Advances of nano-drug delivery systems in the prevention and control of drug-resistant pathogenic bacteria
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摘要:
抗菌药物的不规范使用和细菌选择性压力进化导致耐药病原菌日益增加，严重威胁畜禽养殖和公共卫生安全。随着纳米技术的发展，纳米药物递送系统在递送抗菌药物方面显示出一系列优势，如提高药物的生物利用度、减少毒副作用、降低药物使用成本等，为克服细菌耐药性提供了新的技术和策略。本文从畜禽耐药病原菌的危害及防治现状切入，综述纳米乳液、脂质体、固体脂质纳米粒、纳米胶束、金属纳米颗粒、纳米凝胶这6种纳米药物递送系统在耐药病原菌防控中的研究进展，以期为纳米药物递送系统在畜禽耐药病原菌防控中的应用提供借鉴，助力畜牧养殖业绿色可持续发展。
Abstract:The unregulated use of antimicrobial drugs and the evolution of bacterial selective pressure have led to an increasing number of drug-resistant pathogenic bacteria, which is a serious threat to livestock and poultry breeding as well as public health safety. With the development of nanotechnology, nano-drug delivery systems have shown a series of advantages in delivering antimicrobial drugs, such as improving the bioavailability of drugs, reducing the toxic side effects, and lowering the cost of drug use, which provide the new technologies and strategies for overcoming bacterial drug resistance. In this paper, we reviewed the progress of six nano-drug delivery system types of nanoemulsion, liposome, solid lipid nanoparticle, nano micelle, metal nanoparticle and nano gel in the prevention and control of drug-resistant pathogens, starting from the hazards of drug-resistant pathogens and the current status of their prevention and control in livestock and poultry. We expect to provide a reference for nano-drug delivery system application in the prevention and control of drug-resistant pathogen in livestock and poultry, and help the green and sustainable development of animal husbandry industry.
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在现代养猪产业中，剩余采食量(Residual feed intake, RFI)是衡量猪只生长效率的关键生物指标，具有极高的研究价值。遗传学领域的深入研究[1]已经逐步揭示了RFI的遗传学基础。然而，社会遗传效应(Social genetic effect, SGE)，即个体基因型如何影响群体中其他个体的表型，仍是遗传学研究中较少触及的领域。鉴于猪只在群体中展现出的复杂社会结构和互动行为，深入探究猪只社交行为与生长效率之间的关系，对于制定更精准的养殖策略、提升猪只的生产性能具有重要的实际意义。
高通量测序技术，特别是RNA测序(RNA-seq)技术的发展，极大地推动了我们对复杂生物性状分子机制的认识。RNA-seq技术凭借其高度敏感性和广泛的基因覆盖度，已成为解析饲料效率遗传机制的重要工具，为揭示饲料效率相关的精细基因表达模式提供了强有力的技术支持[2-3]。尽管如此，RNA-seq技术在探索SGE方面的应用潜力尚待进一步挖掘。
