tests, field experiments, swarming motility tests, and acute toxicity tests. Operations and conditions in field experiments were consistent with the Pesticide-Guidelines for the Field Efficacy Trials (GB/T 17980.28-2000), and acute toxicity tests were executed according to Toxicological Test Methods of Pesticides for Registration (GB 15670-1995). In agar diffusion tests in vitro and pot tests in vivo, all lipopeptide samples with different purities significantly inhibited Botrytis cinerea; meanwhile the sample of impurities isolated from target lipopeptides were not effective against B. cinerea. Results of lab-scale tests showed that the target lipopeptides were effective substances against B. cinerea. Thus, partially purified and raw extracted samples were used in field experiments instead of the highly purified sample for cost saving. In the field experiments against rose grey mold, biological control efficacy of 500 mg L−1 lipopeptides reached 67.53%, slightly lower than 74.05% reached by the agrochemical pyrimethanil. However, pyrimethanil severely suppressed B. marinus B-9987, whereas the lipopeptides promoted swarming motility and biocontrol efficacy of Bacillus biomass. Lipopeptides at 87.51% purity were tested for systemic acute toxicity and confirmed as low-toxicity substances. In conclusion, low-toxicity lipopeptides were potential alternatives to agrochemicals, and they also performed good promotion when combined with homologous biological control microorganism. There were 2 breakthroughs in this research: (1) marine-derived bacterial lipopeptides inhibited grey mold caused by B. cinerea in field experiments; and (2) purified bacterial lipopeptides (sample purity: >87.51%) were determined to be low-toxicity substances by systemic acute toxicity tests, satisfying the strict requirement of pesticide registration in China (required purity: >85%). This study provides support for using extracellular Bacillus-derived lipopeptides commercially similar to Bacillus-based biological control agents.

Two novel γ-butyrolactones ghanamycins A (1) and B (2) were isolated from the fermentation broth of marine-derived Streptomyces ghanaensis TXC6-16. Their structures were elucidated by spectroscopic analysis. These two novel compounds exhibited antimicrobial activities against some phytopathogens. The minimum IC (MIC) of 2 against Pseudomonas syringae and Erwinia sp. were 50 μg ml−1.

Nowadays, various machine learning-based approaches using sequence information alone have been proposed for identifying DNA-binding proteins, which are crucial to many cellular processes, such as DNA replication, DNA repair and DNA modification. Among these methods, building a meaningful feature representation of the sequences and choosing an appropriate classifier are the most trivial tasks. Disclosing the significances and contributions of different feature spaces and classifiers to the final prediction is of the utmost importance, not only for the prediction performances, but also the practical clues of biological experiment designs. In this study, we propose a model stacking framework by orchestrating multi-view features and classifiers (MSFBinder) to investigate how to integrate and evaluate loosely-coupled models for predicting DNA-binding proteins. The framework integrates multi-view features including Local_DPP, 188D, Position-Specific Scoring Matrix (PSSM)_DWT and autocross-covariance of secondary structures(AC_Struc), which were extracted based on evolutionary information, sequence composition, physiochemical properties and predicted structural information, respectively. These features are fed into various loosely-coupled classifiers such as SVM and random forest. Then, a logistic regression model was applied to evaluate the contributions of these individual classifiers and to make the final prediction. When performing on the training dataset PDB1075, the proposed method achieves an accuracy of 83.53%. On the independent dataset PDB186, the method achieves an accuracy of 81.72%, which outperforms many existing methods. These results suggest that the framework is able to orchestrate various predicted models flexibly with good performances.

DNA-binding proteins play pivotal roles in alternative splicing, RNA editing, methylating and many other biological functions for both eukaryotic and prokaryotic proteomes. Predicting the functions of these proteins from primary amino acids sequences is becoming one of the major challenges in functional annotations of genomes. Traditional prediction methods often devote themselves to extracting physiochemical features from sequences but ignoring motif information and location information between motifs. Meanwhile, the small scale of data volumes and large noises in training data result in lower accuracy and reliability of predictions. In this paper, we propose a deep learning based method to identify DNA-binding proteins from primary sequences alone. It utilizes two stages of convolutional neutral network to detect the function domains of protein sequences, and the long short-term memory neural network to identify their long term dependencies, an binary cross entropy to evaluate the quality of the neural networks. When the proposed method is tested with a realistic DNA binding protein dataset, it achieves a prediction accuracy of 94.2% at the Matthew's correlation coefficient of 0.961. Compared with the LibSVM on the arabidopsis and yeast datasets via independent tests, the accuracy raises by 9% and 4% respectively. Comparative experiments using different feature extraction methods show that our model performs similar accuracy with the best of others, but its values of sensitivity, specificity and AUC increase by 27.83%, 1.31% and 16.21% respectively. Those results suggest that our method is a promising tool for identifying DNA-binding proteins.

