Thinking about biological thinking: Steady state, half-life & response dynamics

Insights into student thinking & course design, part of the biofundamentals project. 

Something that often eludes both instructors and instructional researchers is a clear appreciation of what it is that students do and do not know, what ideas they can and cannot call upon to solve problems and generate clear, coherent, and plausible explanations. What information – thought to have been presented effectively through past instruction, appears to be unavailable to students. As an example, few instructors would believe that students completing college level chemistry could possibly be confused about the differences between covalent and non-covalent molecular interactions, yet there is good evidence that they are (Williams et al., 2015). Unless these ideas, together with their  conceptual bases and practical applications, are explicitly called out in the design and implementation of instructional materials, they often fail to become a working (relevant) part of the students’ conceptual tool-kit.   

To identify ideas involved in understanding biological systems, we are using an upper division undergraduate course in developmental biology (blog link) to provide context; this is a final “capstone” junior/senior level course that comes after students have completed multiple required courses in chemistry and biology.  Embryonic development integrates a range of molecular level processes, including the control of gene expression, cellular morphology and dynamics, through intrinsic and extrinsic signaling systems.   

A key aspect of the course’s design is the use of formative assessment activities delivered through the beSocratic system. These activities generally include parts in which students are asked to draw a graph or diagram. Students are required to complete tasks before the start of each class meeting; their responses are used to inform in-class discussions, a situation akin to reviewing game film and coaching in sports. Analysis of student drawings and comments, carried out in collaboration with Melanie Cooper and her group at Michigan State University, can reveal unexpected aspects of students’ thinking (e.g. Williams et al., 2015). What emerges from this Socratic give and take is an improved appreciation of the qualities of the tasks that engage students (as well as those that do not), and insights into how students analyze specific tasks, what sets of ideas they see as necessary and which necessary ideas they ignore when generating explanatory and predictive models. Most importantly, they can reveal flaws in how necessary ideas are developed. While at an admittedly early stage in the project, here I sketch out some preliminary findings: the first of these deal with steady state concentration and response dynamics.

The ideas of steady state concentration and pathway dynamics were identified by Loertscher et al (2014)as two of five “threshold concepts” in  biochemistry and presumably molecular biology as well. Given the non-equilibrium nature of biological systems, we consider the concentration of a particular molecule in a cell in dynamic terms, a function of its rate of synthesis (or importation from the environment) together with its rate of breakdown.  On top of this dynamic, the activity of existing molecules can be regulated through various post-translational mechanisms.  All of the populations of molecules within a cell or organism have a characteristic steady state concentration with the exception of genomic DNA, which while synthesized is not, in living organisms, degraded, although it is repaired.

In biological systems, molecules are often characterized by their “half life” but this can be confusing, since it is quite different from the way the term is used in physics, where students are likely to first be introduced to it.[1]  Echos from physics can imply that a molecule’s half-life is an intrinsic feature of the molecule, rather than of the system in which the molecule finds itself.  The equivalent of half-life would be doubling time, but these terms make sense only under specific conditions.  In a system in which synthesis has stopped (synthesis rate = 0) the half life is the time it takes for the number of molecules in the system to decrease by 50%, while in the absence of degradation (degradation rate = 0), the doubling time is the time it takes to double the number of molecules in the system.  Both degradation and synthesis rates are regulateable and can vary, often dramatically, in response to various stimuli.

In the case of RNA and polypeptide levels, the synthesis rate is determined by many distinct processes, including effective transcription factor concentrations, the signals that activate transcription factors, rates of binding of transcription factors to transcription factor binding sites (which can involve both DNA sequences and other proteins), as well as relevant binding affinities, and the rates associated with the recruitment and activation of DNA-dependent, RNA polymerase. Once activated, the rate of gene specific RNA synthesis will be influenced  by the rate of RNA polymerization (nucleotide bases added per second) and the length of the RNA molecules synthesized.  In eukaryotes, the newly formed RNA will generally need to have introns removed through interactions with splicing machinery, as well as other  post-transcriptional reactions, after which the processed RNA will be transported from the nucleus to the cytoplasm through the nuclear pore complex. In the cytoplasm there are rates associated with the productive interaction of RNAs with the translational machinery (ribosomes and associated factors), and the rate at which polypeptide synthesis occurs (amino acids added per second) together with the length of the polypeptide synthesized (given that things are complicated enough, I will ignore processes such as those associated with the targeting of membrane proteins and codon usage, although these will be included in a new chapter in biofundamentals reasonably soon, I hope). On the degradative side, there are rates associated with interactions with nucleases (that breakdown RNAs) and proteinases (that breakdown polypeptides).  These processes are energy requiring; generally driven by reactions coupled to the hydrolysis of adenosine triphosphate (ATP). 

That these processes matter is illustrated nicely in work from Harima and colleagues (2014).   The system, involved in the segmentation of the anterior region of the presomitic mesoderm, responds to signaling by activating the Hes7 gene, while the Hes7 gene product act to inhibit Hes7 gene expression. The result is an oscillatory response that is “tuned” by the length of the transcribed region (RNA length). This can be demonstrated experimentally by generating mice in which two of the genes three introns (Hes7-3) or all three introns (intron-less) are removed. Removing introns changes the oscillatory behavior of the system (Hes7 mRNA -blue and Hes7 protein – green)(Harima et al., 2013).

In the context of developmental biology, we use beSocratic activities to ask students to consider a molecule’s steady state concentration as a function of its synthesis and degradation rates, and to predict how the system would change when one or the other is altered. These ideas were presented in the context of observations by Schwanhausser et al (2011) that large discrepancies between steady state RNA and polypeptide concentrations are common and that there is an absence of a correlation between RNA and polypeptide half-lives (we also use these activities to introduce the general idea of correlation). In their responses, it was common to see students’ linking high steady state concentrations exclusively to long half-lives. Ask to consider the implications in terms of system responsiveness (in the specific context of a positively-acting transcription factor and target gene expression), students often presumed that a longer half-life would lead to higher steady state concentration which in turn would lead to increased target gene expression, primarily because collisions between the transcription factor and its DNA-binding sites would increase, leading to higher levels of target gene expression. This is an example of a p-prim (Hammer, 1996) – the heuristic that “more is more”, a presumption that is applicable to many systems. 

In biological systems, however, this is generally not the case – responses “saturate”, that is  increasing transcription factor concentration (or activity) above a certain level generally does not lead to a proportionate, or any increase in target gene expression. We would not call this a misconception, because this is an example of an idea that is useful in many situations, but generally isn’t in biological systems – where responses are generally inherently limited. The ubiquity and underlying mechanisms of response saturation need to be presented explicitly, and its impact on various processes reinforced repeatedly, preferably by having students use them to solve problems or construct plausible explanations. A related phenomenon that students seemed not to recognize involves the non-linearity of the initial response to a stimulus, in this case, the concentration of transcription factor below which target gene expression is not observed (or it may occur, but only transiently or within a few cells in the population, so as to be undetectable by the techniques used).

