Field of Science

Thinking about a post-pandemic world

I've been trying to think carefully about what our world will be like once the current pandemic is over.  Most people are rightly focused on the current situation and on short term measures to limit the spread of the virus and the harm it causes, but we should also be thinking about, and planning for, what the world is likely to be like once populations reach some sort of equilibrium.  How many people will the virus be infecting or killing every year?  How much difference will a vaccine really make?  Will we still need to wear masks?

 What follows is my non-rigorous back-of-the-envelope analysis.  I made some big assumptions (spelled out) and did some simple arithmetic of a few simple scenarios.  There's no proper modeling here.

What kind of equilibrium we get will depend on how much immunity develops as a result of Covid-19 infection, how rapidly the immunity fades, and whether or not researchers can develop a vaccine that gives the same immunity.  So first we should consider some very broad-brush scenarios.

These outcomes range from very bad (no immunity, no vaccine) to quite good (lifelong immunity from a vaccine).  But how likely are they?  What kind of future should we plan for?

Based on what vaccine scientists have discovered and accomplished so far, I think that we will get a vaccine.  I also think that both ‘no immunity’ and ‘lifelong immunity’ are so unlikely that we shouldn’t waste time thinking about their consequences.

Now let’s do some planning for the most likely scenario.

Below I add more detail to the most likely outcome, that both infection and a vaccine confer moderate immunity.  I consider the effect of many people refusing (or being unable to pay for) vaccination, and of wearing masks and practicing some social distancing.

Let’s assume that the vaccine is a lot like our current flu vaccine, so it reduces your chance of being infected by about 50%, and your chance of having a severe infection or dying by 90%.  If you do get infected, it also reduces your contagiousness by about 50%.  Let’s also assume that you need to be re-vaccinated every year, both because the immunity fades over time and because in some years a virus mutation makes the current vaccine less effective.

What might the annual Covid-19 infection rates and death rates be in a typical year?  Let’s consider four scenarios that capture the most important possibilities:
  • Scenario A: Many people refuse to be vaccinated (I assume 44% vaccine uptake, like the current flu vaccine), and social interactions return to normal.
  • Scenario B: 44% of people refuse to be vaccinated, as in A. However most people practice some social distancing and wear masks when in crowds, which reduces the number of infections by about 50%.
  • Scenario C: Most people are vaccinated annually, and social interactions return to normal. 
  • Scenario D: Most people are vaccinated annually, as in C.  Most people also practice some social distancing and wear masks when in crowds, as in B., which reduces the number of infections by about 50%.
To estimate how much Covid-19 infection we might expect, I decided to use the CDC’s influenza data (  The CDC estimates that influenza has caused 2,400-14,000 infections and 4-19 deaths per 100,000 people each year since 2010.  So I assumed that if Covid-19 vaccine works as well as the flu vaccine but with poor uptake and social interactions returned to normal, we would have about 7000 Covid-19 infections per 100,000 annually.  Initially I used the current Covid-19 death rate of 1% for unvaccinated people, and 0.1% for vaccinated people.

For comparison here are the extremes of the CDC’s estimates of the current influenza burden, drawn to the same scale:

So, if we get a vaccine that works only as well as the influenza vaccine, but most people get vaccinated, we could have an ultimate Covid-19 death burden lower than the current influenza death burden, even without social distancing and masks.

Some qualifications:

How soon will things stabilize?  Covid-19 is spreading rapidly in many parts of the world, and well controlled in others.  A vaccine like the one I consider will probably be available within a year or so.  It might then take a couple more years for vaccination and infection levels to settle into some kind of equilibrium.  But this will not be even across populations, and local outbreaks will continue to occur, since both vaccination rates and infection rates are likely to differ a lot between communities and between societies.

Covid-19 treatments may get better:  The analysis above assumes that the death rate from Covid-19 infection remains about 1%.  But Covid-19 treatments will probably continue to improve, reducing the death rate in both vaccinated and unvaccinated people.

Covid-19 infections often make people sicker than the flu, and for longer:  The analysis above doesn’t consider the disease burden of non-lethal Covid-19 infections, which appears to be much higher than that of influenza.  Although most people who get the flu feel lousy for a week or less, and return to full health within a few weeks, many Covid-19 infections cause more severe effects, with a wide range of debilitating symptoms that may linger for at least several months.  Because infections in vaccinated people are expected to be much less severe, employers (or insurers) might require proof of vaccination.

It's hard to predict the effects of changing contagiousness:  Above I've assumed that having been vaccinated reduces an infected person's ability to transmit the infection by 50%, and my calculations then assumed that this will reduce the overall number of infections at equilibrium by 50%.  But the dynamics of infection spread are complex, and the actual equilibrium reduction might be much stronger.

Response to Ambur et al.

