How can mammalian red blood cells live without a nucleus?

How can mammalian red blood cells live without a nucleus?

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Even though they have a shorter lifespan than other varieties of cells, I would think that 120 days without a nucleus is still quite a long time.

It depends on what you call "to live". RBCs cannot divide, for instance. They cannot synthesise proteins either, so they are decaying after their nucleus is expelled. But they produce ATP using anaerobic glycolysis (as they lack mitochondria), which you may take as a characteristic of "living".

The Cell Nucleus: A Brief Overview

Every well-functioning team needs a group of hardworking members and a leader who ensures everyone does their job. The cells that makeup living things function in much of the same way. Cells are made up of distinct compartments called organelles, each of which carries out a set of specific tasks that ensure the survival of the cell.

The nucleus is a crucial organelle that functions as the control center or ‘leader’ of the cell. The nucleus performs two critical functions it stores the organism’s instruction manual in the form of DNA and regulates all the cell’s activities including growth, reproduction, communication, gene expression and protein synthesis.

The word nucleus is derived from the Latin word ‘nuculeus’ meaning seed just like fruits are embedded with seeds in its center, the nucleus is usually found at the center of the cell. Organisms with cells containing a well-defined nucleus are called eukaryotes, while those without nuclei are called prokaryotes. This article will explore the structural and functional organisation of the cell nucleus in detail.

In all vertebrates, the cell nucleus becomes highly condensed and transcriptionally inactive during the final stages of red cell biogenesis. Enucleation, the process by which the nucleus is extruded by budding off from the erythroblast, is unique to mammals. Enucleation has critical physiological and evolutionary significance in that it allows an elevation of hemoglobin levels in the blood and also gives red cells their flexible biconcave shape. Recent experiments reveal that enucleation involves multiple molecular and cellular pathways that include histone deacetylation, actin polymerization, cytokinesis, cell–matrix interactions, specific microRNAs and vesicle trafficking many evolutionarily conserved proteins and genes have been recruited to participate in this uniquely mammalian process. In this review, we discuss recent advances in mammalian erythroblast chromatin condensation and enucleation, and conclude with our perspectives on future studies.

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Cell Differentiation and Stem Cells

The reversibility and inheritance of patterns of gene activity
What determines the pattern of gene activity in a differentiated cell?
Two possibilites are
1) Loss of genetic material.
2) Regulatory proteins (transcription factors, chromatin proteins?)
Transgenic studies have revealed much about gene activity and differentiation.

Control of transcription involves both general and tissue-specific regulators of transcription
First, the RNA polymerase binds to the start site of transcription in the promoter.
Then the DNA is unwound so that the template DNA can be copied.
The control regions of a gene contribute to the ability of the RNA polymerase and associated proteins to start transcription.
Cooperation of general transcription factors and RNA polymerase II is required to bind to the TATA box of the promoter.
The enhancer elements bind regulatory proteins (transcription factors).
Proteins bound at the enhancer element interact with proteins bound at the promoter region to form a transcription initiation complex and to initiate transcription at a high rate.
Enhancers are usually upstream of the transcriptional start site but can be downstream or within introns.
Enhancer regions can be located some distance away from the promoter.
Some transcription factors are required for a wide range of genes while others are only required for a small set of genes (perhaps one).
Interactions between transcription factors control increases or decreases in binding affinity.

External signals can activate genes
Patterns of gene expression are controlled by external signals such as steroid hormone (that can enter the cell) and protein growth factors that interact with cell surface receptors to generate an intracellular signal.
Steroid hormones are lipid soluble and bind receptor proteins inside the cell.
Steroid/receptor complexes can act as transcription factors binding steroid response elements in the DNA.
Peptide/proteins bind receptors in the cell membrane and signal transduction mechanisms cause the activation or inactivation of transcription factors (by phosphorylation of a transcription factor or release from a cytoplasmic complex).

Maintenance and inheritance of patterns of gene activity may depend on regulatory proteins and DNA modification.
In the differentiated state, some genes are active and others are repressed.
Developmentally important eukaryotic genes often have very complex control regions with binding sites for many transcription factors which can activate or repress transcription.
Continual expression of a gene may require the continual presence of a transcription factor.
The gene product may positively regulate itself to maintain its own expression.
Selector genes remain active throughout development to maintain the developmental pathway of a region.
The state of chromatin packaging can keep a gene inactive for a long period of time.
i.e. The inactivation of one of two X-chromosomes to form a heterochromatic Barr body.
Localized chromatin packaging could have similar effects on gene expression.
Methylation of the DNA can act to modify the activity of genes.
Post-translational modification of histones, such as methylation, acetylation, or phosphorylation, can regulate gene activity (i.e. Hox complex genes during embryogenesis).

All blood cells are derived from pluripotent stem cells.
Through hematopoisis, stem cells give rise to all the blood cells of an adult mammal.
Most blood cells are short lived and must be constantly replaced.
The mammalian embryo begins hematopoiesis in
1) the yolk sac blood islands,
2) then the fetal liver and
3) finally the bone marrow.
The hematopoietic stem cell generates the hierarchy of differentiation.
The pluripotent stem cell reproduces and gives rise to 1) the myloid cells (the erythrocytes (RBC) and
2) the leukocytes (WBC) - eosinophils, neutrophils, basophils, monocytes and megakaryotes) and the lymphoid cells (B and T lymphocytes).
Initially pluripotent, the stem cell first becomes committed to either the lymphoid or myeloid lineages.
This is followed by rounds of replication and further commitment to give the final 8 cell types.
This occurs in the bone marrow and is regulated by growth factors and cytokines.

Colony-stimulating factors and intrinsic changes control differentiation of the hematopoietic lineages.
Hematopoiesis transcription factors have overlapping expression patterns.

20 extracellular colony-stimulating factors affect cell proliferation and cell differentiation such as the
macrophage colony-stimulating factor (M-CSF)
granulocyte colony-stimulating factor (G-CSF), and
granulocyte-macrophage colony-stimulating factor (GM-CSF).

c-Myb (a proto-oncogene) is expressed only in immature cells and is not lineage specific.

20 TF's are lineage specific (i.e. GATA-2 is in all myeloid precursors but not lymphoid cells and
GATA-1 is in some myeloid and is required for RBC differentiation).

Globin gene expression is controlled by distant upstream regulatory sequences.
The erythrocyte requires large amounts of hemoglobin from two sets of globin genes.
Hemoglobin is a tetramer comprised of two alpha and two beta globin chains that come from two multigene clusters.
The human beta globin cluster contain five genes that expressed at different times during development and produce differnt types of hemoglobin at differnt times.
The locus control region (LCR), an element far upstream of the gene cluster confers high levels of expression of the specific developmentally correct family member.
The locus control region contains four core control regions that bind transcription factors including GATA-1.
Developmental control of expression of the different family members over time probably depends upon looping out of the intervening DNA.
Proteins bound to the LCW and to different globin gene promoters can physically interact to promote transcription.

Differentiation of cells that make antibodies is due to changes in DNA
B lymphocytes of the vertebrate immune system recognize and respond to antigens by producing antibodies on their cell surfaces.
The Y-shaped antibody molecule is composed of 2 light and 2 heavy chains.
The antigen binding sites (at the tips of the Yís arms) are made from the variable regions of the light and heavy chains which are encoded by rearranged
Each B lymphocyte expresses just one type of heavy and one light chain to produce a specific antibody.
Rearrangement of the DNA to assemble a V-D-J heavy chain gene and a V-J light chain gene occurs by somatic recombination.

The epithelia of adult mammalian skin and gut are continually replaced.
Vertebrate skin is composed of 3 layers:
1) the dermis (mainly fibroblasts)
2) the protective outer epidermis (mainly keratinocytes)
3) the basal lamina (ECM which separates the epidermis from the dermis).
Stem cells form a population in the basal layer (next to lamina) which divide asymmetrically to form one stem cell and one committed to differentiation.
Cell adhesion molecules play an important role in skin cell development.
The basal cell layer is attached to the basement membrane by hemi-desmosomes and focal contacts.
The epidermal cells of the upper layers are connected by desmosomal junctions.
The epithelial lining of the gut is under continuous turn-over.

A family of genes can activate muscle-specific transcription
Myoblasts are cells that are committed to forming muscle and can proliferate in culture until growth factors are removed from the medium.
This results in the stopping of proliferation and the start of differentiation with the synthesis of muscle specific proteins (actin, myosin II, tropomyosin and creatine phosphate kinase.
The myoblasts become bipolar in shape (via reorganization of the cytoskeleton) then fuse to give multinucleate myotubes.

