Revealing the Origin of Eukaryotes

Tracing the breadcrumbs of deep phylogeny through gene duplications. 

Last week, we discussed the jamboree of mutation that is the MHC locus in animals. One part of that story was the persistent duplication and pseudogene-i-zation (which is to say, the birth and death) of genes in that locus through primate evolution, another consequence of the never-ending arms race with our pathogens. Gene duplication has much deeper significance, however, as it has given rise to new genes throughout biological evolution, as it is merely an accident of DNA (or RNA) replication, thus operationally rather frequent. Significant transitions, such as the advent of eukaryotes, and the advent of plants and of angiosperms, all feature rapid gene duplication, even whole-genome duplication, which provide grist for new functions, refinement of old functions, and speciation.

A recent paper did a deep dive into the origin of eukaryotes, perhaps the watershed event in the history of life, second only to the origin of life itself on the early Earth. It is very difficult to reconstruct what happened during these extremely early events, which these authors put as long as three billion years ago. There has been so much mutational churn in our molecules, and no fossil record to speak of, that, again like the origin of life itself, we do not have a great deal to go on. Thankfully, the discovery of the kingdom of archaea, and its especially eukaryotic-like class, the Asgard archaea, has provided a much better platform for speculating about what the originating organisms might have been like.

Eukaryotes differ from bacteria and archaea by numerous characteristics, implying extensive development/evolution before the last common ancestor from which we can trace all the extant descendants. These include the eponymous nucleus, an internal system of membranes and membrane-bound organelles, a complex cytoskeleton of both actin and microtubules, a mitochondrion, meiosis, a microtubule spindle-driven division system, and many other, more obscure molecular differences. The lack of intermediate forms is certainly frustrating from a scientific perspective, making it difficult to go through the catalog of life today to identify the steps involved in this transition. It also implies that the process ended with an evolutionary bang that in essence caused the final iteration to wipe out all the preceding forms, a bit like humans vs the many other Australopithecines and their descendants. 

These differences are enormous and momentous, making eukaryotic cells far larger, energetically capable, and complex than their antecedents. Another key characteristic is a prevalence of gene duplication and specialization, leading to much larger genomes along with all the other complexity. It has long been theorized that it was the union of the proto-mitochondrion, which was a bacterium, with the archaeal founding cell, that set the whole process off, particularly providing the massive amount of energy required for all this rise in complexity. 

However, the current authors claim that this definitively is not the case- that the mitochondrial symbiosis was a rather late event, coincident with the oxygenation of the atmosphere about two billion years ago. They argue that, since gene duplication and specialization is in any case a clear characteristic of eukaryotes, using molecular clocks to time these many gene duplications can give us relatively specific and data-rich insight into the epochs at which each of the processes that they participate in first developed. 

The authors present estimated times of origin for key duplications involved in RNA synthesis. The RNA polymerases I, II, and III were duplicated from one polymerase in archaea, and are composed of numerous gene products, as well as ancillary regulatory proteins. While some subunits are still shared, the ones that duplicated did so in the range of 2.3 to 2.8 billion years ago, before the symbiosis with the mitochondrial precursor, timed at "mFECA", or the mitochondrial first eukaryotic common ancestor, about 2.2 billion years ago. "nFECA" is nuclear first eukaryotic common ancestor, while "LECA" is last eukaryotic common ancestor. The axis at the top is time before present, in giga-years ago, or Ga.

For example, eukaryotes all have three RNA polymerases, while bacteria and archaea only have one. These RNA polymerases specialize on mRNA synthesis (RNA pol II), ribosomal RNA synthesis (RNA pol I), and tRNA, 5S RNA and U6 snRNA (RNA pol III). Why this specialization exists remains a bit mysterious, though once it started there was no going back. In any case, it happened and is totally characteristic of eukaryotes, so these duplications (which involve the duplication of numerous genes encoding proteins of the polymerase complex itself and its regulators and way-finding helpers) can be used to date eukaryogenesis, using a very carefully calibrated and species-sampled molecular clock. And what they find is that all of these duplications, given as ranges in the graphs above, with green dots at the likeliest time points, all lie prior to the point of "mFECA", which is the mitochondrial first eukaryotic common ancestor, at roughly 2.2 billion years ago. Tabulated over studies of almost a hundred other genes that are similarly duplicated and characteristic of eukaryotes, the authors place the "nFECA", or nuclear eukaryotic common ancestor way back at almost 3 billion years ago. 

That is a very long time! It is a very long time ago that the characteristics of eukaryotes started to come together, and it is also a very long time (well over a billion years) over which that development spanned before culminating in the last common ancestor of all extant eukaryotes, which the authors put at about 1.7 billion years ago. And note that that still comes a billion years before the Cambrian explosion of animal life. We are talking about really deep time here. 

If mitochondrial symbiosis came later, (causing another bout of genomic change as hundreds of bacterial genes moved from the symbiont to the nucleus), just as the atmosphere was getting oxygenated, what was going on in the preceding billion years? One can speculate that these evolving proto-eukaryotes were the top predators of their world, a bit like eukaryotic protists in microbial environments today. The competition would as always have been intense, but there were no higher life forms (i.e. animals or protists) to worry about. Size was not a problem, but rather an advantage. The focus was on quality and effectiveness in finding, disabling, and digesting prey. Thus there was little downside to complexity, unlike the constraints on prey species, which had to optimize for rapid growth and reproduction under both nutrient and predation constraints. New and costly systems for protein secretion, for prey digestion, and intelligent, responsive regulation of all cell processes might have been consistently advantageous.