 The points in purple are objections raised by Ambur et al. to the hypothesis that the main function of DNA uptake by competent bacteria is acquisition of DNA as a nutrient:

These points are typical of those raised when the goal is to dismiss the nutrient hypothesis rather than to carefully consider all the issues.

(i) As yet, there is no clear evidence that the integration of nucleotides taken up by transformation become routed into DNA metabolism.

Yes. Competence has mainly been studied in mucosal commensals, where investigations of metabolism are difficult.  In these organisms absence of evidence is not evidence of absence.

(ii) The presence of exogenous DNA does not appear to induce competence in any transformable species.

Yes, but I don’t see why this is more relevant for the nutrient hypothesis than for other hypotheses.  (Also, Vibrio does use chitin as a signal for competence; its presence indicates biofilms and abundant DNA.)

(iii) Competence in streptococci, like S. pneumoniae, is induced for only a short time period during exponential growth when other resources are highly abundant.

Because laboratory growth conditions for human commensals and pathogens are so different from natural growth conditions, lab cultures are very poor guides to what matters in the real world.  That’s why our work focused on understanding the regulatory machinery.

(iv) Transported DNA is heavily protected against nuclease digestion within the cell, potentially enabling transported fragments to remain intact as a substrate for recombination.

And yet most competent bacteria take up all DNAs they encounter, and DNA that cannot be recombined is efficiently degraded.  The proteins that protect the DNA are also common in non-competent species and so must function outside of transformation.

(v) The hypothesis does not explain why several competent species only take up DNA from close relatives due to conserved DNA uptake sequences (USS and DUS) despite the fact that non-homologous DNA could be used as a source of nucleotides for direct use or degradation.

On the flip side, almost all competent bacteria take up DNA indiscriminately, so DNA’s benefit can’t depend on its information content.  For these exceptions, we have hypothesized that sequence-dependent uptake constraints exist in these species, and have shown that these create molecular drive that causes uptake sequences to accumulate in genomes at frequencies and distributions corresponding to those seen in real genomes with DUS and USS.

Designing better masks

Optimizing design of masks to prevent spread of COVID-19:

(Originally a series of tweets that came out in the wrong order)

1.     COVID-19 is transmitted mainly by droplets and particles in the air we breathe, not by contact with contaminated surfaces.

2.     Surgical and cloth masks only poorly protect an uninfected wearer from becoming infected. 

3.     But these masks CAN reduce virus release by an infectious person, because exhalation produces large wet droplets that are relatively easy to trap on their way out but that rapidly evaporate to smaller dry particles that are hard to trap on their way in (see Wells Curve). 

4.     So the general public should wear masks not to protect themselves from infection but to protect other members of the community, in case the wearer is unknowingly infected. But design of surgical and cloth face masks has not been optimized for this function. 

5.     What properties should such a mask have?
a.     The fabric should block passage of most respiratory droplets.
b.     Most exhaled air should pass through the mask, not around it, even after a cough or sneeze.
c.      Any exhaled air that escapes should escape downward, not upward.
d.     Air and water molecules should pass easily through the mask fabric.
e.     For ease of breathing, exhaled and inhaled air should be filtered over a large area of mask. The mask should not be tightly pressed to the nostrils and mouth.
f.      To maximize air exchange, the mask should not normally enclose a large volume of air.
g.     The space inside the mask should expand in the event of a cough or sneeze, to trap the large volume of air and allow it to be gradually released through the mask (not around it). 

6.     These goals may best be met by long lightweight scarf-type masks that fit snugly around the nose, cheeks and ears, and settle loosely on the shoulders. 

A semi-quantitative framework for long-term thinking about the COVID-19 pandemic

I think the current rush to invoke extreme flatten-the-curve measures needs to be accompanied by careful thought about what we'll do once the measures have had the desired effect.  In particular, how long would restrictive measures need to remain in force, and how will we decide when they can be lifted?  And how can we mitigate the personal, social and economic harms of the measures while they remain in place?

So I've created a series of semi-quantitative graphs to help.  ('Semi-quantitative means that there are numbers on the axes and specific doubling times for periods of exponential growth, but the finer details are rough approximations.)

Here's the tl;dr for the first 6 months:

Points to note:  

  • The Y-axis is log-scale, so small differences in height indicate big differences in numbers of infected people.
  • Five different scenarios are considered, with plausible effects on doubling time of % infected.
  • Restrictive measures are assumed to reduce peak % infected and eventual equilibrium.
  • For all but the most extreme scenario, infection levels remain high (≥1%) even after 6 months.
  • It will be very hard to justify lifting restrictions that have been effective.

