[Sciences/BBB] Why the vitamin K shot in newborn matters

I have seen the topic of vitamin K (VitK) shot coming over and over in various discussion groups, with some parents weighing the need of the VitK in newborns. One of the main argument in favor for the injection of VitK in newborn is its ability to reduce the risk of cerebral bleeding (cerebral hemorrhage).
I thought a post on this topic would provide a great help in understanding the physiological role of VitK, the consequence of brain hemorrhage and conclude on the importance of the VitK shot.

1.What is the vitamin K?

Vitamin K is a fat-soluble vitamin that is mostly obtained by our gut microbiota and accessory from our food intake (in particular leafy greens and liver).

220px-Phylloquinone_structure.svg

During gestation, the fetus obtains it from the mother, as such vitamin passes through the placenta barrier. Vitamin K plays an important role through its biochemical cycle called “the Vitamin K cycle”. Vitamin K can convert glutamyl residues present in proteins into gamma-carboxylglutamyl residues as depicted in the picture below:

F1.large

Such modified glutamyl residues are present in particular set of proteins called “coagulation factors”. These coagulation factors are important pieces of what we refer as the “coagulation cascade”.
400px-coagulation_full-svg

I know this graph is complicated but what we care here is the final part of the cascade. The presence of intrinsic damage or trauma, we have the activation of several coagulation factors. Amongst those that are VitK-dependent, we have factor VII (seven), IX (nine) and X (ten). Prothrombin, upon activation by factor X  is converted into thrombin, which in turn cleaves the soluble fibrinogen into the insoluble fibrin. Fibrin acts as a mesh and forms a fibrin clot that will patch the bleeding area. This is an important physiological response when you rupture a blood vessel. The coagulation cascade will create a clot that will stop the bleeding process, saving you from a risk of loosing too much blood and entering an hypovolemic shock. One organ is particularly sensible to brain bleed, this organ is the brain.

2. Brain hemorrhage: small numbers, big damage

In this section, I will mostly discuss about brain bleeds in regards of hemorrhagic stroke but you can apply the same pathophysiology to brain bleeds induced by brain trauma. Brain bleeds are the second type of stroke. They account for about 15% of total stroke events, but account for 40% of stroke-related deaths.

subarachnoid-800x416

We have different types of brain bleeds. In stroke, we usually have a type of brain bleed called “intracerebral hemorrhage” (ICH) that happens deep inside the brain. There are other types of hemorrhage called “sub-arachnoid hemorrhage”. In that case, the brain bleeds occurs in the sub-arachnoid space, a space between the brain and the skull. This type of bleed results into an ischemic stroke (due to a lack of blood perfusion in blood vessels beyond the bleed site) and a brain swelling (resulting in the crushing of the brain tissue due to increase intracranial pressure).

During the injury heme (from damaged astrocytes, neurons and red blood cells) is released in the extracellular space. Heme is a very strong pro-oxidant molecule resulting in the formation of radical oxygen species (ROS) such as anion superoxide (O2*-) and hydrogen peroxide (H2O2), which in turn further induce oxidative stress and cellular damage.

The major type of cells that suffers of such damage at the greatest extent are neurons. Neurons are highly sensible to such injury and unlike other cell types neurons do not divide anymore (post-mitotic cells). A dead neuron is a dead neuron. There are some studies suggesting a possible regeneration of neurons in certain brain regions in rodents (mice, rats), yet the presence of an evidence pointing out at similar mechanism in humans are yet to be demonstrated. Furthermore, there is still no evidence that stem cells (including cord blood stem cells from umbilical cord) can provide a repair of such brain region following injury.

As of today, a dead neuron is a dead neuron. The ability of a damaged brain region to recover is very limited.

3. Why Vitamin K shots?

As we just have explained here, we know that VitK is essential in coagulation and we also understood the impact of brain bleed on the brain. Thus, reducing such brain bleed can be done in the short-term by the induction of the coagulation cascade.
As we mentioned, babies get their VitK from the placenta, but by the time they are born, they are already coming with a low VitK. We also mentioned that the VitK is primarily produced by the gut microbiota. It will take weeks if not months for babies to get a gut microbiota that is functional enough to produce the VitK (I speculate that such microbiota is not present until the age of 12 months when baby eat a diet similar to adults). We can speculate that food (breast milk or baby formula) should provide a source of VitK but providing a steady and standardized intake from dietary is near impossible to achieve.
Furthermore, there is no lab tests or techniques that can predict the onset of a brain bleed. Furthermore, brain bleed has a very high mortality rate and very high morbidity rate including cerebral palsy and other brain damage.
Therefore, ensuring a source of VitK right at birth is the best approach to ensure the baby has enough VitK to have a functional coagulation cascade. In case of a brain bleed, we can expect to have a rapid response of the body to ensure a emergency clotting process ongoing until the doctors can intervene and stop such bleeding to happen and clean any possible brain bleed.
This is why it is important to opt-in for a VitK shot. Once a brain tissue is damaged, there is no evidence yet that there is regeneration of such area. Neurons do not divide anymore by birth and there is no evidence yet of stem cells (including stem cells from cord blood) able to repair such damage.

 

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[Neurosciences/Aluminum] Does the latest paper from Exley show a link between ASD and aluminum?

Someone brought my attention today about the most recent Exley paper out in the press titled “Aluminium in brain tissue in autism” (the title could have been better but well….) and published in the journal “Journal of Trace Elements in Medicine and Biology“.
Let me put this straight, this is not a paper that has evidence of scientific fraud or data manipulation. There is no duplicated images, no suspicious blots. The problem I have with this paper is its deep experimental flaws and data analysis that nonetheless should not have passed through the peer-review filter.