20 hours, striated muscle is apparent.
myoD expression can induce muscle differentiation by transfection into fibroblasts (which do not normally become muscle).
myoD is a key controlling gene in muscle differentiation and is expressed only in muscle precursors and muscle.
myoD family (myogenin, myf-5 and MRF-4) are basic helix-loop-helix (bHLH) DNA-binding transcription factors.
myoD is the first gene switched on in bird muscle precursors (myf-5 is first in mammals).
myoD & myf-5 are expressed in proliferating myogenic cells but myogenin is only expressed during muscle differentiation.
Each of these genes activate each other's expression.
In myoD knock-out mice, normal striated muscle is made and myf-5 is increased (a compensatory mechanism).
Mice missing both Myf-5 and MyoD lack all skeletal muscle where myogenin knockouts have heart and smooth muscle but lack most skeletal muscle.
The MyoD family of TFs activate transcription by binding the E-box (DNA sequence) in the regulatory region of muscle-specific genes as heterodimers.
MyoD/E2 (E2 is a ubiquitous TF) binds the E box much better than a MyoD homodimer.

The differentiation of muscle cells involves withdrawal from the cell cycle.
Cell proliferation and differentiation of muscle cells are mutually exclusive.
Myoblasts only differentiate after proliferation stops.
When growth factors are present, MyoD & Myf-5 are expressed and the myoblasts proliferate & do not differentiate.
However, additional signal(s) are required for differentiation.
Removal of growth factors will cause the myoblasts to withdraw from the cell cycle, fusion and differentiation follow.
The retinoblastoma protein (Rb) which can block cell growth is inactivated by phosphorylation in proliferating cells.
Dephosphorylation of Rb & blockage of cell cycle is a "differentiation decision".

Complex combinations of transcription factors control cell differentiation.
Liver cell differentiation involves activation of HNF-4 (TF), which activates HNF-1alpha which then activates liver-specific genes such as albumin and Beta-fibrinogen.
HNF-1alpha transcription requires HNF-4 plus ubiquitous Fos & Jun and HNF-3.
TFs act as functional groups such that some contribute to the organization of general epithelium structure.
Others contribute within this context to tissue-specificity (although in other contents the same secondary factor might not have the same tissue specificity).

Neural crest cells give rise to both the chromaffin cells of the adrenal medula (which secrete epinephrine) and to sympathetic neurons (which secrete norepinephrine).
Differentiation of chromaffin cells requires a high concentration of glucocorticoid hormones which are synthesized by the adrenal cortex.
Glucocorticoids inhibit neuronal differentiation and promote maturation of the chromaffin cells.
Neuron differentiation is induced sequentially by fibroblast growth factor (FGF to induce neuron formation) and nerve growth factor (NGF to induce survival of the neurons).
Differentiated chromaffin cells in culture can undergo transdifferentiation into sympathetic neurons is glucocorticoids are removed and FGF is added.

Neural crest diversification involves signals for both specification of cell fate and selection for survival.
Several growth factors direct neural crest cell's fates.
Glial growth factor promotes differentiation of glia and suppresses neuron differentiation.
BMP-2 promotes neuronal differentiation.
Two type of spinal ganglia, the dorsal root sensory ganglia (with cholinergic neurons) and the sympathetic ganglia (most are adrenergic) develop from the neural crest.
Early dorsal ganglia explants can give rise to sympathetic neurons but early sympathetic neural explants never give rise to dorsal sensory neurons.
A factor (probably brain-derived neurotrophic factor:BDNF) produced by the embryonic neural tube causes the sensory cells to survive.
Possibly, the sympathetic ganglia which develops removed from the neural tube does not require BDNF to survive but the sensory ganglia do.

Programmed cell death is under genetic control.
The vertebrate development, apoptosis (programmed cell death) is crucial in the formation of the nervous system.
Cell death genes are highly conserved from nematode to mammals (and humans).
ced-3 & ced-4 are pro-apoptotic and are required for the normal death of the 131 cells that die during development of C. elegans.
Without these genes, the cells survive and develop to resemble their sister cells (worms survive for several weeks).
ced-9 is antiapoptotic and loss of function mutations will cause many cells to die while gain of function mutations will result in no cell death.
The human homologue, Bcl-2, is an oncogene that encodes a mitochondrial membrane protein.
ced-3 and it's homologues are proteases that initiate a proteolytic cleavage cascade which results in cell death.

Nuclei of differentiated cells can support development of the egg
UV-radiation directed to the animal pole destroys the nuclear DNA of the Xenopus egg.
Nuclei from other cells can then be injected into the enucleated eggs.
Nuclei from adult skin, kidney, heart and lungs (and tadpole intestinal cells) can develop into adults (low rate of survival).
Transplantation of nuclei from blastula cells have a high rate of success (1 blastula nuclei into several eggs will produce clones of identical frogs).
Therefore developmental genes are not altered during development.
The transplanted genes act as the original nuclear genes would.

Gene activity can be changed by cell fusion of differentiated cells
When transplantation experiments are difficult, then cell fusion experiments can expose the nucleus of one cell to the cytoplasm of another.
Fusion of chicken red blood cells (inactive but present nucleus) with human cancer cells leads to reactivation of chick-specific gene expression.
Therefore, human cells have cytoplasmic factors that can activate chicken genes.
Fusion of human (non-muscle) cells with rat muscle cells induces human muscle gene activity.
Clearly, the gene expression of cells that are differentiated are controlled by cytoplasmic factors which can be altered.
Cell differentiation can be reversed.

The differentiated state of a cell can change by transdifferentiation
Full differentiation is normally stable.
However, cells can be altered in regenerating tissues.
With transdifferentiation in the newt, a lens can be regenerated from the dedifferentiation of iris pigmented epithelium or cartilage from dedifferentiated limb muscle tissue.
Transdifferentiation occurs in the culture of embryonic chick retina under certain culture conditions where the pigment disappears and lens-specific proteins are made.
In jellyfish cell culture, digestion of the extra-cellular matrix results in the transdifferentiation of striated muscle to smooth muscle then nerve cells.
Alteration of steroid hormone and polypeptide growth factors that specify chromaffin cells can lead to transdifferentiation into sympathetic neurons.

Part 4: Designing and Mining Pathogen Genome Databases: From Genes to Drugs and Vaccines II