Here's the tl;dr if the costly restrictions are lifted after 7 months of misery:

Points to note:
  • In all cases, lifting restrictions makes % infected much worse (remember, log-scale...).
  • The more effective the restrictions were in limiting total infections, the worse the second wave on infection is, and the longer it drags on.
Below are the individual graphs:

If no action were taken (doubling time 3 days):

Infections are assumed to peak at about 30% of the population at weeks 6-10, and then to decline to about 1% of the population since about half of the population will remain susceptible.

If we take actions that have no or low personal cost (doubling time 6 days):
  • Reduce physical contact with other people
  • Don’t touch your face
  • Wash your hands
  • Avoid large groups and crowded places
  • Work from home if this is possible
  • Reduce travel

The peak % infected is lower, maybe 20%,  occurs at weeks 11-16, and declines to about 0.3% provided the restrictions remain in place.

If we take actions that have moderate cost (doubling time 10 days):

  • Cancel pro-sports, concerts, conferences and other large gatherings
  • Close bars and restaurants
  • Cancel university classes

The peak % infected is lower, maybe 15%, occurs at weeks 18-25, and declines to about 0.15% provided the restrictions remain in place.

If we take actions that have high cost (doubling time 20 days):

  • Close all schools and universities
  • Close close non-essential shops and workplaces
  • Close all public buildings
  • Ban all non-essential travel

The peak % infected is lower, about 10%%, occurs at weeks 35-40, and falls to about 1% by week 52 provided the restrictions remain in place.

If we take extreme actions (R0 <1 b="">

  • Lock down the entire population
  • Enforce by police or the National Guard

The % infected slows its increase and begins to decline by week 15.  It continues declining provided the restrictions remain in place.

Planning the GTA work

My goal for the rest of my time in Andrew Lang's GTA lab  is to gather data that constrains estimates of the efficiency of GTA transduction.  I have lots of ideas but they're not very well organized, and I keep getting distracted by the minutiae of GTA biology (and our general ignorance of same).  So this post is an attempt to get a sensible plan written out.

The bottom line for efficiency is how many transductants are generated for each cell that produces GTA and then dies. This depends on many factors, so I'm going to try to break down the steps and evaluate their limitations.

Here are some of the questions I'd like to know the answers to.  (Some of these questions overlap with others, and some of them are addressed by data we already have.)

  1. How many functional GTA particles does a cell produce under 'normal' conditions?  
  2. Are lots of defective particles produced too?
  3. Do individual cells of 'overproducer' mutants produce more GTA particles than normal cells, or is overproduction due only to more cells being producers? 
  4. How stable are GTA particles in the cultures where they are produced?
  5. How stable are GTA particles in more dilute solutions?
  6. Do GTA particles bind to free capsule or to cell-envelope components released by lysed cells?
  7. Do cells in producer cultures bind the GTA particles produced by other cells and take up ands recombine their DNA? 
  8. How good are recipient cells at finding GTA particles when cells and particles are scarce?
  9. Do cells die if they are exposed to too high a concentration of GTA particles?
Experiments I'm going to do:
  1. Measure stability of GTA titers in culture filtrates stored at room temperature.
  2. Measure growth of wildtype and overproducer strains by plating dilutions and counting colonies, in addition to measuring culture density by its turbidity.  At the same time measure accumulation of GTA (see this post and this post).
  3. Compete an overproducer mutant against its isogenic parent during growth under GTA-producing conditions, to estimate the cost of GTA production.  This is especially important since my most recent growth curves don't show much difference between ovverproducer and wildtype strains.  This requires that one strain carry an antibiotic resistance marker the other lacks, so I'm using GTA to transfer a kanamycin-resistance marker from a derivative of the 'wildtype' strain into its overproducer sibling.  Then I can do the competition both ways, starting with the kanR overproducer at low frequency in a background of kanS wildtype cells, or starting with the kanR wildtype at low frequency in a background of kanS overproducer.  I have all these strains now (just confirming that the kanR overproducer does overproduce GTA), so I can start the experiment as soon as I grow up the cultures.  I should also Do a complete growthtiter the amounts fo GTA produced, since the goal is to get the ration of GTA produced to cells died.
  4. Do the same competitions, but between an overproducer and a no-GTA mutant, or between wildtype and no-GTA mutant
  5. Add marked GTA to a producer culture (to multiple different producer cultures) to see how efficiently the cells take up  GTA.  The producer strains are all rifR, so this needs a GTA prep carrying a different marker.  I've made a GTA filtrate that transduces kanR, but this transduction is very inefficient compared to rifR, no doubt partly because the kanR is a big insertion, not a point mutation.
  6. To get an independent antibiotic resistance point mutation, I've just started selecting for a spontaneous mutation giving resistance to streptomycin, by plating GTA-producer strains on streptomycin plates.  Mutations giving strR are common and this selection has been successful for R. capsulatus in the past.
  7. Do a GTA-producer time course analysis that distinguishes between GTA production and GTA accumulation.  Experiments to date have just assayed the amount of GTA in the culture at different times, and there are unexplained peculiarities about the results (see this post:

Scheduling complication:  I'm here until August 12, but I'll be tied up with visitors for part of the time, next week and for the last two weeks of July.  Because R. capsulatus grows slowly, I need to wait two days to see the result of each experiment.  