  1. Before we dive into the paper, lets put the paper into context
    Lets just put the paper in the context. It was received on October 26th (Thursday). Came back in its revised form on November 21st on Tuesday and accepted for publication on November 23rd (Thanksgivings for the US, but since the editor-in-chief (EIC) is in Europe no Thanksgiving here). Let that sink it a bit: in a bit more than three weeks, it got send to review, came back from review and got revised in 26 days. In my standard of reviewing for journals and publishing my papers, thats some faster-than-light peer-reviews. I usually wait 4-5 weeks by the time I submit mine and get the editor reply to my submission with the infamous reviewers comments. Does a fast-reviewed manuscript means a bad manuscript? Not necessarily, but it can mean that maybe the peer-reviewed was not optimal, rushed or even worse just botched. Based on the quality of the data presented, I am leaning towards a botched review. Thats quite disappointing because the journal holds a decent impact factor (~3 for 5-year impact factor) and you expect an okay review.
    Then comes another problem. Exley published this paper (as well as few others) in the journal…..in which he holds a seat in the editorial board. Nobody can exclude the possible conflict of interest. Consider that: if you were an EIC, would you provide the same rigor and objective decision on a paper submitted by a colleague sitting in your editorial board than a paper submitted by Doe and colleagues?
    Not forbidden, but if you can avoid it, avoid it. Transparency is key and publishing in diversified journals (unless it is society-official journals) is an indicator of an healthy research.
    Finally, the last thing to keep in mind before I deconstruct the paper is the funding source. According to the acknowledgment section “The research is supported by a grant from the Children’s Medical Safety Research Institute (CMSRI), a not-for-profit research foundation based in Washington DC, USA.”  Behind the fancy name is just another anti-vaccine foundation that will play “the vaccine safety” card to peddle their pseudosciences. So we can claim that Exley is a shill for CMSRI, since he received monetary support for his research. Does that mean the research is completely bogus? No, but it means it will require further scrutiny, especially when the claim of the study goes against the consensus in the field (aluminum in vaccines is safe).
    Same goes if a study funded by Big Tobacco claimed the absence of correlation between lung cancer and smoking or if Big Sugar claimed the absence of correlation between type 2 diabetes mellitus and consumption of sweetened beverages.
  2. So what is wrong with this paper?
    For those who wants to read the paper with me, you can download it here (I assume it is open-access, so you should not have an issue with the paywall). Exley has a publication record on aluminum, especially when it comes to its possible ecotoxicity and the impact of aluminum on certain biological processes.
    The introduction is damn short, half a page of a double-spaced document but set the tone, this study will investigate the relationship between autism and aluminum in the brain.
    Samples are obtained from the Oxford Brain Bank, but felt short to indicate the source of the tissue (like a catalog number) and how this source of materials was complying with the institutional review boards (IRB). Basically, for any research involving human subjects or human tissues, you have to comply with the IRB that such specimens are used for a certain and defined use and foremost been anonymized.
    We have 5 patients that were diagnosed as on the autism spectrum and immediately we can pinpoint an important issue: there are no controls and that’s one of the big and unforgiving flaw of this paper.
    The authors then used two techniques to localize and quantify the Al in different cortical regions (and sometimes hippocampal regions). They have used three technical replicates (random sampling from the same cortical lobe) for measuring the Al content using an atomic absorption spectrometry and used lumogallion (aka4-chloro-3-(2,4-dihydroxyphenylazo)-2-hydroxybenzene-1-sulphonic acid, a fluorescent dye initially described to localize Al in plant roots). This dye have an excitation/emission spectra close from FITC/Alexa Fluor 488. It has been also used for live cell imaging , in particular to study how macrophages process Al present in vaccines adjuvants (http://www.sciencedirect.com/science/article/pii/S0022175915001222).
    Considering the equipment mentioned in the method, the microscope used provides the right excitation bandwidth filter and provide a long pass emission filter for anything over 510nm.Then things get weird, in the result sections, the authors mention the following:”We examined serial brain sections from 10 individuals (3 females and 7 males) who died with a diagnosis of ASD and recorded the presence of aluminium in these tissues (Table S1).“Where is the number coming from? Why don’t we have the same numbers in the Materials and methods?The other problem is the over interpretation of the data. To be brief, the lumogallion will show some punctuated pictures. The authors show some brightfield pictures overlapping to show the tissue structure but does not really help the reader. A DAPI stain (to stain cell nuclei) as counterstain would have been much more informative, it would helped to distinguish background noise from possible Al inclusion. Again, keep in mind we have no controls. The other issues with immunostaining is the high risk to cherry pick the data. You will be naturally inclined to show the presence of a positive risk but this cannot be used for quantitation. Thus, the use of the second method is welcomed as a complementary technique.
    For those not familiar with fluorescence, there is an important notion to keep in mind when analyzing the data: ensuring you keep the same exposure time, the same brightness or contrast and foremost have a negative control to set your exposure time. You can see a sketch explaining here on one of my fluorescence staining (based on my data, I concluded the expression was weak if not negative).

    Slide2

    The background subtraction is also a bit weird. I acknowledge the assessment of autofluorescence is a good control, but you expect to see a low staining. But foremost, you cannot overlap two distinct slices, as proximal as it can be. For instance, in Figure 1, you see some lumogallion staining and below the fluorescence  from the “control” using the adjacent slice. The lumogallion also seems to have a very high background.
    Picture1
    It seems lipid-rich environment increase dramatically the fluorescence of lumogallion (if you look at the spectra, the dissolution of the dye in Triton-X100 solution (b, a detergent) dramatically increase the excitation and emission spectra compared to water (a)).
    What I found troubling is this sentence in the results section: “We examined serial brain sections from 10 individuals (3 females and 7 males) who died with a diagnosis of ASD and recorded the presence of aluminium in these tissues (Table S1). Excitation of the complex of aluminium and lumogallion emits characteristic orange fluorescence that appears increasingly bright yellow at higher fluorescence intensities. Aluminium, identified as lumogallion-reactive deposits, was recorded in at least one tissue in all 10 individuals. Autofluorescence of immediately adjacent serial sections confirmed“.
    If you are a bit a fluorescence microscopy savvy, you know that the “emission color” we see in the objective is never caught by the CCD camera. These camera have in the most majority a B&W output for the simple reason that they have a much higher sensitivity than color cameras. You can always re-create colors in the micrograph pictures using various “lookup tables” (LUTs) that will give a pseudo color based on the level of grays. This is very useful when you samples different excitation/emission channels (for instance, samples stained with DAPI and two antibodies, one conjugated with Alexa Fluor 488 and the other with Alexa Fluor 546 or further down).
    The problem inherent with fluorescence is you can make thing fluoresce or end up with a false-positive signal if you increase the light beam (usually never happens because it is set) or if you increase the exposure time of your camera (this is the most common issue). As you increase exposure, you increase the risk to capture non-specific signal like autofluorescence signals.
    The other problem here is how to explain this sudden shift from orange to yellow?  This seems more like a subjective observation than something caught on camera.  That can be due to different things. You can have some bleed-through of the dye that is normally emitting in a certain wavelength but if it is strong enough can appears in neighboring emission channels. This thing rarely happens with a good fluorescence microscope that have defined filter cubes that allows the diffusion of certain emission wavelengths (for instance, my microscope have a DAPI, Alexa Fluor 488 and Alexa Fluor 555 cubes that only let the respective emission wavelengths  with 20nm-margin error to cross through the objective and reach the camera and binocular).
    Usually, we have to deal with bleed-through when you use flow cytometry and usually is solved using fluorescent dyes latex beads and by following a protocol called “compensation” (this has the result of removing any noise and keeping only the signals).
    We cannot also exclude that such fluorescence is just an autofluorescence from lipofuscine inclusion bodies. Lipofuscin is a lipid-based compound naturally produced by our cells. It has an important concentration in the central nervous system, however it is normally cleared out by cells. Failure in the clearance of lipofuscin is associated with different diseases called “lipofucsinosis” such as Batten’s disease. Even the author admit the possible presence of lipofuscin inclusions “Intracellular aluminium was identified in likely neurones and glia-like cells and often in the vicinity of or colocalised with lipofuscin (Fig. 5).” Lipofuscin is also capable of autofluorescence, although it is more in the wavelengths matching DAPI. Lipofuscin has an excitation/emission peaks at 360 and 435nm respectively but has been reported to also show fluorescence at 510nm when excited at 488nm (https://www.sciencedirect.com/topics/neuroscience/lipofuscin).
    Compared to the lumogallion excitation/emission spectra (507/567), we cannot exclude the presence of a phenomenon called “FRET” (Fosterman Resonance Energy Transfer) in which the excitation of lipofuscin (as the microscope excitation bandwidth is 470-495nm) provide enough energy to the photons emitted by the lipofucsin to excite nearby lumogallion dyes. Because the microscope setting used in this paper has no restricted bandwidth (it let pass any photons harboring a wavelength of 510nm and more), it may explain this orange-to-yellow transition noted by the author. The presence of a DAPI nuclear stain would greatly helped to identify this region as grey matter (rich in cells) or white matter (rich in lipid-rich myelin sheets). Thus, we can legitimately questions the nature of these as it these punctae labelled as “Al inclusion” are simply lipid inclusion or some artificial noise due to the tissue processing. This is where controls come as critical, it can help you sort the signal from the noise.

