Category Archives: Human Genetic Engineering

SARS-CoV-2, the virus that causes Covid-19, has made our future vulnerability to biological pathogens and what we can learn to help prevent the next pandemic a salient concern. We dont have much evidence one way or the other whether Covids emergence into the world was the result of a lab accident or a natural jump from animal to human. And while the US intelligence communitys current best guess is that the virus probably was not genetically engineered, the theory has been the subject of much debate and has not been definitively ruled out.

The many unknowns we confront underscore the need for a much bigger toolkit to deal with pathogenic threats than we currently have which is why a recent paper about a new advance in tracing genetic editing is particularly exciting.

Bioengineering often leaves traces characteristic patterns in the RNA or DNA of an engineered organism that are a product of a plethora of design decisions that go into synthetic biology. That fact about bioengineered genomes raises an interesting question: What if those traces that gene editing leaves behind were more like fingerprints? That is, what if its possible not just to tell if something was engineered but precisely where it was engineered?

Thats the idea behind genetic engineering attribution: the effort to develop tools that let us look at a genetically engineered sequence and determine which lab developed it. A big international contest among researchers earlier this year demonstrates that the technology is within our reach though itll take lots of refining to move from impressive contest results to tools we can reliably use for bio detective work.

The contest, the Genetic Engineering Attribution Challenge, was sponsored by some of the leading bioresearch labs in the world. The idea was to challenge teams to develop techniques in genetic engineering attribution. The most successful entrants in the competition could predict, using machine-learning algorithms, which lab produced a certain genetic sequence with more than 80 percent accuracy, according to a new preprint summing up the results of the contest.

This may seem technical, but it could actually be fairly consequential in the effort to make the world safe from a type of threat we should all be more attuned to post-pandemic: bioengineered weapons and leaks of bioengineered viruses.

One of the challenges of preventing bioweapon research and deployment is that perpetrators can remain hidden its difficult to find the source of a killer virus and hold them accountable.

But if its widely known that bioweapons can immediately and verifiably be traced right back to a bad actor, that could be a valuable deterrent.

Its also extremely important for biosafety more broadly. If an engineered virus is accidentally leaked, tools like these would allow us to identify where they leaked from and know what labs are doing genetic engineering work with inadequate safety procedures.

Hundreds of design choices go into genetic engineering: what genes you use, what enzymes you use to connect them together, what software you use to make those decisions for you, computational immunologist Will Bradshaw, a co-author on the paper, told me.

The enzymes that people use to cut up the DNA cut in different patterns and have different error profiles, Bradshaw says. You can do that in the same way that you can recognize handwriting.

Because different researchers with different training and different equipment have their own distinctive tells, its possible to look at a genetically engineered organism and guess who made it at least if youre using machine-learning algorithms.

The algorithms that are trained to do this work are fed data on more than 60,000 genetic sequences different labs produced. The idea is that, when fed an unfamiliar sequence, the algorithms are able to predict which of the labs theyve encountered (if any) likely produced it.

A year ago, researchers at altLabs, the Johns Hopkins Center for Health Security, and other top bioresearch programs collaborated on the challenge, organizing a competition to find the best approaches to this biological forensics problem. The contest attracted intense interest from academics, industry professionals, and citizen scientists one member of a winning team was a kindergarten teacher. Nearly 300 teams from all over the world submitted at least one machine-learning system for identifying the lab of origin of different sequences.

In that preprint paper (which is still undergoing peer review), the challenges organizers summarize the results: The competitors collectively took a big step forward on this problem. Winning teams achieved dramatically better results than any previous attempt at genetic engineering attribution, with the top-scoring team and all-winners ensemble both beating the previous state-of-the-art by over 10 percentage points, the paper notes.

The big picture is that researchers, aided by machine-learning systems, are getting really good at finding the lab that built a given plasmid, or a specific DNA strand used in gene manipulation.

The top-performing teams had 95 percent accuracy at naming a plasmids creator by one metric called top 10 accuracy meaning if the algorithm identifies 10 candidate labs, the true lab is one of them. They had 82 percent top 1 accuracy that is, 82 percent of the time, the lab they identified as the likely designer of that bioengineered plasmid was, in fact, the lab that designed it.

