Genes – way weirder than you thought

Pretty much everyone, at least in societies with access to public education or exposure to media in its various forms, has been introduced to the idea of the gene, but “exposure does not equate to understanding” (see Lanie et al., 2004).  Here I will argue that part of the problem is that instruction in genetics (or in more modern terms, the molecular biology of the gene and its role in biological processes) has not kept up with the advances in our understanding of the molecular mechanisms underlying biological processes (Gayon, 2016). spacer bar

Let us reflect (for a moment) on the development of the concept of a gene: Over the course of human history, those who have been paying attention to such things have noticed that organisms appear to come in “types”, what biologists refer to as species. At the same time, individual organisms of the same type are not identical to one  another, they vary in various ways. Moreover, these differences can be passed from generation to generation, and by controlling  which organisms were bred together; some of the resulting offspring often displayed more extreme versions of the “selected” traits.  By strictly controlling which individuals were breddogs
together, over a number of generations, people were able to select for the specific traits they desired (→).  As an interesting aside, as people domesticated animals, such as cows and goats, the availability of associated resources (e.g. milk) led to reciprocal effects – resulting in traits such as adult lactose tolerance (see Evolution of (adult) lactose tolerance & Gerbault et al., 2011).  Overall, the process of plant and animal breeding is generally rather harsh (something that the fanciers of strange breeds who object to GMOs might reflect upon), in that individuals that did not display the desired trait(s) were generally destroyed (or at best, not allowed to breed). spacer bar

Charles Darwin took inspiration from this process, substituting “natural” for artificial (human-determined) selection to shape populations, eventually generating new species (Darwin, 1859).  Underlying such evolutionary processes was the presumption that traits, and their variation, was “encoded” in some type of “factors”, eventually known as genes and their variants, alleles.  Genes influenced the organism’s molecular, cellular, and developmental systems, but the nature of these inheritable factors and the molecular trait building machines active in living systems was more or less completely obscure. 

Through his studies on peas, Gregor Mendel was the first to clearly identify some of the rules for the behavior of these inheritable factors using highly stereotyped, and essentially discontinuous traits – a pea was either yellow or green, wrinkled or smooth.  Such traits, while they exist in other organisms, are in fact rare – an example of how the scientific exploration of exceptional situations can help understand general processes, but the downside is the promulgation of the idea that genes and traits are somehow discontinuous – that a trait is yes/no, displayed by an organism or not – in contrast to the realities that the link between the two is complex, a reality rarely directly addressed (apparently) in most introductory genetics courses.  Understanding such processes is critical to appreciating the fact that genetics is often not destiny, but rather alterations in probabilities (see Cooper et al., 2013).  Without such an more nuanced and realistic understanding, it can be difficult to make sense of genetic information.     spacer bar

A gene is part of a molecular machine:  A number of observations transformed the abstraction of Darwin’s and Mendel’s hereditary factors into physical entities and molecular mechanisms (1).  In 1928 Fred Griffith demonstrated that a genetic trait could be transferred from dead to living organisms – implying a degree of physical / chemical stability; subsequent observations implied that the genetic information transferred involved DNA molecules. The determination of the structure of double-stranded DNA immediately suggested how information could be stored in DNA (in variations of bases along the length of the molecule) and how this information could be duplicated (based on the specificity of base pairing).  Mutations could be understood as changes in the sequence of bases along a DNA molecule (introduced by chemicals, radiation, mistakes during replication, or molecular reorganizations associated with DNA repair mechanisms and selfish genetic elements.  

But on their own, DNA molecules are inert – they have functions only within the context of a living organism (or highly artificial, that is man made, experimental systems).  The next critical step was to understand how a gene works within a biological system, that is, within an organism.  This involve appreciating the molecular mechanisms (primarily proteins) involved in identifying which stretches of a particular DNA molecule were used as templates for the synthesis of RNA molecules, which in turn could be used to direct the synthesis of polypeptides (see previous post on polypeptides and proteins).  In the context of the introductory biology courses I am familiar with (please let me know if I am wrong), these processes are based on a rather deterministic context; a gene is either on or off in a particular cell type, leading to the presence or absence of a trait. Such a deterministic presentation ignores the stochastic nature of molecular level processes (see past post: Biology education in the light of single cell/molecule studies) and the dynamic interaction networks that underlie cellular behaviors.  spacer bar

But our level of resolution is changing rapidly (2).  For a number of practical reasons, when the human genome was first sequence, the identification of polypeptide-encoding genes was based on recognizing “open-reading frames” (ORFs) encoding polypeptides of > 100 amino acids in length (> 300 base long coding sequence).  The increasing sensitivity of mass spectrometry-based proteomic studies reveals that smaller ORFs (smORFs) are present and can lead to the synthesis of short (< 50 amino acid long) polypeptides (Chugunova et al., 2017; Couso, 2015).  Typically an ORF was considered a single entity – basically one gene one ORF one polypeptide (3).  A recent, rather surprising discovery is what are known as “alternative ORFs” or altORFs; these RNA molecules that use alternative reading frames to encode small polypeptides.  Such altORFs can be located upstream, downstream, or within the previously identified conventional ORFalternative orfs
(figure →)(see Samandi et al., 2017).  The implication, particularly for the analysis of how variations in genes link to traits, is that a change, a mutation or even the  experimental  deletion of a gene, a common approach in a range of experimental studies, can do much more than previously presumed – not only is the targeted ORF effected, but various altORFs can also be modified.  

The situation is further complicated when the established rules of using RNAs to direct polypeptide synthesis via the process of translation, are violated, as occurs in what is known as “repeat-associated non-ATG (RAN)” polypeptide synthesis (see Cleary and Ranum, 2017).  In this situation, the normal signal for the start of RNA-directed polypeptide synthesis, an AUG codon, is subverted – other RNA synthesis start sites are used leading to underlying or imbedded gene expression.  This process has been found associated with a class of human genetic diseases, such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) characterized by the expansion of simple (repeated) DNA sequences  (see Pattamatta et al., 2018).  Once they exceed a certain length, such“repeat” regions have been found to be associated with the (apparently) inappropriarepeat region RAN process
te transcription of RNA in both directions, that is using both DNA strands as templates (← A: normal situation, B: upon expansion of the repeat domain).  These abnormal repeat region RNAs are translated via the RAN process to generate six different types of toxic polypeptides. spacer bar

So what are the molecular factors that control the various types of altORF transcription and translation?  In the case of ALS and FTD, it appears that other genes, and the polypeptides and proteins they encode, are involved in regulating the expression of repeat associated RNAs (Kramer et al., 2016)(Cheng et al., 2018).  Similar or distinct mechanisms may be involved in other  neurodegenerative diseases  (Cavallieri et al., 2017).  

So how should all of these molecular details (and it is likely that there are more to be discovered) influence how genes are presented to students?  I would argue that DNA should be presented as a substrate upon which various molecular mechanisms occur; these include transcription in its various forms (directed and noisy), as well as DNA synthesis, modification, and repair mechanisms occur.   Genes are not static objects, but key parts of dynamic systems.  This may be one reason that classical genetics, that is genes presented within a simple Mendelian (gene to trait) framework, should be moved deeper into the curriculum, where students have the background in molecular mechanisms needed to appreciate its complexities, complexities that arise from the multiple molecular machines acting to access, modify, and use the information captured in DNA (through evolutionary processes), thereby placing the gene in a more realistic cellular perspective (4). 

Footnotes:

1. Described greater detail in biofundamentals™

2. For this discussion, I am completely ignoring the roles of genes that encode RNAs that, as far as is currently know, do not encode polypeptides.  That said, as we go on, you will see that it is possible that some such non-coding RNA may encode small polypeptides.  

3. I am ignoring the complexities associated with alternative promoter elements, introns, and the alternative and often cell-type specific regulated splicing of RNAs, to create multiple ORFs from a single gene.  

4. With respects to Norm Pace – assuming that I have the handedness of the DNA molecules wrong or have exchanged Z for A or B. 

literature cited: 

  • Cavallieri et al, 2017. C9ORF72 and parkinsonism: Weak link, innocent bystander, or central player in neurodegeneration? Journal of the neurological sciences 378, 49.
  • Cheng et al, 2018. C9ORF72 GGGGCC repeat-associated non-AUG translation is upregulated by stress through eIF2α phosphorylation. Nature communications 9, 51.
  • Chugunova et al, 2017. Mining for small translated ORFs. Journal of proteome research 17, 1-11.
  • Cleary & Ranum, 2017. New developments in RAN translation: insights from multiple diseases. Current opinion in genetics & development 44, 125-134.
  • Cooper et al, 2013. Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease. Human genetics 132, 1077-1130.
  • Couso, 2015. Finding smORFs: getting closer. Genome biology 16, 189.
  • Darwin, 1859. On the origin of species. London: John Murray.
  • Gayon, 2016. From Mendel to epigenetics: History of genetics. Comptes rendus biologies 339, 225-230.
  • Gerbault et al, 2011. Evolution of lactase persistence: an example of human niche construction. Philosophical Transactions of the Royal Society of London B: Biological Sciences 366, 863-877.
  • Kramer et al, 2016. Spt4 selectively regulates the expression of C9orf72 sense and antisense mutant transcripts. Science 353, 708-712.
  • Lanie et al, 2004. Exploring the public understanding of basic genetic concepts. Journal of genetic counseling 13, 305-320.
  • Pattamatta et al, 2018. All in the Family: Repeats and ALS/FTD. Trends in neurosciences 41, 247-250.
  • Samandi et al, 2017. Deep transcriptome annotation enables the discovery and functional characterization of cryptic small proteins. Elife 6.

Ideas are cheap, theories are hard

In the context of public discourse, there are times when one is driven to simple, reflexive and often disproportionate (exasperated) responses.  That happens to me whenever people talk about the various theories that they apply to a process or event.  I respond by saying (increasingly silently to myself), that what they mean is really that they have an idea, a model, a guess, a speculation, or a comforting “just-so” story. All too often such competing “theories” are flexible enough to explain (or explain away) anything, depending upon one’s predilections. So why a post on theories?  Certainly the  point as been made before (see Ghose. 2013. “Just a Theory”: 7 Misused Science Words“). Basically because the misuse of the term theory, whether by non-scientists, scientists, or science popularizers, undermines understanding of, and respect for the products of the scientific enterprise.  It confuses hard won knowledge with what are often superficial (or self-serving) opinions. When professors, politicians, pundits, PR flacks, or regular people use the word theory, they are all too often, whether consciously or not, seeking to elevate their ideas through the authority of science.    

So what is the big deal anyway, why be an annoying pain in the ass (see Christopher DiCarlo’s video), challenging people, making them uncomfortable, and making a big deal about something so trivial.  But is it really trivial?  I think not, although it may well be futile or quixotic.  The inappropriate use of the word theory, particularly by academics, is an implicit attempt to gain credibility.  It is also an attack on the integrity of science.  Why?  Because like it or not, science is the most powerful method we have to understand how the world works, as opposed to what the world or our existence within the world means.  The scientific enterprise, abiding as it does by explicit rules of integrity, objective evidence, logical and quantifiable implications, and their testing has been a progressive social activity, leading to useful knowledge – knowledge that has eradicated small pox and polio (almost) and produced iPhones, genetically modified organisms, and nuclear weapons.  That is not to say that the authority of science has not been repeatedly been used to justify horrific sociopolitical ideas, but those ideas have not been based on critically evaluated and tested scientific theories, but on variously baked ideas that claim the support of science (both the eugenics and anti-vaccination movements are examples).   

