Back

The Aging Microenvironment Shapes Alveolar Macrophage Identity in Aging

McQuattie-Pimentel, A. C.; Ren, Z.; Joshi, N.; Watanabe, S.; Stoeger, T.; Chi, M.; Lu, Z.; Sichizya, L.; Piseaux, R.; Chen, C.-I.; Soberanes, S.; Reyfman, P. A.; Walter, J. M.; Anekalla, K. R.; Davis, J. M.; Helmin, K. A.; Runyan, C. E.; Abdala-Valencia, H.; Nam, K.; Meliton, A. Y.; Winter, D. R.; Morimoto, R. I.; Mutlu, G. M.; Bharat, A.; Gottardi, C. J.; Perlman, H.; Ridge, K. M.; Chandel, N. S.; Sznajder, J. I.; Balch, W. E.; Singer, B. D.; Misharin, A. V.; Budinger, G. S.

2019-07-28 immunology
10.1101/717033 bioRxiv
Show abstract

A dysfunctional response to inhaled pathogens and toxins drives a substantial portion of the susceptibility to acute and chronic lung disease in the elderly. We used transcriptomic profiling combined with genetic lineage tracing, heterochronic adoptive transfer, parabiosis and treatment with metformin to show that the lung microenvironment defines the phenotype of long-lived alveolar macrophages during aging. While tissue-resident alveolar macrophages persist in the lung without input from monocytes over the lifespan, severe lung injury results in their replacement with monocyte-derived alveolar macrophages. These monocyte-derived alveolar macrophages are also shaped by the microenvironment both during aging and in response to a subsequent environmental challenge to become transcriptionally and functionally similar to tissue-resident alveolar macrophages. These findings show that changes in alveolar macrophage phenotypes during injury and aging are not cell autonomous but instead are shaped by changes in the aging lung microenvironment.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
eLife
● 5828 papers in training set
Top 2%
22.9%
Similar papers in this journal
2
Cell Reports
○ 1498 papers in training set
Top 0.4%
15.4%
Similar papers in this journal
3
Nature Aging
○ 60 papers in training set
Top 0.1%
10.0%
Similar papers in this journal
4
Journal of Experimental Medicine
● 119 papers in training set
Top 0.2%
6.9%
Similar papers in this journal
50% of probability mass above
5
Nature Communications
○ 5641 papers in training set
Top 25%
6.4%
Similar papers in this journal
6
Aging Cell
○ 165 papers in training set
Top 0.7%
5.3%
7
Cell Metabolism
○ 57 papers in training set
Top 0.4%
3.3%
8
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 20%
2.5%
9
EMBO Reports
○ 263 papers in training set
Top 3%
2.0%
10
Science Immunology
● 88 papers in training set
Top 1%
1.8%
11
The Journal of Immunology
◐ 166 papers in training set
Top 1%
1.8%
12
Immunity
○ 67 papers in training set
Top 1%
1.5%
13
JCI Insight
● 277 papers in training set
Top 5%
1.5%
14
iScience
○ 1154 papers in training set
Top 23%
1.4%
15
Science Advances
● 1243 papers in training set
Top 23%
1.4%
16
Nature Immunology
○ 79 papers in training set
Top 2%
1.2%
17
The EMBO Journal
○ 309 papers in training set
Top 6%
0.9%
18
Frontiers in Immunology
○ 638 papers in training set
Top 9%
0.9%
19
Cell
○ 431 papers in training set
Top 9%
0.9%
20
Journal of Clinical Investigation
● 179 papers in training set
Top 5%
0.9%
21
Cell Systems
○ 201 papers in training set
Top 5%
0.6%
22
Developmental Cell
○ 196 papers in training set
Top 4%
0.6%
23
Life Science Alliance
● 285 papers in training set
Top 9%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.