I take this work as being definitive, at least in terms of settling the debate about early vs late symbiosis. It benefits from a clear theoretical basis and new, voluminous sequence data from appropriately related species in its molecular comparisons. I recommend it highly, as I can only scratch the surface of its analysis here. The advent of mitochondria was then a later step change in complexity, but brought incredible metabolic productivity that, at least in oxygen-rich environments, would have definitively superseded all the prior forms of the proto-eukaryote, and (unfortunately for us) erased that record of evolution. A later innovation, placed between mFECA and the last eukaryotic common ancestor "LECA" was sexual meiosis, another fascinating and characteristic innovation of eukaryotes. These authors thus provide a true revolution in our understanding of eukaryogenesis, extending it across well over a billion years.

One more sample from the author's presentation of molecular duplications that help time eukaryogenesis. Here, DNA repair proteins are emphasized, including some involved in meiosis (light blue dots, see legend). Some of these duplications involve genes from the mitochondrial endosymbiont, (MSH4), and some (such as EME1/2 and MUS81) come from older repair processes, but later specialized for meiosis. 

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MHC Through Evolution: Breaking All the Rules

The immunologically critical MHC gene cluster plays by its own rules through the arms race of life.

Last week, I discussed the very general landscape of variation in the human genome, specifically the tradeoff between prevalence in the population and effect size. Given that the vast majority of variants are deleterious, those that affect our phenotypic traits are more heavily selected against the greater effect they have. The result is that at a gross level, most genes and most traits share the same general distribution of lots of variants (alleles) with minor effects, and far fewer with large effects. And those with minor effects also turn out to be tangential for biologists, rarely informative about the nature of the traits they (sort-of) affect.

This week, another paper and another view of evolution, though the eyes of one the more critical genes of the immune system, the major histocompatibility complex, or MHC group of genes. While our adaptive immune system has developed the extraordinary and powerful ability (though semi-controlled DNA recombination of the antibody and TCR genes) to recognize practically any antigen, foreign or domestic, that system requires stringent controls. One of those controls on T cells, which carry the antigen-recognizing TCR receptor, is that it can only see antigens that are "presented" on MHC molecules. MHC proteins have a surface cleft that gets loaded with and holds small peptides (8 to 12 amino acids long) that are cleaved from other proteins, either from pathogens or from the cell itself. The MHC+peptide complex then sits on the surface of the cell, announcing either that 1) I am healthy, full of normal cell proteins, going about their business, nevermind, or 2) I have some other proteins inside, either from a viral infection (class I MHC) or from some bacterium I have just phagocytosed to deal with an infection (class II MHC). In the second case, T cells carrying the TCR receptor lock onto the MHC+peptide complex, and start up the process of killing that cell. 

MHC proteins (beige, pink) present an antigen (red) from one cell, and dock to a T-cell which recognizes the antigen+MHC complex by shape, using the TCR receptor. The additional CD8 or CD4 receptors help to verify the proper binding. While class I MHC are present on all cells, class II MHC are on phagocytosing cells like dendritic cells and macrophages that commonly ingest, reprocess, and re-display bits of encountered pathogens.  

Given that the TCR gene recombination process is unbiassed and produces a galaxy of random binding specificities, how do these cells distinguish self from non-self antigens? This is a deep question that is not fully resolved. But one major mechanism is thymic selection, which is what gives T cells their name. Special cells in the thymus display a wide range of self-antigens, and T cells, which are obliged to pass through the thymus during their maturation, are induced to commit suicide if they react to any of them. A paper from 2018 fascinatingly discussed how it is possible to create a T cell population that knows the "language" of foreign vs domestic after what is known to be a rather haphazard selection process, which displays only a partial range of self-antigens, and leaves quite a few self-reactive T cells around.

At any rate, the MHC proteins do not benefit from hyper-variation provided by genetic recombination. Yet it turns out that variation is beneficial here as well. The way foreign antigen peptides nestle in the MHC groove can be varied by mutations in the MHC molecule, providing a rich field of variation in antigen recognition and thus disease resistance. So, our MHC genes have not just a few alleles in the population, not just a few dozen, but over six thousand alleles. For each individual MHC protein, each person has only two, but over the population, there myriads with different properties. MHC was first recognized for its role in self vs non-self recognition and transplant rejection, (thus the "compatibility" in its name), and it quickly became evident that people vary tremendously in their MHC complement. And this variation plays a big role in keeping us (and all other animals) going as populations, in the face of pathogens that evolve a lot faster than we do. 

A recent paper provided a phylogenetic history of MHC molecules in monkeys, covering the last sixty million years of evolution in our lineage. It is a festival of gene birth, death, and duplication, quite apart from the smaller mutations that are constantly accumulating and cycling through the population. The MHC region carries about 200 related genes, most of which have minor roles, and only six of which (three MHC class I, and three MHC class II) follow the high-mutation pattern because they encode the main antigen presenting proteins. These genes are subject to, quite obviously, unique selective forces. 

How the MHC gene cluster looks, when aligned and identified by gene, over the primates. Note the deep divergence between the new- and old-world primates. Genes A, B, and C are the major MHC class I genes, which vary the most over this time. Note also how some of these genes have gone through extensive duplication in some old-world monkey lineages.

The first force is balancing selection. As soon as one allele becomes common, pathogens evolve to evade its presentation skills, rendering it less effective and less desirable. This results in a population full of minor variants. Indeed, for any individual person, having two MHC molecules that are the same would be bad. Being heterozygous at these genes is highly advantageous, thus enforcing both the retention of minor alleles, and an observed behavior in mating to favor partners with different MHC complements. Apparently, our MHC makeup is reflected in our personal aroma! 

A second force, conversely, is the retention of ancient alleles. It turns out that, across the old-world monkeys, many MHC alleles are preserved and cluster more closely in sequence comparisons with each other than they do with other alleles in the same species. That is, despite the general speed of MHC evolution and constant accumulation of new alleles, old alleles are preserved in all monkey populations as well, due to their distinct capabilities, under balancing selection. This is part of what makes population bottlenecks so damaging to near-extinction species. They lose critically valuable genetic resources (in the form of rare MHC alleles) that represent millions of years of accumulated variation. 

