If there is one thing that university faculty and administrators could do today to demonstrate their commitment to inclusion, not to mention teaching and learning over sorting and status, it would be to ban curve-based, norm-referenced grading. Many obstacles exist to the effective inclusion and success of students from underrepresented (and underserved) groups in science and related programs. Students and faculty often, and often correctly, perceive large introductory classes as “weed out” courses preferentially impacting underrepresented students. In the life sciences, many of these courses are “out-of-major” requirements, in which students find themselves taught with relatively little regard to the course’s relevance to bio-medical careers and interests. Often such out-of-major requirements spring not from a thoughtful decision by faculty as to their necessity, but because they are prerequisites for post-graduation admission to medical or graduate school. “In-major” instructors may not even explicitly incorporate or depend upon the materials taught in these out-0f-major courses – rare is the undergraduate molecular biology degree program that actually calls on students to use calculus or a working knowledge of physics, despite the fact that such skills may be relevant in certain biological contexts – see Magnetofiction – A Reader’s Guide. At the same time, those teaching “out of major” courses may overlook the fact that many (and sometimes most) of their students are non-chemistry, non-physics, and/or non-math majors. The result is that those teaching such classes fail to offer a doorway into the subject matter to any but those already comfortable with it. But reconsidering the design and relevance of these courses is no simple matter. Banning grading on a curve, on the other hand, can be implemented overnight (and by fiat if necessary).
So why ban grading on a curve? First and foremost, it would put faculty and institutions on record as valuing student learning outcomes (perhaps the best measure of effective teaching) over the sorting of students into easy-to-judge groups. Second, there simply is no pedagogical justification for curved grading, with the possible exception of providing a kludgy fix to correct for poorly designed examinations and courses. There are more than enough opportunities to sort students based on their motivation, talent, ambition, “grit,” and through the opportunities they seek after and successfully embraced (e.g., through volunteerism, internships, and independent study projects).
The negative impact of curving can be seen in a recent paper by Harris et al, (Reducing achievement gaps in undergraduate general chemistry …), who report a significant difference in overall student inclusion and subsequent success based on a small grade difference between a C, which allows a student to proceed with their studies (generally as successfully as those with higher grades) and a C-minus, which requires them to retake the course before proceeding (often driving them out of the major). Because Harris et al., analyzed curved courses, a subset of students cannot escape these effects. And poor grades disproportionately impact underrepresented and underserved groups – they say explicitly “you do not belong” rather than “how can I help you learn”.
Often naysayers disparage efforts to improve course design as “dumbing down” the course, rather than improving it. In many ways this is a situation analogous to blaming patients for getting sick or not responding to treatment, rather than conducting an objective analysis of the efficacy of the treatment. If medical practitioners had maintained this attitude, we would still be bleeding patients and accepting that more than a third are fated to die, rather than seeking effective treatments tailored to patients’ actual diseases – the basis of evidence-based medicine. We would have failed to develop antibiotics and vaccines – indeed, we would never have sought them out. Curving grades implies that course design and delivery are already optimal, and the fate of students is predetermined because only a percentage can possibly learn the material. It is, in an important sense, complacent quackery.
Banning grading on a curve, and labelling it for what it is – educational malpractice – would also change the dynamics of the classroom and might even foster an appreciation that a good teacher is one with the highest percentage of successful students, e.g. those who are retained in a degree program and graduate in a timely manner (hopefully within four years). Of course, such an alternative evaluation of teaching would reflect a department’s commitment to construct and deliver the most engaging, relevant, and effective educational program. Institutional resources might even be used to help departments generate more objective, instructor-independent evaluations of learning outcomes, in part to replace the current practice of student-based opinion surveys, which are often little more than measures of popularity. We might even see a revolution in which departments compete with one another to maximize student inclusion, retention, and outcomes (perhaps even to the extent of applying pressure on the design and delivery of “out of major” required courses offered by other departments).
“All a pipe dream” you might say, but the available data demonstrates that resources spent on rethinking course design, including engagement and relevance, can have significant effects on grades, retention, time to degree, and graduation rates. At the risk of being labeled as self-promoting, I offer the following to illustrate the possibilities: working with Melanie Cooper at Michigan State University, we have built such courses in general and organic chemistry and documented their impact, see Evaluating the extent of a large-scale transformation in gateway science courses.
Perhaps we should be encouraging students to seek out legal representation to hold institutions (and instructors) accountable for detrimental practices, such as grading on a curve. There might even come a time when professors and departments would find it prudent to purchase malpractice insurance if they insist on retaining and charging students for ineffective educational strategies.(1)
Acknowledgements: Thanks to daughter Rebecca who provided edits and legal references and Melanie Cooper who inspired the idea. Educate! image from theDorian De Long Arts & Music Scholarship site.
(1) One cannot help but wonder if such conduct could ever rise to the level of fraud. See, e.g., Bristol Bay Productions, LLC vs. Lampack, 312 P.3d 1155, 1160 (Colo. 2013) (“We have typically stated that a plaintiff seeking to prevail on a fraud claim must establish five elements: (1) that the defendant made a false representation of a material fact; (2) that the one making the representation knew it was false; (3) that the person to whom the representation was made was ignorant of the falsity; (4) that the representation was made with the intention that it be acted upon; and (5) that the reliance resulted in damage to the plaintiff.”).
Is the coronavirus-based transition from face to face to on-line instruction yet another step to down-grading instructional quality?
It is certainly a strange time in the world of higher education. In response to the current corona virus pandemic, many institutions have quickly, sometimes within hours and primarily by fiat, transitioned from face to face to distance (web-based) instruction. After a little confusion, it appears that laboratory courses are included as well, which certainly makes sense. While virtual laboratories can be built (see our own virtual laboratories in biology) they typically fail to capture the social setting of a real laboratory. More to the point, I know of no published studies that have measured the efficacy of such on-line experiences in terms of the ideas and skills students master.
Many instructors (including this one) are being called upon to carry out a radical transformation of instructional practice “on the fly.” Advice is being offered from all sides, from University administrators and technical advisors (see as an example Making Online Teaching a Success). It is worth noting that much (all?) of this advice falls into the category of “personal empiricism”, suggestions based on various experiences but unsupported by objective measures of educational outcomes – outcomes that include the extent of student engagement as well as clear descriptions of i) what students are expected to have mastered, ii) what they are expected to be able to do with their knowledge, and iii) what they can actually do. Again, to my knowledge there have been few if any careful comparative studies on learning outcomes achieved via face to face versus virtual teaching experiences. Part of the issue is that many studies on teaching strategies (including recent work on what has been termed “active learning” approaches) have failed to clearly define what exactly is to be learned, a necessary first step in evaluating their efficacy. Are we talking memorization and recognition, or the ability to identify and apply core and discipline-specific ideas appropriately in novel and complex situations?
At the same time, instructors have not had practical training in using available tools (zoom, in my case) and little in the way of effective support. Even more importantly, there are few published and verified studies to inform what works best in terms of student engagement and learning outcomes. Even if there were clear “rules of thumb” in place to guide the instructor or course designer, there has not been the time or resources needed to implement these changes. The situation is not surprising given that the quality of university level educational programs rarely attracts critical analysis, or the necessary encouragement, support, and recognition needed to make it a departmental priority (see Making education matter in higher education). It seems to me that the current situation is not unlike attempting to perform a complicated surgery after being told to watch a 3 minute youtube video. Unsurprisingly patient (student learning) outcomes may not be pretty.
Much of what is missing from on-line instructional scenarios is the human connection, the ability of an instructor to pay attention to how students respond to the ideas presented. Typically this involves reading the facial expressions and body language of students, and through asking challenging (Socratic) questions – questions that address how the information presented can be used to generate plausible explanations or to predict the behavior of a system. These are interactions that are difficult, if not impossible to capture in an on-line setting.
While there is much to be said for active engagement/active learning strategies (see Hake 1998, Freeman et al 2014 and Theobald et al 2020), one can easily argue that all effective learning scenarios involve an instructor who is aware and responsive to students’ pre-existing knowledge. It is also important that the instructor has the willingness (and freedom) to entertain their questions, confusions, and the need for clarification (saying it a different way), or when it may be necessary to revisit important, foundational, ideas and skills – a situation that can necessitate discarding planned materials and “coaching up” students on core concepts and their application. The ability of the instructor to customize instruction “on the fly” is one of the justifications for hiring disciplinary experts in instructional positions, they (presumably) understand the conceptual foundations of the materials they are called upon to present. In its best (Socratic) form, the dialog between student and instructor drives students (and instructors) to develop a more sophisticated and metacognitive understanding of the web of ideas involved in most scientific explanations.
In the absence of an explicit appreciation of the importance of the human interactions between instructor and student, interactions already strained in the context of large enrollment courses, we are likely to find an increase in the forces driving instruction to become more and more about rote knowledge, rather than the higher order skills associated with the ability to juggle ideas, identifying those needed and those irrelevant to a specific situation. While I have been trying to be less cynical (not a particularly easy task in the modern world), I suspect that the flurry of advice on how to carry out distance learning is more about avoiding the need to refund student fees than about improving students’ educational outcomes (see Colleges Sent Students Home. Now Will They Refund Tuition?)
A short post-script (17 April 2020): Over the last few weeks I have put together the tools to make the on-line MCDB 4650 Developmental Biology course somewhat smoother for me (and hopefully the students). I use Keynote (rather than Powerpoint) for slides; since the iPad is connected wirelessly to the project, this enables me to wander around the class room. The iOS version of Keynote enables me, and students, to draw on slides. Now that I am tethered, I rely more on pre-class beSocratic activities and the Mirroring360 application to connect my iPad to my laptop for Zoom sessions. I am back to being more interactive with the materials presented. I am also starting to pick students at random to answer questions & provide explanations (since they are quiet otherwise) – hopefully that works. Below (↓) is my set up, including a good microphone, laptop, iPad, and the newly arrived volume on Active Learning.
Biological systems can be seen as conceptually simple, but mechanistically complex, with hidden features that make “fixing” them difficult.
Biological systems are evolving, bounded, non-equilibrium reaction systems. Based on their molecular details, it appears that all known organisms, both extinct or extant, are derived from a single last universal common ancestor, known as LUCA. LUCA lived ~4,000,000,000 years ago (give or take). While the steps leading to LUCA are hidden, and its precursors are essentially unknowable (much like the universe before the big bang), we can come to some general and unambiguous conclusions about LUCA itself [see Catchpole & Forterre, 2019]. First LUCA was cellular and complex, probably more complex that some modern organisms, certainly more complex than the simplest obligate intracellular parasite [Martinez-Cano et al., 2014]. Second, LUCA was a cell with a semi-permeable lipid bilayer membrane. Its boundary layer is semi-permeable because such a system needs to import energy and matter and export waste in order to keep from reaching equilibrium, since equilibrium = death with no possibility of resurrection. Finally, LUCA could produce offspring, through some version of a cell division process. The amazing conclusion is that every cell in your body (and every cell in every organism on the planet) has an uninterrupted connection to LUCA.
So what are the non-equilibrium reactions within LUCA and other organisms doing? building up (synthesizing) and degrading various molecules, including proteins, nucleic acids, lipids, carbohydrates and such – the components needed to maintain the membrane barrier while importing materials so that the cell can adapt, move, grow and divide. This non-equilibrium reaction network has been passed from parent to offspring cells, going back to LUCA. A new cell does not “start up” these reactions, they are running continuously through out the processes of growth and cell division. While fragile, these reaction systems have been running uninterruptedly for billions of years.