I could do the first 'quick-and-dirty' version of the competition experiment now, starting the cell mixtures tomorrow (Friday) morning and growing them for only 24 or 48 hr, taking time point samples at t=0, t=24 and t=48 (Sunday morning).  Then I could count the colonies on Tuesday morning.  Will I also measure the amount of GTA in each mixture, by its ability to transduce rifR and kanR?

Why doesn't all the GTA get taken up?

I've been modelling the production and uptake of GTA particles in a culture, hoping to understand the cause of the surprising GTA-accumulation curve I described in the previous post.  But this has led me to a more fundamental surprise.
Only a very small fraction of the cells in a GTA+ culture produce GTA particles and lyse, and all the other cells are able to bind GTA particles and take up their DNA.  So why doesn't all the new GTA quickly get taken up by all the surviving cells?
Here are the basic principles I've been assuming, based on what's in the literature:  GTA production:  Cells in exponential growth don't produce GTA.  The GTA genes are turned on as the culture density gets high and growth slows.  Once the culture reaches its stationary-phase density GTA production stops.  GTA uptake:  Cells in exponential growth express the capsule genes at a low level and bind GTA particles with moderate efficiency.  The capsule genes are turned up when culture density reaches a quorum-sensing threshold, and ability to bind GTA particles gradually increases.  Stationary phase cells bind GTA particles efficiently.  GTA decay: BTA particles are moderately unstable, so they fall apart with some unknown probability.

Let's put some numbers to this:

  1. Assume that 1% of cells produce GTA over the course of the permissive stage.
  2. Assume that each producer cell produces 100 particles and then dies.
  3. Assume that each non-producer cell can take up 1 GTA particle.

Result:  All the GTA particles are taken up.  The concentration of GTA particles in the medium falls to zero.

In reality, assumption 1 is likely to be an overestimate, and assumption 3 an underestimate.  I'm going to do some experiments to see if I can clarify what's going on.

Marc Solioz's 1975 PhD thesis on GTA

PhD students, don't assume that your thesis will moulder unread in the library.  More than 40 years after he submitted it, I'm reading Marc Solioz's PhD thesis (The Gene Transfer Agent of Rhodopseudomonas capsulata).  I want to understand the kinetics of GTA production, and his is the only good data I can find.

Here's what he reported:

A. Stability of and transduction by GTA in various solutions:  He tested a wide range of solutions.  In these studies he didn't try to distinguish between conditions that stabilize GTA for storage and conditions that maximize its ability to attach to cells and inject its DNA.  It's happiest in 1mM each of Na+, Mfg+ and Ca++.  This can be buffered with 10 mM Tris, with or without gelatin or BSA (no effect).  It's destabilized by 10% gycerol, even for freezing.  GTA preps made by filtering culture supernatants should be diluted at least 10-fold to reduce the destabilizing effect of the medium constituents.

B. Inactivation by other factors:  GTA's stability is not affected by temperatures up to 50°C.  Keeping it on ice is not better than room temperature, and there was no difference between partially purified and purified stocks.  It's inactivated by proteases but not RNase or DNase.  It's not inactivated by ether or chloroform, or by phospholipases, consistent with the absence of any membrane.

C. Inactivation by UV:  UV damages DNA so it is expected to inactivate the transducing activity of GTA particles.  To control for experimental variation (a big concern with UV experiments), he compared GTA inactivation to inactivation of phage T2 UV'd together in the same solution.  The action spectra are the same for GTA and T2, but GTA inactivation requires much higher doses, consistent with the small amount of DNA in each particle.

D. Conditions and kinetics of GTA production:   1. Production kinetics: This is the same surprising result (Solioz's term) I showed in the previous post. Cells were grown photosynthetically/anaerobically in a yeast extract + peptone medium.  The dashed line approximates the combined growth curves seen in the four independent experiments, but it's in 'arbitrary units' (I think on a log scale) so I have to infer the cell densities from how my cells grow.

He reports that the initial peak and drop were consistently seen across all his experiments, but that sometimes the drop was not followed by the final high-GTA stage.  He saw a similar pattern using a strain that does not absorb GTA (strain H9), so the changes in GTA titre are not due to changes in the removal of GTA particles from the medium.   However this conclusion is weakened by the description of strain H9 in the methods, which just says 'does not act as a recipient of GTA, with no reference'.)  Other tests he did could not rule out effects of transient inhibitory/inactivating factors in the culture supernatant.