Top 1 accuracy is showy, but for biological detective work, top 10 accuracy is nearly as good: If you can narrow down the search for culprits to a small number of labs, you can then use other approaches to identify the exact lab.

Theres still a lot of work to do. The competition looked at only simple engineered plasmids; ideally, wed have approaches that work for fully engineered viruses and bacteria. And the competition didnt look at adversarial examples, where researchers deliberately try to conceal the fingerprints of their lab on their work.

Knowing which lab produced a bioweapon can protect us in three ways, biosecurity researchers argued in Nature Communications last year.

First, knowledge of who was responsible can inform response efforts by shedding light on motives and capabilities, and so mitigate the events consequences. That is, figuring out who built something will also give us clues about the goals they might have had and the risk we might be facing.

Second, obviously, it allows the world to sanction and stop any lab or government that is producing bioweapons in violation of international law.

And third, the article argues, hopefully, if these capabilities are widely known, they make the use of bioweapons much less appealing in the first place.

But the techniques have more mundane uses as well.

Bradshaw told me he envisions applications of the technology could be used to find accidental lab leaks, identify plagiarism in academic papers, and protect biological intellectual property and those applications will validate and extend the tools for the really critical uses.

The past year and a half should have us all thinking about how devastating pandemic disease can be and about whether the precautions being taken by research labs and governments are really adequate to prevent the next pandemic.

The answer, to my mind, is that were not doing enough, but more sophisticated biological forensics could certainly help. Genetic engineering attribution is still a new field. With more effort, itll likely be possible to one day make attribution possible on a much larger scale and to do it for viruses and bacteria. That could make for a much safer future.

Correction, October 25, 9:50 am: A previous version of this story stated that SARS-CoV-2 had been definitively proven not to be a bioengineered virus. While an August 2021 US intelligence report concluded, Most agencies assess with low confidence that SARS-CoV-2 probably was not genetically engineered, and many scientists agree with that assessment, it was an overstatement to claim that the theory has been definitively ruled out. The introduction and conclusion of the story have been updated to reflect this lower level of certainty. (h/t to Alina Chan, biologist at the Broad Institute of MIT and Harvard, for her critique and input)

More here:
How biological detective work can reveal who engineered a virus - Vox.com

Life goes on as gene-edited foods begin to hit the market. Japanese consumers have recently startedbuying tomatoes that fight high blood pressure, and Americans have been consuming soy engineered to produce high amounts of heart-healthy oils for a little over two years. Few people noticed these developments because, as scientists have said for a long time, the safety profile of a crop is not dictated by the breeding method that produced it. For all intents and purposes, it seems that food-safety regulators have done a reasonablejob of safeguarding public health against whatever hypothetical risks gene editing may pose.

But this has not stopped critics of genetic engineering from advocating for more federal oversight of CRISPR and othertechniquesused to make discrete changes to the genomes of plants, animals and other organisms we use for food or medicine. Over at The Conversation, a team of scientists recently made the case for tighter rules in Calling the latest gene technologies natural is a semantic distraction they must still be regulated.

Many scientists have defended gene editing, in part, by arguing that it simply mimics nature. A mutation that boosts the nutrient content of rice, for example, is the same whether it was induced by a plant breeder or some natural phenomenon. Indeed, the DNA of plants and animals we eat contains untold numbers of harmless, naturally occurringmutations. But The Conversation authors will have none of this:

Unfortunately, the risks from technology dont disappear by calling it natural... Proponents of deregulation of gene technology use the naturalness argument to make their case. But we argue this is not a good basis for deciding whether a technology should be regulated.

They have written a very long peer-reviewed article outlining a regulatory framework based on "scale of use."The ideais that the more widely a technology is implemented, the greater risk it may pose to human health and the environment, which necessitates regulatory "control points" to ensure its safe use. It's an interesting proposal, but it's plagued by several serious flaws.

Where's the data?

The most significant issue with a scale-based regulatory approachis that it's a reaction to risks that have never materialized. This isn't to say that a potentially harmful genetically engineered organism will never be commercialized. But if we're going to upend our biotechnology regulatory framework, we need to do so based on real-world evidence. Some experts have actually argued, based on decades of safety data, that the US over-regulates biotech products. As biologist and ACSHadvisorDr. Henry Miller and legal scholar John Cohrssen wrote recently in Nature:

After 35 years of real-world experience with genetically engineered plants and microorganisms, and countless risk-assessment experiments, it is past time to reevaluate the rationale for, and the costs and benefits of, the case-by-case reviews of genetically engineered products now required by the US Environmental Protection Agency (EPA), US Department of Agriculture (USDA) and US Food and Drug Administration (FDA).

