Breaking Up Is Hard To Do

 

Thus Neil Sedaka, the American pop songster back in the 60s. He was crooning about hearts of course but since then we’ve discovered that for our genetic hearts — our DNA — breaking up is not that tough and indeed it’s quite common.

A moving picture worth a thousand words

When I’m trying to explain cancer to non-scientists I often begin by showing a short movie of a cell in the final stages of dividing to form two identical daughter cells. This is the process called mitosis and the end-game is the exciting bit because the cell’s genetic material, its DNA, has been duplicated and the two identical sets of chromosomes are lined up in the middle of the cell. There ensues a mighty tug-of-war as cables (strands of proteins) are attached to the chromosomes to rip them apart, providing a separate genome for each new cell when, shortly after, the parent cell splits into two. When viewed as a speeded-up movie it’s incredibly dramatic and violent — which is why I show it because it’s easy to see how things could go wrong to create broken chromosomes or an unequal division of chromosomes (aneuploidy). It’s the flip side if you like to the single base changes (mutations) — the smallest damage DNA can suffer — that are a common feature of cancers.

In Heir of the Dog we showed pictures of normal and cancerous chromosomes that had been tagged with coloured markers to illustrate the quite staggering extent of “chromosome shuffling” that can occur.

Nothing new there

We’ve known about aneuploidy for a long time. Over 20 years ago Bert Vogelstein and his colleagues showed that the cells in most bowel cancers have different numbers of chromosomes and we know now that chromosomal instability is present in most solid tumours (90%).

Knowing it happens is one thing: being able to track it in real time to see how it happens is another. This difficulty has recently been overcome by Ana C. F. Bolhaqueiro and her colleagues from the Universities of Utrecht and Groningen who took human colorectal tumour cells and grew them in a cell culture system in the laboratory that permits 3D growth — giving rise to clumps of cells called organoids.

Scheme representing how cells grown as a 3D clump (organoid) can be sampled to follow chromosomal changes. Cells were taken from human colon tumours and from adjacent normal tissue and grown in dishes. The cells were labelled with a fluorescent tag to enable individual chromosomes to been seen by microscopy as the cells divided. At time intervals single cells were selected and sequenced to track changes in DNA. From Johnson and McClelland 2019.

Genetic evolution in real time

As the above scheme shows, the idea of organoids is that their cells grow and divide so that at any time you can select a sample and look at what’s happening to its DNA. Furthermore the DNA can be sequenced to pinpoint precisely the genetic changes that have occurred.

It turned out that cancer cells often make mistakes in apportioning DNA between daughter cells whereas such errors are rare in normal, healthy cells.

It should be said that whilst these errors are common in human colon cancers, a subset of these tumours do not show chromosomal instability but rather have a high frequency of small mutations (called microsatellite instability). Another example of how in cancer there’s usually more than one way of getting to the same end.

Building bridges …

The most common type of gross chromosomal abnormality occurs when chromosomes fuse via their sticky ends to give what are called chromatin bridges (chromatin just means DNA complete with all the proteins normally attached to it). Other errors can give rise to a chromosome that’s become isolated — called a lagging chromosome, it’s a bit like a sheep that has wandered off from the rest of the flock. As the cell finally divides and the daughter cells move apart, DNA bridges undergo random fragmentation.

… but where to …

Little is known about how cells deal with aneuploidy and the extent to which it drives tumour development. This study showed that variation in chromosome number depends on the rate at which chromosomal instability develops and the capacity of a cell to survive in the face of changes in chromosome number. More generally for the future, it shows that the organoid approach offers an intriguing opening for exploring this facet of cancer.

Reference

Bolhaqueiro, A.C.F. et al. (2019). Ongoing chromosomal instability and karyotype evolution in human colorectal cancer organoids. Nature Genetics 51, 824–834.

Lengauer, C. et al., (1997). Genetic instability in colorectal cancers. Nature 386, 623-627.

Johnson, S.C. and McClelland, S.E. (2019). Watching cancer cells evolve. Nature 570, 166-167.

Sticky Cancer Genes

 

In the previous blog I talked about Breath Biopsy — a new method that aims to detect cancers from breath samples. I noted that it could end up complementing liquid biopsies — samples of tumour cell DNA pulled out of a teaspoon of blood — both being, as near as makes no difference, non-invasive tests. Just to show that there’s no limit to the ingenuity of scientists, yet another approach to the detection problem has just been published — this from Matt Trau and his wonderful team at The University of Queensland.

This new method, like the liquid biopsy, detects DNA but, rather than the sequence of bases, it identifies an epigenetic profile — that is, the pattern of chemical tags (methyl groups) attached to bases. As we noted in Cancer GPS? cancer cells often have abnormal DNA methylation patterns — excess methylation (hypermethylation) in some regions, reduced methylation in others. Methylation acts as a kind of ‘fine tuner’, regulating whether genes are switched on or off. In the methylation landscape of cancer cells there is an overall loss of methylation but there’s an increase in regions that regulate the expression of critical genes. This shows up as clusters of methylated cytosine bases.

Rather helpfully, a little while ago in Desperately SEEKing … we talked about epigenetics and included a scheme showing how differences in methylation clusters can identify normal cells and a variety of cancers and how these were analysed in the computer program CancerLocator.

The Trau paper has an even better scheme showing how the patterns of DNA decoration differ between normal and cancer cells and how this ‘methylscape’ (methylation landscape) affects the physical behaviour of DNA.

How epigenetic changes affect DNA. Scheme shows methylation (left: addition of a methyl group to a cytosine base in DNA) in the process of epigenetic reprogramming in cancer cells. This change in the methylation landscape affects the solubility of DNA and its adsorption by gold (from Sina et al. 2018).

Critically, normal and cancer epigenomes differ in stickiness — affinity — for metal surfaces, in particular for gold. In a clever ploy this work incorporated a colour change as indicator. We don’t need to bother with the details — and the result is easy to describe. DNA, extracted from a small blood sample, is added to water containing tiny gold nanoparticles. The colour indicator makes the water pink. If the DNA is from cancer cells the water retains its original colour. If it’s normal DNA from healthy cells the different binding properties turns the water blue.

By this test the Brisbane group have been able to identify DNA from breast, prostate and colorectal cancers as well as from lymphomas.

So effective is this method that it can detect circulating free DNA from tumour cells within 10 minutes of taking a blood sample.