2. Effects of growth conditions on production.  Defined medium RCV gave low titres of GTA.  Yields with different concentrations of yeast extract and/or peptone were variuable, apparently depending even on the batch no. of ingredient used.  Variation sin culture growth rate and final density did not correlate with GTA titres.

3. Isolation of mutants:  He attempted to isolate an overproducer mutant but failed.  The original producer strain B10 carried two phages, so he made a derivative strain, SB1003, that was cured of the phages and carried the convenient RifR point mutation.   This new strain is the one I have been using as the standard donor; it's good to know its provenance.

4. Radiolabelling:  He put in a lot of work to find a way to radioactively label GTA.  This was used to guide the purification studies.

E. Purification of GTA particles:  This is a long section that's not of much interest to me.  He tested a wide range fo the available biochemical techniques used for purification of organelles, phage and molecules.

F. Characterization of the nucleic acid:  He used the single-strand-specific nuclease S1 to show that the DNA in GTA particles is double-stranded.  He used CsCl ultracentrifugation to estimate its base composition as 65% G+C, the same as that of the R. capsulatus genome.  Repeating this analysis with heat-denatured DNA confirmed that the DNA is linear, not closed-circular like plasmid DNA.  Banding in a CsSO4 gradient showed that it is not extensively modified.  In sucrose gradients it co-sedimented with SV40 DNA, suggesting a size of 3.6 x 10^6 Daltons.  How big is this in base pairs or kb, you ask - about 5.5 kb.  He says it would be better to run the DNA in an agarose gel, but this emerging technology wasn't available to him yet.

G. Examination of GTA with the Electron Microscope:  He saw lots of tails, and empty heads, some with tails.  Apparently-full heads came in different sizes, from 150-600 Angstroms  in diameter (15-60 nm).  But he thinks much of this may be artefacts of the purification and EM-preparation procedures.

Summary of R. capsulatus Bioscreen growth curves

The previous post (GTA competition experiments) described the results of the follow-up set of R. capsulatus growth curves that I planned at the end of the previous experiment (R. capsulatus growth curves in RCV medium).  But it didn't pull together the results of all the Bioscreen growth curves, nor integrate them with what was previously known/thought).  So here goes:

First, what's already been reported about growth in liquid culture?  Not a lot.  The graphs below are all I could find.  (I asked my colleague here - he says he doesn't know of any others.)

GTA production: 

The only work that measured GTA production along with growth is Solioz et al. 1975, and their 'growth curve' is just a schematic.  The titers of GTA this shows are very peculiar.  The titer is very low while the culture is growing, and rises to about 3x10^4 just before culture density levels off.  But then it dips sharply, falling to about 10^3 over a few hours, and then rises again to its final stable level of about 4x10^5.

I don't understand how the titer can fall that quickly.  Where do the GTA particles go?  The titers are transformants to RifR or StrR, so the total number of active GTA particles per ml is about 1000-fold higher, so ~4x10^7 at he first peak, and 10^6 at the valley.  Perhaps there's an initial burst of GTA production that stops abruptly, and most of the released GTAs are quickly lost because they attach to the remaining cells.  There would be at least 10^8 cells at that stage so this could easily happen.  After a few hours the second wave of GTA production begins.  This produces at least 4x10^8 GTA particles that remain free (and possibly many that attach to cells and are not detected).
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">
Are the GTA titers from my last Bioscreen run comparable?  I got 780 RifR transductants per ml, from a culture that had about 10^9 cells/ml; this is about 20-fold lower than Solioz et al. reported, and about 3-fold lower than I saw in an earlier (not-Bioscreen) culture.   The difference may partly be due to the different culture conditions in the Bioscreen.
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">Effects of PO4:  
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">The Westbye graphs on the lower right come from a study of the effects of phosphate levels on GTA production.  This is in the defined medium RCV, either with its normal 10 mM PO4 or with only 0.5 mM PO4.  Low PO4 allowed higher GTA production.  Differences in PO4 did not affect the culture density of the normal strain SB1003, probably because less than 1% of the cells in a culture produce GTA, but low PO4 caused a drop in the density of the overproducer strain DE442, where up to 20% of cells are thought to produce GTA.  The phosphate effect is thought to be on release of GTA particles from the producer cells, not on GTA synthesis or on stability of parrticles in the medium.
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">In my Bioscreen runs I saw the effect of low PO4 on GTA levels, but no the predicted drop in culture density of DE442.   Instead both DE442 cultures levelled off at densities well below that of both SB1003 cultures.
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">
<10 100-fold="" 20-fold="" 3="" a="" about="" again="" and="" are="" as="" but="" cells="" exponentially="" falls="" final="" going="" growing="" hrs.="" is="" it="" its="" just="" level="" linear="" ml="" nbsp="" of="" on="" over="" p="" quickly="" rapidly="" rises="" scale...="" schematic="" the="" then="" to="" was="" while="" write="" x10="">

GTA competition experiments

I'm in St. John's for the 'summer'*, doing GTA-related experiments in Andrew Lang's lab at Memorial University of Newfoundland ('MUN').

