Abstract:Chitin is the second largest natural polysaccharide after cellulose, which is polymerized by N-acetyl-D-glucosamine, having important application value in agriculture, industry, medical treatment and other fields. Natural chitin exists in a highly crystalline state with complex barrier against degradation. Bacteria can secrete multiple chitinases with special functions to degrade chitin efficiently. Chitinases mainly distributed in GH18 and GH19 families in CAZy database. There are obvious phenomena of gene amplification and multi-domain combination of chitinase genes in bacteria. Chitinases with various action modes in different GH families can act synergistically to break the barrier and complete efficient degradation of crystalline chitin. Therefore, in-depth analysis of the structure and function of bacterial chitinase is of great significance for efficient degradation and high-value conversion of chitin. In this paper, the classification and structural characteristics of bacterial chitinase were introduced, which laid a foundation for further research on the functional mechanism of the enzyme. After that, the action mechanism of chitinases belong to GH18 and GH19 families, including the binding mechanism of enzyme to substrate, catalytic mechanism was summarized to further understand the characteristics of chitinase at molecular level. It is worth noting that processibility is an important characteristic of chitinase to efficiently degrade crystalline chitin, so the molecular mechanism of chitinases, including the effects of polar amino acid residues and aromatic residues on processibility was focused on. In addition, the synergistic degradation modes of extracellular chitin degradation enzymes in 3 different bacterial were summarized, which provided a theoretical basis for the design of efficient chitin degradation enzymes. Through a review of the research progress of molecular modification of chitinases, the role of protein engineering design strategy based on structural bioinformatics and big data deep learning in future modification is prospected, which provides a new perspective and ideas for the design and rational modification of chitinase. To sum up, this paper introduces the relative knowledge of chitinase from structure to mechanism and function to application, which provides a comprehensive foundation for further study of chitinase, the structural and molecular basis for the design of high-functional enzyme, and a theoretical basis for the application of chitinase.

Abstract:Enzymes in the “Auxiliary Activities” (AA) 3 family in the Carbohydrate-Active enZYmes Database (CAZy) belong to the glucose-methanol-choline family. All the members in AA3 family use flaxin-adenine dinucleotide (FAD) as a cofactor, assisting enzymes from other AA families to perform their functions via their reaction products (H₂O₂ or hydroquinone), or facilitating glycoside hydrolase to degrade lignocellulose. According to the structure and sequence similarity, the enzymes from AA3 family were further subdivided into 4 subfamilies, mainly including cellobiose dehydrogenase, aryl-alcohol oxidase and glucose oxidoreductase, alcohol oxidase, pyranose oxidoreductase. On account of the high expression level, efficient catalysis, diversity of biotransformation, and fast electron transport kinetics, AA3 enzymes have become an interesting target for electrochemical biosensors. In order to improve the performance parameters of biosensors, a tremendous development has been obtained in the field ranging from the production of new AA3 enzymes with tailor-designed properties, such as the improved specific activity, low O₂ sensitivity and enhanced redox mediator interaction, to their efficient immobilization in a variety of nanomatrices. Thus in this paper, we provide an overview of the phylogenetic, molecular, and catalytic properties of CAZy-AA3 family enzymes, and the latest research progress of AA3 enzymes used in electrochemical biosensors was also summarised. In the development trend of the future study, it calls for the combination of protein engineering skills with expertise on redox mediator/polymer synthesis and electrochemistry to facilitate the application of AA3 enzymes in biosensor.

Abstract:Inflammasomes are macromolecular multiprotein complexes that exist in the cytoplasm and participate in innate immune defense. They are activated under infection or stress, triggering the release of pro-inflammatory cytokines such as IL-1β and IL-18 and inducing pyroptosis. NLRP3 recognizes various pathogen-associated molecular patterns (PAMP) and danger-associated molecular patterns (DAMP) produced during virus replication, which initiates the NLRP3 inflammasome-dependent antiviral immune response. However, some viruses have evolved complex strategies to evade innate immune surveillance by targeting inflammasomes. IL-1β has profound influence on host immune response to viral infections. Besides, the activation of inflammasome is imperative in the maturation of IL-1β. Therefore, inflammasome is a potential target for both the host and viruses to regulate immune responses. Here, we discuss the crosstalk between the NLRP3 inflammasome and viruses, providing an overview of viral infection-induced NLRP3 inflammasome activation, and the immune escape strategies of viruses through modulating the NLRP3 inflammasome activity.