Modern science is based on theories, ideas about the universe that explain and predict what we will find when we look (smell, hear, touch) carefully at the world around us.  And these theories are rigorously and continually tested, quantitatively – in fact one might say that the ability to translate a theory into a quantitative prediction is one critical hallmark of a real versus an ersatz (non-scientific) theory [here is a really clever approach to teaching students about facts and theories, from David Westmoreland 

So where do (scientific) theories come from?  Initially they are guesses about how the world works, as stated by Richard Feynman and the non-scientific nature of vague “theories”.  Guesses that have evolved based on testing, confirmation, and where wrong – replacement with more and more accurate, logically well constructed and more widely applicable constructs – an example of the evolution of scientific knowledge.  That is why ideas are cheap, they never had, or do not develop the disciplinary rigor necessary to become a theory.  In fact, it often does not even matter, not really, to the people propounding these ideas whether they correspond to reality at all, as witness the stream of tweets from various politicians or the ease with which many apocalyptic predictions are replaced when they turn out to be incorrect.  But how is the average person to identify the difference between a (more or less half-baked) idea and a scientific theory?  Probably the easiest way is to ask, is the idea constantly being challenged, validated, and where necessary refined by both its proponents and its detractors.  One of the most impressive aspects of Einstein’s theory of general relativity is the accuracy of its predictions (the orbit of Mercury, time-dilation, and gravitational waves (link)), predictions that if not confirmed would have forced its abandonment – or at the very least serious revision.  It is this constant application of a theory, and the rigorous testing of its predictions (if this, then that) that proves its worth.  

Another aspect of a scientific theory is whether it is fecund or sterile.  Does its application lead to new observations that it can explain?  In contrast, most ideas are dead ends.  Consider the recent paper on the possibility that life arose outside of the Earth, a proposal known as pan-spermia (1) – “a very plausible conclusion – life may have been seeded here on Earth by life-bearing comets” – and recently tunneling into  the web’s consciousness in stories implying the extra-terrestrial origins of cephalopods (see “no, octopuses don’t come from outer space.”)  Unfortunately, no actual biological insights emerge from this idea (wild speculation), since it simply displaces the problem, if life did not arise here, how did it arise elsewhere?  If such ideas are embraced, as is the case with many religious ideas, their alteration often leads to violent schism rather than peaceful refinement. Consider, as an example, an idea had by an archaic Greek or two that the world was made of atoms. These speculations were not theories, since their implications were not rigorously tested.  The modern atomic theory has been evolving since its introduction by Dalton, and displays the diagnostic traits of a scientific theory.  Once introduced to explain the physical properties of matter, it led to new discoveries and explanations for the composition and structure of atoms themselves (electrons, neutrons, and protons), and then to the composition and properties of these objects, quarks and such (link to a great example.)   

Scientific theories are, by necessity, tentative (again, as noted by Feynman) – they are constrained and propelled by new and more accurate observations.  A new observation can break a theory, leading it to be fixed or discarded.  When that happens, the new theory explains (predicts) all that the old theory did and more.  This is where discipline comes in; theories must meet strict standards – the result is that generally there cannot be two equivalent theories that explain the same phenomena – one (or both) must be wrong in some important ways.  There is no alternative, non-atomic theory that explains the properties of matter.  

The assumption is that two “competing” theories will make distinctly different predictions, if we look (and measure) carefully enough. There are rare cases where two “theories” make the same predictions; the classic example is the Ptolemaic Sun-centered and the Copernican Earth-centered models of the solar system.  Both explained the appearances  of planetary motion more or less equally well, and so on that basis there was really no objective reason to choose between them.  In part, this situation arose from an unnecessary assumption underlying both models, namely that celestial objects moved in perfect circular orbits – this assumption necessitated the presence of multiple “epicycles” in both models.  The real advance came with Kepler’s recognition that celestial objects need not travel in perfect circular orbits, but rather in elliptical orbits; this liberated models of the solar system from the need for epicycles.  The result was the replacement of “theories of solar system movement” with a theory of planetary/solar/galactic motions”.  

Whether, at the end of the day scientific theories are comforting or upsetting, beautiful or ugly remains to be seen, but what is critical is that we defend the integrity of science and call out the non-scientific use of the word theory, or blame ourselve for the further decay of civilization (perhaps I am being somewhat hyperbolic – sorry).

notes: 

1. Although really, pan-oogenia would be better.  Sperm can do nothing without an egg, but an unfertilized egg can develop into an organism, as occurs with bees.  

When is a gene product a protein when is it a polypeptide?

On the left is a negatively-stained electron micrograph of a membrane vesicle isolated from the electric ray Torpedo california, with a muscle-type nicotinic single acetylcholine receptor (AcChR) pointed out . To the right the structure of the AcChR determined to NN resolution using cryoelectron microscopy by Rahman, Teng, Worrell, Noviello, Lee, Karlin, Stowell & Hibbs (2020). “Structure of the native muscle-type nicotinic receptor and inhibition by snake venom toxins.”

As a new assistant professor (1), I was called upon to teach my department’s “Cell Biology” course. I found, and still find, the prospect challenging in part because I am not exactly sure which aspects of cell biology are important for students to know, both in the context of the major, as well as their lives and subsequent careers.  While it seems possible (at least to me) to lay out a coherent conceptual foundation for biology as a whole [see 1], cell biology can often appear to students as an un-unified hodge-podge of terms and disconnected cellular systems, topics too often experienced as a vocabulary lesson, rather than as a compelling narrative. As such, I am afraid that the typical cell biology course often re-enforces an all too common view of biology as a discipline, a view, while wrong in most possible ways, was summarized by the 19th/early 20th century physicist Ernest Rutherford as “All science is either physics or stamp collecting.”  A key motivator for the biofundamentals project [2] has been to explore how to best dispel this prejudice, and how to more effectively present to students a coherent narrative and the key foundational observations and ideas by which to scientifically consider living systems, by any measure the most complex systems in the Universe, systems shaped, but not determined by, physical chemical properties and constraints, together with the historical vagaries of evolutionary processes on an ever-changing Earth. 

Two types of information:  There is an underlying dichotomy within biological systems: there is the hereditary information encoded in the sequence of nucleotides along double-stranded DNA molecules (genes and chromosomes).  There is also the information inherent in the living system.  The information in DNA is meaningful only in the context of the living cell, a reaction system that has been running without interruption since the origin of life.  While these two systems are inextricably interconnected, there is a basic difference between them. Cellular systems are fragile, once dead there is no coming back.  In contrast the information in DNA can survive death – it can move from cell to cell in the process of horizontal gene transfer.  The Venter group has replaced the DNA of bacterial cells with synthetic genomes in an effort to define the minimal number of genes needed to support life, at least in a laboratory setting [see 3, 4].  In eukaryotes, cloning is carried out by replacing a cell’s DNA, with that of another cell (reference).  

Conflating protein synthesis and folding with assembly and function: Much of the information stored in a cell’s DNA is used to encode the sequence of various amino acid polymers (polypeptides).  While over-simplified [see 5], students are generally presented with the view that each gene encodes a particular protein through DNA-directed RNA synthesis (transcription) and RNA-directed polypeptide synthesis (translation).  As the newly synthesized polypeptide emerges from the ribosomal tunnel, it begins to fold, and is released into the cytoplasm or inserted into or through a cellular membrane, where it often interacts with one or more other polypeptides to form a protein  [see 6].  The assembled protein is either functional or becomes functional after association with various non-polypeptide co-factors or post-translational modifications.  It is the functional aspect of proteins that is critical, but too often their assembly dynamics are overlooked in the presentation of gene expression/protein synthesis, which is really a combination of distinct processes. 

Students are generally introduced to protein synthesis through the terms primary, secondary, tertiary, and quaternary structure, an approach that can be confusing since many (most) polypeptides are not proteins and many proteins are parts of complex molecular machines [here is the original biofundamentals web page on proteins + a short video][see Teaching without a Textbook]. Consider the nuclear pore complex, a molecular machine that mediates the movement of molecules into and out of the nucleus.  A nuclear pore is “composed of ∼500, mainly evolutionarily conserved, individual protein molecules that are collectively known as nucleoporins (Nups)” [7]. But what is the function of a particular NUP, particularly if it does not exist in significant numbers outside of a nuclear pore?  Is a nuclear pore one protein?  In contrast, the membrane bound, mitochondrial ATP synthase found in aerobic bacteria and eukaryotic mitochondria, is described as composed “of two functional domains, F1 and Fo. F1 comprises 5 different subunits (three α, three β, and one γ, δ and ε)” while “Fo contains subunits c, a, b, d, F6, OSCP and the accessory subunits e, f, g and A6L” [8].  Are these proteins or subunits? is the ATP synthase a protein or a protein complex?  

Such confusions arise, at least in part, from the primary-quaternary view of protein structure, since the same terms are applied, generally without clarifying distinction, to both polypeptides and proteins. These terms emerged historically. The purification of a protein was based on its activity, which can only be measured for an intact protein. The primary structure of  a polypeptide was based on the recognition that DNA-encoded amino acid polymers are unbranched, with a defined sequence of amino acid residues (see Sanger. The chemistry of insulin).  The idea of a polypeptide’s secondary structure was based on the “important constraint that all six atoms of the amide (or peptide) group, which joins each amino acid residue to the next in the protein chain, lie in a single plane” [9], which led Pauling, Corey and Branson [10] to recognized the α-helix and β-sheet, as common structural motifs.  When a protein is composed of a single polypeptide, the final folding pattern of the polypeptide, is referred to as its tertiary structure and is apparent in the first protein structure solved, that of myoglobin (↓), by Max Perutz and John Kendell. 

Myoglobin’s role in O2 transport depends upon a non-polypeptide (prosthetic) heme group. So far so good, a gene encodes a polypeptide and as it folds a polypeptide becomes a protein – nice and simple (2).  Complications arise from the observations that 1) many proteins are composed of multiple polypeptides, encoded for by one or more genes, and 2) some polypeptides are a part of different proteins.  Hemoglobin, the second protein whose structure was

determined, illustrates the point (←).  Hemoglobin is composed of four polypeptides encoded by distinct genes encoding α- and β-globin polypeptides.  These polypeptides are related in structure, function, and evolutionary origins to myoglobin, as well  as the cytoglobin and neuroglobin proteins (↓).  In

humans, there are a number of distinct α-like globin and β-like globin genes that are expressed in different hematopoetic tissues during development, so functional hemoglobin proteins can have a number of distinct (albeit similar) subunit compositions and distinct properties, such as their affinities for O2 [see 11].  

