What was the evolutionary benefit of enclosing hemoglobin in cells?

What was the evolutionary benefit of enclosing hemoglobin in cells?

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

The ancestral solution to oxygen transport is with hemoglobin (or, similar proteins) dissolved in blood (or, "hemolymph", but, basically, dissolved in water. ) What was the advantage of enclosing the oxygen-transport proteins in cells?

If you enclose the globin in a cell you can achieve a high concentration of the globin, which makes for a faster, readily usable pool available, and it is not subject to degradation (via proteases, or other mechanisms) as if it was just dissolved. In addition, all other functions of the globins (pH regulation, CO2 metabolism, etc) will be more tightly controlled if the globin is on the same site (i.e., inside a cell), instead of being mixed in a milieu. All of that has probably more to do with the evolution of the entire circulatory system, as a whole, and only sequentially as the 'enclosure' of globins in specialized cells. You can find some more details here.

Also, hiding the Iron from invading bacteria is good. Free iron is a limiting factor for bacterial growth.

The adaptive benefit of evolved increases in hemoglobin-O 2 affinity is contingent on tissue O 2 diffusing capacity in high-altitude deer mice

Background: Complex organismal traits are often the result of multiple interacting genes and sub-organismal phenotypes, but how these interactions shape the evolutionary trajectories of adaptive traits is poorly understood. We examined how functional interactions between cardiorespiratory traits contribute to adaptive increases in the capacity for aerobic thermogenesis (maximal O2 consumption, V̇O2max, during acute cold exposure) in high-altitude deer mice (Peromyscus maniculatus). We crossed highland and lowland deer mice to produce F2 inter-population hybrids, which expressed genetically based variation in hemoglobin (Hb) O2 affinity on a mixed genetic background. We then combined physiological experiments and mathematical modeling of the O2 transport pathway to examine the links between cardiorespiratory traits and V̇O2max.

Results: Physiological experiments revealed that increases in Hb-O2 affinity of red blood cells improved blood oxygenation in hypoxia but were not associated with an enhancement in V̇O2max. Sensitivity analyses performed using mathematical modeling showed that the influence of Hb-O2 affinity on V̇O2max in hypoxia was contingent on the capacity for O2 diffusion in active tissues.

Conclusions: These results suggest that increases in Hb-O2 affinity would only have adaptive value in hypoxic conditions if concurrent with or preceded by increases in tissue O2 diffusing capacity. In high-altitude deer mice, the adaptive benefit of increasing Hb-O2 affinity is contingent on the capacity to extract O2 from the blood, which helps resolve controversies about the general role of hemoglobin function in hypoxia tolerance.

Keywords: Complex trait evolution Evolutionary physiology Hemoglobin adaptation High-altitude adaptation O2 transport pathway.

The adaptive benefit of evolved increases in hemoglobin-O2 affinity is contingent on tissue O2 diffusing capacity in high-altitude deer mice

Background Complex organismal traits are often the result of multiple interacting genes and sub-organismal phenotypes, but how these interactions shape the evolutionary trajectories of adaptive traits is poorly understood. We examined how functional interactions between cardiorespiratory traits contribute to adaptive increases in the capacity for aerobic thermogenesis (maximal O2 consumption, V◻O2max, during acute cold exposure) in high-altitude deer mice (Peromyscus maniculatus). We crossed highland and lowland deer mice to produce F2 inter-population hybrids, which expressed genetically based variation in hemoglobin (Hb) O2 affinity on a mixed genetic background. We then combined physiological experiments and mathematical modeling of the O2 transport pathway to examine links between cardiorespiratory traits and V◻O2max.

Results Physiological experiments revealed that increases in Hb-O2 affinity of red blood cells improved blood oxygenation in hypoxia, but were not associated with an enhancement in V◻O2max. Sensitivity analyses performed using mathematical modeling showed that the influence of Hb-O2 affinity on V◻O2max in hypoxia was contingent on the capacity for O2 diffusion in active tissues.

Conclusions These results suggest that increases in Hb-O2 affinity would only have adaptive value in hypoxic conditions if concurrent with or preceded by increases in tissue O2 diffusing capacity. In high-altitude deer mice, the adaptive benefit of increasing Hb-O2 affinity is contingent on the capacity to extract O2 from the blood, which helps resolve controversies about the general role of hemoglobin function in hypoxia tolerance.


Sickle cell hemoglobin (HbS) has existed in humans for thousands of years. Dr. Konotey-Ahulu, a Ghanaian physician, reports that among West African tribes, specific names were assigned to clinical syndromes identifiable as sickle cell anemia (7). However, sickle cells were first described in the peripheral blood of an anemic patient from the West Indies by the Chicago physician Robert Herrick in 1910(8). While homozygous sickle cell anemia is the most common and severe form of sickle cell disease (SCD), other sickling disorders combining HbS with beta or alpha thalassemia, hemoglobin C, hemoglobin D, and other hemoglobins share a similar pathophysiology with common as well as distinguishing clinical features.

HbS results from a single base-pair mutation in the gene for the beta-globin chain of adult hemoglobin. An adenine-to-thymine substitution in the sixth codon replaces glutamic acid with valine in the sixth amino acid position of the beta-globin chain (9, 10). This substitution yields the electrophoretically distinct hemoglobin described by Linus Pauling in 1949 (11). In the deoxygenated form of HbS, the beta-6 valine becomes buried in a hydrophobic pocket on an adjacent beta-globin chain, joining the molecules together to form insoluble polymers (9). In sufficient concentration, these insoluble polymers give rise to the classical sickle morphology. This process causes severe damage to the red cell membrane. Sickled red cells may then aggregate and go on to cause microvascular obstruction. Also, these abnormal red cells adhere to endothelial cells (12) and can interact with various cytokines (13).