00:00:02.06 We're back,
00:00:03.29 and we're now looking live at the Plasmodium genome database
00:00:06.26 at
00:00:10.16 And before we turn to the question
00:00:13.10 that we raised on trying to identify candidate vaccine targets
00:00:17.06 for malaria,
00:00:19.16 let me just provide a little bit of context
00:00:22.26 that may be a little easier to see
00:00:26.05 than what we'd seen before
00:00:28.29 in those canned screen dumps.
00:00:32.10 The first point I'd like to make is that
00:00:34.25 the success of the Plasmodium genome database
00:00:37.12 has been such that it has led to
00:00:40.13 its expansion to encompass a variety of other organisms.
00:00:43.29 The PlasmoDB project
00:00:47.10 morphed into the Apicomplexan Genome Database,
00:00:50.06 APDB,
00:00:52.10 which was itself expanded still further
00:00:54.03 into the Eukaryotic Pathogen Genome Database,
00:00:58.17 encompassing a wide range of organisms --
00:01:02.28 not only apicomplexan parasites,
00:01:05.28 such as Cryptosporidium and Plasmodium
00:01:08.18 and Toxoplasma and Theileria,
00:01:10.20 but also other species as well,
00:01:12.23 such as Giardia and Trichomonas,
00:01:14.24 which we won't be talking about today.
00:01:17.02 This project is in fact just part of a larger
00:01:20.22 Bioinformatics Resource Center project
00:01:23.04 funded by the US NIAID
00:01:25.09 that includes several different genome databases from.
00:01:30.11 dealing with a variety of pathogens.
00:01:33.00 And those of you who are interested in other pathogens
00:01:36.11 might want to explore this further.
00:01:38.24 Now, the purpose of having an overar.
00:01:40.28 overarching website for exploration
00:01:43.02 not only of malaria parasites
00:01:45.06 but many eukaryotic pathogens
00:01:48.05 is that there are a variety of questions
00:01:49.28 that you might want to ask
00:01:52.12 that extend beyond an individual species.
00:01:57.21 And these are several ways that you can explore that.
00:02:00.02 I should also point out that this homepage
00:02:02.13 also provides, in addition to links
00:02:05.02 to some of these other pages,
00:02:07.10 other bits of information.
00:02:09.00 You might, for example, be interested in tutorials
00:02:11.18 that highlight some of the features
00:02:13.15 we'll be talking about.
00:02:15.11 And further down the page, here,
00:02:17.02 you can see links to.
00:02:19.06 links to individual tutorials,
00:02:21.14 links to publications,
00:02:23.09 workshops, exercises, and so on.
00:02:27.25 We can run questions across
00:02:31.00 a variety of these organisms.
00:02:32.17 If we were interested in apicomplexan parasites, for example,
00:02:34.22 we might want to take a look at metabolic pathway maps
00:02:37.18 for these organisms.
00:02:39.20 And in this case, we've taken annotations
00:02:43.01 from the Plasmodium genome database,
00:02:45.11 the Toxoplasma genome database,
00:02:47.08 the Cryptosporidium genome database,
00:02:49.20 and mapped those on top of
00:02:52.23 the KEGG metabolic pathway projects
00:02:54.29 emerging from database projects in Japan.
00:02:57.18 If, for example, we take a look at carbohydrate metabolism,
00:02:59.28 and dive in further to see the glycolytic pathway,
00:03:03.06 the key to this analysis is indicated up at the top,
00:03:07.14 in which Toxoplasma is indicated in red,
00:03:09.20 Plasmodium is indicated in green,
00:03:12.24 Cryptosporidium in yellow,
00:03:14.16 and human in blue.
00:03:16.09 And so, we can see, by looking at the painting
00:03:18.14 of this metabolic pathway,
00:03:20.01 that from top to bottom
00:03:22.22 all of these organisms are capable
00:03:25.06 of carrying out glycolysis.
00:03:27.16 Now, that might not sound very surprising.
00:03:29.28 But we can dive in a little bit deeper
00:03:32.11 and take a look, for example, at the TCA cycle.
00:03:34.25 And now we see a somewhat different pattern,
00:03:37.05 in which the yellow bug
00:03:39.21 -- in this case, Cryptosporidium --
00:03:42.12 doesn't do this pathway.
00:03:45.03 And indeed, that's the case.
00:03:47.02 Cryptosporidium is an anaerobe, which doesn't carry out.
00:03:49.21 which doesn't carry out oxidative phosphorylation.
00:03:54.18 There are many other pathways we could look at
00:03:58.18 if we were interested, for example,
00:04:00.17 in some of those metabolic pathways,
00:04:02.16 which we now know are associated with the apicoplast --
00:04:04.27 for example, pathways involved in the biosynthesis of steroids.
00:04:08.25 We could see that purely from the pattern
00:04:11.26 of gene presence and absence,
00:04:14.07 the red and green organisms
00:04:17.28 -- Toxoplasma and Plasmodium --
00:04:19.29 clearly use a different pathway
00:04:22.24 for synthesizing isoprenoids
00:04:24.25 than the blue organism -- human.
00:04:26.13 And indeed, this is the case.
00:04:28.10 One of the most striking findings from the biochemistry of the apicoplast
00:04:31.27 is that these parasites synthesize isoprenoid subunits
00:04:37.12 via a xylose pathway
00:04:40.15 typically associated with chloroplasts --
00:04:42.25 quite distinct from the HMG CoA-reductase pathway
00:04:45.26 found in humans.
00:04:48.00 Cryptosporidium does neither,
00:04:49.29 presumably salvaging isoprenoid units,
00:04:52.17 which you can use apparently
00:04:54.24 to produce squalene,
00:04:56.15 but of these organisms is capable of converting that squalene
00:05:00.10 all the way into cholesterol,
00:05:02.25 so this is not a sterol biosynthesis pathway,
00:05:05.26 but it's certainly a pathway for the production
00:05:08.16 of isoprenoid precursors.
00:05:11.18 And there are many other fascinating aspects of parasite biochemistry
00:05:15.06 to take a look at.
00:05:17.00 So, let's return to the Eukaryotic Pathogen Genome Database,
00:05:20.18 and return further to the Plasmodium genome database
00:05:23.20 that we.
00:05:25.08 that is the subject of our discussion today.
00:05:28.22 Now, as you've already seen,
00:05:30.18 we can explore this database in many different ways.
00:05:33.01 We can look, for example, at individual genes,
00:05:36.09 and we'll just take a look at a single gene listed here,
00:05:38.29 the default gene on the.
00:05:41.09 on this pathway
00:05:44.06 known as the apical membrane antigen 1,
00:05:46.27 a famous gene in the world of malaria biology
00:05:49.23 because this has been advanced as one of the leading
00:05:55.19 vaccine candidates for malaria parasites,
00:05:58.07 although there are a variety of concerns about AMA1
00:06:01.04 which lead investigators
00:06:04.10 to be interested in identifying other candidates
00:06:07.09 that might also be worth exploration.
00:06:10.02 We can see that AMA1 is present on chromosome 11
00:06:13.05 and, as you've already seen in the illustration
00:06:15.15 from a chromosome-based view,
00:06:17.23 is a highly polymorphic antigen.
00:06:20.22 Many dozens of polymorphisms
00:06:23.16 known to be associated with this gene,
00:06:25.10 and we can see what those polymorphisms are
00:06:28.09 from different species.
00:06:29.24 We can see, for example, that this particular polymorphism
00:06:31.26 changes the coding potential,
00:06:34.13 such that in the reference 3D7 strain
00:06:36.27 the nucleotide C corresponds to a proline
00:06:39.26 whereas in many of the other species on this list.
00:06:42.29 many of the other isolates.
00:06:46.03 a T. C-to-T nucleotide polymorphism
00:06:49.25 results in a proline-to-serine mutation.
00:06:52.07 We can see user comments which have been entered,
00:06:55.28 providing additional information on these genes
00:06:58.01 links to a variety of other gene pages
00:07:00.28 protein features which have been identified
00:07:04.13 by a variety of means
00:07:06.14 predicted structural information
00:07:09.09 proteomic data indicating
00:07:12.21 that there's evidence for expression at the protein level
00:07:15.12 microarray analysis on several different platforms
00:07:18.15 indicating that this gene
00:07:21.24 is most abundantly expressed late in the intraerythrocytic life cycle,
00:07:25.25 as one might expect for a gene
00:07:28.06 that is present in merozoites, the extracellular stage,
00:07:32.22 that one might want to target in a vaccine
00:07:36.16 that would be effective against
00:07:39.20 the disease-causing stage of malaria parasites
00:07:43.24 additional information from other expression studies,
00:07:46.14 from knockout studies
00:07:48.16 sequence information that can be shown here
00:07:51.06 and so forth.
00:07:53.20 But as we described ear. discussed earlier,
00:07:56.21 the real power of this database
00:07:59.15 comes not solely from viewing it as a catalogue
00:08:02.25 of available information
00:08:05.08 but as an opportunity for being able
00:08:09.03 to ask your own questions.
00:08:11.28 So, what kinds of questions can we ask?
00:08:14.03 Here, under the queries and tools link
00:08:18.00 indicated at the upper left-hand corner of your screen,
00:08:21.26 we can see a grid describing
00:08:24.05 a wide range of questions
00:08:26.16 that one might choose to ask.
00:08:28.18 For example, we might imagine that chromosomal location
00:08:32.26 was in some way informative for candidate vaccine targets.
00:08:36.07 I'm not quite sure how that would work
00:08:38.21 it's not really clear to me
00:08:41.24 how proximity to a centromere
00:08:44.00 might be indicative of a good target for vaccine development,
00:08:47.03 so I'm not going to pursue that line of inquiry,
00:08:49.27 but you might want to if you have reason for thinking
00:08:53.15 that chromosomal location is informative
00:08:55.21 for effective vaccine targets.
00:08:58.02 Let's instead start with some more obvious kinds of approaches.
