Predicting hosts and cross-species transmission of Streptococcus agalactiae by interpretable machine learning

无乳链球菌 寄主(生物学) 随机森林 传输(电信) 计算机科学 人工智能 逻辑回归 基因组 机器学习 爆发 计算生物学 支持向量机 鉴定(生物学) 进化生物学 链球菌 生物 遗传学 基因 病毒学 生态学 细菌 电信
作者
Yunxiao Ren,Carmen Li,Dulmini Nanayakkara Sapugahawatte,Chendi Zhu,Sebastian Spänig,Dorota Jamrozy,Julian Rothen,Claudia Daubenberger,Stephen D. Bentley,Margaret Ip,Dominik Heider
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:: 108185-108185
标识
DOI:10.1016/j.compbiomed.2024.108185
摘要

Streptococcus agalactiae, commonly known as Group B Streptococcus (GBS), exhibits a broad host range, manifesting as both a beneficial commensal and an opportunistic pathogen across various species. In humans, it poses significant risks, causing neonatal sepsis and meningitis, along with severe infections in adults. Additionally, it impacts livestock by inducing mastitis in bovines and contributing to epidemic mortality in fish populations. Despite its wide host spectrum, the mechanisms enabling GBS to adapt to specific hosts remain inadequately elucidated. Therefore, the development of a rapid and accurate method differentiates GBS strains associated with particular animal hosts based on genome-wide information holds immense potential. Such a tool would not only bolster the identification and containment efforts during GBS outbreaks but also deepen our comprehension of the bacteria's host adaptations spanning humans, livestock, and other natural animal reservoirs. Here, we developed three machine learning models—random forest (RF), logistic regression (LR), and support vector machine (SVM) based on genome-wide mutation data. These models enabled precise prediction of the host origin of GBS, accurately distinguishing between human, bovine, fish, and pig hosts. Moreover, we conducted an interpretable machine learning using SHapley Additive exPlanations (SHAP) and variant annotation to uncover the most influential genomic features and associated genes for each host. Additionally, by meticulously examining misclassified samples, we gained valuable insights into the dynamics of host transmission and the potential for zoonotic infections. Our study underscores the effectiveness of random forest (RF) and logistic regression (LR) models based on mutation data for accurately predicting GBS host origins. Additionally, we identify the key features associated with each GBS host, thereby enhancing our understanding of the bacteria's host-specific adaptations.
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