But the situation often gets more complicated.  Consider centrin-2, a eukaryotic Ca2+ binding polypeptide that plays roles in organizing microtubules, building cilia, DNA repair, and gene expression [see 12 and references therein].  So, is the centrin-2 polypeptide just a polypeptide, a protein, or a part of a number of other proteins?  As another example, consider the basic-helix-loop-helix family of transcription factors; these transcription factor proteins are typically homo- or hetero-dimeric; are these polypeptides proteins in their own right?  The activity of these transcription factors is regulated in part by which binding partners they contain. bHLH polypeptides also interact with the Id polypeptide (or is it a protein); Id lacks a DNA binding domain so when it forms a dimer with a bHLH polypeptide it inhibits DNA binding (↓).  So is a single bHLH polypeptide a protein or is the protein necessarily a dimer?  More to the point, does the current primary→quaternary view of protein structure help or hinder student understanding of the realities of biological systems?  A potentially interesting bio-education research question.

A recommendation or two:  While under no illusion that the complexities of polypeptide synthesis and protein assembly can be easily resolved – it is surely possible to present them in a more coherent, consistent, and accessible manner.  Here are a few suggestions that might provoke discussion.  Let us first recognize that, for those genes that encode polypeptides: i) they encode polypeptides rather than functional proteins (a reality confused by the term “quaternary structure”).  We might well distinguish a polypeptide from a protein based on the concentration of free monomeric polypeptide (gene product) within the cell.  Then we need to convey the reality to students that the assembly of a protein is no simple process, particularly within the crowded cytoplasm [13], a misconception supported by the simple secondary-tertiary structure perspective. While some proteins assemble on their own, many (most?) cannot.


As an example, consider the protein tubulin (↑). As noted by Nithianantham et al [14], “ Five conserved tubulin cofactors/chaperones and the Arl2 GTPase regulate α- and β-tubulin assembly into heterodimers” and the “tubulin cofactors TBCD, TBCE, and Arl2, which together assemble a GTP-hydrolyzing tubulin chaperone critical for the biogenesis, maintenance, and degradation of soluble αβ-tubulin.”  Without these various chaperones the tubulin protein cannot be formed.  Here the distinction between protein and multiprotein complex is clear, since tubulin protein exists in readily detectable levels within the cell, in contrast to the α- and β-tubulin polypeptides, which are found complexed to the TBCB and TBCA chaperone polypeptides. Of course the balance between tubulin and tubulin polymers (microtubules) is itself regulated by a number of factors. 

 The situation is even more complex when we come to the ribosome and other structures, such as the nuclear pore.  Woolford [15] estimates that “more than 350 protein and RNA molecules participate in yeast ribosome assembly, and many more in metazoa”; in addition to four ribsomal RNAs and ~80 polypeptides (often referred to as ribosomal proteins) that are synthesized in the cytoplasm and transported into the nucleus in association with various transport factors, these “assembly factors, including diverse RNA-binding proteins, endo- and exonucleases, RNA helicases, GTPases and ATPases. These assembly factors promote pre-rRNA folding and processing, remodeling of protein–protein and RNA–protein networks, nuclear export and quality control” [16].  While I suspect that some structural components of the ribosome and the nuclear pore may have functions as monomeric polypeptides, and so could be considered as proteins, at this point it is best (most accurate) to assume that they are polypeptides, components of proteins and larger, molecular machines (past post). 

We can, of course, continue to consider the roles of common folding motifs,  arising from the chemistry of the peptide bond and the environment within and around the assembling protein, in the context of protein structure [17, 18], The knottier problem is how to help students recognize how functional entities, proteins and molecular machines, together with the coupled reaction systems that drive them and the molecular interactions that regulate them, function. How mutations, alleleic variations, and various environmentally induced perturbations influence the behaviors of cells and organisms, and how they generate normal and pathogenic phenotypes. Such a view emphasizes the dynamics of the living state, and the complex flow of information out of DNA into networks of molecular machines and reaction systems. 


Acknowledgements
: Thanks to Michael Stowell for feedback and suggestions and Jon Van Blerkom for encouragement.  All remaining errors are mine. Post updated to include imagines in the right places (and to include the cryoEM structure of the AcChR + minor edits – 16 December 2020.

Footnotes:

  1. Recently emerged from the labs of Martin Raff and Lee Rubin – Martin is one of the founding authors of the transformative “molecular biology of the cell” textbook. 
  2. Or rather quite over-simplistic, as it ignore complexities arising from differential splicing, alternative promoters, and genes encoding non-polypeptide encoding RNAs. 

Literature cited (please excuse excessive self-citation – trying to avoid self-plagarism)

1. Klymkowsky, M.W., Thinking about the conceptual foundations of the biological sciences. CBE Life Science Education, 2010. 9: p. 405-7.

2. 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, in press., 2016. pii: ar70.

3. Gibson, D.G., J.I. Glass, C. Lartigue, V.N. Noskov, R.-Y. Chuang, M.A. Algire, G.A. Benders, M.G. Montague, L. Ma, and M.M. Moodie, Creation of a bacterial cell controlled by a chemically synthesized genome. science, 2010. 329(5987): p. 52-56.

4. Hutchison, C.A., R.-Y. Chuang, V.N. Noskov, N. Assad-Garcia, T.J. Deerinck, M.H. Ellisman, J. Gill, K. Kannan, B.J. Karas, and L. Ma, Design and synthesis of a minimal bacterial genome. Science, 2016. 351(6280): p. aad6253.

5. Samandi, S., A.V. Roy, V. Delcourt, J.-F. Lucier, J. Gagnon, M.C. Beaudoin, B. Vanderperre, M.-A. Breton, J. Motard, and J.-F. Jacques, Deep transcriptome annotation enables the discovery and functional characterization of cryptic small proteins. Elife, 2017. 6.

6. Hartl, F.U., A. Bracher, and M. Hayer-Hartl, Molecular chaperones in protein folding and proteostasis. Nature, 2011. 475(7356): p. 324.

7. Kabachinski, G. and T.U. Schwartz, The nuclear pore complex–structure and function at a glance. J Cell Sci, 2015. 128(3): p. 423-429.

8. Jonckheere, A.I., J.A. Smeitink, and R.J. Rodenburg, Mitochondrial ATP synthase: architecture, function and pathology. Journal of inherited metabolic disease, 2012. 35(2): p. 211-225.

9. Eisenberg, D., The discovery of the α-helix and β-sheet, the principal structural features of proteins. Proceedings of the National Academy of Sciences, 2003. 100(20): p. 11207-11210.

10. Pauling, L., R.B. Corey, and H.R. Branson, The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proceedings of the National Academy of Sciences, 1951. 37(4): p. 205-211.

11. Hardison, R.C., Evolution of hemoglobin and its genes. Cold Spring Harbor perspectives in medicine, 2012. 2(12): p. a011627.

12. Shi, J., Y. Zhou, T. Vonderfecht, M. Winey, and M.W. Klymkowsky, Centrin-2 (Cetn2) mediated regulation of FGF/FGFR gene expression in Xenopus. Scientific Reports, 2015. 5:10283.

13. Luby-Phelps, K., The physical chemistry of cytoplasm and its influence on cell function: an update. Molecular biology of the cell, 2013. 24(17): p. 2593-2596.

14. Nithianantham, S., S. Le, E. Seto, W. Jia, J. Leary, K.D. Corbett, J.K. Moore, and J. Al-Bassam, Tubulin cofactors and Arl2 are cage-like chaperones that regulate the soluble αβ-tubulin pool for microtubule dynamics. Elife, 2015. 4.

15. Woolford, J., Assembly of ribosomes in eukaryotes. RNA, 2015. 21(4): p. 766-768.

16. Peña, C., E. Hurt, and V.G. Panse, Eukaryotic ribosome assembly, transport and quality control. Nature Structural and Molecular Biology, 2017. 24(9): p. 689.

17. Dobson, C.M., Protein folding and misfolding. Nature, 2003. 426(6968): p. 884.

18. Schaeffer, R.D. and V. Daggett, Protein folds and protein folding. Protein Engineering, Design & Selection, 2010. 24(1-2): p. 11-19.

Molecular machines and the place of physics in the biology curriculum

The other day, through no fault of my own, I found myself looking at the courses required by our molecular biology undergraduate degree program. I discovered a requirement for a 5 credit hour physics course, and a recommendation that this course be taken in the students’ senior year – a point in their studies when most have already completed their required biology courses.  Befuddlement struck me, what was the point of requiring an introductory physics course in the context of a molecular biology major?  Was this an example of time-travel (via wormholes or some other esoteric imagining) in which a physics course in the future impacts a students’ understanding of molecular biology in the past?  I was also struck by the possibility that requiring such a course in the students’ senior year would measurably impact their time to degree. 

In a search for clarity and possible enlightenment, I reflected back on my own experiences in an undergraduate biophysics degree program – as a practicing cell and molecular  biologist, I was somewhat confused. I could not put my finger on the purpose of our physics requirement, except perhaps the admirable goal of supporting physics graduate students. But then, after feverish reflections on the responsibilities of faculty in the design of the courses and curricula they prescribe for their students and the more general concepts of instructional (best) practice and malpractice, my mind calmed, perhaps because I was distracted by an article on Oxford Nanopore’s MinION (↓), a “portable real-time device for DNA and RNA sequencing”,a device that plugs into the USB port on one’s laptop!

Distracted from the potentially quixotic problem of how to achieve effective educational reform at the undergraduate level, I found myself driven on by an insatiable curiosity (or a deep-seated insecurity) to insure that I actually understood how this latest generation of DNA sequencers worked. This led me to a paper by Meni Wanunu (2012. Nanopores: A journey towards DNA sequencing)[1].  On reading the paper, I found myself returning to my original belief, yes, understanding physics is critical to developing a molecular-level understanding of how biological systems work, BUT it was just not the physics normally inflicted upon (required of) students [2]. Certainly this was no new idea.  Bruce Alberts had written on this topic a number of times, most dramatically in his 1989 paper “The cell as a collection of molecular machines” [3].  Rather sadly, and not withstanding much handwringing about the importance of expanding student interest in, and understanding of, STEM disciplines, not much of substance in this area has occurred. While (some minority of) physics courses may have adopted active engagement pedagogies (in the meaning of Hake [4]) most insist on teaching macroscopic physics, rather than to focus on, or even to consider, the molecular level physics relevant to biological systems, explicitly the physics of protein machines in a cellular (biological) context. Why sadly, because conventional, that is non-biologically relevant introductory physics and chemistry courses, all to often serve the role of a hazing ritual, driving many students out of biology-based careers [5], in part I suspect, because they often seem irrelevant to students’ interests in the workings of biological systems. (footnote 1)  

Nanopore’s sequencer and Wanunu’s article (footnote 2) got me thinking again about biological machines, of which there are a great number, ranging from pumps, propellers, and oars to  various types of transporters, molecular truckers that move chromosomes, membrane vesicles, and parts of cells with respect to one another, to DNA detanglers, protein unfolders, and molecular recyclers (↓). 

