Musings -- September 2018 to ?? (current posts)

Musings is an informal newsletter mainly highlighting recent science. It is intended as both fun and instructive. Items are posted a few times each week. See the Introduction, listed below, for more information.

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New items

Posted since most recent e-mail; they will be announced in next e-mail, but feel free...


Using bacteria to treat phenylketonuria?

September 16, 2018

Phenylketonuria (PKU) is a genetic disease. Affected people are unable to break down the amino acid phenylalanine (Phe), which leads to major toxic effects. The standard treatment is to restrict the diet: restrict the amount of Phe in the diet. That's not easy; Phe is a standard amino acid, found in all natural proteins.

What about other approaches? Well, we might try to fix the genetic defect in the affected people. That's plausible, but not yet practical. Or perhaps we could add back the ability to metabolize Phe in some other way. Maybe we could get the microbes in the gut to do it.

A new article reports doing just that, with encouraging results in both mice and monkeys.

The following figure shows an example of the results, with monkeys.

In this experiment, the monkeys were given a big dose of Phe. The Phe dose was labeled, with the hydrogen isotope deuterium; that's what the "d5-Phe" in the key means. The deuterium labeling means that the added dose can be measured separately, without confusing it with any Phe already present.

There are two conditions. In one, the monkeys have been "infected" with the treatment bacteria, called SYNB1618. That strain has been designed to metabolize Phe; it is used here as a probiotic. In the other condition, the control, just the Phe is given.

Part e (left) shows the level of the labeled Phe in the blood serum over time. You can see that the presence of the bacteria leads to substantially lower levels of Phe in the serum.

Part f (right) shows the level of (urinary) excretion -- of a product derived from metabolizing the Phe. HA stands for hippuric acid; the added bacteria have been designed so that Phe ends up as HA. You can see that the infected monkeys excrete substantial amounts of labeled HA; the control monkeys excrete none.

   This is part of Figure 6 from the article.


The results above, along with others in the article, show that the bacteria have promise for treating PKU.

The test shown above is with healthy monkeys. The mouse work included a PKU-model. The general point is that the added bacteria reduce the level of Phe in the blood.

The strain of Escherichia coli used here as the starting point for making the current probiotic is one that has long been used to treat humans. It has an excellent safety record, and does not persist in the treated people for more than a few days. Of course, any specific strain proposed for a treatment would need to undergo safety testing.

Translation of a product such as this from mouse and monkey to human is not trivial. The point is that the results are promising, and further work seems warranted. Testing in humans has begun, and a formal trial with PKU patients is starting.


News stories:
* Synthetic Bacteria Help Treat Phenylketonuria in Mice -- The genetically engineered probiotic, already in clinical trials, may ease patients' strict dietary regimes. (D Kwon, The Scientist, August 17, 2018.)
* Have Researchers Developed a Potential Microbial Miracle for Phenylketonuria Patients? (C Jones, Science-Based Medicine, September 7, 2018.) From a pediatrician. A good overview of PKU, with caution about the current story. I find his caution about right. He understands the proposed treatment and the results so far. He just emphasizes that we don't yet know it will be useful in humans. Nothing wrong with doing the tests, but don't assume it will work.

The article: Development of a synthetic live bacterial therapeutic for the human metabolic disease phenylketonuria. (V M Isabella et al, Nature Biotechnology 36:857, September 2018.) The article is from the company developing the treatment.

Previous post about a probiotic: Would a probiotic reduce sepsis in newborn babies? (October 20, 2017).

More on the metabolism of phenylalanine: How bacteria make toluene (May 18, 2018).

Some Musings posts about amino acids are listed on the page Internet Resources for Organic and Biochemistry under Amino acids, proteins, genes.



Baloxavir marboxil: a new type of anti-influenza drug

September 14, 2018

There is a new drug on the scene to treat influenza (flu). A new article reports results from two clinical trials: Phase II and Phase III.

The new drug is called baloxavir marboxil, or just baloxavir. (It's trade name is Xofluza.) In the trials, it was compared not only to a placebo treatment, but also to the current popular drug oseltamivir (Tamiflu).

Use of the new drug soon after getting symptoms shortens the disease course, as judged by symptoms, by about a day. That's about the same as for Tamiflu. That is, both drugs are effective at alleviating the patient's symptoms -- if given very early (within a day or so of the first symptoms).

