Econ_Sep_22

The Economist

September 22nd 2018

Science and technology 73

Cellular ageing

Out with the old

ELLS dividemany times throughout

their lives. But they cannot do it

indefinitely. Once they have reached the

limits of their reproductive powers, they

enter a state called “senescence”, in

which they carry on performing their

duties but stopmaking newcopies of

themselves. For years it was assumed

that, apart from their refusal to divide,

senescent cellswere otherwise identical

to their replicating compatriots.

There ismounting evidence, though,

that this is untrue. One study in 2016

reported that senescent cells in the kid-

neys and heart produce a protein that

causes nearby healthy tissues to deterio-

rate. Another study found that senescent

cells contribute to diseases like athero-

sclerosis and arthritis. Newwork led by

Darren Baker, a biologist at theMayo

Clinic inMinnesota, published in

Nature

thisweek, suggests the accumulation of

senescent cellswithin the brains of mice

causes the animals to develop neurode-

generative diseases—and that clearing

out these cells can help prevent them.

Workingwith a teamof colleagues, Dr

Baker obtained a population ofmice that

had been genetically engineered to

quickly develop fibrous tangles of pro-

tein in their brains. These tangles are

associatedwith the decline inmental

abilities caused by diseases like Alz-

heimer’s. When themicewere four

months old, Dr Baker collected brain

tissue from some, and found senescent

cells accumulating in the hippocampus, a

seahorse-shaped region of the brain

involvedwith learning andmemory. By

sixmonths old, theywere accumulating

in the cerebral cortex aswell—aswere the

tangles that are associatedwith neuro-

logical degeneration.

To seewhat role, if any, senescent cells

were playing in the their diminishing

brainpower, Dr Baker genetically altered

somemice such that their senescent cells

could be eliminatedwith a twice-weekly

dose of a specific chemical. That left a

subgroup ofmice that were still geneti-

cally predisposed to neurological dis-

eases, but which also had their brains

rinsed of senescent cells.

By the time thesemice reached six

months old, the tangleswere almost

entirely absent. When themicewere

presentedwith objects they had encoun-

tered before, they approached them

without hesitation, as healthymice

should. In contrast, micewhose brains

were full of senescent cells approached

the objects tentatively, as if they had

never seen thembefore.

Mice are not people. It remains to be

seenwhether clearing exhausted cells

fromhuman brains could have similar

benefits. Since it is not possible to do

pre-emptive genetic engineering on

humans, some pharmaceutical method

of clearing out senescent cellswill have

to be developed instead. But Dr Baker’s

results suggest that isworth trying. In-

deed, the next project in his lab is to

explorewhether clearing senescent cells

from the brains ofmice that are already

suffering from themurine version of

Alzheimer’smight allow their already

damaged brains to recover.

Removingworn-out cellsmight help treat Alzheimer’s disease

I’m getting too old for this

they are expressed in different cells) and

the proteins forwhich they code (for exam-

ple, their solubility). When they fed these

data to their algorithm, they were able to

explain about 40% of the difference in the

attention paid to each gene (measured by

the number ofpaperspublished) using just

15 features. Essentially, thereweremore pa-

pers on abundantly expressed genes that

encode stable proteins. That suggests re-

searchers—perhaps not unreasonably—fo-

cus on genes that are easier to study. Oddly,

though, the pattern of publication has not

changed much since 2000, despite the

completion of the human genome project

in 2003 and huge advances in

DNA

-se-

quencing technology.

One possible reason for that can be

found in another phenomenon known as

the “Matthew effect”. Pithily summarised

by the adage “the rich get richer”, this pre-

dicts that researchers andmoneywill flow

to subjects that are already well-estab-

lished. To see if this was the case, the team

added the year of each gene’s discovery to

their model and found its explanatory

power jumped to 56%, because earlier dis-

coveries translated into greater attention.

The identification of a new human gene is

often preceded by the discovery of similar

genes in scientificworkhorses such as fruit

flies, rats and mice. When the researchers

added the number of papers relating to

these animal genes, the algorithm’s predic-

tive powers improved even further, to 76%.

All this might be justified if the most-

studied genes were also the most impor-

tant—if, for instance, mutations within

them are associated with serious or com-

mon diseases. The team found that the

most-researched10% of geneswere indeed

between three and five times more likely

to be involved in disease. But they receive

disproportionate attention, accruing thou-

sands of times the number of publications

as the least-researched10%.

The team found these biases were re-

produced in funding decisions made by

America’s National Institutes of Health,

the world’s biggest sponsor of biomedical

research; they also found a similar pattern

in drug development in the private sector.

Drugs are often made to tweak the behav-

iour of the proteins that particular genes

encode. Although there are presently

drugs in development for 30% of disease-

associated genes discovered before 1981,

the same is true foronly2%ofgenesdiscov-

ered since 2001.

No doubt much remains to be learned

about even the best-studied genes. But the

upshot of all this is that a wealth of discov-

eries and treatments is likely to await scien-

tists, and funding agencies, bold enough to

lookelsewhere. Time to shine a light on the

darker parts of the genome.

Have you tried over here?