Abstract:Toll-like receptors (TLRs) belong to the pattern recognition receptor (PRR) family which can recognize multiple pathogen-associated molecular patterns (PAMP) and damage-related molecular patterns (DAMP). TLRs are widely expressed in the innate immune system, and link up the innate and adaptive immunity through indirectly lead to T cell activation by promoting the expression of costimulatory molecules on antigen presenting cells (APC) when binding to their ligands. Previous studies mainly expound the function and mechanism of TLRs in activating the innate immune cells, but few studies on its function in adaptive immune cells. However, it has now become evident that TLRs are also expressed in T cells, and can directly regulate the metabolism and function of T cells in the form of costimulatory molecules without APCs. It has been reported that TLRs can regulate the function of different T cell subsets via directly regulating their metabolism. The present review attempts to summarize the direct regulation of TLR signaling in metabolism and immune function of different T cell subsets, which provides a new idea for the prevention and treatment of T cell-mediated diseases such as cancer, inflammation and autoimmune diseases.

Abstract:The neurons can transform different spatiotemporal patterns of synaptic inputs to the action potential sequences with high temporal precision. This flexible and reliable information coding strategy plays a crucial role in the process by which the nervous system generates the specific activity patterns required by dynamical situation or specific task. The initiation of an action potential follows an all-or-none principle. When the depolarization of membrane potential exceeds a threshold value, the neuron fires an action potential. The action potential threshold is highly variable within and between cells, and its specific dynamics depends on the stimulus input and firing history. In particular, the spike threshold is sensitive to the membrane voltage changes preceding the action potential. Two primary biophysical mechanisms for such state dependence of the spike threshold are Na⁺ inactivation and K⁺ activation. In most neurons, the action potentials are initiated in the axon initial segment, and the threshold variability at this site is the crucial factor that determines how neurons transfer spatiotemporal information. However, the action potentials in electrophysiological experiments are recorded in the cell body or proximal dendrite. The threshold variability at these sites is higher than that in the axon initial segment, which mainly arises from the backpropagation of axonal action potentials. Based on somatic recordings, it is shown that the spike threshold dynamics determines the transformation principle of spatiotemporal information in the neurons, which enhances the temporal coding, feature selectivity, gain modulation, and coincidence detection. In this paper, we first introduce the conception of spike threshold and its calculation methods. Then, we present an exhaustive review on the main findings of the spike threshold variability and its origins in recent years, and mainly discuss the significance of spike threshold variability for neuronal coding. Finally, we raise several key issues on the spike threshold that need to be addressed in the future.

Abstract:Peripheral nerve injury (PNI) is a disease in which peripheral nerve cells are damaged or necrotic due to compression, traction, cutting and ischemia. Pathological changes of peripheral nerve injury include impaired axoplasmic transport, axonal degeneration, schwann cell injury, segmental demyelination and complete Waller’s degeneration. Autogenous nerve transplantation (ANT) is the gold standard for treatment of large peripheral nerve defects (>1 cm in rats and >3 cm in humans). In addition to autologous transplantation, stem cell transplantation will promote peripheral nerve regeneration, improve myelin sheath formation and nerve survival. Neurotrophic factors include nerve growth factor (NGF), glial cell line-derived neurotrophic factor (GDNF), brain-derived neurotrophic factor (BDNF) can promote neuronal differentiation, axon growth and synaptic connection. New biomaterials including chitosan scaffold (CS), silk fibroin (SF), 3-hydroxyoctanoic acid co-3-hydroxydecanoic acid/polycaprolactone (P(3HO-3HD)/PCL75/25) or acellular cauda equina allograft (ACEA) can support and guide the growth of axon. Combined with 3D printing technology, personalized neural conduits can be designed and manufactured. Electroacupuncture stimulation of Huan-jump point (GB 30) and Zusanli point (ST 36) can prevent apoptosis of neurons and promote the growth of axon. The combination of several materials and formation of tissue engineered nerve graft (TENG), will have better effects on repairing of nerve injury. Thus, the role and mechanisms of these methods in the repair of peripheral nerve injury were reviewed, and their clinical application was prospected.