Nanopore’s sequencer works based on the fact that as a single strand of DNA (or RNA) moves through a narrow pore, the different bases (A,C,T,G) occlude the pore to different extents, allowing different numbers of ions, different amounts of current, to pass through the pore. These current differences can be detected, and allows for a nucleotide sequence to be “read” as the nucleic acid strand moves through the pore. Understanding the process involves understanding how molecules move, that is the physics of molecular collisions and energy transfer, how proteins and membranes allow and restrict ion movement, and the impact of chemical gradients and electrical fields across a membrane on molecular movements  – all physical concepts of widespread significance in biological systems (here is an example of where a better understanding of physics could be useful to biologists).  Such ideas can be extended to the more general questions of how molecules move within the cell, and the effects of molecular size and inter-molecular interactions within a concentrated solution of proteins, protein polymers, lipid membranes, and nucleic acids, such as described in Oliverira et al., (2016 Increased cytoplasmic viscosity hampers aggregate polar segregation in Escherichia coli)[6].  At the molecular level, the processes, while biased by electric fields (potentials) and concentration gradients, are stochastic (noisy). Understanding of stochastic processes is difficult for students [7], but critical to developing an appreciation of how such processes can lead to phenotypic  differences between cells with the same genotypes (previous post) and how such noisy processes are managed by the cell and within a multicellular organism.   

As path leads on to path, I found myself considering the (←) spear-chucking protein machine present in the pathogenic bacteria Vibrio cholerae; this molecular machine is used to inject toxins into neighbors that the bacterium happens to bump into (see Joshi et al., 2017. Rules of Engagement: The Type VI Secretion System in Vibrio cholerae)[8].  The system is complex and acts much like a spring-loaded and rather “inhumane” mouse trap.  This is one of a number of bacterial  type VI systems, and “has structural and functional homology to the T4 bacteriophage tail spike and tube” – the molecular machine that injects bacterial cells with the virus’s genetic material, its DNA.

Building the bacterium’s spear-based injection system is control by a social (quorum sensing) system, a way that unicellular organisms can monitor whether they are alone or living in an environment crowded with other organisms. During the process of assembly, potential energy, derived from various chemically coupled, thermodynamically favorable reactions, is stored in both type VI “spears” and the contractile (nucleic acid injecting) tails of the bacterial viruses (phage). Understanding the energetics of this process, exactly how coupling thermodynamically favorable chemical reactions, such as ATP hydrolysis, or physico-chemical reactions, such as the diffusion of ions down an electrochemical gradient, can be used to set these “mouse traps”, and where the energy goes when the traps are sprung is central to students’ understanding of these and a wide range of other molecular machines. 

Energy stored in such molecular machines during their assembly can be used to move the cell. As an example, another bacterial system generates contractile (type IV pili) filaments; the contraction of such a filament can allow “the bacterium to move 10,000 times its own body weight, which results in rapid movement” (see Berry & Belicic 2015. Exceptionally widespread nanomachines composed of type IV pilins: the prokaryotic Swiss Army knives)[9].  The contraction of such a filament has been found to be used to import DNA into the cell, an early step in the process of  horizontal gene transfer.  In other situations (other molecular machines) such protein filaments access thermodynamically favorable processes to rotate, acting like a propeller, driving cellular movement. 

During my biased random walk through the literature, I came across another, but molecularly distinct, machine used to import DNA into Vibrio (see Matthey & Blokesch 2016. The DNA-Uptake Process of Naturally Competent Vibrio cholerae)[10].

This molecular machine enables the bacterium to import DNA from the environment, released, perhaps, from a neighbor killed by its spear.  In this system (←), the double stranded DNA molecule is first transported through the bacterium’s outer membrane; the DNA’s two strands are then separated, and one strand passes through a channel protein through the inner (plasma) membrane, and into the cytoplasm, where it can interact with the bacterium’s  genomic DNA.

The value of introducing students to the idea of molecular machines is that it helps to demystify how biological systems work, how such machines carry out specific functions, whether moving the cell or recognizing and repairing damaged DNA.  If physics matters in biological curriculum, it matters for this reason – it establishes a core premise of biology, namely that organisms are not driven by “vital” forces, but by prosaic physiochemical ones.  At the same time, the molecular mechanisms behind evolution, such as mutation, gene duplication,  and genomic reorganization provide the means by which new structures emerge from pre-existing ones, yet many is the molecular biology degree program that does not include an introduction to evolutionary mechanisms in its required course sequence – imagine that, requiring physics but not evolution? (see [11]).

One final point regarding requiring students to take a biologically relevant physics course early in their degree program is that it can be used to reinforce what I think is a critical and often misunderstood point. While biological systems rely on molecular machines, we (and by we I mean all organisms) are NOT machines, no matter what physicists might postulate -see We Are All Machines That Think.  We are something different and distinct. Our behaviors and our feelings, whether ultimately understandable or not, emerge from the interaction of genetically encoded, stochastically driven non-equilibrium systems, modified through evolutionary, environmental, social, and a range of unpredictable events occurring in an uninterrupted, and basically undirected fashion for ~3.5 billion years.  While we are constrained, we are more, in some weird and probably ultimately incomprehensible way.

Footnotes:

[1]  A discussion with Melanie Cooper on what chemistry is relevant to a life science major was a critical driver in our collaboration to develop the chemistry, life, the universe, and everything (CLUE) chemistry curriculum.  

[2]  Together with my own efforts in designing the biofundamentals introductory biology curriculum. 

literature cited

1. Wanunu, M., Nanopores: A journey towards DNA sequencing. Physics of life reviews, 2012. 9(2): p. 125-158.

2. Klymkowsky, M.W. Physics for (molecular) biology students. 2014  [cited 2014; Available from: http://www.aps.org/units/fed/newsletters/fall2014/molecular.cfm.

3. Alberts, B., The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell, 1998. 92(3): p. 291-294.

4. Hake, R.R., Interactive-engagement versus traditional methods: a six-thousand-student survey of mechanics test data for introductory physics courses. Am. J. Physics, 1998. 66: p. 64-74.

5. Mervis, J., Weed-out courses hamper diversity. Science, 2011. 334(6061): p. 1333-1333.

6. Oliveira, S., R. Neeli‐Venkata, N.S. Goncalves, J.A. Santinha, L. Martins, H. Tran, J. Mäkelä, A. Gupta, M. Barandas, and A. Häkkinen, Increased cytoplasm viscosity hampers aggregate polar segregation in Escherichia coli. Molecular microbiology, 2016. 99(4): p. 686-699.

7. Garvin-Doxas, K. and M.W. Klymkowsky, Understanding Randomness and its impact on Student Learning: Lessons from the Biology Concept Inventory (BCI). Life Science Education, 2008. 7: p. 227-233.

8. Joshi, A., B. Kostiuk, A. Rogers, J. Teschler, S. Pukatzki, and F.H. Yildiz, Rules of engagement: the type VI secretion system in Vibrio cholerae. Trends in microbiology, 2017. 25(4): p. 267-279.

9. Berry, J.-L. and V. Pelicic, Exceptionally widespread nanomachines composed of type IV pilins: the prokaryotic Swiss Army knives. FEMS microbiology reviews, 2014. 39(1): p. 134-154.

10. Matthey, N. and M. Blokesch, The DNA-uptake process of naturally competent Vibrio cholerae. Trends in microbiology, 2016. 24(2): p. 98-110.

11. Pallen, M.J. and N.J. Matzke, From The Origin of Species to the origin of bacterial flagella. Nat Rev Microbiol, 2006. 4(10): p. 784-90.

Is a little science a dangerous thing?

Is the popularization of science encouraging a growing disrespect for scientific expertise? 
Do we need to reform science education so that students are better able to detect scientific BS? 

It is common wisdom that popularizing science by exposing the public to scientific ideas is an unalloyed good,  bringing benefits to both those exposed and to society at large. Many such efforts are engaging and entertaining, often taking the form of compelling images with quick cuts between excited sound bites from a range of “experts.” A number of science-centered programs, such PBS’s NOVA series, are particularly adept and/or addicted to this style. Such presentations introduce viewers to natural wonders, and often provide scientific-sounding, albeit often superficial and incomplete, explanations – they appeal to the gee-whiz and inspirational, with “mind-blowing” descriptions of how old, large, and weird the natural world appears to be. But there are darker sides to such efforts. Here I focus on one, the idea that a rigorous, realistic understanding of the scientific enterprise and its conclusions, is easy to achieve, a presumption that leads to unrealistic science education standards, and the inability to judge when scientific pronouncements are distorted or unsupported, as well as anti-scientific personal and public policy positions.That accurate thinking about scientific topics is easy to achieve is an unspoken assumption that informs much of our educational, entertainment, and scientific research system. This idea is captured in the recent NYT best seller “Astrophysics for people in a hurry” – an oxymoronic presumption. Is it possible for people “in a hurry” to seriously consider the observations and logic behind the conclusions of modern astrophysics? Can they understand the strengths and weaknesses of those conclusions? Is a superficial familiarity with the words used the same as understanding their meaning and possible significance? Is acceptance understanding?  Does such a cavalier attitude to science encourage unrealistic conclusions about how science works and what is known with certainty versus what remains speculation?  Are the conclusions of modern science actually easy to grasp?
The idea that introducing children to science will lead to an accurate grasp the underlying concepts involved, their appropriate application, and their limitations is not well supported [1]; often students leave formal education with a fragile and inaccurate understanding – a lesson made explicit in Matt Schneps and Phil Sadler’s Private Universe videos. The feeling that one understands a topic, that science is in some sense easy, undermines respect for those who actually do understand a topic, a situation discussed in detail in Tom Nichols “The Death of Expertise.” Under-estimating how hard it can be to accurately understand a scientific topic can lead to unrealistic science standards in schools, and often the trivialization of science education into recognizing words rather than understanding the concepts they are meant to convey.

The fact is, scientific thinking about most topics is difficult to achieve and maintain – that is what editors, reviewers, and other scientists, who attempt to test and extend the observations of others, are for – together they keep science real and honest. Until an observation has been repeated or confirmed by others, it can best be regarded as an interesting possibility, rather than a scientifically established fact.  Moreover, until a plausible mechanism explaining the observation has been established, it remains a serious possibility that the entire phenomena will vanish, more or less quietly (think cold fusion). The disappearing physiological effects of “power posing” comes to mind. Nevertheless the incentives to support even disproven results can be formidable, particularly when there is money to be made and egos on the line.

While power-posing might be helpful to some, even though physiologically useless, there are more dangerous pseudo-scientific scams out there. The gullible may buy into “raw water” (see: Raw water: promises health, delivers diarrhea) but the persistent, and in some groups growing, anti-vaccination movement continues to cause real damage to children (see Thousands of cheerleaders exposed to mumps).  One can ask oneself, why haven’t professional science groups, such as the American Association for the Advancement of Science (AAAS), not called for a boycott of NETFLIX, given that NETFLIX continues to distribute the anti-scientific, anti-vaccination program VAXXED [2]?  And how do Oprah Winfrey and Donald Trump  [link: Oprah Spreads Pseudoscience and Trump and the anti-vaccine movement] avoid universal ridicule for giving credence to ignorant non-sense, and for disparaging the hard fought expertise of the biomedical community?  A failure to accept well established expertise goes along way to understanding the situation. Instead of an appreciation for what we do and do not know about the causes of autism (see: Genetics and Autism Risk & Autism and infection), there are desperate parents who turn to a range of “therapies” promoted by anti-experts. The tragic case of parents trying to cure autism by forcing children to drink bleach (see link) illustrates the seriousness of the situation.

