Another OAA “Clinical Trial”

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Continuing the saga of Alan Cash and Terra Biological, trying to get a dietary supplement containing oxaloacetate into clinical trials for all sorts of conditions from long-covid to PMS, there appears to have been a “breakthrough“!

The company finally got around to publishing the results of a clinical trial on the effects of OAA on self-reported symptoms of chronic fatigue syndrome / myalgic encephalomyoelitis (CFS/ME).  Now, this is a condition that scientist George Monbiot has called “The greatest Medical Scandal of the 21st Century”, so straight away that should start raising flags. Why pick a poorly-defined condition, the very existence of which is hotly debated?

Prima facie, the results appear promising and statistically significant, and it’s also commendable that the group chose to provide the complete (not actually) data set.  However, digging into the data there are are several problems…

The number of patients is “wobbly”

The trial started out with 40 patients in the control arm and 42 in the treatment arm. However, there was apparently a greater attrition rate in the controls (12 leaving) than the OAAs (5 leaving).  This would leave 28 controls and 37 OAAs as “completers” of the trial, as nicely explained in the flow diagram in Figure 3 of the paper and in the manuscript text…

…which makes it a bit weird when we go to the original data set (re-hosted here) and see there are actually 29 controls!  Where did the extra control patient come from?

Biased reporting of patients who got worse

Things get real squirrelly when we look at Figure 5 of the paper. The y-axis here is number of patients, the x-axis is the change in fatigue score pre/post trial. The orange are controls and the blue OAAs.

It’s a fairly simple matter to look at the dots and count the number of patients at each score point, then add them up. Doing so, we again arrive at 29 for the controls. BUT, importantly there are only 35 patients in the OAA set. We’re missing two of them, and by looking at the original data we find there were indeed two patients on OAA whose scores got worse (-4). They were simply eliminated from this graph, making it appear as if no patients on OAA got worse.

There are other discrepancies between the data and this graph, as shown in the table below. Anything highlighted blue doesn’t match up. There are 3 instances where a ‘1’ was assigned to the control group in the figure, suggesting a patient got worse, when in-fact there was no patient getting worse at that score level.  Furthermore, there are 2 patients who got better in the control group but were not counted on the graph…

Plotting Figure 5 as shown in the paper (left image below) alongside the real data (on the right) shows the way in which the paper makes it appear the controls got worse. In the published version there are more orange (control) points above the x-axis on the left side of the graph (worse scores).  Notice the missing 2 OAA patients who also got worse (at -4 in the right hand graph).

Just for fun, I also switched the order of the data series, so now the controls (orange) appear on top of the OAAs (blue) in the “real” graph on the right. This highlights the 3 controls who had a big improvement of 11 points… the same as the OAA group.  It’s amazing what little differences like this can make to the perception of a result.

Trial non-completers?

Now remember, Figure 5 is only the folks who completed the trial. As already mentioned, many did not. By comparing the data from the completers vs. the whole set, we can gain some more insight into the non-completers group, as shown in this table below.

What this shows, is of the 11 controls who quit early, they had an average improvement score of 2, and none of them got worse during the trial. However, of the 5 in the OAA arm who quit early, 3 of them actually got worse.

Combining the analysis of the completers and non-completers, overall in the control group 7 patients got worse during the trial, but none of them quit early. However, in the OAA group 8 patients got worse during the trial, and 3 of them quit early. Readers can judge for themselves whether there may have been any sort of “encouragement” applied to certain groups who were feeling worse to quit the trial early, but no such encouragement applied to those in the other group.

Appropriate Statistical Tests

Lastly, I’m not a statistician but my understanding is that when testing if 2 groups are different from each other, without knowing in advance which direction any difference might be, you should use a 2-tailed T-test.  For some reason, here the authors chose to use 1-tailed tests for everything, i.e., they only hypothesized that the results would go in one direction (presumably OAA being better than control), and not the opposite possibility. Needless to say, if you re-do the tests applying the proper criteria, some of the differences get a lot smaller or vanish altogether.  For example, in Figure 4 the p-value of 0.057 is described in the text as “trending toward significance”.