Abstract:Cerebellum, as a classical main brain region of motor control, has been found in many recent studies to be also associated with autism, schizophrenia and reward-related cognitive function and social behavior, therefore, the study of cerebellum has received increasing attention. Studying the neural mechanism of cerebellar participation in movement learning and motion control is one of the most important subjects in neuroscience. Muscular coordination and biokinematic features of eye movement are simpler than the other types of movements, which makes it an ideal model to study the role of cerebellum in movement control. As one of the main ways to collect information, vision is important to our daily life. The 3 main types of eye movements (saccade, smooth-pursuit eye movement (SPEM) and fixation) that are used to ensure clear vision must be precisely controlled by the cerebellum to ensure that stationary or moving objects remain in the center of the fovea. Abnormal eye movement could lead to visual impairment and is used as a clinical indicator for diagnoses of a variety of diseases. Therefore, the study of eye movement control has important medical and biological significance. Although there is a basic understanding of the role of cerebellar cortex and caudal fastigial nuclei in modulating eye movements, the exact neural mechanism of encoding kinematics of eye movements, especially the neural mechanism underlying the control of SPEM in caudal fastigial remains unclear. This review discusses the main open questions in cerebellar researches regarding motor control, cognition and the potential application value of studying cerebellum, summarizes the relevant literatures on cerebellar implication in eye movement control in recent years, and discusses our recent findings using single-cell electrophysiological recordings and mathematical linear regression models, revealing that the neurons in cerebellar cortex and nuclei are both involved in the precise control of different types of eye movements, whereas with different principles for the encoding of different kinematic parameters for different types of eye movements. Moreover, based on previous findings by detecting microsaccades, we discuss possible neural mechanism underlying the involvement of cerebellar nuclei in regulating visual fixation. In addition, this review discusses the new opportunities brought by recent technological advances in neuroscience, and provides new ideas for future cerebellum-related research and the optimization of brain-controlled prosthesis, potentially by improving the control of kinematic parameters separately.

Abstract:Magnetic field is ubiquitous in our life. In order to explore the biological effect of magnetic field, a lot of research work has been carried out. As an emerging model organism, zebrafish plays an important role in exploring the relationship between magnetic fields and physiological functions. This paper reviews the current studies on magnetobiology in zebrafish. Previous studies have shown that magnetic fields can cause developmental malformation, lead to cell apoptosis and delayed zebrafish development, affect zebrafish swimming behavior and direction preference, change their circadian rhythms, and affect reproductive and immune functions. Zebrafish may have more than one magnetic induction mechanism, in addition to the current proposed magnetic ore crystal model, radical-pair mechanism model and electromagnetic induction model. Magnetic field-induced DNA damage, abnormal Ca²⁺ homeostasis, changes in microtubule polymerization rate, stress response, and changes in the expression of the circadian clock gene cry can partially explain the above phenomena. In view of the existing problems such as inconsistent parameters and unclear mechanism in biological magnetic induction research, combined with the advantages of zebrafish, the authors propose the potential direction of zebrafish in magnetic biology research in the future: to establish magnetic fields and biological parameters controllable magnetic biology research model based on zebrafish; non-invasive in vivo tracking of related life activities, visualized study of magnetic biological phenomena; research on the relationship between magnetic fields and biological rhythms based on Cry protein.