The aim of the game — and the reason why so much effort is being expended — is to detect cancers much earlier than current methods (mammography, etc.) can manage. The idea is that if we can do this not weeks or months but perhaps years earlier, at that stage cancers may be much more susceptible to drug treatments. Trau’s paper notes that their test is sensitive enough to detect very low levels of cancer DNA — not the same thing as early detection but suggestive none the less.

So there are now at least three non-invasive tests for cancer: liquid biopsy, Breath Biopsy and the Queensland group’s Methylscape, and in the context of epigenetics we should also bear in mind the CancerLocator analysis programme.

Matt Trau acknowledges, speaking of Methylscape, that “We certainly don’t know yet whether it’s the holy grail for all cancer diagnostics, but it looks really interesting as an incredibly simple universal marker for cancer …” We know already that liquid biopsies can give useful information about patient response to treatment but it will be a while before we can determine how far back any of these methods can push the detection frontier. In the meantime it would be surprising if these tests were not being applied to age-grouped sets of normal individuals — because one would expect the frequency of cancer indication to be lower in younger people.

From a scientific point of view it would be exciting if a significant proportion of ‘positives’ was detected in, say, 20 to 30 year olds. Such a result would, however, raise some very tricky questions in terms of what, at the moment, should be done with those findings.

Reference

Abu Ali Ibn Sina, Laura G. Carrascosa, Ziyu Liang, Yadveer S. Grewal, Andri Wardiana, Muhammad J. A. Shiddiky, Robert A. Gardiner, Hemamali Samaratunga, Maher K. Gandhi, Rodney J. Scott, Darren Korbie & Matt Trau (2018). Epigenetically reprogrammed methylation landscape drives the DNA self-assembly and serves as a universal cancer biomarker. Nature Communications 9, Article number: 4915.

Shifting the Genetic Furniture

 

Readers of these pages will know very well that cells are packets of magic. Of course, we often describe them in the simplest terms: ‘Sacs of gooey stuff with lots of molecules floating around.’ And it’s true that we know a lot about the protein pathways that capture energy from the food we eat and about the machinery that duplicates genetic material, makes new proteins and sustains life. Even so, although we’ve worked out much molecular detail, we have scarcely a clue about how ‘stuff’ in cells is organised. How do the tens of thousands of different types of proteins find their places in the seemingly chaotic jumble of a cell so that they can do their job? If that remains a mystery there’s an even more perplexing one in the form of the nucleus. That’s a smaller sac (i.e. a compartment surrounded by a membrane) that is home to most of our genetic material — i.e. DNA.

Sizing up the problem

It’s easy to see why evolution came up with the idea of a separate enclosure for DNA which only has to do two things: reproduce itself and enable regions of its four base code to be transcribed into molecules that can cross the nuclear membrane to be translated into proteins in the body of the cell. But here’s the puzzle. The nucleus is very small and there’s an awful lot of DNA — over 3,000 million bases in each of the two strands of human DNA (and, of course, two complete sets of chromosomes go to make up the human genome) — so 2 metres of it in every cell. A rather pointless exercise, unless you go in for pub quizzes, is to work out the length of all your DNA if you put it together in a single string. 1013 cells (i.e. 1 followed by 13 zeros) in your body: 2 metres per cell. Answer: your DNA would stretch to the sun and back 67 times.

Mmm. More relevantly, the nucleus of a cell is typically about 6 micrometres (µm) in diameter — that’s six millionths of a metre (6/1,000,000 metre), into which our 2 metres must squeeze.

Time for some serious packing to be done but it’s not just a matter of stuffing it in any old how and sitting on the lid. As we’ve just noted, every time cells divide all the DNA has to be replicated and regions (i.e. genes) are continually being “read” to make proteins. So the machinery in the nucleus has to be able to get at specific regions of DNA and disentangle them sufficiently for code reading. Part of evolution’s solution to these problems has been to add proteins called histones to DNA (the term chromosome refers to DNA together with histone packaging proteins and other proteins). To understand how this leads to “more being less”, consider DNA as a length of cotton. If you just scrunch the cotton up into a ball you get a tangled mess. But if you use cotton reels (aka histones — two or three hundred million per cell), you can reduce the great length to smaller, more organized blocks — which is just as well because they’re all that stands between life and a tangled mess.

Thinking of histones as cotton reels helps a bit in thinking about how the nucleus achieves the seemingly impossible but the fact of the matter is that we have no real idea about how DNA is unravelling is controlled so that the two strands can be unzipped and replicated, yet alone the way in which starting points for reading genes are found by proteins.

Undeterred by our profound ignorance Haifeng Wang and colleagues at Stanford University have just done something really amazing. They came up with a way of moving regions of DNA from the jumble of the nuclear interior to the membrane and they showed that this can change the activity of genes. They used CRISPR (that we described in Re-writing the Manual of Life) to insert a short piece of DNA next to a chosen gene. The insert was tagged with a protein designed to attach to a hormone that also binds to a protein (called emerin) that sits in the nuclear membrane. So the idea was that when the hormone is added to cells it can hook on to the DNA tag and, by attaching to emerin, can drag the chosen gene to the membrane. The coupling agent is a plant hormone (abscisic acid) although it also occurs in other species, including humans. Wang & Co christened their method CRISPR-GO for CRISPR-Genome Organizer.

Tagging a DNA insert with a protein so that a coupling molecule can pull a region of DNA to a protein in the membrane of the nucleus. From Wang et al., 2018.

Repositioning regions of DNA in the nucleus. DNA is labeled blue which defines the shape of the nucleus. Red dots are specific genes before (left) and after (right) adding the coupling agent. From Pennisi 2018.

How did CRISPR-GO go?

Astonishingly well. Not only could it shift tagged DNA from the interior to the membrane of the nucleus but the rearrangements could change the way cells behaved. Depending on which regions were moved and where to, cells grew more slowly or more rapidly.

So this is a really remarkable technical feat — but it’s not just molecular pyrotechnics for fun. It looks as though this approach may offer at long last a way of dissecting how cells go about getting a controlled response out of the mind-boggling complexity that is their genetic material.

References

Wang, H. et al. (2018). CRISPR-Mediated Programmable 3D Genome Positioning and Nuclear Organization. Cell 175, 1405-1417.

Pennisi, E. (2018). Moving DNA to a different part of the nucleus can change how it works. Science Oct. 11th.