There is a second system, more or less fully formed, present in and inherited from LUCA, the DNA-based genetic information storage and retrieval system. The cell’s DNA (its genotype) encodes the “operating system” of the cell. The genotype interacts with and shapes the cell’s reaction systems to produce phenotypes, what the organism looks like and how it behaves, that is how it reacts to and interacts with the rest of the world. Because DNA is thermodynamically unstable, the information it contains, encoded in the sequences of nucleotides within it, and read out by the reaction systems, can be altered – it can change (mutate) in response to its environmental chemicals, radiation, and other processes, such as errors that occur when DNA is replicated. Once mutated, the change is stable, it becomes part of the genotype.
The mutability of DNA could be seen as a design flaw; you would not want the information in a computer file to be randomly altered over time or when copied. In living systems, however, the mutability of DNA is a feature – together with the effects of mutations on a cell’s reproductive success mutations lead to evolutionary change. Over time, they convert the noise of mutation into evolutionary adaptations and diversification of life.
Organisms rarely exist in isolation. Our conceptual picture of LUCA is not complete until we include social interactions (background: aggregative and clonal metazoans). Cells (organisms) interact with one another in complex ways, whether as individuals within a microbial community, as cells within a multicellular organism, or in the context of predator-prey, host-pathogen and symbiotic interactions. These social processes drive a range of biological behaviors including what, at the individual cell level, can be seen as cooperative and self-sacrificing. The result is the production of even more complex biological structures, from microbial biofilms to pangolins and human beings, and complex societies. The breakdown of such interactions, whether in response to pathogens, environmental insult, mutations, politicians’ narcissistic behaviors and the madness of crowds, underlie a wide range of aberrant and pathogenic outcomes – after all cancer is based on the anti-social behavior of tumor cells.
The devil is in the details – from the conceptual to the practical: What a biologist/ bioengineer rapidly discovers when called upon to fix the effects of a mutation, defeat a pathogen, or repair a damaged organ is that biological systems are mechanistically more complex that originally thought, and are no means intelligently designed. There are a number of sources for this biological complexity. First, and most obviously, modern cells (as well as LUCA) are not intelligently designed systems – they are the product of evolutionary processes, through which noise is captured in useful forms. These systems emerge rather than are imposed (as is the case with humanly designed objects). Second, within the cell there is a high concentration of molecules that interact with one another, often in unexpected ways. As examples of molecular interactions that my lab has worked on, the protein β-catenin – originally identified as playing a role in cell adhesion and cytoskeletal organization, has a second role as a regulator of gene expression (link). The protein Chibby, a component of the basal body of cilia (a propeller-like molecular machine involved in moving fluids) has a second role as an inhibitor of β-catenin’s gene regulatory activity (link), while centrin-2. another basal body component, plays a role in the regulation of DNA repair and gene expression (link). These are interactions that have emerged during the process of evolution – they work, so they are retained.
More evidence as to the complexity of biological systems is illustrated by studies that examined the molecular targets of specific anti-cancer drugs (see Lowe 2019. Your Cancer Targets May Not Be Real). The authors of these studies used the CRISPR-Cas9 system to knock out the gene encoding a drugs’ purported target; they found that the drug continued to function (see Lin et al., 2019). At the same time, a related study raises a note of caution. Smits et al (2019) examined the effects of what were expected to be CRISPR-CAS9-induced “loss of function” mutations. They found expression of the (mutated) targeted gene, either by using alternative promoters (RNA synthesis start sites) or alternative translation start sites. The results were mutant polypeptides that retained some degree of wild type activity. Finally, in a system that bears some resemblance to the CRISPR system was found in mutations that induce what is known as non-sense mediated decay. A protection against the synthesis of aberrant (toxic) mutant polypeptides, one effect of non-sense mediated decay is to lead to the degradation of the mutant RNA. As described by Wilkinson (2019. Genetic paradox explained by nonsense) the resulting RNA fragments can be transported back into the nucleus where they interact with proteins involved in the regulation of gene expression, leading to the expression of genes related to the originally mutated gene. The expression of these related genes can modify the phenotype of the original mutation.
Biological systems are further complicated by the fact that the folding of polypeptides and the assembly of proteins (background: polypeptides and proteins) is mediated by a network of chaperone proteins, that act to facilitate correct, and suppress incorrect, folding, interactions, and assembly of proteins. This chaperone network helps explain the ability of cells to tolerate a range of genetic variations; they render cells more adaptive and “non-fragile”. Some chaperones are constitutively expressed and inherited when cells divide, the synthesis of others is induced in response to environmental stresses, such as increased temperatures (heat shock). The result is that, in some cases, the phenotypic effects of a mutation on a target protein may not be primarily due to the absence of the mutated protein, but rather to secondary effects, effects that can be significantly ameliorated by the expression of molecular chaperones (discussed in Klymkowsky. 2019 Filaments and phenotypes).
As one more, but certainly not the last, complexity, there is the phenomena by which “normal” cells interact with cells that are discordant with respect to some behavior (Di Gregorio et al 2016).1 These cells, termed “fit and unfit” and “winners and losers”, clearly socially inappropriate and unfortunate terms, interact in unexpected ways. The eccentricity of these cells can be due to various stochastic processes, including monoallelic expression (Chess, 2016), that lead to clones that behave differently (background: Biology education in the light of single cell/molecule studies). Akieda et al (2019) describe the presence of cells that respond inappropriately to a morphogen gradient during embryonic development. These eccentric cells are “out of step” with their neighbors are induced to die. Experimentally blocking their execution leads to defects in subsequent development. Similar competitive effects are described by Ellis et al (2019. Distinct modes of cell competition shape mammalian tissue morphogenesis). That said, not all eccentric behaviors lead to cell death. In some cases the effect is more like an ostracism, cells responding inappropriately migrate to a more hospitable region (Xiong et al., 2013).
All of which is to emphasize that while conceptually simple, biologically systems, and their responses to mutations and other pathogenic insults, are remarkably complex and unpredictable – a byproduct of the unintelligent evolutionary processes that produced them.
When a building is named after a scientist, it is generally in order to honor that person’s scientific contributions. The scientist’s ideological opinions are rarely considered explicitly, although they may influence the decision at the time. In general, scientific contributions are timeless in that they represent important steps in the evolution of a discipline, often by establishing a key observation, idea, or conceptual framework upon which subsequent progress is based – they are historically important. In this sense, whether a scientific contribution was correct (as we currently understand the natural world) is less critical than what that contribution led to. The contribution marks a milestone or a turning point in a discipline, understanding that the efforts of many underlie disciplinary progress and that those contributors made it possible for others to “see further.” (2)
Since science is not about recognizing or establishing a single unchanging capital-T-Truth, but rather about developing an increasingly accurate model for how the world works, it is constantly evolving and open to revision. Working scientists are not particularly upset when new observations lead to revisions to or the abandonment of ideas or the addition of new terms to equations.(3)
Compare that to the situation in the ideological, political, or religious realms. A new translation or interpretation of a sacred text can provoke schism and remarkably violent responses between respective groups of believers. The closer the groups are to one another, the more horrific the levels of violence that emerge often are. In contrast, over the long term, scientific schools of thought resolve, often merging with one another to form unified disciplines. From my own perspective, and not withstanding the temptation to generate new sub-disciplines (in part in response to funding factors), all of the life sciences have collapsed into a unified evolutionary/molecular framework. All scientific disciplines tend to become, over time, consistent with, although not necessarily deducible from, one another, particularly when the discipline respects and retains connections to the real (observable) world.(4) How different from the political and ideological.
The historical progression of scientific ideas is dramatically different from that of political, religious, or social mores. No matter what some might claim, the modern quantum mechanical view of the atom bears little meaningful similarity to the ideas of the cohort that included Leucippus and Democritus. There is progress in science. In contrast, various belief systems rarely abandon their basic premises. A politically right- or left-wing ideologue might well find kindred spirits in the ancient world. There were genocidal racists, theists, and nationalists in the past and there are genocidal racists, theists, and nationalists now. There were (limited) democracies then, as there are (limited) democracies now; monarchical, oligarchical, and dictatorial political systems then and now; theistic religions then and now. Absolutist ideals of innate human rights, then as now, are routinely sacrificed for a range of mostly self-serving or politically expedient reasons. Advocates of rule by the people repeatedly install repressive dictatorships. The authors of the United States Constitution declare the sacredness of human rights and then legitimized slavery. “The Bible … posits universal brotherhood, then tells Israel to kill all the Amorites.” (Phil Christman). The eugenic movement is a good example; for the promise of a genetically perfect future, existing people are treated inhumanely – just another version of apocalyptic (ends justify the means) thinking.
Ignoring the simpler case of not honoring criminals (sexual and otherwise), most calls for removing names from buildings are based on the odious ideological positions espoused by the honored – typically some version of racist, nationalistic, or sexist ideologies. The complication comes from the fact that people are complex, shaped by the context within which they grow up, their personal histories and the dominant ideological milieu they experienced, as well as their reactions to it. But these ideological positions are not scientific, although a person’s scientific worldview and their ideological positions may be intertwined. The honoree may claim that science “says” something unambiguous and unarguable, often in an attempt to force others to acquiesce to their perspective. A modern example would be arguments about whether climate is changing due to anthropogenic factors, a scientific topic, and what to do about it, an economic, political, and perhaps ideological question.(5)
So what to do? To me, the answer seems reasonably obvious – assuming that the person’s contribution was significant enough, we should leave the name in place and use the controversy to consider why they held their objectionable beliefs and more explicitly why they were wrong to claim scientific justification for their ideological (racist / nationalist / sexist / socially prejudiced) positions.(6) Consider explicitly why an archeologist (Flinders Petrie), a naturalist (Francis Galton), a statistician (Karl Pearson), and an advocate for women’s reproductive rights (Marie Stopes) might all support the non-scientific ideology of eugenics and forced sterilization. We can use such situations as a framework within which to delineate the boundaries between the scientific and the ideological.
Understanding this distinction is critical and is one of the primary justifications for why people not necessarily interested in science or science-based careers are often required to take science courses. Yet all too often these courses fail to address the constraints of science, the difference between political and ideological opinions, and the implications of scientific models. I would argue that unless students (and citizens) come to understand what constitutes a scientific idea or conclusion and what reflects a political or ideological position couched in scientific or pseudo-scientific terms, they are not learning what they need to know about science or its place in society. That science is used as a proxy for Truth writ large is deeply misguided. It is much more important to understand how science works than it is to remember the number of phyla or the names of amino acids, the ability to calculate the pH of a solution, or to understand processes going on at the center of a galaxy or the details of a black hole’s behavior. While sometimes harmless, misunderstanding science and how it is used socially can result in traumatic social implications, such as drawing harmful conclusions about individuals from statistical generalizations of populations, avoidable deaths from measles, and the forced “eugenic” sterilization of people deemed defective. We should seek out and embrace opportunities to teach about these issues, even if it means we name buildings after imperfect people.
The location of some of my post-doc work.
In the words of Isaac Newton, “If I have seen further than others, it is by standing upon the shoulders of giants.”
Unless, of course, the ideas and equations being revised or abandoned are one’s own.