Abstract:The unique three-dimensional structure of protein is closely related to its biological function. Therefore, investigating the three-dimensional structure of protein is helpful to reveal its biological function mechanism. The study of protein three-dimensional structure in the solution state using nuclear magnetic resonance (NMR) spectroscopy can accurately reveal the relationship between protein structure and biological function. The aim of this article is to provide an effective strategy for accurate analysis of protein three-dimensional structure using NMR and combination with other biophysical means such as molecular modeling and computation methods through reviewing the research progress and latest technology in these fields. Firstly, we summarize the theory of NMR for studying protein three-dimensional structure. Secondly, we deeply review the theory and technology of NMR analysis of protein three-dimensional structure, including isotope labeling of proteins (labeling methods, expression systems, and purification techniques), NMR data acquisition and analysis software, analysis of amino acid sequence, secondary structural unit and three-dimensional structure of protein using NMR and combination with other biophysical means (F?rster/fluorescence resonance energy transfer (FRET), chemical cross-linking coupled with mass spectrometry (CXMS), small angel X-ray scattering (SAXS), and cryo-electron microscopy (Cryo-EM)), and analysis of excited state structure of protein molecules using Carr-Purcell-Meiboom-Gill relaxation dispersion (CPMG RD) and chemical exchange saturation transfer (CEST) techniques. Thirdly, we overview recent researches about application of NMR for analysis of three-dimensional structure of high molecular mass single chain protein and supramolecular protein complex. Fourthly, we elaborate the latest progress in the field of NMR combined with molecular modeling and computation methods. Lastly, we summarize challenges and prospects of application of NMR for studying protein three-dimensional structure in the future.

Abstract:In the past 40 years, metal nanomaterials have developed rapidly. Because the special properties of metal nanomaterials differ from macroscopic crystals, they have gradually played indispensable roles in all walks of life. At present, human beings are facing increasingly serious ecological problems, such as lack of resources and environmental pollution. Therefore, the green ecological model combining metal nanomaterials with biology is an irresistible general trend. This article mainly focuses on the bio-green synthesis methods of the preparation of the metal and metal oxide nanomaterials with various plant extracts, microorganisms, proteins and other biological materials as reducing agents. These methods are easy to operate and utilize biological reagents with unique physiological structures, which are not only environmentally friendly, but can also limit the growth of nanomaterials, overcome the enormous surface energy, and prevent the enlargement of the metal nanomaterials in size and structure due to Ostwald ripening or agglomeration. In addition, the combination of specific structures of biomaterials with metal nanomaterials usually exhibits synergy or new physicochemical and physiological properties. The surface plasmon resonance of metal nanomaterials will be enhanced under laser irradiation and can emit energy in the form of heat, so it can yield unusually brilliant results in the treatment of tumors. At the same time, with the enhancement of surface plasmon resonance effect, the Raman scattering of the material can be significantly enhanced, so Raman scattering bioimaging can be used to monitor the condition of the upper tumor cells in conjunction with photothermal therapy. Bacteriostatic and antibacterial is a characteristic of most metals, so metal nanomaterials are regarded as a new class of antibacterial biological reagents. The difficulty of wound self-healing lies in bacterial infection, so metal nanomaterials with antibacterial properties have become candidates for treating wounds and other conditions. Many characteristics of metal nanomaterials can be used as media for identifying biological signals and converting them into photoelectric signals to monitor changes with instruments, which has more convenient operation and higher accuracy. Metal nanomaterials prepared from biomaterials can improve and supplement existing medical methods, and accomplish medical goals conveniently and effectively. The biochemical preparation of metal nanomaterials will bring more intersections between the nanometer and biological fields in the future. There will be more interdisciplinary workers to work hard on the existing challenges, and there will be an indispensable figure of metal nanomaterials in medical field in the future.