本项研究聚焦于剩余采食量的社会遗传效应(SGE-RFI)表现存在显著差异的杜洛克猪，通过对肝脏转录组的深入分析，旨在识别调控RFI的关键基因及其所涉及的信号通路。这项研究不仅可加深我们对RFI分子机制的理解，而且可为杜洛克猪的遗传改良提供坚实的科学基础，具有深远的科学意义和实际应用价值。
1. 材料与方法
1.1 试验动物与样品采集
本研究在四川新希望六和集团的养猪场进行，共选取209头杜洛克母猪作为研究对象。利用奥斯本公司的采食记录设备，详细收集猪只的体质量和采食量数据。基于这些数据，计算每头猪的RFI和SGE，具体方法参见文献[4]。为了深入探究SGE对RFI的影响，我们根据SGE评分的高低，筛选出2个极端组别：高RFI-SGE组(HRS组)和低RFI-SGE组(LRS组)，每组各包含4只猪。猪只屠宰后，收集猪只的肝脏样本以供后续的转录组研究。
1.2 试验指标
使用以下公式计算平均日增质量(Average daily gain, ADG)、平均日采食量(Average daily feed intake, ADFI)、平均代谢体质量(Average metabolic weight, AMW)和RFI：
$$ {\mathrm{ADG}}=\dfrac{{W}_2-{W}_1}{{t}}{\text{，}} $$ (1) 式中：W1 为开始测定时的体质量，W2 为结束测定时的体质量，t为测定期间的总天数。
$$ {\mathrm{ADFI}}=\dfrac{{\mathrm{TFI}}}{\mathit{t}}{\text{，}} $$ (2) 式中：TFI 为总采食量。
$$ {\mathrm{AWM}}=\dfrac{({\mathit{W}_2}^{1.6}-{\mathit{W}_1}^{1.6})}{1.6 \mathit{ }(\mathit{W}_2-\mathit{W}_1)}{\text{，}} $$ (3) $$ {\mathrm{RFI}}={\mathrm{ADFI}}-1.41\;{\mathrm{ADG}}-2.83\;{\mathrm{BFT}}-110.9\;{\mathrm{AMW}}{\text{，}} $$ (4) 式中：BFT为背膘厚。
RFI 的 SGE 使用以下公式表示：
$$ {\boldsymbol{Y}}_{\bf{R}\bf{F}\bf{I}}=\boldsymbol{X}\boldsymbol{B}+{\boldsymbol{Z}}_{\bf{d}}{\boldsymbol{a}}_{\bf{d}}+{\boldsymbol{Z}}_{\bf{s}}{\boldsymbol{a}}_{\bf{s}}+\boldsymbol{W}\boldsymbol{l}+\boldsymbol{V}\boldsymbol{g}+\boldsymbol{e}{\text{，}} $$ (5) 式中：YRFI为 RFI 表型值向量；B为固定效应向量，包括测定年月、出生年月；ad和as分别为直接遗传效应和社会遗传效应向量；l为随机窝效应向量；g为随机组效应向量；e为随机残差向量；X、Zd、Zs、W、V为对应的关联矩阵。
1.3 RNA-seq流程
依照TRIzol试剂盒指南，我们从猪只肝脏样本中提取总RNA。使用Agilent 2100和NanoDrop 2000对RNA进行质量控制，确保其满足试验要求。随后，利用华大基因BGISEQ-500平台进行链特异性RNA测序。测序数据采用HISAT[5]进行序列比对，StringTie软件[6]进行转录组组装。Cuffdiff软件[7]用于量化表达差异。DESeq2 R包进一步识别组间的差异表达基因(Differently expressed genes，DEGs)，以LRS组为对照，以|log2FC|>1和Padj<0.05为筛选标准(FC表示差异倍数)。
1.4 功能注释分析
利用DAVID Bioinformatics Resources[8]对DEGs进行KEGG和GO富集分析。通过Benjamini-Hochberg方法对P值进行校正，以P<0.05为显著性的界定标准。
1.5 蛋白−蛋白互作网络构建
通过STRING数据库[9]构建蛋白−蛋白互作网络(Protein-protein interaction networks, PPI)。使用Cytoscape软件[10]对PPI网络进行可视化，并运用CytoHubba插件的Degree、EPC、EcCentricity和MNC算法来筛选网络中的核心基因。
1.6 实时荧光定量PCR验证转录组测序结果