The problem with scale

Real-world data aside for the moment, there are some theoretical problems with the scalabilitymodel as well. Theargument assumes thatrisks associated with gene editing proliferate as use of the technology expands, because each gene edit carries a certain level of risk. This is a false assumption, as plant geneticist Kevin Folta pointed out on a recent episode of the podcast we co-host (21 minute mark).

Scientists have a variety of tools with which to monitor and limit the effects of specific gene edits. For example, proteins known as anti-CRISPRs can be utilized to halt the gene-editing machinery so it makes only the changes we want it to. University of Toronto biochemist Karen Maxwell has explained how this could work in practice:

In genome editing applications, anti-CRISPRs may provide a valuable 'off switch for Cas9 activity for therapeutic uses and gene drives. One concern of CRISPR-Cas gene editing technology is the limited ability to control its activity after it has been delivered to the cell . which can lead to off-target mutations. Anti-CRISPRs can potentially be exploited to target Cas9 activity to particular tissues or organs, to particular points of the cell cycle, or to limit the amount of time it is active

Suffice it to say that these and other safeguards significantly alter the risk equation and weaken concerns about a gene-edits-gone-wild scenario. Parenthetically, scientists design these sorts of preventative measures as they develop more genetic engineering applications for widespread use. This is why the wide variety of cars in production today have safety features that would have been unheard of in years past.

Absurdity alert: The A-Bomb analogy

To bolster their argument, The Conversation authors made the following analogy:

Imagine if other technologies with the capacity to harm were governed by resemblance to nature. Should we deregulate nuclear bombs because the natural decay chain of uranium-238 also produces heat, gamma radiation and alpha and beta particles? We inherently recognize the fallacy of this logic. The technology risk equation is more complicated than a supercilious 'its just like nature' argument

If someone has to resort to this kind of rhetoric, the chances are excellent that their argument is weak. Fat Man and Little Boy, the bombs dropped on Japan in 1945, didn't destroy two cities because a nuclear physicist in New Mexico made a technical mistake. These weapons are designed to wreak havoc. Tomatoes bred to produce more of an amino acid, in contrast, are not.

The point of arguing that gene-editing techniques mimic natural processes isn't to assert that natural stuff is good; therefore, gene editing is also good. Instead, the point is to illustrate that inducing mutations in the genomes of plants and animals is not novel or uniquely risky. Even the overpriced products marketed as all-natural have been improved by mutations resulting from many years of plant breeding.

Nonetheless, some scientists have argued that reframing the gene-editing conversation in terms of risk vs benefit would be a smarter approach than making comparisons to nature. I agree with them, so let's start now. The benefits of employing gene editing to improve our food supply and treat disease far outweigh the potential risks, which we can mitigate. Very little about modern life is naturaland it's time we all got over it.

Read more from the original source:
CRISPR Revolution: Do We Need Tighter Gene-Editing Regulations? No - American Council on Science and Health

Left to right: Gary Hon, Ph.D., UTSW Assistant Professor of Obstetrics and Gynecology; Nikhil Munshi, M.D., Ph.D., Associate Professor of Internal Medicine and Molecular Biology; W. Lee Kraus, Ph.D., Professor and Director of the Cecil H. and Ida Green Center for Reproductive Biology Sciences

The Human Genome Project identified and mapped all of the genes of the human genome, achieving the worlds largest international, collaborative biological project. That opened the door to a wide array of innovative research projectsincluding a prestigious one that UT Southwestern has just joined.

A team ofUT Southwestern faculty led by Gary Hon, Ph.D.,has been awarded a five-year, $8.8 million grant to participate in the National Human Genome Research Institutes Impact of Genomic Variation on Function (IGVF) Consortium. The consortiums goal is understanding how developmental variants contribute to developmental diseases.

Dr. Hon is an assistant professor of obstetrics and gynecology in the Cecil H. and Ida Green Center for Reproductive Biology Sciences and a member of the Lyda Hill Department of Bioinformatics.