00:09:02.18 We certainly would expect that a target
00:09:05.27 for vaccine development
00:09:08.14 would have to be antigenic in some way.
00:09:10.20 And so, here we can take advantage
00:09:13.10 of extensively curated information
00:09:15.26 that comes from the Immune Epitope Database Project,
00:09:18.05 whose research on other databases
00:09:20.21 has been incorporated into this database.
00:09:22.28 And we can look for genes that have been annotated
00:09:25.06 with a very high confidence of antigenic function
00:09:29.15 against Plasmodium falciparum.
00:09:32.05 And what we see is several genes --
00:09:35.13 41 to be exact.
00:09:38.18 Now, that's a disappointingly small number.
00:09:41.10 It includes the merozoite surface protein 1, which,
00:09:45.10 with AMA1,
00:09:47.15 is also viewed as a promising candidate for antimalarial vaccine development,
00:09:50.20 and a variety of other merozoite surface proteins,
00:09:53.01 as one as one might expect.
00:09:55.23 But, surely, there must be more than 41 candidate targets
00:10:00.20 in a genome of many thousands of genes.
00:10:03.29 So, let's modify this query
00:10:07.06 to ask a different question,
00:10:10.00 ask not just those antigens
00:10:12.22 that have a high confidence of immune reactivity
00:10:17.04 but those that have any confidence of immune reactivity.
00:10:21.09 And now we come up with a much larger list of genes,
00:10:24.05 which may have lower.
00:10:26.06 for which we may have lower confidence,
00:10:28.12 but things that we might want to explore further.
00:10:32.12 What else might we want to ask?
00:10:34.15 We'll return to our query grid here
00:10:36.29 and ask about other information.
00:10:38.27 So, we've asked about genes
00:10:41.19 that show some evidence -- based on manual curation --
00:10:44.09 of being effective epitopes.
00:10:47.08 We might also imagine that genes
00:10:49.20 that would be effective targets
00:10:53.10 for vaccine development
00:10:58.13 would have to be expressed in the right place and at right time.
00:11:03.08 By in the right place, we mean presumably
00:11:05.27 on the surface of an infected red blood cell
00:11:08.22 or on the surface of the parasite itself,
00:11:11.08 and we can gauge that information
00:11:14.11 by looking at cellular location, here.
00:11:16.17 We know that signal peptides
00:11:18.29 are likely to be involved in targeting proteins outside of the cell,
00:11:21.10 so let's ask for all proteins in a malaria parasite
00:11:24.01 that have a predicted signal sequence.
00:11:27.07 And we're interested, in this case, in Plasmodium falciparum,
00:11:29.24 although we could interrogate other malaria parasites as well.
00:11:33.04 And when we ask a question like that,
00:11:35.19 we get a list of many hundreds of genes,
00:11:39.05 including genes that certainly wouldn't be at the top
00:11:43.01 of anyone's list as a vaccine target,
00:11:45.28 such as this pseudogene that's listed.
00:11:48.04 that's indicated here.
00:11:50.28 Interestingly, as I look at this number,
00:11:54.02 while we find many hundreds of genes,
00:11:56.25 there are not that many hundreds of genes.
00:11:59.01 I would have naively expected that for an organism
00:12:01.12 that makes its living by secreting proteins to modify the host cell,
00:12:05.14 using those specialized apical secretory organelles
00:12:08.20 that we discussed in the first lecture of the series,
00:12:11.24 surely more than 10% of the parasite genome would be.
00:12:16.10 would be secreted.
00:12:19.08 What could possibly account for this.
00:12:22.19 for this shortfall?
00:12:24.22 These organisms, remember, are eukaryotic organisms.
00:12:27.26 And this brings us face to face
00:12:30.08 with the bane of genome annotation
00:12:34.10 in eukaryotic species,
00:12:36.23 and that is the following.
00:12:39.08 that while we are quite good at identifying coding sequence,
00:12:42.19 it's quite difficult to identify every single exon
00:12:46.19 that is encoded into a.
00:12:48.25 that's translated into a protein in eukaryotic species.
00:12:52.02 And that's particularly true
00:12:56.22 at the extreme 5' end of the gene
00:12:59.03 the first exon is the most difficult to identify.
00:13:02.01 And that, in turn, manifests itself
00:13:04.24 as an inability to accurately predict signal sequences.
00:13:10.10 So, we can imagine expanding our search
00:13:13.23 a little more broadly
00:13:16.15 to identify more proteins.
00:13:18.10 Let's imagine, for example,
00:13:20.10 if we go back to the grid of questions that we've asked,
00:13:22.29 that we might want to ask
00:13:26.03 not only for proteins that have a recognizable signal peptide
00:13:29.27 but also for proteins that have recognizable transmembrane domains,
00:13:34.12 anticipating that those that have a transmembrane domain
00:13:36.19 without a signal sequence
00:13:39.14 are probably proteins for which we weren't really able
00:13:42.10 to recognize the signal sequence accurately.
00:13:44.29 Once again, we will look only in Plasmodium falciparum.
00:13:47.24 I'm not interested in proteins
00:13:50.06 with two or ten transmembrane domains.
00:13:52.06 I'm really interested in proteins that have at least one.
00:13:54.12 I don't care how many.
00:13:56.05 you know, at least one,
00:13:58.01 and no more than a thousand transmembrane domains.
00:14:00.22 And now we see a slightly larger number --
00:14:03.11 actually, about double the number. 1,700+ proteins.
00:14:07.23 Now, some of those proteins.
00:14:09.29 so, this will presumably include many of those proteins
00:14:12.28 with signal peptides.
00:14:14.17 Some of them will be secreted without a transmembrane domain.
00:14:17.01 But it includes many other proteins
00:14:19.14 that we have some confidence are associated at least with a membrane,
00:14:22.09 although we have no confidence that it's associated
00:14:26.03 with the surface membrane of those proteins.
00:14:28.18 Now, those of you with sharp eyes may have noticed
00:14:31.24 that over on the far left-hand end of the screen
00:14:34.28 is a box indicated as "My Query History,"
00:14:38.17 and this is a history of all of the questions
00:14:42.27 that we've asked in the context of this session.
00:14:45.27 We asked, first of all,
00:14:48.14 for this individual gene, AMA1.
00:14:51.06 Secondly, we asked for the.
00:14:53.24 for the high confidence epitopes,
00:14:56.28 and here for epitopes with even low confidence,
00:15:01.28 proteins with signal peptides or with transmembrane domains.
00:15:04.22 And what we're really interested in for.
00:15:07.08 from the standpoint of location
00:15:11.13 is genes that have either a signal peptide or a transmembrane domain,
00:15:14.24 and so I'm going to combine these queries using a combination, here,
00:15:18.14 to look for the results of prote.
00:15:20.22 of our search for a signal peptide --
00:15:23.22 that is, query 4 --
00:15:26.01 or a transmembrane domain.
00:15:28.10 And the result that I get will be a much larger set
00:15:32.16 -- or a somewhat larger set --
00:15:35.04 of about 2,000 proteins that have either a signal peptide
00:15:37.05 or a transmembrane domain.
00:15:38.23 Once again, it includes many proteins
00:15:41.09 that I don't think any of us would advocate
00:15:43.07 as vaccine targets.
00:15:45.02 the cytochrome oxidase genes
00:15:46.28 associated presumably with the parasite mitochondrion.
00:15:49.24 But we can see, now, the results of this query,
00:15:51.28 a new question which I'm going to rename,
00:15:53.27 just so I don't lose track of it.
00:15:56.02 And I'll just call this "signal peptide or transmembrane domain,"
00:16:01.00 just so I don't forget about what the question is that I've asked.
00:16:06.01 So, we can imagine a wide range of other questions,
00:16:08.11 and I would encourage any of you who have questions
00:16:11.02 you would like to ask
00:16:13.28 to explore this query grid
00:16:17.15 for accessible questions that may be relevant to the ways
00:16:22.06 that you choose to interrogate the database.
00:16:24.17 We might also want to know about proteins
00:16:27.07 that are not only in the right place on the surface
00:16:30.00 but also at the right time.
00:16:31.26 Remember that intraerythrocytic life cycle --
00:16:34.16 in which a parasite invades into an erythrocyte,
00:16:38.21 sets up its home as a ring stage parasite,
00:16:40.25 develops and metabolizes
00:16:43.09 and grows as a trophozoite,
00:16:44.28 finally emerging by assembling daughter parasites
00:16:47.18 as a schizont,
00:16:49.15 before rupturing outside of the red blood cell to release merozoites --
00:16:53.08 we might expect that if we were interested in a vaccine that targeted
00:16:56.27 the red blood cell stage of malaria
00:17:00.12 that's responsible for the clinical symptoms,
00:17:03.04 we'd be interested in targeting those merozoites,
00:17:05.27 a very short-lived form about which it's difficult
00:17:09.14 to gather detailed information.
00:17:11.16 We could ask, for example, for proteomic da.
00:17:14.05 for protein data,
00:17:16.29 looking for mass spec-based data.
00:17:19.27 evidence of expression on merozoites.
00:17:23.03 And you may wish to explore that.
00:17:25.10 the datasets associated with transcription,
00:17:28.03 some of which were described in Joseph DeRisi's iBio seminar
00:17:31.27 on malaria.
00:17:35.05 are probably more extensive and more.
00:17:37.13 and more comprehensive.
00:17:39.04 So, I'm going to, instead,
00:17:42.13 interrogate the expression profile for. from trans.
00:17:46.22 from transcript levels.
00:17:49.25 And there are a number of queries that can be used
00:17:52.14 against various different organisms using various different datasets.
00:17:54.17 Since you may be familiar with the data set generated in the DeRisi lab,