So what ideas do students need to call upon when they consider steady state concentration, how it changes, and the impact of such changes on system behavior?  It seems we need to go beyond synthesis and degradation rates and include the molecular processes associated with setting the system’s response onset and saturation concentrations.  First we need to help students appreciate why such behaviors (onset and saturation) occur – why doesn’t target gene expression begin as soon as a transcription factor appears in a cell?  Why does gene expression level off when transcription factor concentrations rise above a certain level?  The same questions apply to the types of threshold behaviors often associated with signaling systems.  For example, in quorum sensing among unicellular organisms, the response of cells to the signal occurs over a limited concentration range, from off to full on.  A related issue is associated with morphogen gradients (concentration gradients over space rather than time), in which there are multiple distinct types of “threshold” responses. One approach might be to develop a model in which we set the onset concentration close to the saturation concentration. The difficulty (or rather instructional challenge) here is that these are often complex processes involving cooperative as well as feedback interactions.

Our initial approach to steady state and thresholds has been to build activities based on the analysis of a regulatory network presented by Saka and Smith (2007), an analysis based on studies of early embryonic development in the frog Xenopus laevis. We chose the system because of its simplicity, involving only four components (although there are many other proteins associated with the actual system).  Saka and Smith modeled the regulatory network controlling the expression of the transcription factor proteins Goosecoid (Gsc) and Brachyury (Xbra) in response to the secreted signaling protein activin (↓), a member of

the TGFβ superfamily of secreted signaling proteins (see Li and Elowitz, 2019).   The network involves the positive action of Xbra on the gene encoding the transcription factor protein Xom.  The system’s behavior depends on the values of various parameters, parameters that include response to activator (Activin), rates of synthesis and the half-lives of Gsc, Xbra, and Xom, and the degrees of regulatory cooperativity and responsiveness.

Depending upon these parameters, the system can produce a range of complex responses.  In different regimes (→),  increasing concentrations of activin (M) can lead, initially, to increasing, but mutually exclusive, expression of either Xba (B) or Gsc (A) as well as sharp transitions in which expression flips from one to the other, as Activin concentration increases, after which the response saturates. There are also conditions at very low Activin concentration (marked by ↑) in which both Xbra and Gsc are expressed at low levels, a situation that students are asked to explain.

Lessons learned: Based on their responses, captured through beSocratic and revealed during in class discussions, it appears that there is a need to be more explicit (early in the course, and perhaps the curriculum as well) when considering the mechanisms associated with response onset and saturation, in the context of how changes in the concentrations of regulatory factors (through changes in synthesis, turn-over, and activity) impact system responses. This may require a more quantitative approach to molecular dynamics and system behaviors. Here we may run into a problem, the often phobic responses of biology majors (and many faculty) to mathematical analyses.  Even the simplest of models, such as that of Saka and Smith, require a consideration of factors generally unfamiliar to students, concepts and skills that may well not be emphasized or mastered in prerequisite courses. The trick is to define realistic, attainable, and non-trivial goals – we are certainly not going to succeed in getting late stage molecular biology students with rudimentary math skills to solve systems of differential equations in a developmental biology course.  But perhaps we can build up the instincts needed to appreciate the molecular processes involved in the behavior of systems whose behavior evolves overtime in response to various external signals (which is, of course, pretty much every biological system).

Footnotes

[1] A similar situation exists in the context of the term “spontaneous” in chemistry and biology.  In chemistry spontaneous means thermodynamically favorable, while in standard usage (and generally in biology) spontaneous implies that a reaction is proceeding at a measurable, functionally significant rate.  Yet another insight that emerged through discussions with Melanie Cooper. 

Mike Klymkowsky

Literature cited

Hammer, D. (1996). Misconceptions or p-prims. How might alternative perspectives of cognitive structure influence instructional perceptions and intentions. Journal of the Learning Sciences 5, 97-127.

Harima, Y., Imayoshi, I., Shimojo, H., Kobayashi, T. and Kageyama, R. (2014). The roles and mechanism of ultradian oscillatory expression of the mouse Hes genes. In Seminars in cell & developmental biology, pp. 85-90: Elsevier.

Harima, Y., Takashima, Y., Ueda, Y., Ohtsuka, T. and Kageyama, R. (2013). Accelerating the tempo of the segmentation clock by reducing the number of introns in the Hes7 gene. Cell Reports 3, 1-7.

Li, P. and Elowitz, M. B. (2019). Communication codes in developmental signaling pathways. Development 146, dev170977.

Loertscher, J., Green, D., Lewis, J. E., Lin, S. and Minderhout, V. (2014). Identification of threshold concepts for biochemistry. CBE—Life Sciences Education 13, 516-528.

Saka, Y. and Smith, J. C. (2007). A mechanism for the sharp transition of morphogen gradient interpretation in Xenopus. BMC Dev Biol 7, 47.

Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W. and Selbach, M. (2011). Global quantification of mammalian gene expression control. Nature 473, 337.

Williams, L. C., Underwood, S. M., Klymkowsky, M. W. and Cooper, M. M. (2015). Are Noncovalent Interactions an Achilles Heel in Chemistry Education? A Comparison of Instructional Approaches. Journal of Chemical Education 92, 1979–1987.

Conceptual simplicity and mechanistic complexity: the implications of un-intelligent design

Using “Thinking about the Conceptual Foundations of the Biological Sciences” as a jumping off point. “Engineering biology for real?” by Derek Lowe (2018) is also relevant

Biological systems can be seen as conceptually simple, but mechanistically complex, with hidden features that make “fixing” them difficult.  

Biological systems are evolving, bounded, non-equilibrium reaction systems. Based on their molecular details, it appears that all known organisms, both extinct or extant, are derived from a single last universal common ancestor, known as LUCA.  LUCA lived ~4,000,000,000 years ago (give or take).  While the steps leading to LUCA are hidden, and its precursors are essentially unknowable (much like the universe before the big bang), we can come to some general and unambiguous conclusions about LUCA itself [see Catchpole & Forterre, 2019].  First LUCA was cellular and complex, probably more complex that some modern organisms, certainly more complex than the simplest obligate intracellular parasite [Martinez-Cano et al., 2014].  Second, LUCA was a cell with a semi-permeable lipid bilayer membrane. Its boundary layer is semi-permeable because such a system needs to import energy and matter and export waste in order to keep from reaching equilibrium, since equilibrium = death with no possibility of resurrection. Finally, LUCA could produce offspring, through some version of a cell division process. The amazing conclusion is that every cell in your body (and every cell in every organism on the planet) has an uninterrupted connection to LUCA. 

 So what are the non-equilibrium reactions within LUCA and other organisms doing?  building up (synthesizing) and degrading various molecules, including proteins, nucleic acids, lipids, carbohydrates and such – the components needed to maintain the membrane barrier while importing materials so that the cell can adapt, move, grow and divide. This non-equilibrium reaction network has been passed from parent to offspring cells, going back to LUCA. A new cell does not “start up” these reactions, they are running continuously through out the processes of growth and cell division. While fragile, these reaction systems have been running uninterruptedly for billions of years. 

There is a second system, more or less fully formed, present in and inherited from LUCA, the DNA-based genetic information storage and retrieval system. The cell’s DNA (its genotype) encodes the “operating system” of the cell. The genotype interacts with and shapes the cell’s reaction systems to produce phenotypes, what the organism looks like and how it behaves, that is how it reacts to and interacts with the rest of the world.  Because DNA is thermodynamically unstable, the information it contains, encoded in the sequences of nucleotides within it, and read out by the reaction systems, can be altered – it can change (mutate) in response to its environmental chemicals, radiation, and other processes, such as errors that occur when DNA is replicated. Once mutated, the change is stable, it becomes part of the genotype.