Taking Aim at Cancer’s Heart

 

Cancer is a unique paradox. At one level it’s as easy as can be to describe: damage to DNA (aka mutations) drives cells to behave abnormally — to make more of themselves when they shouldn’t.

But we all know that cancer’s fiendishly complicated — at least at the level of fine detail. Over the last decade or so the avalanche of sequenced DNA has revealed that every cell in a tumour is different: compare one cell to its neighbour and you’ll find variations in the individual units (the bases A, C, G & T) that make up the chains of DNA.

It’s a nightmare: every cancer is different so we need an infinite number of treatments to control or cure each one. Time to give up and retire to the pub.

Drivers and passengers

Not quite. DNA sequencing has also revealed that, amongst all the genetic mayhem, some mutations are more important than others. The movers and shakers have been dubbed ‘drivers’: those that come along for the ride are ‘passengers’. The hangers-on are heavily in the majority but, even so, several hundred drivers (i.e. mutated genes that give rise to abnormal proteins) have been identified. As it needs a group of drivers to work together to make a cancer we still have the problem that the number of critical combinations that can arise is essentially infinite.

One way of reducing the scale of the problem has been to look at what ‘driver’ proteins do in cells and to target those acting at key points to push cell proliferation beyond the normal.

Playing games

Just recently Giulio CaravagnaAndrea Sottoriva and colleagues at the Institute of Cancer Research, London and the University of Edinburgh have come up with a different approach. The idea goes back to the 1950s when a clever chap from Kansas by the name of Arthur Samuel came up with a program for IBM’s first commercial computer so that it could play draughts (checkers as our American friends call it) in its spare time. The program defined the patterns that could be formed by the pieces on the chequerboard so that, given enough of these, IBM 701 could indicate the optimal moves. Samuel called this machine learning, a precursor of the idea of artificial intelligence.

Perhaps the most famous moment in this saga came in 1997 when a later IBM computer, Deep Blue, beat the then world chess champion Garry Kasparov. Unsurprisingly, Kasparov was a bit miffed and accused IBM of cheating — to wit, getting a human to tell the machine what to do. Let’s hope that in the end he came to terms with the fact that Deep Blue could crank through 200 million positions per second and, however many games Grandmasters have in their heads, they can’t compete with that.

The cancer team realized that the mutations driving the evolution of cancer cells emerge as patterns in the sequence of DNA as a cell moves towards becoming independent of normal controls. Think of each cancer as a family tree of mutations, the key question being which branch leads to the most potent combination.

To pick out these patterns they applied a machine-learning approach known as transfer learning to the DNA sequences from a large number of cancers. They called this ‘repeated evolution in cancer’ — REVOLVER — aimed at picking out mutation patterns at the heart of cancer that foreshadow future genetic changes and can be used to predict how they will evolve.

Identifying patterns of mutation common to different tumours.

Samples are taken from different regions of a patient’s tumour (represented by the coloured dots). Their DNA sequences will have multiple variations that can mask underlying patterns of driver mutations present in some subgroups. The five trees show mutations picked up in those patients. REVOLVER uses transfer learning to screen the sequence data from many patients and pull out evolutionary trajectories shared by subgroups. The dotted red lines highlight common patterns that are represented in the lower strip. From Caravagna et al. 2018.

REVOLVER was applied to sequences from lung, breast, kidney and bowel cancers but there’s no reason it shouldn’t work with other tumours. The big attraction is that if these mini-sequence mutation patterns can be associated generally with how a given tumour develops they should help to inform treatment options and predict survival.

We have in the past referred to the ways cancers evolve as ‘genetic roulette’ — so perhaps it’s appropriate if game-playing computer programs turn out to be useful in teasing out behavioural clues.

Reference

Caravagna, G. et al. (2018). Detecting repeated cancer evolution from multi-region tumor sequencing data. Nature Methods 15, 707–714.

Now wash your hands!

 

You must have spent the last 20 years on a distant planet if you’re unaware that we’re heading for Antibiotic Armaggedon — the rise of “Superbugs”, i.e., bacteria resistant to once-successful medication. Microbes resistant to multiple antimicrobials are called multidrug resistant. It’s a desperate matter because it means trivial infections may become fatal and currently safe surgical procedures may become dangerous.

Time-line of the discovery of different antibiotic classes in clinical use. The key point is that the last antibiotic class to become a successful treatment was discovered in 1987.

What’s the problem?
It’s 30 years since we came up a new class of antibiotics. The golden age launched by Fleming’s celebrated discovery of penicillin is long gone and while the discovery curve has drifted ever downwards since 1960 the bugs have been busy.

Just how busy a bug can be was shown by a large-scale experiment carried out by Roy Kishony and friends. They built a “Mega-Plate” — a Petri Dish 2 ft by 4 ft filled with a jelly for the bacteria to grow in. The bugs were seeded into channels at either end so they would grow towards the middle. The only thing stopping them was four channels dosed with antibiotic at increasing concentrations — 10 times more in each successive channel.

The bugs grow until they hit a wall of antibiotic. There they pause for a think — and, after a bit, an intrepid little group start to make their way into the higher dose of drug. Gradually the number of groups expand until a tidal wave sweeps over that barrier. This is repeated at each new ‘wall’ — four times until the whole tray is a bug fest.

When they pause at each new ‘wall’ they’re not ‘thinking’ of course. They’re just picking up random mutations in their DNA until they are able to advance into the high drug environment. So this experiment is a fantastic visual display of bugs becoming drug-resistant. And it’s terrifying because it takes about 11 days for them to overcome four levels of drug. It’s even more scary in the speeded-up movie as that lasts less than two minutes.

Sound familiar?
It should do as this is a cancer column and readers will know that cancers arise by picking up mutations. To highlight the similarities the picture below is the left-hand half of the bug tray with new colonies shown as linked dots. You could perfectly well think of these as early stage cancer cells acquiring mutations in ‘driver’ genes that push them towards tumour formation.

So that’s pretty scary too and the only good news is that animal cells reproduce much more slowly than bacteria. The fastest they can manage is about 48 hours to grow and divide into two new cells and for many it’s much slower than that. Bugs, on the other hand, can do it in 20 minutes if you feed them enough of the right stuff.

Which is why we don’t all get zonked by cancer at an early age.

