System immunology-based identification of blood transcriptional modules correlating to antibody responses in sheep.
System immunology-based identification of blood transcriptional modules correlating to antibody responses in sheep.
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DOI：
10.1038/s41541-018-0078-0
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发表时间：
2018
期刊：
影响因子：
9.2
通讯作者：
Summerfield A
中科院分区：
文献类型：
--
作者：
Braun RO;Brunner L;Wyler K;Auray G;García-Nicolás O;Python S;Zumkehr B;Gaschen V;Stoffel MH;Collin N;Barnier-Quer C;Bruggmann R;Summerfield A
由于缺乏免疫原性，灭活疫苗需要强有力的佐剂。为了了解它们的作用，我们使用了基于系统免疫学的分析绵羊血液转录模块（BTM）来剖析与抗体或触珠蛋白水平相关的先天免疫应答。使用灭活的口蹄疫病毒作为抗原，我们比较了无佐剂的脂质体配制的疫苗，补充或不补充TLR4和TLR7配体。接种后早期，与骨髓细胞、先天免疫应答、树突状细胞和抗原呈递相关的BTM呈正相关，而与T细胞和自然杀伤细胞以及细胞周期相关的BTM与抗体应答呈负相关。有趣的是，与骨髓细胞、炎症和抗原呈递相关的BTM也与触珠蛋白相关，但以相反的方式，表明急性全身性炎症对早期抗体应答无益。疫苗依赖性BTM调节的分析显示，脂质体制剂诱导与抗体水平相关的那些类似的应答，而TLR配体的添加减少了骨髓细胞、炎症和抗原呈递BTM表达，尽管促进了抗体应答。此外，该疫苗在下调T和自然杀伤细胞BTM方面更有效。当分析接种前BTM时，我们发现与低应答者相比，高疫苗应答者表达更高水平的细胞周期和骨髓细胞BTM。总之，我们已经将人BTM转移到绵羊身上，并确定了与抗体水平或不必要的炎症相关的早期疫苗诱导反应。这种读数适用于其他兽医物种，并且对于鉴定有效的疫苗佐剂、其作用机制以及与低应答相关的因素非常有用。下一代识别疫苗接种后遗传变化的方法开启了疫苗和佐剂效应的详细研究。由瑞士伯尔尼大学的Artur萨默菲尔德领导的研究人员给绵羊接种了口蹄疫病毒抗原，无论是单独的，佐剂（在免疫增强配方中），还是与推测有助于产生对口蹄疫的长期免疫力的分子佐剂。研究小组发现，只有后一组刺激宿主免疫力达到被认为是保护性的水平，尽管变异性很高。基因表达的差异被鉴定为与病毒中和反应相关。该团队使用了一种强大的生物信息学工具，测量“血液转录模块（BTM）”，以确定绵羊特异性免疫过程。BTM方法提供了一种有利的方式来鉴定疫苗和佐剂的作用，直到遗传水平。这项研究还提供了数据，为未来的口蹄疫疫苗开发工作提供信息。
Lacking immunogenicity, inactivated vaccines require potent adjuvants. To understand their effects, we used a system immunology-based analysis of ovine blood transcriptional modules (BTMs) to dissect innate immune responses relating to either antibody or haptoglobin levels. Using inactivated foot-and-mouth disease virus as an antigen, we compared non-adjuvanted to liposomal-formulated vaccines complemented or not with TLR4 and TLR7 ligands. Early after vaccination, BTM relating to myeloid cells, innate immune responses, dendritic cells, and antigen presentation correlated positively, whereas BTM relating to T and natural killer cells, as well as cell cycle correlated negatively with antibody responses. Interestingly, BTM relating to myeloid cells, inflammation and antigen presentation also correlated with haptoglobin, but in a reversed manner, indicating that acute systemic inflammation is not beneficial for early antibody responses. Analysis of vaccine-dependent BTM modulation showed that liposomal formulations induced similar responses to those correlating to antibody levels, while addition of TLR ligands reduced myeloid cells, inflammation and antigen presentation BTM expression despite promoting antibody responses. Furthermore, this vaccine was more potent at downregulating T and natural killer cell BTM. When pre-vaccination BTM were analyzed, we found that high vaccine responders expressed higher levels of cell cycle and myeloid cell BTMs as compared with low responders. In conclusion, we have transferred human BTM to sheep and identified early vaccine-induced responses associated with antibody levels or unwanted inflammation. Such readouts are applicable to other veterinary species and very useful to identify efficient vaccine adjuvants, their mechanism of action, and factors related to low responders. A next-generation method to identify genetic changes in response to vaccination opens up the detailed study of vaccine and adjuvant effects. Researchers led by the Swiss University of Bern’s Artur Summerfield inoculated sheep with foot-and-mouth disease (FMD) virus antigens, either alone, adjuvanted (in an immunity-boosting formulation), or adjuvanted with molecules speculated to help generate long-term immunity against FMD. The team found only the latter group stimulated host immunity to a level considered protective, albeit with high variability. Differences in gene expression were identified that correlated to virus-neutralizing responses. The team used a powerful bioinformatics tool, measuring ‘blood transcriptional modules (BTM),’ to identify ovine specific immune processes. The BTM approach offers an advantageous way to identify vaccine and adjuvant effects down to the genetic level. This study also offers data to inform future FMD vaccine development efforts.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
9.4
作者：
LaRocco, Michael;Krug, Peter W.;Rodriguez, Luis L.
通讯作者：
Rodriguez, Luis L.
DOI：
10.1073/pnas.1621489114
发表时间：
2017-02-28
影响因子：
11.1
作者：
Kazmin, Dmitri;Nakaya, Helder I.;Pulendran, Bali
通讯作者：
Pulendran, Bali
影响因子：
32.4
作者：
Obermoser, Gerlinde;Presnell, Scott;Chaussabel, Damien
通讯作者：
Chaussabel, Damien