The process of microthrombosis and microembolization is the foundation of SCD pathology. Occlusion of the microvasculature by sickled erythrocytes causes painful crises, priapism, pulmonary emboli, and osteonecrosis, and ultimately damages every organ system including the retinae, spleen, liver, and kidneys. Many patients with SCD have hematocrits of 20% to 35% and chronic reticulocytosis. Clinical symptoms can be precipitated by fever, infection, excessive exercise, temperature changes, hypoxia, and hypertonic solutions. The clinical severity of the symptoms experienced is related to the concentration of HbS in the red blood cell and expression of other hemoglobins, endothelial factors, nitric oxide and other factors. Also, patients with SCD have a higher proportion of dense, dehydrated erythrocytes (14).

In about 11% of SCD patients under 20 years of age, stroke occurs because of stenotic cranial artery lesions, demonstrable by transcranial Doppler ultrasonography. A regular program of transfusion aimed at reducing the sickle cell population to 㱐% prevents about 90% of stroke cases. Unfortunately, the high risk of stroke returns after transfusion is discontinued (15).

The surface of HbS consists mainly of hydrophilic amino acid side chains together with some smaller hydrophobic side chains. Since adult hemoglobin is present at a very high concentration within the red cell and yet appears to remain free from aggregation at all levels of saturation with oxygen, the amino acids on the surface of the molecule must be arranged so as to avoid attraction between adjacent molecules. Of the majority of hemoglobin variants with surface amino acid substitutions, only a minority are associated with any significant clinical abnormalities. Except for HbS, none of those more common hemoglobins found in the homozygous state, such as hemoglobins C, D, and E, are associated with any greater abnormality than mild anemia. The surface of hemoglobin A is therefore able to accommodate a variety of different amino acid changes without its structure or function being affected (16).

The valine-for-glutamic acid substitution has very little effect on the oxygenated form of HbS (17). However, when the concentration of deoxygenated HbS becomes sufficiently great, its properties differ markedly from those of deoxygenated hemoglobin A, causing the formation of insoluble fibers and bundles, which distort the red blood cell into the sickle shape.

Since the discovery of HbS, the clinical symptomatology and associated pathophysiology of SCD have gradually been elucidated (18). SCD is characterized by anemia and four types of crises: painful (vasoocclusive), sequestrative, hemolytic, and aplastic. Damage to the red blood cell membrane gives rise to reduced cell survival and chronic hemolytic anemia. If severe enough, this damage increases the risk of bilirubin gallstone formation, stroke, and heart failure. Also, the anemia is aggravated by the mechanical impedance to blood flow caused by sickled red blood cells, resulting in widespread vasoocclusive complications. Interestingly, the anemia to some degree can be protective against vasoocclusive complications, as it moderates the increase in viscosity associated with sickling in the microcirculation. Hence, judicious exchange transfusion therapy and blood transfusion is indicated for the prevention of pain crises, stroke, pulmonary hypertension, and other related conditions (19).

Blood transfusion not only increases the oxygen-carrying capacity of blood but also decreases the percentage of cells capable of sickling. It is recommended that transfusion should be carried out with phenotypically matched, leuko-reduced, sickle-cell–negative blood in order to attain a posttransfusion hematocrit of about 36%. (20). The complications of transfusion are well known and include allo- and autoimmunization, iron overload, and the transmission of infectious diseases such as hepatitis and HIV. Also, a considerable number of patients with sickle cell anemia worldwide have undergone successful bone marrow transplantation (21). Only selected patients are eligible for the procedure. Even then, bone marrow transplantation was associated with a 5% to 10% mortality, mostly from graft-versus-host disease.

Another approach to reducing the effect of HbS polymer formation has been to augment the production of fetal hemoglobin (HbF). Through population and clinical observation, it has long been recognized that higher blood HbF levels correlate with fewer clinical manifestations of SCD. Pharmacologic manipulation of HbF in the therapy of sickling disorders has been proposed since the mid-1950s. To date, several agents have been tried, but the safest and most effective has proven to be hydroxyurea(22). The mechanism of increased HbF production by hydroxyurea is not fully understood. Also, recent studies have found that hydroxyurea contributes to the production of nitric acid, a potent endothelial relaxing factor (23).

Numerous inflammatory markers associated with endothelial surfaces and white blood cells are elevated in SCD, including C-reactive protein. Baseline granulocyte counts are often increased. Leukocytosis itself is a risk factor for increased mortality (24). Finally, laminin, a constituent of the endothelial matrix that binds to the Lutheran antigen on red cells, is expressed on sickled red blood cells in greater quantities than on normal red blood cells (25).

Almost every aspect of hemostasis tending to hypercoagulability has been described in SCD (26). However, it is not known whether the hypercoagulability is the cause or the result of vasoocclusion. Thrombocytosis is due to hyposplenia, and platelet aggregation is increased (27). Antiphospholipid antibodies may be elevated, and protein C and S levels are decreased (28). Also, high levels of von Willebrand factor and factor VIII can be found (29). Therapeutic trials of heparins, coumadin, and antiplatelet agents have been limited, yielding inconclusive information, but they are ongoing.

Materials and Methods

Specimen collection

Working with animals we always aim to limit the effect our research afflict populations and individuals. Whenever possible we collaborate with other sources, such as commercial fisheries or museums. This way, no animals need to be euthanized to serve our scientific purpose alone. The tissue samples used in this study are either from museum specimen or commercially fished individuals intended for human consumption. The commercially caught fish were immediately stunned by bleeding, following standard procedures by a local fisherman. Sampling in this manner does not fall under any specific legislation in Norway, but it is in accordance with the guidelines set by the ‘Norwegian consensus platform for replacement, reduction and refinement of animal experiments’ ( For more information regarding the samples see 9 .