00:17:57.05 we'll take a look at that data here,
00:17:59.03 looking at expression timing
00:18:01.21 based on glass slide microarrays,
00:18:03.20 although there are other ways that you can interrogate this data as well.
00:18:06.15 Now, we've already seen,
00:18:09.01 from looking at the expression profile of AMA1,
00:18:11.24 that the transcripts were most abundant
00:18:15.17 towards the end of that 48-hour window
00:18:18.22 of replication inside an erythrocyte.
00:18:21.00 And that makes sense if you imagine
00:18:23.23 that transcription is going to precede translation,
00:18:26.17 and so we might imagine that in that schi.
00:18:29.12 stage of schizogony,
00:18:32.10 proteins are most like. is the most likely time to transcribe genes
00:18:36.24 that are going to be translated for protein in merozoites.
00:18:40.21 And so, I'm going to ask for genes
00:18:42.26 that are maximally expressed at.
00:18:45.18 in the last third of that intraerythrocytic.
00:18:48.28 of that intraerythrocytic life cycle,
00:18:51.20 that is, the last 16 hours.
00:18:53.13 In other words, we're looking for things.
00:18:56.06 genes where expression is maximal at
00:19:01.03 40 plus or minus 8 hours.
00:19:04.01 I don't care when the gene is turned off.
00:19:06.11 But I'm going to look for genes that are upregulated by 4-fold
00:19:11.01 -- you can change these parameters if you wish --
00:19:14.03 and that are reasonably abundant,
00:19:16.19 let's say in the top 60th percentile
00:19:20.08 of all genes in the genome.
00:19:23.16 And now we'll run this query, and presumably return hundreds of genes that are.
00:19:28.26 that fulfill those criteria --
00:19:31.24 600 genes in this particular question.
00:19:38.04 600 genes.
00:19:39.14 And we can see, if I stand aside, the actual expression profile.
00:19:41.27 This hypothetical protein shows, indeed, the pattern that we expect:
00:19:46.16 maximally expressed towards the end of the intraerythrocytic life cycle
00:19:49.14 in all three of these strains
00:19:52.22 symbolized by the red, blue, and yellow curves.
00:19:56.21 Alright.
00:19:59.01 Are there other questions that we. that we might want to address?
00:20:03.05 Well, as a geneticist,
00:20:05.27 I guess I would be particularly interested in taking advantage of some
00:20:08.11 of the most exciting new datasets
00:20:10.16 that have emerged for malaria parasites,
00:20:12.24 from resequencing projects designed to assess the diversity of parasites
00:20:16.18 throughout the world.
00:20:18.15 And these have. and as a result of such studies,
00:20:21.28 we can identify polymorphisms,
00:20:24.08 single nucleotide polymorphisms, or SNPs,
00:20:28.04 that distinguish one gene from another.
00:20:30.09 And so, we'll consider comparing
00:20:35.11 any two strains of our choosing,
00:20:37.01 and I'm going to compare the reference strain, 3D7,
00:20:39.02 the strain whose complete genome was first sequenced,
00:20:43.01 with a field isolate,
00:20:46.24 a field isolate from Ghana, the GHANA1 strain.
00:20:49.23 And we can set our parameters in various different ways.
00:20:53.10 We could ask, for example,
00:20:55.16 for polymorphisms that are known to affect
00:20:59.00 coding potential
00:21:00.22 or for the density of polymorphisms.
00:21:02.25 Just to keep things simple,
00:21:04.22 I'm going to ask for any gene that has
00:21:07.08 at least five known polymorphisms.
00:21:10.06 But once again, you may want to manipulate these parameters.
00:21:14.05 And asking a question like this
00:21:16.27 gives us back several hundred genes,
00:21:19.09 including, as we might expect,
00:21:21.16 the variant surface antigens, PfEMP1 genes
00:21:24.08 that I don't think are.
00:21:27.15 would be likely advocated as a single-subunit vaccine,
00:21:32.13 but certainly genes that are likely to be highly polymorphic.
00:21:39.10 There are many other questions you can consider asking,
00:21:41.22 and this is.
00:21:43.15 this has already become a fairly long session,
00:21:45.10 so I'm going to just limit myself to one more question,
00:21:47.09 a question related to the evolutionary biology of these parasites.
00:21:51.11 One might imagine,
00:21:54.08 if we were looking for candidate vaccine targets,
00:21:58.21 that we'd be interested in genes that are specific to malaria parasites,
00:22:03.16 so we can interrogate for genes
00:22:06.16 across the range of life,
00:22:08.25 for where those genes are found.
00:22:11.07 And we might imagine,
00:22:13.20 as we scroll down to look at eukaryotic organisms,
00:22:16.03 and the apicomplexa in particular,
00:22:18.10 that we'd be interested in genes that are found in Plasmodium falciparum -- of course --
00:22:22.15 and perhaps, if we wanted to consider a candidate target
00:22:25.07 with broad-spectrum activity,
00:22:27.17 we might want to look for genes that are also present in Plasmodium vivax,
00:22:30.12 the second leading cause of malaria in humans.
00:22:35.21 But we're certainly not interested in genes
00:22:38.04 that are present in humans.
00:22:39.22 And so, I'm going to.
00:22:41.10 I'm going to ask in this particular question
00:22:43.23 for genes that are absent from humans
00:22:45.27 or maybe absent from mammals all together.
00:22:49.25 And running a question like this gives me a large fraction of the genome,
00:22:55.09 a third of the parasite genome,
00:22:57.07 which is distinctive in being present in Plasmodium falciparum.
00:23:00.11 Most of these proteins, we know nothing about.
00:23:02.21 hypothetical proteins.
00:23:04.18 So, let's return to our now rather long list of questions
00:23:09.13 that we suggest might be relevant
00:23:11.27 to vaccine development.
00:23:17.13 We've asked for proteins that have antigenicity.
00:23:23.00 That was our question number 3.
00:23:26.01 And I'm going to try to combine that information with the other questions I've asked.
00:23:32.16 I'm going to ask for proteins now,
00:23:34.25 not just for. not for the union of proteins with signal peptides
00:23:38.05 and transmembrane domains,
00:23:40.04 as we asked earlier,
00:23:41.25 but for the intersection of these various different queries.
00:23:43.21 I'm going to ask for genes
00:23:46.12 that have some level of immunogenicity
00:23:48.03 and also have either a signal peptide
00:23:50.17 or a transmembrane domain
00:23:53.16 -- so, that was my question number 6 --
00:23:56.01 were also present at the right time,
00:23:58.25 expressed abundantly in schizonts
00:24:02.14 and were highly polymorphic,
00:24:05.24 indicating diversifying selection,
00:24:08.00 presumably under control of the immune system
00:24:10.01 and also showed this evolutionary profile
00:24:13.15 that was present in these particular species.
00:24:16.02 So, this is a question
00:24:18.20 that I've actually never asked in exactly this same way,
00:24:21.11 although I've certainly run many similar sorts
00:24:25.25 of questions in the past.
00:24:27.28 And I can see that in this particular set of queries,
00:24:30.18 I come up with a list of 23 proteins.
00:24:34.15 Let me turn off this track ind.
00:24:38.20 showing the expression profiling
00:24:40.26 so we can see these a little bit better,
00:24:42.23 and I'm going to display all of them on one page.
00:24:47.05 And now, we can ask a little more readily
00:24:50.04 about the various proteins we've looked at.
00:24:53.02 So, let's scroll down the list.
00:24:57.01 First on the list -- just first alphabetically --
00:24:58.27 is a hypothetical protein.
00:25:00.23 It's a conserved hypothetical protein.
00:25:02.21 Is this a vaccine antigen. I don't know.
00:25:05.14 But my eye is immediately drawn to what you might think of
00:25:08.24 for this computational experiment as a positive control,
00:25:13.20 that AMA1 protein,
00:25:16.22 the protein that is one of the leading vaccine targets
00:25:19.00 for antimalarial vaccine development.
00:25:22.00 A number of other proteins: a guanylyl cyclase, a kinase,
00:25:26.09 several other hypothetical proteins.
00:25:28.22 It's hard for me to believe that a tRNA ligase
00:25:31.17 would be a good vaccine candidate,
00:25:34.03 but here's the second of my positive controls,
00:25:37.06 MSP1, the other of these leading candidates
00:25:40.06 for an intraerythrocytic or an erythrocytic stage vaccine,
00:25:44.05 and several other proteins which have certainly been considered.
00:25:47.14 This CLAG9 protein has been advanced, for example,
00:25:50.12 as a candidate target for vaccine development.
00:25:54.10 So, my point here is not to argue
00:25:57.16 that computational approaches,
00:26:00.03 considered in and of themselves,
00:26:02.06 are ever going to be sufficie
00:26:05.07 for identifying successful vaccine targets.
00:26:07.29 That would be as absurd as saying
00:26:11.23 that we can identify the function of the apicoplast
00:26:14.24 solely by using cell biological approaches
00:26:19.08 of organelle purification without any biochemical or genetic characterization.
00:26:25.09 But certainly, in a few minutes sitting here at the computer,
00:26:28.04 we're been able to filter the many thousands of genes
00:26:32.24 in the parasite genome
00:26:35.13 down to a rather short list, a list of 23,
00:26:37.12 that includes both of our positive control antigens,
00:26:41.04 AMA1 and MSP1.
00:26:43.05 And I would imagine that if I were interested
00:26:46.02 in vaccine development for malaria,
00:26:49.01 I would certainly want to explore further
00:26:51.22 the other 21 proteins on this list,
00:26:54.07 as a manageable set that might be worth exploring
00:26:57.16 for candidate genes
00:27:01.02 that may be as good as or even better
00:27:04.25 than AMA1 or MSP1 as vaccine targets
00:27:08.23 for antimalarial development.