So why do a large percentage of the public ignore the conclusions of disciplinary experts?  I would argue that an important driver is the way that science is taught and popularized [3]. Beyond the obvious fact that a range of politicians and capitalists (in both the West and the East) actively distain expertise that does not support their ideological or pecuniary positions [4], I would claim that the way we teach science, often focussing on facts rather than processes, largely ignoring the historical progression by which knowledge is established, and the various forms of critical analyses to which scientific conclusions are subjected to, combines with the way science is popularized, erodes respect for disciplinary expertise. Often our education systems fail to convey how difficult it is to attain real disciplinary expertise, in particular the ability to clearly articulate where ideas and conclusions come from and what they do and do not imply. Such expertise is more than a degree, it is a record of rigorous and productive study and useful contributions, and a critical and objective state of mind. Science standards are often heavy on facts, and weak on critical analyses of those ideas and observations that are relevant to a particular process. As Carl Sagan might say, we have failed to train students on how to critically evaluate claims, how to detect baloney (or BS in less polite terms)[5].

In the area of popularizing scientific ideas, we have allowed hype and over-simplification to capture the flag. To quote from a article by David Berlinski [link: Godzooks], we are continuously bombarded with a range of pronouncements about new scientific observations or conclusions and there is often a “willingness to believe what some scientists say without wondering whether what they say is true”, or even what it actually means.  No longer is the in-depth, and often difficult and tentative explanation conveyed, rather the focus is on the flashy conclusion (independent of its plausibility). Self proclaimed experts pontificate on topics that are often well beyond their areas of training and demonstrated proficiency – many is the physicist who speaks not only about the completely speculative multiverse, but on free will and ethical beliefs. Complex and often irreconcilable conflicts between organisms, such as those between mother and fetus (see: War in the womb), male and female (in sexually dimorphic species), and individual liberties and social order, are ignored instead of explicitly recognized, and their origins understood. At the same time, there are real pressures acting on scientific researchers (and the institutions they work for) and the purveyors of news to exaggerate the significance and broader implications of their “stories” so as to acquire grants, academic and personal prestige, and clicks.  Such distortions serve to erode respect for scientific expertise (and objectivity).

So where are the scientific referees, the individuals that are tasked to enforce the rules of the game; to call a player out of bounds when they leave the playing field (their area of expertise) or to call a foul when rules are broken or bent, such as the fabrication, misreporting, suppression, or over-interpretation of data, as in the case of the anti-vaccinator Wakefield. Who is responsible for maintaining the integrity of the game?  Pointing out the fact that many alternative medicine advocates are talking meaningless blather (see: On skepticism & pseudo-profundity)? Where are the referees who can show these charlatans the “red card” and eject them from the game?

Clearly there are no such referees. Instead it is necessary to train as large a percentage of the population as possible to be their own science referees – that is, to understand how science works, and to identify baloney when it is slung at them. When a science popularizer, whether for well meaning or self-serving reasons, steps beyond their expertise, we need to call them out of
bounds!  And when scientists run up against the constraints of the scientific process, as appears to occur periodically with theoretical physicists, and the occasional neuroscientist (see: Feuding physicists and The Soul of Science) we need to recognize the foul committed.  If our educational system could help develop in students a better understanding of the rules of the scientific game, and why these rules are essential to scientific progress, perhaps we can help re-establish both an appreciation of rigorous scientific expertise, as well as a respect for what is that scientists struggle to do.



Footnotes and references:

  1. And is it clearly understood that they have nothing to say as to what is right or wrong.
  2.  Similarly, many PBS stations broadcast pseudoscientific infomercials: for example see Shame on PBS, Brain Scam, and the Deepak Chopra’s anti-scientific Brain, Mind, Body, Connection, currently playing on my local PBS station. Holocaust deniers and slavery apologists are confronted much more aggressively.
  3.  As an example, the idea that new neurons are “born” in the adult hippocampus, up to now established orthodoxy, has recently been called into question: see Study Finds No Neurogenesis in Adult Humans’ Hippocampi
  4.  Here is a particular disturbing example: By rewriting history, Hindu nationalists lay claim to India
  5. Pennycook, G., J. A. Cheyne, N. Barr, D. J. Koehler and J. A. Fugelsang (2015). “On the reception and detection of pseudo-profound bullshit.” Judgment and Decision Making 10(6): 549.

Making education matter in higher education


It may seem self-evident that providing an effective education, the type of educational experiences that lead to a useful bachelors degree and serve as the foundation for life-long learning and growth, should be a prime aspirational driver of Colleges and Universities (1).  We might even expect that various academic departments would compete with one another to excel in the quality and effectiveness of their educational outcomes; they certainly compete to enhance their research reputations, a competition that is, at least in part, responsible for the retention of faculty, even those who stray from an ethical path. Institutions compete to lure research stars away from one another, often offering substantial pay raises and research support (“Recruiting or academic poaching?”).  Yet, my own experience is that a department’s performance in undergraduate educational outcomes never figures when departments compete for institutional resources, such as supporting students, hiring new faculty, or obtaining necessary technical resources (2).

 I know of no example (and would be glad to hear of any) of a University hiring a professor based primarily on their effectiveness as an instructor (3).

In my last post, I suggested that increasing the emphasis on measures of departments’ educational effectiveness could help rebalance the importance of educational and research reputations, and perhaps incentivize institutions to be more consistent in enforcing ethical rules involving research malpractice and the abuse of students, both sexual and professional. Imagine if administrators (Deans and Provosts and such) were to withhold resources from departments that are performing below acceptable and competitive norms in terms of undergraduate educational outcomes?

Outsourced teaching: motives, means and impacts

Sadly, as it is, and particularly in many science departments, undergraduate educational outcomes have little if any impact on the perceived status of a department, as articulated by campus administrators. The result is that faculty are not incentivized to, and so rarely seriously consider the effectiveness of their department’s course requirements, a discussion that would of necessity include evaluating whether a course’s learning goals are coherent and realistic, whether the course is delivered effectively, whether it engages students (or is deemed irrelevant), and whether students’ achieve the desired learning outcomes, in terms of knowledge and skills achieved, including the ability to apply that knowledge effectively to new situations.  Departments, particularly research focussed (dependent) departments, often have faculty with low teaching loads, a situation that incentivizes the “outsourcing” of key aspects of their educational responsibilities.  Such outsourcing comes in two distinct forms, the first is requiring majors to take courses offered by other departments, even if such courses are not well designed, delivered, or (in the worst cases) relevant to the major.  A classic example is to require molecular biology students to take macroscopic physics or conventional calculus courses, without regard to whether the materials presented in these courses is ever used within the major or the discipline.  Expecting a student majoring in the life sciences to embrace a course that (often rightly) seems irrelevant to their discipline can alienate a student, and poses an unnecessary obstacle to student success, rather than providing students with needed knowledge and skills.  Generally, the incentives necessary to generate a relevant course, for example, a molecular level physics course that would engage molecular biology students, are simply not there.  A version of this situation is to require courses that are poorly designed or delivered (general chemistry is often used as the poster child for such a course). These are courses that have high failure rates, sometimes justified in terms of “necessary rigor” when in fact better course design could (and has) resulted in lower failure rates and improved learning outcomes.  In addition, there are perverse incentives associated with requiring “weed out” courses offered by other departments, as they reduce the number of courses a department’s faculty needs to teach, and can lead to fewer students proceeding into upper division courses.

The second type of outsourcing involves excusing tenure track faculty from teaching introductory courses, and having them replaced by lower paid instructors or lecturers.  Independently of whether instructors, lecturers, or tenure track professors make for better teaching, replacing faculty with instructors sends an implicit message to students.  At the same time, the freedom of instructors/lecturers to adopt an effective (socratic) approach to teaching is often severely constrained; common exams can force classes to move in lock step, independently of whether that pace is optimal for student engagement and learning. Generally, instructors/lecturers do not have the freedom to adjust what they teach, to modify the emphasis and time they spend on specific topics in response to their students’ needs. How an instructor instructs their students suffers when teachers do not have the freedom to customize their interactions with students in response to where they are intellectually.  This is particularly detrimental in the case of underrepresented or underprepared students. Generally, a flexible and adaptive approach to instruction (including ancillary classes on how to cope with college: see An alternative to remedial college classes gets results) can address many issues, and bring the majority of students to a level of competence, whereas tracking students into remedial classes can succeed in driving them out of a major or college (see Colleges Reinvent Classes to Keep More Students in Science and Redesigning a Large-Enrollment Introductory Biology Course and Does Remediation Work for All Students? )

How to address this imbalance, how can we reset the pecking order so that effective educational efforts actually matter to a department? 

My (modest) suggestion is to base departmental rewards on objective measures of educational effectiveness.   And by rewards I mean both at the level of individuals (salary and status) as well as support for graduate students, faculty positions, start up funds, etc.  What if, for example, faculty in departments that excel at educating their students received a teaching bonus, or if the number of graduate students within a department supported by the institution was determined not by the number of classes these graduate students taught (courses that might not be particularly effective or engaging) but rather by a departments’ undergraduate educational effectiveness, as measured by retention, time to degree, and learning outcomes (see below)?  The result could well be a drive within a department to improve course and curricular effectiveness to maximize education-linked rewards.  Given that laboratory courses, the courses most often taught by science graduate students, are multi-hour schedule disrupting events, of limited demonstrable educational effectiveness, that complicate student course scheduling, removing requirements for lab courses deemed unnecessary (or generating more effective versions), would be actively rewarded (of course, sanctions for continuing to offer ineffective courses would also be useful, but politically more problematic.)

A similar situation applies when a biology department requires its majors to take 5 credit hour physics or chemistry courses.  Currently it is “easy” for a department to require its students to take such courses without critically evaluating whether they are “worth it”, educationally.  Imagine how a department’s choices of required courses would change if the impact of high failure rates (which I would argue is a proxy for poorly designed  and delivered courses) directly impacted the rewards reaped by a department. There would be an incentive to look critically at such courses, to determine whether they are necessary and if so, well designed and delivered. Departments would serve their own interests if they invested in the development of courses  that better served their disciplinary goals, courses likely to engage their students’ interests.

So how do we measure a department’s educational efficacy?

There are three obvious metrics: i) retention of students as majors (or in the case of “service courses” for non-majors, whether students master what it is the course claims to teach); ii) time to degree (and by that I mean the percentage of students who graduate in 4 years, rather than the 6 year time point reported in response to federal regulations (six year graduation rate | background on graduation rates); and iii) objective measures of student learning outcomes attained and skills achieved. The first two are easy, Universities already know these numbers.  Moreover they are directly influenced by degree requirements – requiring students to take boring and/or apparently irrelevant courses serves to drive a subset of students out of a major.  By making courses relevant and engaging, more students can be retained in a degree program. At the same time, thoughtful course design can help students  pass through even the most rigorous (difficult) of such courses. The third, learning outcomes, is significantly more challenging to measure, since universal metrics are (largely) missing or superficial.  A few disciplines, such as chemistry, support standardized assessments, although one could argue with what such assessments measure.  Nevertheless, meaningful outcomes measures are necessary, in much the same way that Law and Medical boards and the Fundamentals of Engineering exam serve to help insure (although they do not guarantee) the competence of practitioners. One could imagine using parts of standardized exams, such as discipline specific GRE exams, to generate outcomes metrics, although more informative assessment instruments would clearly be preferable. The initiative in this area could be taken by professional societies, college consortia (such as the AAU), and research foundations, as a critical driver for education reform, increased effectiveness, and improved cost-benefit outcomes, something that could help address the growing income inequality in our country and make success in higher education an important factor contributing to an institution’s reputation.