The first experiments I'm going to do are growth competitions between GTA-producing strains and otherwise-identical non-producer strains created by deleting the GTA genes.  Because GTA production requires cell lysis, we predict that the non-producers should outcompete the producers.

While I was still in Vancouver I did detailed growth curves of the various strains.  Preliminary ones are described here, and I'll paste the graph from the latest ones below:

I wanted to check the effect of phosphate concentration in GTA production and culture growth, so I only used two strains, SB1003 (wildtype) and DE442 (a GTA overproducer).  I used two PO4 concentrations; 0.1 mM, which should allow high GTA production and reduced growth, and 10 mM, which should cause low GTA production and better growth.  The growth differences should be detectable only for DE442.  (I also used three different cell densities.  I'm only showing the results for cultures started at the highest density, but the others grew similarly with the expected delays.)

I measured GTA production at two times, by removing cultures from some wells, filtering out the cells, and using the cell-free supernatants to transduce an RifS strain to RifR.

The results are below.  (The upper graph is plotted on a linear scale, and the lower graph is the same data plotted on a log scale, for easier comparison of growth rates.)  The growth curves are very similar to those from a previous experiment (RR#1438) where I didn't measure GTA production.

The GTA production happened as expected.  SB1003 produced no significant GTA in high PO4, and a modest amount (780 transductants per ml) in low PO4.  DE442 produced lots more GTA under all conditions, but about 4-fold more in low PO4 than in high PO4.

On the linear scale the two strains appear to have very similar exponential growth rates, but the log scale reveals that DE442 (the GTA overproducer) is slower in exponential growth.  DE442 also reaches a lower final densities (SB1003, OD ~ 1 - 1.08; DE442 OD ~ 0.7).

The growth differences are unlikely to be directly due to the lysis required by GTA production, because the GTA differences caused by the different PO4 levels do not correlate with OD differences.

DE442 is not isogenic with SB1003; it carries a mutation that blocks synthesis of the red accessory pigment.  Could DE4432’s pigment phenotype be responsible for its poorer growth?  These were aerobic cultures in a dark room, so the growth difference is not a direct consequence of differences in photosynthesis.

I don't think it would be straightforward to transfer the ‘overproducer’ mutation into the SB1003 background, since typical transduction frequencies are less than 1/1000, and we have no way to select for overproducer colonies against the background of normal colonies.  If the pigment difference causes the growth difference, we could transfer the wildtype pigment allele into DE442 or the mutant allele into SB1003.  I wonder how the parent strain of DE442 (Y262, I think) grows.

* It's definitely not summer yet here.  Icy winds anywhere near the coast, and several thin snowfalls in the last few days. I remain hopeful, because most of the trees are finally getting their leaves, and the spring bulbs are blooming.

Wait, there's a much simpler explanation! (For CRISPR-Cas, not for GTA)

I'm in Halifax for a couple of weeks, visiting Ford Doolittle and his philosophical colleagues,  We've spent much of the time considering the extent to which CRISPR-Cas systems can or should be considered 'Lamarckian'.  I started with the simplistic perspective that of course it is, because an acquired character (immunity to future phage or plasmid infection) becomes inherited because the Cas proteins insert short phage- or plasmid-derived DNA sequences as a CRISPR 'spacer' into the chromosome.

Here's a very detailed diagram I made of the evolutionary events (mutation and selection) affecting CRISPR-Cas systems (click to embiggen):

We ended up concluding that 'directed mutation' was a better perspective.

But, once our ideas started settling down, this detailed diagram got me thinking about how uncertain and far in the future the 'immunity to future infection' benefit is.  That's a problem for CRISPR-Cas evolution, since this uncertainty greatly weakens the selection maintaining and refining the system.  Iv selection is too weak, the system shouldn't be maintained at all.

A more urgent problem is that the cell needs to survive the immediate infection/invasion before it has any chance of benefiting from the long-term immunity.  This becomes especially important if the bias against potentially-lethal self-spacers arises because the cell contains many copies of the invader genome.

But the cell does have a very nice mechanism to clear the invader, because it has just created an invader-specific spacer in its CRISPR array.  Transcribing this new spacer would give it many copies of an invader-specific crRNA with which Cas9 can destroy all the copies of the invader genome.