The second result is a little more interesting, and is shown in the following graph.

The graph shows the amount of virus (the "titer") in the patients over time.

The virus titer is shown relative to the baseline value, on day 1. It is shown on a log scale: "-1" means that the virus titer is 1/10 the baseline value.

Results are shown for patients treated with the new drug (baloxavir; red triangles) or with the current drug (oseltamivir; blue circles).

The main result of interest is the lower levels of virus on days 2 and 3 for those treated with baloxavir. That is, baloxavir appears to inhibit virus production more quickly than does oseltamivir. This should mean that the person is less able to transmit the virus to others.

Is the better reduction of virus titer significant? The asterisks indicate that it is by the usual statistical tests. This is one trial, and it is clear that the variability is high. (The error bars show the standard deviations. There are about 350 patients for each drug.) One trial does not prove the case, no matter the statistics for that trial. But for now, all we can do is to present this one trial.

   This is Figure 3B from the article.


So, the results suggest that the new drug is better at reducing virus titer, and about as good at reducing symptoms, as the current drug.

But there is another point to be made, which is why baloxavir is of special interest. It is a new type of anti-flu drug. Oseltamivir targets the enzyme neuraminidase, affecting the release of virus particles. Baloxavir affects replication of the viral nucleic acid. Affecting the earlier step is probably good, but simply having drugs with different actions is good. Resistance to the two drugs is likely to be independent; in fact, data so far shows that baloxavir is active against strains that are resistant to oseltamivir. (Resistance to oseltamivir has already been a problem. Resistance to baloxavir was seen in the current studies.) And using two drugs with different action together may sometimes be helpful.

Miscellaneous...

Another advantage of baloxavir... It remains active longer in the body, and can therefore be given as a single dose. In contrast, use of oseltamivir involves taking the drug twice a day for a few days. All else equal, taking a single dose is obviously easier. (I have no information on the cost of the two drugs.)

Baloxavir was approved for use in Japan earlier this year. It is currently being considered for approval in the US.


News stories:
* New single-dose antiviral cuts flu symptoms, viral loads. (S Soucheray CIDRAP, September 5, 2018.)
* New Flu Pill Stopped Influenza Virus Shedding in Just One Day -- Xofluza (baloxavir marboxil) was reported superior to Tamiflu (Oseltamivir). (D W Hackett, Precision Vaccinations, September 6, 2018.)

* News story accompanying the article: A Step Forward in the Treatment of Influenza. (T M Uyeki, New England Journal of Medicine 379:975, September 6, 2018.)
* The article,: Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents. (F G Hayden et al, New England Journal of Medicine 379:913, September 6, 2018.)

An earlier post about oseltamivir: Transparency of clinical trials -- Is the flu drug Tamiflu worthless? (May 4, 2014). Be sure to see the follow-up post noted at the end.

Many posts on various flu issues are listed on the supplementary page: Musings: Influenza.



September 12, 2018 (Current e-mail)


Briefly noted...

September 12, 2018


How did the first people get to the Americas? By crossing the Bering Strait from Siberia to Alaska. Somehow. Musings has noted pieces of the story, but it is hard to get the big picture from individual articles. A new review article tries to provide that big picture, discussing evidence for and against the various models. The general conclusion is that neither of the major proposed routes (inland or coastal) should be excluded from consideration at this point. It is even possible that both routes were used. A good news story: The Peopling of the Americas: Evidence for Multiple Models. (C Tarlach, Dead Things (blog at Discover), August 8, 2018.) It links to the article, which is freely available. Background post: Man's migration from Asia to America? Did it really happen by land? (August 16, 2016).



Predicting the toxicity of chemicals

September 11, 2018

Testing of chemicals for safety is a big issue -- with no simple answer. Over recent years there has been an effort to develop computer programs that can predict chemical safety. A new article is a progress report on the effort. It's interesting and promising -- and very complicated.

The following figure illustrates one analysis with the new computer system, which is called Read-Across Structure Activity Relationships (RASAR).

The analysis here deals with the property of skin-sensitization. Thousands of chemicals, with known effect, were "tested" by the RASAR program. The program returns a "hazard probability" score, between 0 and 1; that is shown on the x-axis. The y-axis shows the "count" of chemicals with that score.

Results are shown separately for chemicals known to be negative (red bars) and positive (blue bars).