The mutability of DNA could be seen as a design flaw; you would not want the information in a computer file to be randomly altered over time or when copied. In living systems, however, the mutability of DNA is a feature – together with the effects of mutations on a cell’s reproductive success mutations lead to evolutionary change.  Over time, they convert the noise of mutation into evolutionary adaptations and diversification of life.  

 Organisms rarely exist in isolation. Our conceptual picture of LUCA is not complete until we include social interactions (background: aggregative and clonal metazoans). Cells (organisms) interact with one another in complex ways, whether as individuals within a microbial community, as cells within a multicellular organism, or in the context of predator-prey, host-pathogen and symbiotic interactions. These social processes drive a range of biological behaviors including what, at the individual cell level, can be seen as cooperative and self-sacrificing. The result is the production of even more complex biological structures, from microbial biofilms to pangolins and human beings, and complex societies. The breakdown of such interactions, whether in response to pathogens, environmental insult, mutations, politicians’ narcissistic behaviors and the madness of crowds, underlie a wide range of aberrant and pathogenic outcomes – after all cancer is based on the anti-social behavior of tumor cells.

The devil is in the details – from the conceptual to the practical: What a biologist/ bioengineer rapidly discovers when called upon to fix the effects of a mutation, defeat a pathogen, or repair a damaged organ is that biological systems are mechanistically more complex that originally thought, and are no means intelligently designed. There are a number of sources for this biological complexity. First, and most obviously, modern cells (as well as LUCA) are not intelligently designed systems – they are the product of evolutionary processes, through which noise is captured in useful forms. These systems emerge rather than are imposed (as is the case with humanly designed objects). Second, within the cell there is a high concentration of molecules that interact with one another, often in unexpected ways.  As examples of molecular interactions that my lab has worked on, the protein β-catenin – originally identified as playing a role in cell adhesion and cytoskeletal organization, has a second role as a regulator of gene expression (link). The protein Chibby, a component of the basal body of cilia (a propeller-like molecular machine involved in moving fluids) has a second role as an inhibitor of β-catenin’s gene regulatory activity (link), while centrin-2. another basal body component, plays a role in the regulation of DNA repair and gene expression (link).  These are interactions that have emerged during the process of evolution – they work, so they are retained.    

More evidence as to the complexity of biological systems is illustrated by studies that examined the molecular targets of specific anti-cancer drugs (see Lowe 2019. Your Cancer Targets May Not Be Real).  The authors of these studies used the CRISPR-Cas9 system to knock out the gene encoding a drugs’ purported target; they found that the drug continued to function (see Lin et al., 2019).  At the same time, a related study raises a note of caution.  Smits et al (2019) examined the effects of what were expected to be CRISPR-CAS9-induced “loss of function” mutations. They found expression of the (mutated) targeted gene, either by using alternative promoters (RNA synthesis start sites) or alternative translation start sites. The results were mutant polypeptides that retained some degree of wild type activity.  Finally, in a system that bears some resemblance to the CRISPR system was found in mutations that induce what is known as non-sense mediated decay.  A protection against the synthesis of aberrant (toxic) mutant polypeptides, one effect of non-sense mediated decay is to lead to the degradation of the mutant RNA.  As described by Wilkinson (2019. Genetic paradox explained by nonsense) the resulting RNA fragments can be transported back into the nucleus where they interact with proteins involved in the regulation of gene expression, leading to the expression of genes related to the originally mutated gene. The expression of these related genes can modify the phenotype of the original mutation.   

Biological systems are further complicated by the fact that the folding of polypeptides and the assembly of proteins (background: polypeptides and proteins) is mediated by a network of chaperone proteins, that act to facilitate correct, and suppress incorrect, folding, interactions, and assembly of proteins. This chaperone network helps explain the ability of cells to tolerate a range of genetic variations; they render cells more adaptive and “non-fragile”. Some chaperones are constitutively expressed and inherited when cells divide, the synthesis of others is induced in response to environmental stresses, such as increased temperatures (heat shock). The result is that, in some cases, the phenotypic effects of a mutation on a target protein may not be primarily due to the absence of the mutated protein, but rather to secondary effects, effects that can be significantly ameliorated by the expression of molecular chaperones (discussed in Klymkowsky. 2019 Filaments and phenotypes). 

The expression of chaperones along with other genetics factors complicate our understanding of what a particular gene product does, or how variations (polymorphisms) in a gene can influence human health.  This is one reason why genetic background effects are important when making conclusions as the health (or phenotypic) effects of inheriting a particular allele (Schrodi et al., 2014. Genetic-based prediction of disease traits: prediction is very difficult, especially about the future). 

As one more, but certainly not the last, complexity, there is the phenomena by which “normal” cells interact with cells that are discordant with respect to some behavior (Di Gregorio et al 2016).1  These cells, termed “fit and unfit” and “winners and losers”, clearly socially inappropriate and unfortunate terms, interact in unexpected ways. The eccentricity of these cells can be due to various stochastic processes, including monoallelic expression (Chess, 2016), that lead to clones that behave differently (background: Biology education in the light of single cell/molecule studies).  Akieda et al (2019) describe  the presence of cells that respond inappropriately to a morphogen gradient during embryonic development. These eccentric cells are “out of step” with their neighbors are induced to die. Experimentally blocking their execution leads to defects in subsequent development.  Similar competitive effects are described by Ellis et al (2019. Distinct modes of cell competition shape mammalian tissue morphogenesis). That said, not all eccentric behaviors lead to cell death.  In some cases the effect is more like an ostracism, cells responding inappropriately migrate to a more hospitable region (Xiong et al., 2013). 

All of which is to emphasize that while conceptually simple, biologically systems, and their responses to mutations and other pathogenic insults, are remarkably complex and unpredictable – a byproduct of the unintelligent evolutionary processes that produced them.  

  1. Adapted from a F1000 review recommendation.

Gradients and Molecular Switches (a biofundamentalist perspective)

Embryogenesis is based on a framework of social (cell-cell) interactions, initial and early asymmetries, and cascades of cell-cell signaling and gene regulatory networks (DEVO posts one, two, & three). The result is the generation of embryonic axes, germ layers (ectoderm, mesoderm, endoderm), various organs and tissues (brains, limbs, kidneys, hearts, and such), their patterning, and their coordination into a functioning organism. It is well established that all animals share a common ancestor (hundreds of millions of years ago) and that a number of molecular  modules were already present in this common ancestor.  