Hon developed Mosaic-seq, a genome engineering technique that helped lead to the awarding of the $8.8 million grant. In a statement, he saidthe IGVF Consortium is the National Human Genome Research Institutes next step to unveiling the genomes role in disease.

The Human Genome Project told us that most of the genome doesnt contain genes, Hon said. One big surprise from genome-wide association studies is that gene-poor regions contain many disease signatures.

It turns out that the signatures largely overlap with DNA elements, found by the Encyclopedia of DNA Elements (ENCODE) Consortium, that control when genes turn on, Hon added. The goal of this consortium is to fill in the gaps, linking DNA sequences to genes, cell phenotypes, and disease. Ultimately, this knowledge will allow us to interpret the disease potential of any persons genome sequence.

In their work with the consortium, the UTSW teamwill combine molecular biology, genomics, high throughput screens, and computational analyses to focus on potential disease-causing genetic variations in the cardiovascular, nervous, and placental systems.

Besides Hon, the teamalso includes principal investigators Nikhil Munshi, M.D., Ph.D., associate professor of internal medicine and molecular biology, and W. Lee Kraus, Ph.D., professor and director of the Green Center.

Mosaic-seq allows high throughput analysis of the molecular events that occur during programming of embryonic stem cells into other cell types. This technique uses single-cell sequencing to study different regions of the genome at the same time.

Just one experiment can perturb thousands of regions in the genome to better understand their function, according to the UTSW team.

With Mosaic-seq, researchers no critical have to study one region at a time. Hons lab received national attention in 2017 for this significant advance, which was part of his teams grant application.

UTSW now joins Harvard, Stanford, and Yale universities as one of the 30 research sites taking part in the IGVF Consortium nationwide.The consortium will study noncoding regions of the human genome that are known to contribute to genetic diseasesincluding congenital heart disease, autoimmune disease, and blood disorders.

Dr. Kraus, a professor of obstetrics and gynecology and pharmacology who holds the Cecil H. and Ida Green Distinguished Chair in reproductive biology sciences, will use additional CRISPR-based technologies in the consortium research project. Kraus will use them to study how genetic variation in non-coding RNAs originating from the regulatory elements impacts the development of the placenta.

The placentas development is important because it supports the human fetus as it grows, as well as the fetuss heart and central nervous system.

Studying the role of genetic variation in the embryonic development of these key organs could point the way to understanding human diseases in adults, Kraus said in the statement.

Dr. Munshi believes the IGVF Consortium initiativecould potentially fill in huge pieces of the puzzle for many diseases.

If we candetermine all of the noncoding elements in the genome that impact a particular developmental pathway, then those could become candidates fordisease-associated mutations, Munshi said.

By generating catalogs of tens of thousands offunctionalvariants, we dont have to search the billons of basepairs to find where thedisease-causingmutations might lie, he added. We can really focus the search on thesetens of thousands of variants. It really gives us an encyclopediatonarrow the search.

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Earlier this week, surgeons at New York Universitys Langone Transplant Institute successfully performed a pig kidney transplant. This in itself would be unremarkable. What does mark the achievement as unprecedented is the identity of the donor a genetically modified pig.

Some days post-surgery, the recipient, a brain-dead patient whose family consented to the experimental procedure, has not rejected the kidney and tests show that it is functioning normally. This incredible feat is significant both as a demonstration of scientific control over biological systems and as a beacon of hope to others in line for a transplant.

The idea of using other species for organ transplants is not new; we have used pig heart-valves for over 50 years. Yet whole organs have presented several challenges, most notably the risk of rejection. This occurs because the body believes the transplant is an invader that must be destroyed, leading to an immune response that attacks the organ. While the triggers for rejection are not completely understood, one of the biggest barriers to cross-species transplantation is a molecule known as alpha-gal, a carbohydrate that immediately elicits a massive immune response.

To counteract this, scientists used a powerful tool of genetic engineering, CRISPR, to modify the pigs genome so that it does not produce alpha-gal. CRISPR has existed for less than a decade, yet its ability to accurately cut and paste specific pieces of genomes is already leading to breakthroughs in many areas of biology including in the development of COVID-19 vaccines.