Fatty acids (FAs) play essential roles in regulating differentiation and proliferation by affecting gene expression in various cell types. However, their potential functions in bovine cells remain unclear. Herein, we examine the differentiation and proliferation of bovine skeletal muscle-derived satellite cells (MDSCs) after incubation with three types of representative FAs (palmitic acid, oleic acid and docosahexaenoic acid) by western blotting, immunofluorescence assays, flow cytometry analysis and EdU incorporation assays. The myotube fusion rate, myotube length and expression levels of muscle differentiation-related gene myogenin (MYOG) and myosin heavy chain 3 (MYH3) increased significantly, although the FAs did not affect proliferation. Additionally, FA-induced bovine MDSC differentiation increased ELOVL3 expression and relocation of ELOVL3 to cytoplasmic lipid droplets in the differentiation of bovine MDSCs. Moreover, the effect of FAs on bovine MDSC differentiation was inhibited upon ELOVL3 downregulation. Collectively, these data indicate that FAs promote bovine MDSC differentiation by regulating ELOVL3 expression.

ELOVL3 is involved in elongating saturated and monounsaturated fatty acids, and is a critical enzyme for lipid accumulation in brown adipocytes during the early phase of tissue recruitment. In addition, ELOVL3 is related to increased fatty acid oxidation in brown adipocytes. However, the potential functions of ELOVL3 in bovine cells remain unclear. Herein, we aimed to elucidate the effect of the ELOVL3 on the differentiation of bovine skeletal muscle-derived satellite cells (MDSCs). Western blot and immunofluorescence analyses were used for elucidating ELOVL3 expression pattern in bovine MDSCs during differentiation in vitro. We activated or inhibited ELOVL3 to study the effect of alterations in its expression on in vitro differentiation of bovine MDSCs. ELOVL3 expression increased gradually during bovine MDSC differentiation, and its levels were higher in the more highly differentiated myotubes. Activation of ELOVL3 promoted MDSC differentiation, while inhibition of ELOVL3 hindered differentiation of these cells. Here, for the first time, we demonstrate the importance of ELOVL3 during bovine MDSC differentiation, which may assist in increasing beef cattle muscularity.

Objectives Paclitaxel (PTX) is frequently used in the clinical treatment of solid tumors. But the PTX-resistance is a great obstacle in cancer treatment. Exploration of the mechanisms of drug resistance suggests that tumor suppressor genes (TSGs) play a key role in the response of chemotherapeutic drugs. TSGs, a set of genes that are often inactivated in cancers, can regulate various biological processes. In this study, an overview of the contribution of TSGs to PTX resistance and their underlying relationship in cancers are reported by using GeneMANIA, a web-based tool for gene/protein function prediction. Methods Using PubMed online database and Google web site, the terms “paclitaxel resistance” or “taxol resistance” or “drug resistance” or “chemotherapy resistance”, and “cancer” or “carcinoma”, and “tumor suppressor genes” or “TSGs” or “negative regulated protein” or “antioncogenes” were searched and analyzed. GeneMANIA data base was used to predict gene/protein interactions and functions. Results We identified 22 TSGs involved in PTX resistance, including BRCA1, TP53, PTEN, APC, CDKN1A, CDKN2A, HIN-1, RASSF1, YAP, ING4, PLK2, FBW7, BLU, LZTS1, REST, FADD, PDCD4, TGFBI, ING1, Bax, PinX1 and hEx. The TSGs were found to have direct and indirect relationships with each other, and thus they could contribute to PTX resistance as a group. The varied expression status and regulation function of the TSGs on cell cycle in different cancers might play an important role in PTX resistance. Conclusion A further understanding of the roles of tumor suppressor genes in drug resistance is an important step to overcome chemotherapy tolerance. Tumor suppressor gene therapy targets the altered genes and signaling pathways and can be a new strategy to reverse chemotherapy resistance.