The Bird Heart: A Look At The Circulatory System & Blood

Active flapping flight needs a lot of energy to maintain.

This, in turn, necessitates an efficient and effective circulatory system.

Birds have evolved such a system and it is very similar to a mammal’s. Bird blood is similar to ours, in that it contains both red (erythrocytes) and white blood cells called leucocytes.

The red blood cells are iron-based proteins like ours – and do the work of moving oxygen around the system and taking the waste carbon dioxide away from the muscles and other organs. However, unlike ours, a birds red blood cells are nucleated, i.e. they have a nucleus where our red corpuscles have no nucleus.

Flight muscles need a lot of oxygen on a regular basis. To get it, the blood must be kept moving rapidly around the system.

To achieve, this birds have (like mammals) evolved a four chambered heart (reptiles have only a three chambered heart).

Two of these chambers are basically receiving vessels called atria – into them the blood flows at the end of its journey around the body, or to and from the lungs. The other two chambers, called ventricles, are the pumping power houses that send the blood off on its endless journey again.

Thus the blood travels in a figure 8, as in mammals.

The oxygenated blood (red), is pumped out to the various parts of the body by the left ventricle. Where after giving up its life fuelling oxygen and collecting the carbon dioxide, it returns, as deoxygenated blood (blue) to the right atrium through three large veins called the caval veins – (left caval, right caval and post caval).

From here it is shunted to the right ventricle, which pumps it out to the bird’s lungs via the pulmonary arch – where the carbon dioxide is dumped to be exhaled (breathed out) and a new load of oxygen picked up.

This newly reoxygenated (red) blood returns to the left atrium of the bird’s heart via four large pulmonary veins. (We mammals only have two pulmonary veins).

From here, it is shunted to the left ventricle so that the cycle can start all over again.

The possession of four pulmonary veins – along with the fact that a bird’s heart is generally larger and more muscular per pound (or kilogram) or body weight than ours – explains why a bird’s circulatory system is more efficient than ours.

The left ventricle in the bird heart is by far the largest chamber and has to work exceptionally hard in small birds which have hovering flight, such as humming birds.

Birds’ resting heartbeat rates (beats per minute)

It is a general rule in nature, that smaller animals have larger hearts in proportion to their body size and faster heart rates.

The relative size of a bird’s heart is also affected by its lifestyle – Tinamous are flightless birds and therefore do not need such athletic hearts.

Birds with primarily gliding flight will also need less capable hearts than those that practice active flight – particularly hovering. Like us, a bird’s heart rate rapidly increases when it is involved in exercise and the heart rates of small birds can easily rise above 1000 beats per minute during flight.

Red Blood Cell Production

Red blood cell (RBC) production (erythropoiesis) takes place in the bone marrow under the control of the hormone erythropoietin (EPO). Juxtaglomerular cells in the kidney produce erythropoietin in response to decreased oxygen delivery (as in anemia and hypoxia) or increased levels of androgens. In addition to erythropoietin , red blood cell production requires adequate supplies of substrates, mainly iron, vitamin B12, folate, and heme.

RBCs survive about 120 days. They then lose their cell membranes and are then largely cleared from the circulation by the phagocytic cells of the spleen and liver. Hemoglobin is broken down primarily by the heme oxygenase system with conservation (and subsequent reutilization) of iron, degradation of heme to bilirubin through a series of enzymatic steps, and reutilization of amino acids. Maintenance of a steady number of RBCs requires daily renewal of 1/120 of the cells immature RBCs (reticulocytes) are continually released and constitute 0.5 to 1.5% of the peripheral RBC population.

With aging, hemoglobin and hematocrit (Hct) decrease slightly, but not below normal values. In menstruating women, the most common cause of lower RBC levels is iron deficiency due to chronic blood loss resulting from menstruation.


In a 1981 review of the history of mitochondria in the Journal of Cell Biology, authors Lars Ernster and Gottfried Schatz note that the first true observation of mitochondria was by Richard Altmann in 1890. While Altmann called them &ldquobioblasts,&rdquo their current, visually descriptive name was given by Carl Benda in 1898, based on his observations of developing sperm. &ldquoMitochondria&rdquo derives from two Greek words: &ldquomitos&rdquo meaning thread, and &ldquochondros&rdquo meaning granule. As described by Karen Hales, a professor of biology at Davidson College, in Nature Education, these organelles are dynamic, and constantly fuse together to form chains, and then break apart.

Individual mitochondria are capsule shaped, with an outer membrane and an undulating inner membrane, which resembles protruding fingers. These membranous pleats are called cristae, and serve to increase the overall surface area of the membrane. When compared to cristae, the outer membrane is more porous and is less selective about which materials it lets in. The matrix is the central portion of the organelle and is surrounded by cristae. It contains enzymes and DNA. Mitochondria are unlike most organelles (with an exception of plant chloroplasts) in that they have their own set of DNA and genes that encode proteins.

Plant mitochondria were first observed by Friedrich Meves in 1904, as mentioned by Ernster and Schatz (Journal of Cell Biology, 1981). While plant and animal mitochondria do not differ in their basic structure, Dan Sloan, an assistant professor at the University of Colorado said, their genomes are quite different. They vary in size and structure.

According to Sloan, the genomes of most flowering plants are about 100,000 base pairs in size, and can be as large as 10 million base pairs. In contrast, mammalian genomes are about 15,000 to 16,000 base pairs in size. Moreover, while the animal mitochondrial genome has a simple circular configuration, Sloan said that the plant mitochondrial genome, though depicted as circular, could take on alternate forms. &ldquoTheir actual structure in vivo [within the plant] is not well understood. They might be complex branched molecules,&rdquo he said.

The impact of red blood cell lifespan on HbA1c measurement

Diabetes mellitus affects over 30 million Americans, and 1.5 million Americans are diagnosed with diabetes every year. In addition to monitoring whole blood glucose, it is recommended by the American Diabetes Association (ADA) to test diabetic patients for hemoglobin A1c (HbA1c) two to four times per year. The completed HbA1c results in a patient’s medical record is used as an indicator of the quality of medical care and can play a role in monetary reimbursement. Regarding these guidelines and reimbursement practices, and knowing the absence of a HbA1c data point may result in a lowered quality score for the clinician, are there clinical reasons why a patient should not have HbA1c reported or be reported with caution? This article will discuss the role of red blood cell (RBC) lifespan on HbA1c results, clinical interference on HbA1c results, and cases where HbA1c should not be reported for clinical reasons. This article will provide a perspective to those laboratorians who, depending upon the test method, must answer the question, “Why didn’t the laboratory provide a result on my patient today?”

Assumptions related to RBC survival

The HbA1c results are used to provide an estimation of the patient’s glycemic control over the last two to three months, assuming the RBCs have an average circulating lifespan of 120 days. During that time period, glucose in the blood permanently binds to the hemoglobin in the RBC by the Amadori rearrangement forming HbA1c from the wild type (or typical) HbA. The higher the level of circulating glucose the higher the percentage of HbA1c will be formed, in turn, an average estimated glucose level (eAG) can be calculated from the percentage of HbA1c. Recently Cohen, et al, has summarized altered RBC lifespan will affect the eAG from a calculated HbA1c result. 1

Current interpretation of HbA1c values, which corresponds to the calculated (eAG), assumes that the RBC life span is the same for all patients. However, even modest variation in red cell survival—that would not be apparent in routine hematological studies—could have a significant impact on the HbA1c level. 2 Therefore, the detection of some of the more common causes of decreased (or increased) RBC survival would be important in determining whether the HbA1c level was an accurate reflection of a patient’s level of glycemic control. In general, a shorter RBC life span would yield lower levels of HbA1c at a given average whole blood glucose concentration as compared to that of a normal patient.