At present, over 100,000 people in the United States are awaiting an organ donation, among whom 83%, ~91,000, are in need of a kidney. Though 54% of US citizens are registered organ donors, less than 1% of deaths result in useable organs, so supply will always outstrip demand.

Consequently, wait times for a kidney can range from four months to six years depending on blood type, geographic location, disease severity, immune system activity, and other factors. Most of those on the waiting list must have their blood cleaned via hemodialysis, a process that entails commuting to a dialysis centre and spending four hours a day, three times a week, attached to a machine simply to stay alive. The longer they are on dialysis, the smaller their chance of a successful kidney transplant becomes as the procedure can only partially compensate for the damaged organ.

Every year, 5,000 people die waiting for a transplant and another 5,000 are removed from the list because they are no longer healthy enough to receive it, meaning that only 65% of those placed on transplant lists will receive a kidney in time. This latest development could prove to be a gamechanger.

But there will be difficult questions about the ethics of modifying other species to fit our needs, and the event may spark further dialogue on the conditions pigs and other animals are currently raised in. There are also still many unanswered questions surrounding the efficacy of cross-species transplantation. Can pig kidney transplants to humans save lives? Well, before we get to an answer, more robust, longer-term trials will have to take place.

Yet the significance of this pig kidney transplant demonstration should not be underestimated this is a momentous step towards saving the lives of tens of thousands of people awaiting a transplant, not to mention the half a million with kidney failure who do not even qualify because of scarcity. It also speaks to the potential of biotechnology more broadly to transform the health outcomes of millions of people.

Written by

Cameron Fox, Project Specialist, Shaping the Future of Health and Healthcare, World Economic Forum

The views expressed in this article are those of the author alone and not the World Economic Forum.

Continued here:
A kidney transplant from a pig to a human has worked. What you need to know - World Economic Forum

Image credit: Hannes Richter viaUnsplash.

For years weve been told that human and chimp DNA is some 99 percent identical. The genetic similarity statistic is then used to make an argument for human-ape common ancestry, and human-ape common ancestry is then employed in service of the larger philosophical point that humans are just modified apes, and nothing special. It all amounts to an argument against human exceptionalism. This sort of thinking is embodied by Bill Nye (The Science Guy) in his 2014 bookUndeniable:

As our understanding of DNA has increased, we have come to understand that we share around 98.8 percent of our gene sequence with chimpanzees. This is striking evidence for chimps and chumps to have a common ancestor.

BioLogos-affiliated biologist Dennis Venemahas also arguedthat we are but a hand-breadth away from our evolutionary cousins at the DNA level. But is this really true? In response to the newly released episode ofScience Uprisingon human origins, we have recently received questions about the true degree of human-chimp similarity. With that in mind, lets review some past coverage on the issue.

In 2007, not long after the chimp genome was first sequenced, the journalSciencepublished an article, Relative Differences: The Myth of 1%, which called the idea that humans are only 1 percent genetically different from chimps a myth and a truism [that] should be retired. It observed that the genetic differences between humans and chimps amount to 35 million base-pair changes, 5 million indels [sequences of multiple nucleotide bases] in each species, and 689 extra genes in humans. The article further reported that if we consider the number of copies of genes in the human and chimp genomes, human and chimpanzee gene copy numbers differ by a whopping 6.4%.

The old statistic that we are about 99 percent or 98 percent similar to chimps pertains only to alignable protein-coding sequences. In fact the statistic first originated based upon similarity between humans and chimps in just one single gene! But many non-coding sequences are highly dissimilar, and there are sequences of the human and chimp genomes that are so different that they cant be aligned for comparison. For example, there are some parts of our genome, such as thehuman y chromosome, that are radically different from the chimp genome.

Geneticist Richard Buggs has tried to refine the methods for comparing human and chimp genomes. In a 2018 post, he observesthat The percentage of nucleotides in the human genome that had one-to-one exact matches in the chimpanzee genome was 84.38%. In 2020 he co-published anarticle in the journalFrontiers in Geneticsproviding a different method of estimating of human-chimp genetic differences, finding that human-chimp genetic similarity is about 96 percent. This papers estimate of ~4 percent genetic difference includes both coding and non-coding DNA, but it does not include centromeric DNA. If that DNA were included, the percent of genetic similarity between humans and chimps could drop to as low as ~93 percent, but probably not lower. Computational biologist Steve Schaffner has roughly estimated human-chimp genetic similarity to be ~95 percent. However, one criticism Ive heard of all current estimates is that they are based upon versions of the chimp genome that used the human genome as a scaffolding, potentially making certain sections of the chimp genome more humanlike than they ought to be. This could also artificially inflate the degree of human-chimp similarity.