     

    The second big issue with this paper is the over-interpretation of what the experimenter see. The experimenter wants to see Al inclusion in monocytes? So be it: “Aluminium-loaded mononuclear white blood cells, probably lymphocytes, were identified in the meninges and possibly in the process of entering brain tissue from the lymphatic system“. Or maybe these are astrocytes, or neurons, or microglial cells, or blood vessels….or whatever the author wants to believe in: “Aluminium could be clearly seen inside cells as either discrete punctate deposits or as bright yellow fluorescence. Aluminium was located in inflammatory cells associated with the vasculature (Fig. 2). In one case what looks like an aluminium-loaded lymphocyte or monocyte was noted within a blood vessel lumen surrounded by red blood cells while another probable lymphocyte showing intense yellow fluorescence was noted in the adventitia (Fig. 2b). Glial cells including microglia-like cells that showed positive aluminium fluorescence were often observed in brain tissue in the vicinity of aluminium-stained extracellular deposits (Figs. 3&4). Discrete deposits of aluminium approximately 1m in diameter were clearly visible in both round and amoeboid glial cell bodies (e.g. Fig. 3b). Intracellular aluminium was identified in likely neurones and glia-like cells and often in the vicinity of or colocalised with lipofuscin (Fig. 5). Aluminium-selective fluorescence microscopy was successful in identifying aluminium in extracellular and intracellular locations in neurones and non-neuronal cells and across all brain tissues studied (Figs.1-5). The method only identifies aluminium as evidenced by large areas of brain tissue without any characteristic aluminium-positive fluorescence (Fig. S1).
    This is the second big mistake of this paper. If the author wants to make the claim he proposed here, then he has the obligation to show a counterstain using selective markers for neurons (e.g. MAP2, bIII-tubulin, NeuN….), astrocytes (e.g. GFAP), microglial cells (CD11b), leukocytes (CD3), macrophages (CD45), blood vessels (e.g. PECAM-1, claudin-5). This could have been easily performed (using a secondary antibody conjugated with Alexa Fluor 555 or better Alexa Fluor 647)  and would have give support to this claim.
    If the author can identify cells by the naked eye, he is either equipped with  Superman X-ray eyes or he is just imagining things.

    The discussion quickly gets into an anti-vaxxer diatribe and throws the minimal amount of scientific data under the bus.
    For example, the author throws this sentence as is: “We recorded some of the highest values for brain aluminium content ever measured in healthy or diseased tissues in these male ASD donors including values of 17.10, 18.57 and 22.11 g/g dry wt. (Table 1).” Firstly, where does it get this data? You cannot sum technical replicates, you have to average them (even with considering the huge variability between technical replicates). Secondly, how can the author make a claim like this without providing values from controls (well there are no controls) or from the literature. It is like “we have recorded the highest amount of leukocytes in ASD patients blood samples with values of 11.3, 12.0 and 11.5 x10e3 cells/mm3.” I cannot make an interpretation or conclusion without knowing the reference from the normal population (normal range 4.5-11x 10e3 cells/mm3) or from control groups. The average Al level was 2.38-4.79 microg/g tissues in male ASD and 1.15 in the female ASD patient. Such levels were very similar to those reported in samples from patients suffering from familial form of Alzheimer’s disease.
    Slide3

    The data is interesting but we are lacking additional female samples to make a claim as he did: “All 4 male donors had significantly higher concentrations of brain aluminium than the single female donor.” He lacks the proper conditions to run the statistics (you need same number of patients in male and female to make such claims) and even the important inter-individual variability makes it unlikely that he could achieve the statistical significance. This is a statement that would put a graduate student in shame for overconfidence in the data.
    Then goes the tirade “What discriminates these data from other analyses of brain aluminium in other diseases is the age of the ASD donors. Why, for example would a 15 year old boy have such a high content of aluminium in their brain tissues? There are no comparative data in the scientific literature, the closest being similarly high data for a 42 year old male with familial Alzheimer’s disease (fAD) [19].” (another Exley paper published…..in the same journal). We are dealing with the same issues (lack of controls, huge variability in the technical replicates…..).
    Now if you plot the average patient Al levels agains the age, regardless of the condition, you end up with an homogenous cloud. Now, two things have to be noted here: seems there is no impact of Al levels based on the disease (only age seems to matter between ASD and AD) and there is no correlation between increase in brain Al and age, at least in the very small sample size.
    Data 2

    No pun intended, but the data scatter looks vaguely like the United States map. Again, it shows the need of data from asymptomatic patients to estimate the burden of Al in the brain.
    Since we have not access to Al content in the brain, we have to see some values in the literature. A study by Andrasi and colleagues (https://content.iospress.com/articles/journal-of-alzheimers-disease/jad00432) provide some Al levels in control samples. According to their study, the average Al content in control samples were between 1.4 to 2.5µg/g dry tissue. We are indeed not far from the value reported by this study, especially when you consider the important standard deviation in these samples.

    Maybe it is also to consider the other study by Exler on Al level in brain samples from patients associated with familial form of Alzheimers disease (fAD) and familial dementia. In that study, all reported with Alzheimers (some with early onset, some with late onset based on age), the Al values reported were ranging from 0.34microg/g tissue (male) to 6.55microg/g (female, presenting a mutation in the PSEN1 gene, a known gene in FAD). So are we just measuring noise and try to extrapolate data from noise? Thats some bold statement that should have been smashed already by a decent reviewer in the field of neurosciences.
    But seeing these two papers went through in a apparent free ride is not looking good for the journal integrity.

  3. Conclusive Remarks
    To make a claim is one thing, to back it up with robust data is another thing. I think Exley jumped the shark a while ago and started to aluminum as the big bad wolf in every little things. But a wolf can be tamed, kept out from showing danger to the community and somehow co-exist. But for Exley, like Shaw, like Gherardi, aluminum is the devil incarnate. God forbid it has been used for 70 years and showed barely more than simple coincidence in its association with some disease, aluminum is their dead horse that worth being beaten again and again. If your funding sponsor will give you money for showing a link between aluminum and autism, lets give them what they want. Ethically it is insane, but when you need to keep your lab and your faculty position afloat, sometimes making the pact with the devil and throwing the scientific integrity and the philosophism that is given to you  following your thesis defense can be tempting. Sometimes, it feels that anti-vaccines researchers are like Faust and succumbed to the offer made by Mephistopheles offer. But this come with a price and a hefty price to pay: the loss of your integrity as a scientist.
    So my question is what is coming next to patients on the spectrum: does this study will be used to support the anti-vaccine agenda (another reason to yell “Aluminum is a chemikillz” in parenting groups?) and breakdown the herd immunity? Bogus remedies by bleach enemas and drops (the infamous CD/MMS)? or give a support to chelation therapy? gluten-free/casein-free diet? Or like Exley once claimed have these people drink ad nauseam silicon-rich water like Fiji water or Volvic water with the magic claims that the silicon with drain your brain from the Al contained inside it?
    This kind of deeply-flawed studies, lacking proper controls and driven by an ideology over the facts are dangerous because they prey on the meek and enrich modern snake oil sellers.

 

[Sciences/BBB] About the Thanksgiving tryptophan comatose and the BBB

Happy Thanksgiving everyone, I hope you are enjoying your family gathering. I know many of you are dreading to meet the family and extended family to discuss about controversial topics and differences in opinion.
But the other big menace coming in, that is particularly feared by the Black Friday shoppers: “The Thanksgiving turkey comatose” myth. This myth is perpetuating the idea that the Thanksgiving feast will induce a lethargic state attributed to the tryptophan present in turkey. Lets use this time to talk about tryptophan, turkey and of course the BBB in all that.

1. What is tryptophan?

Tryptophan is one of the 22 amino acids forming the building bricks of each of our proteins. It belongs to one of the few amino acids that our body cannot produce and therefore has to get it from our food supply.
In addition to its role in proteins, tryptophan is also an interesting molecule for the central nervous system, because it serves as a precursor for serotonin (a neurotransmitter also known as 5-hydroxytryptamine) and melatonin (commonly known as the “clock hormone”). You can see the similarities in structure of these molecules below:
Picture1

Tryptophan is particularly enriched in meat. According to the USDA, turkey meat contains the highest level of tryptophan from all foods, followed by white eggs, soybean and seaweeds. This partly support the claim of turkey being rich in tryptophan.

2. How does the tryptophan enters the central nervous system?

Like you expect, the blood-brain barrier is impermeable to any charged molecule. This is the case of many amino acids circulating in the blood (pH=7.4). Thus amino acids can enter the brain only by using special “revolving door” called solute carriers (SLCs). Tryptophan is transported by a particular amino acid transporter called large amino acid transporter 1 (LAT1). LAT1 is a particular transporter because it is formed by two subunits named SLC3A2 (also named CD98) and SLC7A5.
LAT1 is not specific to tryptophan, it also allows the transport of other aromatic amino acids like phenylalanine and tyrosine, but also chained amino acids such as leucine or arginine.

The impact of dysfunction in LAT1 remains poorly understood, however a study by Mykkaenen and colleagues noted several point mutations in SLC7A7 with a rare disease named lysinuric protein intolerance, a rare autosomal disease primarily described in patients from Finnish and Japanese origin marked by the impaired transport and elimination of basic amino acids following a protein-rich diet.

3. What is the function of tryptophan in the brain?

As I have previously mentioned, tryptophan is the precursor of two major neuromediators: serotonin and melatonin.
Serotonin is produced by a certain type of neurons named “serotoninergic neurons”. Like other neurons expressing a particular neurotransmitter other than glutamate or gamma-aminobutyrate (GABA), these neurons are restricted to a certain localization usually referred as “nucleus” (kernel, core). These neurons can project their axons all through the brain via a process called projections, allowing these neurons to interact with far-fetched neurons localized in a remote location.

In the case of serotoninergic (5-HT) neurons, these neurons are located in a structure called “raphe nucleus” and project to areas in which such neurotransmitter interact with 5-HT receptors. Through the interactions with the receptor, serotonin plays an important role in the modulation of several behavior including appetite, emotional (depression, anxiety), cognitive (schizophrenia) motor and autonomous (for instance emesis, the scientific term of “puking“).

In addition to the biological effects on the brain, the serotonin system is also linked to the circadian rhythm system (what we can call the “biological clock”) as depicted in the picture below:

sadserotoninfigure

We are diurnal animals as our main activity occurs during daylight and concludes with our sleep cycle during the dark period. In opposite, some animals like rodents are nychthemeral animals (active during dark phase and sleeping during daylight).

The light/dark cycle phase is determined by our eyes and retina. Such retina will transmit the presence of light to a particular nucleus named “suprachiasmatic nucleus” (SCN) . This nucleus is consisted by cells and nuclei functioning as oscillators. You can think about a pendulum in perpetual movement or a ticking clock. When darkness settles, the retina start to slowdown the information coming to the SCN.
In turn, the SCN becomes less active and relieve the blockade of the activity of the pineal gland. The pineal gland in turn start to secrete melatonin (aka the sleep hormone) that act as a “negative feedback loop” further shutting down the SCN and stimulate the production of serotonin via the raphe nucleus. All these events ultimately giving us the feeling of being sleepy and the process of sleeping.

4. So why we claim the “turkey comatose” is real?

As you can see in this myth, we are facing a post-hoc ergo fallacy. “I feel sleepy after Thanksgiving dinner. I ate large amount of turkey meat at Thanksgiving dinner. Turkey contains tryptophan and sleep is controlled by melatonin (a tryptophan derivative). Thus the tryptophan contained in the turkey meat is responsible of the food comatose”.

As you have seen, this does not make sense as the sleep/wake cycle is driven by the light exposure. This is also explaining partly why some people feel more tired and less motivated during winter times.

One explanation we can discuss is the particular food intake we all face during Thanksgiving that exceed our usual amount of food. We rarely experience such a feast and copious meal during the year. The table is furnished with so different plates, rich in proteins and carbohydrates.
This create a spike in food intake and food digestion that will likely create a urge of blood flow towards the gastrointestinal tract. This physiological phenomenon is named “postpandrial torpor”, making you feel sleepy and tired after a large meal, even if the meal was completely turkey-free.

So in conclusion, if you want to avoid “the turkey comatose”, don’t blame it on the turkey. Blame it on your eyes having a bigger appetite that your stomach can sustain. Keep it in moderation and now you know about the tryptophan transport at the BBB.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[Sciences/Stem Cells] Case report of a successful treatment of a junctional epidermolysis bullosa (Hirsch et al., Nature 2017)

This is a very groundbreaking case report here on a patient suffering from junctional epidermolysis bullosa (JEB). This is a genetic condition marked by mutation in the LAMB2 gene, a gene encoding for laminin beta-2 chain of the laminin complex. Laminins are part of the extracellular matrix and play an important role in cell adhesion such as skin cells. Imagine a concrete slab by which your house sits firmly.
In patients suffering from JEB, this slab is wobbly and crackle under the pressure due to some issues in the concrete composition. These patients end up having a very fragile skin that rapidly detach and have a low life expectancy due to high risk of infection and important handicap. Until now, no treatment has been successful, including skin grafts.
In this case report, the authors have taken skin stem cells from this patient and have corrected the mutation by inserting a correct gene copy of LAMB2 via retrovirus. Such stem cells were maintained and expanded into Petri dishes to form skin graft islands. Such islands where then implanted to the patients and successfully grafted in. Apparently the patient has been able to recover and live an almost normal life.
Another “Yes!” moment for science! If you are interested to read more about it, you can find it here:
https://www.nature.com/articles/nature24487

 

[Neurosciences/Junk Sciences] Autopsy of a flawed study of aluminum and brain inflammation (Li et al., J Inorg Biochem 2017)

Note: This is a special blog post coauthored by The Mad Virologist and The Blood-Brain Barrier Scientist (this article will be co-published on both our blogs). Another post has already been published on this paper, but we wanted to take a deeper look at everything that is wrong with this paper.

[UPDATE2] The study in question got retracted according to RetractionWatch:
http://retractionwatch.com/2017/10/09/journal-retract-paper-called-anti-vaccine-pseudoscience/

[UPDATE] I would strongly recommend the reader to look at the comments on Pubpeer about this paper. It is terrifying to think how it percolated through peer-review.
https://pubpeer.com/publications/4AEB7C8F30015079E2611157CF8983#undefined

A recent paper by ophthalmologist Chris Shaw was published and immediately touted as being proof positive that the aluminum adjuvants found in some vaccines are responsible for causing autism. Before we get into the paper, I have a few choice things to say about Chris Shaw. Despite not being an immunologist, Shaw has ventured into studying how vaccines and vaccine adjuvants cause neurological disorders such as autism. Shaw made headlines in 2016 when a paper he co-authored that claimed to show a link between the HPV vaccine and neurological disorders was retracted after being accepted by the journal Vaccine. It turns out that the statistics used in the paper were completely inappropriate and there were undisclosed conflicts of interests for some of the authors, including Shaw.These issues should have prevented the paper from being accepted in the first place, but mistakes do happen and science tends  to be self correcting. More surprising is that Shaw claimed that he didn’t know why the paper was retracted and that the science was of the highest quality. Shaw’s previous work has also been described by the WHO as deeply flawed and rejected by that body. This isn’t being brought up to dismiss the paper out of hand but to help illustrate why Shaw’s work is deserving of additional scrutiny. Hopefully by the end of this post, the logic behind the need for additional scrutiny of anything Shaw publishes is abundantly clear. We’ll begin by examining the methods used by Shaw’s research group and point out some of the issues.

Background for experimental design flaws: PK and species issues

One problem that is recurrent with Shaw is his “vaccination schedule” tries to consider rodents, such as mice and rats, as humans in miniature. It is wrong to assume that rodent and human primate species are alike, they’re not and there are notable physiological differences between rodents and non-rodents. For example, there are a couple of studies by Terasaki and colleagues (http://onlinelibrary.wiley.com/doi/10.1111/j.1471-4159.2011.07208.x/abstract) that have shown differences in the expression of solute carriers and drug transporters at the blood-brain barrier. We cannot exclude that such differences may bias the outcome observed in his studies, but this bias applies intrinsically to any in vivo studies based on a rodent model.
There is also the issue of brain development and mapping the vaccination schedule and the brain maturation. In this study (as well in the previous ones), Shaw and colleagues consider that applying vaccines from post-natal day (PND) 3 to 12 is representative of a human infant vaccine schedule. There is some differences in the literature, as previous studies from Clancy and colleagues mapped the PND12 to the 7th gestational months in humans (https://blogs.cornell.edu/bfinlay/files/2015/06/ClancyNeurosci01-17kkli7.pdf), some more recent publications map PND21 to 6th month post natal in humans, making the PND12 around the 3rd month infancy following full-term birth (http://www.sciencedirect.com/science/article/pii/S2352154615001096). You can easily appreciate that by following Shaw flawed experimental design, the total amount of Al administered during a 2 year period has been indeed administered within 90 days of birth, whereas the vaccination schedule according to the CDC does not start before the 2nd month of infancy if we exclude the two injections of Hepatitis B vaccines at birth and after the first month respectively (https://www.cdc.gov/vaccines/schedules/hcp/imz/child-adolescent.html).

In addition to a flaw in the experimental design, we cannot exclude some differences in the pharmacokinetic profile of Al adjuvants between mice and humans. The data available is fairly limited but a recent study from Kim and colleagues (https://www.ncbi.nlm.nih.gov/pubmed/26437923) failed to show a significant brain uptake of Al compared to controls following the single oral administration of different Al oxide nanoparticles at a concentration of 10mg/kg. Furthermore, the approximation of Shaw in terms of total burden of Al from vaccines (550 microg/kg) is not an accurate metric as we have a dynamic process involving absorption, distribution and elimination to occur simultaneously. A daily burden of Al from vaccines is a much more reliable parameter to consider. Yokel and McNamara (https://www.ncbi.nlm.nih.gov/pubmed/11322172) established it about 1.4-8 microg/day for based on 20 injections spanning over a 6-year period in a 20kgs individual.
If we consider Shaw calculation, then the total burden at age 6 would be 1650 microg/kg or 33’000 microg for a 20kgs 6-year old child. That’s about 15 microg/day of daily Al burden from vaccines, a value that is 2 to 10 folds higher than applied to humans. It makes therefore very difficult to compare apples to oranges, as Shaw experimental paradigm is flawed and not representative of a clinical scenario.

Selection of genes to measure:

Selecting which genes to measure is a crucial step in a study like this. If care is not given to ensure that the correct genes are selected, then the study will be a wasted effort. Shaw stated in the paper that they selected genes that were previously published. However, not all of the genes that they measured came from this paper. Only 14 of the genes were from this paper (KLK1, NFKBIB, NFKBIE, SFTPB, C2, CCL2, CEBPB, IFNG, LTB, MMP9, TNFα, SELE, SERPINE1, and STAT4). This leaves 17 genes the were measured but not found in the paper. Two of these can be explained. One gene, ACHE, was mentioned as having been selected because of other work, so it is sourced. The second gene, is the internal control gene beta-actin. This is a housekeeping gene that is often used as an internal control to provide a relative expression from. This leaves 15 genes unaccounted for. We suspect that these genes were selected because they are involved in the innate immune response, but no reason is stated in the paper.

The way these genes were selected is problematic. Because half of the genes seemed to be selected for uncited reasons, this study is what is known in science as a “fishing expedition.” There’s nothing inherently wrong with this type of research and indeed it can lead to new discoveries that expand our understanding of the natural world (this study that increased the number of sequenced viral genomes by nearly tenfold is a good example of this). But what fishing expeditions can show is limited. These types of studies can lead to other studies but they do not show causality. Shaw is claiming causality with his fishing expedition here.

There is also the problem that they used old literature to select their gene targets when much more recent research has been done. By happenstance, they did measure some of these same genes in their study. However, their results do not match has has been measured in children that have been diagnosed with autism. For example, RANTES was shown to be decreased in children with autism. In Shaw’s work there was no statistical difference in RANTES expression between mice given the aluminum treatment and those receiving saline. Likewise, MIP1alpha  was shown to be decreased in developmentally delayed children but was shown to be increased in the aluminum treated mice. This was also the case for ILIb which was found to be elevated in children with moderate autism yet there was no statistical difference between the mice receiving the aluminum treatment and those receiving saline. In fact IL-4 was the only gene to follow an expression pattern similar to what was found in children with severe autism (elevated in both cases). However, there is something odd with the gel in this case. This was the image for figure 4 that was included in the online version of the paper (we have not altered the image in any way). Look closely at the top right panel at the IL-4 samples and the IL-6 samples. You’ll notice that the bands for the control and the aluminum treated mice have different color backgrounds (We enlarged the image to help highlight this but did not adjust the contrast). If these came from the same gel, there would not be a shift in color like this where the treated bands have a lighter color encircling them. The only way this could happen is if the gel was assembled in photoshop. The differences could be real; however, since this image was modified we do not know for sure and this is scientific misconduct. Papers get retracted for this all the time and people have lost their degrees for doing this in their dissertations. These gel results cannot be trusted and the paper hinges on them. The Western blots and issues with them will be discussed below.

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The unaltered figure 4.

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A close up of the panel with the regions in question highlighted.

Semi-quantitative RT-PCR:

In order to quantify the gene expression levels of the genes that Shaw’s group selected, they used an older technique called semi-quantitative RT-PCR. This technique uses the exponential increase in PCR products in order to show differences between expression of a gene under different conditions. There’s nothing wrong with the technique provided one understands what the limitations are. Let’s say you have a large number of genes that you want to measure expression of, but you aren’t sure which genes are going to be responsive and you have limited funds. Semi-quantitative RT-PCR is a good method to screen for specific genes to be examined further by more precise techniques, such as Real-Time RT-PCR, but it’s not appropriate to use this technique and then make statements about precise quantification. Where semi-quantitative RT-PCR excels is with genes that are normally not expressed but can be expressed after some sort of stimulus, such as terpene biosynthesis genes that are induced by insect feeding.

To put it bluntly, semi-quantitative RT-PCR was not used properly in the paper by Shaw. The way that it was used implied that it would be quantitative when the technique is not that precise. Without verification by another method, ideally Real-Time PCR which can determine what the exact abundance of a given target is, these results should be taken with a grain of salt. This would still be the case if there weren’t irregularities in the gel images. With those irregularities, this is absolutely essential and should have prevented this paper from being accepted.

Western-blots and data manipulationPCR and Western-blots data: the owl is not what it seems
As The Mad Virologist mentioned, the semi-quantitative PCR is an old-fashioned RNA quantitation method, with the use of Real-Time quantitative PCR (that quantifies the amplification product at each cycle, using a fluorescent dye as an indicator) is a much more accepted method nowadays (see his section for more details). For Western-blots, the semi-quantitative approach is more accepted but it is important to show data that are consistent between what you show (qualitative) from what you count (quantitative). In Western-blot analysis, we measure the relative darkness of a protein band (the black lines that you see in papers) between treatments and controls. Because you cannot exclude some errors due to the amount of protein loading, we also measure the band intensity for proteins that are very abundant, usually referred as housekeeping proteins (because they play essential functions in cells). In this case, beta-actin (named ACT in the paper was used).
Once you normalize to beta-actin, you can compare the effect of a treatment by comparing the relative band intensity ratios. In both cases (semi-quantitative PCR and Western-blots), “what you see is what you measure” or you have to show a “representative Western-blot” alongside a quantitative data to demonstrate that your quantification matches with band densities. The common practice is the use of image acquisition software like ImageJ to determine band density. Showing Western-blot is nice, but not foolproof. Indeed, Western-blots data (with fluorescence images) is amongst the most common method by which some researchers can manipulate or even falsify data but also the most common type of data that spark a paper retraction. Someone notice something fuzzy on a Western-blot data, creating some questioning reaching to the editors and asking access to the full dataset (usually the X-ray film or the original full scan of the blot). Often, the author will use the excuse “the dog ate the flash drive” or “the hard drive containing the data crashed” if they cannot provide such data.
There are some methods to spot some image manipulation on Western-Blots and include playing with the brightness/contrast, requesting the presence of quantitative data in addition of a representative blot, samples must be coming from a same gel (you cannot use a cookie-cutter and build-your-own perfect gel). There is an excellent article that describe the pitfalls and cases of bad Western-blot data representation if not image manipulation. (https://www.elsevier.com/editors-update/story/publishing-ethics/the-art-of-detecting-data-and-image-manipulationThere are, at this time, different issues raised both in the Western-blots pictures and their subsequent analysis raising the reliability of the data presented in this study.

In this post, we have used the full-resolution pictures provided by the journal website (http://www.sciencedirect.com/science/article/pii/S0162013417300417), opened just pictures in ImageJ to convert such pictures into 8-bit format, invert the lookup tables (LUT) and adjusted the brightness and contrast. We have exported such pictures in Powerpoint to ease the annotation and comments. We recommend the reader to judge by himself/herself and download the full-resolution images as well.

The first concern is by looking at Figure 1C. First, this is the original Fig.1.

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Then, this is the close-up analysis for Fig.1C

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There are several issues. First there are some bands that appears as band splicings, in which the author create a custom blots by assembling different bands from different gels. This is a no-no in Western-blots: all bands showed in a blot should come from the same gel. This is why Western-blot is a torture for graduates students and postdocs, you need to show your best blot with all bands showing the same behavior for your quantitative analysis.
Second, the presence of a rectangular grey piece that was added on the top of control 3 TNF band. This is a possible data manipulation and fraud, as you are voluntary masking a band and hiding it. Thats a big red flag on the paper. The third issue of Fig.1C is the consistent feeling of seeing bands either cropped on a grey rectangle or what I call a “Photoshop brushing” in which you brush off using the brush function area of the gel you consider not looking good enough. You can clearly see it with actin as we have a clear line between the blurred blot and a sharp and uniform grey in the bottom half of the blot, compared to the wavy top of the blot. This a grey area that I am not familiar with Western-blot but this is a no-no for any immunofluorescence picture. Any image manipulation that goes beyond the brightness/contrast adjustment and involves alteration of the acquired picture is considered as data manipulation. If you analyze the data upon correcting for the inconsistency of Figure 1C, the graph looks much more different and failed to show any differences between Al-treated and control, when you restrict yourself in over-normalizing it and plot straight the protein/actin band density ratios.

What is also concerning and surprising is the conclusion from the authors that males, not females, showing an inflammatory response. Of course, the authors failed to show the same outcomes from female animals and expect us to trust them on this. The problem is that such conclusion is in direct contradiction with the literature. There is a solid literature supporting the presence of a sexual dimorphism in terms of inflammatory response, in particular in terms of neuroinflammation and autoimmune disorders such as multiple sclerosis (https://www.ncbi.nlm.nih.gov/pubmed/28647490; https://www.ncbi.nlm.nih.gov/pubmed/27870415). There is also a growing call to the scientific community to provide results for both sexes (males and females alike). Although Shaw reports the study was performed in both males and females, he gives us this explanation at the end of section 3.1: Taken together, a number of changes indicative of the activation of the immune-mediated NF-κB pathway were observed in both male and female mice brains as a result of Al-injection, although females seemed to be less susceptible than males as fewer genes were found altered in female brains.

Yet the interesting part comes when Shaw try to compare ikB phosphorylation between males and females following Al injection (Fig.3C). When you analyze the data, you are raising concerns very rapidly. First, we have a possible case of cookie-cutter band in which you just paste a band that seems nice enough in a blank space. This is a very suspicious activity as you can make up data as easy as this. Second, there is again this “Photoshopping brushing/erasing” taking place in that figure, in which I suspect a case of fraudulent activity. As you can see in female, it is as if someone tried to mask some bands that should not have been here. Remember when he said that males but not females showed an inflammatory response? Is it trying to dissimulate data that contradict his claims?

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Again, lets bring up Figure 3 at its full resolution.
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Finally, the same issues are persistent and even more obvious in Fig.5A. Again, we have a mixture of different Western-blots image manipulations including bands splicing, Photoshop brushing, cookie-cutter bands……

First, the unedited picture:
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And below the close up of Fig.5A

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These are some serious concerns that raise the credbility of this study and can only be addressed by providing a full-resolution (300dpi) of the original blots (X-ray films or the original picture file generated by the gel acquisition camera).  There has been a lot of chatter on PubPeer discussing this paper and many duplicated bands and other irregularities have been identified by the users there. If anyone is unsure of how accurate the results are, we strongly suggest looking at what has been identified on PubPeer as it suggests that the results are not entirely accurate and until the original gels and Western blots have been provided, it looks like the results were manufactured in Photoshop.

 

Statistics:
Long time followers know that I tend to go right to the statistics that are used in papers to see if what they are claiming is reasonable or not. Poor use of statistics has been the downfall of many scientists, even if they are making honest mistakes. It’s a common problem that scientists have to be wary of. One easy solution is to consult with a statistician before submitting a paper for publication. These experts can help point out if the statistical tests that were run are the correct or not. The Shaw paper could have benefited from this expertise. They used a Student’s T Test for all of their statistics comparing the control to the aluminum treated. This is problematic for a couple of reasons. These aren’t independent tests and the data likely does not have a normal distribution, so a T Test isn’t appropriate. Better statistical tests would have been either Hotelling’s T-squared distribution or Tukey’s HSD.  Another issue is how the authors used standard error (SE) instead of standard deviation (SD). To understand why this matters, it helps to understand what the SE and what the SD measure and what these statistics show. The SD measures the variation in samples and how far the measurements are from the mean of the measurements. A smaller SD means that there is low variability in the measurements. The SE measures the likelihood that a measurement varies from the mean of the measurements within a population. Both the SE and SD can be used; however, using the SE is not always appropriate, especially if you are trying to use it as a descriptive statistic (in other words if you are trying to summarize data). Simply put, the SE is an estimation and only shows the variation between the sample mean and the population mean. If you are trying to show descriptive statistics, then you need to use the SD. The misuse of SE when the SD needs to be shown is a common mistake in many research publications. In fact, this is what the GraphPad manual has to say about when to use the SD and when to use the SE:

If you want to create persuasive propaganda:
If your goal is to emphasize small and unimportant differences in your data, show your error bars as SEM,  and hope that your readers think they are SD. If our goal is to cover-up large differences, show the error bars as the standard deviations for the groups, and hope that your readers think they are a standard errors.” This approach was advocated by Steve Simon in his excellent weblog. Of course he meant it as a joke. If you don’t understand the joke, review  the differences between SD and SEM.” The bottom line is that there is an appropriate time to use the SE but not when you are trying to summarize data.

Another issue is the number of animals used in the study. A consensus in published study is to provide a minimal number of animals (usually n=8) needed to achieve statistical significance but also maintain to a minimum to ensure proper welfare and humane consideration for lab animals. In this study, such number is half (n=5). Also the authors are bringing some confusion by blurring the lines between biological replicates (n=5) and technical replicates (n=3). By definition, biological replicates are different organisms that are measured and are essential for statistical analysis as these replicates are independent from each other. Technical replicates are dependent on each other as they come from the same biological samples and are repeated measurements. By considering the latter as statistical relevant, you are biasing yourself to consider a fluke as a biological phenomenon.

 

Conclusions:
Based on the methods that were used in this paper, Shaw et al. went too far in declaring that aluminum adjuvants cause autism. But there are six other key points that limit what conclusions can be drawn from this paper:
1) They selected genes based on old literature and ignored newer publications.
2) The method for PCR quantification is imprecise and cannot be used as an absolute quantification of expression of the selected genes.
3) They used inappropriate statistical tests that are more prone to giving significant results which is possibly why they were selected.
4) Their dosing regime for the mice makes assumptions on the development of mice that are not correct.
5) They gave the mice far more aluminum sooner than the vaccine schedule exposes children to.
6) There are irregularities in both the semi-quantitative RT-PCR and Western blot data that strongly suggests that these images were fabricated. This is probably the most damning thing about the paper. If the data were manipulated and images fabricated, then the paper needs to be retracted and UBC needs to do an investigation into research misconduct by the Shaw lab.

Maybe there’s a benign explanation for the irregularities that we’ve observed, but until these concerns are addressed this paper cannot be trusted.

[BBB/Junk Sciences] Polysorbate 80 and the BBB or how to put anti-vaxxers into a blowing cognitive dissonance

Here we go again, anti-vaxxers keeping on moving the goalpost to fit their belief instead to change to adjust it to the facts. First it was mercury, then it was formaldehyde, then aluminum, today the “ingredient du jour” is polysorbate 80 and tomorrow they will blame it to PBS saline solution.

The latest fad as I have seen is to blame polysorbate 80 as a source of “vaccine-injury” with the bold claim that it breaks down the blood-brain barrier (BBB). Lets put the fact straight and debunk this one for all. But what is even better is the “what if” counter-argument. What if polysorbate 80 was indeed a good ingredient? I will come to that later.

Polysorbate (aka Tween 80) is a amphiphile compound   as you can see the molecular structure below (source Wikipedia):
1200px-polysorbate_80

You can see the structure made of a lipophilic (loves fat) tail and a series of hydrophilic  (loves water) tails, loaded with oxygen and hydroxyl groups. This is a typical structure of a detergent: one side will mix well with water, the other will mix very well with fat and oils. The result? You can form microspheres that can dissolve well in water and dissolve fat into water. This is how a detergent works, it helps to breakdown fats into small spheres and dissolve them in the drain water.
Polysorbate 80, due to this property, is very good to dissolve drugs and medicines that under normal condition would barely dissolve into biological fluids. This is why we have it in vaccines, but we also have it in medicines. Thats the job of biopharmaceutics: finding formulations to dissolve drugs into the body and allow them to reach a concentration high enough to display their therapeutic activity.