Performing this test with 2 tails yields a far-less impressive p-value of 0.114, quite literally nothing to write home about.

Wrapping this up

Let’s not get into the numerous typos in the paper that really speak more about the shitty editorial standards at Frontiers than anything else. Further illustrating such problems – one person who reviewed the paper (Alison Bested) has published with at least two authors on the paper (Yellman and Bateman) as recently as 2021, so it was reviewed by familiar folks. The discussion of the paper repeats many of the mistakes regarding the simple biochemistry of OAA that I’ve written about in the past. Bottom line, yet again, the company shilling a $600 a year supplement has managed to get something published with a veneer of scientific legitimacy in a not very good (predatory?) journal. A not-very-deep-dive shows problems with data reporting and the basic arithmetic keeping track of numbers of patients. Don’t human patients with hard-to-diagnose-and-measure chronic diseases deserve better than this?

The Guy Foundation & Quantum Biology – No Mitochondria do not Communicate With Light!

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I’ve been meaning to write about this for a while, but what with running the lab, teaching, trying to stay on top of the poly-crisis of the NIH funding situation, starting an MBA, and doing my best to avoid GenAI wherever possible, it got put down the priority list. That’s unfortunate because this is a fun story…

If you work in mitochondria circles, you may have heard of the Guy Foundation?  It’s a charitable trust in the UK – a philanthropic effort from Geoffrey Guy, who made his money as co-founder of GW pharmaceuticals, the first company to get cannabis derived medications approved for human use. GW was sold to a US company for $7.2bn in 2022. Dr. Guy seems like a decent… guy… (sorry), and let me be clear this post is not intended as a personal attack on him, the company he founded, or the mission or operations of the Guy Foundation (GF).  Rather, I want to highlight the absolute bonkers batshit crazy science the foundation is spending its money on.

Flashy Science

Let’s get right into it, with the idea that mitochondria can communicate using light, explained in a YT video from Dr. Guy himself. Building on the widely accepted idea that mitochondria run on electricity (something, something, membrane potential, millivolts – go read Peter Mitchell’s “little grey book”), the GF seems to think mitochondria produce photons as well.  In-fact, much of the “science” funded by the GF is on the topic of biophotonics – the release and sensing of photons by biological systems.

Aside: There are myriad examples of biological systems that produce light – firefly luciferase being most familiar. These have been extensively characterized at the molecular level and reconstituted outside the original organisms – all useful and well-documented science.  That’s not what this is about.

The paper that warrants the batshit label is this one. Published in that bastion of scientific excellence Frontiers in Physiology, it comes from the lab of Jimmy Bell at the University of Westminster in London. Bell and first author Rhys Mould are part of the Research Center for Optimal Health at Westminster. The work was funded by the GF, as proudly claimed on their website and noted in the paper.

So what’s the problem?  Well, let’s start with a description of the experimental set-up from figure 1 of the paper: 3 cuvets with stir-bars at the bottom, each containing a suspension of isolated mitochondria. The left cuvet is separated from the others by a piece of cardboard (the thick black line), while the one on the right is next to the central one (i.e., “unshielded”).  Into each cuvet is placed a needle-type O2 electrode, to measure O2 consumption by the mitochondria.  Let’s number these cuvets 1, 2, 3 from left to right.

Now here’s the kicker… they add the mitochondrial inhibitor antimycin to the central cuvet (#2), and then claim the mitochondria in cuvet #3 respond by changing their O2 consumption (respiration), but the mito’s in the cuvet #1 do not. Not only that, the mechanism of communication involves mitochondria in cuvet #2 emitting photons and those in cuvet #2 sensing them.

Let’s ignore for just a second a couple of other minor problems with the methods, such as the use of 244uM antimycin (enough to tranq’ a horse) and the complete lack of any added metabolic substrate to support the mitochondrial respiration.  Let’s instead focus on section 3.4 of the discussion…

“3.4 Comparing “light” and “dark” experiments. Finally, we compared the change in OCR between unshielded mitochondria in light conditions versus dark conditions, shown in Figure 6. In MCF7, the rate of OCR change was significantly higher in light conditions compared to dark conditions.”