Incidentally, the trees shown here again reinforce the history of primate evolution, with new world monkeys splitting off from the old-world monkeys quite early on and developing a very distinct set of MHC molecules.

So, while virtually every other gene in the genome is being relentlessly optimized, sticking to its knitting, doing one thing and being beaten down whenever any mutation steers it from its optimized path, the MHC genes follow quite a different path, at least in portions of their sequence that provide variation in antigen binding and presentation. These genes revel in endless diversity, throw off pseudogenes at a high rate, wink out of existence and come back in other forms. Natural selection is the motor in each case, but meets the challenge of survival in different ways.

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Performing Search, as a Transcription Regulator

Billions of years have created some weird tricks in DNA search.

Search is all around us, as we increasingly rely on search engines to find everything we need on the internet, want to watch, or want to buy. Search looks into databases, which hold the sought-after information. All our accounts, all the domain names, all the products... everything is held in databases of one kind or another, and those databases are indexed in clever ways to provide virtually instant pointers from the question we ask to the answer held online. AI merely puts a linguistic gloss on this, and most people are still encountering AI first as a feature of search, such as the top of current Google search results.

Well, our genomes are databases as well- rich and ancient storehouses of jewels that encode the body and its doings. How does search work there, and what is search even for? At any moment, each cell of the body has certain needs, stresses, and goals, as expressed in its DNA programming. The tools available are proteins and RNAs, which carry out the cell's functions. The needs may arise from signals coming from previously expressed receptors, say, for insulin, which may trigger and tell the cell to take up glucose from the blood. The receptor turns on a kinase, which may turn on another kinase, which turns on a transcription regulator, which goes into the nucleus and ... does a search. This regulator is searching for places (specific sequences) in the genomic DNA where it can bind, after which it helps to turn on (or off) the nearby gene, executing the desired function / tool. 

General introduction to transcription regulators (or "factors") and their role in gene activation and the whole process of gene expression.

Obviously a very different kind of search than what Google does on our behalf across documents, but there are similarities. Internet search depends on patterns, matching the user's input with the vast corpus of the internet also held as text symbols. Transcription regulators match patterns, in this case patterns of DNA that they like to bind, which may occur only once in the genome, or occur tens of thousands of times. The pattern here is a complementary physical/electrochemical shape, rather than an abstract same-symbol match. The genome is, to a protein, truly vast. Our three billion-base genome is forty million times larger than an average regulatory protein of, say, fifty kilodaltons (kDa). Search is also, here, a difficult problem, which researchers have been wondering about for decades. Several recent papers discuss different aspects of the problem and shed some modern light on it.

We have roughly 1600 transcription regulators in our genomes, so there is something going on all the time. DNA is always being queried. And what it replies with is RNA- a transcript issued/copied from a gene, which either goes off to instruct creation of a protein, or is itself functional in some way. So, how do proteins bind to DNA, executing their search? It was transformative when the first atomic structures of such proteins were solved. They were clearly complementary with their DNA targets, with nicely positioned positive charges to mate with the backbone of the DNA and amino acid fingers reaching into the helix to feel the shapes of the nucleotides they wanted to bind. All very neat, and paradigmatic for bacteria whose genomes are quite small. But there is more to the story. Binding sites in human genes tend to be quite short- five to seven bases. That really isn't enough to be very specific, across a vast genome. Eukaryotes have developed several weird tricks, as it were, to encourage efficient search over much larger genomes and at the same time increase precision while maintaining evolvability and flexibility.

Eukaryotes have nucleosomes, chromatin, and packaging. The DNA is not just splayed out randomly, but wound up on protein spools. One would think that this would impair search by regulators. But paradoxically, there is a fine balance between hunting around on a given piece of DNA for a preferred site (one-dimensional search, 1D), and jumping off, letting go, and trying somewhere else (by diffusion; three-dimensional search, 3D). The compaction of genomic DNA into nucleosomes that wind up most of the DNA while leaving linking DNA in between free appears to provide a nice balance of landing spots that allow searching regulators to jump very long distances (in linear DNA terms) while not going very far in absolute terms. Regulators vary in how aggressively they can plow through nucleosomes to try out their internal DNA sites, but many (called pioneer factors) can do so.

Secondly, transcription regulators cooperate with other proteins to create longer, more complex DNA sites for precise gene identification and higher binding affinity. As biologists have characterized the enhancers and promoters of important developmental genes, they have found that DNA binding sites occur in bunches, and have much weaker effects when broken down and separated. Sometimes there is direct side-to-side cooperation between two regulators that bind the DNA. At other times, they combine with other non-DNA-binding proteins to create complexes at such sites. The DNA recognition sequences of these combinatorial sites can be changed significantly, even beyond (our) recognition, by the addition of cooperative proteins. This is something that makes prediction of where a given regulator binds particularly perilous. 

Thirdly, many regulators contain not only DNA binding domains, but also extra disordered domains that facilitate DNA search and binding. This has been a recent realization that accounts for some of the speed and flexibility of regulator search and DNA interaction in eukaryotes. The stable crystal structures of paradigmatic bacterial regulators are not the whole story, and indeed are insufficient to explain what is happening in the much larger setting of our own cells. The authors note that eighty percent of human gene regulators have large disordered domains, (called IDRs, for intrinsically disordered region), upwards of 500 amino acids long. These never showed up in crystal structures, naturally. Being disordered, they are also poorly conserved. So, they have been difficult to study. 

Comparison of binding by one regulator, MSN2, which has a large IDR, to its genomic sites. At top is its native binding pattern, across a whole genome. At bottom are mapped its core motif occurrences on that DNA. Second from top is the MSN2 protein mutated to contain only its core motif-binding domain, and third from top is the MSN2 protein mutated to remove that domain and retain everything else. Note how different the patterns of binding by each of these proteins are, though how each approximates to some degree the wild-type pattern.