Perhaps the most striking exception occurs in physics on the subjects of quantum mechanics and relativity, but as I am not a physicist, I am not sure about that.
Perhaps people are “meant” to go extinct.
The situation is rather different outside of science, because the reality of progress is more problematic and past battles continue to be refought. Given the history of Reconstruction and the Confederate “Lost Cause” movement [see PBS’s Reconstruction] following the American Civil War, monuments to defenders of slavery, no matter how admirable they may have been in terms of personal bravery and such, reek of implied violence, subjugation, and repression, particularly when the person honored went on to found an institution dedicated to racial hatred and violent intimidation [link]. There would seem little doubt that a monument in honor of a Nazi needs to be eliminated and replaced by one to their victims or to those who defeated them.
Michael Reiss, a professor of science education at University College London and an Anglican Priest, suggests that “we need to rethink the way we teach evolution” largely because conventional approaches can be unduly confrontational and “force religious children to choose between their faith and evolution” or to result in students who”refuse to engage with a lesson.” He suggests that a better strategy would be akin to those use to teach a range of “sensitive” subjects “such as sex, pornography, ethnicity, religion, death studies, terrorism, and others” and could “help some students to consider evolution as a possibility who would otherwise not do so.” [link to his original essay and a previous post on teaching evolution: Go ahead and teach the controversy].
There is no doubt that an effective teacher attempts to present materials sensitively; it is the rare person who will listen to someone who “teaches” ideas in a hostile, alienating, or condescending manner. That said, it can be difficult to avoid the disturbing implications of scientific ideas, implications that can be a barrier to their acceptance. The scientific conclusion that males and females are different but basically the same can upset people on various sides of the theo-political spectrum.
In point of fact an effective teacher, a teacher who encourages students to question their long held, or perhaps better put, familial or community beliefs, can cause serious social push-back – Trouble with a capital T. It is difficult to imagine a more effective teacher than Socrates (~470-399 BCE). Socrates “was found guilty of ‘impiety’ and ‘corrupting the young’, sentenced to death” in part because he was an effective teacher (see Socrates was guilty as charged). In a religious and political context, challenging accepted Truths (again with a capital T) can be a crime. In Socrates’ case”Athenians probably genuinely felt that undesirables in their midst had offended Zeus and his fellow deities,” and that, “Socrates, an unconventional thinker who questioned the legitimacy and authority of many of the accepted gods, fitted that bill.”
So we need to ask of scientists and science instructors, does the presentation of a scientific, that is, a naturalistic and non-supernatural, perspective in and of itself represent an insensitivity to those with a super-natural belief system. Here it is worth noting a point made by the philosopher John Gray, that such systems extend beyond those based on a belief in god(s); they include those who believe, with apocalyptic certainty, in any of a number of Truths, ranging from the triumph of a master race, the forced sterilization of the unfit, the dictatorship of the proletariat, to history’s end in a glorious capitalist and technological utopia. Is a science or science instruction that is “sensitive” to, that is, uncritical of or upsetting to those who hold such beliefs, possible?
My original impression is that one’s answer to this question is likely to be determined by whether one considers science a path to Truth, with a purposeful capital T, or rather that the goal of scientists is to build a working understanding of the world around and within us. Working scientists, and particularly biologists who must daily confront the implications of apparently un-intelligent designed organisms (due to ways evolution works) are well aware that absolute certainty is counterproductive. Nevertheless, the proven explanatory and technological power of the scientific enterprise cannot help but reinforce the strong impression that there is some deep link between scientific ideas and the way the world really works. And while some scientists have advocated unscientific speculations (think multiverses and cosmic consciousness), the truth, with a small t, of scientific thinking is all around us.
Photograph of the Milky Way by Tim Carl photography, used by permission
A science-based appreciation of the unimaginable size and age of the universe, taken together with compelling evidence for the relatively recent appearance of humans (Homo sapiens from their metazoan, vertebrate, tetrapod, mammalian, and primate ancestors) cannot help but impact our thinking as to our significance in the grand scheme of things (assuming that there is such a, possibly ineffable, plan)(1). The demonstrably random processes of mutation and the generally ruthless logic by which organisms survive, reproduce, and evolve, can lead even the most optimistic to question whether existence has any real meaning.
Consider, as an example, the potential implications of the progress being made in terms of computer-based artificial intelligence, together with advances in our understanding of the molecular and cellular connection networks that underlie human consciousness and self-consciousness. It is a small step to conclude, implicitly or explicitly, that humans (and all other organisms with a nervous system) are “just” wet machines that can (and perhaps should) be controlled and manipulated. The premise, the “self-evident truth”, that humans should be valued in and of themselves, and that their rights should be respected (2) is eroded by the ability of machines to perform what were previously thought to be exclusively human behaviors.
Humans and their societies have, after all, been around for only a few tens of thousands of years. During this time, human social organizations have passed from small wandering bands influenced by evolutionary kin and group selection processes to produce various social systems, ranging from more or less functional democracies, pseudo-democracies (including our own growing plutocracy), dictatorships, some religion-based, and totalitarian police states. Whether humans have a long term future (compared to the millions of years that dinosaurs dominated life on Earth) remains to be seen – although we can be reasonably sure that the Earth, and many of its non-human inhabitants, will continue to exist and evolve for millions to billions of years, at least until the Sun explodes.
So how do we teach scientific conclusions and their empirical foundations, which combine to argue that science represents how the world really works, without upsetting the most religiously and politically fanatical among us? Those who most vehemently reject scientific thinking because they are the most threatened by its apparently unavoidable implications. The answer is open to debate, but to my mind it involves teaching students (and encouraging the public) to distinguish empirically-based, and so inherently limited observations and the logical, coherent, and testable scientific models they give rise to from unquestionable TRUTH- and revelation-based belief systems. Perhaps we need to focus explicitly on the value of science rather than its “Truth”. To reinforce what science is ultimately for; what justifies society’s support for it, namely to help reduce human suffering and (where it makes sense) to enhance the human experience, goals anchored in the perhaps logically unjustifiable, but nevertheless essential acceptance of the inherent value of each person.
Apologies to “Good Omens”
For example, “We hold these truths to be self-evident, that all men are created equal, that they are endowed by their creator with certain unalienable rights, that among these are life, liberty and the pursuit of happiness.”
Embryogenesis is based on a framework of social (cell-cell) interactions, initial and early asymmetries, and cascading cell-cell signaling and gene regulatory networks (DEVO posts one, two, & three). The result is the generation of embryonic axes, germ layers (ectoderm, mesoderm, endoderm), various organs and tissues (brains, limbs, kidneys, hearts, and such) and their characteristic cell types, their patterning, and their coordination into a functioning organism. It is well established that all animals share a common ancestor (hundreds of millions of years ago) and that a number of molecularmodules were already present in that common ancestor.
At the same time evolutionary processes are, and need to be, flexible enough to generate the great diversity of organisms, with their various adaptations to particular life-styles. The extent of both conservation and flexibility (new genes, new mechanisms) in developmental systems is, however, surprising. Perhaps the most striking evidence for the depth of this conservation was supplied by the discovery of the organization of the Hox gene cluster in the fruit fly Drosophila and in the mouse (and other vertebrates). In both, the Hox genes are arranged and expressed in a common genomic and expression patterns. But as noted by Denis Duboule (2007) Hox gene organization is often presented in textbooks in a distorted manner (↓).
The Hox gene clusters of vertebrates are compact, but are split, disorganized, and even “atomized” in other types of organisms. Similarly, processes that might appear foundational, such as the role of the Bicoid gradient in the early fruit fly embryo (a standard topic in developmental biology textbooks), is in fact restricted to a small subset of flies (Stauber et al., 1999). New genes can be generated through well defined processes, such as gene duplication and divergence, or they can arise de novo out of sequence noise (Carvunis et al., 2012; Zhao et al., 2014 – see Van Oss & Carvunis 2019. De novo gene birth). Comparative genomic analyses can reveal the origins of specific adaptations (see Stauber et al., 1999).The result is that organisms as closely related to each other as the great apes (including humans) have significant species-specific genetic differences (see Florio et al., 2018; McLean et al., 2011; Sassa, 2013 and references therein) as well as common molecular and cellular mechanisms.
A universal (?) feature of developing systems – gradients and non-linear responses: There is a predilection to find (and even more to teach) simple mechanisms that attempt to explain everything (witness the distortion of the Hox cluster, above) – a form of physics “theory of everything” envy.But the historic nature, evolutionary plasticity, and need for regulatory robustness generally lead to complex and idiosyncratic responses in biological systems.Biological systems are not “intelligently designed” but rather cobbled together over time through noise (mutation) and selection (Jacob, 1977)(see blog post).
That said, acommon (universal?) developmental process appears to be the transformation of asymmetries into unambiguous cell fate decisions. Such responses are based on threshold events controlled by a range of molecular behaviors, leading to discrete gene expression states. We can approach the question of how such decisions are made from both an abstract and a concrete perspective. Here I outline my initial approach – I plan to introduce organism specific details as needed.I start with the response to a signaling gradient, such as that found in many developmental systems, including the vertebrate spinal cord (top image Briscoe and Small, 2015) and the early Drosophila embryo (Lipshitz, 2009)(↓).
We begin with a gradient in the concentration of a “regulatory molecule” (the regulator).The shape of the gradient depends upon the sites and rates of synthesis, transport away from these sites, and turnover (degradation and/or inactivation). We assume, for simplicity’s sake, that the regulator directly controls the expression of target gene(s). Such a molecule binds in a sequence specific manner to regulatory sites, there could be a few or hundreds, and lead to the activation (or inhibition) of the DNA-dependent, RNA polymerase (polymerase), which generates RNA molecules complementary to one strand of the DNA. Both the binding of the regulator and the polymerase are stochastic processes, driven by diffusion, molecular collisions, and binding interactions.(1)
Let us now consider the response of target gene(s) as a function of cell position within the gradient. We might (naively) expect that the rate of target gene expression would be a simple function of regulator concentration. For an activator, where the gradient is high, target gene expression would be high, where the gradient concentration is low, target gene expression would be low – in between, target gene expression would be proportional to regulator concentration.But generally we find something different, we find that the expression of target genes is non-uniform, that is there are thresholds in the gradient: on one side of the threshold concentration the target gene is completely off (not expressed), while on the other side of the threshold concentration, the target gene is fully on (maximally expressed).The target gene responds as if it is controlled by an on-off switch. How do we understand the molecular basis for this behavior?
Distinct mechanisms are used in different systems, but we will consider a system from the gastrointestinal bacteria E. coli that students may already be familiar with; these are the genes that enable E. coli to digest the mammalian milk sugar lactose.They encode a protein needed to importlactose into a bacterial cell and an enzyme needed to break lactose down so that it can be metabolized.Given the energetic cost to synthesize these proteins, it is in the bacterium’s adaptive self interest to synthesize them only when lactose is present at sufficient concentrations in their environment.The response is functionally similar to that associated with quorum sensing, which is also governed by threshold effects. Similarly cells respond to the concentration of regulator molecules (in a gradient) by turning on specific genes in specific domains, rather than uniformly.