The evolution of bacteria on a “Mega-Plate” Petri Dish. The vertical red lines mark the boundaries of increasing antibiotic concentrations. You could equally think of each dot that represents a new bacterial colony being early stage cancer cells acquiring mutations in ‘driver’ genes (white arrows) that push them towards tumour formation. From Roy Kishony’s Laboratory at Harvard Medical School.

Enough of that!
But for once I don’t want to talk about cancer but about a really fascinating piece of work that caught my eye in the journal Cell Reports. It’s by Gianni Panagiotou, Kang Kang and colleagues from The University of Hong Kong and The Hans Knöll Institute, Jena, Germany and it’s all about their travels on the Hong Kong MTR (Mass Transit Railway). This is the network of over 200 km of railway lines with 159 stations that serves the urbanised areas of Hong Kong IslandKowloon, and the New Territories and has a cross- border connection to the neighboring city of Shenzhen in mainland China.

An MTR train on the Tung Chung line that links Lantau Island with Hong Kong Island.

Being scientists of course they weren’t just having a day out. They wanted to know the contents of the microbiome that they and their fellow travellers picked up on the palms of their hands when riding the rails. ‘Microbiome’ means all of the collection of microorganisms — though in practice this is almost entirely bacteria. So they swabbed the palms of volunteers and then threw the full power of modern DNA sequencing and genetic analysis at what they’d scraped off. Or, as they put it: “We conducted a metagenomic study of the Hong Kong MTR system.”

And if you’re thinking it might be possible to take a trip on the Hong Kong Metro without grabbing a handrail or otherwise engaging in what on the London Underground used to be called ‘strap-hanging’ you clearly haven’t tried it!

Hong Kong MTR.

 

The MTR System and Sampling Procedure. Left: The eight urban lines studied: the Airport Express line and Disneyland Resort branch were excluded. The Central-Hong Kong station and the cross-border rail stations connecting with the MTR and the Shenzhen metro system are labeled. Right: The sampling procedure included handwashing, handrail touching for 30 min and swabbing. From Kang et al. 2018.

Hold very tight please! 

It’s going to become a seriously bumpy ride. The major findings were:

  1. Four groups (phyla) of bacteria dominated: Actinobacteria [51%], Proteobacteria [27%], Firmicutes [11%] and Bacteroidetes [2%]. Followers of this blog will be delighted to spot the last two (B & F) as we’ve met them several times before (in Hitchhiker Or Driver?, Fast Food Fix Focuses on Fibre, Our Inner Self, The Best Laid Plans In Mice and Men, and, of course, in it’s a small world) — that’s how important they are in the context of cancer.
  2. The dominant organism (29% of the community) was P. acnes (one of the Actinobacteria — it’s the bug linked to the skin condition of acne).
  3. Some non-human-associated species (e.g., soil organisms) also popped up that varied enormously in amount from day to day — perhaps because of weather conditions (e.g., humidity).
  4. Variation in the make-up of the microbial communities picked up depended, more than anything else, on the time of day. There was a marked decrease in diversity in afternoon samples compared with those taken in the morning.
  5. Specific species of bacteria associated with individual metro lines. That is, sets of bug types are relatively abundant on a given line compared with all other lines, giving a kind of line-specific signature — though the distinction declines from morning to afternoon. The most physically isolated line, MOS (Ma On Shan), had a greater number of signature species. The MOS runs entirely above ground alongside the Shing Mun Channel, a polluted brackish river, and its ‘signature’ includes bacteria found in sewage.
  6. All of which brings us to bugs with antibiotic resistance genes (ARGs). Across the network 136 ARG families were detected including 24 that are clinically important. Strikingly, lines closer to Shenzhen (ER (East Rail) and MOS) tend to have higher ARG input during the day. Critically, the ER line a.m. signatures become p.m.-enriched in all MTR lines far from Shenzhen — that is, these ARG families spread over the network during the day.

Simplified map of the Hong Kong MTR indicating how antibiotic resistance genes spread during the day from the ER and MOS lines to the entire network. Tetracycline resistance genes: tetA, tetO, tetRRPP and tetMWOS; vancomycin resistance genes: vanC, vanX. From Kang et al. 2018.

These results clearly suggest that the ER line, the only cross-border line linked to mainland China, may be a source of clinically important ARGs, especially against tetracycline, a commonly used antibiotic in China’s swine feedlots. Antibiotics, including tetracycline, can be detected in the soil in the Pearl River Delta area where the cities of Hong Kong and Shenzhen are located.

It should be said that this is by no means the first survey of bugs on rails. Notable ones have looked at the New York and Boston metro systems and they too revealed the potential health risks of the bug communities found on trains and in the stations, including the presence of pathogens and antibiotic resistance. The Boston survey highlighted that different types of materials have surfaces that are preferred by different microbes with high variation in functional capacity and pathogenic potential.

One obvious suggestion from these studies is that world-wide we could do a lot to improve sanitation, e.g., by having hand sanitizer dispensers in all sensible places (at the exits of metro, railway and bike-sharing stations and airports and of course in hospitals). The Hong Kong data are seriously frightening and most people seem blissfully unaware that the invisible world they reveal carries the potential for the destruction of us all.

But, as ever, there’s two sides to the matter. We’ve evolved over millions of years to live with bugs and they with us. However you wash your hands you won’t get rid of every bug and anyway, as what’s-his-name almost says, “They’ll be back!” We all carry around micro-organisms that can be fatal if they get to the wrong place. But, if you’re reasonably fit, there’s a lot to be said for simply following sensible, basic hygiene rules with a philosophy of ‘live and let live.’

Have a nice day commuters, wherever you are!

References

Kang K., et al. (2018). The Environmental Exposures and Inner- and Intercity Traffic Flows of the Metro System May Contribute to the Skin Microbiome and Resistome. Cell Reports 24, 1190–1202.

Wu, N., Qiao, M., Zhang, B., Cheng, W.D., and Zhu, Y.G. (2010). Abundance and diversity of tetracycline resistance genes in soils adjacent to representative swine feedlots in China. Environ. Sci. Technol. 44, 6933–6939.

Li, Y.W., Wu, X.L., Mo, C.H., Tai, Y.P., Huang, X.P., and Xiang, L. (2011). Investigation of sulfonamide, tetracycline, and quinolone antibiotics in vegetable farmland soil in the Pearl River Delta area, southern China. J. Agric. Food Chem. 59, 7268–7276.

Leung, M.H., Wilkins, D., Li, E.K., Kong, F.K., and Lee, P.K. (2014). Indoor-air microbiome in an urban subway network: diversity and dynamics. Appl. Environ. Microbiol. 80, 6760–6770.