Whole-genome sequencing

We selected 27 species, which represent most of the lineages in the Gadiformes order, in addition to its closest living relatives, Stylephorus chordatus, Zeus faber and Percopsis transmontanta 9, 55 . We sequenced paired end libraries with an average insert size of 350 bp (2 × 150 bp reads on Illumina HiSeq. 2000) with coverage ranging from 18 to 40x (average coverage 28x). This sequence strategy gives contigs spanning the average median gene-length 56 , making it ideal for finding and identifying genes, but without substantial gene-order information (synteny). The Celera assembler 57 was used to assemble the genomes, with contig N50 ranging from 3.1 to 8.1 kb with an average of 4.1 kb. CEGMA 58 and BUSCO 59 were used to evaluate gene completeness CEGMA gave, on average, complete or partial hits for 69% of the conserved eukaryotic genes included in the CEGMA analysis and BUSCO gave, on average, 68% of the conserved genes belonging to the Actinopterygii clade in the BUSCO analysis. A list of species with relevant genome statistics is given in Supplementary Table 1. For further information regarding the sequencing see 9, 55 .

Gene mining and annotation

Hb genes were annotated by tBLASTn 60 searches with known Hb sequences from Gadus morhua, Oryzias latipes, Tetraodon nigroviridis, Oreochromis niloticus, Gasterosteus aculeatus, Salmo salar and Danio rerio (annotation and nomenclature following 11 ). For paralogous genes that have recently been duplicated or are similar due to gene conversion, gene copies can collapse in the assembly process. In contrast, with polymorphic genes alleles could be misjudged as copies. However, by manually inspecting alignments of intronic sequences it was possible to distinguish paralogous gene copies from alleles.

Phylogenetic tree construction

To identify orthologous Hb sequences phylogenetic gene trees were constructed, α and β sequences were analyzed separately. Amino acid sequences were aligned using ClustalW 61 as implemented in MEGA7 62 with default settings for all species (alignments of α and β sequences are in Supplementary Data 1). Using the model selection tool in MEGA7 we determined that the best model (i.e. having the lowest AIC score) for molecular evolution was TN93 + G + I for α-sequences and GTR + G + I for β-sequences. Phylogenetic trees were constructed based on codon triplets using maximum likelihood (ML) implemented in MEGA7 and a Bayesian method in MrBayes 3.2.2 63 . A ML tree was constructed based on the models of molecular evolution stated above, with 1000 bootstrap replicates. Bayesian trees were run using standard priors, with four chains of simulations for 1 × 10 7 generations sampling every 1 × 10 3 generation. The GTR + G + I model was used for both α and β as the TN93 + G + I is not available in this program. A given run was considered to have reached convergence when the likelihood scores leveled off asymptotically. All trees sampled prior to convergence were discarded and support (posterior probability) was calculated based on a consensus of the last 2250 trees. Previous work on teleost Hbs shows that Hb from the frog Xenopus tropicalis is clearly outside the clade constituting teleost Hbs 11 , therefore it was chosen as an outgroup species.

The identified α and β genes were then mapped on a phylogenomic species tree based on 567 exons of 111 genes, selected after stringent filtering for single-copy orthologous markers. Branching times were estimated in BEAST v.2.2 64 using a relaxed clock model and 17 fossil constraints. This phylogeny is a modified version from 9 , which describes the procedures in more detail.

Ancestral reconstruction of the number of Hbs

The ancestral reconstruction of number of Hbs in gadiformes was estimated using the function ace implemented in the R package APE 65 . Percopsis transmontana, Zeus faber and Stylephorus chordatus were not included as many of the Hbs found in these species are not 1:1 orthologs with gadiform Hbs. We used maximum likelihood estimation of the ancestral state for discrete characters with three different models, an equal rates model (ER), an all rates different model (ARD) and a symmetrical model (SYM), goodness of fit was estimated using a Chi-square test. All statistics was carried out in R v3.1.3 66 .

Phylogenetic comparative analyses

We used a phylogenetic comparative method called SLOUCH (Stochastic Linear Ornstein-Uhlenbeck models for Comparative Hypotheses) 24, 67,68,69,70 implemented in R v3.1.3 66 , to investigate whether the number of Hb genes has evolved as a response to changes in maximum depth and latitude, respectively (data was obtained for the different species in the global information system FishBase 6 ). The assumed model of trait evolution (trait is here the number of Hb genes) is an Ornstein-Uhlenbeck (OU) process, where the trait evolves towards an optimum that is assumed to be a linear function of a predictor x, as ( heta =a+_x) , the regression parameters are informative of the relationship between the optimum and the trait. The deterministic pull of the trait towards the optimum is can be quantified with the phylogenetic half-life, (_<1/2>=frac,2>,,,) the average time it takes for a species to move half the way from an ancestral state to a new optimum i.e. a half-life above zero indicates adaptation is not immediate. SLOUCH returns an “optimal regression”, which represents the best fit of the estimated primary optimum 67 on Hb copy number. In other words, this optimal regression describes the expected relationship between the number of Hb genes and the predictor in the model if adaptation was instantaneous (i.e. there are no constraints on the evolution of number of Hb genes towards the optimal state). A model that includes a predictor variable can be contrasted with an intercept-only model where no predictor variables are included. Phylogenetic effect is a measure of how well the phylogeny alone explains the distribution of the trait (number of Hb genes). Model comparisons are done using the small sample-size corrected version of Akaike information criterion (AICc).

Analyses of natural selection

For each Hb gene translated amino acid sequences from all species available for that gene in the dataset were aligned following same procedure as described above (alignments presented in Supplementary Data 1). To test for diversifying and purifying selection we used the SLAC, FEL and REL analyses 22 as implemented in the Hyphy software package on the Datamonkey server ( and using the phylogenies in Fig. 1 (referred to as the species tree), and Figs 2 and 3 (referred to as the gene trees).