采用实时荧光定量PCR(RT-qPCR)技术验证RNA-seq结果。以GAPDH作为内参基因，并采用2−△△CT方法分析基因表达的差异，确保结果的准确性。
2. 结果与分析
2.1 试验动物的确定
根据RFI的SGE，选取SGE最高和最低的猪各4只，并对它们进行转录组分析。相关数据见表1。
表 1 试验猪只剩余采食量与社会遗传效应数据1)Table 1. Data on residual feed intake (RFI) and social genetic effects (SGE) of test pigs猪只
Pig分组
Group样品编号
Sample number剩余采食量/kg
RFI社会遗传效应
SGEDDJYZC120019500 LRS LRS1 0.3283 − 0.0179 DDJYZC120019491 LRS2 0.3372 − 0.0168 DDJYZC120019494 LRS3 0.3383 − 0.0144 DDJYZC120019492 LRS4 0.3385 − 0.0141 DDJYZC120022569 HRS HRS1 − 0.2406 0.0127 DDJYZC120022570 HRS2 − 0.2452 0.0098 DDJYZC120022571 HRS3 − 0.2483 0.0091 DDJYZC120022575 HRS4 − 0.2609 0.0089 1) LRS：低RFI-SGE；HRS：高RFI-SGE。
1) LRS: Low RFI-SGE; HRS: High RFI-SGE.2.2 测序结果质量汇总
转录组测序数据经过原始数据过滤、测序错误率检查及 GC 含量分布检查后，获得用于后续分析的Clean reads。其中，Q20 > 95%，Q30 > 90%，40% < GC含量 < 50%，这些指标确保了测序数据满足后续生物信息分析要求。
2.3 DEGs在肝脏组织中的分布统计
转录组测序技术被用于探究不同SGE猪肝脏RNA表达的差异。火山图(图1)清晰地揭示了全基因组转录水平分析识别出的360个DEGs。其中，HRS组相较于LRS组发现了262个显著上调和98个显著下调的DEGs，这些基因的表达满足|log2FC| > 1且Padj< 0.05的标准。层次聚类分析进一步描绘了基因表达的整体格局(图2)。聚类图将基因表达较高和较低的群体明显区分开来，证实了2组间存在显著的基因表达模式差异。
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图 1 高RFI-SGE组较低RFI-SGE组肝脏组织中差异表达mRNA的火山图分析Figure 1. Volcano plot analysis of differentially expressed mRNAs in high RFI-SGE group compared with low RFI-SGE group![]()
图 2 高RFI-SGE(HRS)与低RFI-SGE(LRS)组肝脏组织中差异表达mRNA的层次聚类热图Figure 2. Hierarchical clustering heatmap of differentially expressed mRNAs in high RFI-SGE (HRS) and low RFI-SGE (LRS) groups表2列出了HRS组中上调和下调表达最显著的前10位基因。其中，TCN1基因表达显著上调(log2FC = 5.48, P = 3.14×10−6, Padj = 3.31×10−4)；而CA3基因表达显著下调(log2FC = −3.88, P = 6.27×10−5, Padj = 2.87×10−3)。这些结果为研究SGE对猪肝脏转录调控的影响提供了重要的分子证据。
表 2 高RFI-SGE组肝脏组织Top 10上调与下调基因Table 2. Top 10 up- and down-regulated genes in liver tissue of high RFI-SGE group基因 Gene log2FC P Padj TCN1 5.48 3.14×10−6 3.31×10−4 HBM 3.77 4.57×10−4 1.20×10−2 HBB 3.31 2.23×10−4 7.29×10−3 C2H11orf86 2.73 2.84×10−7 5.26×10−5 LOC100737768 2.62 9.25×10−4 1.98×10−2 ARF4 2.54 7.27×10−14 3.59×10−10 PNPLA3 2.53 3.36×10−5 1.84×10−3 LOC100517779 2.41 1.75×10−7 3.45×10−5 APOA4 