In related work, researchers have divided up such proteins into the core binding site part and the IDR part. They find that both parts work partially, directing binding to some of the native sites around the genome. In fact, the IDR part does a more statistically accurate job than the core DNA binding motif. This is fascinating, showing that in eukaryotes, a new search mechanism arose, supplementing discrete and precise binding with a floppy / fuzzy code in the IDR and its binding sites. It turns out that regions of hundreds of bases around core target sites (which in one case amount to only the motif AGGGG) are preferentially bound by the respective IDR protein domain, with multiple weak interactions that remain structurally uncharacterized. In fact, neither the protein structures responsible, nor the DNA sites they bind are known yet, though deletion studies through IDR domains show that binding is distributed throughout.

Relationship between IDR binding site size, and the ratio of 1D vs 3D search time, by simulation. The bottom axis is size of the IDR binding region, the Y axis is time taken for search. Time spent in total (yellow) goes down to minimum at an optimum between 1D search that is slowed by longer IDR-binding regions, while 3D search is strongly accelerated by longer IDR-binding regions.

The combination of core binding and loosely unstructured binding in one regulatory / search protein provides powerful benefits. In dimensionality terms, if the effective landing site is expanded from five to five hundred bases, then the time required for 3D search through the space of the nucleus is dramatically shortened. Secondly, loose binding by the IDR then promotes an "octopus"-like 1D search along the local DNA, resulting in efficient settling on the core binding site to get ultimately precise positioning. The ultimate affinity of the regulator with the local DNA is also enhanced compared to what it could manage over a five base pair site. The researchers conclude that with these domains, the search problem is, in net terms, reduced by one dimension, from 3D to 2D. The surrounding areas of DNA that have marginal affinity for the IDR domain are called "antenna" regions, and the author's simulations show how they alter search behavior.

Schematic explanation of the current work, describing how IDR domains help to speed up the transition from 3D search through space, to 1D search across the DNA. And then also to facilitate 1D search by preventing full detachment from the DNA while the core binding motif continues to search by diffusion for its binding site (yellow).

For computers and databases, search is a huge problem that has led to technical innovation, as well as large drains on resources. Every search engine combs the internet, gobbling up all available information, creating indexes, and updating them constantly in order to give us the instant access we want. This infrastructure has been raised to a new level by AI, which transforms search into a new form, combining it with language translation and prediction methods that allow a search for corkscrew to bring back results for wine. Whether it understands anything is unlikely, but the desire to upgrade search from a simply determinative process to one that is more fuzzy and richly interpretive, and thus more useful, is not a new phenomenon.

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Strides in Cancer Treatment

A new paper shows that CART therapies can be unleashed against solid tumors.

We are finally in the payoff period in the decades-long war on cancer. Slowly, painfully, precision approaches are being developed to treat specific molecular lesions in ways that are superior to the old blunderbuss kill-everything approaches. At first, these treatments had only marginal effects, at astounding costs. But increasingly, the effects are lengthening and cures are in sight in some forms of cancer. One unexpected area of revolutionary progress has been immunotherapies, which in various ways help our immune systems attack cancers. It turns out that many cancers have tricks to hide from the immune system, and once those tricky dampening molecules are circumvented, dramatic reductions are possible. One paper recently described an anticancer vaccine made up of a witch's brew of targeting molecules, cancer antigens, and adjuvants, that achieves strong anti-melanoma action.

Another one of these immunotherapies is CART, or chimeric antigen receptor T-cell therapy. T cells have a receptor repertoire, just as B-cells do, which target things to be attacked- foreign pathogens, diseased states, etc. at molecules called antigens. One problem in cancer is that the cells are, originally at least, our own, so they mostly evade immune detection by having few "foreign" antigens. But there are nevertheless some antigens, comprised of normal molecules that are out of place (such as DNA found outside the cell) and "neoantigens" that are proteins expressed from the mutations in cancer cells. Additionally, as mentioned above, cancer cells express additional molecules (PD-L1) that can dampen even the immune response that does get generated by these few cancer antigens. So, the chimeric part of CART is taking the patient's own T-cells and engineering some of them to express new anti-antigen receptors that are relevant to the patient's cancer. Perhaps there is a mutant fusion protein that the cancer depends on. Perhaps the cancer displays an unusual surface molecule. Perhaps the tables need to be turned and PD-L1 targeted. There are many possible targets. 

CART therapies have, to date, been mostly directed at blood tumors. Solid tumors have extra protection in their micro-environments, and have not been good targets, though they necessarily have blood supplies and thus exposure to systemic T-cells. A recent paper blows open this field by revealing a magic molecule that plays a very significant role in the structure of solid tumors- the urokinase receptor. The urokinase plasminogen activator receptor (uPAR) is heavily expressed on senescent cells and many solid tumors, but rarely expressed elsewhere. Indeed, its expression correlates with tumor aggressiveness. Plasminogen is a protease that is sort of a cleanup crew for the circulatory system and body generally. It breaks up blood clots, and digests follicle tissues allowing ovulation. It encourages wound healing and discourages fibrosis- the buildup of scar tissue. However, in the cancer setting, the same activity seems to encourage fibrosis in a sort of constant wound healing state. Reviews in this field are rather confused about the direction of action. But one thing is clear- uPAR has myriad signaling activities relevant to tissue repair and immune activation that are not all dependent on the uPA (plasminogen activator) and plasmin activation system. Indeed, it is expressed not just in cancers, but in many other fibrotic settings.

A wide array of proteins are assessed here for their expression in a cancer tissue sample. uPAR is in red at the upper left. The matrix on the right shows the correlation of expression in a wide variety of cell types and tissues, like cancer-associated fibroblasts (CAFs), monocytes/macrophages (Mo/Mac), and with the protein fibroblast activation protein alpha (FAP).