Homology model building

A 3D protein model was created using the SWISS-MODEL Workspace and the DeepView software 71 for Gadus morhua Hb-I (α1 and β1) based on homology. A template search was carried out in the SWISS-MODEL Workspace, identifying hemoglobin from Trematomus bernacchii (Protein Data Bank (PDB) code 1HBH) as the best template. Gadus morhua α1, α2, α3, α4 and β1, respectively were aligned to the template in DeepView, the alignment was then submitted to the SWISS-MODEL Workspace under project mode. The automated modeler procedure gave one model with high quality (QMEAN4 = 1.34) of a Hb tetramer with two β1 units, and two alpha units of either α1, α2, α3 or α4. This gave four different Hb tetramers in total, which are all shown in Supplementary Data 2.

Selection forecast

To forecast evolution, we need to go a step further and link phenotypes to fitness. At the coarsest level this could entail statistical description of covariation between phenotype and fitness (e.g., for the Price equation Price 1972, Queller 2017), or between genotype and fitness. Currently, we have the statistical tools for this approach. Although this might suffice for making predictions over very short timescales, a lack of mechanistic understanding in this approach limits future projections in changing environmental contexts. Projecting into the future requires a functional understanding of how traits affect fitness, drawing on biomechanics, behavior, ecology, and so on. We lack the capacity, at present, to model how present-day traits, let traits that do not yet exist, generate variation in fitness in as-yet-unobserved environments. However, with improved conceptual models and quality data we can begin to tackle these questions.

As we gain better understanding of genetic process, and selection, we can use existing tools of quantitative or population genetics to forecast the course of evolution. This genetic knowledge can in some instances be simplified by omitting mechanistic detail and taking a quantitative genetic approach (e.g., the breeder's equation), or population genetics for the rare case of simple single-gene traits (Walsh and Lynch 2018). This is an established approach that works well over short timescales, but will break down over longer timescales because we are still refining our mechanistic models of how genetic variance–covariance matrices themselves evolve (we know that they do evolve Roff 2000), and how selective pressures will change. Therefore, the more mechanistic approach outlined in the above genotype to phenotype predictions provides a potentially robust framework, but one that is harder to parameterize (if possible at all). Whether one takes a mechanistic, quantitative, or population genetics approach, the key is incorporating knowledge about the available genetic variation, how this affects fitness, and how response to the resulting selection is constrained.

Forecasting evolutionary responses to known selective pressures works well when the present-day environment can be safely trusted to remain constant. However, selection on focal species depends on abiotic conditions, and biotic interactions, both of which change through time and we must forecast for evolutionary prediction. To do so, we will need to draw on fields ranging from meteorology (climate change being a major driver of evolution during the Anthropocene), to toxicology (from human pollution), to epidemiology and ecology more generally. We therefore need detailed data on the present-day state of multivariate environmental and ecological factors (e.g., species densities of predators, parasites, prey, competitors, mutualists), and the rules of how these change through time (e.g., how species interact to drive each other's changing population densities).

With models of changing environmental conditions in hand (e.g., the Earth system models used by the IPCC for environmental changes IPCC 2014), we need to draw inferences about changing selection pressures. To do so, we need to revisit the phenotype to function to fitness mapping by describing how this mapping (particularly function to fitness) changes depending on the future environments (including possible future communities and interactions). This will allow us to forecast how the fitness landscape will shift through time to favor different trait values, trait combinations, and genotypes at various points in the future.

Finally, the selective pressures acting on a focal species may depend on the genotypes of other species, not just their presence, absence, or abundance. Therefore, evolution by other species may modify the direction of evolution of our focal species (i.e., Darwin's tangled bank). Conversely, evolution by our focal species can have reciprocal effects on the abundance, genotypes, phenotypes, and fitness of all the other species with which it interacts directly (or perhaps indirectly Whitham et al. 2003, Johnson and Stinchcombe 2007). Therefore, effective evolutionary forecasts might also need to consider entire multispecies communities simultaneously in these ecoevolutionary feedback loops.

The above information we need to know to forecast evolution is sobering. And it is likely we will never know enough to effectively forecast evolution with high levels of precision and mechanism over long timescales and in natural environments. This reflects both constraints on our input data, and fundamental stochasticity of biological processes at all levels of organization. However, in many cases we don't need to have absolute precision for practical predictive outcomes. Returning to the subject of what we seek to predict, it may be sufficient to predict that adaptation will occur (Fisher's fundamental theorem), or that a given trait will increase or decrease. For example, predictive evolutionary models are used to determine influenza strain vaccines annually. Although there is limitation in the accuracy of the current models used and evolution of the virus is not entirely predictable, the partial level of accuracy still provides an effective annual vaccine (Agor and Ozaltin 2018). Likewise, in conservation efforts we can predict that in cases of genetic rescue, if we introduce new alleles to a very small, endangered population, issues involving inbreeding depression can be improved. This may be all the information needed for a critically endangered population, but more advanced knowledge about population level adaptations in gene pools across a species's range will help improve models to predict potential outbreeding depression consequences if deleterious alleles are introduced into an already threatened population (Frankham et al. 2011) or help conservation biologist weigh the relative risks of inbreeding or outbreeding depression in a given threatened organism (Edmands 2007). Although precise prediction of evolution is an important goal of evolution biology, our current state of evolutionary prediction still enables many important practical outcomes.

How can evolutionary biology explain why we get cancer?