Now let us look in a little more detail at the behavior of the lactose utilization system in E. coli following an analysis by Vilar et al (2003)(2).At an extracellular lactose concentration below the threshold, the system is off.If we increase the extracellular lactose concentration above threshold the system turns on, the lactose permease and β-galactosidase proteins are made and lactose can enter the cell and be broken down to produce metabolizable sugars.By looking at individual cells, we find that they transition, apparently stochastically from off to on (→), but whether they stay on depends upon the extracellular lactose concentration. We can define a concentration, the maintenance concentration, below the threshold, at which “on” cells will remain on, while “off” cells will remain off.
The circuitry of the lactose system is well defined(Jacob and Monod, 1961; Lewis, 2013; Monod et al., 1963)(↓).The lacI gene encodes the lactose operon repressor protein and it is expressed constituately at a low level; it binds to sequences in the lac operon and inhibits transcription.The lac operon itself contains three genes whose expression is regulated by a constituatively active promoter.LacY encodes the permease while the lacZ encodes β-galactosidase.β-galactosidase has two functions: it catalyzes the reaction that transforms lactose into allolactone and it cleaves lactose into the metabolically useful sugars glucose and galactose. Allolactone is an allosteric modulator of the Lac repressor protein; if allolactone is present, it binds to lac epressor proteins and inactivates them, allowing lac operon expression.
The cell normally contains only ~10 lactose repressor proteins. Periodically (stochastically), even in the absence of lactose, and so its derivative allolactone, the lac operon promoter region is free of repressor proteins, and a lactose operon is briefly expressed – a few LacY and LacZpolypeptides are synthesized (↓).This noisy leakiness in the regulation of the lac operon allows the cell to respond if lactose happens to be present – some lactose molecules enter the cell through the permease, are converted to allolactone by β-galactosidase.Allolactone is an allosteric effector of the lac repressor; when present it binds to and inactivates the lac repressor protein so that it no longer binds to its target sequences (the operator or “O” sites).In the absence of repressor binding, the lac operon is expressed.If lactose is not present, the lac operon is inhibited and lacY and LacZ disappear from the cell by turnover or growth associated dilution.
The question of how the threshold concentration for various signal-regulated decisions is set often involves homeostatic processes that oppose the signaling response. The binding and activation of regulators can involve cooperative interactions between molecular components and both positive and negative feedback effects.
In the case of patterning a tissue, in terms of regional responses to a signaling gradient, there can be multiple regulatory thresholds for different genes, as well as indirect effects, where the initiation of gene expression of one set of target genes impacts the sensitive expression of subsequent sets of genes.One widely noted mechanism, known as reaction-diffusion, was suggested by the English mathematician Alan Turing (see Kondo and Miura, 2010) – it postulates a two component system. One component is an activator of gene expression, which in addition to its own various targets, positively regulates its own expression. The second component is a repressor of the first.Both of these two regulator molecules are released by the signaling cell or cells; the repressor diffuses away from the source faster than the activator does.The result can be a domain of target gene expression (where the concentration of activator is sufficient to escape repression), surrounded by a zone in which expression is inhibited (where repressor concentration is sufficient to inhibit the activator).Depending upon the geometry of the system, this can result in discrete regions (dots or stripes) of primary target gene expression (see Sheth et al., 2012).In real systems there are often multiple gradients present; their relative orientations can produce a range of patterns.
The point of all of this, is that when we approach a particular system – we need to consider the mechanisms involved.Typically they are selected to produce desired phenotypes, but also to be robust in the sense that they need to produce the same patterns even if the system in which they occur is subject to perturbations, such as embryo/tissue size (due to differences in cell division / growth rates) and temperature and other environmental variables.
note: figures returned – updated 13 November 2020.
While stochastic (random) these processes can still be predictable.A classic example involves the decay of an unstable isotope (atom), which is predictable at the population level, but unpredictable at the level of an individual atom.Similarly, in biological systems, the binding and unbinding of molecules to one another, such as a protein transcription regulator to its target DNA sequence is stochastic but can be predictable in a large enough population.
[21st Century DEVO-3] Embryonic development is the process by which a fertilized egg becomes an independent organism, an organism capable of producing functional gametes, and so a new generation. In an animal, this process generally involves substantial growth and multiple rounds of mitotic cell division; the resulting organism, a clone of the single-celled zygote, contains hundreds, thousands, millions, billions, or trillions of cells [link]. These dividing, migrating, differentiating, and sometimes dying cells that interact to form the adult and its various tissues and organ systems. The various cell types generated can be characterized by the genes that they express, the shapes they assume, the behaviors that they display, and how they interact with neighboring and distant cells (1). Based on first principles, one could imagine (at least) two general mechanisms that could lead to differences in gene expression between cells. The first would be that different cells contain different genes while the other is that while all cells contain all genes, which genes are expressed in a particular cell varies,it is regulated by molecular processes that determine when, where, and to what the levels particular genes are expressed (2). Turns out, there are examples of both processes among the animals, although the latter is much more common.
The process of discarding genomic DNA in somatic cells is known as chromatin diminution. During the development of the soma, but not the germ line, regions of the genome are lost. In the germ line, for hopefully obvious reasons, the full genome is retained. The end result is that somatic cells contain different subsets of genes and non-coding DNA compared to the full genome. The classic case of chromosome diminution was described in the parasitic nematode of horses, now named Parascaris univalens (originally Ascaris megalocephala) by Theodore Boveri in 1887 (reviewed in Streit and Davis, 2016)[pdf link]. Based on its occurrence in a range of distinct animal lineages, chromatin diminution appears to be an emergent rather than an ancestral trait, that is, a trait present in the common ancestor of the animals.
While, as expected for an emergent trait, the particular mechanism of chromatin diminution appears to vary between different organisms: the best characterized example occurs in Parascaris. In the somatic cell lineages in which chromatin diminution occurs, double-stranded breaks are made inchromosomal DNA molecules, and teleomeric sequences are added to ends of the resulting DNA molecules (↓).
You may have learned that chromosomes interact with spindle microtubules through a localized regions on the chromosomes, known as centromeres. Centromeres are identified through their association with proteins that form the kinetochore, which is a structure that mediates interactions between condensed chromosomes and mitotic (and meiotic) spindle microtubules. While many organisms have a discrete spot-like (localized) centromere, in many nematodes centromere-binding proteins are found distributed along the length of the chromosomes, a situation known as a holocentric centromere.At higher resolution it appears that centromere components are preferentially associated with euchromatic, that is, molecularly accessible chromosomal regions, which are (typically) the regions where most expressed genes are located.Centromere components are largely excluded from heterochromatic (condensed and molecularly inaccessible) chromosomal regions. After chromosome fragmentation, those DNA fragments associated with centromere components can interact with the spindle microtubules and are accurately segregated to daughter cells during mitosis, while those, primarily heterochromatic fragments (without associated centromeric components) are degraded and lost. In contrast the integrity of the genome is maintained in those cells that come to form the germ line, the cells that can undergo meiosis to produce gametes.Looking forward to the reprogramming of somatic cells (the process of producing what are known as induced pluripotent stem cells – iPSCs), one prediction is that it should not be possible to reprogram a somatic cell that has undergone chromatin diminution to form a functional germ line cell – you should be able to explain why, or what would have to be the case for such reprogramming to be successful.
The origins of cellular asymmetries:Clearly, there must be differences between the cells that undergo chromatin diminution and those that do not; at the very least the nuclease(s) that cuts the DNA during chromatin diminution will need to be active in somatic cells and inactive in germ line cells, or it may simply not be present – the genes that encode it are not expressed in germ line cells. We can presume that similar cytoplasmic differences play a role in the differential regulation of gene expression in different cell types during the development of organisms in which the genome remains intact in somatic cells. So how might such asymmetries arise?There are three potential, but certainly not mutually exclusive, mechanisms that can lead to cellular/cytoplasmic asymmetries: they can be inherited based on pre-existing asymmetries in the parental cell, they could emerge based on asymmetries in the signaling environments occupied by the two daughters, or they could arise from stochastic fluctuations in gene expression (see Chen et al., 2016; Neumüller and Knoblich, 2009).
One example of how an asymmetry can be established occurs in the free-living nematode Caenorhabditiselegans, where the site of sperm fusion with the egg leads to the recruitment and assembly of proteins around the site of sperm entry, the future posterior side of the embryo.After male and female pronuclei fuse, mitosis begins and cytokinesis divides the zygote into two cells; the asymmetry initiated by sperm entry leads to an asymmetric division (↑); the anterior AB blastomere is larger, and molecularly distinct from the smaller posterior P1 blastomere.These differences set off a regulatory cascade, in which the genes expressed at one stage influence those expressed subsequently, and so influence subsequent cell divisions / cell fate decisions.
Other organisms use different mechanisms to generate cellular asymmetries. In organisms that have external fertilization, such as the clawed frog Xenopus, development proceeds rapidly once fertilization occurs. The egg is large, since in contains all of the materials necessary for the formation until the time that the embryo can feed itself. The early embryo is immotile and vulnerable to predation, so early development in such species tends to be rapid, and based on materials supplied by the mother (leading to maternal effects on subsequent development).In such cases, the initial asymmetry is built into the organization of the oocyte.
Formed through a mitotic division the primary oocyte enters meiotic prophase I, during which it undergoes a period of growth. Maternal and paternal chromosomes align (syngamy) and undergo crossing-over (recombination).The oocyte contains a single centrosome, a cytoplasmic structure that surrounds the centrioles of the oocyte’s inherited mitotic spindle pole. Cytoplasmic components become organized around the pole and then move from the pole toward the cell cortex (↓ image from Gard and Klymkowsky, 1998); this movement defines an “animal-vegetal” axisof the oocyte, which upon fertilization will play a role in generating the head-tail (anterior-posterior) and back-belly (dorsal-ventral) axes of the embryo and adult.
The primary oocyte remains in prophase I throughout oogenesis. The asymmetry of the oocyte becomes visible through the development of a pigmented animal hemisphere, largely non-pigmented vegetal hemisphere, and an large (~300 um diameter) and off-centered nucleus (known as the germinal vesicle or GV)(3).Messenger RNA molecules, encoding different polypeptides, are differentially localized to the animal and vegetal regions of the late stage oocyte. The translation of these mRNAs is regulated by factors activated by subsequent developmental events, leading to molecular asymmetries between embryonic cells derived from the animal and vegetal regions of the oocyte.In preparation for fertilization, the oocyte resumes active meiosis,leading to the formation of two polar bodies and the secondary oocyte, the egg. Fertilization occurs within the pigmented animal hemisphere; the site of sperm entry (↓) produces a second driver of asymmetry, in addition to the animal-vegetal axis, albeit through a mechanism distinct from that used in C. elegans (De Domenico et al., 2015).
Asymmetries in oocytes and eggs, and sperm entry points are not always the primary drivers of subsequent embryonic differentiation.In the mouse, and other placental mammals, including humans, embryonic development occurs within, and is supported by and dependent upon the mother.The mouse (mammalian) egg appears grossly symmetric, and sperm entry itself does not appear to impose an asymmetry.Rather, as the zygote divides, the first cells formed appear to be similar to one another. As cell division continue, however, some cells find themselves on the surface while others are located within the interior of the forming ball of cells, or morula (↓).