The authors sought to target CART cells against uPAR, principally as a targeting device, since this marks many solid tumors and correlates with metastasis and rapid cancer progression, in addition to inflammation and fibrosis. While only tested in mice, the results were remarkable. 

"CAR T cells targeting the D2-D3 domain of uPAR display broad antitumor activity in xenograft, syngeneic, and patient-derived models, including in adjuvant and combination settings, supporting the concept that targeting conserved malignant cell states can enable therapeutic strategies that transcend tumor type. ... our prior work shows that uPAR CAR T cells targeting senescent cells remodel fibrotic tissues, and, as shown herein, this remodeling is associated with CAR T cell infiltration and cytotoxic activity. Similarly, parallel work demonstrates that uPAR CAR T cells exhibit potent efficacy in glioblastoma models and can co-target supportive stromal cells."

This is to say that these CART cells target not only tumor cells, but the surrounding solid tissues (stromal cells) that they rely on. That is the key to defeating solid tumors. It also indicates that other autoimmune and fibrotic conditions may be addressable with this therapy as well. 

Treatment effects from the CART therapy in mice, against several tumors. The red graphs are controls, and the blue graphs are treatments. Top is the tumor volume over time, while at bottom is survival of the mice over time.  Lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), high-grade serous ovarian carcinoma (HGSOC), and pancreatic ductal adenocarcinoma (PDAC).

The results of treatment of xenografted human ovarian tumors into susceptible mice, at 3 weeks, bottom. On the left is the control, while the other two sets were treated with CART cells against uPAR.

The authors note that relapses were seen occasionally, but that in these cases, the uPAR target was still highly expressed. That suggests firstly that it is difficult for tumors of these targeted types to do without uPAR, and secondly that something else went wrong with the tailored CART therapy, other than that its target went away. Perhaps future work can enhance its penetration or activity. The researchers also strained to make their model systems as human-relevant as possible, using cancer tissue transplanted (xenografted) from human cell lines, human CART cells, and mice with transplanted immune systems from humans. This work is thus not only a scientific breakthrough of the highest order, but is a technical tour de force as well. It also ends up with a variety of patent declarations and commercial ties, indicating that this breakthrough is being fully milked by its inventors and commercialized at breakneck speed.

One major problem with this mode of therapy is that CART cells require a great deal of engineering. First, antibodies against uPAR were developed in mice or other species. Then the genes from those immune systems were recovered from those mice, to get the precisely recombined gene that expressed the antibody with highest binding activity against uPAR. Then that gene, hooked up to new transmembrane and intracellular domains, (specially selected to activate the T cell they will be put into), was introduced into a transformation vector and put into the T cells collected from the diseased mice. In humans, this treatment routinely runs a half a million dollars. It is incredibly ornate, and one expects that gene therapy will someday allow the patient's T cells to be directly modified in the body, without all the collection and laboratory work, (which takes months), given a high-quality gene encoding the antibody fragment that is generally applicable- not tailored to a specific patient.

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The History and Future of a Single Mutation

The CCR5delta32 confers resistance to HIV. Where did it come from?

We are edging into an age of precision medicine, where the causes of our maladies will be known in molecular detail, allowing treatments that address them at the root. Given the parlous state of medicine today, in the midst of financial breakdown and a continued mediocre level of basic diagnosis, it is hard to believe this is a corner we can turn. But vaccines have long been in this category, of addressing the precise pathogenic causes of disease, and oncology is fitfully getting there, given advances in DNA sequencing and in treatments based on specific mutations.

HIV is also a beneficiary of this approach, since the discovery of its pathogen led directly to a variety of effective (if not yet permanent) treatments. A researcher in China created gene-edited humans with a specific mutation that will render them resistant to HIV. The mutation he chose for this work is called CCR5delta32, and it does not naturally exist in Chinese populations. 

But it does exist in European populations, at a roughly 10% rate in single copy. When present in two copies, it provides complete immunity to HIV, while if present in one copy, it slows infection substantially. A recent paper rooted through the available ancient and present genomes to figure out where this mutation came from. 

CCR5 is a cell surface receptor for cytokines 3, 4, and 5. These are all pro-inflammatory cytokines, and they interact with multiple receptors. Here, as in so many other respects, the immune system is riven with redundancy, so that it can grapple with as many contingencies as possible. Cytokines are signaling molecules for the immune system, which is an unusual organ, being dispersed all over the body with numerous cell types all patrolling around, and communicating with each other by long- and short-range chemical messages. It turns out that the major form of HIV uses the CCR5 protein to get into our immune cells, explaining why CCR5delta32, which is totally non-functional, has such a dramatic effect on HIV susceptibility. 

While people carrying CCR5delta32 are generally fine, this defect does confer a variety of subtle changes to their susceptibility to other infectious diseases and cancers. That explains why this mutation has settled at its low level in the European populations, probably balancing the occasional benefit against a specifically CCR5-seeking pathogen against its natural functions that form the basis of its existence in the first place as a part of immune system that is conserved in all mammals. The Chinese gene-editing researcher came under withering criticism not only for breaching the generally agreed moratorium on human germline gene editing, but also because the net effect of this mutation is, on the whole, negative, raising risks of numerous diseases, despite its beneficial effect on HIV. 

The authors run several models and populations in an attempt to time the origin of the CCR5delta32 mutation, and portray its positive selection over the ensuing millenia.  CHG- Caucasus hunter-gatherer; EHG- Eastern hunter-gatherer; WHG- Western hunter-gatherer; ANA- Anatolian Neolithic ancestry. The bottom axis is time, and the Y axis is the frequency of the mutation in these populations. "Modern DAF" refers to the inclusion of the data set of current (not ancient) population frequencies, (top), which the authors claim leads to continued rates of selection (last 2,000 years) that are artifactual.