2.41 4.93×10−7 8.21×10−5 SPATA22 2.39 1.78×10−7 3.47×10−5 LOC102164346 −2.43 4.47×10−5 2.25×10−3 CYP1A1 −2.44 1.99×10−5 1.22×10−3 COLCA1 −2.46 2.95×10−4 8.86×10−3 LOC110259967 −2.47 5.12×10−5 2.47×10−3 GALP −2.69 8.77×10−4 1.90×10−2 LOC100154757 −3.01 1.77×10−4 6.13×10−3 KCNH7 −3.14 1.14×10−4 4.49×10−3 LOC110261964 −3.24 5.52×10−4 1.37×10−2 ASIC1 −3.35 1.74×10−4 6.08×10−3 CA3 −3.88 6.27×10−5 2.87×10−3 2.4 DEGs在肝脏组织中的功能分析
GO富集分析对DEGs的功能进行了分类，共有115个GO条目显著富集。在生物过程类别中，前5项最显著富集的是质子动力驱动的 ATP 合成、氧化磷酸化、三磷酸核糖核苷生物合成过程、三磷酸核苷生物合成过程和嘌呤核糖核苷三磷酸的生物合成过程；在分子功能类别中，前5项最显著富集的是质子通道活性、质子跨膜转运体活性、异构酶活性、连接酶活性和蛋白质折叠伴侣；在细胞组分类别中，前5项最显著富集的是线粒体内膜蛋白复合体、线粒体内膜、线粒体含蛋白复合物、线粒体包膜和细胞器内膜。
本研究还对DEGs进行了KEGG富集分析，以确定SGE影响RFI的主要生物学通路。KEGG分析发现28条生物学通路显著富集。其中，最显著的前10条通路，包括氧化磷酸化、帕金森病、阿尔茨海默病、代谢途径、亨廷顿病、产热、非酒精性脂肪肝(NAFLD)、逆行内源性大麻素信号、蛋白酶体和类固醇生物合成。
2.5 通过PPI网络鉴定关键基因
本研究根据DEGs的结果，通过STRING网站构建PPI网络，设置置信度> 0.9，结果如图3所示，在肝脏组织中共有336个节点，317个连线，PPI富集的P< 1.0×10−16。基于STRING数据库，使用4种中心化算法提取PPI网络中的前10种核心基因(Hub genes)，并取交集。通过这种方式来发掘SGE影响RFI的候选核心功能基因。最终，本研究确定了4个关键核心基因，分别是ATP5F1D、ATP5MG、NDUFA8和NDUFB9。
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图 3 肝脏组织部分差异表达基因蛋白−蛋白互作网络Figure 3. Protein-protein interaction network of selected differentially expressed genes in liver tissue2.6 RT-qPCR验证RNA-seq结果
为了验证RNA-seq和分析结果的可靠性，本研究从共同富集的差异基因中随机挑选了6个基因ABCD3、APOA4、CPXM2、LPIN1、PI16和PLCE1，进行RT-qPCR。结果(图4)表明，RT-qPCR和RNA-seq的结果基本一致，这说明RNA-seq的结果是可靠的，试验重复性良好。
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图 4 肝脏组织中差异表达基因的RT-qPCR验证Figure 4. RT-qPCR validation of differentially expressed genes in liver tissue3. 讨论与结论
在本研究中，我们利用RNA-seq技术对杜洛克猪的肝脏样本进行了转录组分析，旨在探究SGE对RFI的影响机制。数据分析识别出360个DEGs，并通过功能注释和PPI网络分析，初步描绘了SGE调控下的肝脏转录活动图景，并锁定了4个关键基因，它们在肝脏能量代谢和RFI调控中发挥核心作用。
线粒体作为细胞的能量工厂，其功能完整性对有机体的能量平衡至关重要[11]。尽管目前对线粒体功能与动物社会行为之间交互作用的了解有限，但已有研究证实线粒体功能与神经疾病和认知之间存在联系[12-17]。然而，代谢状态对社交能力的具体影响机制仍需进一步探究。此外，社会地位对个体心理健康和幸福感的影响[18]，也揭示了社会因素与生理机能之间可能存在复杂的联系。
GO和KEGG富集分析显示，DEGs在与线粒体活性及能量代谢途径相关的功能分类中显著富集，并且在神经疾病中也表现出显著的富集趋势。特别是，4个核心基因均定位于线粒体内，直接参与氧化磷酸化过程，其表达模式的变化与SGE的高低显著相关。根据这一发现得出一个假设，即线粒体功能的减弱可能是LRS组猪能量代谢失衡的根本原因，进而影响其社会行为，成为SGE状态低下的潜在驱动因素。
