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肠道微生物变化表明小鼠模型中黑色素瘤的存在及细菌相互作用

Gut Microbial Shifts Indicate Melanoma Presence and Bacterial Interactions in a Murine Model.

作者信息

Rossi Marco, Aspromonte Salvatore M, Kohlhapp Frederick J, Newman Jenna H, Lemenze Alex, Pepe Russell J, DeFina Samuel M, Herzog Nora L, Donnelly Robert, Kuzel Timothy M, Reiser Jochen, Guevara-Patino Jose A, Zloza Andrew

机构信息

Rush University Medical Center, Chicago, IL 60612, USA.

Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.

出版信息

Diagnostics (Basel). 2022 Apr 12;12(4):958. doi: 10.3390/diagnostics12040958.

Abstract

Through a multitude of studies, the gut microbiota has been recognized as a significant influencer of both homeostasis and pathophysiology. Certain microbial taxa can even affect treatments such as cancer immunotherapies, including the immune checkpoint blockade. These taxa can impact such processes both individually as well as collectively through mechanisms from quorum sensing to metabolite production. Due to this overarching presence of the gut microbiota in many physiological processes distal to the GI tract, we hypothesized that mice bearing tumors at extraintestinal sites would display a distinct intestinal microbial signature from non-tumor-bearing mice, and that such a signature would involve taxa that collectively shift with tumor presence. Microbial OTUs were determined from 16S rRNA genes isolated from the fecal samples of C57BL/6 mice challenged with either B16-F10 melanoma cells or PBS control and analyzed using QIIME. Relative proportions of bacteria were determined for each mouse and, using machine-learning approaches, significantly altered taxa and co-occurrence patterns between tumor- and non-tumor-bearing mice were found. Mice with a tumor had elevated proportions of , .g_rc4.4, and as well as significant information gains and ReliefF weights for , , , and . , , and were also implicated through shifting co-occurrences and PCA values. Using these seven taxa as a melanoma signature, a neural network reached an 80% tumor detection accuracy in a 10-fold stratified random sampling validation. These results indicated gut microbial proportions as a biosensor for tumor detection, and that shifting co-occurrences could be used to reveal relevant taxa.

摘要

通过大量研究,肠道微生物群已被公认为是体内稳态和病理生理学的重要影响因素。某些微生物分类群甚至可以影响癌症免疫疗法等治疗方法,包括免疫检查点阻断。这些分类群可以通过从群体感应到代谢产物产生的机制,单独或共同影响这些过程。由于肠道微生物群在胃肠道远端的许多生理过程中普遍存在,我们推测在肠外部位携带肿瘤的小鼠与未携带肿瘤的小鼠相比,会表现出独特的肠道微生物特征,并且这种特征将涉及随着肿瘤出现而共同变化的分类群。从用B16-F10黑色素瘤细胞或PBS对照攻击的C57BL/6小鼠的粪便样本中分离出的16S rRNA基因确定微生物OTU,并使用QIIME进行分析。确定每只小鼠的细菌相对比例,并使用机器学习方法,发现携带肿瘤和未携带肿瘤的小鼠之间显著改变的分类群和共现模式。患有肿瘤的小鼠中, 、 、g_rc4.4和 的比例升高,并且 、 、 和 的信息增益和ReliefF权重显著。 、 和 也通过共现变化和主成分分析值而受到牵连。使用这七个分类群作为黑色素瘤特征,神经网络在10倍分层随机抽样验证中达到了80%的肿瘤检测准确率。这些结果表明肠道微生物比例可作为肿瘤检测的生物传感器,并且共现变化可用于揭示相关分类群。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dade/9029337/6c0e2d585c94/diagnostics-12-00958-g001.jpg

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