So where did it come from? The new authors gather up a large variety of population samples from around the world, and from ancient humans, back to about ten thousand years ago. They find the first instance of the mutation in one sample at 5.8 thousand years ago. After that, its frequency rises dramatically, up to about two thousand years ago, when it levels off. They conclude that this mutation originated about seven to nine thousand years ago, in the steppes of Eastern Europe / Western Asia, and was under strong positive selection at first, spreading to the current frequency of about 10% of the population / alleles. All occurrences on other continents can be accounted by the spread from this source.

Does this mean that HIV was prevalent long, long before the current pandemic? Hardly. The authors can not say anything about it, but one theory would be that some other disease had a similar profile. It certainly was not the Black Death, as the authors show that this mutation had no change in frequency over that gruesome pandemic. Another hypothesis is that general reduction in inflammatory response might be beneficial in some settings, as has been found for Covid-19, though here again, this mutation does not have any known positive or negative net effect on Covid-19 susceptibility or course. 

It is amazing that we have enough sequences of ancient DNA to be able to reconstruct this kind of thing- to be able to trace where and when some influential mutation occurred, and how it traveled and spread. It is a tour-de-force of bio-archeological reconstruction.

• When you escape reality, and morality.
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Pumping Calcium

An ornate ion pump manages rapid outflow of calcium.

In the beginning, the egg cell experienced a wave of calcium release, triggered by union with a sperm cell. This blocked other sperm from entering, and prepared the egg to become a zygote and embark on embryogenesis. It is but one example of the pervasive role of calcium signaling among animals. Another is the muscle activation cycle, which relies on calcium release from the specialized sarcoplasmic reticulum (in response to a nerve activation) to get the cell as a whole contracting. Generally, calcium is kept very low in the cytoplasm, and high in the endoplasmic reticulum and outside the cell. Thus, channels gated by electrical activation or other signals can cause rapid cytoplasmic calcium spikes and signal widely within a cell. 

On the flip side, there have to be pumps that keep the cytoplasmic concentration low, and a recent paper elucidates the structure of one such pump that is remarkably fast, while also closely regulated. It is an impressive machine. PMCA2 is an ATP-using calcium pump that sits in the plasma membrane and carries out what is called the Post-Albers cycle. This is a flip-switch mechanism for pumping ions, where ATP drives conformational switches alternately exposing ion binding sites to each side of the membrane. When the pore is open to the cytoplasm, there is no competition from higher concentrations outside, so the active site can bind one internal calcium, given a high-affinity site. Then, after the conformational switch, the pore is exposed to the outside, and at the same time the site is reconfigured to be lower-affinity, releasing the calcium ion into a high concentration environment. Neurons especially use calcium signaling extensively to operate synapses and regulate growth and development. Their rapid and frequent signaling requires a pump that has especially high capacity. PMCA2 operates at a maximal rate of several thousands of Ca2+ ions pumped per second.

Cartoon of the Post-Albers cycle, which is shared by a large family of active ATP-using pumps that transfer ions against their chemical concentration gradient. M is the main transmembrane domain of the pump, where the ions traverse the membrane. The N, P, and A domains are regulatory, especially binding and cleaving ATP  at an interface between the N, P, and A domain. The cycle links power steps (1,2) with conformational changes that carefully gate the pumping process.

And that is not all. Since calcium has a charge of 2+ and this pump does not intend to alter charge across the membrane, the pump simultaneously has binding sites for counter-ions (generally two OH-) that are transferred in the opposite direction from the calcium. Not only that, but every pump of this kind requires regulation of various kinds. PMAC2 is activated by phosphatidyl inositol 4,5 bisphosphate (PIP2), which is another important signaling molecule generated by specific PI kinases in response to activation of G-protein coupled receptors or protein kinase C, which may respond to external signals. In very general terms, these tend to be pro-growth or stress-induced pathways. These regulatory processes can tune the overall rate of recovery from rapid Ca2+ signaling events, by adjusting the level and activity of pumps like PMAC2. 

ATP binds at the N/P/A domain interface, and its hydrolysis (and loss of ADP) generates extensive shape changes, including into the transmembrane M domain. At the very bottom, the calcium ion is shown in green, bound inside the M domain pumping channel. The motions here are subtle, but enough to dramatically reshape the calcium channel.

The authors, using various substrate variants and other tricks, were able to develop structures of PMAC2 in several steps of the pumping cycle, using cryo-electron microscopy. The ATPase site in the N domain (red) is far from the channel that conducts the calcium ion (brown, far bottom). They show extensive shape changes from binding or losing the ATP molecule, though they mostly concern the intracellular domains (red, blue, yellow). The effects on the transmembrane pore domain are rather subtle, shown on right. The authors claim that, compared to other pumps of this large family, the structural changes are significantly less, suggesting that evolution for speed has caused the mechanism to become more efficient, with less wasted motion per conduction event, at least in the channel region itself.

Relation of the PIP2 binding domain (orange/red stick figures) to the calcium core binding site. PIP2 appears to be essential for rapid pump operation. At bottom is shown some schematics of the gating provided by PIP2 in bound and unbound states, especially via the D873 side chain (negatively charged aspartic acid).

They also find that the activating molecule PIP2 is neatly parked right next to the main calcium binding and conduction region, and is more or less essential for enzyme activity. In the graph above (e), they show that several single mutations made in the calcium binding high affinity site, for example switching the negatively charged D873 for the positively charged K (lysine), kills ion pumping activity. Mutation of the PIP2 binding pocket (KKQ->TLL, around position 347) likewise kills enzyme activity.

Relation of the counter-ion channel (red dots) with the calcium channel. Both are essential parts of the mechanism. Closeups with the coordinating protein side chains shown on the right.

The whole mechanism is alluded to in the last figure, where the central calcium binding site is shown, with the general direction of calcium pumping. The counter-ion transport area is shown nearby as a flurry of red dots (standing for water molecules, which at this scale are interchangeable with OH ions). Specific single mutations in either area, either changing negatively charged E412 to positively charged lysine at the calcium binding pocket, or changing polar S877 in the water/hydroxy binding area to the bulky and hydrophobic F (phenylalanine), each kill pumping activity (graph). 