So here's my new hypothesis:
The primary function of CRISPR-Cas systems is the detection and immediate destruction of phage and /or plasmid DNA.  Benefits from immunity to future infection are relatively unimportant. 
Things I need to find out:

 Is this a new idea?  I don't remember seeing it anywhere, but if any reader knows of a prior proposal please let me know in the comments or via Twitter (@rosieredfield).

Is relevant data available?  The basic experiment is, in principle at least, quite simple.  Do cells with an intact CRISPR-Cas system survive phage infection better than cells with a defective system?  Do they become transformed less efficiently by plasmids?  These tests would be most sensitively done under sub-optimal infection conditions.

How is transcription of the Cas genes and CRISPR array regulated?  In particular, how efficiently is the CRISPR array transcribed and processed immediately after a new spacer has been added?  In the context of my GTA-as-CRISPR-vaccine ideas (see this post from a few months ago) I'd been looking for reports that new CRISPR spacers can be immediately transcribed, creating crRNAs that can immediately attack the original invader.  I didn't find any solid data, but neither did I find anything that ruled this out. 

R. capsulatus growth curves in RCV medium

My upstairs GTA colleague and I were surprised that the Bioscreen growth curves in the previous post didn't show a dip in OD600 of the GTA-overproducer strain like that seen in manual (non-automated) growth curves.  This dip is thought to be caused by the lysis of GTA-producing cells as GTA production peaks when cells hit stationary phase.

We thought part of the problem might be that I used the standard YPS medium which is based on modest concentrations of yeast extract and peptone.  The clearest/most-recent published demonstration that GTA-producing cultures used RCV, a simpler 'defined' medium based on malate, and showed that the apparent lysis occurred in medium with 0.5 mM PO4 but not in medium with 10 mM PO4.

So I redid the growth curves for all 6 strains, using both high-P and low-P versions of RCV (kindly supplied by my upstairs colleague).  The results are not inconsistent with the Westbye results, but they're not at all compelling.  None of the strains decreases in OD600

The problem is that there's quite a bit of between-strain variation in growth and in the stability of the stationary phase OD.  (Within each strain the replicate wells give very similar results, with one exception.)

The graph below shows growth in the high-phosphate medium.  The main graph shows OD600 on a log scale (appropriate to exponential growth), and all the strains appear to stably reach similar densities.  But the inset shows the same data on a linear scale, which makes the variation look more significant.  The overproducer strain stops growing abruptly at OD600 = 0.7 a lower density than the other strains.

Here's the cells in the low-phosphate medium.  There's an initial drop in OD600, over the first 10 hours, but then all the strains grow steadily except strain YW1, where the individual wells grew at different rates for no apparent reason.  Again the linear-scale inset shows the substantial variation at stationary phase.  The overproducer DE442 again stops growing, this time at OD600 = 0.8, and now its OD falls by about 20% over the next 40 hours.

I really don't feel comfortable drawing any solid conclusions from this one experiment, especially since there's a blip in many of the growth curves at a point where I stopped and restarted the runs to add more time when I realized that 3 days wasn't going to be long enough.  Even though the shaking only stopped for 2-3 minutes, and the trays of cells remained in their holder with the lid closed, most of the strains had an abrupt change in OD600.  (You can see the blips at hour 63.)

Plan:  Do the run again.  This time I'll pre-grow the cells into log phase in high-P and low-P RCV.  medium (the upstairs colleague has offered me enough medium to do this).  And I'll plan on pausing the run at key times to take samples that I can assay for GTA production.

What can we learn from growth curves?

Here's the results of the Bioscreen growth curves I ran for Rhodobacter capsulatus strains:

Each dot is the mean OD600 of 15 replicate wells, each containing 300 µl of culture, with ODs read every 20 minutes for 45 hours.  The cultures all grew up at about the same times, but I've shifted the X-axes so the curves don't overlap.  OD values below about 0.015 are not significantly above the backround absorption of the culture medium. The Y-axis is a log scale, so when doubling time is constant the dots will fall in a straight line.

I did these runs 'just-in-case', because I'm going to be working with Rhodobacter capsulatus at Memorial University in Newfoundland for the next few months (on sabbatical leave) and thought they probably wouldn't have a convenient Bioscreen that I could use.

Now I need to figure out what we learn from these, and whether I should do any more experiments before I leave UBC.

The simplest expectation is that once the cells have adjusted to the medium (after 'lag phase') they will grow at a constant rate until they run out of nutrients or experience other bad consequences of high cell density (little oxygen, accumulation of toxic byproducts).  But all of these cultures instead exhibit 'diauxy', a mid-growth shift from one resource to another.  We see  this as a brief slowing or even cessation of growth at about OD=0.05 (orange shaded band), after which growth resumes, often at a different rate.  The pause occurs because the cells need time to adjust their metabolism to a change they've caused in the medium, such as exhaustion of one nutrient or new availability of another. 