Extrinsic causes of decreased RBC survival include pernicious anemia, acquired hemolytic anemia, pregnancy, nephritis, hepatic disease, burns, sepsis, and anemia associated with malignancy. Intrinsic causes include hemoglobinopathy, paroxysmal nocturnal hemoglobinuria, congenital hemolytic jaundice, and elliptocytosis. Renal and hepatic disease may be detected by scrutiny of the results of routine serum chemistry profiles. Hemolytic anemia is rare, and may be suspected with a normocytic, normochromic pattern of anemia. Rarely will a patient with diabetes have testing which is specifically focused on determining if red cell survival is diminished due to congenital causes, with the most common condition being the presence of a hemoglobinopathy.

Interferences to be considered

  1. Analytical interference: Most newer methods for HbA1c have minimal analytical interference from the presence of the major hemoglobin variants (HbS, HbC, HbE, HbD) in the specimen. The reader is referred to the NGSP (National Glycohemoglobin Standardization Program) website for a more detailed table by manufacturer and methodology.
  2. Clinical interference: There are clinical conditions which will limit the ability to use the HbA1c value as an estimate of the degree of glycemic control. “This issue is of particular concern when using assays for HbA1c (e.g. immunoassay) that will produce an HbA1c result for homozygous Hb variants, without providing information that an Hb variant is present in the sample.” 3

The summary of the 2018 Standards of Medical Care in Diabetes by The American Diabetes Association stresses that the A1c test can give skewed results in people with certain genetic traits that alter the molecules in their red blood cells, such as hemoglobinopathies. 3

Most methods are free from analytical interference from common hemoglobinopathies however, the clinical interference may not be known if the patient’s results do not indicate the presence of a hemoglobinopathy or other disease state that can alter the RBC lifespan. 4

The decision related to the method to be used for measurement of HbA1c would be easier if one knew that each patient being tested had a normal RBC lifespan. If this were the case, then the decision could be made based on test cost and the ability to automate the pre and post-analytical components of this analysis. Unfortunately, there is a small percentage of patients being tested by non-separation methods, such as immunoassay, enzymatic, or boronate affinity, that have undetected shortening of RBC survival, to a degree that will cause a reduction in HbA1c that is unrelated to the patient’s average glucose level during the prior two to three months. How is one to determine when HbA1c, in the presence of a disease state, such as a hemoglobinopathy, is clinically inaccurate due to RBC survival issues? A methodology that indicates if a hemoglobin variant is present must be used.

It has been suggested that prior clinical information in the medical record could be reviewed to determine if there are any conditions that will cause significant shortening of the red cell survival time. This approach may not be feasible in a high-volume reference laboratory, or in facilities that have isolated medical records for outpatient and inpatient encounters. It will not be useful in selecting patients that have a clinically silent hemoglobinopathy but have never been tested for this condition.

Another solution is to implement a method for HbA1c testing which will also detect most hemoglobinopathies and allow the laboratory to report the comment: “The presence of a hemoglobinopathy in this patient may cause a reduction in red cell survival, which could falsely reduce the measured HbA1c level. Please consider fructosamine or glycated albumin testing to monitor this patient’s level of glycemic control.”

In many healthcare systems, the use of HbA1c is mandated by predetermined practice guidelines that are tied to reimbursement and a quality scorecard. These electronic monitoring systems cannot accept this comment as satisfying the requirement for a quantitative HbA1c result. It may be necessary to modify these quality systems to allow for these selected patients to meet the quality guidelines by alternative testing methods.


HbA1c testing has been promoted as a required test to monitor glycemic control in all diabetic patients. Many methods have been evaluated for analytical interferences from the presence of common, abnormal hemoglobin molecules, and laboratory acceptance of certain methods have been based solely on a manufacturer’s claim of lack of analytical interferences.

There is growing evidence that it is also important to identify the clinical status of a patient where there is significantly decreased red cell survival, as the HbA1c will be falsely lowered. While many conditions which shorten RBC life can be suspected by a review of the patient’s medical record or prior laboratory results, there are patients with inherited abnormalities in hemoglobin structure or globin synthesis rate that have reduced RBC survival times which are clinically silent. These are the patients that will benefit from the use of a method for HbA1c that highlights the presence of the abnormal hemoglobin molecules.

In these cases, one is not seeking a HbA1c result that is free of analytical interferences, but instead one which allows the testing to be directed to another method which is less dependent on the assumption of a normal RBC lifespan, such as fructosamine or glycated albumin.

