Selective alterations of the antibody response to HIV-1

HIV infection leads to progressive alterations of humoral immune functions, including B-cell hyperplasia, hypergammaglobulinemia, elevated autoantibody titers, a poor response to neoantigens and mitogens, polyclonal B-cell activation, monoclonal gammopathies, and a significant deterioration of the antigen-specific humoral response. There is also an important isotypic imbalance of the antibody (Ab) response in the systemic compartment and a profound modification of mucosal immune functions. These abnormalities may contribute to disease progression and development of opportunistic infections, despite the presence of serum-neutralizing anti-HIV Abs. Equally important are the abnormal selection mechanisms of the Ab repertoire that seem to be responsible for B-cell clonal deletions. The V_H3 gene family, which encodes for approx 50% of immunoglobulins expressed by peripheral B-cells from normal adults, is underrepresented in human monoclonal antibodies to HIV-1 and in the peripheral B-cells of AIDS patients. These abnormalities, together with features of germinal center alteration, could be responsible for the clonal elimination of a subset of B-cells, and could contribute to HIV pathogenesis.     

Exploring bioanalytical applications of assisted protein reassembly

Reassembly of protein from its peptide fragments is a technique that can have many applications in the bioanalytical field. Typically, a reporter protein fragmented into its two peptides is employed as a label in this study. This fragments of peptide can reassemble yielding an active functional reporter. This reassembly of the protein can be assisted by non-covalently interacting peptides or proteins, which are attached to the fragmented reporter. This technique has been employed in several applications including study of protein–protein interactions, antibody screening, immunoassays, and high-throughput screening. This review focuses on different reporters employed in the study of reassembly of proteins and applications of this strategy in bioanalysis.     

Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis

Type 2 diabetes mellitus (T2DM) is a metabolic disease caused by multiple etiologies, the development of which can be divided into three states: normal state, critical state/pre-disease state, and disease state. To avoid irreversible development, it is important to detect the early warning signals before the onset of T2DM. However, detecting critical states of complex diseases based on high-throughput and strongly noisy data remains a challenging task. In this study, we developed a new method, i.e., degree matrix network entropy (DMNE), to detect the critical states of T2DM based on a sample-specific network (SSN). By applying the method to the datasets of three different tissues for experiments involving T2DM in rats, the critical states were detected, and the dynamic network biomarkers (DNBs) were successfully identified. Specifically, for liver and muscle, the critical transitions occur at 4 and 16 weeks. For adipose, the critical transition is at 8 weeks. In addition, we found some “dark genes” that did not exhibit differential expression but displayed sensitivity in terms of their DMNE score, which is closely related to the progression of T2DM. The information uncovered in our study not only provides further evidence regarding the molecular mechanisms of T2DM but may also assist in the development of strategies to prevent this disease.     

先天性白内障一家系的致病基因研究

目的 对一个来自浙江省杭州地区的三代先天性白内障家系进行常染色体显性遗传基因的突变分析,以寻找其可能的致病基因及突变位点。方法 该家系共10例成员,其中包括4例患者。10 例家系成员在浙江省人民医院眼科中心接受眼科专科检查及全身检查,以排除存在白内障以外的眼部及全身疾患。10例家系成员各抽取外周血5ml,提取基因组DNA。针对国内外文献报道的与常染色体显性遗传先天性白内障相关的18 个基因（CRYAA、CRYAB、CRYBA1、CRYBA2、CRYBA4、CRYBB1、CRYBB2、CRYGC、CRYGD、CRYGS、GJA3、GJA8、MIP、BFSP、HSF4、PITX3、EPHA2、PAX6）设计引物,进行PCR 扩增,对扩产物进行测序和序列分析,了解这10例家系成员的以上基因是否存在相应的序列。结果 临床眼科检查显示该家系先天性白内障类型为粉尘状白内障。候选基因序列测定显示在CRYAA 第1 个外显子中第6 位碱基发生C→T 置换,氨基酸同为天门冬氨酸。该家系中所有患者均有此改变,而所有的正常家系成员均无此改变。结论 CRYAA 第1个外显子中第6位碱基发生C→T的同义突变可能是导致该家系先天性白内障发生的致病原因。     

4-Ketozeinoxanthin,a novel carotenoid produced in Escherichia coli through metabolic engineering using carotenogenic genes of bacterium and liverwort

In order to produce a novel keto-carotenoid in Escherichia coli, we introduced the marine bacterial carotenoid ketolase gene (crtW) into pathway-engineered E. coli producing carotenoids of plant origin, which carried the lycopene biosynthesis genes (crtE, crtB, and crtI) from soil bacterium Pantoea ananatis and the liverwort Marchantia polymorpha genes that encode lycopene β-cyclase (MpLCYb), lycopene ε-cyclase (MpLCYe), and β-carotenoid hydroxylase (MpBHY). A novel keto-carotenoid (1) was produced by these carotenoid biosynthesis genes in E. coli along with α-echinenone, adonirubin, and adonixanthin. The structure of 1 was determined as (3S,6′R)-3-hydroxy-β,ε-caroten-4-one based on Uv–vis, MS, ¹H NMR, and CD spectral data. This compound was named 4-ketozeinoxanthin and showed anti-tumor-promoting activity.     

Differentially expressed genes selection via Laplacian regularized low-rank representation method

With the rapid development of DNA microarray technology and next-generation technology, a large number of genomic data were generated. So how to extract more differentially expressed genes from genomic data has become a matter of urgency. Because Low-Rank Representation (LRR) has the high performance in studying low-dimensional subspace structures, it has attracted a chunk of attention in recent years. However, it does not take into consideration the intrinsic geometric structures in data.In this paper, a new method named Laplacian regularized Low-Rank Representation (LLRR) has been proposed and applied on genomic data, which introduces graph regularization into LRR. By taking full advantages of the graph regularization, LLRR method can capture the intrinsic non-linear geometric information among the data. The LLRR method can decomposes the observation matrix of genomic data into a low rank matrix and a sparse matrix through solving an optimization problem. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Therefore, the differentially expressed genes can be selected according to the sparse matrix. Finally, we use the GO tool to analyze the selected genes and compare the P-values with other methods.The results on the simulation data and two real genomic data illustrate that this method outperforms some other methods: in differentially expressed gene selection.     

QUANTITATIVE EXPLANATION ON STRUCTURE-CARCINOGENIC ACTIVITY RELATIONSHIP OF AROMATIC AMINES BY DI-REGION THEORY

Di-region theory, the theory for the mechanism of carcinogenesis, has been extendedsuccessfully on the quantitative Structure-carcinogenic activity relationship (QSCAR) of 63aromatic amines. A quantitative equation for the QSCAR of aromatic amines has been estab-lished by the mechanism conception of the specialized di- functional alkylation between thenitrenium ion of the amino group and the epoxide of the aromatic ring. The agreementbetween calculation and experiment comes up to 98%. Thus, it can now express the puzzlingvariation of the carcinogenicity of aromatic amines, as a comprehensible structure-chemicalreactivity relationship.