此外，在LRS组中，我们观察到APOA1、APOC3和APOA4 3个载脂蛋白基因表达水平的降低。已有研究表明APOA1的表达在社会地位较高的个体中呈现上调趋势[19]，而载脂蛋白家族成员与神经系统疾病也有关联[20-22]，这提示我们需对LRS组猪的神经功能及其对SGE的影响进行更深入的研究。这些发现强调了神经功能在维持正常SGE水平中的重要性。
本研究采用转录组学和生物信息学的综合分析方法，对杜洛克猪肝脏中的分子机制进行了系统性探索，这些机制与SGE和RFI的调控密切相关。通过这一过程，我们鉴定了多个关键基因，这些基因在氧化磷酸化、电子传递链和神经疾病等生物过程中起着至关重要的作用。这些发现不仅拓展了我们对SGE调控网络的认识，而且为优化猪种的遗传改良策略、提升养殖效率提供了坚实的理论基础和科学依据。
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图 1 纳米药物递送系统优势示意图
Figure 1. Schematic diagram of the advantages of nano-drug delivery systems
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图 2 6种纳米药物递送系统示意图
Figure 2. Schematic diagram of six kinds of nano-drug delivery systems
表 1 不同纳米药物递送系统及其特点
Table 1 Different nano-drug delivery systems and their features
种类
Type优点
Advantage缺点
Disadvantage装载药物
Loading drug病原体
Pathogen抑菌机制
Antibacterial mechanism参考文献
Reference纳米乳液
Nanoemulsion水相、油相混合，增加药物溶解度；提高药物生物利用度 工艺成本相对较高；不稳定，保质期较短 己醛、壳聚糖 副溶血性弧菌 细菌形态变化并破坏细胞膜 [101] 香芹酚 大肠埃希菌、肠炎沙门氏菌 提高香芹酚的生物活性 [102] 脂质体
Liposome封装水、脂溶性药物；提高药物稳定性；延长作用时间；减少药物不良反应 整体稳定性较差；生物利用度低；易引发免疫或毒性反应 妥布霉素 铜绿假单胞菌 增强破坏生物膜的能力 [103] 橄榄叶、橙皮提取物 金黄色葡萄球菌 提高组成成分的抗菌活性 [104] 氨苄西林 藤黄微球菌 提高氨苄西林的稳定性和抗菌活性 [105] 固体脂质纳米颗粒
Solid lipid nanoparticle改善生物利用度；保护药物免受酶分解或其他环境因素的破坏；实现缓慢持续的药物释放 长期储存稳定性差；可能引起细胞毒性；给药途径有限 多黏菌素B 铜绿假单胞菌 增加多黏菌素B的溶解度，增强对细菌细胞膜的作用 [106] 乳链菌肽 齿垢密螺旋体 保护乳链菌肽不被降解，延长作用时间 [107] 利福平 铜绿假单胞菌 抑制铜绿假单胞生物膜形成 [108] 纳米胶束
Nano micelle提高药物的生物利用度和溶解度；减少药物的副作用和毒性；有效改善药物的稳定性和溶解性 制备过程较复杂，成本较高；稳定性和储存性较差 脱氧胆酸、壳聚糖 大肠埃希菌、金黄色葡萄球菌 破坏细胞膜 [109] 纳米银、儿茶酚功能化季铵化壳聚糖 大肠埃希菌、金黄色葡萄球菌 靶向细菌，光热联合杀菌 [110] 金属纳米颗粒
Metal nanoparticle高效的杀菌能力；通过尺寸、形状、浓度等参数来调整抗菌性能 高浓度对机体有一定的毒性，易在体内蓄积；易发生沉淀、团聚 银、铜 大肠埃希菌、金黄色葡萄球菌 促进细菌活性氧产生 [111] 银、磺胺嘧啶 大肠埃希菌、金黄色葡萄球菌 提高磺胺嘧啶水溶性，增强磺胺嘧啶的释放和抗菌活性 [112] 纳米氧化铜、纳米氧化锌 大肠埃希菌、金黄色葡萄球菌、耐甲氧西林金黄色葡萄球菌 协同抗菌作用，促进细菌活性氧产生；引起脂质过氧化 [113] 纳米凝胶
Nano gel提供反应、吸附和催化活性位点，提高材料性能；通过改变反应条件控制其大小、形状和孔径等参数 合成过程复杂；性能易受温度、湿度、pH等因素影响 过氧化物酶 大肠埃希菌、金黄色葡萄球菌 消耗谷胱甘肽 [114] 茴香精油 金黄色葡萄球菌 药物与载体之间存在特殊互作，增强与药物的接触 [115] 纳米氧化锌 铜绿假单胞菌、金黄色葡萄球菌、大肠埃希菌 与纳米氧化锌协同抗菌 [116] 
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