Abstract:Objective PAMM (peroxiredoxin-like 2 activated in M-CSF stimulated monocytes) is a secreted protein which shows high expression in white adipose tissues, but the roles of PAMM in many biological processes are still unknown. To provide new clues for PAMM function research, this study is intended to investigate the possible role of PAMM in white adipogenesis as well as the downstream genes regulated by PAMM.Methods Adipogenic differentiation and adipogenic inhibition models of human ADSCs (adipose-derived stem cells) were established using adipogenic cocktail (AC) medium or AC plus IL-1α, respectively. Expression of PAMM in ADSCs was suppressed or overexpressed using siRNA interference or plasmid transfection. Gene array, mRNA sequencing and quantitative RT-PCR were employed to detect the mRNA expression level. Western blot was used to evaluate protein expression and Oil red O staining was adopted to assess lipid droplets accumulation.Results Expression of PAMM was increased following white adipogenic differentiation of ADSCs and decreased following adipogenic inhibition. However, when PAMM was knocked down or overexpressed before adipogenic differentiation of ADSCs, the downregulation and upregulation of PAMM expression generally did not exert evident influence on the formation of lipid droplets and the expression of adipogenesis-related genes. Similarly, PAMM knockdown in highly differentiated adipocytes had no obvious effect on cellular morphology and the accumulation of lipid droplets. Finally, a bunch of functional genes and gene sets regulated by PAMM, such as SULF1, A2M genes and P53 stability gene set, were screened out and confirmed through siRNA interference, mRNA sequencing and qRT-PCR.Conclusion This study suggests PAMM could serve as a useful marker of white adipogenic differentiation of ADSCs, but it exerts no evident effect on white adipogenic differentiation. The unveiled downstream genes and gene sets regulated by PAMM would provide new clues for the functional research of PAMM.

Abstract:Objective Human secretory phospholipase A₂ group IIA (sPLA₂-IIA) plays an important role in the regulation of cellular lipid metabolism and signal transmission, and participates in a variety of acute and chronic inflammatory responses. Investigating the relationship between their dynamics, allostery and functions is of important significance.Methods The elastic network model (ENM), perturbation-response scanning (PRS) and protein structure network (PSN) methods are utilized to analyze the structural dynamics and allostery of 31 human sPLA₂-IIA members, and explore the relationship between their shared/specific dynamics and functions.Results The results show that the catalytic residues and cysteine residues involved in disulfide bond formation, important for the enzyme’s catalysis and structural stability respectively, are of minimal mobility, which are the requirements for the enzyme’s shared functions; however, the 5 regions involved in the association with calcium ion/membrane are of high mobility, which embody the specificity of sPLA₂-IIA members. Additionally, the PRS analysis reveals that the above five regions have a high sensitivity to external perturbations, suggesting their important roles in allosteric modulation, while those residues with a low sensitivity play an important role in maintaining structural stability. Finally, the ANM analysis indicates that the strong correlation movements around the catalytic sites of sPLA₂, are helpful for the enzyme’s catalytic function exertion.Conclusion This study is helpful for the deep understanding of the dynamics and functionally allosteric mechanism of human sPLA₂-IIA, and can provide a guide for drug design and accurate design of proteins with finely tuned activities.

Abstract:Objective Allostery plays important roles in regulating protein biological functions. How to effectively identify the allosteric signal transduction pathway and the related key residues from the tertiary structure of proteins is a hot scientific problem in the research field of protein structure-function relationship.Methods In the present work, a method based on elastic network model (ENM) combined with force distribution calculation was used to investigate the response of proteins to the external loading forces, and then the allosteric pathway and associated key residues in proteins were identified based on the analysis of internal force distribution. In this method, external forces were exerted on the allosteric site of the protein, and then the deformations and internal force distributions within the protein in response to the external loading forces were analyzed. Based on these analyses, the key sites that are coupled with the deformation of the force-loading region were identified, and the transduction pathway of the force signals in the protein was obtained.Results By using the proposed method, two proteins, the human phosphoglycerate kinase (hPGK) and protein tyrosine phosphatase (PTP) PDZ2 domain, were investigated to identify the allosteric pathway and the related key residues in the systems. For hPGK, two allosteric pathways were identified, which mediate the transduction of force signals from the substrate binding site to the hinge region of the protein. For PTP PDZ2, two long-range allosteric pathways, from the ligand binding site to the distantly opposite side of the protein, were also successfully revealed. The calculation results are consistent with the experimental observations and the results obtained with molecular dynamics simulations.Conclusion This study provides an effective method for the identification of key residues and allosteric pathway in proteins.