At the same time evolutionary processes are, and need to be, flexible enough to generate the great diversity of organisms, with their various adaptations to particular life-styles. The extent of both conservation and flexibility (new genes, new mechanisms) in developmental systems is, however, surprising. Perhaps the most striking evidence for the depth of this conservation was supplied by the discovery of the organization of the Hox gene cluster in the fruit fly Drosophila and in the mouse (and other vertebrates); in both the genes are arranged and expressed in a common genomic and expression patterns. But as noted by Denis Duboule (2007) Hox gene organization is often presented in textbooks in a distorted manner (→). The Hox clusters of vertebrates are compact, but are split, disorganized, and even “atomized” in other types of organisms. Similarly, processes that might appear foundational, such as the role of the Bicoid gradient in the early fruit fly embryo (a standard topic in developmental biology textbooks), are in fact restricted to a small subset of flies (Stauber et al., 1999). New genes can be generated through well defined processes, such as gene duplication and divergence, or they can arise de novo out of sequence noise (Carvunis et al., 2012; Zhao et al., 2014). Comparative genomic analyses can reveal the origins of specific adaptations (see Stauber et al., 1999).  The result is that organisms as closely related to each other as the great apes (including humans) have significant species-specific genetic differences (see Florio et al., 2018; McLean et al., 2011; Sassa, 2013 and references therein) as well as common molecular and cellular mechanisms.

A universal (?) feature of developing systems – gradients and non-linear responses: There is a predilection to find (and even more to teach) simple mechanisms that attempt to explain everything (witness the distortion of the Hox cluster, above) – a form of physics “theory of everything” envy.  But the historic nature, evolutionary plasticity, and need for regulatory robustness generally lead to complex and idiosyncratic responses in biological systems.  Biological systems are not “intelligently designed” but rather cobbled together over time through noise (mutation) and selection (Jacob, 1977). 
That said, a  common (universal?) developmental process appears to be the transformation of asymmetries into unambiguous cell fate decisions. Such responses are based on threshold events controlled by a range of molecular behaviors, leading to discrete gene expression states. We can approach the question of how such decisions are made from both an abstract and a concrete perspective. Here I outline my initial approach – I plan to introduce organism specific details as needed.  I start with the response to a signaling gradient, such as that found in many developmental systems, including the vertebrate spinal cord (top image Briscoe and Small, 2015) and the early Drosophila embryo (Lipshitz, 2009)(→).  

We begin with a gradient in the concentration of a “regulatory molecule” (the regulator).  The shape of the gradient depends upon the sites and rates of synthesis, transport away from these sites, and turnover (degradation and/or inactivation). We assume, for simplicity’s sake, that the regulator directly controls the expression of target gene(s). Such a molecule binds in a sequence specific manner to regulatory sites, there could be a few or hundreds, and leads to the activation (or inhibition) of the DNA-dependent, RNA polymerase (polymerase), which generates RNA molecules complementary to one strand of the DNA. Both the binding of the regulator and the polymerase are stochastic processes, driven by diffusion, molecular collisions, and binding interactions.(1) 

Let us now consider the response of target gene(s) as a function of cell (nuclear) position within the gradient.  We might (naively) expect that the rate of target gene expression would be a simple function of regulator concentration. For an activator, where the gradient is high, target gene expression would be high, where the gradient concentration is low, target gene expression would be low – in between, target gene expression would be proportional to regulator concentration.  But generally we find something different, we find that the expression of target genes is non-uniform, that is there are thresholds in the gradient: on one side of the threshold concentration the target gene is completely off (not expressed), while on the other side of the threshold concentration, the target gene is fully on (maximally expressed).  The target gene responds as if it is controlled by an on-off switch. How do we understand the molecular basis for this behavior? 

Distinct mechanisms are used in different systems, but we will consider a system from the gastrointestinal bacteria E. coli that students may already be familiar with; these are the genes that enable E. coli to digest the mammalian milk sugar lactose.  They encode a protein needed to import  lactose into a bacterial cell and an enzyme needed to break lactose down so that it can be metabolized.  Given the energetic cost to synthesize these proteins, it is in the bacterium’s adaptive self interest to synthesize them only when lactose is present at sufficient concentrations in their environment.  The response is functionally similar to that associated with quorum sensing, which is also governed by threshold effects. Similarly cells respond to the concentration of regulator molecules (in a gradient) by turning on specific genes in specific domains, rather than uniformly. 

Now let us look in a little more detail at the behavior of the lactose utilization system in E. coli following an analysis by Vilar et al (2003)(2).  At an extracellular lactose concentration below the threshold, the system is off.  If we increase the extracellular lactose concentration above threshold the system turns on, the lactose permease and β-galactosidase proteins are made and lactose can enter the cell and be broken down to produce metabolizable sugars.  By looking at individual cells, we find that they transition, apparently stochastically from off to on (→), but whether they stay on depends upon the extracellular lactose concentration. We can define a concentration, the maintenance concentration, below the threshold, at which “on” cells will remain on, while “off” cells will remain off.  

The circuitry of the lactose system is well defined  (Jacob and Monod, 1961; Lewis, 2013; Monod et al., 1963)(↓).  The lacI gene encodes the lactose operon repressor protein and it is expressed constituately at a low level; it binds to sequences in the lac operon and inhibits transcription.  The lac operon itself contains three genes whose expression is regulated by a constituatively active promoter.  LacY encodes the permease while the lacZ encodes β-galactosidase.  β-galactosidase has two functions: it catalyzes the reaction that transforms lactose into allolactone and it cleaves lactose into the metabolically useful sugars glucose and galactose. Allolactone is an allosteric modulator of the Lac repressor protein; if allolactone is present, it binds to lac epressor proteins and inactivates them, allowing lac operon expression.  

The cell normally contains only ~10 lactose repressor proteins. Periodically (stochastically), even in the absence of lactose, and so its derivative allolactone, the lac operon promoter region is free of repressor proteins, and a lactose operon is briefly expressed – a few LacY and LacZ  polypeptides are synthesized (↓).  This noisy leakiness in the regulation of the lac operon allows the cell to respond if lactose happens to be present – some lactose molecules enter the cell through the permease, are converted to allolactone by β-galactosidase.  Allolactone is an allosteric effector of the lac repressor; when present it binds to and inactivates the lac repressor protein so that it no longer binds to its target sequences (the operator or “O” sites).  In the absence of repressor binding, the lac operon is expressed.  If lactose is not present, the lac operon is inhibited and lacY and LacZ disappear from the cell by turnover or growth associated dilution.     

The question of how the threshold concentration at which a genetic switch is set, whether for quorum sensing  or simpler regulated gene expression, is established is complex, and as we will see, different systems have different solutions – although often the exact mechanism remains to be resolved. The binding and activation of regulators can involve cooperative interactions between regulatory proteins and other positive and negative feedback interactions. 

In the case of patterning a tissue in terms of regional responses to a signaling gradient, there can be multiple regulatory thresholds for different genes, as well as indirect effects, where the initiation of gene expression of one set of target gene impacts the sensitive expression of subsequent sets of genes.  One widely noted mechanism, known as reaction-diffusion, was suggested by the English mathematician Alan Turing (see Kondo and Miura, 2010) – it postulates a two component system, regulated by a either a primary regulatory gradient or the stochastic activation of a master regulator. One component is an activator of gene expression, which in addition to its own various targets, positively regulates its own expression as well as a second gene.  This second gene encodes a repressor of the first.  Both of these two regulator molecules are released by the signaling cell or cells; the repressor diffuses away from the source faster than the activator does.  The result can be a domain of target gene expression (where the concentration of activator is sufficient to escape repression), surrounded by a zone in which expression is inhibited (where repressor concentration is sufficient in inhibit the activator).  Depending upon the geometry of the system, this can result in discrete regions (dots) of 1º target gene expression or stripes of 1º gene expression (see Sheth et al., 2012).  In real system there are often multiple gradients involved and their relative orientations can produce a range of patterns.   

