Fun With The Tabula Muris (Senis)

post by sarahconstantin · 2024-09-20T18:20:01.901Z · LW · GW · 0 comments

Contents

  Brain
    TL;DR
    Upregulated With Age
        C4b
        H2-K1
        LGALS3BP
        CTSS
        H2D1
        LYZ2
        GFAP
        LGALS3
        IFIT3
        C3
        NEAT1
        H2-Q6
        H2-Q7
    Downregulated with Age
        GPR7
    Progression
  Muscle
    TL;DR
    Upregulated With Age
        H1fnt
        Alpi
        Defa30
    Downregulated with Age
        Cilp2
        Col1a2
        Col1a1
    Progression
  Spleen
    TL;DR
    Upregulated with Age
        C130026I21Rik
        Snhg11
        Gzmk
    Progression
  Conclusions
      Speculative Research Directions:
      Maybe Research Idea-Generation At Scale Is Valuable?
None
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A very cool project, sponsored by the Chan Zuckerberg Initiative, was the Tabula Muris and the Tabula Muris Senis — a single-cell gene expression “atlas” of the mouse, and then mice at various ages from 3 months (young adulthood) to 27 months (near the end of their lifespan.)

There’s a handy-dandy differential expression app that lets you see which genes are more expressed, or less expressed, in different tissues with age.

This is great for basic exploration that could give us insights into the mechanisms of aging (and ultimately inform research towards treatments of the diseases of aging.). So let’s play around with it and see if anything interesting is going on!

I’m going to just compare 27-month-old to 3-month-old mice. I’ll refer to genes as “upregulated” (with age) if they’re more abundant in the 27-month-olds, and “downregulated” (with age) if they’re more abundant in the 3-month-olds.

Brain

TL;DR

The aging brain is chronically inflamed, especially in neurodegenerative diseases of aging (Alzheimer’s, dementia, etc). The upregulated genes in old mouse brains are all markers of inflammation.

Interestingly, GPR7, the receptor for neuropeptides B and W, is downregulated with age. This may be a factor in age-related obesity.

Upregulated With Age

Downregulated with Age

Progression

Muscle

TL;DR

Mice, like humans, lose muscle and strength with age; mice begin to show elevated levels of muscle-loss-associated proteins quite young, before “middle age”. At least one of them, a defensin, belongs to a class of proteins known to cause muscle loss when administered experimentally. Perhaps anti-defensin strategies might be a good research direction for preventing sarcopenia?

Also, collagen genes are downregulated with age; this might be a side effect of collagen accumulation in muscles (a known age-related phenomenon.)

Upregulated With Age

Downregulated with Age

Progression

Alpi begins to be upregulated super early, at 9 months. H1fnt and Defa30 are also upregulated by 15 months. Downregulated Cilp, Col1a2, and Col1a1 show up later, at 18-21 months.

Spleen

TL;DR

The aging spleen is interesting because removing it altogether seems to make animals live longer. What is the aged spleen doing wrong, at a gene expression level?

Looks like, granzyme K-positive cytotoxic T cells. They accumulate with age and promote inflammation throughout the body. (They aren’t a unique “culprit” for age-related inflammation, though; inject them into a young mouse and they don’t promote the same phenotypes.)

Upregulated with Age

Progression

Snhg11 and Gzmk start to get upregulated at middle age (12 months); C130026I21Rik pops up only slightly later, at 18 months.

Conclusions

This is just a starting point (I picked a couple tissues to play with but there are lots more.)

But already it’s easy to observe patterns that make sense in context (old brains are inflamed; old muscles have weird collagen; old spleens are enriched for particular populations of immune cells) and to generate ideas for interventional research directions.

Speculative Research Directions:

The majority of drug discovery programs, remarkably enough, do not seem to have come from any kind of genetic analysis of this kind, where you notice that the relevant target is overexpressed (or mutated, or whatever) in disease.

Obviously, the “sit on your butt and read/play with omics data” approach to idea generation offers only a slim chance of actually coming up with something successful; but I keep seeing data points suggesting that, despite how easy it is to do, it’s underrated and under-practiced in biomedical research.