These two cellpopulations are exposed to different environments, environments that influence patterns of gene expression. The cells on the surface differentiate to form the trophectoderm, which in turn differentiates into extra-embryonic placental tissues, the interface between mother and developing embryo.The internal cells becomes the inner cell mass, which differentiate to form the embryo proper, the future mouse (or human). Early on inner cell mass cells appear similar to one another, but they also experience different environments, leading to emerging asymmetries associated with the activation of different signaling systems, the expression of different sets of genes, and difference in behavior – they begin the process of differentiating into distinct cell lineages and types forming, as embryogenesis continues, different tissues and organs.
The response of a particular cell to a particular environment will depend upon the signaling molecules present, typically expressed by neighboring cells, the signaling molecule receptors expressed by the cell itself, and how the binding of signaling molecules to receptors alters receptor activity or stability. For example, an activated receptor can activate (or inhibit) a transcription factor protein that could influence the expression of a subset of genes. These genes may themselves encode regulators oftranscription, signals, signal receptors, or modifiers of the cellular localization, stability, activity, or interactions with other molecules. While some effects of signal-receptor interactions can be transient, leading to reversible changes in cell state (and gene expression), during embryonic development activating and responding to a signal generally starts a cascade of effects that leads to irreversible changes, and the formation of altered differentiated states. Acell’s response to a signal can be variable, and influenced by the totality of the signals it receives and its past history.For example, a signal could lead to a decrease in the level of a receptor, or an increase in an inhibitory protein, making the cell unresponsive to the signal (a negative feedback effect) or more sensitive (a positive feedback effect) or could lead to a change in its response to a signal – different genes could be regulated as time goes by following the signal.Such emerging patterns of gene expression, based on signaling inputs, are the primary driver of embryonic development.
Not all genes are differentially expression, however – some genes, known as housekeeping genes, are expressed in essential all cells.
Hopefully it is clear what the term “expressed” means – namely that part of the gene is used to direct the synthesis of RNA (through the process of transcription (DNA-dependent, RNA polymerization).Some such RNAs (messenger or mRNAs) are used to direct the synthesis of a polypeptide through the process of translation (RNA-directed, amino acid polymerization) others do not encode polypeptides, such non-coding RNAs (ncRNAs) can play roles in a number of processes, from catalysis to the regulation of transcription, RNA stability, and translation.
Eggs are laid in water and are exposed to the sun; the pigmentation of the animal hemisphere is thought to protect the oocyte/zygote/early embryo’s DNA from photo-damage.
Chen et al., (2016). The ins (ide) and outs (ide) of asymmetric stem cell division. Current opinion in cell biology43, 1-6.
De Domenico et al., (2015). Molecular asymmetry in the 8-cell stage Xenopus tropicalis embryo described by single blastomere transcript sequencing. Developmental biology408, 252-268.
Gard & Klymkowsky. (1998). Intermediate filament organization during oogenesis and early development in the clawed frog, Xenopus laevis. In Intermediate filaments (ed. H. Herrmann & J. R. Harris), pp. 35-69. New York: Plenum.
Neumüller & Knoblich. (2009). Dividing cellular asymmetry: asymmetric cell division and its implications for stem cells and cancer. Genes & development23, 2675-2699.
Streit & Davis. (2016). Chromatin Diminution. In eLS: John Wiley & Sons Ltd, Chichester.
21st Century DEVO-2 In the first post in this series [link], I introduced the observation that single celled organisms can change their behaviors, often in response to social signals.They can respond to changing environments and can differentiate from one cellular state to the another. Differentiation involves changes in which sets of genes are expressed, which polypeptides and proteins are made [previous post], where the proteins end up within the cell, and which behaviors are displayed by the organism. Differentiation enables individuals to adapt to hostile conditions and to exploit various opportunities.
The ability of individuals to cooperate with one another, through processes such as quorum sensing, enables them to tune their responses so that they are appropriate and useful. Social interactions also makes it possible for them to produce behaviors that would be difficult or impossible for isolated individuals.Once individual organisms learn, evolutionarily, how to cooperate, new opportunities and challenges (cheaters) emerge. There are strategies that can enable an organism to adapt to a wider range of environments, or to become highly specialized to a specific environment,through the production of increasingly complex behaviors.As described previously, many of these cooperative strategies can be adopted by single celled organisms, but others require a level of multicellularity.Multicellularity can be transient – a pragmatic response to specific conditions, or it can be (if we ignore the short time that gametes exist as single cells) permanent, allowing the organism to develop the range of specialized cells types needed to build large, macroscopic organisms with complex and coordinated behaviors. In appears that various forms of multicellularity have arisen independently in a range of lineages (Bonner, 1998; Knoll, 2011). We can divide multicellularity into two distinct types, aggregative and clonal – which we will discuss in turn (1). Aggregative (transient) multicellularity:Once organisms had developed quorum sensing, they can monitor the density of related organisms in their environment and turn or (or off) specific genes (or sets of genes, necessary to produce a specific behavior.While there are many variants, one model for sucha behavior isa genetic toggle switch, in which a particular gene (or genes) can be switched on or off in response to environmental signals acting as allosteric regulators of transcription factor proteins (see Gardner et al., 2000).Here is an example of an activity (↓) that we will consider in class to assess our understanding of the molecular processes involved.
One outcome of such a signaling system is to provoke the directional migration of amoeba and their aggregation to form the transient multicellular “slug”.Such behaviors has been observedin a range of normally unicellular organisms (see Hillmann et al., 2018)(↓). The classic example isthe cellular slime mold Dictyostelium discoideum (Loomis, 2014).Under normal conditions, these
unicellular amoeboid eukaryotes migrate, eating bacteria and such. In this state, the range of an individual’s movement is restricted to short distances.However when conditions turn hostile, specifically a lack of necessary nitrogen compounds, there is a compelling reason to abandon one environment and migrate to another, more distant that a single-celled organism could reach. This is a behavior that depends upon the presence of a sufficient density (cells/unit volume) of cells that enables them to: 1) recognize one another’s presence (through quorum sensing), 2) find each other through directed (chemotactic) migration, and 3) form a multicellular slug that can go on to differentiate. Upon differentiation about 20% of the cells differentiate (and die), forming a stalk that lifts the other ~80% of the cells into the air.These non-stalk cells (the survivors) differentiate into spore (resistant to drying out) cells that are released into the air where they can be carried to new locations, establishing new populations.
The process of cellular differentiation in D. discoideum has been worked out in molecular detail and involves two distinct signaling systems: the secreted pre-starvation factor (PSF) protein and cyclic AMP (cAMP).PSF is a quorum signaling protein that also serves to activate the cell aggregation and differentiation program (FIG. ↓)
If bacteria, that is food, are present, the activity of PSF is inhibited andcells remain in their single cell state. The key regulator of downstream aggregation and differentiation is the cAMP-dependent protein kinase PKA.In the unicellular state, PKA activity is inhibited by PufA.As PSF increases, while food levels decrease, YakA activity increases, inactivating PufA, leading to increased PKA activity.Active PKA induces the synthesis of two downstream proteins, adenylate cyclase (ACA) and the cAMP receptor (CAR1). ACA catalyzes cAMP synthesis, much of which is secreted from the cell as a signaling molecule. The membrane-bound CAR1 protein acts as a receptor for autocrine (on the cAMP secreting cell) and paracrine (on neighboring cells) signaling.The binding of cAMP to CAR1 leads to further activation of PKA, increasing cAMP synthesis and secretion – a positive feed-back loop. As cAMP levels increase, downstream genes are activated (and inhibited) leading cells to migrate toward one another, their adhesion to form a slug.Once the slug forms and migrates to an appropriate site, the process of differentiation (and death) leading to stalk and spore formation begins. The fates of the aggregated cells is determined stochastically, but social cheaters can arise. Mutations can lead to individuals that avoid becoming stalk cells.In the long run, if all individuals were to become cheaters, it would be impossible to form a stalk, so the purpose of social cooperation would be impossible to achieve.In the face of environmental variation, populations invaded by cheaters are more likely to become extinct.For our purposes the various defenses against cheaters are best left to other courses (see here if interested Strassmann et al., 2000).
Clonal (permanent) multicellularity:The type of multicellularity that most developmental biology courses focus on is what is termed clonal multicellularity – the organism is a clone of an original cell, the zygote, a diploid cell produced by the fusion of sperm and egg, haploid cells formed through the process of meiosis (2).It is during meiosis that most basic genetic processes occur, that is the recombination between maternal and paternal chromosomes leading to the shuffling of alleles along a chromosome, and the independent segregation of chromosomes to form haploid gametes, gametes that are genetically distinct from those present in either parent. Once the zygote forms, subsequent cell divisions involve mitosis, with only a subset of differentiated cells, the cells of the germ line, capable of entering meiosis.
Non-germ line, that is somatic cells, grow and divide. They interact with one another directly and through various signaling processes to produce cells with distinct patterns of gene expression, and so differentiated behaviors.A key difference from a unicellular organism, is that the cells will (largely) stay attached to one another, or to extracellular matrix materials secreted by themselves and their neighbors.The result is ensembles of cells displaying different specializations and behaviors.As such cellular colonies get larger, they face a number of physical constraints – for example, cells are open non-equilibrium systems, to maintain themselves and to grow and reproduce, they need to import matter and energy from the external world. Cells also produce a range of, often toxic, waste products that need to be removed.As the cluster of zygote-derived cells grows larger, and includes more and more cells, some cells will become internal and so cut off from necessary resources. While diffusive processes are often adequate when a cell is bathed in an aqueous solution, they are inadequate for a cell in the interior of a large cell aggregate (3).The limits of diffusive processes necessitate other strategies for resource delivery and waste removal; this includes the formation of tubular vascular systems (such as capillaries, arteries, veins) and contractile systems (hearts and such) to pump fluids through these vessels, as well as cells specialized to process and transport a range of nutrients (such as blood cells).As organisms get larger, their movements require contractile machines (muscle, cartilage, tendons, bones, etc) driving tails, fins, legs, wings, etc. The coordination of such motile systems involves neurons, ganglia, and brains. There is also a need to establish barriers between the insides of an organism and the outside world (skin, pulmonary, and gastrointestinal linings) and the need to protect the interior environment from invading pathogens (the immune system).The process of developing these various systems depends upon controlling patterns of cell growth, division, and specialization (consider the formation of an arm), as well as the controlled elimination of cells (apoptosis), important in morphogenesis (forming fingers from paddle-shaped appendages), the maturation of the immune system (eliminating cells that react against self), and the wiring up, and adaptation of the nervous system. Such changes are analogous to those involved in aggregative multicellularity.
Origins of multicellularity:While aggregative multicellularity involves an extension of quorum sensing and social cooperation between genetically distinct, but related individuals, we can wonder whether similar drivers are responsible for clonal multicellularity.There are a number of imaginable adaptive (evolutionary) drivers but two spring to mind: a way to avoid predators by getting bigger than the predators and as a way to produce varied structures needed to exploit various ecological niches and life styles. An example of the first type of driver of multicellularity is offered by the studies of Boraas et al(1998). They cultured the unicellular green alga Chlorella vulgaris, together with a unicellular predator, the phagotrophic flagellated protist Ochromonas vallescia. After less than 100 generations (cell divisions), they observed the appearance of multicellular, and presumable inedible (or at least less easily edible), forms. Once selected, this trait appears to be stable, such that “colonies retained the eight-celled form indefinitely in continuous culture”.To my knowledge, the genetic basis for this multicellularity remains to be determined.