While it would be ideal to have a more dynamic representation of what is going on, the new structures give tremendous detail, including the associated ATP, PIP2, calcium, and water molecules. The mutations also nail down several functional points. Obviously a rather intricate and well-oiled machine that keeps its bit of cellular calcium homeostasis on an even keel. It is hard to believe that the sum of thousands of machines like this one is life, but the deeper we look the more true that appears to be.

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Not Every Transcript is Golden

 Reflections on junk DNA, and junk transcripts.

Some time ago, a large project in molecular biology determined that most regions of the genome are transcribed. The authors and most observers took this to mean that most regions are functional, quite in contrast to the reigning theory up to that point, that our genomes host a smattering of genes floating in a sea of "junk" DNA. That theory was based on the now-ancient observations of reannealing curves for bulk DNA from humans and other species which found that most of our DNA re-anneals very quickly, due to the fact that it is repetitive. Most of our genomes (60%) are taken up with LINE repeats, SINE repeats, old retro-transposons, stray duplications, and other repetitive material that, at a first glance, seems like junk. There has been a battle ever since, between proponents of junk DNA and those who see function around every corner. As we learn more about the genome, many more functions have indeed come to light, like distant enhancers and regulatory RNAs of many flavors. But overall, there still seems to be a lot of junk. 

A recent paper took an oblique shot at this field, looking at the profusion of alternative gene transcripts, which can number into the hundreds for a single gene. (This was also reviewed.) These are generally called isoforms, and arise due to variable ways one gene's RNA products can be initiated, terminated, and spliced. So not only are most regions of the genome transcribed in some form, actively transcribed regions can be transcribed and processed in myriad ways to lead to different RNA products. Here again, there has been an analogous argument, about whether every such isoform has a function, or whether isoforms might arise from more or less sporadic processes, often as unintended and non-functional sparks coming out of the machinery. The importance of isoforms is very well documented in many cases, so the possibility of function, sometimes highly conserved, is not in question. Only the importance of every last variation in combinatorial collections of isoforms that can number into the hundreds.

Here is an image from the first page (of about six pages) of RNA transcripts coming off the notorious BRCA1 gene, which is intensely studied for its role in breast cancer. Each line is a distinct mRNA transcript. Each darker bar is an exon, which are separated by introns. The darker colored exons are in the protein coding region, while the lighter exons signify the untranslated upstream and downstream ends. I count about 315 transcripts described for this genetic locus. The idea that each of these has some evolutionarily constrained and important function is, on the face of it, absurd.

The authors took an interesting evolutionary approach, reasoning that species with larger population sizes experience more stringent purifying selection, and thus should (in theory) show tighter control over stray genomic products such as isoforms, if most transcript isoforms are neutral (or even deleterious) accidents, rather than intentional and functional forms. Thankfully, animals come in a wide range of population sizes, from insects to crocodiles and primates; very large to very small. While population size is hard to calculate, several convenient proxies are known, like lifespan, body size, etc. When they totted everything up, they saw clear correlations between these proxies and the number of alternative RNA products per gene- also termed transcript diversity. They sliced up the data by organ where the RNA was expressed, and by the source of the RNA variation- either different initiation, different termination, different splicing. In all cases the trend was the same. In species with larger population sizes, the diversity of transcripts was lower, agreeing with their hypothesis that when greater selecive force is available, the slop from the transcription and transcript processing machinery declines.

The authors draw correlations between alternative splicing (AI) diversity in an organism's cells and its population size. 

The authors additionally note that there is a similar relationship between alternative splice site usage and expression level of a gene. That is, the higher the gene expression, the less likely that minor splice sites are used, indicating that here again, higher selective pressure helps to clear out non-functional off-products of the transcription apparatus.

The correlations found here are only that- correlations. While significant, they are not terribly strong, let alone stark. So it is evident that our gene expression machinery has a lot of play in it, and this falls on a spectrum from deleterious to critically functional. It is, after all, machinery, not divine. It is also grist for evolution itself- it is useful to have some slop so that there is always some diversity in the gearing to accommodate new selective pressures. But the idea that just because a distinct transcript exists, it is biologically functional, or that, similarly, because a genomic region is transcribed, it is a "gene" rather than junk DNA.. that does not hold water. Every nucleotide in the genome has its own unique selective constraints, and for many of them, that constraint is zero.

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How 5S rRNA Gets Into the Ribosome

For a minor component, it gets a lot of molecular love.

As mentioned several times in this space, the ribosome, which synthesizes proteins according to mRNA instructions, is an extremely ancient and complicated machine. Its core, including the catalytic site, is RNA. This marks it as a hold-over from the RNA world, as the thing that made proteins, (probably tiny proteins at first), before proteins had become a thing. But boy has there been a lot of duct-taping since then. In humans, there are four ribosomal RNAs, eighty proteins pasted on the outside, and hundreds of other proteins or RNAs involved in assembling the ribosome, not to mention dozens of initiation factors and other regulators that help during translation.

A recent paper discussed the maturation of 5S ribosomal RNA, which is the smallest rRNA, and one whose function is more peripheral than the large central 16S and 23S rRNAs. It is present in all life forms, though ribosomes inside mitochondria do without it. Its processing is an interesting case study of the complexity that has accumulated over the eons. Exactly what the 5S rRNA does remains a bit unclear, though it clearly contributes to the dynamics of the large ribosomal subunit, and occupies the "central protuberance". One group ligated it into the large subunit 23S rRNA, showing that translation still worked quite well with the 5S portion stably tacked into the structure. But then they also found that these ribosomes fell with high frequency into an unproductive locked state, suggesting that the independent nature of the 5S rRNA plays an important role in the dynamics of the ribosome. 