 

A footnote or two…
 
1. My comments are primarily focused on research universities, since that is where my experience lies; these are, of course, the majority of the largest universities (in a student population sense).
 
2. Although my experience is limited, having spent my professorial career at a single institution, conversations with others leads me to conclude that it is not unique.
 
3. The one obvious exception would be the hiring of  coaches of sports teams, since their success in teaching (coaching) is more directly discernible and impactful on institutional finances and reputation).
 
minor edits – 16 March 2020

Balancing research prestige, human decency, and educational outcomes.


Or why do academic institutions shield predators?  Many working scientists, particularly those early in their careers or those oblivious to practical realities, maintain an idealistic view of the scientific enterprise. They see science as driven by curious, passionate, and skeptical scholars, working to build an increasingly accurate and all encompassing understanding of the material world and the various phenomena associated with it, ranging from the origins of the universe and the Earth to the development of the brain and the emergence of consciousness and self-consciousness (1).  At the same time, the discipline of science can be difficult to maintain (see PLoS post:  The pernicious effects of disrespecting the constraints of science). Scientific research relies on understanding what people have already discovered and established to be true; all too often, exploring the literature associated with a topic can reveal that one’s brilliant and totally novel “first of its kind” or “first to show” observation or idea is only a confirmation or a modest extension of someone else’s previous discovery. That is the nature of the scientific enterprise, and a major reason why significant new discoveries are rare and why graduate students’ Ph.D. theses can take years to complete.

Acting to oppose a rigorous scholarly approach are the real life pressures faced by working scientists: a competitive landscape in which only novel observations  get rewarded by research grants and various forms of fame or notoriety in one’s field, including a tenure-track or tenured academic position. Such pressures encourage one to distort the significance or novelty of one’s accomplishments; such exaggerations are tacitly encouraged by the editors of high profile journals (e.g. Nature, Science) who seek to publish “high impact” claims, such as the claim for “Arsenic-life” (see link).  As a recent and prosaic example, consider a paper that claims in its title that “Dietary Restriction and AMPK Increase Lifespan via Mitochondrial Network and Peroxisome Remodeling” (link), without mentioning (in the title) the rather significant fact that the effect was observed in the nematode C. elegans, whose lifespan is typically between 300 to 500 hours and which displays a trait not found in humans (and other vertebrates), namely the ability to assume a highly specialized “dauer” state that can survive hostile environmental conditions for months. Is the work wrong or insignificant? Certainly not, but it is presented to the unwary (through the Harvard Gazette under the title, “In pursuit of healthy aging: Harvard study shows how intermittent fasting and manipulating mitochondrial networks may increase lifespan,” with the clear implication that people, including Harvard alumni, might want to consider the adequacy of their retirement investments


Such pleas for attention are generally quickly placed in context and their significance evaluated, at least within the scientific community – although many go on to stimulate the economic health of the nutritional supplement industry.  Lower level claims often go unchallenged, just part of the incessant buzz associated with pleas for attention in our excessively distracted society (see link).  Given the reward structure of the modern scientific enterprise, the proliferation of such claims is not surprising.  Even “staid” academics seek attention well beyond the immediate significance of their (tax-payer funded) observations. Unfortunately, the explosively expanding size of the scientific enterprise makes policing such transgressions (generally through peer review or replication) difficult or impossible, at least in the short term.

The hype and exaggeration associated with some scientific claims for attention are not the most distressing aspect of the quest for “reputation.”  Rather, there are growing number of revelations of academic institutions protecting those guilty of abusing their dependent colleagues. These reflect how scientific research teams are organized. Most scientific studies involve groups of people working with one another, generating data, testing ideas, and eventually publishing their observations and conclusions, and speculating on their broader implications.

Research groups can vary greatly in size.  In some areas, they involve isolated individuals, whether thinkers (theorists) or naturalists, in the mode of Darwin and Wallace.  In other cases, these are larger and include senior researchers, post-doctoral  fellows, graduate students, technicians, undergraduates,  and even high school students. Such research groups can range from the small (2 to 3 people) to the significantly larger (~20-50 people); the largest of such groups are associated mega-projects, such as the human genome project and the Large Hadron Collider-based search for the Higgs boson (see: Physics paper sets record with more than 5,000 authors).  A look at this site [link] describing the human genome project reflects two aspects of such mega-science: 1) while many thousands of people were involved [see Initial sequencing and analysis of the human genome], generally only the “big names” are singled out for valorization (e.g., receiving a Nobel Prize). That said, there would be little or no progress without general scientific community that evaluates and extends ideas and observations. In this context, “lead investigators” are charged primarily with securing the funds needed to mobilize such groups, convincing funders that the work is significant; it is members of the group that work out the technical details and enable the project to succeed.

As with many such social groups, there are systems in play that serve to establish the status of the individuals involved – something necessary (apparently) in a system in which individuals compete for jobs, positions, and resources.  Generally, one’s status is established through recommendations from others in the field, often the senior member(s) of one’s research group or the (generally small) group of senior scientists who work in the same or a closely related area. The importance of professional status is particularly critical in academia, where the number of senior (e.g. tenured or tenure-track professorships) is limited. The result is a system that is increasingly susceptible to the formation of clubs, membership in which is often determined by who knows who, rather than who has done what (see Steve McKnight’s “The curse of committees and clubs”). Over time, scientific social status translates into who is considered productive, important, trustworthy, or (using an oft-misused term) brilliant. Achieving status can mean putting up with abusive and unwanted behaviors (particularly sexual). Examples of this behavior have recently been emerging with increasing frequency (which has been extensively described elsewhere: see Confronting Sexual Harassment in Science; More universities must confront sexual harassment; What’s to be done about the numerous reports of faculty misconduct dating back years and even decades?; Academia needs to confront sexism; and The Trouble With Girls’: The Enduring Sexism in Science).

So why is abusive behavior tolerated?  One might argue that this reflects humans’ current and historical obsession with “stars,” pharaohs, kings, and dictators as isolated geniuses who make things work. Perhaps the most visible example of such abused scientists (although there are in fact many others : see History’s Most Overlooked Scientists) is Rosalind Franklin, whose data was essential to solving the structure of double stranded DNA, yet whose contributions were consistently and systematically minimized, a clear example of sexual marginalization. In this light, many is the technician who got an experiment to “work,” leading to their research supervisor’s being awarded the prizes associated with the breakthrough (2).

Amplifying the star effect is the role of research status at the institutional level;  an institution’s academic ranking is often based upon the presence of faculty “stars.” Perhaps surprisingly to those outside of academia, an institution’s research status, as reflected in the number of stars on staff, often trumps its educational effectiveness, particularly with undergraduates, that is the people who pay the bulk of the institution’s running costs. In this light, it is not surprising that research stars who display various abusive behavior (often to women) are shielded by institutions from public censure.

So what is to be done? My own modest proposal (to be described in more detail in a later post) is to increase the emphasis on institution’s (and departments within institutions) effectiveness at undergraduate educational success. This would provide a counter-balancing force that could (might?) place research status in a more realistic context.

a footnote or two:

  1.  on the assumption that there is nothing but a material world.
  2. Although I am no star, I would acknowledge Joe Dent, who worked out the whole-mount immunocytochemical methods that we have used extensively in our work over the years).
  3. Thanks to Becky for editorial comments as well as a dramatic reading!

Humanized mice & porcinized people

mouse and pig

Updates:  12 January 2022

7 December 2020: US FDA declares genetically modified pork ‘safe to eat

A practical benefit, from a scientific and medical perspective, of the evolutionary unity of life (link) are the molecular and cellular similarities between different types of organisms. Even though humans and bacteria diverged more than 2 billion years ago (give or take), the molecular level conservation of key systems makes it possible for human insulin to be synthesized in and secreted by bacteria and pig-derived heart valves to be used to replace defective human heart valves (see link). Similarly, while mice, pigs, and people are clearly different from one another in important ways they have, essentially, all of the same body parts. Such underlying similarities raise interesting experimental and therapeutic possibilities.

A (now) classic way to study the phenotypic effects of human-specific versions of genes is to introduce these changes into a model organism, such as a mouse (for a review of human brain-specific human genes – see link).  A example of such a study involves the gene that encodes the protein foxp2, a protein involved in the regulation of gene expression (a transcription factor). The human foxp2  protein differs from the foxp2 protein in other primates at two positions; foxP2 evolution these two amio acid changes alter the activity of the human protein, that is the ensemble of genes that it regulates. That foxp2 has an important role in humans was revealed through studies of individuals in a family that displayed a severe language disorder linked to a mutation that disrupts the function of the foxp2 protein. Individuals carrying this mutant  foxp2 allele display speech apraxia, a “severe impairment in the selection and sequencing of fine oral and facial movements, the ability to break up words into their constituent phonemes, and the production and comprehension of word inflections and syntax” (cited in Bae et al, 2015).  Male mice that carry this foxp2 mutation display changes in the “song” that they sing to female mice (1), while mice carrying a humanized form of foxp2 display changes in “dopamine levels, dendrite morphology, gene expression and synaptic plasticity” in a subset of CNS neurons (2).  While there are many differences between mice and humans, such studies suggest that changes in foxp2 played a role in human evolution, and human speech in particular.

Another way to study the role of human genes using mouse as a model system is to generate what are known as chimeras, named after the creature in Greek mythology composed of parts of multiple organisms.  A couple of years ago, Goldman and colleagues (3) reported that human glial progenitor cells could, when introduced into immune-compromised mice (to circumvent tissue rejection), displaced the mouse’s own glia, replacing them with human glia cells.iPSC transplant Glial cells are the major non-neuronal component of the central nervous system. Once thought of as passive “support” cells, it is now clear that the two major types of glia, known as astrocytes and oligodendrocytes, play a number of important roles in neural functioning [back track post].  In their early studies, they found that the neurological defects associated with the shaker mutation, a mutation that disrupts the normal behavior of oligodendrocytes, could be rescued by the implantation of normal human glial progenitor cells (hGPCs)(4).  Such studies confirmed what was already known, that the shaker mutation disrupts the normal function of myelin, the insulating structure around axons that dramatically speeds the rate at which neuronal signals (action potentials) move down the axons and activate the links between neurons (synapses). In the central nervous system, myelin is produced by oligodendrocytes as they ensheath neuronal axons.  Human oligodendrocytes derived from hGPCs displaced the mouse’s mutation carrying oligodendrocytes and rescued the shaker mouse’s mutation-associated neurological defect.

golgi staining- diagramSubsequently, Goldman and associates used a variant of this approach to introduce hGPCs (derived from human embryonic stem cells) carrying either a normal or mutant version of the  Huntingtin protein, a protein associated with the severe neural disease Huntington’s chorea (OMIM: 143100)(5).  Their studies strongly support a model that locates defects associated with human Huntington’s disease to defects in glia.  This same research group has generated hGPCs from patient-derived, induced pluripotent stem cells (patient-derived HiPSCs). In this case, the patients had been diagnosed with childhood-onset schizophrenia (SCZ) [link](6).  Skin biopsies were taken from both normal and children diagnosed with SCZ; fibroblasts were isolated, and reprogrammed to form human iPSCs. These iPSCs were treated so that they formed hGPCs that were then injected into mice to generate chimeric (human glial/mouse neuronal) animals. The authors reported systematic differences in the effects of control and SCZ-derived hGPCs; “SCZ glial mice showed reduced prepulse inhibition and abnormal behavior, including excessive anxiety, antisocial traits, and disturbed sleep”, a result that suggests that defects in glial behavior underlie some aspects of the human SCZ phenotype.