Maybe Research Idea-Generation At Scale Is Valuable?

Someone (maybe me!) should start compiling a database of “someone should try this” experimental approaches suggested by systematic “omics” datasets (like the Tabula Muris Senis and CellxGene, and GWAS studies, and much much more.)

This requires a common-sense filtering step based on context from the research literature — I don’t think writing a simple script to automate it would be very useful — but it is of course accelerated by LLM tools like Perplexity that help you search for research papers that answer your questions.

Once fully populated, this sort of resource might be valuable to researchers; it might even be a higher-quality version of some of the functions that fully automated “AI for target discovery” tools aim to provide biotech companies.

1

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2

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3

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4

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5

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6

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7

Palmer, Alexandra L., and Shalina S. Ousman. "Astrocytes and aging." Frontiers in Aging Neuroscience 10 (2018): 337.

8

Liu, W-Q., et al. "Effects of long non-coding RNA NEAT1 on sepsis-induced brain injury in mice via NF-κB." European Review for Medical & Pharmacological Sciences 23.9 (2019).

9

Li, Kun, and Ziqiang Wang. "lncRNA NEAT1: Key player in neurodegenerative diseases." Ageing Research Reviews 86 (2023): 101878.

10

Nagata-Kuroiwa, Ruby, et al. "Critical role of neuropeptides B/W receptor 1 signaling in social behavior and fear memory." PLoS One 6.2 (2011): e16972.

11

Ishii, Makoto, Hong Fei, and Jeffrey M. Friedman. "Targeted disruption of GPR7, the endogenous receptor for neuropeptides B and W, leads to metabolic defects and adult-onset obesity." Proceedings of the National Academy of Sciences 100.18 (2003): 10540-10545.

12

Engel, W. King, and GUY G. CUNNINGHAM. "Alkaline phosphatase-positive abnormal muscle fibers of humans." Journal of Histochemistry & Cytochemistry 18.1 (1970): 55-57.

13

Petermann-Rocha, Fanny, et al. "Biomarkers profile of people with sarcopenia: a cross-sectional analysis from UK biobank." Journal of the American Medical Directors Association 21.12 (2020): 2017-e1.

14

Yamaguchi, Yasuhiro, et al. "β-Defensin overexpression induces progressive muscle degeneration in mice." American Journal of Physiology-Cell Physiology 292.6 (2007): C2141-C2149.

15

though, note that this is a beta defensin and the gene in question is for an alpha defensin.

16

Güttsches, Anne-Katrin, et al. "Human β-defensin-3 correlates with muscle fibre degeneration in idiopathic inflammatory myopathies." Innate Immunity 20.1 (2014): 49-60.

17

Chen, Wan-Jing, et al. "Aged skeletal muscle retains the ability to remodel extracellular matrix for degradation of collagen deposition after muscle injury." International journal of molecular sciences 22.4 (2021): 2123.

18

Karaky, Mohamad, et al. "SP140 regulates the expression of immune-related genes associated with multiple sclerosis and other autoimmune diseases by NF-κ B inhibition." Human molecular genetics 27.23 (2018): 4012

19

Xu, Wei, et al. "Circulating lncRNA SNHG11 as a novel biomarker for early diagnosis and prognosis of colorectal cancer." International journal of cancer 146.10 (2020): 2901-2912.

20

Geng, Y-B., et al. "Long non-coding RNA SNHG11 promotes cell proliferation, invasion and migration in glioma by targeting miR-154-5p." European Review for Medical & Pharmacological Sciences 24.9 (2020).

21

Huang, Wei, et al. "LncRNA SNHG11 promotes proliferation, migration, apoptosis, and autophagy by regulating hsa-miR-184/AGO2 in HCC." OncoTargets and therapy (2020): 413-421.

22

Verschoor, Chris P., et al. "NK-and T-cell granzyme B and K expression correlates with age, CMV infection and influenza vaccine-induced antibody titres in older adults." Frontiers in Aging 3 (2023): 1098200.

23

that is, derived from the same parent cells

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