Robertson, C.E., Baumgartner, L.K., Harris, J.K., Peterson, K.L., Stevens, M.J., Frank, D.N., and Pace, N.R. (2013). Culture-independent analysis of aerosol microbiology in a metropolitan subway system. Appl. Environ. Microbiol. 79, 3485–3493.

Afshinnekoo, E., Meydan, C., Chowdhury, S., Jaroudi, D., Boyer, C., Bernstein, N., Maritz, J.M., Reeves, D., Gandara, J., Chhangawala, S., et al. (2015). Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics. Cell Syst 1, 72–87.

Hsu, T., Joice, R., Vallarino, J., Abu-Ali, G., Hartmann, E.M., Shafquat, A., Du- Long, C., Baranowski, C., Gevers, D., Green, J.L., et al. (2016). Urban Transit System Microbial Communities Differ by Surface Type and Interaction with Humans and the Environment. mSystems 1, e00018–e00016.

Fantastic Stuff

 

It certainly is for Judy Perkins, a lady from Florida, who is the subject of a research paper published last week in the journal Nature Medicine by Nikolaos Zacharakis, Steven Rosenberg and their colleagues at the National Cancer Institute in Bethesda, Maryland. Having reached a point where she was enduring pain and facing death from metastatic breast cancer, the paper notes that she has undergone “complete durable regression … now ongoing for over 22 months.”  Wow! Hard to even begin to imagine how she must feel — or, for that matter, the team that engineered this outcome.

How was it done?

Well, it’s a very good example of what I do tend to go on about in these pages — namely that science is almost never about ‘ground-breaking breakthroughs’ or ‘Eureka’ moments. It creeps along in tiny steps, sideways, backwards and sometimes even forwards.

You may recall that in Self Help – Part 2, talking about ‘personalized medicine’, we described how in one version of cancer immunotherapy a sample of a patient’s white blood cells (T lymphocytes) is grown in the lab. This is a way of either getting more immune cells that can target the patient’s tumour or of being able to modify the cells by genetic engineering. One approach is to engineer cells to make receptors on their surface that target them to the tumour cell surface. Put these cells back into the patient and, with luck, you get better tumour cell killing.

An extra step (Gosh! Wonderful GOSH) enabled novel genes to be engineered into the white cells.

The Shape of Things to Come? took a further small step when DNA sequencing was used to identify mutations that gave rise to new proteins in tumour cells (called tumour-associated antigens or ‘neoantigens’ — molecular flags on the cell surface that can provoke an immune response – i.e., the host makes antibody proteins that react with (stick to) the antigens). Charlie Swanton and his colleagues from University College London and Cancer Research UK used this method for two samples of lung cancer, growing them in the lab to expand the population and testing how good these tumour-infiltrating cells were at recognizing the abnormal proteins (neo-antigens) on cancer cells.

Now Zacharakis & Friends followed this lead: they sequenced DNA from the tumour tissue to pinpoint the main mutations and screened the immune cells they’d grown in the lab to find which sub-populations were best at attacking the tumour cells. Expand those cells, infuse into the patient and keep your fingers crossed.

Adoptive cell transfer. This is the scheme from Self Help – Part 2 with the extra step (A) of sequencing the breast tumour. Four mutant proteins were found and tumour-infiltrating lymphocytes reactive against these mutant versions were identified, expanded in culture and infused into the patient.

 

What’s next?

The last step with the fingers was important because there’s almost always an element of luck in these things. For example, a patient may not make enough T lymphocytes to obtain an effective inoculum. But, regardless of the limitations, it’s what scientists call ‘proof-of-principle’. If it works once it’ll work again. It’s just a matter of slogging away at the fine details.

For Judy Perkins, of course, it’s about getting on with a life she’d prepared to leave — and perhaps, once in while, glancing in awe at a Nature Medicine paper that does not mention her by name but secures her own little niche in the history of cancer therapy.

References

McGranahan et al. (2016). Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 10.1126/science.aaf490 (2016).

Zacharakis, N. et al. (2018). Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nature Medicine 04 June 2018.

Now You See It

 

In the pages of this blog we’ve often highlighted the power of fluorescent tags to track molecules and see what they’re up to. It’s a method largely pioneered by the late Roger Tsien and it has revolutionized cell biology over the last 20 years.

In parallel with molecular tagging has come genetic engineering that permits novel genes, usually carried by viruses, to be introduced to cells and animals. As we saw in Gosh! Wonderful GOSH and Blowing Up Cancer, various ‘virotherapy’ approaches have been used with some success to treat leukemias and skin cancers and a trial is underway in China treating metastatic non-small cell lung cancer.

A major aim of genetic engineering is to be able to control the expression of novel genes (i.e. protein production from the encoding DNA sequence) that have been introduced into an animal — in the jargon, to ‘switch’ on or off at will. That can be done but only by administering a drug or some other regulator, either in drinking water, by injection or squirting directly into the lungs. An ideal would be something that’s more controlled and less invasive. How about shining a light on the relevant spot?!

Wacky or what?

That may sound as though we’re veering towards science fiction but reflect for a moment that every animal with vision, however rudimentary, sees by transforming light entering the eyes into electrical signals that the brain turns into a picture of the world around them. This relies on photoreceptor proteins that span the membranes of retinal cells.

How vision works. Light passes through the lens and falls on the retina at the back of the eye. The photoreceptor cells it activates are rod cells (that respond to low light levels — there’s about 100 million of them) and cone cells (stimulated by bright light). Sitting across the membranes of these cells are photoreceptor proteins — rhodopsin in rods and photopsin in cones. Photoreceptor proteins change shape when light falls on them — the driver for this being a small chemical attached to the proteins called retinal, one of the many forms of vitamin A. This shape change allows the proteins to ‘talk’ to the inside of the cell, i.e. to interact with other proteins to switch on enzymes and change the level of ions (sodium and calcium). The upshot is that the signal is passed through neural cells in the optic nerve to the brain where the incoming light signals are processed into the images that we perceive. © Arizona Board of Regents / ASU Ask A Biologist.

The seemingly far-fetched notion of controlling genes by light was floated by Francis Crick in 1999. The field was launched in 2002 by Boris Zemelman and Gero Miesenböck who engineered neurons to express one form of rhodopsin. This gave birth to the subject of optogenetics — using light to control cells in living tissues that have been genetically modified to express light-sensitive ion channels such as rhodopsin. By 2010 optogenetics had advanced to being the ‘Method of the Year’ according to the research journal Nature Methods.