Cell Differentiation:One feature of simple colonial organisms is that when dissociated into individual cells, each cell is capable of regenerating a new organism. The presence of multiple (closely related) cells in a single colony opens up the possibility of social interactions; this is distinct from the case in aggregative multicellularity, where social cooperation came first. Social cooperation within a clonal metazoan means that most cells “give up” their ability to reproduce a new organism (a process involving meiosis). Such irreversible social interactions mark the transition from a colonial organism to a true multicellular organism. As social integration increases, cells can differentiate so as to perform increasingly specialized functions, functions incompatible with cell division. Think for a moment about a human neuron or skeletal muscle cell – in both cases, cell division is no longer possible (apparently). Nevertheless, the normal functioning of such cells enhances the reproductive success of the organism as a whole – a classic example of inclusive fitness (remember heterocysts?)Modern techniques of single cell sequencing and data analysis have now been employed to map this process of cellular differentiation in increasingly great detail, observations that will inform our later discussions (see Briggs et al., 2018 and future posts). In contrast, the unregulated growth of a cancer cell is an example of an asocial behavior, an asocial behavior that is ultimately futile, except in those rare cases (four known at this point) in which a cancer cell can move from one organism to another (Ujvari et al., 2016).
Unicellular affordances for multicellularity:When considering the design of a developmental biology course, we are faced with the diversity of living organisms – the basic observation that Darwin, Wallace, their progenitors and disciplinary descendants set out to solve. After all there are many millions of different types of organisms; among the multicellular eukaryotes, there are six major group : the ascomycetes and basidiomycetes fungi, the florideophyte red algae, laminarialean brown algae, embryophytic land plants and animals
(Knoll, 2011 ↑).Our focus will be on animals. “All members of Animalia are multicellular, and all are heterotrophs (i.e., they rely directly or indirectly on other organisms for their nourishment). Most ingest food and digest it in an internal cavity.” [Mayer link].From a macroscopic perspective, most animals have (or had at one time during their development) an anterior to posterior, that is head to tail, axis. Those that can crawl, swim, walk, or fly typically have a dorsal-ventral or back to belly axis, and some have a left-right axis as well.
But to be clear, a discussion of the various types of animals is well beyond the scope of any introductory course in developmental biology, in part because there are 35 (assuming no more are discovered) different “types” (phyla) of animals – nicely illustrated at this website [BBC: 35 types of animals, most of whom are really weird)].So again, our primary focus will be on one group, the vertebrates – humans are members of this group.We will also consider experimental insights derived from studies of various “model” systems, including organisms from another metazoan group, theecdysozoa (organisms that shed their outer layer as they grow bigger), a group that includes fruit flies and nematode worms.
My goal will be to ignore most of the specialized terminology found in the scholarly literature, which can rapidly turn a biology course into a vocabulary lesson and that add little to understanding of basic processes relevant to a general understanding of developmental processes (and relevant to human biology, medicine, and biotechnology). This approach is made possible by the discovery that the basic processes associated with animal (and metazoan) development are conserved. In this light, no observation has been more impactful than the discovery that the nature and organization of the genes involved in specifying the head to tail axes of the fruit fly and vertebrates (such as the mouse and human) is extremely similar in terms of genomic organization and function (Lappin et al., 2006 ↓), an observation that we will return to repeatedly.Such molecular similarities extend to cell-cell and cell-matrix adhesion systems, systems that release and respond to various signaling molecules, controlling cell behavior and gene expression, and reflects the evolutionary conservation and the common ancestry of all animals (Brunet and King, 2017; Knoll, 2011).
What can we know about the common ancestor of the animals?Early on in the history of comparative cellular anatomy, the striking structural similarities between the feeding system of choanoflagellate protozoans, a motile (microtubule-based) flagellum a surrounded by a “collar”of microfilament-based microvilli) and a structurally similar organelle in a range of multicellular organisms led to the suggestion that choanoflagellates and animals shared a common ancestor.The advent of genomic sequencing and analysis has only strengthened this hypothesis, namely that choanoflagellates and animals form a unified evolutionary clade, the ‘Choanozoa’(see tree↑ above)(Brunet and King, 2017).Moreover, “many genes required for animal multicellularity (e.g., tyrosine kinases, cadherins, integrins, and extracellular matrix domains) evolved before animal origins”.The implications is that the Choanozoan ancestor was predisposed to exploit some of the early opportunities offered by clonal multicellularity. These pre-existing affordances, together with newly arising genes and proteins (Long et al., 2013) were exploited in multiple lineages in the generation of multicellular organisms (see Knoll, 2011).
Basically to understand what happened next, some ~600 million years ago or so, we will approach the various processes involved in the shaping of animal development.Because all types of developmental processes, including the unicellular to colonial transition, involve changes in gene expression, we will begin with the factors involved in the regulation of gene expression.
Footnotes: 1). Please excuse the inclusive plural, but it seems appropriate in the context of what I hope will be a highly interactive course.
2). I will explicitly ignore variants as (largely) distractions, better suited for more highly specialized courses. 3). We will return to this problem when (late in the course, I think) we will discuss the properties of induced pluripotent stem cell (iPSC) derived organoids.
Literature cited: Bonner, J. T. (1998). The origins of multicellularity. Integrative Biology: Issues, News, and Reviews: Published in Association with The Society for Integrative and Comparative Biology 1, 27-36.
Boraas, M. E., Seale, D. B. and Boxhorn, J. E. (1998). Phagotrophy by a flagellate selects for colonial prey: a possible origin of multicellularity. Evolutionary Ecology 12, 153-164.
Briggs, J. A., Weinreb, C., Wagner, D. E., Megason, S., Peshkin, L., Kirschner, M. W. and Klein, A. M. (2018). The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360, eaar5780.
Brunet, T. and King, N. (2017). The origin of animal multicellularity and cell differentiation. Developmental cell 43, 124-140.
Gardner, T. S., Cantor, C. R. and Collins, J. J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339-342.
Hillmann, F., Forbes, G., Novohradská, S., Ferling, I., Riege, K., Groth, M., Westermann, M., Marz, M., Spaller, T. and Winckler, T. (2018). Multiple roots of fruiting body formation in Amoebozoa. Genome biology and evolution 10, 591-606.
Knoll, A. H. (2011). The multiple origins of complex multicellularity. Annual Review of Earth and Planetary Sciences 39, 217-239.
Lappin, T. R., Grier, D. G., Thompson, A. and Halliday, H. L. (2006). HOX genes: seductive science, mysterious mechanisms. The Ulster medical journal 75, 23.
Long, M., VanKuren, N. W., Chen, S. and Vibranovski, M. D. (2013). New gene evolution: little did we know. Annual review of genetics 47, 307-333.
Loomis, W. F. (2014). Cell signaling during development of Dictyostelium. Developmental biology 391, 1-16.
Strassmann, J. E., Zhu, Y. and Queller, D. C. (2000). Altruism and social cheating in the social amoeba Dictyostelium discoideum. Nature 408, 965-967.
Ujvari, B., Gatenby, R. A. and Thomas, F. (2016). Transmissible cancers, are they more common than thought? Evolutionary applications 9, 633-634.
On teaching developmental biology and trying to decide where to start: differentiation
Having considered the content of courses in chemistry  andbiology [2, 3], and preparing to teach developmental biology for the first time, I find myself reflecting on how such courses might be better organized.In my department, developmental biology (DEVO) has returned after a hiatus as the final capstone course in our required course sequence, and so offers an opportunity within which to examine what students have mastered as they head into their more specialized (personal) educational choices.Rather than describe the design of the course that I will be teaching, since at this point I am not completely sure what will emerge, what I intend to do (in a series of posts) is to describe, topic by topic, the progression of key concepts, the observations upon which they are based, and the logic behind their inclusion.
Modern developmental biology emerged during the mid-1800s from comparative embryology  and was shaped by the new cell theory (the continuity of life and the fact that all organisms are composed of cells and their products) and the ability of cells to differentiate, that is, to adopt different structures and behaviors .Evolutionary theory was also key.The role of genetic variation based on mutations and selection, in the generation of divergent species from common ancestors, explained why a single, inter-connected Linnaean (hierarchical) classification system (the phylogenic tree of life →) of organisms was possible and suggested that developmental mechanisms were related to similar processes found in their various ancestors.
So then, what exactly are the primary concepts behind developmental biology and how do they emerge from evolutionary, cell, and molecular biology?The concept of “development” applies to any process characterized by directional changes over time.The simplest such process would involve the progress from the end of one cell division event to the beginning of the next; cell division events provide a convenient benchmark.In asexual species, the process is clonal, a single parent gives rise to a genetically identical (except for the occurrence of new mutations) offspring. Often there is little distinction between parent and offspring.In sexual species, a dramatic and unambiguous benchmark involves the generation of a new and genetically distinct organism.This “birth” event is marked by the fusion of two gametes (fertilization) to form a new diploid organism.Typically gametes are produced by a complex cellular differentiation process (gametogenesis), ending with meiosis and the formation of haploid cells.In multicellular organisms, it is often the case that a specific lineage of cells (which reproduce asexually), known as the germ line, produce the gametes.The rest of the organism, the cells that do not produce gametes, is known as the soma, composed of somatic cells. Cellular continuity remains, however, since gametes are living (albeit haploid) cells.
It is common for the gametes that fuse to be of two different types, termed oocyte and sperm.The larger, and generally immotile gamete type is called an oocyte and an individual that produces oocytes is termed female. The smaller, and generally motile gamete type is called a sperm; individuals that produces sperm are termed male. Where a single organism can produce both oocytes and sperm, either at the same time or sequentially, they are referred to as hermaphrodites (named after Greek Gods, the male Hermes and the female Aphrodite). Oocytes and sperm are specialized cells; their formation involves the differential expression of genes and the specific molecular mechanisms that generate the features characteristic of the two cell types.The fusion of gametes, fertilization,leads to a zygote, a diploid cell that (usually) develops into a new, sexually mature organism.
An important feature of the process of fertilization is that it requires a level of social interaction, the two fusing cells (gametes) must recognize and fuse with one another.The organisms that produce these gametes must cooperate; they need to produce gametes at the appropriate time and deliver them in such a way that they can find and recognize each other and avoid “inappropriate” interactions”. The specificity of such interactions underlie the reproductive isolation that distinguishes one species from another.The development of reproductive isolation emerges as an ancestral population of organisms diverges to form one or more new species.As we will see, social interactions, and subsequent evolutionary effects, are common in the biological world.
The cellular and molecular aspects of development involve the processes by which cells grow, replicate their genetic material (DNA replication), divide to form distinct parent-offspring or similar sibling cells, and may alter their morphology (shape), internal organization, motility, and other behaviors, such as the synthesis and secretion of various molecules, and how these cells respond to molecules released by other cells.Developmental processes involve the expression and the control of all of these processes.
Essentially all changes in cellular behavior are associated with changes in the activities of biological molecules and the expression of genes, initiated in response to various external signaling events – fertilization itself is such a signal.These signals set off a cascade of regulatory interactions, often leading to multiple “cell types”, specialized for specific functions (such as muscle contraction, neural and/or hormonal signaling, nutrient transport, processing, and synthesis, etc.).For specific parts of the organism, external or internal signals can result in a short term “adaptive” response (such as sweating or panting in response to increased internal body temperature), after which the system returns to its original state, or in the case of developing systems, to new states, characterized by stable changes in gene expression, cellular morphology, and behavior.
Development in bacteria (and other unicellular organisms):In most unicellular organisms, the cell division process is reasonably uneventful, the cells produced are similar to the original cell – but not always.A well studied example is the bacterium Caulobacter crescentus (and related species) [link][link].In cases such as this, the process of growthleads to phenotypically different daughters.While it makes no sense to talk about a beginning (given the continuity of life after the appearance of the last universal common ancestor or LUCA), we can start with a “swarmer” cell, characterized by the presence of a motile flagellum (a molecular machine driven by coupled chemical reactions – see past blogpost] that drives motility [figure modified from 6 ↓].
A swarmer will eventually settle down, loose the flagellum, and replace it with a specialized structure (a holdfast) designed to anchor the cell to a solid substrate.As the organism grows, the holdfast develops a stalk that lifts the cell away from the substrate.As growth continues, the end of the cell opposite the holdfast begins to differentiate (becomes different) from the holdfast end of the cell – it begins the process leading to the assembly of a new flagellar apparatus.When reproduction (cell growth, DNA replication, and cell division) occurs, a swarmer cell is released and can swim away and colonize another area, or settle nearby.The holdfast-anchored cell continues to grow, producing new swarmers. This process is based on the inherent asymmetry of the system – the holdfast end of the cell is molecularly distinct from the flagellar end [see 7].
The process of swarmer cell formation in Caulobacter is an example of what we will term deterministic phenotypic switching.Cells can also exploit molecular level noise (stochastic processes) that influence gene expression to generate phenotypic heterogeneity, different behaviors expressed by genetically identical cells within the same environment [see 8, 9].Molecular noise arises from the random nature of molecular movements and the rather small (compared to macroscopic systems) numbers of most molecules within a cell.Most cells contain one or two copies of any particular gene, and a similarly small number of molecular sequences involved in their regulation .Which molecules are bound to which regulatory sequence, and for how long, is governed by inter-molecular surface interactions and thermally driven collisions, and is inherently noisy.There are strategies that can suppress but not eliminate such noise [see 11].As dramatically illustrated by Elowitzand colleagues (↑), molecular level noise can produce cells with different phenotypes.Similar processes are active in eukaryotes (including humans), and can lead to the expression of one of the two copies of a gene (mono-allelic expression) present in a diploid organism.This can lead to effects such as haploinsufficiency and selective (evolutionary) lineage effects if the two alleles are not identical [12, 13]. Such phenotypic heterogeneity among what are often genetically identical cells is a topic that is rarely discussed (as far as I can discern) in introductory cell, molecular, or developmental biology courses [past blogpost].
The ability to switch phenotypes can be a valuable trait if an organism’s environment is subject to significant changes.As an example, when the environment gets hostile, some bacterial cells transition from a rapidly dividing to a slow or non-dividing state.Such “spores” can differentiate so as to render them highly resistant to dehydration and other stresses.If changes in environment are very rapid, a population can protect itself by continually having some cells (stochastically) differentiating into spores, while others continue to divide rapidly. Only a few individuals (spores) need to survive a catastrophic environmental change to quickly re-establish the population.
Dying for others – social interactions between “unicellular” organisms:Many students might not predict that one bacterial cell would “sacrifice” itself for the well being of others, but in fact there are a number of examples of this type of self-sacrificing behavior, known as programmed cell death, which is often a stochastic process.An interesting example is provided by cellular specialization for photosynthesis or nitrogen fixation in cyanobacteria [see 9].These two functions require mutually exclusive cellular environments to occur, in particular the molecular oxygen (O2) released by photosynthesis inhibits the process of nitrogen fixation.Nevertheless, both are required for optimal growth.The solution?some cells differentiate into what are known as heterocysts, cells committed to nitrogen fixation (← a heterocyst in Anabaena spiroides, adapted from link), while most ”vegetative” cells continue with photosynthesis.Heterocysts cannot divide, and eventually die – they sacrifice themselves for the benefit of their neighbors, the vegetative cells, cells that can reproduce.
The process by which the death of an individual can contribute resources that can be used to insure or enhance the survival and reproduction of surrounding individuals is an inherently social process, and is subject of social evolutionary mechanisms [14, 15][past blogpost]. Social behaviors can be selected for because the organism’s neighbors, the beneficiaries of their self-sacrifice are likely to be closely (clonally) related to themselves. One result of the social behavior is, at the population level, an increase in one aspect of evolutionary fitness,termed “inclusive fitness.”
Such social behaviors can enable a subset of the population to survive various forms of environmental stress (see spore formation above).An obvious environmental stress involves the impact of viral infection.Recall that viruses are completely dependent upon the metabolic machinery of the infected cell to replicate. While there are a number of viral strategies, a common one is bacterial lysis – the virus replicates explosively, kills the infected cells, leading to the release of virus into the environment to infect others.But, what if the infected cell kills itself BEFORE the virus replicates – the dying (self-sacrificing, altruistic) cell “kills” the virus (although viruses are not really alive) and stops the spread of the infection.Typically such genetically programmed cell death responses are based on a simple two-part system, involving a long lived toxin and a short-lived anti-toxin.When the cell is stressed, for example early during viral infection, the level of the anti-toxin can fall, leading to the activation ofthe toxin.
Other types of social behavior and community coordination (quorum effects): Some types of behaviors only make sense when the density of organisms rises above a certain critical level.For example,it would make no sense for an Anabaena cellto differentiate into a heterocyst (see above) if there are no vegetative cells nearby.Similarly, there are processes in which a behavior of a single bacterial cell, such as the synthesis and secretion of a specific enzyme, a specific import or export machine, or the construction of a complex, such as a DNA uptake machine, makes no sense in isolation – the secreted molecule will just diffuse away, and so be ineffective, the molecule to be imported (e.g. lactose) or exported (an antibiotic) may not be present, or there may be no free DNA to import.However, as the concentration (organisms per volume) of bacteria increases, these behaviors can begin to make biological sense – there is DNA to eat or incorporate and the concentration of secreted enzyme can be high enough to degrade the target molecules (so they are inactivated or can be imported as food).
So how does a bacterium determine whether it has neighbors or whether it wants to join a community of similar organisms?After all, it does not have eyes to see. The process used is known as quorum sensing.Each individual synthesizes and secretes a signaling molecule and a receptor protein whose activity is regulated by the binding of the signaling molecule. Species specificity in signaling molecules and receptors insures that organisms of the same kind are talking to one another and not to other, distinct types of organisms that may be in the environment. At low signaling molecule concentrations, such as those produced by a single bacterium in isolation, the receptor is not activated and the cell’s behavior remains unchanged.However, as the concentration of bacteria increases, the concentration of the signal increases, leading to receptor activation. Activation of the receptor can have a number of effects, including increased synthesis of the signal and other changes, such as movement in response to signals through regulation of flagellar and other motility systems, such a system can lead to the directed migration (aggregation) of cells [see 16].
In addition to driving the synthesis of a common good (such as a useful extracellular molecule), social interactions can control processes such as programmed cell death. When the concentration of related neighbors is high, the programmed death of an individual can be beneficial, it can lead to release of nutrients (common goods, including DNA molecules) that can be used by neighbors (relatives)[17, 18] – an increase in the probability of cell death in response to a quorum can increased in a way that increases inclusive fitness.On the other hand,if there are few related individuals in the neighborhood, programmed cell death “wastes” these resources, and so is likely to be suppressed (you might be able to generate a plausible mechanism that could control the probability of programmed cell death).
As we mentioned previously with respect to spore formation, the generation of a certain percentage of “persisters” – individuals that withdraw from active growth and cell division, can enable a population to survive stressful situations, such as the presence of an antibiotic. On the other hand, generating too many persisters may place the population at a reproductive disadvantage. Once the antibiotic is gone, the persisters can return into active division. The ability of bacteria to generate persisters is a serious problem in treating people with infections, particularly those who stop taking their antibiotics too early .
Of course, as in any social system, the presumption of cooperation (expending energy to synthesize the signal, sacrificing oneself for others) can open the system to cheaters [blogpost].All such “altruistic” behaviors are vulnerable to cheaters.*For example, a cheater that avoids programmed cell death (for example due to an inactivating mutation that effects the toxin molecule involved) will come to take over the population.The downside, for the population, is that if cheaters take over, the population is less likely to survive the environmental events that the social behavior was evolve to address.In response to the realities of cheating, social organisms adopt various social-validation and policing systems [see 20 as an example]; we see this pattern of social cooperation, cheating, and social defense mechanism throughout the biological world.
* Such as people who fail to pay their taxes or disclose their tax returns.
1. Cooper, M.M. and M.W. Klymkowsky, Chemistry, life, the universe, and everything: a new approach to general chemistry, and a model for curriculum reform. J. Chem. Educ. 2013. 90: 1116-1122 & Cooper, M. M., R. Stowe, O. Crandell and M. W. Klymkowsky. Organic Chemistry, Life, the Universe and Everything (OCLUE): A Transformed Organic Chemistry Curriculum. J. Chem. Educ. 2019. 96: 1858-1872.
2. Klymkowsky, M.W., Teaching without a textbook: strategies to focus learning on fundamental concepts and scientific process. CBE Life Sci Educ, 2007. 6: 190-3.
3. Klymkowsky, M.W., J.D. Rentsch, E. Begovic, and M.M. Cooper, The design and transformation of Biofundamentals: a non-survey introductory evolutionary and molecular biology course. LSE Cell Biol Edu, 2016. pii: ar70.
4. Arthur, W., The emerging conceptual framework of evolutionary developmental biology. Nature, 2002. 415: 757.
5. Wilson, E.B., The cell in development and heredity. 1940.
6. Jacobs‐Wagner, C., Regulatory proteins with a sense of direction: cell cycle signalling network in Caulobacter. Molecular microbiology, 2004. 51:7-13.
7. Hughes, V., C. Jiang, and Y. Brun, Caulobacter crescentus. Current biology: CB, 2012. 22:R507.
8. Elowitz, M.B., A.J. Levine, E.D. Siggia, and P.S. Swain, Stochastic gene expression in a single cell. Science, 2002. 297:1183-6.
9. Balázsi, G., A. van Oudenaarden, and J.J. Collins, Cellular decision making and biological noise: from microbes to mammals. Cell, 2011. 144: 910-925.
10. Fedoroff, N. and W. Fontana, Small numbers of big molecules. Science, 2002. 297:1129-1131.
11. Lestas, I., G. Vinnicombe, and J. Paulsson, Fundamental limits on the suppression of molecular fluctuations. Nature, 2010. 467:174-178.
12. Zakharova, I.S., A.I. Shevchenko, and S.M. Zakian, Monoallelic gene expression in mammals. Chromosoma, 2009. 118:279-290.
13. Deng, Q., D. Ramsköld, B. Reinius, and R. Sandberg, Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science, 2014. 343: 193-196.
14. West, S.A., A.S. Griffin, A. Gardner, and S.P. Diggle, Social evolution theory for microorganisms. Nature reviews microbiology, 2006. 4:597.
15. Bourke, A.F.G., Principles of Social Evolution. Oxford series in ecology and evolution. 2011, Oxford: Oxford University Press.
16. Park, S., P.M. Wolanin, E.A. Yuzbashyan, P. Silberzan, J.B. Stock, and R.H. Austin, Motion to form a quorum. Science, 2003. 301:188-188.
