Enrichment and characterization of an anaerobic cellulolytic microbial consortium SQD-1.1 from mangrove soil

Enrichment of microbial consortia provides an approach to simulate and investigate microbial communities in natural environments. In this study, a cellulolytic microbial consortium SQD-1.1 was enriched from mangrove soil of Qinglan port (Hainan, China) by 27 times continuous subcultivation under anaerobic static conditions. The consortium could completely degrade 0.2 % (w/v) filter paper within 3 days and utilized it as the sole carbon source. PCR-denaturing gradient gel electrophoresis analysis revealed a stable microbial community structure in the incubation process of 10 days and in the procedure of subcultivation. Twenty-four operational taxonomic units belonging to seven phyla were obtained from the full-length 16S rRNA gene library. Five clones, closest related to the genera Alkaliflexus, Clostridium, Alistipes, Spirochaeta, and Trichococcus, were the predominant ones. Among them, M117, phylogeneticly showing high similarity (16S rRNA gene identity, 95.3 %) with the cellulolytic anaerobic bacterium Clostridium straminisolvens CSK1^T, was the potential key cellulolytic bacterium. Using the plate cultivation method, 12 strains, including one potential new species and four potential new species of new genera, were isolated. The strain P2, corresponding to the most frequently detected clone (M05) in the 16S rRNA gene library, showed both CMCase and xylanase activity and may be another important cellulolytic bacterium. The findings of cellulase activity in cell pellet and cohesion and dockerin domains in metagenome data further suggested the potential of utilization of cellulosomes by the consortium to degrade cellulose. Consortium SQD-1.1 provides a candidate for investigating the mechanism of cellulose degradation under anoxic conditions in natural environments.     

Effects of porcine acellular dermal matrix treatment on wound healing and scar formation: Role of Jag1 expression in epidermal stem cells

Skin wound healing involves Notch/Jagged1 signaling. However, little is known how Jag1 expression level in epidermal stem cells (ESCs) contributes to wound healing and scar formation. We applied multiple cellular and molecular techniques to examine how Jag1 expression in ESCs modulates ESCs differentiation to myofibroblasts (MFB) in vitro, interpret how Jag1 expression in ESCs is involved in wound healing and scar formation in mice, and evaluate the effects of porcine acellular dermal matrix (ADM) treatment on wound healing and scar formation. We found that Jag1, Notch1 and Hes1 expression was up-regulated in the wound tissue during the period of wound healing. Furthermore, Jag1 expression level in the ESCs was positively associated with the level of differentiation to MFB. ESC-specific knockout of Jag1 delayed wound healing and promoted scar formation in vivo. In addition, we reported that porcine ADM treatment after skin incision could accelerate wound closure and reduce scar formation in vivo. This effect was associated with decreased expression of MFB markers, including α-SMA Col-1 and Col-III in wound tissues. Finally, we confirmed that porcine ADM treatment could increase Jag1, Notch1 and Hesl expression in wound tissues. Taken together, our results suggested that ESC-specific Jag1 expression levels are critical for wound healing and scar formation, and porcine ADM treatment would be beneficial in promoting wound healing and preventing scar formation by enhancing Notch/Jagged1 signaling pathway in ESCs.     

T and B cell Epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research

COVID-19 caused by SARS-CoV-2 is pandemic with a severe morbidity and mortality rate across the world. Despite the race for effective vaccine and drug against further expansion and fatality rate of this novel coronavirus, there is still lack of effective antiviral therapy. To this effect, we deemed it necessary to identify potential B and T cell epitopes from the envelope S protein. This can be used as potential targets to develop anti-SARS-CoV-2 vaccine preparations. In this study, we used immunoinformatics to identify conservative B and T cell epitopes for S proteins of SARS-CoV-2, which might play roles in the initiation of SARS-CoV-2 infection. We identified the B cell and T cell peptide epitopes of S protein and their antigenicity, as well as the interaction between the peptide epitopes and human leucocyte antigen (HLA). Among the B cell epitopes, ‘EILDITPCSFGGVS’ has the highest score of antigenicity and great immunogenicity. In T cell epitopes, MHC-I peptide ‘KIADYNYKL’ and MHC-II peptide ‘LEILDITPC’ were identified as high antigens. Besides, docking analysis showed that the predicted peptide ‘KIADYNYKL’ was closely bound to the HLA-A*0201. The results of molecular dynamics simulation through GROMACS software showed that ‘HLA-A*0201~peptide’ complex was very stable. And the peptide we selected could induce the T cell response similar to that of SARS-CoV-2 infection. Moreover, the predicted peptides were highly conserved in different isolates from different countries. The antigenic epitopes presumed in this study were effective new vaccine targets to prevent SARS-CoV-2 infection.     

Detection of tumor marker CA125 in ovarian carcinoma using quantum dots

The fluorescent labeling of biological materials usingsmall-molecule organic dyes is widely employed in bio-logical imaging and clinical diagnosis. Organic fluoro-phores, however, have certain characteristics that limittheir advantages in some applications. These limitationsinclude narrow excitation bands and broad emissionbands with red spectral tails, which make the simultaneousevaluation of several light-emitting probes difficult due tospectral overlap. Also, many organic dyes exhibit highp…     

Quantitative analysis of micro-CT imaging and histopathological signatures of experimental arthritis in rats

Micro-computed tomographic (micro-CT) imaging provides a unique opportunity to capture 3-D architectural information in bone samples. In this study of pathological joint changes in a rat model of adjuvant-induced arthritis (AA), quantitative analysis of bone volume and roughness were performed by micro-CT imaging and compared with histopathology methods and paw swelling measurement. Micro-CT imaging of excised rat hind paws (n = 10) stored in formalin consisted of approximately 600 30-mum slices acquired on a 512 x 512 image matrix with isotropic resolution. Following imaging, the joints were scored from H&E stained sections for cartilage/bone erosion, pannus development, inflammation, and synovial hyperplasia. From micro-CT images, quantitative analysis of absolute bone volumes and bone roughness was performed. Bone erosion in the rat AA model is substantial, leading to a significant decline in tarsal volume (27%). The result of the custom bone roughness measurement indicated a 55% increase in surface roughness. Histological and paw volume analyses also demonstrated severe arthritic disease as compared to controls. Statistical analyses indicate correlations among bone volume, roughness, histology, and paw volume. These data demonstrate that the destructive progression of disease in a rat AA model can be quantified using 3-D micro-CT image analysis, which allows assessment of arthritic disease status and efficacy of experimental therapeutic agents.     

Global analysis of an epidemic model with nonmonotone incidence rate

In this paper we study an epidemic model with nonmonotonic incidence rate, which describes the psychological effect of certain serious diseases on the community when the number of infectives is getting larger. By carrying out a global analysis of the model and studying the stability of the disease-free equilibrium and the endemic equilibrium, we show that either the number of infective individuals tends to zero as time evolves or the disease persists.     

Prediction of protein secondary structure content for the twilight zone sequences

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure.