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In RuBisCo and haemoglobin both can bind to CO2 as well as O2. Do these proteins have any structural similarity?
Try reading about these proteins. Apart from wikipedia, you can check out pfam which classifies proteins into families. Globins and RuBisCO are very different protein families. Perhaps the only similarity is that both these are made up of amino acids!
Evolutionary trends in RuBisCO kinetics and their co-evolution with CO2 concentrating mechanisms
RuBisCO-catalyzed CO2 fixation is the main source of organic carbon in the biosphere. This enzyme is present in all domains of life in different forms (III, II, and I) and its origin goes back to 3500 Mya, when the atmosphere was anoxygenic. However, the RuBisCO active site also catalyzes oxygenation of ribulose 1,5-bisphosphate, therefore, the development of oxygenic photosynthesis and the subsequent oxygen-rich atmosphere promoted the appearance of CO2 concentrating mechanisms (CCMs) and/or the evolution of a more CO2-specific RuBisCO enzyme. The wide variability in RuBisCO kinetic traits of extant organisms reveals a history of adaptation to the prevailing CO2/O2 concentrations and the thermal environment throughout evolution. Notable differences in the kinetic parameters are found among the different forms of RuBisCO, but the differences are also associated with the presence and type of CCMs within each form, indicative of co-evolution of RuBisCO and CCMs. Trade-offs between RuBisCO kinetic traits vary among the RuBisCO forms and also among phylogenetic groups within the same form. These results suggest that different biochemical and structural constraints have operated on each type of RuBisCO during evolution, probably reflecting different environmental selective pressures. In a similar way, variations in carbon isotopic fractionation of the enzyme point to significant differences in its relationship to the CO2 specificity among different RuBisCO forms. A deeper knowledge of the natural variability of RuBisCO catalytic traits and the chemical mechanism of RuBisCO carboxylation and oxygenation reactions raises the possibility of finding unrevealed landscapes in RuBisCO evolution.
Xerophytes, such as cacti and most succulents, also use
phosphoenolpyruvate (PEP) carboxylase to capture carbon dioxide in a process called crassulacean acid metabolism (CAM). In contrast to C4 metabolism, which physically separates the CO2 fixation to PEP from the Calvin cycle, CAM temporally separates these two processes.
CAM plants have a different leaf anatomy from C3 plants, and fix the CO2 at night, when their stomata are open. CAM plants store the CO2 mostly in the form of malic acid via carboxylation of phosphoenolpyruvate to oxaloacetate, which is then reduced to malate. Decarboxylation of malate during the day releases CO2 inside the leaves, thus allowing carbon fixation to 3-phosphoglycerate by RuBisCO. Sixteen thousand species of plants use CAM.
Figure (PageIndex<1>): Cross section of agave, a CAM plant: Cross section of a CAM (crassulacean acid metabolism) plant, specifically of an agave leaf. Vascular bundles shown. Drawing based on microscopic images courtesy of Cambridge University Plant Sciences Department.
The family of `novel' vertebrate globins
In addition to Ngb, other `novel' vertebrate globins have been found, which differ in expression patterns and evolutionary history(Fig. 1): cytoglobin [Cygb(Burmester et al., 2002 Trent and Hargrove, 2002)],globin E [GbE (Kugelstadt et al.,2004)], globin X [GbX (Roesner et al., 2005)] and globin Y [GbY(Fuchs et al., 2006)]. Cygb resides in fibroblast-like cells and in distinct populations of neurons. Cygb is related to Mb from which it diverged early in the evolution of vertebrates(Burmester et al., 2002)(Fig. 1). It is unlikely that Cygb functions in O2 supply to the respiratory chain, but it may be involved in detoxification of reactive oxygen or nitrogen species (ROS/RNS),or in oxygen supply to particular enzymatic reactions(Hankeln et al., 2005). While Ngb and Cygb are presumably present in all vertebrates, three other globin types only occur in some taxa. GbX is weakly expressed in amphibia and fish and its function is not yet known (Fuchs et al., 2006 Roesner et al.,2005). In the eye of birds, another globin type (GbE) has been detected (Kugelstadt et al.,2004). GbY has only been identified so far in Xenopus(Fuchs et al., 2006). Both,GbE and GbY are distant relatives of Cygb or Mb, but little is known about ligand binding characteristics or functions.
Phylogenetic history of vertebrate globins. The simplified tree has been combined from different sources (Burmester et al., 2002 Burmester et al.,2000 Kugelstadt et al.,2004 Roesner et al.,2005). Globins with clear respiratory function (i.e. Mb and Hb)are shaded in grey. Note that haemoglobins from Agnatha and Gnathostomata(haemoglobin α and β) are not monophyletic.
Phylogenetic history of vertebrate globins. The simplified tree has been combined from different sources (Burmester et al., 2002 Burmester et al.,2000 Kugelstadt et al.,2004 Roesner et al.,2005). Globins with clear respiratory function (i.e. Mb and Hb)are shaded in grey. Note that haemoglobins from Agnatha and Gnathostomata(haemoglobin α and β) are not monophyletic.
Covid-19: Debunking the Hemoglobin Story
In recent days, I’ve had a number of people ask me for my thoughts on a now-deleted Medium blog post entitled “Covid-19 had us all fooled, but now we might have finally found its secret.” It seems that, even following its deletion, this post has become widely shared in an archived form, largely by people who seem to entirely accept its premise. That premise, to be very brief, is essentially that the SARS-CoV-2 virus harms patients entirely through its interactions with the oxygen transport protein hemoglobin (Hb). A Google search for the title will still turn up the post, should you wish to read (or re-read) it.
A bit about me, and why people have sent me this blog post: in December 2019, I completed my MD degree at the University of Pittsburgh through the Medical Scientist Training Program (MD/PhD program). As part of that same program, I spent 4 years completing a PhD in Bioengineering the focus of my dissertation was the molecular biology, biochemistry, and physiology of mammalian heme globins. As a result, I’ve spent the last 7+ years at the intersection of clinical medicine and heme globin research and felt compelled to offer my perspective on this blog post. I’ve been assisted in writing this piece by Drs. Anthony DeMartino, PhD and Matt Dent, PhD, both postdoctoral scholars in the lab where I completed my PhD and both ten times better chemists than I could ever hope to be.
But back to the post: the Medium blog post in question simultaneously puts forth two related narratives, one “scientific” (or at least presented to give that appearance) and one clinical. Both are told with an overriding tone of authority and certainty unfortunately, both are also almost entirely incorrect in their overall conclusions and the specific details used to support those conclusions. As is so often the case, refuting this sort of misinformation requires a good deal more effort (and words) than propagating it, but we have done our best to address everything.
The Purportedly “Scientific” Narrative
Before getting into the details, I want to take a brief aside to describe hemoglobin. A single hemoglobin protein consists of two parts: heme (which itself is made up of a small chemical ring called a porphyrin + an iron atom in the center), and the globin, a large protein that holds the heme. The hemoglobin molecule in our red blood cells is actually comprised of four hemes and their four respective proteins (two alpha proteins and two beta) that are linked together to form a tetramer. In each of these chains, the heme is surrounded by its respective protein, which forms a small space referred to as the “heme pocket” around the heme. This pocket is just large enough to accommodate oxygen, carbon monoxide, and other small molecules that bind to the heme iron.
The blog post’s “scientific” narrative begins with the SARS-CoV-2 virus entering red blood cells (RBCs). Once inside the RBCs, the post states that the virus rapidly removes the iron from RBC hemoglobin molecules, leading to 1) depletion of functional hemoglobin (with the virus bound to its porphyrin ring) and 2) accumulation of toxic iron in the bloodstream. All of the clinical manifestations of Covid-19 are subsequently attributed to this process, despite the fact that there’s effectively no evidence to support such a mechanism of viral entry into RBCs and interaction with hemoglobin. Alarmingly, the blog post relies on a series of assumptions that have little to no support within the current scientific literature.
First, it is unclear that the virus enters red blood cells at all. Reviewing the currently published literature, I am unable to find any evidence for significant SARS-CoV-2 entry into red blood cells. While it is possible that interactions between the virus and RBCs may have been overlooked (the majority of research has understandably focused on lung disease), there is currently no evidence to suggest that red blood cells are a significant site of virus localization or replication. If the hypothesis is that most of this virus’s toxic effect arises from interactions with Hb, documenting viral entry into RBCs would be an important first step.
That said, we do have some idea of where this virus is going. For example, one study examined lung tissue samples from a patient who died of Covid-19 and found results consistent with diffuse alveolar damage (damage to the small air sacs in the lungs where gas exchange occurs) . The same study found that the virus itself localized primarily to the epithelial cells lining those same alveoli. While RBCs appear to have been washed out before the tissue samples were examined (leaving empty blood vessels), the blood vessels themselves, as well as the tissue between the air sacs, showed little to no virus. Overall, the study suggests that the virus, and the resultant damage, are found primarily in the lung alveoli.
The blog post author presumes that the virus does enter RBCs, and that viral “glycoproteins bond to the heme, and in doing so that special and toxic oxidative iron ion is ‘disassociated’ (released).” This spurious claim, for which the blog post author provides no evidence, seems to derive from a misinterpretation of a recent preprint of a paper in ChemRxiv. This pre-print manuscript proposes a possible mechanism for the virus to “attack” (a term they never define) hemoglobin and release the heme from the protein . While the blog post author does not cite this work (or any other work, for that matter), the conclusions and language are similar enough that it seems very likely the scientific paper inspired the blog post.
On a close reading, the ChemRxiv paper is itself seriously flawed, and provides nothing that I or my colleagues consider meaningful evidence of a mechanism by which SARS-CoV-2 could “attack” hemoglobin. I do plan to work on a second piece further discussing the problems with this paper, but for now, here is a summary of that work: the authors claim to provide evidence that certain viral proteins can bind to isolated porphyrin (without the iron and not bound to any protein). They also argue that the virus may somehow force the heme out of the protein, and subsequently the iron out of the heme, to allow this sort of binding. This is all based on rather rudimentary analysis, relying solely on protein sequence similarity and questionable modeling of molecular docking. Notably, the work was entirely performed in silico (via computer models), which is usually an initial screening step that has to be verified with in vitro (experimental, e.g., in a test tube or petri dish) data. The authors themselves state in their abstract that “[t]his paper is only for academic discussion, the correctness needs to be confirmed by other laboratories”. Aside from this introductory disclaimer, the authors do a poor job of qualifying their results and emphasizing the highly preliminary nature of their work. It is easy to see how a reader without a healthy dose of scientific skepticism could overinterpret the results given the strong language used throughout the manuscript.
Nevertheless, the Medium blog post seems to take this questionable work as hard truth and proceeds to extend the conclusion several steps further, claiming that the virus will go right into the heme pocket and replace the intact heme iron, all while the porphyrin remains bound to the protein. Beyond the questionable evidence for virus binding the porphyrin at all, the issue here is that the heme/porphyrin is still in the heme pocket, a space barely large enough for two-atom molecules like oxygen (O2). Despite that, the blog post author seems to believe the virus (which is larger than the entire hemoglobin protein) will be able to enter the pocket, kick out the iron, and bind the porphyrin while leaving the porphyrin and protein otherwise totally intact. To put it charitably, this would be an entirely novel and seemingly impossible sort of chemistry, and there is absolutely no scientific evidence that supports such a possibility. It’s this seemingly impossible interaction that forms the foundation of the blog post’s entire argument, and so the remainder of the conclusions drawn by the blogger simply don’t carry any weight.
The clinical story
From here, using this faulty scientific narrative as a basis, the author creates an equally faulty narrative of the clinical progression of the disease. The failure of the scientific narrative largely invalidates the subsequent clinical narrative, which is almost entirely based on that faulty science. Thus, rather than pick apart the entire clinical model, I’m going to highlight some key points that I want to refute specifically. First, while this narrative is a bit more difficult to follow, I will attempt to summarize it herein.
The blog post suggests (paraphrasing here outside of direct quotes): As the patient’s Hb loses iron, that patient will desaturate (lose oxygen from their hemoglobin). This desaturation has nothing to do with lung dysfunction as “there is no ‘pneumonia’ nor ARDS” and “the patient’s lungs aren’t ‘tiring out’, they’re pumping just fine. The red blood cells just can’t carry [oxygen], end of story”. The free iron that has been released overwhelms the lung’s defense mechanisms against this toxic free iron, leading to bilateral lung damage, which is held to be significant by the author because “Pneumonia rarely ever does that [causes damage in both lungs], but COVID-19 does… EVERY. SINGLE. TIME.”
Again, this probably sounds like a compelling, reasonable series of events to a lay person. In reality, it is essentially nonsense built upon a deeply flawed understanding of physiology and pathophysiology. Some key points, and my responses:
Blog post says:Patients desaturate as their hemoglobin loses iron
Reality: Even if the virus were to eject the iron from hemoglobin (which it almost certainly does not), it would not likely result in a measurable desaturation. Saturation is most commonly measured via pulse oximetry (pulseox), which uses light to differentiate Hb with oxygen from Hb without oxygen. Both these forms of Hb, however, have the iron present, and most clinical pulse oximeters only work when these two forms — and only these two forms — of Hb are present . A novel form of Hb with the virus in place of the iron would absorb light very differently from either of these forms, and such a protein (if it could exist) would almost certainly result in incomprehensible pulseox readings, not a desaturation.
Even ignoring these technical aspects, a far more likely explanation for a measured desaturation in Covid-19 patients would be inadequate oxygenation of the blood due to lung disease/damage (which we know is present). Indeed, we know that Covid-19 patients who are oxygenating poorly respond to supplemental oxygen, as the author seems to acknowledge when suggesting oxygen as a therapy. Improvement with more oxygen effectively rules out iron loss as a cause of this desaturation, as providing more oxygen will increase oxygen binding to normal Hb with intact iron but could not put iron back into Hb that had lost it.
Blog post says: Release of iron from Hb is the source of all observed pathology in Covid-19, including bilateral lung damage, which pneumonia “rarely ever” causes.
Reality: There’s simply no evidence that SARS-CoV-2 infection leads to the large-scale release of iron from Hb, or that such release would be sufficient to overwhelm the body’s numerous mechanisms for regulation of free iron. Even if it did, however, I’m unable to find evidence that pure iron overload (in the absence of other pathologies) leads to significant lung damage, much less the bilateral pneumonia-like pattern seen in many Covid-19 patients . In contrast, bilateral lung damage is actually a fairly common manifestation of pneumonia caused by viral infections .
Blog post says: “There is no ‘pneumonia’ nor ARDS. At least not the ARDS with established treatment protocols and procedures we’re familiar with.”
Reality: Both are clearly present. The clinical picture, despite what the author might think, is generally consistent with viral pneumonia, and progression to ARDS has been well-documented. One study in China found that, out of 201 patients with confirmed Covid-19, roughly 42% developed a clinical picture consistent with ARDS . The mortality rate among these patients was over 52%, while there were no deaths among those that did not develop ARDS. The blog post may be somewhat correct about the resultant ARDS being atypical. There is a letter out of Northern Italy suggesting that ARDS arising from Covid-19 may not require or could even be harmed by high-pressure mechanical ventilation , but this same letter suggests that intubation and mechanical ventilation without high pressures should be prioritized for patients who are struggling to breathe, not avoided as suggested in the blog post.
Finally, and perhaps most troublingly, the author of the blog post, who has no medical background, suggests a number of therapies for their imagined mechanism of this disease.
Treatment 1: “Max oxygen”, or hyperbaric chamber with 100% O2 at multiple atmospheres of pressure
It’s unclear what the author thinks this would achieve. If their model of virus-induced hemoglobin dysfunction via iron loss is true (it isn’t, but if it was), the affected Hb absolutely CANNOT bind oxygen. Providing more oxygen, via a ventilator or a hyperbaric chamber, would not magically put the iron back in Hb. To take a generous interpretation, the author may be suggesting that free iron eventually causes lung damage, which subsequently prevents oxygen from getting into the blood, even though our current understanding is that this damage is in fact caused by the virus and our immune response. Regardless of the source of lung damage, however, intubation and mechanical ventilation remains the standard of care in critically ill patients with hypoxic respiratory failure, as even the report of atypical ARDS from Italy suggests .
EDIT, 04/13/2020: A reader, Dr. Merveldt-Guevara, brought to my attention that hyperbaric oxygen therapy (HBOT) likely would benefit patients with iron loss from Hb by allowing more oxygen to be dissolved directly in the blood without binding to hemoglobin. She is absolutely correct about this, and I want to thank her for setting me straight. While there remains no compelling reason to suspect such iron loss, HBOT is well-documented to increase the amount of oxygen that reaches the blood, and thus may have therapeutic potential for these patients even if their Hb remains entirely normal. I have reached out to some far more qualified colleagues for their opinions on this, and will update if I hear back.
Treatment 2: Blood transfusion with “normal hemoglobin”
The blog post is correct that a transfusion of donor red blood cells (or whole blood) would temporarily increase the oxygen carrying capacity of the blood. However, beyond the blog post’s unfounded assertions, I can find no case reports or any other data suggesting that profound anemia or loss of oxygen carrying capacity exacerbates the effects of Covid-19 in patients, and so there’s no reason to believe a transfusion of RBCs would result in clinical improvement.
Even if the author were correct, a red blood cell transfusion would likely do more harm than good after a brief initial improvement. For example, we know that some degree of hemolysis (RBC destruction) occurs during storage of blood and after transfusion, eventually leading to release of toxic byproducts such as free heme. Furthermore, if the core premise of the blog post is accepted, the transfused RBCs would also have their Hb attacked by the virus, negating any increase in oxygen carrying capacity and worsening the accumulation of iron in the blood. A transfusion, if we accept the author’s argument about hemoglobin and iron, amounts to throwing logs onto a raging fire, claiming you’re putting the fire out because those logs haven’t burned up yet, and then watching the fire grow bigger as it consumes those logs as well.
Just to clarify, there is some evidence in favor of a plasma transfusion from recovered Covid-19 patients, as the antibodies contained therein can augment the recipient’s immune function.The blog post, however, seems very dismissive of this therapy, suggesting it would be ineffective without a simultaneous transfusion of red blood cells despite the lack of any evidence to support this claim.
Treatment 3: Hydroxychloroquine
The author of the blog post also recommends early treatment with hydroxychloroquine (HCQ), which in their words is “…suspected to bind to DNA and interfere with the ability to work magic on hemoglobin”. A preface: I am not making a broader claim here about the effectiveness of HCQ in Covid-19, which remains under investigation. But this author’s specific arguments about HCQ do not stand up to scrutiny.
For example, I’m not sure where the author found this “suspected” mechanism of action. The true mechanism of action of HCQ and other quinoline-based anti-malaria drugs has been studied extensively. It is known that these drugs prevent the malaria parasite from sequestering free heme (the result of hemoglobin consumption) in food vacuoles, where the toxic heme molecules are normally converted to relatively harmless, crystalline deposits of hemozoin . Importantly, HCQ does not prevent the release of toxic iron from heme, nor does the drug prevent an interaction with hemoglobin (the protein component of which is still consumed by the parasite). Instead, HCQ disrupts formation of the inert hemozoin crystals, thereby allowing the accumulation of toxic heme (porphyrin and iron together), which causes oxidative damage that ultimately kills the parasite.
Also, the virus is a protein envelope surrounding a length of coding RNA (it’s an RNA virus) and contains literally not a single piece of DNA anywhere, so a DNA binding mechanism would have no relevance here. Even beyond this virus, I cannot find anything suggesting DNA binding is a significant mediator of HCQ’s effects on malaria, autoimmunity, or any other disease state. Its primary effect is thought to occur in lysosomes/food vacuoles, where it prevents acidification as a weak base and may otherwise inhibit hemozoin formation (in malaria) and antigen presentation/immune activation (in autoimmune disease) [9, 10]. As a final thought, HCQ being a weak base means that the author’s statement that it “lowers the pH which can interfere with the replication of the virus” is certainly incorrect, as it is a base and thus would prevent lowering of pH (acidification).
The above discussion is by no means an exhaustive list of the blog post’s incorrect statements or conclusions. Nonetheless, I hope it has been sufficient to make clear that the blog post, and even the scientific article that likely inspired it, should not be viewed as a source of any meaningful insight into SARS-CoV-2, how it affects patients, or how the virus might be treated. What I still don’t know is why the blog post author, under a pseudonym, chose to present such an incorrect description of this disease and the underlying pathophysiology with such confidence. That they would go so far as to suggest treatments for the disease despite a lack of any medical training, and in virtually the same paragraph condemn “armchair pseudo-physicians” who push incorrect information, is truly mind-boggling. Tragically, whether it arises from genuine malice, unfounded arrogance, or just simple ignorance, this sort of misinformation about a deadly pandemic can genuinely put lives at risk, and it’s up to those of us who work in this field to fight back against it in whatever way we can.
Finally, while I’ve been very critical of this blog post author, I do have to give them credit for making one very insightful comment, right near the end, that I want to single out for praise:
“Whatever, I don’t know the full breadth and scope because I’m not a physician.”
On this, at least, we can agree.
1. Zhang, H., et al., Histopathologic Changes and SARS-CoV-2 Immunostaining in the Lung of a Patient With COVID-19. Ann Intern Med, 2020.
2. Wenzhong, L. and L. Hualan, COVID-19: Attacks the 1-Beta Chain of Hemoglobin and Captures the Porphyrin to Inhibit Human Heme Metabolism. ChemRxiv, 2020.
3. Jubran, A., Pulse oximetry. Crit Care, 2015. 19: p. 272.
4. Ganz, T., Does Pathological Iron Overload Impair the Function of Human Lungs? EBioMedicine, 2017. 20: p. 13–14.
5. Galvan, J.M., O. Rajas, and J. Aspa, Review of Non-Bacterial Infections in Respiratory Medicine: Viral Pneumonia. Arch Bronconeumol, 2015. 51(11): p. 590–7.
6. Wu, C., et al., Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med, 2020.
7. Gattinoni, L., et al., Covid-19 Does Not Lead to a “Typical” Acute Respiratory Distress Syndrome. Am J Respir Crit Care Med, 2020.
8. Coronado, L.M., C.T. Nadovich, and C. Spadafora, Malarial hemozoin: from target to tool. Biochim Biophys Acta, 2014. 1840(6): p. 2032–41.
9. Fox, R.I., Mechanism of action of hydroxychloroquine as an antirheumatic drug. Semin Arthritis Rheum, 1993. 23(2 Suppl 1): p. 82–91.
10. Liu, J., et al., Hydroxychloroquine, a less toxic derivative of chloroquine, is effective in inhibiting SARS-CoV-2 infection in vitro. Cell Discov, 2020. 6: p. 16.
Extended Data Fig. 1 Reconstruction of ancestral haemoglobin and precursors.
a, Phylogeny of Hb and related globins. Node supports are shown as approximate likelihood ratio statistic 59,60 . The number of sequences in each group is shown in parentheses. Ancestral sequences reconstructed in this study are shown as coloured circles. Arrow, branch swap that differentiates this phylogeny from the unconstrained ML phylogeny, which requires additional gene gains and losses. The tree is rooted on neuroglobin and globin X, paralogues that duplicated before the divergence of deuterostomes and protostomes 61 . Inset, pairwise sequence identities among extant (human, Hsa) and reconstructed ancestral globins. b, Distribution across sites of the posterior probabilities (PP) of maximum a posteriori states for reconstructed ancestral proteins. c, Thermal stability of ancestral globins. Points, fraction of secondary structure lost as temperature increases in Ancα/β (purple), Ancα + Ancβ (blue) and AncMH (black), measured by circular dichroism spectroscopy at 222 nm, relative to signal at 23 °C. Tm and its s.e. were estimated by nonlinear regression the best-fit curve (lines) are shown. Each point is the mean of four measurements. d, Native mass spectra of globin Y from elephant shark (top, Callorhinchus milii) and African clawed frog (bottom, X. laevis) at 30 μM. Charge states of haem-bound monomer shown. Asterisk, cleavage products. Spectra were collected once. e, Sequence alignment of reconstructed ancestral globins. Dots, states identical to Ancα/β yellow, IF2 sites orange, IF1 sites h, sites 4 Å away from the haem a, sites that link the haem-coordinated proximal histidine (H95) to IF2. f, Statistical test of cooperativity of oxygen binding for ancestral proteins and mutants. An F-test was used to compare the fit of a model in which the Hill coefficient (n) is a free parameter to a null model with no cooperativity (n = 1). Computed P value and degrees of freedom (df) are shown. N, number of concentrations measured. *P < 0.05. Data were pooled across replicate experiments for nonlinear regression.
Extended Data Fig. 2 Stoichiometric characterization of ancestral globin complexes.
a, Homology model of Ancα + Ancβ (template 1A3N) showing haem (tan spheres). Blue cartoon, Ancβ subunits red, Ancα. Helices and interfaces are labelled. Green, proximal histidine. b, SEC and multiangle light scattering of Ancα/β (90 μM) and Ancα + Ancβ (60 μM). Black, relative refractive index red, estimated molar mass. Dashed lines, Ancα/β solid lines, Ancα+Ancβ. Dashed horizontal lines, expected mass for dimers and tetramers. c, SEC of human Hb (dashed) and Ancα + Ancβ (solid) at 100 μM. Top inset, SDS–PAGE of these complexes, with bands corresponding to α- and β-subunits. Bottom inset, masses estimated by denaturing MS of Ancα + Ancβ, compared to expected masses based on primary sequence. d, SEC of Ancα/β across a series of concentrations. Dashed vertical lines, elution peak volumes of human haemoglobin tetramer and myoglobin monomer. e, Tandem MS of the heterotetrameric peak in the Ancα + Ancβ nMS (indicated in Fig. 1b). Ejected monomer and trimer charge series and the subunits they contain are shown. Pink, Ancα blue, Ancβ. f, nMS of Ancα + Ancβ and Ancα/β at 4 μM and 100 μM. Charge series and fitted stoichiometries are indicated. *Unhaemed apo form. g, Monomer–dimer association by Ancα/β. Abundances of monomers and dimers were characterized using nMS across a range of concentrations. Circles, fraction of all subunits that were assembled into dimers as a function of the concentration of subunits in all states. Nonlinear regression (line) was used to estimate the dissociation constant (Kd, with s.e.). h, SEC of Ancα/β at high concentrations (purple and grey lines). Black curves show SEC traces of human Hb and myoglobin for comparison. i, nMS of human Hb at 50 μM. j, SEC of AncMH (cyan) at a high concentration. SEC traces of human Hb and myoglobin (black) are shown for reference. Dashed line, Ancα/β dimer elution peak volume (see f). k, Alternative estimation of affinity of dimer–tetramer association by nMS. For human Hb (green) and Ancα/β14 + Ancα (orange), the fraction of heterodimers incorporated into heterotetramers includes both haem-deficient and holo-heterodimers. For Ancα + Ancβ (red), caesium iodide adduct was included. Compare to Figs. 1d and 3d. Kd values (with s.e.) were estimated by nonlinear regression (lines). All concentrations are expressed in terms of monomer. All nMS and SEC experiments were performed once at each concentration.
Extended Data Fig. 3 Stoichiometric analysis of Ancα, Ancβ, and AncMH.
a, SEC of Ancα at 75 μM. b, nMS spectrum (top, at 20 μM) and SEC–MALS (bottom) of Ancβ. Blue, UV absorption red, molar mass estimated by light scattering. c, Colorimetric haemoglobin concentration assay. Absorbance spectra before (black) and after (red) adding 150 μl Triton/NaOH reagent to 50 μl purified Ancα/β. In the presence of reagent, globins absorb at 400 nm. d, SEC of crude cell lysate after expression of AncMH (purple) and Ancα/β (black). Dashed lines, expected elution volumes for monomer (human myoglobin) and dimer (Ancα/β). e, Colorimetric haemoglobin concentration assay on collected SEC fractions of crude lysate containing AncMH (purple) and Ancα/β (black). f, nMS of His-tagged AncMH at 70 μM, with monomer charge series indicated. *Cleavage product. Green, apo. Fractional occupancy of the monomeric form is shown. All experiments were performed once.
Extended Data Fig. 4 Biochemical inferences about ancestral Hbs are robust to uncertainty in sequence reconstructions.
a–e, Maximum parsimony inferences of ancestral stoichiometry and interface losses or gains based on the distribution of stoichiometries among extant globins. a, Hbs in all extant lineages of jawed vertebrates are heterotetramers, supporting the inference that AncHb was heterotetrameric. Stoichiometries from representative species’ Hbs are shown with PDB IDs. b–e, Each panel shows a hypothetical set of ancestral stoichiometries, plotted on the phylogeny of extant Hb subunits and closely related globins, with the minimal number of changes required by each scenario. b, The most parsimonious reconstruction is that Ancα/β was a homodimer and AncMH was a monomer. c, For Ancα/β to have been a tetramer, early gain and subsequent loss of IF2 in Hbα would be required. d, For Ancα/β to have been a monomer, IF1 would have to have been independently gained in Hbα and Hbβ. e, For AncMH to have been a dimer, IF1 would have to have been lost in lineages leading to the monomers myoglobin (Mb) and globin E (GbE) 12,13 . The dimeric globins most closely related to Hb—agnathan ‘haemoglobin’ (aHb) and cyotoglobin (Cyg)—use interfaces that are structurally distinct from those in Hb 15,16 , indicating independent acquisition. f–j, Alternative reconstructions of Ancα/β are biochemically similar to the ML reconstruction. f, Alternative ancestral versions of Ancα/β were constructed, each containing the the ML state at every unambiguously reconstructed site and the second most likely state at all ambiguously reconstructed sites, using different thresholds of ambiguity. For each alternative reconstruction, the table shows the threshold posterior probability (PP) used to define an ambiguous site, as well as the fold-difference in total PP of the entire sequence and the number of sites that differ from the ML reconstruction. g, SEC at 75 μM of ML reconstruction of Ancα/β and AltAll reconstructions, which contain all plausible alternative states with PP above a threshold. Dashed lines show elution peak volumes for the dimeric ML α/β and monomeric human myoglobin. Constructs that elute between the expected volumes for dimer and monomer indicate dimers that partially dissociate during the run. None tetramerize all form predominantly dimers, except AltAll(PP >0.2), which is
62,000 times less probable than ML, which is mostly monomeric. UV traces were collected once for each construct. h, Oxygen binding curves of Ancα/β-AltAll(0.25), the dimeric AltAll with the lowest PP, with and without 2× IHP. Dissociation constant (P50, with s.e.) estimated by nonlinear regression is shown. Lack of cooperativity is indicated by the Hill coefficient (n50 =
1.0). Oxygen binding at each concentration was measured once. i, Alternate globin phylogeny that is more parsimonious than the ML topology with respect to gene duplications and synteny but has a lower likelihood given the sequence data. A version of Ancα/β (Ancα/β-AltPhy) was reconstructed on this phylogeny. j, SEC of Ancα/β-AltPhy. Dashed lines show expected elution volumes for various stoichiometric forms.
Extended Data Fig. 5 HDX-MS of Ancα/β.
a–c, Deuterium uptake measurements across time for three peptides. Left vertical axis, raw deuterium incorporation right vertical axis, deuterium incorporation divided by the total number of exchangeable amide hydrogens per peptide. Uptake curves for four concentrations of mutants IF1rev and P127R are shown. Each point shows mean ± s.e. of three replicate measurements. d–f, Raw MS spectra for the peptides shown in a–c, respectively, at 0.67 μM (red, at which the protein is monomeric), and 75 μM (purple, at which it is entirely dimeric: see Extended Data Fig. 2). The traces are slightly offset to allow visualization. One replicate at each incubation time is shown. g, Amino acids 99 to 111 contact IF1 (orange) or IF2 (yellow). The homology model of one chain of Ancα/β (cartoon and sticks) was aligned to the α-subunit of human Hb (PDB 1A3N) β-subunits are shown as surfaces. h, Normalized deuterium uptake difference (mean ± s.e. from three replicates), defined as the uptake difference between monomer and dimer divided by the uptake of the monomer, observed for peptides containing amino acids 99–111. Grey N-terminal residues do not contribute to uptake. Amino acid sequences are aligned and labelled (orange dots, IF1 yellow dots, IF2).
Extended Data Fig. 6 Statistical analysis of HDX-MS results for peptides containing interface residues.
a, Residues in human Hb (PDB 1A3N) that bury at least 50% of their surface area in either IF1 (orange) or IF2 (yellow) are shown as spheres. Red and pink, α-subunits blue, β-subunits. b, Homology models of Ancα/β dimer across IF1 (left) and IF2 (right). Two subunits of Ancα/β were computationally docked using HADDOCK using the α1/β1 interface (IF1, left) or α1/β2 interface (IF2, right) of human Hb (1A3N) as a template. c, Coverage of peptides produced by trypsinization of Ancα/β, assessed by MS. Orange and yellow, sites that bury surface area at IF1 and IF2 in the modelled dimeric structures, respectively. d, Classification of trypsin-produced peptides that contribute to IF1 or IF2. Each circle represents one peptide, plotted by average surface area per residue buried at each interface (total buried area divided by total number of residues). Dashed lines, cutoffs to classify peptides as contributing to IF1 (orange) or IF2 (yellow). e, f, Correlation between change in deuterium uptake and burial of surface area at IF1 or IF2. Each point is one of 47 peptides, plotted according to the normalized difference in deuterium uptake between concentrations at which monomer or dimer predominates (0.67 or 75 μM, normalized by uptake at 75 μM) and average buried surface area at IF1 or IF2. r, Pearson correlation coefficient. g, Permutation test to evaluate the difference in deuterium uptake at two time points by peptides containing IF1 versus all other peptides (orange), or IF2 versus all other peptides (yellow). To avoid non-independence, the experimental data were reduced to a set of nonoverlapping peptides by sampling without replacement. Peptides were categorized by whether they contained residues at IF1, IF2, or neither peptides that contributed to both IFs were excluded. For each interface, the mean uptake by peptides contributing to the interface was calculated, as was the mean uptake by peptides not in that category, and the difference in means was recorded. Peptide assignment to categories was then randomized, and the difference in mean uptake recorded this permutation process was repeated until all possible randomized assignment schemes for those peptides had been sampled once. P value, fraction of permuted assignment schemes with a difference in mean uptake between categories greater than or equal to that from the true scheme. This process was repeated for 1,000 nonoverlapping peptide sets the histogram shows the frequency of P values across these sets. Dashed line, P = 0.05.
Extended Data Fig. 7 Dissection of IF1 and IF2 by HDX-MS and mutagenesis.
a, b, Peptides with residues contributing to IF1 (a) or IF2 (b) that have the largest relative uptake difference upon dimerization are shown as purple tubes. Sticks, side chains predicted to contact the other subunit (orange surface, IF1 yellow surface, IF2). Side chains are coloured orange (IF1) or yellow (IF2) if they were substituted between AncMH and Ancα/β purple, unchanged in that interval green, site for targeted mutation P127 blue, Q40. Circled numbers show the rank of each peptide among all peptides for the normalized difference in deuterium uptake between monomer and dimer conditions. Homology models of the Ancα/β dimer using half-tetramers of human Hb (1A3N) are shown. In a, the dimer is modelled using the α1/β1 subunits in b, it is modelled on the α1/β2 subunits. c, d, nMS of interface mutants Q40R (at IF2) and P127R (at IF1) and for mutants IF1rev and IF2rev, in which interface residues in Ancα/β were reverted to their states in AncMH. All assays at 20 μM. Stoichiometries and charge states are labelled. Unhaemed peak series due to haem ejection during nMS is labelled. Spectra were collected once.
Extended Data Fig. 8 Alternative methods to normalize deuterium uptake.
a, Deuterium uptake difference between monomer (0.67 μM) and dimer (75 μM) at each time point was normalized by the length of each peptide. Peptides were categorized by the interface to which they contribute, as in Fig. 2c. *Interface peptide sets that show significantly increased uptake upon dilution when compared to peptides outside of that interface, as determined by a permutation test (see Extended Data Fig. 6). Each point shows the mean ± s.e. from three replicates. b, Permutation test to evaluate the difference in deuterium uptake at 60 min by peptides at each interface, when uptake difference per peptide is normalized by length (as described in Extended Data Fig. 6g). Orange, peptides with IF1-containing residues versus those with no IF1 residues. Yellow, IF2-containing peptides versus those with no IF2 residues. Dashed line, P = 0.05. c, d, Average deuterium uptake difference per residue (c) and uptake difference normalized by dimer uptake (d) for peptides at different time points. Orange, IF1 sites yellow, IF2 sites. Each rectangle shows the position of the peptide in the linear sequence and its uptake (mean of three replicates).
Extended Data Fig. 9 Effect of interface-disrupting mutations on Ancα/β.
a, b, SEC of mutants at IF2 (Q40R and IF2rev, which reverts all substitutions that occurred between AncMH and Ancα/β at IF2 sites) and at IF1 (P127R and IF1rev) at 100 μM. Dashed line, elution peak volume for Ancα/β. c, Circular dichroism spectra for P127R and Ancα/β, showing comparable helical structure. d, SEC from IF1 mutant V119A at 64 μM, compared to Ancα/β. e, nMS of Ancα/β, P127R and IF1rev at 10 μM. Stoichiometries and charges are shown. For a–d, nMS and SEC experiments were performed once per concentration. f, Normalized deuterium uptake by IF1-containing peptide 106–111 in HDX-MS of Ancα/β (75 μM) and mutants P127R (2 μM) and IF1rev (2 μM). Mean ± s.e. of three replicates. g, h, Difference between deuterium uptake by each peptide in Ancα/β and uptake by the same peptide in IF1 mutants P127R (g) and IF1rev (h), both at 2 μM, normalized by uptake in Ancα/β. Peptides are classified by interface category. Mean ± s.e. of three replicates. *Peptide sets that have significantly increased relative uptake (by permutation test, see Extended Data Fig. 6) compared to all other peptides (peptides containing both IF1 and IF2 residues excluded).
Extended Data Fig. 10 Genetic mechanisms of tetramer evolution.
a, c, SEC of Ancα/β containing sets of historical substitutions, when coexpressed and purified with Ancα. Dashed lines, elution volumes of known stoichiometries (4-mer, Ancα + Ancβ 2-mer, Ancα/β monomer, human myoglobin). Pie charts, relative proportions of α (pink) and α/β mutant (purple) subunits in fractions corresponding to each peak, as determined by high-resolution MS (Extended Data Fig. 11). b, nMS of tetrameric fraction in a at 20 μM (monomer concentration). *Apparent impurity. Together, a and b show that tetramers formed by coexpression of Ancα/β4 + Ancα incorporate virtually no α-subunits. Occupancy from this experiment is shown in Fig. 3b. d, f, nMS of unfractionated purified protein complexes of Ancα/β5 + α and Ancα/β14 + α at 20 μM. Charge series, stoichiometries indicated. Red arrows, peaks isolated for further characterization by tandem MS (Extended Data Fig. 11). e, Homology model of Ancα/β14 + α using Human Hb (1A3N) as template. Yellow and cyan sticks, Ancβ-lineage substitutions on IF2 orange sticks, Ancβ substitutions on IF1 yellow surface, αIF2 orange surface, αIF1 green, five β substitutions close to the interfaces included in Ancα/β14 + α. g, nMS of Ancα/β2 across concentrations. Charge series and stoichiometries indicated. h, Similarity between interfaces in Ancα/β14 + Ancα homology model and X-ray crystal structure of Human Hb. Venn diagrams show sites buried at IF1 and IF2 in one or both structures. Small circle, number of shared interface sites with identical amino acid state. i, Hydrogen-bond contacts at interfaces in Ancα/β14 + α homology model are also found in X-ray crystal structures of extant haemoglobins. Residue pairs hydrogen-bonded in Ancα/β14 + α IF2 (yellow) and IF1 (orange) are listed +also present in crystal structure *interactions discussed in the main text. PDB identifiers are shown. j, Oxygen equilibrium curves of Ancα/β14 + α, Ancα/β4, Ancα/β2. All experiments were performed once per concentration. Lines, best-fit curves by nonlinear regression.
Extended Data Fig. 11 Stoichiometric characterization of Ancα/β containing historical substitutions.
a, SEC of Ancα/β5. Circles show stoichiometry associated with each peak’s elution volume. b, High-resolution accuracy mass spectrometry (HRA-MS) of Ancα/β5 + α. Purple circles, peaks associated with Ancα/β5 pink, Ancα. c, HRA-MS of tetramer-containing SEC fraction of Ancα/β4 + Ancα. d, HRA-MS of monomer-containing SEC fraction of Ancα/β4 + Ancα. *922 m/z calibration reference standard. e, HRA-MS of Ancα/β9 + Ancα. f, nMS of tetramer-containing SEC fraction of Ancα/β4 + Ancα (Fig. 3a, b). Black circle, most abundant peak used for tandem MS. g, Tandem MS of isolated most-abundant peak in f, showing trimer-containing peaks. Charge states and number of haems (h) in the 8+ peak are indicated. h, Monomer-containing (M) peaks. i–k, nMS (i) and tandem MS (j, k) of Ancα/β14 + Ancα (Fig. 3f) as in f–h. l–n, nMS and tandem MS of Ancα/β5 + Ancα (Fig. 3c, d) as in f–h. Black dots in n mark charge species produced by cleavage of Ancα/β5. All experiments were performed once.
Difference Between Haemoglobin and Iron
It is always considered that iron is only found in the blood, especially the erythrocytes. Though majority of Iron does circulate in the blood as a part of the haemoglobin protein, iron and haemoglobin are two separate entities. Iron is also found in other parts of the body. Let us look at the difference between the two.
Haemoglobin – The oxygen carrier
Haemoglobin is the protein that imparts the red colour to the red blood cells circulating in the blood. Combination of haem protein and iron molecule forms the haemoglobin protein molecule. The main function of haemoglobin is to carry oxygen from the lungs to the rest of the body tissues and to bring back carbon di oxide from the rest of the body back to the lungs so as to remove it through exhalation.
Normal haemoglobin levels are around 12-14 gm% in women and 14-16 gm% in men. Haemoglobin levels lower than this indicates that the person is in a state of anaemia. The patient is advised to increase the haemoglobin levels by increasing the intake of iron and vitamin C through diet and supplements. Extremely severe cases of anaemia are treated through blood transfusion.
Anaemia usually occurs after heavy blood loss. Gastrointestinal blood loss is a common cause of anaemia in men and post-menopausal women. Genitourinary loss is the main cause of anaemia in women of reproductive age group.
Iron is an important macronutrient required by the human body. Around 70 percent of the iron is found in the blood as a part of the haemoglobin molecule. The total iron in the body is approximately 3.9g, of which 2.5g is a part of haemoglobin, 500mg is stored in the heart and 250 mg is stored in the liver. The bone marrow holds another 150mg of iron. Myoglobin or the enzymes present in the muscles contains 300mg of iron. The other enzymes present in the body make up the remaining 150 mg. The plasma also carries 5 mg of iron bounded to transferrin protein. This distribution of iron shows how important Iron is for various respiratory and metabolic activities. Apart from this it also plays a vital role in collagen synthesis and formation of neurotransmitters. Body immunity also depends on iron levels as it dictates the haemoglobin levels.
The iron stored in the body is in the form of ferritin that circulates in the blood. There is difference in iron stores in men and women with men having around 1000 mg of stores iron and women having 300 mg. Minimum daily requirement of iron is around 1.8 mg out of which only 10-30 percent is actually absorbed. To maximize the absorption of iron it is advised to increase the intake of Vitamin C. If a diet deficient in iron is consumed over a prolonged period (or prolonged starvation) it may lead to depletion of the iron stores in the body causing iron deficiency anemia.
To summarize Iron is an important constituent of our diet as it combines with many important molecules in the body and aids in cellular respiration and metabolism. Deficiency of iron can lower the Hemoglobin levels causing reduced oxygen transport to the body tissues. This puts the body in a state of fatigue and low energy levels.
The Interworkings of the Calvin Cycle
In plants, carbon dioxide (CO2) enters the chloroplast through the stomata and diffuses into the stroma of the chloroplast—the site of the Calvin cycle reactions where sugar is synthesized. The reactions are named after the scientist who discovered them, and reference the fact that the reactions function as a cycle. Others call it the Calvin-Benson cycle to include the name of another scientist involved in its discovery (Figure 5.14).
Figure 5.14 Light-dependent reactions harness energy from the sun to produce ATP and NADPH. These energy-carrying molecules travel into the stroma where the Calvin cycle reactions take place.
The Calvin cycle reactions (Figure 5.15) can be organized into three basic stages: fixation, reduction, and regeneration. In the stroma, in addition to CO2, two other chemicals are present to initiate the Calvin cycle: an enzyme abbreviated RuBisCO, and the molecule ribulose bisphosphate (RuBP). RuBP has five atoms of carbon and a phosphate group on each end.
RuBisCO catalyzes a reaction between CO2 and RuBP, which forms a six-carbon compound that is immediately converted into two three-carbon compounds. This process is called carbon fixation, because CO2 is “fixed” from its inorganic form into organic molecules.
ATP and NADPH use their stored energy to convert the three-carbon compound, 3-PGA, into another three-carbon compound called G3P. This type of reaction is called a reduction reaction, because it involves the gain of electrons. A reduction is the gain of an electron by an atom or molecule. The molecules of ADP and NAD + , resulting from the reduction reaction, return to the light-dependent reactions to be re-energized.
One of the G3P molecules leaves the Calvin cycle to contribute to the formation of the carbohydrate molecule, which is commonly glucose (C6H12O6). Because the carbohydrate molecule has six carbon atoms, it takes six turns of the Calvin cycle to make one carbohydrate molecule (one for each carbon dioxide molecule fixed). The remaining G3P molecules regenerate RuBP, which enables the system to prepare for the carbon-fixation step. ATP is also used in the regeneration of RuBP.
Figure 5.15 The Calvin cycle has three stages. In stage 1, the enzyme RuBisCO incorporates carbon dioxide into an organic molecule. In stage 2, the organic molecule is reduced. In stage 3, RuBP, the molecule that starts the cycle, is regenerated so that the cycle can continue.
In summary, it takes six turns of the Calvin cycle to fix six carbon atoms from CO2. These six turns require energy input from 12 ATP molecules and 12 NADPH molecules in the reduction step and 6 ATP molecules in the regeneration step.
Among 31,906 singleton pregnancies, 4% of women had Hb <110 g/L and 10% had Hb 140+ g/L at ≤20 weeks of pregnancy. Our results suggest that both low and high Hb at ≤20 weeks are associated with adverse outcomes at the time of birth, in a U-shaped relationship that rises on either side of the lowest risk point at 120–129 g/L. The association between the low Hb and adverse outcomes was relatively stronger than that between high Hb and adverse outcomes. Only transfusion had a linear relationship, with risk increasing with lower Hb and decreasing with higher Hb. The U-shaped relationship between Hb and adverse outcomes that we found has also been shown in a study in Peru in both high and low altitude pregnancies. 
Of the women with a low Hb at ≤20 weeks, almost 40% had their Hb restored in the second half of pregnancy. Restoration of Hb did not appear to change risk of PPH, preterm birth, SGA or a composite indicator including transfer to higher care, stillbirth and very low birthweight, but did lower the risk of postpartum transfusion. These data are consistent with a review of trial data suggesting iron supplementation improved Hb levels in pregnant women but did not conclusively improve pregnancy outcomes. The reasons why improvements in Hb do not translate into improved perinatal outcomes require further study. There may be a critical window for the impact of low Hb on outcomes, or the low Hb may be a symptom of an underlying condition that is itself the cause of the poor outcome. Another possibility is that restoring Hb does in fact improve some outcomes, but not those specifically measured in our study.
The higher rate of PPH demonstrated in the low compared with normal Hb groups is in line with previous evidence suggesting anaemia is associated with a higher risk of PPH.[24, 25] Women with high antenatal Hb also had a slightly higher PPH rate than those with normal Hb (although not significantly so), but were less likely to be transfused than those with low Hb, which may have been due to better iron reserves or the treating clinicians being more willing to tolerate blood loss before deciding to transfuse. We also found a significantly higher risk of adverse outcomes such as preterm birth, very low birthweight and transfer to higher care or stillbirth for those with high Hb result, as has been found in other studies,[26, 27] possibly due to inadequate plasma volume expansion, or the impaired response to inflammation and infection,[5, 26] or possibly due to high Hb levels before pregnancy.
Antepartum haemorrhage and abnormal placenta site can cause anaemia and are also associated with adverse pregnancy outcomes.[28, 29] These factors were adjusted for in our analysis, but were also unlikely to have influenced anaemia in the first 20 weeks of pregnancy, as bleeding due to these factors usually occurs later in the pregnancy.
Australian data on the prevalence of anaemia in pregnancy is limited. Our estimate of low Hb (4%) was similar to a 2015 South Australian estimate of women with anaemia in pregnancy (6.6%). International studies have found much higher rates of maternal anaemia with a global estimate of 38% in 2011. The high proportion of women with a history of iron-deficiency anaemia in our population (15%), particularly in the low Hb group, suggest there may have been opportunities to correct low Hb due to iron deficiency before the pregnancy.
We were able to obtain Hb results for a large cohort of pregnant women and examine outcomes by Hb levels at ≤20 weeks gestation. However, limitations of these data were that only Hb results that were manually entered in birth data by midwives or were obtained from in-hospital pathology laboratories (at Royal North Shore) or in-hospital or linked pathology laboratories (at Westmead) were available. This meant there were 13% of pregnant women (n = 4621) who did not have a valid Hb result in the first 20 weeks of pregnancy. These women were broadly similar to those in the final study population, though. Also, we did not know the cause of the low Hb or what measures were taken to restore Hb, and could only infer treatment based on changes in Hb results. From a previous survey, and clinical experience, we assume that a majority of women were taking supplemental iron, either as part of a multivitamin or in an iron-only supplement, but without information on which supplements, and how much iron they contained, collected in the database, we were unable to examine how this impacted on Hb or outcomes. The country of birth results suggest that some thalassaemia/sickle cell anaemia cases may have been missed, as these conditions are more common in Africa and the Middle East, where the low Hb women were more likely to be born.
The high-speed development of biological techniques such as next generation sequencing has greatly improved efficiency of cancer prediction and disease diagnosis. However, intricate phenotype ontology and high genetic heterogeneity have stunted further improvement of disease identification. As an useful and powerful tool, HPO-based phenotype semantic similarity could fill this gap and accelerate the disease diagnosis effectively. In this paper, we proposed an unique and novel phenotype similarity measurement, called DisPheno, which integrates multiple types of information: hierarchical structure, phenotype term annotation and text description. Compared with existing five state-of-art methods on the optimal and noisy datasets, our method performs much better than the others. In summary, DisPheno accelerates the efficiency of disease identification significantly and it also shows greatly potentiality in practical clinical studies.