Abstract:Objective The prediction model based on PSSM (position-specific scoring matrix) has achieved good results, and various optimization methods based on PSSM are also being continuously developed. However, the accuracy rate is relatively lower. In order to further improve the prediction accuracy rate, this paper does further research based on the CNN algorithm.Methods In this paper, PSSM is used to process the letter sequence into a numeric matrix, and through a convolutional neural network (CNN) algorithm for classification. The 3 promoter sequences of Sigma38, Sigma54 and Sigma70 of E.coli K-12 (Escherichia coli K-12, hereinafter referred to as Escherichia coli) are used as the positive sets, and the sequences of the Coding and Non-coding regions of Escherichia coli are the negative set.Results In the prediction of Escherichia coli for the two-classification for promoters, the accuracy rate reaches 99%, and the success rate of promoter prediction is close to 100%; in the three-classification for Sigma38, Sigma54 and Sigma70 promoters, the prediction accuracy rate is 98%, and for each the prediction accuracy of these sequences can reach 0.98 or more. Finally, we tried 4 classifications of 3 promoters of Sigma38, Sigma54 and Sigma70 with Coding area or Non-coding area sequences respectively, the accuracy of prediction was 0.98. The prediction accuracy of the ten-fold cross-validation of the balanced samples of the Sigma promoters can reach more than 0.95, the Hamming distance is 0.016, and the Kappa coefficient is 0.97.Conclusion Compared with other classification algorithms such as SVM (support vector machine), the CNN classification algorithm has more advantages, and based on the classification advantages of CNN, the coding method can also be simplified.

Abstract:Objective Male pattern baldness (MPB), or androgenetic alopecia (AGA), is a common type of hair loss in men, with an estimation that approximately 80% of the phenotypic variance can be explained by genetic factors. Most prediction models were developed in European and few MPB associated (single nucleotide polymorphisms,SNPs) have been validated in East Asian population. In this study, MPB associated SNPs in European were verified in Chinese population, and MPB risk prediction models were built based on those SNP data.Methods We examined 486 genetic variants previously reported associated with MPB, and assessed their impacts on hair loss in 312 Chinese individuals. Different sets of SNPs were selected by stepwise regression and Lasso regression. Logistic regression algorithm was used to construct the prediction models and the evaluations were conducted by the method of 10-fold cross validation. We further compared the prediction accuracy among logistic regression, k-nearest neighbor classifier, random forest and support vector machine.Results 174 SNPs demonstrated significant associations with MPB (P<0.05). Among those SNP markers, 22 SNPs and 25 SNPs were selected by different screening methods. Two logistic regression model considering the genotypes of 22 and 25 SNPs demonstrate that the risk of MPB were predictable at AUC (area under curve) level of 0.85 and 0.84. Prediction accuracy was slightly reduced after performing 10-fold cross validation, 0.81 and 0.77 respectively. Moreover, the AUC of both models reaches maximum (0.89) when age was added as a predictive factor. From the running results, the logistic regression prediction model had obvious advantages.Conclusion Overall, although the accuracy obtained here has not reached a clinically desired level, our model still has great potential for genetic prediction of MPB, which may assist decision making on early MPB intervention actions and in forensic investigations.