I don't know enough about R. capsulatus metabolism to speculate about what the change might be, but it might affect production of Gene Transfer Agent particles.  The pause isn't due to lysis of GTA-producing cells, because it's not changed in the ∆∆ strains, which have deletions of the GTA gene cluster and lysis gene.

SB1003, B10 and YW1 are all 'wildtype' strains, I think.  Strain YW1 grows much slower than the others, although it still speeds up after the growth pause, and it reaches a slightly lower final density.

Strain DE442 carries a mutation that causes over-expression of the GTA genes and over-production of GTA particles.  Growth curves in a 2013 paper found that this strain had a substantial drop in OD once growth ceased, thought to be due to lytic release of GTA particles, but no drop is seen in the Bioscreen culture.  That work used a low-phosphate version of a different medium, RCV.  But an earlier paper found strong lysis with the same complex medium I used (YPS), and low lysis with the high-phosphate (10 mM) standard RCV medium.

The lab upstairs has both low-phosphate and high-phosphate versions of the RCV medium, so I'm going to repeat the time course with both.

growth time courses

In a few weeks I'll be headed for the Maritimes, for the final part of my sabbatical work on Gene Transfer Agent.  But before I leave here I want to run some detailed growth time courses on GTA-producing strains, taking advantage of the BioScreen machine belonging to the lab next door.

I'll first do a trial run with all the strains I have,  to check the basic growth kinetics under the Bioscreen growth conditions.  Then I'll see if I can combine the growth measurements with testing for the amounts of GTA produced.

Phage plaqueing still sucks - what to do now?

I feel like I've been sucked down a hole of trying to get consistently countable plaques from the Rhodobacter capsulatus phage I'm testing.  After seven weeks of plaqueing with various combinations of strains and agar concentrations and cell densities, I'm no closer to having a well-behaved phage I can use to test the GTA-as-vaccine hypothesis.

Along the way I've eliminated various sub-hypotheses:

1.  The plaques are tiny/faint/blurry/invisible because the phage capsids have long fibers that reduce diffusion through the top agar:  Test - use increasingly dilute top agar.  Top agar us usually 0.75% agar; I've taken this down to 0.3% (the lowest concentration that's still stable enough to handle). The first time I got somewhat larger plaques, but this was not reproducible.

2.  The plaques are tiny/faint/blurry/invisible because GTA gene products contribute to phage production:  Test:  Plaque phage on a GTA overproducer strain.  Result:  On the first try, plaques on the overproducer seemed larger.  But this was not reproducible.

3.  The plaques are tiny/faint/blurry/invisible because the GTA-as-vaccine hypothesis is true:  (Plaques can't grow because rapid diffusion of GTA particles allows surrounding cells to become CRISPR-resistant to the phage before the phage gets to them.)  Tested by plaqueing the phage on cells deleted for the entire GTA operon and for the separate endolysin.  Result:  Plaques on these '∆∆' strains are just as lousy (maybe more lousy) than on the GTA-producer parents.

4.  Variant (large) plaques contain mutations that increase infectivity or diffusion:  I made new lysates from a couple of big plaques that spontaneously appeared among the tiny plaques, but these lysates still gave tiny or no plaques

I know that the phage lysates do infect and kill the cells, and do produce progeny phages.   When I put a spot of sufficiently-concentrated phage onto a lawn, all the cells die, and when I make a lysate with this 'clear' top agar, I get way more phage then I put in.  Can I use the lysate to test the GTA-as-vaccine hypothesis even though I don't have countable plaques?

What would I do?  Here's an earlier blog post where I laid out a crude plan and a list of all the things I'd need to find out before actually doing the experiment that would test the hypothesis:

Luckily, after I wrote the above I made another grand attempt at titering the phages on the various strains.  Well, I made a sloppy attempt, learned from at and made a better attempt, which more-or-less worked. 

Basic test:  Pour lawns of the test strains, using cells concentrated from 400 µl of culture, in 1.5 ml of 0.4% top agar.  Put 10 µl spots of different dilutions of phage lysates onto these lawns, let the liquid absorb, and check the next day.  Yesterday I did this using photosynthetically grown cells (supposed to make better lawns) and today I've repeated it using cells grown aerobically in the dark.  Here's yesterday's result for one of the two phage and one of the six strains:

The central clear spot is undiluted lysate, and the other spots are 10-fold dilutions of that.  For undiluted, 10^-1, 10^-2 and 1-^-3, the spot is clear (all the cells have been lysed).  The 10^-4 spot still has patches of non-lysed lawn, and the 10^-5 and 10^-6 spots have distinguishable plaques.  Two of the four healthy strains gave countable plaques (27 and 29), which is nicely consistent.

I'll wait for tomorrow's results before proceeding.