The use of human glia chimeric mice provides a powerful research tool for examining the molecular and cellular bases for a subset of human neurological disorders.  Does it raise a question of making mice more human?  Not for me, but perhaps I do not appreciate the more subtle philosophical and ethical issues involved. The mice are still clearly mice, most of their nervous systems are composed of mouse cells, and the overall morphology, size, composition, and organization of their central nervous systems are mouse-derived and mouse-like. The situation becomes rather more complex and potentially therapeutically useful when one talks about generating different types of chimeric animals or of using newly developed genetic engineering tools (the CRISPR CAS9 system found in prokaryotes), that greatly simplify and improve the specificity of the targeted manipulation of specific genes (link).  In these studies the animal of choice is not mice, but pigs – which because of their larger size produce organs for transplantion that are similar in size to the organs of people (see link).  While similar in size, there are two issues that complicate pig to human organ transplantation: first there is the human immune system mediated rejection of foreign  tissue and second there is the possibility that transplantation of porcine organs will lead to the infection of the human recipient with porcine retroviruses.

The issue of rejection (pig into human), always a serious problem, is further exacerbated by the presence in pigs of a gene encoding the enzyme α-1,3 galactosyl transferase (GGTA1). GGTA1 catalyzes the addition of the gal-epitope to a number of cell surface proteins. The gal-epitope is “expressed on the tissues of all mammals except humans and subhuman primates, which have antibodies against the epitope” (7). The result is that pig organs provoke an extremely strong immune (rejection) response in humans.  The obvious technical fix to this (and related problems) is to remove the gal-epitope from pig cells by deleting the GGTA1 enzyme (see 8). It is worth noting that “organs from genetically engineered animals have enjoyed markedly improved survivals in non-human primates” (see Sachs & Gall, 2009).

pig to humanThe second obstacle to pig → human transplantation is the presence of retroviruses within the pig genome.  All vertebrate genomes, including those of humans, contain many inserted retroviruses; almost 50% of the human genome is retrovirus-derived sequence (an example of unintelligent design if ever there was one). Most of these endogenous retroviruses are “under control” and are normally benign (see 9). The concern, however, is that the retroviruses present in pig cells could be activated when introduced into humans. To remove (or minimize) this possibility, Niu et al set out to use the CRISPR CAS9 system to delete these porcine endogenous retroviral sequences (PERVs) from the pig genome; they appear to have succeeded, generating a number of genetically modified pigs without PERVs (see 10).  The hope is that organs generated from PERV-minus pigs from which antigen-generating genes, such as α-1,3 galactosyl transferase, have also been removed or inactivated together with more sophisticated inhibitors of tissue rejection, will lead to an essentially unlimited supply of pig organs that can be used for heart and other organ transplantation (see 11), and so alleviate the delays in transplantation, and so avoid deaths in sick people and the often brutal and criminal harvesting of organs carried out in some countries.

The final strategy being explored is to use genetically modified hosts and patient derived iPSCs  to generate fully patient compatible human organs. To date, pilot studies have been carried out, apparently successfully, using rat embryos with mouse stem cells (see 12 and 13), with much more preliminary studies using pig embryos and human iPSCs (see 14).  The approach involves what is known as chimeric  embryos.  In this case, host animals are genetically modified so that they cannot generate the organ of choice. Typically this is done by mutating a key gene that encodes a transcription factor directly involved in formation of the organ; embryos missing pancreas, kidney, heart, human pig embryo chimeraor eyes can be generated.  In an embryo that cannot make these organs, which can be a lethal defect, the introduction of stem cells from an animal that can form these organs can lead to the formation of an organ composed primarily of cells derived from the transplanted (human) cells.

At this point the strategy appears to work reasonably well for mouse-rat chimeras, which are much more closely related, evolutionarily, than are humans and pigs. Early studies on pig-human chimeras appear to be dramatically less efficient. At this point, Jun Wu has been reported as saying of human-pig chimeras that “we estimate [each had] about one in 100,000 human cells” (see 15), with the rest being pig cells.  The bottom line appears to be that there are many technical hurdles to over-come before this method of developing patient-compatible human organs becomes feasible.  Closer to reality are PERV-free/gal-antigen free pig-derived, human compatible organs. The reception of such life-saving organs by the general public, not to mention religious and philosophical groups that reject the consumption of animals in general, or pigs in particular, remains to be seen.

figures reinserted & minor edits 23 October 2020 – new link 17 December 2020.
references cited

  1. A Foxp2 Mutation Implicated in Human Speech Deficits Alters Sequencing of Ultrasonic Vocalizations in Adult Male Mice.
  2. A Humanized Version of Foxp2 Affects Cortico-Basal Ganglia Circuits in Mice
  3. Modeling cognition and disease using human glial chimeric mice.
  4. Human iPSC-derived oligodendrocyte progenitor cells can myelinate and rescue a mouse model of congenital hypomyelination.
  5. Human glia can both induce and rescue aspects of disease phenotype in Huntington disease
  6. Human iPSC Glial Mouse Chimeras Reveal Glial Contributions to Schizophrenia.
  7.  The potential advantages of transplanting organs from pig to man: A transplant Surgeon’s view
  8. see Sachs and Gall. 2009. Genetic manipulation in pigs. and Fisher et al., 2016. Efficient production of multi-modified pigs for xenotransplantation by ‘combineering’, gene stacking and gene editing
  9. Hurst & Magiokins. 2017. Epigenetic Control of Human Endogenous Retrovirus Expression: Focus on Regulation of Long-Terminal Repeats (LTRs)
  10. Nui et al., 2017. Inactivation of porcine endogenous retrovirus in pigs using CRISPR-Cas9
  11. Zhang  2017. Genetically Engineering Pigs to Grow Organs for People
  12. Kobayashi et al., 2010. Generation of rat pancreas in mouse by interspecific blastocyst injection of pluripotent stem cells.
  13. Kobayashi et al., 2015. Targeted organ generation using Mixl1-inducible mouse pluripotent stem cells in blastocyst complementation.
  14. Wu et al., 2017. Interspecies Chimerism with Mammalian Pluripotent Stem Cells
  15. Human-Pig Hybrid Created in the Lab—Here Are the Facts

Reverse Dunning-Kruger effects and science education

The Dunning-Kruger (DK) effect is the well-established phenomenon that people tend to over estimate their understanding of a particular topic or their skill at a particular task, often to a dramatic degree [link][link]. We see examples of the DK effect throughout society; the current administration (unfortunately) and the nutritional supplements / homeopathy section of Whole Foods spring to mind as examples. But there is a less well-recognized “reverse DK” effect, namely the tendency of instructors, and a range of other public communicators, to over-estimate what the people they are talking to are prepared to understand, appreciate, and accurately apply. The efforts of science communicators and instructors can be entertaining but the failure to recognize and address the reverse DK effect results in ineffective educational efforts. These efforts can themselves help generate the illusion of understanding in students and the broader public (discussed here). While a confused understanding of the intricacies of cosmology or particle physics can be relatively harmless in their social and personal implications, similar misunderstandings become personally and publicly significant when topics such as vaccination, alternative medical treatments, and climate change are in play.

There are two synergistic aspects to the reverse DK effect that directly impact science instruction: the need to understand what one’s audience does not understand together with the need to clearly articulate the conceptual underpinnings needed to understand the subject to be taught. This is in part because modern science has, at its core, become increasingly counter-intuitive over the last approximately 100 years or so, a situation that can cause serious confusions that educators must address directly and explicitly. The first reverse DK effect involves the extent to which the instructor (and by implication the course and textbook designer) has an accurate appreciation of what students think or think they know, what ideas they have previously been exposed to, and what they actually understand about the implications of those ideas.  Are they prepared to learn a subject or does the instructor first have to acknowledge and address conceptual confusions and build or rebuild base concepts?  While the best way to discover what students think is arguably a Socratic discussion, this only rarely occurs for a range of practical reasons. In its place, a number of concept inventory-type testing instruments have been generated to reveal whether various pre-identified common confusions exist in students’ thinking. Knowing the results of such assessments BEFORE instruction can help customize how the instructor structures the learning environment and content to be presented and whether the instructor gives students the space to work with these ideas to develop a more accurate and nuanced understanding of a topic.  Of course, this implies that instructors have the flexibility to adjust the pace and focus of their classroom activities. Do they take the time needed to address student issues or do they feel pressured to plow through the prescribed course content, come hell, high water, or cascading student befuddlement.

A complementary aspect of the reverse DK effect, well-illustrated in the “why magnets attract” interview with the physicist Richard Feynman, is that the instructor, course designer, or textbook author(s) needs to have a deep and accurate appreciation of the underlying core knowledge necessary to understand the topic they are teaching. Such a robust conceptual understanding makes it possible to convey the complexities involved in a particular process and explicitly values appreciating a topic rather than memorizing it.  It focuses on the general, rather than the idiosyncratic. A classic example from many an introductory biology course is the difference between expecting students to remember the steps in glycolysis or the Krebs cycle reaction system, as opposed to the general principles that underlie the non-equilibrium reaction networks involved in all biological functions, a reaction network based on coupled chemical reactions and governed by the behaviors of thermodynamically favorable and unfavorable reactions. Without a explicit discussion of these topics, all too often students are required to memorize names without understanding the underlying rationale driving the processes involved; that is, why the system behaves as it does.  Instructors also give false “rubber band” analogies or heuristics to explain complex phenomena (see Feynman video 6:18 minutes in). A similar situation occurs when considering how molecules come to associate and dissociate from one another, for example in the process of regulating gene expression or repairing mutations in DNA. Most textbooks simply do not discuss the physiochemical processes involved in binding specificity, association, and dissociation rates, such as the energy changes associated with molecular interactions and thermal collisions (don’t believe me? look for yourself!). But these factors are essential for a student to understand the dynamics of gene expression [link], as well as the specificity of modern methods involved in genetic engineering, such as restriction enzymes, polymerase chain reaction, and CRISPR CAS9-mediated mutagenesis. By focusing on the underlying processes involved we can avoid their trivialization and enable students to apply basic principles to a broad range of situations. We can understand exactly why CRISPR CAS9-directed mutagenesis can be targeted to a single site within a multibillion-base pair genome.

