Identification of copy number variations using high density whole-genome single nucleotide polymorphism markers in Chinese Dongxiang spotted pigs

Chengbin Wang,Hao Chen,Xiaopeng Wang,Zhongping Wu,Weiwei Liu,Yuanmei Guo,Jun Ren,Nengshui Ding 아세아·태평양축산학회 2019 Animal Bioscience Vol.32 No.12

Objective: Copy number variations (CNVs) are a major source of genetic diversity complementary to single nucleotide polymorphism (SNP) in animals. The aim of the study was to perform a comprehensive genomic analysis of CNVs based on high density whole-genome SNP markers in Chinese Dongxiang spotted pigs. Methods: We used customized Affymetrix Axiom Pig1.4M array plates containing 1.4 million SNPs and the PennCNV algorithm to identify porcine CNVs on autosomes in Chinese Dongxiang spotted pigs. Then, the next generation sequence data was used to confirm the detected CNVs. Next, functional analysis was performed for gene contents in copy number variation regions (CNVRs). In addition, we compared the identified CNVRs with those reported ones and quantitative trait loci (QTL) in the pig QTL database. Results: We identified 871 putative CNVs belonging to 2,221 CNVRs on 17 autosomes. We further discarded CNVRs that were detected only in one individual, leaving us 166 CNVRs in total. The 166 CNVRs ranged from 2.89 kb to 617.53 kb with a mean value of 93.65 kb and a genome coverage of 15.55 Mb, corresponding to 0.58% of the pig genome. A total of 119 (71.69%) of the identified CNVRs were confirmed by next generation sequence data. Moreover, functional annotation showed that these CNVRs are involved in a variety of molecular functions. More than half (56.63%) of the CNVRs (n = 94) have been reported in previous studies, while 72 CNVRs are reported for the first time. In addition, 162 (97.59%) CNVRs were found to overlap with 2,765 previously reported QTLs affecting 378 phenotypic traits. Conclusion: The findings improve the catalog of pig CNVs and provide insights and novel molecular markers for further genetic analyses of Chinese indigenous pigs.

Biodegradable charged polyester-based vectors (BCPVs) as an efficient non-viral transfection nanoagent for gene knockdown of the BCR-ABL hybrid oncogene in a human chronic myeloid leukemia cell line

Yang, Chengbin,Panwar, Nishtha,Wang, Yucheng,Zhang, Butian,Liu, Maixian,Toh, Huiting,Yoon, Ho Sup,Tjin, Swee Chuan,Chong, Peter Han Joo,Law, Wing-Cheung,Chen, Chih-Kuang,Yong, Ken-Tye The Royal Society of Chemistry 2016 Nanoscale Vol.8 No.17

<P>First-line therapy of chronic myelogenous leukemia (CML) has always involved the use of BCR-ABL tyrosine-kinase inhibitors which is associated with an abnormal chromosome called Philadelphia chromosome. Although the overall survival rate has been improved by the current therapeutic regime, the presence of resistance has resulted in limited efficacy. In this study, an RNA interference (RNAi)-based therapeutic regime is proposed with the aim to knockdown the BCR-ABL hybrid oncogene using small interfering RNA (siRNA). The siRNA transfection rates have usually been limited due to the declining contact probability among polyplexes and the non-adherent nature of leukemic cells. Our work aims at addressing this limitation by using a biodegradable charged polyester-based vector (BCPV) as a nano-carrier for the delivery of BCR-ABL-specific siRNA to the suspension culture of a K562 CML cell line. BCR-ABL siRNAs were encapsulated in the BCPVs by electrostatic force. Cell internalization was facilitated by the BCPV and assessed by confocal microscopy and flow cytometry. The regulation of the BCR-ABL level in K562 cells as a result of RNAi was analyzed by real-time polymerase chain reaction (RT-PCR). We observed that BCPV was able to form stable nanoplexes with siRNA molecules, even in the presence of fetal bovine serum (FBS), and successfully assisted in vitro siRNA transfection in the non-adherent K562 cells. As a consequence of downregulation of BCR-ABL, BCPV-siRNA nanoplexes inhibited cell proliferation and promoted cell apoptosis. All results were compared with a commercial transfection reagent, Lipofectamine2000 (TM), which served as a positive control. More importantly, this class of non-viral vector exhibits biodegradable features and negligible cytotoxicity, thus providing a versatile platform to deliver siRNA to non-adherent leukemia cells with high transfection efficiency by effectively overcoming extra-and intra-cellular barriers. Due to the excellent in vitro transfection results from BCPV-siRNA, a newly developed biodegradable transfection agent, BCPV, is being probed for transfection performance in an animal model.</P>

Fabrication of novel compound SERS substrates composed of silver nanoparticles and porous gold nanoclusters: A study on enrichment detection of urea

Li, Yali,Li, Qianwen,Sun, Chengbin,Jin, Sila,Park, Yeonju,Zhou, Tieli,Wang, Xu,Zhao, Bing,Ruan, Weidong,Jung, Young Mee Elsevier 2018 APPLIED SURFACE SCIENCE - Vol.427 No.2