The point of all of this, is that when we approach a particular system – we need to consider the mechanisms involved.  Typically they are selected to produce desired phenotypes, but also to be robust in the sense that they need to produce the same patterns even if the system in which they occur is subject to perturbations, such as embryo/tissue size (due to differences in cell division / growth rated) and temperature and other environmental variables. 

 

n.b.clearly there will be value in some serious editing and reorganization of this and other posts.  

Footnotes:

  1. While stochastic (random) these processes can still be predictable.  A classic example involves the decay of an unstable isotope (atom), which is predictable at the population level, but unpredictable at the level of an individual atom.  Similarly, in biological systems, the binding and unbinding of molecules to one another, such as a protein transcription regulator to its target DNA sequence is stochastic but can be predictable in a large enough population.   
  2. and presented in biofundamentals ( pages 216-218). 

literature cited: 

Briscoe & Small (2015). Morphogen rules: design principles of gradient-mediated embryo patterning. Development 142, 3996-4009.

Carvunis et al  (2012). Proto-genes and de novo gene birth. Nature 487, 370.

Duboule (2007). The rise and fall of Hox gene clusters. Development 134, 2549-2560.

Florio et al (2018). Evolution and cell-type specificity of human-specific genes preferentially expressed in progenitors of fetal neocortex. eLife 7.

Jacob  (1977). Evolution and tinkering. Science 196, 1161-1166.

Jacob & Monod (1961). Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology 3, 318-356.

Kondo & Miura (2010). Reaction-diffusion model as a framework for understanding biological pattern formation. Science 329, 1616-1620.

Lewis (2013). Allostery and the lac Operon. Journal of Molecular Biology 425, 2309-2316.

Lipshitz (2009). Follow the mRNA: a new model for Bicoid gradient formation. Nature Reviews Molecular Cell Biology 10, 509.

McLean et al  (2011). Human-specific loss of regulatory DNA and the evolution of human-specific traits. Nature 471, 216-219.

Monod Changeux & Jacob (1963). Allosteric proteins and cellular control systems. Journal of Molecular Biology 6, 306-329.

Sassa (2013). The role of human-specific gene duplications during brain development and evolution. Journal of Neurogenetics 27, 86-96.

Sheth et al (2012). Hox genes regulate digit patterning by controlling the wavelength of a Turing-type mechanism. Science 338, 1476-1480.

Stauber et al (1999). The anterior determinant bicoid of Drosophila is a derived Hox class 3 gene. Proceedings of the National Academy of Sciences 96, 3786-3789.

Vilar et al (2003). Modeling network dynamics: the lac operon, a case study. J Cell Biol 161, 471-476.

Zhao et al (2014). Origin and Spread of de Novo Genes in Drosophila melanogaster Populations. Science.

Establishing Cellular Asymmetries (a biofundamentalist perspective)

[21st Century DEVO-3]  Embryonic development is the process by which a fertilized egg becomes an independent organism, an organism capable of producing functional gametes, and so a new generation. In an animal, this process generally involves substantial growth and multiple rounds of mitotic cell division; the resulting organism, a clone of the single-celled zygote, contains hundreds, thousands, millions, billions, or trillions of cells [link]. As cells form, they begin the process of differentiation, forming a range of cell types; these differentiating (and sometime migrating) cells that interact to form the adult and its various tissues and organ systems. These various cell types can be characterized by the genes that they express, the shapes they assume, the behaviors that they display, and how they interact with neighboring and distant cells (1).  Based on first principles, one could imagine (at least) two general mechanisms that could lead to differences in gene expression between cells. The first would be that different cells contain different genes while the other is that while all cells contain all genes, which genes are expressed in a particular cell varies, it is regulated by molecular processes that determine when, where, and to what the levels particular genes are expressed (2).  Turns out, there are examples of both processes among the animals, although the latter is much more common.

The process of discarding genomic DNA in somatic cells is known as chromatin diminution. During the development of the soma, but not the germ line, regions of the genome are lost. In the germ line, for hopefully obvious reasons, the full genome is retained. The end result is that somatic cells contain different subsets of genes and non-coding DNA compared to the full genome. The classic case of chromosome diminution was described in the parasitic nematode of horses, now named Parascaris univalens (originally Ascaris megalocephala) by Theodore Boveri in 1887 (reviewed in Streit and Davis, 2016)[pdf link]. Based on its occurrence in a range of distinct animal lineages, chromatin diminution appears to be an emergent rather than an ancestral trait, that is, a trait present in the common ancestor of the animals.

While, as expected for an emergent trait, the particular mechanism of chromatin diminution appears to vary between different organisms: the best characterized example occurs in Parascaris. In the somatic cell lineages in which chromatin diminution occurs, double-stranded breaks are made in  chromosomal DNA molecules, and teleomeric sequences are added to ends of the resulting DNA molecules (↓).  You may have learned that chromosomes interact with spindle microtubules through a localized regions on the chromosomes, known as centromeres. Centromeres are identified through their association with proteins that form the kinetochore, which is a structure that mediates interactions between condensed chromosomes and mitotic (and meiotic) spindle microtubules. While many organisms have a discrete spot-like (localized) centromere, in many nematodes centromere-binding proteins are found distributed along the length of the chromosomes, a situation known as a holocentric centromere.  At higher resolution it appears that centromere components are preferentially associated with euchromatic, that is, molecularly accessible chromosomal regions, which are (typically) the regions where most expressed genes are located.  Centromere components are largely excluded from heterochromatic (condensed and molecularly inaccessible) chromosomal regions. After chromosome fragmentation, those DNA fragments associated with centromere components can interact with the spindle microtubules and are accurately segregated to daughter cells during mitosis, while those, primarily heterochromatic fragments (without associated centromeric components) are degraded and lost. In contrast the integrity of the genome is maintained in those cells that come to form the germ line, the cells that can undergo meiosis to produce gametes.  Looking forward to the reprogramming of somatic cells (the process of producing what are known as induced pluripotent stem cells – iPSCs), one prediction is that it should not be possible to reprogram a somatic cell that has undergone chromatin diminution to form a functional germ line cell – you should be able to explain why, or what would have to be the case for such reprogramming to be successful. 

The origins of cellular asymmetries: Clearly, there must be differences between the cells that undergo chromatin diminution and those that do not; at the very least the nuclease(s) that cuts the DNA during chromatin diminution will need to be active in somatic cells and inactive in germ line cells, or it may simply not be present – the genes that encode it are not expressed in germ line cells. We can presume that similar cytoplasmic differences play a role in the differential regulation of gene expression in different cell types during the development of organisms in which the genome remains intact in somatic cells. So how might such asymmetries arise?  There are three potential, but certainly not mutually exclusive, mechanisms that can lead to cellular/cytoplasmic asymmetries: they can be inherited based on pre-existing asymmetries in the parental cell, they could emerge based on asymmetries in the signaling environments occupied by the two daughters, or they could arise from stochastic fluctuations in gene expression (see Chen et al., 2016; Neumüller and Knoblich, 2009).  