Of course, as in the case of recognizing and responding to student misunderstandings and knowledge gaps, a thoughtful consideration of underlying processes takes course time, time that trades the development of a working understanding of core processes and principles for broader “coverage” of frequently disconnected facts, the memorization and regurgitation of which has been privileged over understanding why those facts are worth knowing. If our goal is for students to emerge from a course with an accurate understanding of the basic processes involved rather than a superficial familiarity with a plethora of unrelated facts, however, a Socratic interaction with the topic is essential. What assumptions are being made, where do they come from, how do they constrain the system, and what are their implications?  Do we understand why the system behaves the way it does? In this light, it is a serious educational mystery that many molecular biology / biochemistry curricula fail to introduce students to the range of selective and non-selective evolutionary mechanisms (including social and sexual selection – see link), that is, the processes that have shaped modern organisms.

Both aspects of the reverse DK effect impact educational outcomes. Overcoming the reverse DK effect depends on educational institutions committing to effective and engaging course design, measured in terms of retention, time to degree, and a robust inquiry into actual student learning. Such an institutional dedication to effective course design and delivery is necessary to empower instructors and course designers. These individuals bring a deep understanding of the topics taught and their conceptual foundations and historic development to their students AND must have the flexibility and authority to alter the pace (and design) of a course or a curriculum when they discover that their students lack the pre-existing expertise necessary for learning or that the course materials (textbooks) do not present or emphasize necessary ideas. Radiation-kills-in-BoulderUnfortunately, all too often instructors, particularly in introductory level college science courses, are not the masters of their ships; that is, they are not rewarded for generating more effective course materials. An emphasis on course “coverage” over learning, whether through peer-pressure, institutional apathy, or both, generates unnecessary obstacles to both student engagement and content mastery.  To reverse the effects of the reverse DK effect, we need to encourage instructors, course designers, and departments to see the presentation of core disciplinary observations and concepts as the intellectually challenging and valuable endeavor that it is. In its absence, there are serious (and growing) pressures to trivialize or obscure the educational experience – leading to the socially- and personally-damaging growth of fake knowledge.

empty images holders removed, new image added – 17 December 2020

Is it time to start worrying about conscious human “mini-brains”?

A human iPSC cerebral organoid in which pigmented retinal epithelial cells can be seen (from the work of McClure-Begley et al.).   Also see “Can lab-grown brains become conscious?” by Sara Readon Nature 2020.

The fact that experiments on people are severely constrained is a major obstacle in understanding human development and disease.  Some of these constraints are moral and ethical and clearly appropriate and necessary given the depressing history of medical atrocities.  Others are technical, associated with the slow pace of human development. The combination of moral and technical factors has driven experimental biologists to explore the behavior of a wide range of “model systems” from bacteria, yeasts, fruit flies, and worms to fish, frogs, birds, rodents, and primates.  Justified by the deep evolutionary continuity between these organisms (after all, all organisms appear to be descended from a single common ancestor and share many molecular features), experimental evolution-based studies of model systems have led to many therapeutically valuable insights in humans – something that I suspect a devotee of intelligent design creationism would be hard pressed to predict or explain (post link).

While humans are closely related to other mammals, it is immediately obvious that there are important differences – after all people are instantly recognizable from members of other closely related species and certainly look and behave differently from mice. For example, the surface layer of our brains is extensively folded (they are known as gyrencephalic) while the brain of a mouse is smooth as a baby’s bottom (and referred to as lissencephalic). In humans, the failure of the brain cortex to fold is known as lissencephaly, a disorder associated with severe neurological defects. With the advent of more and more genomic sequence data, we can identify human specific molecular (genomic) differences. Many of these sequence differences occur in regions of our DNA that regulate when and where specific genes are expressed.  Sholtis & Noonan (1) provide an example: the HACNS1 locus is a 81 basepair region that is highly conserved in various vertebrates from birds to chimpanzees; there are 13 human specific changes in this sequence that appear to alter its activity, leading to human-specific changes in the expression of nearby genes (↓). At this point ~1000 genetic elements that are different in humans compared to other vertebrates have been identified and more are likely to emerge (2).  Such human-specific changes can make modeling human-specific behaviors, at the cellular, tissue, organ, and organism level, in non-human model systems difficult and problematic (3, 4).   It is for this reason that scientists have attempted to generate better human specific systems.

human sequence divergence

One particularly promising approach is based on what are known as embryonic stem cells (ESCs) or pluripotent stem cells (PSCs). Human embryonic stem cells are generated from the inner cell mass of a human embryo and so involve the destruction of that embryo – which raises a number of ethical and religious concerns as to when “life begins” (5).  Human pluripotent stem cells are isolated from adult tissues but in most cases require invasive harvesting methods that limit their usefulness.  Both ESCs and PSCs can be grown in the laboratory and can be induced to differentiate into what are known as gastruloids.  Such gastruloids can develop anterior-posterior (head-tail), dorsal-ventral (back-belly), and left-right axes analogous to those found in embryos (6) and adults (top panel ↓). In the case of PSCs, the gastruloid (bottom panel ↓) is essentially a twin of the organism from which the PSCs were derived, a situation that raises difficult questions: is it a distinct individual, is it the property of the donor or the creation of a technician.  The situation will be further complicated if (or rather, when) it becomes possible to generate viable embryos from such gastruloids.

Axes

gastruloid-embryo-comparisonThe Nobel prize winning work of Kazutoshi Takahashi and Shinya Yamanaka (7), who devised methods to take differentiated (somatic) human cells and reprogram them into ESC/PSC-like cells, cells known as induced pluripotent stem cells (iPSCs)(8), represented a technical breakthrough that jump-started this field. While the original methods derived sample cells from tissue biopsies, it is possible to reprogram kidney epithelial cells recovered from urine, a non-invasive approach (910).  Subsequently, Madeline Lancaster, Jurgen Knōblich, and colleagues devised an approach by which such cells could be induced to form what they termed “cerebral organoids” (although Yoshiki Sasai and colleagues were the first to generate neuronal organoids); they used this method to examine the developmental defects associated with microencephaly (11).  The value of the approach was rapidly recognized and a number of studies on human conditions, including  lissencephaly (12), Zika-virus infection-induced microencephaly (13), and Down’s syndrome (14);  investigators have begun to exploit these methods to study a range of human diseases – and rapid technological progress is being made.

The production of cerebral organoids from reprogrammed human somatic cells has also attracted the attention of the media (15).  While “mini-brain” is certainly a catchier name, it is a less accurate description of a cerebral organoid, itself possibly a bit of an overstatement, since it is not clear exactly how “cerebral” such organoids are. For example, the developing brain is patterned by embryonic signals that establish its asymmetries; it forms at the anterior end of the neural tube (the nascent central nervous system and spinal cord) and with distinctive anterior-posterior, dorsal-ventral, and left-right asymmetries, something that simple cerebral organoids do not display.  Moreover, current methods for generating cerebral organoids involve primarily what are known as neuroectodermal cells – our nervous system (and that of other vertebrates) is a specialized form of the embryo’s surface layer that gets internalized during development. In the embryo, the developing neuroectoderm interacts with cells of the circulatory system (capillaries, veins, and arteries), formed by endothelial cells and what are known as pericytes that surround them. These cells, together with interactions with glial cells (astrocytes, a non-neuronal cell type) combine to form the blood brain barrier.  Other glial cells (oligodendrocytes) are also present; in contrast, both types of glia (astrocytes and oligodendrocytes) are rare in the current generation of cerebral organoids. Finally, there are microglial cells,  immune system cells that originate from outside the neuroectoderm; they invade and interact with neurons and glia as part of the brain’s dynamic neural capillary and neuronssystem. The left panel of the figure shows, in highly schematic form how these cells interact (16). The right panel is a drawing of neural tissue stained by the Golgi method (17), which reveals ~3-5% of the neurons present. There are at least as many glial cells present, as well as microglia, none of which are visible in the image. At this point, cerebral organoids typically contain few astrocytes and oligodendrocytes, no vasculature, and no microglia. Moreover, they grow to be about 1 to 3 mm in diameter over the course of 6 to 9 months; that is significantly smaller in volume than a fetal or newborn’s brain. While cerebral organoids can generate structures characteristic of retinal pigment epithelia (top figure) and photo-responsive neurons (18), such as those associated with the retina, an extension of the brain, it is not at all clear that there is any significant sensory input into the neuronal networks that are formed within a cerebral organoid, or any significant outputs, at least compared to the role that the human brain plays in controlling bodily and mental functions.

The reasonable question, then, must be whether a  cerebral organoid, which is a relatively simple system of cells (although itself complex), is conscious. It becomes more reasonable as increasingly complex systems are developed, and such work is proceeding apace. Already researchers are manipulating the developing organoid’s environment to facilitate axis formation, and one can anticipate the introduction of vasculature. Indeed, the generation of microglia-like cells from iPSCs has been reported; such cells can be incorporated into cerebral organoids where they appear to respond to neuronal damage in much the same way as microglia behave in intact neural tissue (19).

We can ask ourselves, what would convince us that a cerebral organoid, living within a laboratory incubator, was conscious? How would such consciousness manifest itself? Through some specific pattern of neural activity, perhaps?  As a biologist, albeit one primarily interested in molecular and cellular systems, I discount the idea, proposed by some physicists and philosophers as well as the more mystical, that consciousness is a universal property of matter (20,21).  I take consciousness to be an emergent property of complex neural systems, generated by evolutionary mechanisms, built during embryonic and subsequent development, and influenced by social interactions (BLOG LINK) using information encoded within the human genome (something similar to this: A New Theory Explains How Consciousness Evolved). While a future concern, in a world full of more immediate and pressing issues, it will be interesting to listen to the academic, social, and political debate on what to do with mini-brains as they grow in complexity and perhaps inevitably, towards consciousness.

Footnotes and references

Thanks to Rebecca Klymkowsky, Esq. and Joshua Sanes, Ph.D. for editing and disciplinary support. Minor updates and the reintroduction of figures 22 Oct. 2020.

  1. Gene regulation and the origins of human biological uniqueness
  2.  See also Human-specific loss of regulatory DNA and the evolution of human-specific traits
  3. The mouse trap
  4. Mice Fall Short as Test Subjects for Some of Humans’ Deadly Ill
  5. The status of the human embryo in various religions
  6. Interactions between Nodal and Wnt signalling Drive Robust Symmetry Breaking and Axial Organisation in Gastruloids (Embryonic Organoids)
  7.  Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors
  8.  How iPS cells changed the world
  9.  Generation of Induced Pluripotent Stem Cells from Urine
  10. Urine-derived induced pluripotent stem cells as a modeling tool to study rare human diseases
  11. Cerebral organoids model human brain development and microcephaly.
  12. Human iPSC-Derived Cerebral Organoids Model Cellular Features of Lissencephaly and Reveal Prolonged Mitosis of Outer Radial Glia
  13. Using brain organoids to understand Zika virus-induced microcephaly
  14. Probing Down Syndrome with Mini Brains
  15. As an example, see The Beauty of “Mini Brains”
  16. Derived from Central nervous system pericytes in health and disease
  17. Golgi’s method .
  18. Cell diversity and network dynamics in photosensitive human brain organoids
  19. Efficient derivation of microglia-like cells from human pluripotent stem cells
  20. The strange link between the human mind and quantum physics – BBC:
  21. Can Quantum Physics Explain Consciousness?