The use of polysorbate 80 in drug delivery of anti-cancerous drug is probably the first and foremost main driving factor on investigating its effect on the BBB. Brain tumors (primary and metastatic alike) are up until now one of the most dreaded and deadliest form of cancer. For instance, the average expected lifespan upon diagnosis of a grade IV glioma (aka glioblastoma multiforme) is grim: 18-months, with less than 5% survival after 5 years. The major issue is being able to deliver drugs and chemotherapy across the BBB. As reported by Pr. William Partridge (UCLA) the BBB remains the bottleneck in drug development for the treating neurological disorders (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC539316/?fref=gc&dti=873247819461536)

The first report of the investigation of polysorbate 80 on the BBB is probably by Spiegelman and colleagues in 1984 (http://thejns.org/doi/pdf/10.3171/jns.1984.61.4.0674), investigating the effect of the solvent used in etoposide solution for treating cancer. According to their  result, they noted a statistical difference in the BBB permeability  (using Evans Blue and 99mTc as tracers) following the injection of 1.125ml/kg. According to their paper, 5mL solution contained 400mg of polysorbate 80 or a concentration of 80mg/mL. Based on this, we can assume that the BBB effect was observed for a dose of 90mg/kg. Thats a very huge dose.
If we go back to the manure anti-vaxxers say, the amount injected via vaccines is enough to cause a barrier opening. According to John Hopkins University Institute of Vaccine Safety (http://www.vaccinesafety.edu/components-DTaP.htm), the expected concentration of polysorbate is lesser or equal to 100mcg or micrograms. Thats 0.1mg per dose. If we assume such dose is injected to a newborn (average weight ~3 kgs), then the amount injected is about 0.033mg/kg. Thats 2700 times less than what has been reported to induce a BBB disruption. Also you have to factor the bioavailability of polysorbate (that is 100% upon IV route) making this number a very optimistic number.
Now, the interesting twist about polsyorbate 80 is its use to enhance some drug carriers and its widely used for finding novel formulation to enhance the delivery of anti-cancerous drugs across the BBB. You can find a list of publications on Pubmed about that aspect (https://www.ncbi.nlm.nih.gov/pubmed/?term=polysorbate+80+blood-brain+barrier). What if polysorbate 80 not only will not injure your brain, but actually may help deliver drugs to help your brain fight disease?

 

Keep in mind that polysorbate 80 is good at dissolving lipid in water solutions but it is not good to let charged molecules accross the BBB, just in case someone comes with the claims that it conjugates with aluminum. Thats some high-school chemistry level.

 

 

[Sciences/Neurosciences] International/European Society of Neurochemistry (ISN-ESN) Meeting 2017 – Paris (France). A summary

Today is the last day of the ISN-ESN biannual meeting taking place this year in Paris (France). The venue was taking place at the Palais Des Congres near Porte Maillot (right on the periphery of Paris). I thought it was a great place for the venue, first by its location (excentered from inner Paris, giving more affordable options for lodging), but also by hosting a shopping mall in the basement level (with affordable lunch options including a Galeries Gourmandes and a Paul Patisserie). Another special perk was the presence of complimentary coffee during the morning and afternoon session breaks.
The presence of vendors was fairly minimal but the welcome package provided by ISN was fairly nice. It included:

A mug of your choice (I took molecular basis of disease of course),
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And a set of 10 RATP tickets allowing you to wander inside Paris when the urge of sightseeing overcomes your thirst of science:
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This is a first time I am attending a ISN meeting, following the acceptance of my paper by the Journal of Neurochemistry. It is a small conference (maybe 500 attendees, this is a high estimate) but it does not mean the quality of science was small too. The conference was taking place on four full days (21-24 August) with morning plenary lectures including a senior keynote speaker and a junior keynote speaker, followed by two breakout sessions (one morning, one afternoon) covering different topics including development, gene and genetics, synapses and neurotransmission, molecular basis of diseases, neurodegeneration or cell energetics.

One of the nice thing was this huge crowd-sourced timeline in which attendees could fill it with stickers indicating their first publication in Journal of Neurochemistry, their first enrollment in one of the different societies.
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Interesting fact, the first ISN took place in Strasbourg (my hometown) in 1967 and 50 years later, one attendee was still attending the same ISN meeting! Hail to the elders!

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Senior keynote lectures were very instructive including a keynote lecture by Pr. Tamas Horvath (Yale University, USA) on the selective depletion of Agouti-gene related protein neurons and its impact on feeding behavior. These neurons are present are very few numbers (3000-6000) but play important role in feeding. The take home message? Resistance (to chocolate cake) is futile!
Another interesting keynote lecture was from Pr. Yoshi Hirabayashi (RIKEN, Japan) on glycolipids, their known impact on Gaucher’s disease and more interestingly their contribution into Parkinson’s disease. One slide to highlight the complexity of the topic is this one summarizing the different types of glycosphingolipids present in mammalian brains. Yes, this will be part of your next biochemistry quiz.

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Finally, todays senior keynote lecture by Pr. Giovanna Malluci (Cambridge University, UK) on the importance of unfolded-protein response stress and its contribution to several neurodegenerative diseases (in particular on prion diseases), with the importance of elongation factor 2E (elF2E) as a rescue pathway in neurodegeneration. More interestingly was the description in the second part of the cold-shock response and the contribution of RBM3 as a neuroprotective agent. I was aware of the importance of cold in hypoxia tolerance (drowning in frigid water decreases the gravity of brain injury inflicted by hypoxia compared to warm water) but I was always skeptical on the use of cooling blanket on stroke patients to cool their body down. It seems there is some vestigial molecular pathways initially used in evolutionary adaptation in hibernating animals that maybe still present in non-hibernating animals via RBM3. It would be interesting to see how this pathway cross-talk with the HIF-1 pathway.

Other concurrent sessions were interesting including one on transporters in the CNS (especially one on glutathione handling in astrocytes through MRPs), the importance of TDP-43 in ALS and other diseases, SIRT6 and its importance in neurodegenerative (including the possible involvement of Wnt and HIF-1 pathways), mitochondria bioenergetics and the discussion and debate on mitochondria movements in astrocytes and neurons (with even the discussion on Eng Lo’s paper on mitochondria transfer following stroke injury) or novel aspects of neural development and neurogenesis.

The poster sessions were well designed with the exception of the manned poster sessions. Poster sessions were initially scheduled between the morning and afternoon concurrent sessions but the presence of poster authors was requested only during the evening socials after 6:00PM. By principle, I am done with science by 5:00PM if I have been bathing in since the morning, so I ended up seeing a lot of “empty” posters and wished I could have a chance to chat and talk to the poster authors. I think this is were SfN poster session is more adapted: you have half-day to showcase your poster and have a time period (2 hours) to stand next your poster. Maybe the organizers could take this into account for ISN2019 taking place in Montreal.

Finally, the ISN see themselves through the Neurochemistry consortium as funny people and hell yeah they know how to bring fun with a complimentary funny photomaton booth. Another opportunity for me to let the weird and funny coming out of me 🙂

See you in probably the ASN meeting 2018 in Riverside, CA and ISN2019 in Montreal (Quebec, Canada)!

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