         One example of how an asymmetry can be established occurs in the free-living nematode Caenorhabditis elegans, where the site of sperm fusion with the egg leads to the recruitment and assembly of proteins around the site of sperm entry, the future posterior side of the embryo.  After male and female pronuclei fuse, mitosis begins and cytokinesis divides the zygote into two cells; the asymmetry initiated by sperm entry leads to an asymmetric division (←); the anterior AB blastomere is larger, and molecularly distinct from the smaller posterior P1 blastomere.  These differences set off a regulatory cascade, in which the genes expressed at one stage influence those expressed subsequently, and so influence subsequent cell divisions / cell fate decisions.

Other organisms use different mechanisms to generate cellular asymmetries. In organisms that have external fertilization, such as the clawed frog Xenopus, development proceeds rapidly once fertilization occurs. The egg is large, since in contains all of the materials necessary for the formation until the time that the embryo can feed itself. The early embryo is immotile and vulnerable to predation, so early development in such species tends to be rapid, and based on materials supplied by the mother (leading to maternal effects on subsequent development).  In such cases, the initial asymmetry is built into the organization of the oocyte. 

Formed through a mitotic division the primary oocyte enters meiotic prophase I, during which it undergoes a period of growth. Maternal and paternal chromosomes align (syngamy) and undergo crossing-over (recombination).  The oocyte contains a single centrosome, a cytoplasmic structure that surrounds the centrioles of the oocyte’s inherited mitotic spindle pole. Cytoplasmic components become organized around the pole and then move from the pole toward the cell cortex (↓ image from Gard and Klymkowsky, 1998); this movement defines an “animal-vegetal” axisof the oocyte, which upon fertilization will play a role in generating the head-tail (anterior-posterior) and back-belly (dorsal-ventral) axes of the embryo and adult. The primary oocyte remains in prophase I throughout oogenesis. The asymmetry of the oocyte becomes visible through the development of a pigmented animal hemisphere, largely non-pigmented vegetal hemisphere, and an large (~300 um diameter) and off-centered nucleus (known as the germinal vesicle or GV)(3).  Messenger RNA molecules, encoding different polypeptides, are differentially localized to the animal and vegetal regions of the late stage oocyte. The translation of these mRNAs is regulated by factors activated by subsequent developmental events, leading to molecular asymmetries between embryonic cells derived from the animal and vegetal regions of the oocyte.  In preparation for fertilization, the oocyte resumes active meiosis,  leading to the formation of two polar bodies and the secondary oocyte, the egg. Fertilization occurs within the pigmented animal hemisphere; the site of sperm entry (↓) produces a second driver of asymmetry, in addition to the animal-vegetal axis, albeit through a mechanism distinct from that used in C. elegans (De Domenico et al., 2015). populations areAsymmetries in oocytes and eggs, and sperm entry points are not always the primary drivers of subsequent embryonic differentiation.  In the mouse, and other placental mammals, including humans, embryonic development occurs within, and is supported by and dependent upon the mother.  The mouse (mammalian) egg appears grossly symmetric, and sperm entry itself does not appear to impose an asymmetry.  Rather, as the zygote divides, the first cells formed appear to be similar to one another. As cell division continue, however, some cells find themselves on the surface while others are located within the interior of the forming ball of cells, or morula (↓).  These two cell  populations are exposed to different environments, environments that influence patterns of gene expression. The cells on the surface differentiate to form the trophectoderm, which in turn differentiates into extra-embryonic placental tissues, the interface between mother and developing embryo.  The internal cells becomes the inner cell mass, which differentiate to form the embryo proper, the future mouse (or human). Early on inner cell mass cells appear similar to one another, but they also experience different environments, leading to emerging asymmetries associated with the activation of different signaling systems, the expression of different sets of genes, and difference in behavior – they begin the process of differentiating into distinct cell lineages and types forming, as embryogenesis continues, different tissues and organs.   

The response of a particular cell to a particular environment will depend upon the signaling molecules present, typically expressed by neighboring cells, the signaling molecule receptors expressed by the cell itself, and how the binding of signaling molecules to receptors alters receptor activity or stability. For example, an activated receptor can activate (or inhibit) a transcription factor protein that could influence the expression of a subset of genes. These genes may themselves encode regulators of  transcription, signals, signal receptors, or modifiers of the cellular localization, stability, activity, or interactions with other molecules. While some effects of signal-receptor interactions can be transient, leading to reversible changes in cell state (and gene expression), during embryonic development activating and responding to a signal generally starts a cascade of effects that leads to irreversible changes, and the formation of altered differentiated states.
       A  cell’s response to a signal can be variable, and influenced by the totality of the signals it receives and its past history.  For example, a signal could lead to a decrease in the level of a receptor, or an increase in an inhibitory protein, making the cell unresponsive to the signal (a negative feedback effect) or more sensitive (a positive feedback effect) or could lead to a change in its response to a signal – different genes could be regulated as time goes by following the signal.  Such emerging patterns of gene expression, based on signaling inputs, are the primary driver of embryonic development. 

footnotes:

  1. Not all genes are differentially expression, however – some genes, known as housekeeping genes, are expressed in essential all cells.
  2.  Hopefully it is clear what the term “expressed” means – namely that part of the gene is used to direct the synthesis of RNA (through the process of transcription (DNA-dependent, RNA polymerization).  Some such RNAs (messenger or mRNAs) are used to direct the synthesis of a polypeptide through the process of translation (RNA-directed, amino acid polymerization) others do not encode polypeptides, such non-coding RNAs (ncRNAs) can play roles in a number of processes, from catalysis to the regulation of transcription, RNA stability, and translation.  
  3. Eggs are laid in water and are exposed to the sun; the pigmentation of the animal hemisphere is thought to protect the oocyte/zygote/early embryo’s DNA from photo-damage.

Literature cited

Chen et al.,  (2016). The ins (ide) and outs (ide) of asymmetric stem cell division. Current opinion in cell biology 43, 1-6.

De Domenico et al., (2015). Molecular asymmetry in the 8-cell stage Xenopus tropicalis embryo described by single blastomere transcript sequencing. Developmental biology 408, 252-268.

Gard & Klymkowsky. (1998). Intermediate filament organization during oogenesis and early development in the clawed frog, Xenopus laevis. In Intermediate filaments (ed. H. Herrmann & J. R. Harris), pp. 35-69. New York: Plenum.

Neumüller & Knoblich. (2009). Dividing cellular asymmetry: asymmetric cell division and its implications for stem cells and cancer. Genes & development 23, 2675-2699.

Streit & Davis. (2016). Chromatin Diminution. In eLS: John Wiley & Sons Ltd, Chichester.

Aggregative and clonal metazoans (a biofundamentalist perspective)

21st Century DEVO-2  In the first post in this series [link], I introduced the observation that single celled organisms can change their behaviors, often in response to social signals.  They can respond to changing environments and can differentiate from one cellular state to the another. Differentiation involves changes in which sets of genes are expressed, which polypeptides and proteins are made [previous post], where the proteins end up within the cell, and which behaviors are displayed by the organism. Differentiation enables individuals to adapt to hostile conditions and to exploit various opportunities. 