OCR here stands for “oxygen consumption rate”.  In other words, this magical communication between cuvets via photons was actually more pronounced when the system was flooded with gazillions of photons from the room lights, versus in the dark – exactly opposite to how low-intensity light phenomena are supposed to work. Batshit is not nearly a strong enough descriptor.

Yeah yeah nit-picky details, what’s the real problem?  Dear reader, take a look again at Figure 1 above.  Do you see any type of lid on the apparatus, or anything separating the liquid part (blue) from the air above it?

The problem is, the way to measure mitochondrial respiration / OCR / O2 consumption, is in a CLOSED system such as a Clark type oxygen electrode, of which there are numerous vendors available, or alternative platforms. Every one of these instrument makers will tell you that the liquid in which [O2] is measured (e.g. a suspension of mitochondria or cells at the bottom of a well) MUST be sealed off from the atmosphere… even the tiniest air bubble will mess up the readings.

This is simple chemistry – air is 21% oxygen – much higher than the solubility of oxygen in water (about 200 micromolar at 37C). If there’s any contact between the liquid and the air, any O2 consumed by mitochondria in solution will be instantly replaced by that from the air, so the concentration of O2 in the liquid will stay the same. It’s the CHANGE in O2 concentration that’s used to determine the RATE of O2 consumption (that’s the “R” in OCR).  No change in [O2]?  No rate.

Simply put… it is damn near impossible to measure OCR in an open system such as this (well, technically it’s possible using precise mixing at the gas-liquid interface, a complex series of engineering formulas, lots of math, and a full understanding of the problem just described, but that’s not what happened here).

Show Me The Data

The “data” in the paper are presented in a manner completely opaque and unfamiliar to anyone who does these types of experiments.  The change in oxygen consumption rate (remember – there is no rate) in cuvets #1 and #3 following antimycin addition to cuvet #2 is shown in Figure 2…

Your eyes do not deceive you.. there was a 0.004% change in OCR in the cuvet that was exposed to a big old dose of pew-pew mito lazer beemz (#3, pink bar) but only a 0.002% change in the mito’s that were shielded by a bit of cardboard (cuvet #1, black). Tip – remember the direction these bars are pointing, for later on.

Far be it from me (a non-statistician) to wonder at the levels of intricacy and convolution required to detect a 0.004% change in anything, but this does not seem to be: (A) at all measurable, (B) of any importance. For context, this is like someone who earns $100k a year getting a $4 pay cut.  It should therefore come as no surprise that the statistics section of the paper will also appear odd to anyone familiar with such experiments…

“Differences in the rate of mitochondrial oxygen consumption
were analysed using a mixed linear effects model written in R”

Complete overkill. This is not how it’s done. You calculate the rate (slope of the O2/time trace) and (after checking for normalcy) use simple statistical tests to see if the values are different, with appropriate multiple-testing corrections if necessary. If you want to get fancy you could even use the real-statistics plug-in for Excel, but you absolutely do not need a mixed linear effects model to see the difference between 2 diagonal lines on a trace. Here’s an example from one of my very old (fuzzy) papers.  This is what real oxygen consumption traces look like.  You can see that one line is different from the other.  Compare this to what you’re about to see further down the page.

Show Me The Data – Part 2

As a polite science critic, my first course of action upon reading this paper (well, OK, not my first choice) was to make a nice post over on PubPeer, bringing up the above points and more.

To the authors’ credit, they did respond to clarify some of the experimental details, but in some cases this only confirmed my concerns (e.g. there was a lid made of parafilm, but still a head-space, and indeed the buffer contained no fuel for the mitochondria to respire on). Most importantly, the authors were gracious enough to share the original data set on FigShare.  Absolute stellar move. Kudos. Love it. I’ve been using FigShare since 2016 and everyone should do this!  The problem is when you actually plot the data from the Excel sheet, as predicted from the impossibilities discussed above, there is no rate.  The concentration of oxygen is flat over time, because it’s at equilibrium with the atmosphere.