Dropping like flies

One of the most dramatic demonstrations of the power of optogenetics has come from Robert Kittel and colleagues in Würzburg and Göttingen who made a mutant form of a protein called channelrhodopsin-1 (found in green algae) and expressed it in fruit flies (Drosophila melanogaster). The mutant protein (ChR2-XXL) carries very large photocurrents of ions (critically sodium and calcium) with the result that photostimulation can drastically change the behaviour of freely moving flies.

Light-induced stimulation of motor neurons in adult flies expressing a mutant form of rhodopsin ChR2-XXL. Click to run movie.

Left hand tube: Activation of ChR2-XXL in motor neurons with white light LEDs caused reversible immobilization of adult flies. In contrast (right hand tube) flies expressing normal (wild-type) channelrhodopsin-2 showed no response. From Dawydow et al., 2014.

Other optogenetic experiments on flies can be viewed on You Tube, e.g., the TED talk of Gero Miesenböck and the Manchester Fly Facility video of fly maggots, engineered to have a channel protein (channelrhodopsin) in their neurons, responding to blue light.

Of flies … and mice … and men

This is stunning science and it’s opened a new vista in neurobiology. But what about the things we’re concerned with in these pages — treating diseases like diabetes and cancer?

Scheme showing how genetic engineering can make the release of insulin from cells controllable by light. Normally cells of the pancreas (beta cells) take up glucose when its level in the circulation rises (via a glucose transporter protein). The rise in glucose triggers ATP production in the cell. This in turn causes potassium channels in the membrane to close (called depolarization) and this opens calcium channels. The increase in calcium in the cell drives insulin secretion. From Kushibiki et al., 2015.

The left-hand scheme above shows how glucose triggers the pancreas to produce the hormone insulin. Diabetes occurs when either the pancreas doesn’t make enough insulin or when cells of the body don’t respond properly to insulin by taking up glucose.

As a first step to see whether optogenetic regulation of calcium levels in pancreatic cells could trigger insulin release, Toshihiro Kushibiki and colleagues at the National Defense Medical College in Saitama, Japan engineered the channelrhodopsin-1 protein into mouse cells and hit them with laser light of the appropriate frequency. An hour after a short burst of light (a few seconds) the insulin levels had doubled.

The photo below shows a clump of these cells: the nuclei are blue and the channel protein (yellow) can be seen sitting across the cell membranes.

 

Cells expressing a fluorescently tagged channelrhodopsin protein (yellow). Nuclei are blue. From Kushibiki et al., 2015.

 

 

To show that this could work in animals they suspended the engineered cells in a gel and inoculated blobs of the goo under the skin of diabetic mice. Laser burst again: blood glucose levels fell and they showed this was due to the irradiated, implanted cells producing insulin.

Fast forward three years

Those brilliant results highlighted the potential of optogenetic technology as a completely novel approach to a disease that afflicts over 300 million people worldwide.

Scheme showing a Smartphone can be used to regulate the release of insulin from engineered cells implanted in a mouse with diabetes. The key events in the cell are that the light-activated receptor turns on an enzyme (BphS) that in turn controls a transcription regulator (FRTA) that binds to a DNA construct to switch on the Gene Of Interest (GOI) — in this case encoding insulin. (shGLP1, short human glucagon-like peptide 1, is a hormone that has the opposite effect to insulin). From Shao et al., 2017.

In a remarkable confluence of technologies Jiawei Shao and colleagues from a number of institutes in Shanghai, including the Shanghai Academy of Spaceflight Technology, and from ETH Zürich have recently published work that takes the application of optogenetics well and truly into the twenty-first century.

They figured that, as these days nearly everyone lives with their smartphone, the world could use a diabetes app. Essentially they designed a home server SmartController to process wireless signals so that a smartphone could control insulin production by cells in gel capsules implanted in mice. There are differences in the genetic engineering of these cells from those used by Kushibiki’s group but the critical point is unchanged: laser light stimulates insulin release. The capsules carry wirelessly powered LEDs.

The only other thing needed is to know glucose levels. Because mice are only little and they’ve already got their gel capsule, rather than implanting a monitor they took a drop of blood from the tail and used a glucometer. However, looking ahead to human applications, continuous glucose monitors are now available that, placed under the skin, can transmit a radio signal to the controller and, ultimately, it will be possible for the gel capsules to have a built-in battery plus glucose sensor and the whole thing could work automatically.

Any chance of illuminating cancer?

This science is so breathtaking it seems cheeky to ask but, well, I’d say ‘yes but not just yet.’ So long as the ‘drug’ you wish to use can be made biologically (i.e. from DNA by the machinery of the cell), rather than by chemical synthesis, Shao’s Smartphone set-up can readily be adapted to deliver anti-cancer drugs. This might be hugely preferable to the procedures currently in use and would offer an additional advantage by administering drugs in short bursts of lower concentration — a regimen that in some mouse cancer models at least is more effective.

References

Dawydow, A., Kittel, R.J. et al., 2014. Channelrhodopsin-2–XXL, a powerful optogenetic tool for low-light applications. PNAS 111, 13972-13977.

Kushibiki et al., (2015). Optogenetic control of insulin secretion by pancreatic beta-cells in vitro and in vivo. Gene Therapy 22, 553-559.

Shao, J. et al., 2017. Smartphone-controlled optogenetically engineered cells enable semiautomatic glucose homeostasis in diabetic mice. Science Translational Medicine 9, Issue 387, eaal2298.

No It Isn’t!

 

It’s great that newspapers carry the number of science items they do but, as regular readers will know, there’s nothing like the typical cancer headline to get me squawking ‘No it isn’t!” Step forward The Independent with the latest: “Major breakthrough in cancer care … groundbreaking international collaboration …”

Let’s be clear: the subject usually is interesting. In this case it certainly is and it deserves better headlines.

So what has happened?