The ability of individuals to cooperate with one another, through processes such as quorum sensing, enables them to tune their responses so that they are appropriate and useful. Social interactions also makes it possible for them to produce behaviors that would be difficult or impossible for isolated individuals.  Once individual organisms learn, evolutionarily, how to cooperate, new opportunities and challenges (cheaters) emerge. There are strategies that can enable an organism to adapt to a wider range of environments, or to become highly specialized to a specific environment,  through the production of increasingly complex behaviors.  As described previously, many of these cooperative strategies can be adopted by single celled organisms, but others require a level of multicellularity.  Multicellularity can be transient – a pragmatic response to specific conditions, or it can be (if we ignore the short time that gametes exist as single cells) permanent, allowing the organism to develop the range of specialized cells types needed to build large, macroscopic organisms with complex and coordinated behaviors. In appears that various forms of multicellularity have arisen independently in a range of lineages (Bonner, 1998; Knoll, 2011). We can divide multicellularity into two distinct types, aggregative and clonal – which we will discuss in turn (1).  Aggregative (transient) multicellularity:  Once organisms had developed quorum sensing, they can monitor the density of related organisms in their environment and turn or (or off) specific genes (or sets of genes, necessary to produce a specific behavior.  While there are many variants, one model for such  a behavior is  a genetic toggle switch, in which a particular gene (or genes) can be switched on or off in response to environmental signals acting as allosteric regulators of transcription factor proteins (see Gardner et al., 2000).  Here is an example of an activity (↓) that we will consider in class to assess our understanding of the molecular processes involved.

One outcome of such a signaling system is to provoke the directional migration of amoeba and their aggregation to form the transient multicellular “slug”.  Such behaviors has been observed  in a range of normally unicellular organisms (see Hillmann et al., 2018)(↓). The classic example is  the cellular slime mold Dictyostelium discoideum (Loomis, 2014).  Under normal conditions, these unicellular amoeboid eukaryotes migrate, eating bacteria and such. In this state, the range of an individual’s movement is restricted to short distances.  However when conditions turn hostile, specifically a lack of necessary nitrogen compounds, there is a compelling reason to abandon one environment and migrate to another, more distant that a single-celled organism could reach. This is a behavior that depends upon the presence of a sufficient density (cells/unit volume) of cells that enables them to: 1) recognize one another’s presence (through quorum sensing), 2) find each other through directed (chemotactic) migration, and 3) form a multicellular slug that can go on to differentiate. Upon differentiation about 20% of the cells differentiate (and die), forming a stalk that lifts the other ~80% of the cells into the air.  These non-stalk cells (the survivors) differentiate into spore (resistant to drying out) cells that are released into the air where they can be carried to new locations, establishing new populations.  

The process of cellular differentiation in D. discoideum has been worked out in molecular detail and involves two distinct signaling systems: the secreted pre-starvation factor (PSF) protein and cyclic AMP (cAMP).  PSF is a quorum signaling protein that also serves to activate the cell aggregation/differentiation program (FIG. ↑). If bacteria, that is food, are present, the activity of PSF is inhibited and  cells remain in their single cell state. The key regulator of downstream aggregation and differentiation is the cAMP-dependent protein kinase PKA.  In the unicellular state, PKA activity is inhibited by PufA.  As PSF increases, while food levels decrease, YakA activity increases, inactivating PufA, leading to increased PKA activity.  Active PKA induces the synthesis of two downstream proteins, adenylate cyclase (ACA) and the cAMP receptor (CAR1). ACA catalyzes cAMP synthesis, much of which is secreted from the cell as a signaling molecule. The membrane-bound CAR1 protein acts as a receptor for autocrine (on the cAMP secreting cell) and paracrine (on neighboring cells) signaling.  The binding of cAMP to CAR1 leads to further activation of PKA, increasing cAMP synthesis and secretion – a positive feed-back loop. As cAMP levels increase, downstream genes are activated (and inhibited) leading cells to migrate toward one another, their adhesion to form a slug.  Once the slug forms and migrates to an appropriate site, the process of differentiation (and death) leading to stalk and spore formation begins. The fates of the aggregated cells is determined stochastically, but social cheaters can arise. Mutations can lead to individuals that avoid becoming stalk cells.  In the long run, if all individuals were to become cheaters, it would be impossible to form a stalk, so the purpose of social cooperation would be impossible to achieve.  In the face of environmental variation, populations invaded by cheaters are more likely to become extinct.  For our purposes the various defenses against cheaters are best left to other courses (see here if interested Strassmann et al., 2000).  

Clonal (permanent) multicellularity:  The type of multicellularity that most developmental biology courses focus on is what is termed clonal multicellularity – the organism is a clone of an original cell, the zygote, a diploid cell produced by the fusion of sperm and egg, haploid cells formed through the process of meiosis (2).  It is during meiosis that most basic genetic processes occur, that is the recombination between maternal and paternal chromosomes leading to the shuffling of alleles along a chromosome, and the independent segregation of chromosomes to form haploid gametes, gametes that are genetically distinct from those present in either parent. Once the zygote forms, subsequent cell divisions involve mitosis, with only a subset of differentiated cells, the cells of the germ line, capable of entering meiosis.  

Non-germ line, that is somatic cells, grow and divide. They interact with one another directly and through various signaling processes to produce cells with distinct patterns of gene expression, and so differentiated behaviors.  A key difference from a unicellular organism, is that the cells will (largely) stay attached to one another, or to extracellular matrix materials secreted by themselves and their neighbors.  The result is ensembles of cells displaying different specializations and behaviors.  As such cellular colonies get larger, they face a number of physical constraints – for example, cells are open non-equilibrium systems, to maintain themselves and to grow and reproduce, they need to import matter and energy from the external world. Cells also produce a range of, often toxic, waste products that need to be removed.  As the cluster of zygote-derived cells grows larger, and includes more and more cells, some cells will become internal and so cut off from necessary resources. While diffusive processes are often adequate when a cell is bathed in an aqueous solution, they are inadequate for a cell in the interior of a large cell aggregate (3).  The limits of diffusive processes necessitate other strategies for resource delivery and waste removal; this includes the formation of tubular vascular systems (such as capillaries, arteries, veins) and contractile systems (hearts and such) to pump fluids through these vessels, as well as cells specialized to process and transport a range of nutrients (such as blood cells).  As organisms get larger, their movements require contractile machines (muscle, cartilage, tendons, bones, etc) driving tails, fins, legs, wings, etc. The coordination of such motile systems involves neurons, ganglia, and brains. There is also a need to establish barriers between the insides of an organism and the outside world (skin, pulmonary, and gastrointestinal linings) and the need to protect the interior environment from invading pathogens (the immune system).  The process of developing these various systems depends upon controlling patterns of cell growth, division, and specialization (consider the formation of an arm), as well as the controlled elimination of cells (apoptosis), important in morphogenesis (forming fingers from paddle-shaped appendages), the maturation of the immune system (eliminating cells that react against self), and the wiring up, and adaptation of the nervous system. Such changes are analogous to those involved in aggregative multicellularity.      