Cardiovascular Terms

  • angioblast - the stem cells in blood islands generating endothelial cells which will form the walls of both arteries and veins. (More? Blood Vessel)
  • angiogenesis - the formation of new blood vessels from pre-existing vessels following from vasculogenesis in the embryo. (More? Blood Vessel)
  • anlage (German, anlage = primordium) structure or cells which will form a future more developed or differentiated adult structure.
  • blood islands - earliest sites of blood vessel and blood cell formation, seen mainly on yolk sac chorion.
  • cardinal veins - paired main systemic veins of early embryo, anterior, common, posterior.
  • cardiogenic region - region above prechordal plate in mesoderm where heart tube initially forms.
  • ectoderm - the layer (of the 3 germ cell layers) which form the nervous system from the neural tube and neural crest and also generates the epithelia covering the embryo.
  • endoderm - the layer (of the 3 germ cell layers) which form the epithelial lining of the gastrointestinal tract (GIT) and accessory organs of GIT in the embryo.
  • endocardium - lines the heart. Epithelial tissue lining the inner surface of heart chambers and valves.
  • endothelial cells - single layer of cells closest to lumen that line blood vessels.
  • extraembryonic mesoderm - mesoderm lying outside the trilaminar embryonic disc covering the yolk sac, lining the chorionic sac and forming the connecting stalk. Contributes to placental villi development.
  • haemocytoblasts - stem cells for embryonic blood cell formation.
  • anastomose - to connect or join by a connection (anastomosis) between tubular structures.
  • chorionic villi - the finger-like extensions which are the functional region of the placental barrier and maternal/fetal exchange. Develop from week 2 onward as: primary, secondary, tertiary villi.
  • estrogens - support the maternal endometrium.
  • growth factor - usually a protein or peptide that will bind a cell membrane receptor and then activates an intracellular signaling pathway. The function of the pathway will be to alter the cell directly or indirectly by changing gene expression. (eg VEGF, shh)
  • intra-aortic hematopoietic cluster - (IAHC) blood stem cells associated with the endothelial layer of aorta and large arteries.
  • maternal decidua - region of uterine endometrium where blastocyst implants. undergoes modification following implantation, decidual reaction.
  • maternal sinusoids - placental spaces around chorionic villi that are filled with maternal blood. Closest maternal/fetal exchange site.
  • Megakaryocytopoiesis - the process of bone marrow progenitor cells developMENT into mature megakaryocytes.
  • mesoderm - the middle layer of the 3 germ cell layers of the embryo. Mesoderm outside the embryo and covering the amnion, yolk and chorion sacs is extraembryonic mesoderm.
  • myocardium - muscular wall of the heart. Thickest layer formed by spirally arranged cardiac muscle cells.
  • pericardium - covers the heart. Formed by 3 layers consisting of a fibrous pericardium and a double layered serous pericardium (parietal layer and visceral epicardium layer).
  • pericytes - (Rouget cells) cells located at the abluminal surface of microvessels close to endothelial cells, mainly found associated with CNS vessels and involved in vessel formation, remodeling and stabilization.
  • pharyngeal arches (=branchial arches, Gk. gill) series of cranial folds that form most structures of the head and neck. Six arches form but only 4 form any structures. Each arch has a pouch, membrane and groove.
  • placenta - (Greek, plakuos = flat cake) refers to the discoid shape of the placenta, embryonic (villous chorion)/maternal organ (decidua basalis)
  • placental veins - paired initially then only left at end of embryonic period, carry oxygenated blood to the embryo (sinus venosus).
  • protein hormone - usually a protein distributed in the blood that binds to membrane receptors on target cells in different tissues. Do not easliy cross placental barrier.
  • sinus venosus - cavity into which all major embryonic paired veins supply (vitelline, placental, cardinal).
  • splanchnic mesoderm - portion of lateral plate mesoderm closest to the endoderm when coelom forms.
  • steroid hormone - lipid soluble hormone that easily crosses membranes to bind receptors in cytoplasm or nucleus of target cells. Hormone+Receptor then binds DNA activating or suppressing gene transcription. Easliy cross placental barrier.
  • syncitiotrophoblast extraembryonic cells of trophoblastic shell surrounding embryo, outside the cytotrophoblast layer, involved with implantation of the blastocyst by eroding extracellular matrix surrounding maternal endometrial cells at site of implantation, also contribute to villi. (dark staining, multinucleated).
  • truncus arteriosus - an embryological heart outflow structure, that forms in early cardiac development and will later divides into the pulmonary artery and aorta. Term is also used clinically to describe the malformation where only one artery arises from the heart and forms the aorta and pulmonary artery.
  • vascular endothelial growth factor - (VEGF) A secreted protein growth factor family, which stimulates the proliferation of vasular endotheial cells and therefore blood vessel growth. VEGF's have several roles in embryonic development. The VEGF family has 7 members (VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGF-E, VEGF-F, and PlGF) that have a common VEGF homology domain. PIGF is the placental growth factor. They act through 3 VEGF tyrosine kinase membrane receptors (VEGFR-1 to 3) with seven immunoglobulin-like domains in the extracellular domain, a single transmembrane region, and an intracellular tyrosine kinase sequence.
  • vasculogenesis - the formation of new blood vessels from mesoderm forming the endothelium. Compared to angiogenesis that is the process of blood vessel formation from pre-existing vessels.
  • vitelline blood vessels - blood vessels associated with the yolk sac.
  • waste products - products of cellular metabolism and cellular debris, e.g.- urea, uric acid, bilirubin.
  • anastomose - a direct connection between arteries, veins or arteries and veins. The Circle of Willis is an arterial anastomosis.
  • angioblasts - stem cells in blood islands generating endothelial cells
  • angiogenesis - the formation of blood vessels also called vasculogenesis in the embryo
  • anlage - (Ger. ) primordium, structure or cells which will form a future structure.
  • atrial septal defects - (A.S.D.)
  • blood islands - earliest sites of blood vessel and blood cell formation, seen mainly on yolk sac chorion
  • branched villi - or terminal villi, grow from sides of stem villi, region of main exchange, surrounded by maternal blood in intervillous spaces
  • cardinal veins - paired main systemic veins of early embryo, anterior, common, posterior.
  • cardiogenic region - region above precordal plate in mesoderm where ceart tube initially forms.
  • cord knotting- umbilical cord knotting occurs in 1%, prevents the passage of placental blood. pseudoknots also occur usually with no effect.
  • cotyledons - on maternal face of placenta, form cobblestone appearance, originally placental septa formed grooves
  • cytotrophoblast - extraembryonic cells of trophoblastic shell surrounding embryo, contribute to villi and placental membranes.
  • decidua basalis-
  • decidual reaction -
  • ectoderm - the layer (of the 3 germ cell layers) which form the nervous system from the neural tube and neural crest and also generates the epithelia covering the embryo.
  • endoderm - the layer (of the 3 germ cell layers) which form the epithelial lining of the gastrointestinal tract (GIT) and accessory organs of GIT in the embryo.
  • endothelial cells - single layer of cells closest to lumen that line blood vessels
  • extraembryonic mesoderm - mesoderm lying outside the trilaminar embryonic disc
  • fetal erythroblastosis - see [#Haemolytic Disease Haemolytic Disease of the Newborn]
  • haemocytoblasts - stem cells for embryonic blood cell formation
  • Haemolytic Disease of the Newborn - fetal erythroblastosis, fetus Rh+ /maternal Rh-, fetus causes anti Rh antibodies, dangerous for 2nd child 
  • chorionic villi - the finger-like extensions which are the functional region of the placental barrier and maternal/fetal exchange. Develop from week 2 onward as: primary, secondary, tertiary villi.
  • estrogens - support maternal endometrium
  • fetal drug addiction - occurs when drugs used maternally cross the placental barrier and can establish addiction in the unborn fetus.
  • growth factor - usually a protein or peptide that will bind a cell membrane receptor and then activates an intracellular signaling pathway. The function of the pathway will be to alter the cell directly or indirectly by changing gene expression. (eg VEGF, shh)
  • hCG - [#hCG see Human chorionic gonadotrophin]
  • Human chorionic gonadotrophin - (hCG) like leutenizing hormone, supports corpus luteum
  • Human chorionic somatommotropin - (hCS) or placental lactogen stimulate mammary development
  • Human chorionic thyrotropin - (hCT) placental derived hormone equivilant to thyroid
  • Human chorionic corticotropin - (hCACTH) placental derived hormone equivilant to
  • maternal antibodies - immune molecules capable of crossing placental barrier
  • maternal decidua - region of uterine endometrium where blastocyst implants. undergoes modification following implantation, decidual reaction.
  • maternal sinusoids - placental spaces around chorionic villi that are filled with maternal blood. Closest maternal/fetal exchange site.
  • mesoderm - the middle layer of the 3 germ cell layers of the embryo. Mesoderm outside the embryo and covering the amnion, yolk and chorion sacs is extraembryonic mesoderm.
  • neural crest - cell region at edge of neural plate, then atop the neural folds, that remains outside and initially dorsal to the neural tube when it forms. These paired dorsal lateral streaks of cells migrate throughout the embryo and can differentiate into many different cell types(=pluripotential). Neural crest cells also contribute to major cardiac outflow vessels.
  • patent ductus arteriosus - (P.D.A.)
  • pharyngeal arches - (=branchial arches, Gk. gill) form structures of the head. Six arches form but only 4 form any structures. Each arch has a pouch, membrane and groove.
  • placenta - (Gk. plakuos= flat cake) refers to the discoid shape of the placenta, embryonic (villous chorion)/maternal organ (decidua basalis)
  • placenta accreta - abnormal adherence of placenta, with absence of decidua basalis
  • placental arteries - paired, carry deoxygenated blood (from dorsal aorta) and waste products to the placental villi
  • placental lactogen - see [#hCS Human chorionic somatommotropin]
  • placenta percreta - villi of placenta penetrate myometrium
  • placenta previa - placenta overlies internal os of uterus, abnormal bleeding, cesarian delivery
  • placental veins - paired initially then only left at end of embryonic period, carry oxygenated blood to the embryo (sinus venosus)
  • primary villi - week 2, first stage of chorionic villi development, trophoblastic shell cells (syncitiotrophoblasts and cytotrophoblasts) form finger-like extensions into maternal decidua.
  • protein hormone - usually a protein distributed in the blood that binds to membrane receptors on target cells in different tissues. Do not easliy cross placental barrier.
  • relaxin - hormone
  • secondary villi - week 3, second stage of chorionic villi development, extraembryonic mesoderm grows into villi, covers entire surface of chorionic sac
  • sinus venosus - cavity into which all major embryonic paired veins supply (vitelline, placental, cardinal)
  • splanchnic mesoderm - portion of lateral plate mesoderm closest to the endoderm when coelom forms.
  • stem villi - or anchoring villi, cytotrophoblast cells attached to maternal tissue.
  • steroid hormone - lipid soluble hormone that easily crosses membranes to bind receptors in cytoplasm or nucleus of target cells. Hormone+Receptor then binds DNA activating or suppressing gene transcription. Easliy cross placental barrier.
  • syncitiotrophoblast - extraembryonic cells of trophoblastic shell surrounding embryo, outside the cytotrophoblast layer, involved with implantation of the blastocyst by eroding extracellular matrix surrounding maternal endometrial cells at site of implantation, also contribute to villi. (dark staining, multinucleated)
  • tetralogy of Fallot- Named after Etienne-Louis Arthur Fallot (1888) who described it as "la maladie blue". The syndrome consists of a number of a number of cardiac defects possibly stemming from abnormal neural crest migration.
  • tertiary villi - third stage of chorionic villi development, mesenchyme differentiates into blood vessels and cells, forms arteriocapillary network, fuse with placental vessels, developing in connecting stalk
  • umbilical cord
  • umbilical cord knotting
  • vascular endothelial growth factor - (VEGF) protein growth factor family that stimulates blood vessel growth, a similar factor can be found in the placenta (PIGF).
  • ventricular septal defects - (V.S.D.)
  • virus - small infectious agent able to cross placental barrier. Can infect embryo and cause developmental abnormalities. (e.g. cytomegalovirus, rubella, measles)
  • vitelline blood vessels - blood vessels associated with the yolk sac.
  • waste products - products of cellular metabolism and cellular debris, e.g.- urea, uric acid, bilirubin