What this means is that until more accurate and complete versions of the chimp genome are produced, any estimate of human-chimp genetic similarity will undoubtedly be refined in the future, and current numbers may very well be overestimates. Nonetheless, any of the above estimates of human-chimp genetic similarity 96 percent, 95 percent, 93 percent, 84 percent carries meaning in different contexts. But what exactly do they mean?

Whatever the exact percentage of human-chimp genetic similarity (however you want to measure it) turns out to be, lets grant that it will be fairly high, probably 84 percent or greater. Does this necessarily require the conclusion of common ancestry? Is the case for common ancestry, based upon the degree of similarity, an objective or rigorous argument thats capable of being falsified? For example, if a 1 percent genetic difference implies common ancestry, but then that statistic turns out to be wrong, then does a 4 percent genetic difference mean common ancestry is false? How about 7 percent or 10 percent genetic difference? 25 percent? At what point does the comparison cease to support common ancestry? Why does the percent genetic similarity even matter? Its not clear that there is an objective standard for falsification here, any identifiable reason why a particular percentage of genetic similarity should be taken to indicate common ancestry.

Indeed, Dennis Venema even seems to acknowledge this point, writing in 2018:

No one is more interested in the % genome identity thing than folks trying to cast doubt on common ancestry. Its just not a precise value that scientists are interested in, because it doesnt answer interesting scientific questions in the way other values do (emphasis added)

Thats quite a bold quote from Professor Venema when earlier he was seen emphasizing how humans are a mere genetic hand-breadth away from chimps, as part of a case for common ancestry. This is in keeping with numerous other evolution apologists over the years who have cited the 1% statistic in favor of human-chimp common ancestry. They are the ones who invented and promoted this fallacious argument, and we are simply responding to it. Yet somehow us Darwin-skeptics get blamed for spreading a fallacious argument.

Perhaps Dr. Venema has changed his mind about the import of the statisticwhich he is fully entitled to do. Whatever the case, we agree with his point here that the % genome identity provides no rigorous argument for common ancestry and does not answer very many interesting questions within this particular debate.

The case for human-chimp common ancestry is further significantly weakened once one realizes that there are other potential explanations for functional similarities: notably, design based upon a common blueprint.

Intelligent agents often re-use parts and components that perform common functions in different designs. Its a good engineering design principle to follow! Everyday examples of this include wheels used on both cars and airplanes, or touchscreen keyboards used on both phones and tablets.

It should be noted that common design, as an argument, is not intended to prove species were specially created or designed separately. Rather, its a rejoinder put forth to defeat the evolutionist assertion that genetic similarity necessarily indicates common ancestry. Genetic similarity doesnt necessarily indicate common ancestry because intelligent agents can and do independently use common parts in different designs to fulfill common functional goals. High genetic similarity could reflect design with a common blueprint rather than common ancestry.Biologist Ann Gauger, mathematician Ola Hssjer, and statistician Colin Reevesexplain this wellin Chapter 15 of the 2017 bookTheistic Evolution:

[T]here are some basic differences between the way evidence is approached by evolutionary biologists and design biologists. The chief assumption made by evolutionary biologists is that the genetic changes responsible for evolutionary change are random, and therefore, if a group of species share a trait in common that is not found in other related species, it is presumed that the common ancestor of the group developed that trait, and they all share it because of common descent. On the other hand, if genetic change is directed rather than random,the trait is most likely shared because the organisms use similar solutions to a physiological need.

Humans and chimps thus have similarities that reflect functional constraints due to design based upon a common blueprint. Gauger and her team indicate what this means for some of the basic molecular, cellular, metabolic, and physiological similarities between humans and chimps:

First, our basic building blocks, the proteins out of which our cells are made and the enzymes that carry out cellular metabolism, are very similar to those of chimpanzees, almost identical in many cases. One can think of our genes as being like the bricks and mortar, nails and wood, shingles and wires out of which houses are made. Two houses may look different but be composed of the same basic building blocks. By analogy, the building blocks out of which we are made, the genes, are very similar for chimps and humans, even if our bodily forms are different.