The y-axis here is dissolved oxygen (in micromolar, you’ll note it’s a bit higher than the usual 200 uM, likely because these experiments were done at room temp’ and O2 solubility is an inverse function of temperature). The x-axis is time in seconds, and the antimycin A was added at 120s. The black lines are 8 samples from the unshielded cuvet, and the red lines are the shielded cuvet.

Put simply THERE IS NO DOWNWARD SLOPE, THERE IS NO OXYGEN CONSUMPTION RATE TO SPEAK OF. Some of the lines even slope upwards, so the [O2] in solution is actually increasing over time.  If we calculate the rates before and after the antimycin addition, the numbers (mean +/- standard error) come out as follows:

The chart shows the average rate (N=8 per group) for the unshielded cuvets (left 2 bars) vs. the shielded cuvets (right 2 bars), pre vs. post antimycin addition to the central cuvet. There is no difference between bars 1 and 2 – if anything the slope of the line (which is already close to zero!) increases instead of decreasing as claimed in the paper (compare this to Figure 2, above). The error bars (standard error of the mean) also show that whatever difference there may be is statistically non-significant – no fancy linear mixed regression what-have-you.  Looking at the unshielded vs. shielded groups at baseline (bar #1 vs. #3) you could even say they show a bigger difference than pre/post antimycin.  In other words, regardless of any mito’ communication voodoo, having a bit of cardboard next to you causes your O2 consumption to drop from very close to zero, to very actually zero, before any mito’ poisons are added to your neighbor.

Furthermore, the control data (cuvet #2) are not included, so we don’t even know if the massive dose of antimycin did anything.  It probably inhibited the mitochondria, but there would be no change in [O2] because, as explained above, any O2 consumed by the mito’s would be replaced by that from the atmosphere, so the trace would remain flat.  The difference between a flat line and a flat line, is nothing.

Anyway, I’d encourage anyone who enjoys playing around in Excel or R to take a look at these data and produce a single shred of evidence that the main conclusion is valid.

Let me be clear – not only is the key claim of this paper utterly bonkers, it’s based on a total lack of evidence, with a complete misunderstanding of how to measure mitochondrial function.

Surely this should be retracted?

Of course it should!  But since when did doing the right thing matter for publisher Frontiers? (just ask Leo Schneider). I wrote to the person who edited the paper, YoungChan Kim at the University of Surrey, UK, but he didn’t bother to respond. Neither did John Imig the chief editor for this field section of Frontiers in Physiology. A good friend who is involved with the journal did respond to my inquiry, but they were not involved with handling the paper and so could not do anything about it.  There was a bit of chatter in the PubPeer thread, which died down as soon as I posted a “no rate” graph similar to the one above.

Importantly, David Fernig raised the point that this is not an impossible experiment to do properly. You just need a very sensitive photomultiplier tube to detect photons, and tightly controlled other conditions (sealed chambers, dark room, temperature regulation, filters to control the wavelengths of light passing between samples, etc).  Maybe this IS something worth investigating?  Maybe the GF should give the money instead to someone who understands this?

How will this “knowledge” be used?

The paper is still out there, and people are still using it to make ridiculous claims.  The GF is lauding the work in their newsletter, as well as the aforementioned video, the lead author also talks about it in video format.  The foundation appears very proud of the work it funded, which is unfortunate because it’s not very good at all.

Of course, if you believe mitochondria can influence each other via light, it’s a small step away to hack that biology by shining bright lights on various body parts, which brings us neatly into crazy anti-aging therapies, such as the claims that red light can energise your mito’s. I’ve made my thoughts on the shit-show that is anti-aging biotech abundantly clear before. While there are certainly biological effects that can be attributed to certain wavelengths of light, they are usually based on rigorous experimentation (such as the paper just cited, from a well-known group in the field at MCW). The Frontiers paper is not an example of quality science.

There’s a lot more to be written on the various out there projects and papers claiming all sorts of weird quantum effects on mitochondria. It’s not just light… there are all kinds of papers about electromagnetic fields and mitochondria.  Claims that mitochondria operate at high temperatures inside cells. Mitochondria allegedly respond to music and other sounds.