<P><B>Abstract</B></P> <P>A new type of surface-enhanced Raman scattering (SERS) substrate was fabricated through the layer-by-layer self-assembly of silver nanoparticles (AgNPs, av. 45nm in diameter) and porous gold nanoclusters/nanoparticles (AuNPs, av. 143nm in diameter). The development of the porosity of the AuNPs was investigated, and successful SERS applications of the porous AuNPs were also examined. As compared with AgNP films, the enhancement factor of Ag-Au compound substrates is increased 6 times at the concentration of 10<SUP>−6</SUP> M. This additional enhancement contributes to the trace-amount-detection of target molecules enormously. The contribution is generated through the increase of the usable surface area arising from the nanoscale pores distributed three-dimensionally in the porous AuNPs, which enrich the adsorption sites and hot spots for the adsorption of probe molecules, making the developed nanofilms highly sensitive SERS substrates. The substrates were used for the detection of a physiological metabolite of urea molecules. The results reached to a very low concentration of 1mM and exhibited good quantitative character over the physiological concentration range (1∼20mM) under mimicking biophysical conditions. These results show that the prepared substrate has great potential in the ultrasensitive SERS-based detection and in SERS-based biosensors.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A new type of SERS substrate, consisting of silver nanoparticles and porous gold nanoparticles was fabricated. </LI> <LI> An additional enhancement contribution generated from the porous characteristic of gold nanoparticles. </LI> <LI> The substrates were used for the detection of a physiological metabolite of urea molecules. </LI> <LI> The results exhibited good quantitative character over all the physiological concentration range (1∼20 mM). </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Real-time unmanned aerial vehicle flight path prediction using a bi-directional long short-term memory network with error compensation

Chen Sifan,Chen Baihe,Shu Peng,Wang Zhensheng,Chen Chengbin 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1

In recent years, unmanned aerial vehicle (UAV) autonomous flight technology has been applied in many fields. However, in the process of autonomous operation, the UAV may deviate from the set flight path due to various disturbance factors, which results in mission failure. In order to find the abnormal situation in time and take corresponding measures, it is necessary to monitor the operation state of the UAV. Predicting the UAV flight path is the main monitoring method at present; however, the accuracy and real-time of the existing prediction methods are limited. Therefore, this paper proposes an error compensation Bessel bidirectional long short-term memory real-time path prediction model deployed in ground stations. First, because of inconsistency of the units in all directions of the original positioning information provided by global positioning system, the Bessel geodetic coordinate transformation is introduced to unify the units of three-dimensional coordinate data, so as to improve the prediction accuracy. Second, considering the problems of poor data quality and data missing in the operation process, the least square fitting method is used to supplement and correct the positioning coordinate data to obtain more reliable and accurate path observation values as the model input. Finally, a deep learning path prediction model based on bi-directional long short-term memory (BiLSTM) network is constructed, and the appropriate network parameters are determined with the prediction accuracy and time as evaluation indicators. In order to further improve the prediction accuracy, a compensator based on proportional integral differential error control theory is designed according to the output characteristics of the BiLSTM network, which is used for providing compensation values for the prediction results of the model. The training and testing results using the actual flight data of UAV operation show that, under the experimental environment built, the model proposed in this paper can complete the UAV flight path prediction with root mean square error < 1 meter within 0.1 second, and has better performance and higher prediction accuracy than other neural network models.

Identification of loci affecting teat number by genome-wide association studies on three pig populations

Tang, Jianhong,Zhang, Zhiyan,Yang, Bin,Guo, Yuanmei,Ai, Huashui,Long, Yi,Su, Ying,Cui, Leilei,Zhou, Liyu,Wang, Xiaopeng,Zhang, Hui,Wang, Chengbin,Ren, Jun,Huang, Lusheng,Ding, Nengshui Asian Australasian Association of Animal Productio 2017 Animal Bioscience Vol.30 No.1

Objective: Three genome-wide association studies (GWAS) and a meta-analysis of GWAS were conducted to explore the genetic mechanisms underlying variation in pig teat number. Methods: We performed three GWAS and a meta-analysis for teat number on three pig populations, including a White Duroc${\times}$Erhualian $F_2$ resource population (n = 1,743), a Chinese Erhualian pig population (n = 320) and a Chinese Sutai pig population (n = 383). Results: We detected 24 single nucleotide polymorphisms (SNPs) that surpassed the genome-wide significant level on Sus Scrofa chromosomes (SSC) 1, 7, and 12 in the $F_2$ resource population, corresponding to four loci for pig teat number. We highlighted vertnin (VRTN) and lysine demethylase 6B (KDM6B) as two interesting candidate genes at the loci on SSC7 and SSC12. No significant associated SNPs were identified in the meta-analysis of GWAS. Conclusion: The results verified the complex genetic architecture of pig teat number. The causative variants for teat number may be different in the three populations