17. West, S.A., S.P. Diggle, A. Buckling, A. Gardner, and A.S. Griffin, The social lives of microbes. Annual Review of Ecology, Evolution, and Systematics, 2007: 53-77.
18. Durand, P.M. and G. Ramsey, The Nature of Programmed Cell Death. Biological Theory, 2018: 1-12.
19. Fisher, R.A., B. Gollan, and S. Helaine, Persistent bacterial infections and persister cells. Nature Reviews Microbiology, 2017. 15:453.
20. Queller, D.C., E. Ponte, S. Bozzaro, and J.E. Strassmann, Single-gene greenbeard effects in the social amoeba Dictyostelium discoideum. Science, 2003. 299: 105-106.
Links between genetics and race crop up periodically in the popular press (link; link), but the real, substantive question, and the topic of a number of recent essays (see Saletan. 2018a. Stop Talking About Race and IQ) is whether the idea of “race” as commonly understood, and used by governments to categorize people (link), makes scientific sense.More to the point, do biology educators have an unmet responsibility to modify and extend their materials and pedagogical approaches to address the non-scientific, often racist, implications of racial characterizations.Such questions are complicated by a second factor, independent of whether the term race has any useful scientific purpose, namely to help students understand the biological (evolutionary) origins of racism itself, together with the stressors that lead to its periodic re-emergence as a socio-political factor. In times of social stress, reactions to strangers (others) identified by variations in skin color or overt religious or cultural signs (dress), can provoke hostility against those perceived to be members of a different social group.As far as I can tell, few in the biology education community, which includes those involved in generating textbooks, organizing courses and curricula, or the design, delivery, and funding of various public science programs, including PBS’s NOVA, the science education efforts of HHMI and other private foundations, and programs such as Science Friday on public radio, directly address the roots of racism, roots associated with biological processes such as the origins and maintenance of multicellularity and other forms of social organization among organisms, involved in coordinating their activities and establishing defenses against social cheaters and processes such as cancer, in an organismic context (1).These established defense mechanisms can, if not recognized and understood, morph into reflexive and unjustified intolerance, hostility toward, and persecution of various “distinguishable others.”I will consider both questions, albeit briefly, here.
Two factors have influenced my thinking about these questions.The first involves the design of the biofundamentals text/course and its extension to include topics in genetics (2).This involved thinking about what is commonly taught in genetics, what is critical for students to know going forward (and by implication what is not), and where materials on genetic processes best fit into a molecular biology curriculum (3).While engaged in such navel gazing there came an email from Malcolm Campbell describing student responses to the introduction of a chapter section on race and racism in his textbook Integrating Concepts in Biology.The various ideas of race, the origins of racism, and the periodic appearance of anti-immigrant, anti-religious and racist groups raise important questions – how best to clarify what is an undeniable observation, that different, isolated, sub-populations of a species can be distinguished from one another (see quote from Ernst Mayr’s 1994 “Typological versus Population thinking” →), from the deeper biological reality, that at the level of the individual these differences are meaningless. In what I think is an interesting way, the idea that people can be meaningfully categorized as different types of various platonic ideals (for example, as members of one race or the other) based on anatomical / linguistic differences between once distinct sub-populations of humans is similar to the dichotomy between common wisdom (e.g. that has influenced people’s working understanding of the motion of objects) and the counter-intuitive nature of empirically established scientific ideas (e.g. Newton’s laws and the implications of Einstein’s theory of general relativity).What appears on the surface to be true but in fact is not.In this specific case, there is a pressure toward what Mayr terms “typological” thinking, in which we class people into idealized (platonic) types or races (↓).
As pointed out most dramatically, and repeatedly, by Mayr (1985; 1994; 2000), and supported by the underlying commonality of molecular biological mechanisms and the continuity of life, stretching back to the last universal common ancestor, there are only individuals who are members of various populations that have experienced various degrees of separation from one another.In many cases, these populations have diverged and, through geographic, behavioral, and structure adaptations driven by natural, social, and sexual selection together with the effects of various events, some non-adaptive, such as bottlenecks, founder effects, and genetic drift, may eventually become reproductively isolated from one another, forming new species.An understanding of evolutionary principles and molecular mechanisms transforms biology from a study of non-existent types to a study of populations with their origins in common, sharing a single root – the last universal common ancestor (LUCA). Over the last ~200,000 years the movement of humans first within Africa and then across the planet has been impressive (→). These movements have been accompanied by the fragmentation of human populations. Campbell and Tishkoff (2008) identified 13 distinct ancestral African populations while Busby et al (2016) recognized 48 sub-saharan population groups. The fragmentation of the human population is being reversed (or rather rendered increasingly less informative) by the effects of migration and extensive intermingling (↓).
Ideas, such as race (and in a sense species), try to make sense of the diversity of the many different types of organisms we observe. They are based on a form of essentialist or typological thinking – thinking that different species and populations are completely different “kinds” of objects, rather than individuals in a population connected historically to all other living things. Race is a more pernicious version of this illusion, a pseudo-scientific, political and ideological idea that postulates that humans comein distinct, non-overlapping types (quote ↓ again, from Mayr).Such a weird idea underlies various illogical and often contradictory legal “rules” by which a person’s “race” is determined.
Given the reality of the individual and the unreality of race, racial profiling (see Satel,
2002) can lead to serious medical mistakes, as made clear in the essays by Acquaviva & Mintz (2010) “Are We Teaching Racial Profiling?”, Yudell et al(2016) “Taking Race out of Human Genetics”, and Donovan (2014) “The impact of the hidden curriculum”.
The idea of race as a type fails to recognize the dynamics of the genome over time.If possible (sadly not) a comparative analysis of the genome of a “living fossil”, such as modern day coelacanths and their ancestors (living more than 80 million years ago) would likely reveal dramatic changes in genomic DNA sequence.In this light the fact that between 100 to 200 new mutations are introduced into the human genome per generation (see Dolgin 2009 Human mutation rate revealed) seems like a useful number to be widely appreciated by students, not to mention the general public. Similarly, the genomic/genetic differences between humans, our primate relatives, and other mammals and the mechanisms behind them (Levchenko et al., 2017)(blog link) would seem worth considering and explicitly incorporating into curricula on genetics and human evolution.
While race may be meaningless, racism is not.How to understand racism?Is it some kind of political artifact, or does it arise from biological factors.Here, I believe, we find a important omission in many biology courses, textbooks, and curricula – namely an introduction and meaningful discussion of social evolutionary mechanisms. Many is the molecular/cell biology curriculum that completely ignores such evolutionary processes. Yet, the organisms that are the primary focus of biological research (and who pay for such research, e.g. humans) are social organisms at two levels.In multicellular organisms somatic cells, which specialize to form muscular, neural, circulatory and immune systems, bone and connective tissues, sacrifice their own inter-generational reproductive future to assist their germ line (sperm and/or eggs) relatives, the cells that give rise to the next generation of organisms, a form of inclusive fitness (Dugatkin, 2007).Moreover, humans are social organisms, often sacrificing themselves, sharing their resources, and showing kindness to other members of their group. This social cooperation is threatened by cheaters of various types (POST LINK).Unless these social cheaters are suppressed, by a range of mechanisms, and through processes of kin/group selection, multicellular organisms die and socially dysfunctional social populations are likely to die out.Without the willingness to cooperate, and when necessary, self-sacrifice, social organization is impossible – no bee hives, no civilizations.Imagine a human population composed solely of people who behave in a completely selfish manner, not honoring their promises or social obligations.
A key to social interactions involves recognizing those who are, and who are not part of your social group.A range of traits can serve as markers for social inclusion.A plausible hypothesis is that the explicit importance of group membership and defined social interactions becomes more critical when a society, or a part of society, is under stress.Within the context of social stratification, those in the less privileged groups may feel that the social contract has been broken or made a mockery of.The feeling (apparent reality) that members of “elite” or excessively privileged sub-groups are not willing to make sacrifices for others serves as evidence that social bonds are being broken (4). Times of economic and social disruption (migrations and conquests) can lead to increased explicit recognition of both group and non-group identification.The idea that outsiders (non-group members) threaten the group can feed racism, a justification for why non-group members should be treated differently from group members.From this position it is a small (conceptual) jump to the conclusion that non-group members are somehow less worthy, less smart, less trustworthy, less human – different in type from members of the group – many of these same points are made in an op-ed piece by Judis. 2018. What the Left Misses About Nationalism.
That economic or climatic stresses can foster the growth of racist ideas is no new idea; consider the unequal effects of various disruptions likely to be associated with the spread ofautomation (quote from George Will↓) and the impact of climate change on migrations of groups within and between countries (see Saletan 2018b: Why Immigration Opponents Should Worry About Climate Change) are likely to spur various forms of social unrest, whether revolution or racism, or both – responses that could be difficult to avoid or control.
So back to the question of biology education – in this context understanding the ingrained responses of social creatures associated with social cohesion and integrity need to be explicitly presented. Similarly, variants of such mechanisms occur within multicellular organisms and how they work is critical to understanding how diseases such as cancer, one of the clearest forms of a cheater phenotype, are suppressed.Social evolutionary mechanisms provide the basis for understanding a range of phenomena, and the ingrained effects of social selection may be seen as one of the roots of racism, or at the very least a contributing factor worth acknowledging explicitly.
Thanks to Melanie Cooper and Paul Strode for comments. Minor edits 4 May 2019.
It is an interesting possibility whether the 1%, or rather the super 0.1% represent their own unique form of social parasite, leading periodically to various revolutions – although sadly, new social parasites appear to re-emerge quite quickly.
At this point it is worth noting that biofundamentals itself includes sections on social evolution, kin/group and sexual selection (see Klymkowsky et al., 2016; LibreText link).
One might be forgiven for thinking that rich and privileged folk who escape paying what is seen as their fair share of taxes, might be cast as social cheaters (parasites) who, rather than encouraging racism might lead to revolutionary thoughts and actions.
Acquaviva & Mintz. (2010). Perspective: Are we teaching racial profiling? The dangers of subjective determinations of race and ethnicity in case presentations. Academic Medicine85, 702-705.
Busby et al. (2016). Admixture into and within sub-Saharan Africa. Elife5, e15266.
Campbell & Tishkoff. (2008). African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu. Rev. Genomics Hum. Genet.9, 403-433.
Donovan, B.M. (2014). Playing with fire? The impact of the hidden curriculum in school genetics on essentialist conceptions of race. Journal of Research in Science Teaching51: 462-496.
Dugatkin, L. A. (2007). Inclusive fitness theory from Darwin to Hamilton. Genetics176, 1375-1380.
Levchenko et al., (2017). Human accelerated regions and other human-specific sequence variations in the context of evolution and their relevance for brain development. Genome biology and evolution10, 166-188.
Mayr, E. (1985). The Growth of Biological Thought: Diversity, Evolution, and Inheritance. Cambridge, MA: Belknap Press of Harvard University Press.
Mayr, E. (1994). Typological versus population thinking. Conceptual issues in evolutionary biology, 157-160.
—- (2000). Darwin’s influence on modern thought. Scientific American283, 78-83.
Satel, S. (2002). I am a racially profiling doctor. New York Times5, 56-58.
Yudell et al., (2016). Taking race out of human genetics. Science351, 564-565.