As an example, look at hazard probability of 0.25. You can see that about 150 "negatives" gave this score, but only about 50 "positives".

More generally, you can see that the distribution for the negatives is shifted toward the left (low scores), whereas the distribution for the positives is toward the right (high scores).

   This is Figure 6A from the article. (I added the labeling on the x-axis, based on how it is labeled at the bottom of the full figure.)


That is, the RASAR computer program can predict whether a chemical is or is not a skin sensitizer. Well, sort of.

The important question is whether the quality of the predictions, as illustrated by one example above, is "good", or "worthwhile". One part of the answer is that the program results are similar to the results from animal testing. Such animal testing is now the key source of information about chemical toxicity before humans are exposed. But limitations of such animal testing have long been recognized. No animal is just like a human.

The principle behind the new work is straightforward: chemicals with similar structures are likely to have similar properties. The problem, of course, is working out the details.

The authors have collected toxicity data on about 80,000 chemicals, mainly using publicly available databases. That toxicity data comes from the type of animal testing noted above. They then developed algorithms to calculate the probability that a "new" chemical will show a specific toxic effect. Of course, running the test on known chemicals allows them to test how well their algorithms are doing.

The graph above shows one example of how well it does. Good, but not perfect. In a general sense, that is just like traditional animal testing. In some cases, the computer testing appeared to be correct more often than the animal testing, but it varied.

Computer testing of chemicals is fast, cheap, and safe. Animal testing is slow and expensive, and not safe for the animals used.

It seems likely that people will start using such computer models. A chemical that is considered unsafe by the computer, perhaps by more than one independent computer model, is probably not a good candidate for development. The computer prediction software should also find use in testing the vast number of chemicals that have long been in use without testing. Being fast and cheap and "pretty good" makes it suitable for that use.

Working out the limitations of the computer modeling, and how such tests should be used alongside animal testing, will presumably evolve over time. There is considerable hype about the new work, with some suggesting it is "the answer". However, it does not pretend to be. It only addresses certain tests at this point. And its accuracy, while good, is not understood.


News stories:
* Database analysis more reliable than animal testing for toxic chemicals. (Science Daily, July 11, 2018.)
* Software-Based Chemical Screen Could Minimize Animal Testing -- Researchers develop a machine-learning tool for toxicity analyses that is more consistent in predicting chemical hazards than assays on animals. (A Azvolinsky, The Scientist, July 13, 2018.)

The article, which is freely available: Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility. (T Luechtefeld et al, Toxicological Sciences 165:198, September 1, 2018.)

Among many posts dealing with toxicity issues...
* Largest field trials yet... Neonicotinoid pesticides may harm bees -- except in Germany; role of fungicide (August 20, 2017).
* Designing a less toxic form of an antibiotic (April 19, 2015).
* A better mouse -- it has a humanized liver (August 12, 2014).
* Is lipstick toxic? (July 2, 2013).



Why is ice slippery?

September 9, 2018

Some data, from a recent article...

It's a complicated figure. Just look at that "key", in the inner box. One thing at a time.

The x-axis is the temperature (T) -- of ice, There are multiple y-axes and types of things shown.

The black-circle data points are for measurements of the friction between a piece of steel and the ice surface. The frictional coefficient μ is shown on the left-hand (black) y-axis scale. You can see that the friction is inversely related to T: less friction as the ice gets closer to melting. (That holds except very near the melting point, where the curve changes direction. We can ignore that. The blue curve relates to this point.) The values for μ at the warmer T are certainly indicative of slipperiness.

The red-triangle points relate to the diffusion coefficient (D) of water molecules at the ice surface as a function of T. I say "relate to" because what is actually plotted is the reciprocal, D-1 (or 1/D). See the right-hand (red) y-axis scale. You may already see why they plotted the reciprocal; if not, hang on for a moment.

   This is Figure 1a from the article.


Notice that the two sets of data points seem to follow the same curve. What is that curve? It's the green curve, for μd; the equation is shown in the key. It's the equation for something that follows a simple (Arrhenius) activation energy model. The green curve shown there is for an activation energy ΔE = 11.5 kJ/mol.

That's the point. As ice warms up, the surface molecules become more mobile; that's reflected in the higher diffusion coefficient (and lower D-1). That effect seems just enough to explain why the friction between a piece of steel and the ice is also reduced as the ice warms.

