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2.
J Genet Genomics ; 47(1): 27-35, 2020 01 20.
Article in English | MEDLINE | ID: mdl-32111437

ABSTRACT

B cells express B-cell receptors (BCRs) which recognize antigen to trigger signaling cascades for B-cell activation and subsequent antibody production. BCR activation has a crucial influence on B-cell fate. How BCR is activated upon encountering antigen remains to be solved, although tremendous progresses have been achieved in the past few years. Here, we summarize the models that have been proposed to explain BCR activation, including the cross-linking model, the conformation-induced oligomerization model, the dissociation activation model, and the conformational change model. Especially, we elucidate the partially resolved structures of antibodies and/or BCRs by far and discusse how these current structural and further immunogenomic messages and more importantly the future studies may shed light on the explanation of BCR activation and the relevant diseases in the case of dysregulation.


Subject(s)
B-Lymphocytes/immunology , Lymphocyte Activation/immunology , Protein Conformation , Receptors, Antigen, B-Cell/genetics , Genome, Human/immunology , Humans , Lymphocyte Activation/genetics , Receptors, Antigen, B-Cell/immunology , Signal Transduction/genetics , Signal Transduction/immunology
3.
Genomics ; 112(4): 2784-2793, 2020 07.
Article in English | MEDLINE | ID: mdl-32209379

ABSTRACT

Acinetobacter haemolyticus (A. haemolyticus) is a significant Acinetobacter pathogen, and the resistance of A. haemolyticus continues to rise due to abuse of antibiotics and the frequent gene exchange between bacteria in hospital. In this study, we performed complete genome sequencing of two A. haemolyticus strains TJR01 and TJS01 to improve our understanding of pathogenic and resistance of A. haemolyticus. Both TJR01 and TJS01 contain one chromosome and two plasmids. Compared to TJS01, more virulence factors (VFs) associated pathogenicity and resistant genes were predicted in TJR01 due to T4SS and integron associated with combination and transport. Antimicrobial susceptibility results were consistent with sequencing. We suppose TJS01 was a susceptive strain and TJR01 was an acquired multidrug resistance strain due to plasmid-mediated horizontal gene transfer. We hope these findings may be helpful for clinical treatment of A. haemolyticus infection and reduce the risk of potential outbreak infection.


Subject(s)
Acinetobacter/genetics , Genome, Bacterial , Acinetobacter/isolation & purification , Acinetobacter/metabolism , Acinetobacter/pathogenicity , Bacterial Proteins/genetics , Drug Resistance, Bacterial , Genes, Bacterial , Genomics , Humans , Molecular Sequence Annotation , Phylogeny , Respiratory Tract Infections/microbiology , Sputum/microbiology , Virulence Factors/genetics
4.
Sci Rep ; 7(1): 13332, 2017 10 17.
Article in English | MEDLINE | ID: mdl-29042583

ABSTRACT

Salinity effects on microbial communities in saline soils is still unclear, and little is known about subsurface soil microbial communities especially in saline or hypersaline ecosystems. Here we presented the survey of the prokaryotic community in saline soils along a salinity gradient (17.3-148.3 dS/m) in surface (0-10 cm) and subsurface (15-30 cm) saline soils of Qarhan Salt Lake, China. Moreover, we compared them with three paired nonsaline normal soils. Using the high-throughput sequencing technology and several statistical methods, we observed no significant community difference between surface soils and subsurface soils. For environmental factors, we found that TOC was the primary driver of the prokaryotic community distribution in surface saline soils, so was pH in subsurface saline soils. Salinity had more effects on the prokaryotic community in subsurface saline soils than in surface saline soils and played a less important role in saline soils than in saline waters or saline sediments. Our research provided references for the prokaryotic community distribution along a salinity gradient in both surface and subsurface saline soils of arid playa areas.


Subject(s)
Geologic Sediments/chemistry , Geologic Sediments/microbiology , Prokaryotic Cells , Salinity , Soil Microbiology , Soil/chemistry , Biodiversity , Environment , Metagenome , Metagenomics/methods , Microbiota , RNA, Ribosomal, 16S/genetics
5.
Article in English | MEDLINE | ID: mdl-28286741

ABSTRACT

Identifying intestinal microbiota is arguably an important task that is performed to determine the pathogenesis of inflammatory bowel diseases (IBD); thus, it is crucial to collect and analyze intestinally-associated microbiota. Analyzing a single niche to categorize individuals does not enable researchers to comprehensively study the spatial variations of the microbiota. Therefore, characterizing the spatial community structures of the inflammatory bowel disease microbiome is critical for advancing our understanding of the inflammatory landscape of IBD. However, at present there is no universally accepted consensus regarding the use of specific sampling strategies in different biogeographic locations. In this review, we discuss the spatial distribution when screening sample collections in IBD microbiota research. Here, we propose a novel model, a three-dimensional spatial community structure, which encompasses the x-, y-, and z-axis distributions; it can be used in some sampling sites, such as feces, colonoscopic biopsy, the mucus gel layer, and oral cavity. On the basis of this spatial model, this article also summarizes various sampling and processing strategies prior to and after DNA extraction and recommends guidelines for practical application in future research.


Subject(s)
Dysbiosis/diagnosis , Gastrointestinal Microbiome , Inflammatory Bowel Diseases/microbiology , Intestinal Mucosa/microbiology , Microbiota , Specimen Handling/methods , Humans , Inflammatory Bowel Diseases/pathology , Intestinal Mucosa/pathology
6.
Front Microbiol ; 6: 1049, 2015.
Article in English | MEDLINE | ID: mdl-26483774

ABSTRACT

As the replication of genomic DNA is arguably the most important task performed by a cell and given that it is controlled at the initiation stage, the events that occur at the replication origin play a central role in the cell cycle. Making sense of DNA replication origins is important for improving our capacity to study cellular processes and functions in the regulation of gene expression, genome integrity in much finer detail. Thus, clearly comprehending the positions and sequences of replication origins which are fundamental to chromosome organization and duplication is the first priority of all. In view of such important roles of replication origins, tremendous work has been aimed at identifying and testing the specificity of replication origins. A number of computational tools based on various skew types have been developed to predict replication origins. Using various in silico approaches such as Ori-Finder, and databases such as DoriC, researchers have predicted the locations of replication origins sites for thousands of bacterial chromosomes and archaeal genomes. Based on the predicted results, we should choose an effective method for identifying and confirming the interactions at origins of replication. Here we describe the main existing experimental methods that aimed to determine the replication origin regions and list some of the many the practical applications of these methods.

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