A big flurry of research papers has just emerged from a joint project of the National Cancer Institute and the National Human Genome Research Institute to make something called The Cancer Genome Atlas (TCGA). This massive initiative is, of course, an offspring of the Human Genome Project, the first full sequencing of the 3,000 million base-pairs of human DNA, completed in 2003. The intervening 15 years have seen a technical revolution, perhaps unparalled in the history of science, such that now genomes can be sequenced in an hour or two for a few hundred dollars. TCGA began in 2006 with the aim of providing a genetic data-base for three cancer types: lung, ovarian, and glioblastoma. Such was its success that it soon expanded to a vast, comprehensive dataset of more than 11,000 cases across 33 tumor types, describing the variety of molecular changes that drive the cancers. The upshot is now being called the Pan-Cancer Atlas — PanCan Atlas, for short.

What do we need to know?

Fortunately not much of the humungous amounts of detail but the scheme below gives an inkling of the scale of this wonderful endeavour — it’s from a short, very readable summary by Carolyn Hutter and Jean Claude Zenklusen.

TCGA by numbers. The scale of the effort and output from The Cancer Genome Atlas. From Hutter and Zenklusen, 2018.

The first point is obvious: sequencing 11,000 paired tumour and normal tissue samples produced mind-boggling masses of data. 2.5 petabytes, in fact. If you have to think twice about your gigas and teras, 1 PB = 1,000,000,000,000,000 B, i.e. 1015 B or 1000 terabytes. A PB is sometimes called, apparently, a quadrillion — and, as the scheme helpfully notes, you’d need over 200,000 DVDs to store it.

The 33 different tumour types included all the common cancers (breast, bowel, lung, prostate, etc.) and 10 rare types.

The figure of seven data types refers to the variety of information accumulated in these studies (e.g., mutations that affect genes, epigenetic changes (DNA methylation), RNA and protein expression, duplication or deletion of stretches of DNA (copy number variation), etc.

After which it’s worth pausing for a moment to contemplate the effort and organization involved in collecting 11,000 paired samples, sequencing them and analyzing the output. It’s true that sequencing itself is now fairly routine, but that’s still an awful lot of experiments. But think for even longer about what’s gone into making some kind of sense of the monstrous amount of data generated.

And it’s important because?

The findings confirm a trend that has begun to emerge over the last few years, namely that the classification of cancers is being redefined. Traditionally they have been grouped on the basis of the tissue of origin (breast, bowel, etc.) but this will gradually be replaced by genetic grouping, reflecting the fact that seemingly unrelated cancers can be driven by common pathways.

The most encouraging thing to come out of the genetic changes driving these tumours is that for about half of them potential treatments are already available. That’s quite a surprise but it doesn’t mean that hitting those targets will actually work as anti-cancer strategies. Nevertheless, it’s a cheering point that the output of this phenomenal project may, as one of the papers noted, serve as a launching pad for real benefit in the not too distant future.

What should science journalists do to stop upsetting me?

Read the papers they comment on rather than simply relying on press releases, never use the words ‘breakthrough’ or ‘groundbreaking’ and grasp the point that science proceeds in very small steps, not always forward, governed by available methods. This work is quite staggering for it is on a scale that is close to unimaginable and, in the end, it will lead to treatments that will affect the lives of almost everyone — but it is just another example of science doing what science does.

References

Hutter, C. and Zenklusen, J.C. (2018). The Cancer Genome Atlas: Creating Lasting Value beyond Its Data. Cell 173, 283–285.

Hoadley, K.A. et al. (2018). Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Cell 173, 291–304.

Hoadley, K.A. et al. (2014). Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin. Cell 158, 929–944.

John Sulston: Biologist, Geneticist and Guardian of our Heritage

 

Sir John Sulston died on 6 March 2018, an event reported world-wide by the press, radio and television. Having studied in Cambridge and then worked at the Salk Institute in La Jolla, California, he joined the Laboratory of Molecular Biology in Cambridge to investigate how genes control development and behaviour, using as a ‘model organism’ the roundworm Caenorhabditis elegans. This tiny creature, 1 mm long, was appealing because it is transparent and most adult worms are made up of precisely 959 cells. Simple it may be but this worm has all the bits required for to live, feed and reproduce (i.e. a gut, a nervous system, gonads, intestine, etc.). For his incredibly painstaking efforts in mapping from fertilized egg to mature animal how one cell becomes two, two becomes four and so on to complete the first ‘cell-lineage tree’ of a multicellular organism, Sulston shared the 2002 Nobel Prize in Physiology or Medicine with Bob Horvitz and Sydney Brenner.

Sir John Sulston

It became clear to Sulston that the picture of how genes control development could not be complete without the corresponding sequence of DNA, the genetic material. The worm genome is made up of 100 million base-pairs and in 1983 Sulston set out to sequence the whole thing, in collaboration with Robert Waterston, then at the University of Washington in St. Louis. This was a huge task with the technology available but their success indicated that the much greater prize of sequencing of the human genome — ten times as much DNA as in the worm — might be attainable.

In 1992 Sulston became head of a new sequencing facility, the Sanger Centre (now the Sanger Institute), in Hinxton, Cambridgeshire that was the British component of the Human Genome Project, one of the largest international scientific operations ever undertaken. Astonishingly, the complete human genome sequence, finished to a standard of 99.99% accuracy, was published in Nature in October 2004.

As the Human Genome Project gained momentum it found itself in competition with a private venture aimed at securing the sequence of human DNA for commercial profit — i.e., the research community would be charged for access to the data. Sulston was adamant that our genome belonged to us all and with Francis Collins — then head of the US National Human Genome Research Institute — he played a key role in establishing the principle of open access to such data, preventing the patenting of genes and ensuring that the human genome was placed in the public domain.

One clear statement of this intent was that, on entering the Sanger Centre, you were met by a continuously scrolling read-out of human DNA sequence as it emerged from the sequencers.

In collaboration with Georgina Ferry, Sulston wrote The Common Thread, a compelling account of an extraordinary project that has, arguably, had a greater impact than any other scientific endeavour.

For me and my family John’s death was a heavy blow. My wife, Jane, had worked closely with him since inception of the Sanger Centre and not only had his scientific influence been immense but he had also become a staunch friend and source of wisdom. At the invitation of John’s wife Daphne, a group of friends and relatives gathered at their house after the funeral. As darkness fell we went into the garden and once again it rang to the sound of chatter and laughter from young and old as we enjoyed one of John’s favourite party pastimes — making hot-air lanterns and launching them to drift, flickering to oblivion, across the Cambridgeshire countryside. John would have loved it and it was a perfect way to remember him.

