Research press release





今回、Yueh-hsiu Chien、Purvesh Khatriたちの研究グループは、潜伏感染に至る免疫状態と活動性結核への進行に伴う免疫状態の変化を調べるため、さまざまなヒトのコホートを対象として、マスサイトメトリー解析と遺伝子発現データセットの検討を組み合わせた研究を行い、結核の潜伏感染者、活動性結核患者、および非感染者の免疫細胞集団の差異の特定を試みた。



Higher levels of natural killer cells are associated with tuberculosis latency, reports a paper published online this week in Nature. The findings raise the question as to whether natural killer cells might play an active role in controlling tuberculosis infections.

Tuberculosis (TB) is a bacterial disease and a leading cause of infection-related deaths. The majority of TB infections are latent - manifesting, without outward symptoms, in a contained state. It is estimated that a quarter of the world’s population has latent TB, although fewer than 10% of latent TB cases end up progressing to an active state. The immune factors that influence a given individual’s infection outcome, however, are poorly understood.

To investigate the immune state that leads to latency and how that changes if the disease progresses, Yueh-hsiu Chien, Purvesh Khatri, and colleagues conducted studies of various human cohorts combining mass cytometry analysis with an examination of gene expression datasets to identify differences in immune cell populations between uninfected subjects and those with latent or active TB.

They find that latent manifestations of TB are associated with higher numbers of natural killer cells - white blood cells that can kill certain pathogens - with enhanced anti-toxin responses in comparison to uninfected individuals. In subjects with an active infection, levels of natural killer cells were diminished, but abundances returned to baseline levels when the infection was cured. However, the findings cannot prove a causal relationship between natural killer cells and TB latency.

Additionally, the authors show that measurements of natural killer cell levels can be used to determine the activity level and burden of TB infection in a patient’s lungs - a finding that could help to assess disease progression and optimize treatments.

doi: 10.1038/s41586-018-0439-x

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