Second, the vast majority of our DNA does not code for protein but functions like an operating system, determining what files (genes) should be used when, and where. The routine processes of life are carried out by this operating system, and we share these basic routines with chimps. Thus in many respects our operating systems are the same as those of chimps.

Of course some will cite shared NON-functional (as opposed to functional) genetic similarities between humans and chimps as better evidence for common ancestry. I agree that non-functional shared DNA could be a potential argument for common ancestry, but Im skeptical that many of the DNA elements cited in these arguments are actually non-functional. Aswe saw recently, a new paper inGenome Biology and Evolutiondeclared, The days of junk DNA are over. Even pseudogenes, commonly cited as a form of genetic junk that supports common ancestry, have had their junk status severely questioned in recent years seehere,here,here,here, andherefor discussions.

Since many of the building blocks used by humans and chimps are similar, its no wonder that our protein-coding DNA is also so similar. Common design can explain these similarities. But its important to bear in mind that one can use identical building blocks bricks, mortar, wood, and nails to build very different houses. So its not just about having similar building blocks, but how you use them. This is where genetic similarities between humans and chimps probably arent so meaningful, when you consider how the building blocks being used can be very different.

Gauger and her colleagues thus explain that the percentage of nucleotide similarity does not tell the whole story about human-chimp genetic differences since many of the most crucial differences lie outside the protein-coding DNA:

[C]ounting raw difference is not the best way to calculate how different we are genetically speaking We now know that when, where, and how our DNA is used matters much more than an overall count of nucleotide differences. Human-specific differences in gene regulation, as we will see, are what make us unique.

They recount some of the crucial differences between humans and chimps:

And this leaves aside the vast cognitive and behavioral gulf between humans and chimpanzees. We are the only species that uses fire and technology. We are the only species that composes music, writes poetry, and practices religion. We are also the only species that seeks to investigate the natural world through science. We write papers about chimps; not the other way around. All of this is possible because we humans are the only species that uses complex language.

The human race has unique and unparalleled moral, intellectual, and creative abilities. Regardless of the level of similarity of human protein-coding DNA to chimps, clearly that similarity is only a small part of the story. If anything, it testifies that protein-coding DNA sequences are only one of multiple crucial interacting factors that determine an organisms biology and behavior.

Original post:
Human-Chimp Similarity: What Does It Mean? - Discovery Institute

Completed Business Combination with Big Cypress Acquisition Corp.

Common stock to commence trading on the Nasdaq Global Market October 25, 2021, under the ticker symbol SABS

SIOUX FALLS, S.D., Oct. 25, 2021 (GLOBE NEWSWIRE) --SAB Biotherapeutics, Inc. (Nasdaq: SABS), (SAB), a clinical-stage biopharmaceutical company with a novel immunotherapy platform that produces specifically targeted, high-potency, fully-human polyclonal antibodies without the need for human donors, today announced the completion of its business combination with Big Cypress Acquisition Corp. (Nasdaq: BCYP) (Big Cypress), a publicly-traded special purpose acquisition company (SPAC) focused on innovative biopharmaceutical firms. The common stock and warrants of the resulting combined company, SAB Biotherapeutics Inc. will commence trading on the Nasdaq Global Market (the NASDAQ) under the ticker symbol SABS and SABSW, respectively, on October 25, 2021.

The stockholders of Big Cypress approved the transaction at a Special Meeting held on October 20, 2021. The transaction was previously approved by SABs shareholders. SABs management team will be led by Eddie Sullivan, Ph.D., Co-Founder, President, and Chief Executive Officer, who previously served as President, and Chief Executive Officer. Samuel J. Reich, formerly Chief Executive Officer and Chief Financial Officer of Big Cypress, and Jeffrey G. Spragens, Big Cypress Chairman of the Board of Directors, will join the SAB Board of Directors, with Mr. Reich assuming the role of Executive Chairman.