Then …

When John Sulston set out to ‘map the worm’ the tools he used could not have been more basic: a microscope — with pencil and paper to sketch what he saw as the animal developed. His hundreds of drawings tracked the choreography of the worm to its final 959 cells and showed that, along the way, 131 cells die in a precisely orchestrated programme of cell death. The photomontage and sketch below are from his 1977 paper with Bob Horvitz and give some idea of the effort involved.

Photomontage of a microscope image (top) and (lower) sketch of the worm Caenorhabditis elegans showing cell nuclei. From Sulston and Horvitz, 1977.

 … and forty years on

It so happened that within a few days of John’s death Achim Trubiroha and colleagues at the Université Libre de Bruxelles published a remarkable piece of work that is really a descendant of his pioneering studies. They mapped the development of cells from egg fertilization to maturity in a much bigger animal than John’s worms — the zebrafish. They focused on one group of cells in the early embryo (the endoderm) that develop into various organs including the thyroid. Specificially they tracked the formation of the thyroid gland that sits at the front of the neck wrapped around part of the larynx and the windpipe (trachea). The thyroid can be affected by several diseases, e.g., hyperthyroidism, and in about 5% of people the thyroid enlarges to form a goitre — usually caused by iodine deficiency. It’s essential to determine the genes and signalling pathways that control thyroid development if we are to control these conditions.

For this mapping Trubiroha’s group used the CRISPR method of gene editing to mutate or knock out specific targets and to tag cells with fluorescent labels — that we described in Re-writing the Manual of Life.

A flavor of their results is given by the two sets of fluorescent images below. These show in real time the formation of the thyroid after egg fertilization and the effect of a drug that causes thyroid enlargement.

Live imaging of transgenic zebrafish to follow thyroid development in real-time (left). Arrows mark chord-like cell clusters that form hormone-secreting follicles (arrowheads) during normal development. The right hand three images show normal development (-) and goiter formation (+) induced by a drug. From Trubiroha et al. 2018.

John would have been thrilled by this wonderful work and, with a chuckle, I suspect he’d have said something like “Gosh! If we’d had gene editing back in the 70s we’d have mapped the worm in a couple of weeks!”

References

International Human Genome Sequencing Consortium Nature 431, 931–945; 2004.

John Sulston and Georgina Ferry The Common Thread: A Story of Science, Politics, Ethics and the Human Genome (Bantam Press, 2002).

Sulston, J.E. and Horvitz, H.R. (1977). Post-embryonic Cell Lineages of the Nematode, Caenorhabitis elegans. Development Biology 56, 110-156.

Trubiroha, A. et al. (2018). A Rapid CRISPR/Cas-based Mutagenesis Assay in Zebrafish for Identification of Genes Involved in Thyroid Morphogenesis and Function. Scientific Reports 8, Article number: 5647.

Bonkers Really … but …

 

This is just in case you spotted the headline in January 2018: ‘Scientists Counted All The Protein Molecules in a Cell And The Answer Really Is 42. This is so perfect.’ 

Them scientists eh! The things they get up to!! The scallywags in this case were Brandon Ho & chums from the University of Toronto and Signe Dean, the journalist who came up with the headline, was referring, of course, to Douglas Adams’s “Answer to the Ultimate Question of Life …” in The Hitchhiker’s Guide to the Galaxy — though it may be noted that Ho’s paper includes neither the number 42 nor mention of Douglas Adams.

The cult that has evolved around this number is both amusing and bizarre, not least because Adams himself explained that he dreamed 42 up out of the blue. In a different context a while ago (talking about how the way you get to work might affect your life expectancy) I recounted happy evenings spent carousing in The Baron (well, having a quiet jar or two) with Douglas Adams and friends from which it was clear that he was not into abstruse mathematics, astrology or the occult. He just had a vivid imagination.

Anything for a catchy headline but

Aside from the whimsy, is there anything interesting in this paper? Well, yes. Ho & Co studied a type of yeast (Saccharomyces cerevisiae) that is mighty important because it’s been a foundation for brewing and baking since ancient times. So no merry sessions in The Baron of Beef without it! Its cells are about the same size as red blood cells (5–10 microns in diameter) but you can actually see them sometimes as films on the skin of fruit. It’s played a huge role in biology as a ‘model organism’ for studying how we work because the proteins it makes that are essential for life are pretty well identical to those in human cells — so much so that you can swap those that control cell growth and division between the two. Yeast proteins work just fine in human cells and vice versa.

 

Yeast on the skin of a grape. Photo: Barbara W. Beacham

 

The question Ho & Co asked was ‘how many protein molecules are there in one cell?’ In the age when you can sequence the DNA of practically anything at the drop of a hat, you might think we’d know the answer already but in fact it’s not been at all clear. Accordingly, what these authors did was to pull together all the relevant studies that have been done to come up with an absolute figure. The answer that emerged was that the number of protein molecules per yeast cell is 4.2 x 107 — which, of course, can also be written as 42 million. Eureka! We have our headline!! Albeit, as the authors noted, with a two-fold error range.

Does anyone care?

Now you’re just being awkward. You should be grateful to be made to picture for a moment tens of millions of proteins jiggling around in little sacs so small you could get tens of thousands of these cells on the head of a pin. And somehow, in that heaving molecular city, each protein manages to carry out its own task so that the cell works. It is quite staggering.

Mention of tasks leads to the other question Ho et al looked at: how many copies are there of the different types of protein? We know from its DNA sequence that this yeast has about 6,000 genes (Saccharomyces Genome Database). So that’s at least 6,000 different proteins. Not surprisingly, it turns out that about two thirds of them are in the middle in terms of abundance — i.e. there’s between 1,000 and 10,000 molecules of each sort per cell. The rest are either low abundance (up to about 800 molecules per cell) or at the high end — 140,000 to 750,000, i.e. somewhere in the region of half a million copies of each type of protein.

Does this distribution make sense in terms of what these proteins do?

You know the answer because if it didn’t the Toronto team wouldn’t have got their work published but, indeed, proteins present in large numbers are, for example, part of the machinery that makes new proteins (so they’re slaving away all the time) whereas, those present in small numbers do things like repair and replicate DNA and drive cells to divide — important jobs but ones that are only intermittently needed.

These results aren’t going to turn science on its head but it is awe-inspiring when a piece of work really brings us face-to-face with stunning complexity of biology. And if it takes a bonkers headline to catch our eye, so be it!

Reference

Ho, B. et al. (2018). Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome. Cell Systems. Published online: January 23, 2018.