As the ice warms, more water molecules on the surface lose one hydrogen bond. That reduces their number of hydrogen bonds from three to two. The more weakly bound molecules with only two H-bonds are responsible for the increased mobility.

(The activation energy noted above is about half the energy of a typical hydrogen bond in the ice.)

The high mobility of surface molecules in "warm" ice has another consequence. Warm ice is self-healing. Scratch the surface, and the scratch will heal within minutes. Figure 3 of the article illustrates this. A deep scratch in ice at -10 °C is almost completely healed within 7 minutes. Unfortunately, they do not show results for any colder T.

Friction between steel and ice? Think ice-skating. You may have heard another explanation offered for why it works. The current work offers a new explanation, and the authors seem to have the numbers to support their case.


News story: A new study reveals why ice gets so slippery - and it wasn't what we expected. (A Micu, ZME Science, May 10, 2018.)

The article, which is freely available: Molecular Insight into the Slipperiness of Ice. (B Weber et al, Journal of Physical Chemistry Letters 9:2838, June 7, 2018.)

Most recent post that included ice: Large organic molecules found on Enceladus (September 7, 2018). That's the post immediately below.

Among other posts about ice:
* Ice in your diamond? (April 23, 2018).
* How rocks travel (November 14, 2014).



Large organic molecules found on Enceladus

September 7, 2018

Collect a sample of stuff from deep underground on Enceladus, and run it through the mass spectrometer. A recent article reports the results: large organic molecules.

Here is an example of the mass spec analysis...

The y-axis is the amount detected, in arbitrary units ("a.u." on the axis scale). The x-axis is travel time in the mass spec, a parameter that relates to the mass of the particle. Some actual masses are shown on the figure itself. For example, the big peak on the right side is labeled "1500-2100 u", where u stands for atomic mass unit.

For perspective... Mass 2100 u would correspond to 175 atoms of carbon, if this were pure carbon. If it were an Earthly protein, it would have about 20 amino acids.

The extreme right edge of the spectrum corresponds to mass about 8000 u.

This is part of Extended Data Figure 5 from the article. (The Extended Data figures are not in the print edition. Figure 1 of the article itself shows the "high-resolution" part of the spectrum, out to 200 u.)


The analysis here is from the Cassini spacecraft, which spent many years in the Saturn system.

How did the orbiting spacecraft get these samples from deep underground? Enceladus emits plumes of material -- mostly water, but with small amounts of other things from inside the moon.

The emphasis here is on finding high molecular weight material -- "macromolecular organic compounds", as the authors say in the title of the article. What are these compounds? They don't know. The high-mass part of the spectrum (to the right of the vertical dashed line in the figure above) is obtained at low-resolution; exact mass information is not available. However, for the lower mass part of the spectrum (to the left), there is a series of peaks that are about 13 u apart. That suggests almost pure carbon, with a very small amount of hydrogen. Something like benzene, or larger compounds of similar structure.

The plume material that is collected includes ice grains, from the crust. It is likely that the large organic molecules are carried on the ice.

The specific peaks seen in the high-res spectrum make it likely that some of the organic molecules contain O and N atoms.

Some spectra also contain peaks for rhodium. That comes from the collection device on the spacecraft.

There are no big conclusions, beyond finding large C-containing chemicals inside Enceladus. They are the largest organic molecules detected beyond Earth. As so often, we learn about our Solar System neighbors one small step at a time.


News stories:
* Complex organics bubble from the depths of ocean-world Enceladus. (Phys.org, June 28, 2018.)
* Complex Organic Molecules On Saturn's Moon Enceladus. (Heidelberg University, June 28, 2018.) From the lead institution.

The article: Macromolecular organic compounds from the depths of Enceladus. (F Postberg et al, Nature 558:564, June 28, 2018.)

Among posts about Enceladus:
* Is there food on Enceladus? (May 21, 2017).
* Enceladus and its plume (November 17, 2009).

Added September 9, 2018. More ice: Why is ice slippery? (September 9, 2018). Immediately above.



September 5, 2018


Briefly noted...