Over 500 billion cells in our bodies will be replaced daily, yet natural selection has enabled us to develop defenses against the cellular mutations which could cause cancer. It is this relationship between evolution and the body's fight against cancer which is explored in a new special issue of the Open Access journal Evolutionary Applications.

"Cancer is far from a single well-defined disease which we can identify and eradicate," said Dr Athena Aktipis, Director, Human and Social Evolution, Center for Evolution and Cancer at the University of California, San Francisco. "It is highly diverse and evolutionary theory allows us to consider cancer as a highly complex and evolving ecosystem. This approach can improve the understanding, treatment and prevention of a number of different cancer types."

By applying the principles of evolutionary biology papers in the special issue ask: Why do we get cancer, despite the body's powerful cancer suppression mechanisms? How do evolutionary principles like natural selection, mutation, and genetic drift, work in a cancer ecosystem? How can we use evolutionary theory to minimize the rate of cancers worldwide?

"Nowhere is the diversity of cancer better revealed than the many reasons why we remain vulnerable to it," said Dr Aktipis. "Evolutionary medicine allows us to see explanations for traits that leave organisms vulnerable to disease."

These evolutionary explanations include the role of environmental factors, such as the relationship between tobacco availability and lung cancer co-evolution with fast evolving pathogens constraints on what selection can do trade-offs, such as the capacity for tissue repair vs. risk of cancer reproductive success at the expense of health defenses with costs as well as benefits, such as inflammation.

"An evolutionary approach can unite and explain the many avenues of cancer research by allowing us to see cancer as an ecosystem," concluded Dr Aktipis. "Just as a forest depends on the individual characteristics of trees as well as the interactions of each tree with its environment similarly tumors can be comprised of genetically distinct cells, which depend on both cell-to-cell interactions within the tumor, as well as on the interactions of tumor itself with the body."

Papers in the Special Issue Include:

From forest and agro-ecosystems to the microecosystems of the human body: what can landscape ecology tell us about tumor growth, metastasis, and treatment options?
Simon P. Daoust, Lenore Fahrig, Amanda E. Martin and Frédéric Thomas, DOI: 10.1111/eva.12031

Cancer stem cells as 'units of selection'
Mel Greaves, DOI: 10.1111/eva.12017

The real war on cancer: the evolutionary dynamics of cancer suppression
Leonard Nunney, DOI: 10.1111/eva.12018

Cancer as a moving target: understanding the composition and rebound growth kinetics of recurrent tumors
Jasmine Foo, Kevin Leder and Shannon M. Mumenthaler, DOI: 10.1111/eva.12019

The origin of cells was the most important step in the evolution of life on Earth. The birth of the cell marked the passage from pre-biotic chemistry to partitioned units resembling modern cells. The final transition to living entities that fulfill all the definitions of modern cells depended on the ability to evolve effectively by natural selection. This transition has been called the Darwinian transition.

If life is viewed from the point of view of replicator molecules, cells satisfy two fundamental conditions: protection from the outside environment and confinement of biochemical activity. The former condition is needed to keep complex molecules stable in a varying and sometimes aggressive environment the latter is fundamental for the evolution of biocomplexity. If the freely floating molecules that code for enzymes are not enclosed in cells, the enzymes will automatically benefit the neighbouring replicator molecules. The consequences of diffusion in non-partitioned life forms might be viewed as "parasitism by default." Therefore, the selection pressure on replicator molecules will be lower, as the 'lucky' molecule that produces the better enzyme has no definitive advantage over its close neighbors. If the molecule is enclosed in a cell membrane, then the enzymes coded will be available only to the replicator molecule itself. That molecule will uniquely benefit from the enzymes it codes for, increasing individuality and thus accelerating natural selection.

Partitioning may have begun from cell-like spheroids formed by proteinoids, which are observed by heating amino acids with phosphoric acid as a catalyst. They bear much of the basic features provided by cell membranes. Proteinoid-based protocells enclosing RNA molecules could have been the first cellular life forms on Earth. [5]

Another possibility is that the shores of the ancient coastal waters may have served as a mammoth laboratory, aiding in the countless experiments necessary to bring about the first cell. Waves breaking on the shore create a delicate foam composed of bubbles. Shallow coastal waters also tend to be warmer, further concentrating the molecules through evaporation. While bubbles made mostly of water tend to burst quickly, oily bubbles are much more stable, lending more time to the particular bubble to perform these crucial experiments. The phospholipid is a good example of a common oily compound prevalent in the prebiotic seas. [6]

Both of these options require the presence of a massive amount of chemicals and organic material in order to form cells. This large gathering of materials most likely came from what scientists now call the prebiotic soup. The prebiotic soup refers to the collection of every organic compound that appeared on earth after it was formed. This soup would have most likely contained the compounds necessary to form early cells. [7]

Phospholipids are composed of a hydrophilic head on one end, and a hydrophobic tail on the other. They possess an important characteristic for the construction of cell membranes they can come together to form a bilayer membrane. A lipid monolayer bubble can only contain oil, and is not conducive to harbouring water-soluble organic molecules, but a lipid bilayer bubble [1] can contain water, and was a likely precursor to the modern cell membrane. [ citation needed ] If a protein came along that increased the integrity of its parent bubble, then that bubble had an advantage. [ citation needed ] Primitive reproduction may have occurred when the bubbles burst, releasing the results of the experiment into the surrounding medium. Once enough of the right compounds were released into the medium, the development of the first prokaryotes, eukaryotes, and multi-cellular organisms could be achieved. [8] [ citation needed ]

The common ancestor of the now existing cellular lineages (eukaryotes, bacteria, and archaea) may have been a community of organisms that readily exchanged components and genes. It would have contained:

    that produced organic compounds from CO2, either photosynthetically or by inorganic chemical reactions that obtained organics by leakage from other organisms that absorbed nutrients from decaying organisms
  • Phagotrophs that were sufficiently complex to envelop and digest particulate nutrients, including other organisms.