At any rate, the assembly of 5S into the rest of the structure is a story in itself. There are multiple steps involved, some involving ATP-using helicases. As it comes off the gene, 5S rRNA is bound by two proteins- the TFIIIA regulatory factor that activates its transcription, and also La protein (aka La antigen), which is a storage protein, named after systemic lupus, for which it is one immune target. To be incorportated into ribosomes, the RNA is next bound by a complex of Rpl5 and Rpl11, which will remain with the 5S RNA and become part of the eventual ribosome. Next come Rpf2 and Rrs1, which are two assembly facilitators that bind as a complex. Then comes Rsa4, which is similarly an assembly protein that helps the whole mess bind to the proper place on the (immature) large ribosomal subunit. Lastly, Rea1 (called MDN1 in humans) is an ATP-driven RNA helicase that wrenches the whole 5S-containing protuberance into its final and quite different position. 

The authors provide a scheme for the stepwise processing and assembly of 5S rRNA into the ribosome, involving numerous assembly factors, ribosomal proteins, and a helicase. 

It is quite an amazing story of progressive assembly, all to attach an element of the ribosome that is hardly central, but is rather a relatively late accretion on the machinery. Nevertheless, it evidently deserves specialized attention for correct placement. 

A less schematic view of various steps heading toward ribosomal assembly. 5S rRNA is in teal, and the helicase Rea1 is in dark gold, mounted like a wrench at the top of the (late) structure.

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We Have Rocks in Our Heads ... And Everywhere Else, Too

On the evolution and role of iron-sulfur complexes.

Some of the more persuasive ideas about the origin of life have it beginning in the rocks of hydrothermal vents. Here was a place with plenty of energy, interesting chemistry, and proto-cellular structures available to host it. Some kind of metabolism would by this theory have come first, followed by other critical elements like membranes and RNA coding/catalysis. This early earth lacked oxygen, so iron was easily available, not prone to oxidation as now. Thus life at this early time used many minerals in its metabolic processes, as they were available for free. Now, on today's earth, they are not so free, and we have complex processes to acquire and manage them. One of the major minerals we use is the iron-sulfur complex, (similar to pyrite), which comes in a variety of forms and is used by innumerable enzymes in our cells. 

The more common iron-sulfur complexes, with sulfur in yellow, iron in orange.

The principle virtue of the iron-sulfur complex is its redox flexibility. With the relatively electronically "soft" sulfur, iron forms semi-covalent-style bonds, while being able to absorb or give up an electron safely, without destroying nearby chemicals as iron alone typically does. Depending on the structure and liganding, the voltage potential of such complexes can be tuned all over the (reduction potential) map, from -600 to +400 mV. Many other cofactors and metals are used in redox reactions, but iron-sulfur is the most common by far.

Reduction potentials (ability to take up an electron, given an electrical push) of various iron-sulfur complexes.

Researchers had assumed that, given the abundance of these elements, iron-sulfur complexes were essentially freely acquired until the great oxidation event, about two to three billion years ago, when free oxygen started rising and free iron (and sulfur) disappeared, salted away into vast geological deposits. Life faced a dilemma- how to reliably construct minerals that were now getting scarce. The most common solution was a three enzyme system in mitochondria that 1) strips a sulfur from the amino acid cysteine, a convenient source inside cells, 2) scaffolds the construction of the iron-sulfur complex, with iron coming from carrier proteins such as frataxin, and 3) employs several carrier proteins to transfer the resulting complexes to enzymes that need them. 

But a recent paper described work that alters this story, finding archaeal microbes that live anaerobically and make do with only the second of these enzymes. A deep phylogenetic analysis shows that the (#2) assembly/scaffold enzymes are the core of this process, and have existed since the last common ancestor of all life. So they are incredibly ancient, and it turns out to that iron-sulfur complexes can not just be gobbled up from the environment, at least not by any reasonably advanced life form. Rather, these complexes need to be built and managed under the care of an enzyme.

The presented structures of the dimer of SmsB (orange) and SmsC (blue) that dimerize again to make up a full iron-sulfur scaffolding and production enzyme in the archaean Methanocaldococcus jannaschii. Note the reaction scheme where ATP comes in and evicts the iron-sulfur cluster. On right is shown how ATP fits into the structure, and how it nudges the iron-sulfur binding area (blue vs green tracing).

A recent paper from this group extended their analysis to the structure of the assembly/scaffold enzyme. They find that, though it is a symmetrical dimer of a complex of two proteins, it only deals with one iron-sulfur complex at at time. It also binds and cleaves ATP. But ATP seems to have more of an inhibitory role than one that stimulates assembly directly. The authors suggest that high levels of ATP signal that less iron-sulfur complex is needed to sustain the core electron transport chains of metabolism, making this ATP inhibition an allosteric feedback control mechanism in these archaeal cells. I might add, however, that ATP binding may well also have a role in extricating the assembled iron-sulfur cluster from the enzyme, as that complex is quite well coordinated, and could use a push to pop out into the waiting arms of target enzymes.

"These ancestral systems were kept in archaea whereas they went through stepwise complexification in bacteria to incorporate additional functions for higher Fe-S cluster synthesis efficiency leading to SUF, ISC and NIF." - That is, the three-component systems present in eukaryotes, which come in three types.

In the author's structure, the iron-sulfur complex, liganded by three cysteines within the SmsC protein. But note how, facing the viewer, the complex is quite exposed, ready to be taken up by some other enzyme that has a nice empty spot for it.

Additionally, these archaea, with this simple one-step iron cluster formation pathway, get their sulfur not from cysteine, but from ambient elemental sulfur. Which is possible, as they live only in anaerobic environments, such as deep sea hydrothermal vents. So they represent a primitive condition for the whole system as may have occurred in the last common ancestor of all life. This ancestor is located at the split between bacteria and archaea, so was a fully fledged and advanced cell, far beyond the earlier glimmers of abiogenesis, the iron sulfur world, and the RNA world.

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