We are excited to enter the public markets at such a pivotal time when next-generation immunotherapies like ours are essential in driving improvement in the global health landscape. We extend our gratitude to the Big Cypress team for being our partner in driving our vision of developing scalable and highly potent polyclonal antibody therapies, said Dr. Eddie Sullivan. We would also like to thank the SAB team, as well as our new and existing shareholders, who are making our important work possible. The SAB team is committed to progressing our science and expanding the reach of our unique DiversitAb platform, now as a public company.

Dr. Sullivan added, We look forward to reporting clinical data from a number of our programs in the coming months. SAB expects to announce topline clinical data for our seasonal influenza program before the end of the year, and we expect to report clinical data from our NIH-sponsored COVID-19 clinical trials as soon as it becomes available.

SABs innovative and versatile DiversitAb platform and talented team bring a unique approach to the development of immunotherapies, which is why we chose them as our merger partner, said Samuel Reich. Our experienced biopharmaceutical team was initially impressed by the ability of SABs platform to produce high-potency fully-human polyclonal antibodies with the potential to address a variety of serious diseases with high unmet medical need. In the few months since we announced our intention to merge, SAB has achieved multiple significant milestones, reinforcing our confidence in their ability to execute and deliver on the promise of their technology. Im delighted to be joining the SAB team to advance the companys clinical programs and business strategy, with the goal of building a differentiated biopharmaceutical company committed to creating shareholder value and having a significant positive impact on human health.

Summary of TransactionOn June 22, 2021, SAB entered into a definitive business combination agreement with Big Cypress Acquisition Corp., a blank check company focused on innovative biopharmaceutical firms, which was created for the purpose of entering into a business combination with a selected biopharmaceutical company and bringing the combined entity to the NASDAQ.

The gross proceeds from the transaction after redemptions and before deducting transaction fees are approximately $30 million. SAB intends to use the proceeds from the transaction to progress its pipeline programs and to augment its recent $60.5 million award from the government, in addition to approximately $27 million remaining from previous awards.

Recent Developments Demonstrate Momentum Across Key Initiatives

AdvisorsLazard served as exclusive financial advisor to SAB. Stradling Yocca Carlson & Rauth is serving as legal counsel to SAB. Chardan served as exclusive M&A advisor and financial advisor to Big Cypress and Dentons US LLP is serving as legal counsel. Ladenburg Thalmann & Co. Inc. is acting as a capital markets advisor to Big Cypress.

About SAB Biotherapeutics, Inc.SAB Biotherapeutics, Inc. (SAB) is a clinical-stage, biopharmaceutical company advancing a new class of immunotherapies leveraging fully human polyclonal antibodies. SAB has applied advanced genetic engineering and antibody science to develop transchromosomic (Tc) Bovine that produce fully-human antibodies targeted at specific diseases, including infectious diseases such as COVID-19 and influenza, immune system disorders including type 1 diabetes and organ transplantation, and cancer. SABs versatile DiversitAb platform is applicable to a wide range of serious unmet needs in human diseases. It produces natural, specifically targeted, high-potency, human polyclonal immunotherapies. SAB is currently advancing multiple clinical programs and has collaborations with the US government and global pharmaceutical companies. For more information on SAB, visit: https://www.sabbiotherapeutics.com and follow @SABBantibody on Twitter.

Contact:Melissa Ullerich+1 605-679-4609mullerich@sabbiotherapeutics.com

Courtney Turiano (investors)Stern IR212-698-8687Courtney.Turiano@sternir.com

Forward-Looking Statements Certain statements made herein that are not historical facts are forward-looking statements for purposes of the safe harbor provisions under The Private Securities Litigation Reform Act of 1995. Forward-looking statements generally are accompanied by words such as believe, may, will, estimate, continue, anticipate, intend, expect, should, would, plan, predict, potential, seem, seek, future, outlook and similar expressions that predict or indicate future events or trends or that are not statements of historical matters. These forward-looking statements include, but are not limited to, statements regarding future events. These statements are based on the current expectations of SAB and are not predictions of actual performance. These forward-looking statements are provided for illustrative purposes only and are not intended to serve as, and must not be relied on, by any investor as a guarantee, an assurance, a prediction or a definitive statement of fact or probability. Actual events and circumstances are difficult or impossible to predict, will differ from assumption and are beyond the control of SAB.

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SAB Biotherapeutics Debuts as Publicly Traded Next-Generation Immunotherapy Company - BioSpace