But for now, we just have to wonder how does a charitable foundation hand over non-trivial amounts money to fund “pew-pew mito space photon lazer beemz”, and then when the project execution and results are complete garbage, they just accept everything at face value?  No admission that anything silly happened.  No acknowledgement that the work is absolutely fundamentally flawed, and was not performed in a way that could give meaningful outcomes. It’s an alternate reality!

Spring 2025 Update

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It’s been a while! Several updates to the lab over the past few months…

  • Rahiim Lake passed his PhD qualifier exam last fall (congrats!)
  • We hosted the 11th annual TRiMAD (Translational Research in Mitochondria Aging & Disease) conference here in October, attended by over 160 people.
  • Paul spoke at a number of conferences and outside institutions, including a conference at UPenn on research integrity, resulting in this paper.
  • The University finally adopted and published the Open Access Publishing Guidelines that we worked to bring through the faculty senate. It only took 7 years!
  • Paul went back to school in the Executive MBA program at UofR’s Simon School of Business (anticipated graduation May 2026). It resulted in finally (reluctantly) getting a linked-in page.
  • Post-doc Sabarna Chowdhury got married in April 2025 (congrats!)
  • Lots of other papers got published, including some exciting work on taurine metabolism with the Bajaj lab in the (newly NCI designated) Cancer Center, which is accepted for publication in Nature.

Buchi Rotavap Parts

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A rotary evaporator (RotaVap) is a core piece of equipment for any lab that does chemical synthesis, and Buchi make some of the best ones. Unfortunately, ours is about 25 years old and they no longer make parts for it.

In the heart of the apparatus there’s a worm-drive… the motor is connected to a helical spindle which drives a ring gear. This is what spins the round-bottomed flask.

Over the past few months we’d been noticing that at low spin speeds, the rotation would skip.  It would intermittently speed up and slow down through the rotation cycle.  So, we opened up the gearbox to see what was inside (note – you may need to use a hammer to get the parts to separate out).

The problem is, Buchi decided to make the gear out of plastic, and years of exposure to grease and chemicals causes the plastic to crack, as shown here…

The result is the motor spins but the plastic rung just grinds around and doesn’t spin the metal part that’s eventually attached to the flask.  The worm gear is basically slipping once per rotation. We tried buying a used model off eBay, and upon dissection it had exactly the same problem, so it appears this is a common fault on this particular model (RE-111).

The solution is not as simple as “glue it back together”. There’s too much grease everywhere, so it needs completely stripping down to remove all that so the glue will stick. And then, removing the ring gear can only be done by snapping it in two. The problem then is there’s not much surface area to glue back together so it will probably break again soon.

Enter the 3D printer!

With some quick work in Sketchup  I was able to make a model of the gear with a break in the middle and plenty of surface area for gluing the 2 halves together, as well as gluing to the metal spindle inside the machine.

Here’s the new gear, printed in Elegoo ABS-like resin on a Mars 3 printer. The STL file is here to download if your Buchi Rotavap is in need of a fix!

 

 

Spring ’24 Update

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Lots of happenings in the lab since last fall:

  • Grad’ student Rahiim Lake (Biochem Grad Program) joined in summer 2023 and is getting up to speed on his project looking at MGO modification targets in mitochondria.
  • New Post-Doc’ Sabarna Chowdhury arrived from India in January and is learning the various methods of the lab.  Sabarna is a zebrafish biologist by training, but also a bona fide mitochondriac!  She will (hopefully) be attending the CSHL course on metabolomics this summer.
  • Our paper on ROS generation by reverse electron transfer at complex I was published in Redox Biology.
  • Paul is now back on the Faculty Senate’s Research Policy Committee (RPC), which this year will be reviewing several policies including the one on research misconduct. Yummy!
  • We continue to fight misinformation and bad science, especially in the field of mitochondrial biology.  Most recently this has included calling out a truly awful study claiming that mitochondria can communicate with each other across distances of centimeters by using photons!
  • A successful Biochemistry retreat was held, in which we hosted Rita Alevriadou from University of Buffalo, who gave a fascinating talk on mitochondria inside extracellular vesicles.