The eukaryotic cell seems to have evolved from a symbiotic community of prokaryotic cells. DNA-bearing organelles like mitochondria and chloroplasts are remnants of ancient symbiotic oxygen-breathing bacteria and cyanobacteria, respectively, where at least part of the rest of the cell may have been derived from an ancestral archaean prokaryote cell. This concept is often termed the endosymbiotic theory. There is still debate about whether organelles like the hydrogenosome predated the origin of mitochondria, or vice versa: see the hydrogen hypothesis for the origin of eukaryotic cells.

How the current lineages of microbes evolved from this postulated community is currently unsolved but subject to intense research by biologists, stimulated by the great flow of new discoveries in genome science. [9]

Modern evidence suggests that early cellular evolution occurred in a biological realm radically distinct from modern biology. It is thought that in this ancient realm, the current genetic role of DNA was largely filled by RNA, and catalysis also was largely mediated by RNA (that is, by ribozyme counterparts of enzymes). This concept is known as the RNA world hypothesis.

According to this hypothesis, the ancient RNA world transitioned into the modern cellular world via the evolution of protein synthesis, followed by replacement of many cellular ribozyme catalysts by protein-based enzymes. Proteins are much more flexible in catalysis than RNA due to the existence of diverse amino acid side chains with distinct chemical characteristics. The RNA record in existing cells appears to preserve some 'molecular fossils' from this RNA world. These RNA fossils include the ribosome itself (in which RNA catalyses peptide-bond formation), the modern ribozyme catalyst RNase P, and RNAs. [10] [11] [12] [13]

The nearly universal genetic code preserves some evidence for the RNA world. For instance, recent studies of transfer RNAs, the enzymes that charge them with amino acids (the first step in protein synthesis) and the way these components recognise and exploit the genetic code, have been used to suggest that the universal genetic code emerged before the evolution of the modern amino acid activation method for protein synthesis. [10] [11] [14] [15] [16]

The evolution of sexual reproduction may be a primordial and fundamental characteristic of the eukaryotes, including single cell eukaryotes. Based on a phylogenetic analysis, Dacks and Roger [17] proposed that facultative sex was present in the common ancestor of all eukaryotes. Hofstatter and Lehr [18] reviewed evidence supporting the hypothesis that all eukaryotes can be regarded as sexual, unless proven otherwise. Sexual reproduction may have arisen in early protocells with RNA genomes (RNA world). [19] Initially, each protocell would likely have contained one RNA genome (rather than more than one) since this maximizes the growth rate. However, the occurrence of damages to the RNA which block RNA replication or interfere with ribozyme function would make it advantageous to fuse periodically with another protocell to restore reproductive ability. This early, simple form of genetic recovery is similar to that occurring in extant segmented single-stranded RNA viruses (see influenza A virus). As duplex DNA became the predominant form of the genetic material, the mechanism of genetic recovery evolved into the more complex process of meiotic recombination, found today in most species. It thus appears likely that sexual reproduction arose early in the evolution of cells and has had a continuous evolutionary history.

Although the evolutionary origins of the major lineages of modern cells are disputed, the primary distinctions between the three major lineages of cellular life (called domains) are firmly established.

In each of these three domains, DNA replication, transcription, and translation all display distinctive features. There are three versions of ribosomal RNAs, and generally three versions of each ribosomal protein, one for each domain of life. These three versions of the protein synthesis apparatus are called the canonical patterns, and the existence of these canonical patterns provides the basis for a definition of the three domains - Bacteria, Archaea, and Eukarya (or Eukaryota) - of currently existing cells. [20]

Instead of relying a single gene such as the small-subunit ribosomal RNA (SSU rRNA) gene to reconstruct early evolution, or a few genes, scientific effort has shifted to analyzing complete genome sequences. [21]

Evolutionary trees based only on SSU rRNA alone do not capture the events of early eukaryote evolution accurately, and the progenitors of the first nucleated cells are still uncertain. For instance, analysis of the complete genome of the eukaryote yeast shows that many of its genes are more closely related to bacterial genes than they are to archaea, and it is now clear that archaea were not the simple progenitors of the eukaryotes, in contradiction to earlier findings based on SSU rRNA and limited samples of other genes. [22]

One hypothesis is that the first nucleated cell arose from two distinctly different ancient prokaryotic (non-nucleated) species that had formed a symbiotic relationship with one another to carry out different aspects of metabolism. One partner of this symbiosis is proposed to be a bacterial cell, and the other an archaeal cell. It is postulated that this symbiotic partnership progressed via the cellular fusion of the partners to generate a chimeric or hybrid cell with a membrane bound internal structure that was the forerunner of the nucleus. The next stage in this scheme was transfer of both partner genomes into the nucleus and their fusion with one another. Several variations of this hypothesis for the origin of nucleated cells have been suggested. [23] Other biologists dispute this conception [9] and emphasize the community metabolism theme, the idea that early living communities would comprise many different entities to extant cells, and would have shared their genetic material more extensively than current microbes. [24]

"The First Cell arose in the previously pre-biotic world with the coming together of several entities that gave a single vesicle the unique chance to carry out three essential and quite different life processes. These were: (a) to copy informational macromolecules, (b) to carry out specific catalytic functions, and (c) to couple energy from the environment into usable chemical forms. These would foster subsequent cellular evolution and metabolism. Each of these three essential processes probably originated and was lost many times prior to The First Cell, but only when these three occurred together was life jump-started and Darwinian evolution of organisms began." (Koch and Silver, 2005) [25]