September 5, 2018


1. How many moons hath Earth? Did you count 2006 RH120? Did you exclude it because it is not in Earth orbit any more? But it was for about 13 months. What about the numerous ones seen over Toronto on February 9, 1913? A recent review article surveys the field -- and suggests that there should be many more, though they largely remain undetected. News story: Earth may have 'mini-moons' that could answer some interesting astronomy. (T Puiu, ZME Science, August 16, 2018.) Links to the article, which is freely available. Much of the article is very readable -- and fun. And it includes a beautiful painting of that 1913 event in Toronto. (This is all about natural moons, not manmade ones. The article addresses the problem of distinguishing the two classes.)


2. An update on the use of CRISPR for humans. News story: CRISPR Inches Toward the Clinic -- The gene-editing technology is already in trials for some rare conditions, with more human testing on the horizon. (S Williams, The Scientist, August 1, 2018.) A background post: CRISPR: an overview (February 15, 2015). It includes a complete list of regular Musings posts on CRISPR and other gene-editing tools.



Low-carb diets: Long-term effects?

September 4, 2018

Diets low in carbohydrates ("carb") have attracted attention in recent years. There is evidence that they do promote weight loss.

But what about the long-term? A new article looks at the long-term effect of carbohydrate level in the diet. It is part of a larger study, involving thousands of people over 25 years. It uses a simple end-point: all-cause mortality, or death.

The following graph summarizes the results...

The x-axis is the percentage of carbohydrate in the diet. It is expressed here as percent by energy.

The y-axis is the hazard ratio -- the risk of dying. It is shown here relative to the risk at 50% carb in the diet.

The blue line shows the best fit to the data; the shaded band shows a confidence interval. (It's probably 95%, but that isn't clear.)

The picture is clear: There is an optimum, at about 50% carb. Mortality is higher at either higher or lower levels of carb.

   This is Figure 1 from the article.


It looks simple. But is it?

The work involved collecting data from 15,000 people over many years. 40% of them died during the study. The results were then analyzed taking into account all the things known about the people. The figure legend offers a clue to the complexity of the analysis: "Results are adjusted for age, sex, race, ARIC test centre, total energy consumption, diabetes, cigarette smoking, physical activity, income level, and education." (ARIC? Stands for the name of the study: Atherosclerosis Risk in Communities.)

Analysis of the ARIC data is the focus of the current article. The authors also did a meta-analysis, combining data from several studies. The big conclusions held over the larger data set. That full data set included 432,179 participants, with 40,181 deaths.

One of the sub-group analyses is striking. When the carb component of the diet is reduced, what replaces it? (Remember, the carb content here is by percent, not total dietary intake.) Less carb means more protein and/or fat. From what? Broadly, from plant or animal sources. Breaking down the overall data set suggests that replacement of carb by animal products leads to greater mortality. In contrast, replacement of carb by plant products leads to slightly reduced mortality. Not all protein or fat is equal. Or maybe it isn't the protein or fat that is the issue, but something else associated with the source. (And not all carb is equal either.)

Human nutrition is a complex and difficult topic. It's complex because we are fundamentally omnivorous, and need many things from our diet. Further, people vary in their metabolism; not everyone has the same nutritional needs or optima. Studying human nutrition is difficult; the current study is huge both in number of people and time. There are questions about the data, such as food intake being based only on self-reporting, and that only at certain times during the long study period.

Overall, the study offers some interesting results, but still raises questions.


News stories:
* Low-carb diets may be cutting years off your life, new study says. (T Puiu, ZME Science, August 17, 2018.)
* Moderate carbohydrate intake may be best for health, study suggests. (Science Daily, August 17, 2018.)
* Expert reaction to study looking at carbohydrate intake and health. (Science Media Centre, August 16, 2018.) Interesting, even amusing. Even the "experts" can get agitated over the topic of human nutrition.

* "Comment" accompanying the article: Evolving evidence about diet and health. (A Mente & S Yusuf, Lancet Public Health 3:e408, September 2018.) A useful overview, with discussion of limitations of the study.
* The article, which is freely available: Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis. (S B Seidelmann et al, Lancet Public Health 3:e419, September 2018.) It includes a nice "Research in context" summary (page 2 of the pdf).

A post about a problem with meat: Red meat and heart disease: carnitine, your gut bacteria, and TMAO (May 21, 2013).

A caution about extreme diets... How the giant panda survives on a poor diet (August 2, 2015).

My page Organic/Biochemistry Internet resources has a section on Carbohydrates. It includes a list of related Musings posts.



Older items are on the archive pages, starting with 2018 (May-August).


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