Abstract:Objective Bees are born with rich olfactory recognition capabilities. Foraging, mating, navigation and social activities all rely on their olfactory system. It is an ideal model for studying behaviors and neural mechanisms of olfactory perception, learning and memory. Bees can distinguish a compound odor as a configural character, and can distinguish the components individually as well, but yet it is not clear whether the feature component of a compound odor is stored into the memory as a key cue in a feature-dependent context.Methods In the feature-positive (FP: AB+, B-) and feature-negative (FN: AB-, B+) olfactory discrimination tasks, we train bees to learn to associate an odor and a sugar reward. During the mid-term memory (3 h) and long-term memory (24 h) tests, response to the trained odors AB and B, and the feature odor A were tested.Results We found that in the FP task, bees can form stable mid-term and long-term memories of the trained odors. The memory of the feature odor was well stored as the rewarded compound. In the FN task, bees were able to distinguish the two trained odors, but their response to the unrewarded compound increased with the passage of time.Conclusion Our results suggest that bees selectively consolidate the reward associated information into long-term memory no matter it is the compound or the components. Interestingly, the feature component is not the key factor to be consolidated into the memory system. Our study indicates that selective memory consolidation is supposed to be an important strategy for simple animals to efficiently encode survival-related information.

Abstract:Objective Long non-coding RNAs (lncRNAs) participate in a variety of vital biological processes and closely relate with various human diseases. The prediction of lncRNA-disease associations can help to understand the mechanisms of human disease at the molecular level, and also contribute to diagnosis and treatment of diseases. Most existing methods of predicting the lncRNA-disease associations ignore the deep embedding features hiding in lncRNA/disease network topological structures. Moreover, randomly selecting the negative samples will affect the robustness of predictors.Methods Here we first set up a high quality dataset by using an effective strategy to select the negative samples (i.e., pairs of non lncRNA-disease association) with relatively higher quality instead of randomly selecting the negative samples, then proposed a novel method (called NELDA) to predict the potential lncRNA-disease associations by building 4 deep auto-encoder models to learn the low dimensional network embedding features from the lncRNA/disease similarity networks, and lncRNA-disease association network, respectively. NELDA takes the lncRNA/disease similarity network embedding features as the input of one support vector machine (SVM) classifier, and the lncRNA/disease association network embedding features as the input of another SVM classifier. The prediction results of these two SVM classifiers are fused by the weighted average strategy to obtain the final prediction results.Results In 10-fold cross-validation (10 CV) test, the AUC of NELDA achieves 0.982 7 on high quality dataset, which is 0.062 7 and 0.020 7 higher than that of other two state-of-the-art methods of LDASR and LDNFSGB, respectively. In the case studies of stomach cancer and breast cancer, 29/40 (72.5%) novel predicted lncRNAs associated with stomach and breast cancers are supported by recent literatures and public datasets.Conclusion These experimental results demonstrate that NELDA is a superior method for predicting the potential lncRNA-disease associations. It has the ability to discover the new lncRNA-disease associations.

Abstract:Objective In order to construct a viral vector that can simultaneously express two non-fusion proteins in the whole host plants.Methods Agrobacterium infectious clone pYL156 containing tobacco rattle virus (TRV) genomic RNA2 was used to construct dual expression vector pTRV2e² by deleting 279 bp of 5" end of 2b gene, changing initiation codon of 2b gene ATG to AGG, and introducing the subgenomic promoter of pea early-browning virus (PEBV) coat protein (cp) gene. Different exogenous genes were cloned into the downstream of 2b and PEBV cp subgenomic promoters to measure the ability of virus TRVe² to express two foreign proteins, assess the stability of reconstructed TRVe² and analyze the function of proteins in the seedlings of Nicotiana benthamiana.Results TRVe² could simultaneously and rapidly produce two non-fused target proteins and express at least a 70 ku foreign protein in the whole host plants; TRVe² harboring 2.0 kb exogenous gene could stably exist in N. benthamina plants and could be served as technical means for analyzing the biological function of the proteins and the interaction between two proteins.Conclusion Recombinant virus TRVe² constructed in this study provide a toolbox for fast and efficient production of double foreign proteins and for analysis of the interaction between two proteins in the host plants.