"The evolution of modern cells is arguably the most challenging and important problem the field of Biology has ever faced. In Darwin's day the problem could hardly be imagined. For much of the 20th century it was intractable. In any case, the problem lay buried in the catch-all rubric "origin of life"---where, because it is a biological not a (bio)chemical problem, it was effectively ignored. Scientific interest in cellular evolution started to pick up once the universal phylogenetic tree, the framework within which the problem had to be addressed, was determined . But it was not until microbial genomics arrived on the scene that biologists could actually do much about the problem of cellular evolution." (Carl Woese, 2002) [26]

  1. ^ Schopf, JW, Kudryavtsev, AB, Czaja, AD, and Tripathi, AB. (2007). Evidence of Archean life: Stromatolites and microfossils. Precambrian Research 158:141-155.
  2. ^ Schopf, JW (2006). Fossil evidence of Archaean life. Philos Trans R Soc Lond B Biol Sci 29361(1470):869-85.
  3. ^ Peter Hamilton Raven George Brooks Johnson (2002). Biology . McGraw-Hill Education. p. 68. ISBN978-0-07-112261-0 . Retrieved 7 July 2013 .
  4. ^
  5. Cooper, Geoffrey M. (2000). The Origin and Evolution of Cells. The Cell: A Molecular Approach (2nd ed.).
  6. ^
  7. Fox, Sidney W. Dose, Klaus (1972). Molecular evolution and the origin of life. San Francisco: W.H. Freeman. ISBN978-0-7167-0163-7 . OCLC759538.
  8. ^
  9. "Big Picture". Big Picture . Retrieved 1 October 2019 .
  10. ^
  11. "The Prebiotic Soup". . Retrieved 1 October 2019 .
  12. ^ This theory is expanded upon in The Cell: Evolution of the First Organism by Joseph Panno
  13. ^ ab
  14. Kurland, CG Collins, LJ Penny, D (2006). "Genomics and the irreducible nature of eukaryote cells". Science. 312 (5776): 1011–4. Bibcode:2006Sci. 312.1011K. doi:10.1126/science.1121674. PMID16709776. S2CID30768101.
  15. ^ ab
  16. Poole AM, Jeffares DC, Penny D (1998). "The path from the RNA world". J Mol Evol. 46 (1): 1–17. Bibcode:1998JMolE..46. 1P. doi:10.1007/PL00006275. PMID9419221. S2CID17968659.
  17. ^ ab
  18. Jeffares DC, Poole AM, Penny D (1998). "Relics from the RNA world". J Mol Evol. 46 (1): 18–36. Bibcode:1998JMolE..46. 18J. doi:10.1007/PL00006280. PMID9419222. S2CID2029318.
  19. ^
  20. Orgel LE (2004). "Prebiotic chemistry and the origin of the RNA world". Crit Rev Biochem Mol Biol. 39 (2): 99–123. CiteSeerX10.1.1.537.7679 . doi:10.1080/10409230490460765. PMID15217990.
  21. ^
  22. Benner SA, Ellington AD, Tauer A (1989). "Modern metabolism as a palimpsest of the RNA world". Proc Natl Acad Sci U S A. 86 (18): 7054–8. Bibcode:1989PNAS. 86.7054B. doi:10.1073/pnas.86.18.7054. PMC297992 . PMID2476811.
  23. ^
  24. Hohn MJ, Park HS, O'Donoghue P, Schnitzbauer M, Söll D (2006). "Emergence of the universal genetic code imprinted in an RNA record". Proc Natl Acad Sci U S A. 103 (48): 18095–100. Bibcode:2006PNAS..10318095H. doi:10.1073/pnas.0608762103. PMC1838712 . PMID17110438.
  25. ^
  26. O'Donoghue P, Luthey-Schulten Z (2003). "On the Evolution of Structure in Aminoacyl-tRNA Synthetases". Microbiol Mol Biol Rev. 67 (4): 550–73. doi:10.1128/MMBR.67.4.550-573.2003. PMC309052 . PMID14665676.
  27. ^ Gesteland, RF et al. eds.(2006) The RNA World: The Nature of Modern RNA Suggests a Prebiotic RNA (2006) (Cold Spring Harbor Lab Press, Cold Spring Harbor, NY,).
  28. ^ Dacks J, Roger AJ (June 1999). "The first sexual lineage and the relevance of facultative sex". Journal of Molecular Evolution. 48 (6): 779–783. Bibcode:1999JMolE..48..779D. doi:10.1007/PL00013156. 10229582. S2CID 9441768
  29. ^ Hofstatter PG, Lahr DJG. All Eukaryotes Are Sexual, unless Proven Otherwise: Many So-Called Asexuals Present Meiotic Machinery and Might Be Able to Have Sex. Bioessays. 2019 Jun41(6):e1800246. doi: 10.1002/bies.201800246. Epub 2019 May 14. 31087693
  30. ^ Bernstein H, Byerly HC, Hopf FA, Michod RE. Origin of sex. J Theor Biol. 1984 Oct 5110(3):323-51. doi: 10.1016/s0022-5193(84)80178-2. 6209512
  31. ^
  32. Olsen, GJ Woese, CR Ibba, M. Soll, D. (1997). "Archaeal genomics: an overview". Cell. 89 (7): 991–4. doi:10.1016/S0092-8674(00)80284-6. PMID9215619. S2CID7576095.
  33. ^
  34. Daubin, V Moran, NA Ochman, H (2003). "Phylogenetics and the cohesion of bacterial genomes". Science. 301 (5634): 829–32. Bibcode:2003Sci. 301..829D. doi:10.1126/science.1086568. PMID12907801. S2CID11268678.
    • Eisen, JA Fraser, CM (2003). "Viewpoint phylogenomics: intersection of evolution and genomics". Science. 300 (5626): 1706–7. Bibcode:2003Sci. 300.1706E. doi:10.1126/science.1086292. PMID12805538. S2CID42394233.
    • Henz, SR Huson, DH Auch, AF Nieselt-Struwe, K Schuster, SC (2005). "Whole-genome prokaryotic phylogeny". Bioinformatics. 21 (10): 2329–35. doi: 10.1093/bioinformatics/bth324 . PMID15166018.
  35. ^
  36. Esser, C Ahmadinejad, N Wiegand, C Rotte, C Sebastiani, F Gelius-Dietrich, G Henze, K Kretschmann, E et al. (2004). "A genome phylogeny for mitochondria among alpha-proteobacteria and a predominantly eubacterial ancestry of yeast nuclear genes". Molecular Biology and Evolution. 21 (9): 1643–60. doi: 10.1093/molbev/msh160 . PMID15155797.
  37. ^
  38. Esser, C Ahmadinejad, N Wiegand, C Rotte, C Sebastiani, F Gelius-Dietrich, G Henze, K Kretschmann, E et al. (2004). "A genome phylogeny for mitochondria among alpha-proteobacteria and a preedominantly eubacterial ancestry of yeast nuclear genes". Mol Biol Evol. 21 (9): 1643–50. doi: 10.1093/molbev/msh160 . PMID15155797.
  39. ^
  40. Woese, C (2002). "On the evolution of cells". Proc Natl Acad Sci USA. 99 (13): 8742–7. Bibcode:2002PNAS. 99.8742W. doi:10.1073/pnas.132266999. PMC124369 . PMID12077305.
  41. ^
  42. Koch, AL Silver, S (2005). The first cell. Advances in Microbial Physiology. 50. pp. 227–59. doi:10.1016/S0065-2911(05)50006-7. ISBN9780120277506 . PMID16221582.
  43. ^
  44. Woese, CR (2002). "On the evolution of cells". Proceedings of the National Academy of Sciences of the United States of America. 99 (13): 8742–7. Bibcode:2002PNAS. 99.8742W. doi:10.1073/pnas.132266999. PMC124369 . PMID12077305.

This article incorporates material from the Citizendium article "Evolution of cells", which is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License but not under the GFDL.

HbA2 : biology, clinical relevance and a possible target for ameliorating sickle cell disease

HbA2 , a tetramer of α- and δ-globin chains, provides a diagnostic clue to the presence of β-thalassaemia trait. This minor haemoglobin, which forms about 2-3% of the total, has no known physiological role, but has the interesting property of preventing polymerization of deoxy-sickle haemoglobin. If it were possible to increase the level of HbA2 sufficiently it could have a benefit in sickle cell disease similar to that of foetal haemoglobin. Moreover, HbA2 is present in all erythrocytes, an advantage not found with foetal haemoglobin, which is heterocellularly expressed. The molecular basis of HbA2 gene (HBD) expression is partially understood, and with new molecular tools, it might be possible to induce levels of HbA2 that could be clinically important. However, high concentrations of this positively charged haemoglobin might damage the erythrocyte membrane also, the reciprocal relationship of δ- and γ-globin gene (HBD and HBG1/2, respectively) expression might negate any benefit of increasing transcription of the former.

Keywords: HbS polymer delta-globin gene foetal haemoglobin haemoglobin switching sickle haemoglobin.

Why is hemoglobin bound in red blood cells?

Why isn't hemoglobin directly synthesized and released into the blood plasma? Producing it only contained in red blood cells seems like a lot of wasted energy to me.

To keep it safe and separate from all the other nasty stuff in your blood. Primarily proteases that would chop up hemoglobin. Cell biology is all about compartmentalizing relevant reactions and processes. It's basically the same reason that the cell has organelles like the lysosome or the nucleus, to keep things that need to be separate

To add to this, carbonic anhydrase is another factor in blood chemistry and it is highly active in red blood cells. Carbonic anhydrase maintains the pH balance, which is impacted by CO2/O2 balance (carbon dioxide is more acidic, so carbonic anhydrase generates the buffer H2CO3). Carbonic anhydrase is commonly cited as the fastest working enzyme, but part of the reason for its speed is that it is contained within red blood cells, the area where its reactant (carbon dioxide) is at highest concentration.

A red cell provides a host of services to the hemoglobin it contains. Red cells produce diphosphoglycerate, shunting it off the glycolytic pathway. Without proper DPG levels, oxygen would be too strongly bound for release in the tissues. Red cells also have high levels of glutathione an essential redox buffer necessary that maintains the proper disulfide-sulfhydral balance of the protein portions of hemoglobin among other functions to do with oxidative stress. However, I wouldn't be surprised if the original evolutionary advantage of packaging hemoglobin in erythrocytes were about protecting iron from infective microorganisms.

If we want to jump back some in our evolutionary timeline, lots of more primitive species of arthropods use hemolymph, where the pigments are not bound in cells, but there are hemocytes that function as a rudimentary immune system.

Jumping forward into more complex organisms, the next level you can look at would be Deuterostomes, but they also contain a dissolved pigment, not a cell bound one (and not very much of it, really). Tunicates also don't have cell bound respiratory pigments. I think the first place erythrocytes show up is in Craniata? Hagfishes have them.

I'm inclined to believe it has more to do with immune function and keeping oxygen sequestered away. All blood cells come off of the hematopoietic stem cell, which evolved before red blood cells. Keeping oxygen sequestered has a twofold benefit: First, without the cells, the maximum oxygen carrying capacity of the blood is limited by the amount of oxygen that can be dissolved in it - sequestering that in cells means the blood can essentially always dissolve more. Second, it prevents the hemoglobin from being attacked by rogue adaptive immune cells (which would very quickly prove to be fatal) and keeps it from being available to bacterial infections, for the same reasons.