Origins of multicellularity:  While aggregative multicellularity involves an extension of quorum sensing and social cooperation between genetically distinct, but related individuals, we can wonder whether similar drivers are responsible for clonal multicellularity.  There are a number of imaginable adaptive (evolutionary) drivers but two spring to mind: a way to avoid predators by getting bigger than the predators and as a way to produce varied structures needed to exploit various ecological niches and life styles. An example of the first type of driver of multicellularity is offered by the studies of Boraas et al  (1998). They cultured the unicellular green alga Chlorella vulgaris, together with a unicellular predator, the phagotrophic flagellated protist Ochromonas vallescia. After less than 100 generations (cell divisions), they observed the appearance of multicellular, and presumable inedible (or at least less easily edible), forms. Once selected, this trait appears to be stable, such that “colonies retained the eight-celled form indefinitely in continuous culture”.  To my knowledge, the genetic basis for this multicellularity remains to be determined.  

Cell Differentiation:  One feature of simple colonial organisms is that when dissociated into individual cells, each cell is capable of regenerating a new organism. The presence of multiple (closely related) cells in a single colony opens up the possibility of social interactions; this is distinct from the case in aggregative multicellularity, where social cooperation came first. Social cooperation within a clonal metazoan means that most cells “give up” their ability to reproduce a new organism (a process involving meiosis). Such irreversible social interactions mark the transition from a colonial organism to a true multicellular organism. As social integration increases, cells can differentiate so as to perform increasingly specialized functions, functions incompatible with cell division. Think for a moment about a human neuron or skeletal muscle cell – in both cases, cell division is no longer possible (apparently). Nevertheless, the normal functioning of such cells enhances the reproductive success of the organism as a whole – a classic example of inclusive fitness (remember heterocysts?)  Modern techniques of single cell sequencing and data analysis have now been employed to map this process of cellular differentiation in increasingly great detail, observations that will inform our later discussions (see Briggs et al., 2018 and future posts). In contrast, the unregulated growth of a cancer cell is an example of an asocial behavior, an asocial behavior that is ultimately futile, except in those rare cases (four known at this point) in which a cancer cell can move from one organism to another (Ujvari et al., 2016).  

Unicellular affordances for multicellularity:  When considering the design of a developmental biology course, we are faced with the diversity of living organisms – the basic observation that Darwin, Wallace, their progenitors and disciplinary descendants set out to solve. After all there are many millions of different types of organisms; among the multicellular eukaryotes, there are six major group : the ascomycetes and basidiomycetes fungi, the florideophyte red algae, laminarialean brown algae, embryophytic land plants and animals (Knoll, 2011 ↑).  Our focus will be on animals. “All members of Animalia are multicellular, and all are heterotrophs (i.e., they rely directly or indirectly on other organisms for their nourishment). Most ingest food and digest it in an internal cavity.” [Mayer link].  From a macroscopic perspective, most animals have (or had at one time during their development) an anterior to posterior, that is head to tail, axis. Those that can crawl, swim, walk, or fly typically have a dorsal-ventral or back to belly axis, and some have a left-right axis as well.  

But to be clear, a discussion of the various types of animals is well beyond the scope of any introductory course in developmental biology, in part because there are 35 (assuming no more are discovered) different “types” (phyla) of animals – nicely illustrated at this website [BBC: 35 types of animals, most of whom are really weird)].  So again, our primary focus will be on one group, the vertebrates – humans are members of this group.  We will also consider experimental insights derived from studies of various “model” systems, including organisms from another metazoan group, the  ecdysozoa (organisms that shed their outer layer as they grow bigger), a group that includes fruit flies and nematode worms. 

My goal will be to ignore most of the specialized terminology found in the scholarly literature, which can rapidly turn a biology course into a vocabulary lesson and that add little to understanding of basic processes relevant to a general understanding of developmental processes (and relevant to human biology, medicine, and biotechnology). This approach is made possible by the discovery that the basic processes associated with animal (and metazoan) development are conserved. In this light, no observation has been more impactful than the discovery that the nature and organization of the genes involved in specifying the head to tail axes of the fruit fly and vertebrates (such as the mouse and human) is extremely similar in terms of genomic organization and function (Lappin et al., 2006 ↓), an observation that we will return to repeatedly.  Such molecular similarities extend to cell-cell and cell-matrix adhesion systems, systems that release and respond to various signaling molecules, controlling cell behavior and gene expression, and reflects the evolutionary conservation and the common ancestry of all animals (Brunet and King, 2017; Knoll, 2011). 

What can we know about the common ancestor of the animals?  Early on in the history of comparative cellular anatomy, the striking structural similarities between  the feeding system of choanoflagellate protozoans, a motile (microtubule-based) flagellum a surrounded by a “collar”of microfilament-based microvilli) and a structurally similar organelle in a range of multicellular organisms led to the suggestion that choanoflagellates and animals shared a common ancestor.  The advent of genomic sequencing and analysis has only strengthened this hypothesis, namely that choanoflagellates and animals form a unified evolutionary clade, the ‘Choanozoa’  (see tree↑ above)(Brunet and King, 2017).  Moreover, “many genes required for animal multicellularity (e.g., tyrosine kinases, cadherins, integrins, and extracellular matrix domains) evolved before animal origins”.  The implications is that the Choanozoan ancestor was predisposed to exploit some of the early opportunities offered by clonal multicellularity. These pre-existing affordances, together with newly arising genes and proteins (Long et al., 2013) were exploited in multiple lineages in the generation of multicellular organisms (see Knoll, 2011).

Basically to understand what happened next, some ~600 million years ago or so, we will approach the various processes involved in the shaping of animal development.  Because all types of developmental processes, including the unicellular to colonial transition, involve changes in gene expression, we will begin with the factors involved in the regulation of gene expression.  


Footnotes:
1). Please excuse the inclusive plural, but it seems appropriate in the context of what I hope will be a highly interactive course.
2). I will explicitly ignore variants as (largely) distractions, better suited for more highly specialized courses.
3). We will return to this problem when (late in the course, I think) we will discuss the properties of induced pluripotent stem cell (iPSC) derived organoids.

Literature cited:
Bonner, J. T. (1998). The origins of multicellularity. Integrative Biology: Issues, News, and Reviews: Published in Association with The Society for Integrative and Comparative Biology 1, 27-36.

Boraas, M. E., Seale, D. B. and Boxhorn, J. E. (1998). Phagotrophy by a flagellate selects for colonial prey: a possible origin of multicellularity. Evolutionary Ecology 12, 153-164.

Briggs, J. A., Weinreb, C., Wagner, D. E., Megason, S., Peshkin, L., Kirschner, M. W. and Klein, A. M. (2018). The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360, eaar5780.

Brunet, T. and King, N. (2017). The origin of animal multicellularity and cell differentiation. Developmental cell 43, 124-140.

Gardner, T. S., Cantor, C. R. and Collins, J. J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339-342.

Hillmann, F., Forbes, G., Novohradská, S., Ferling, I., Riege, K., Groth, M., Westermann, M., Marz, M., Spaller, T. and Winckler, T. (2018). Multiple roots of fruiting body formation in Amoebozoa. Genome biology and evolution 10, 591-606.

Knoll, A. H. (2011). The multiple origins of complex multicellularity. Annual Review of Earth and Planetary Sciences 39, 217-239.

Lappin, T. R., Grier, D. G., Thompson, A. and Halliday, H. L. (2006). HOX genes: seductive science, mysterious mechanisms. The Ulster medical journal 75, 23.

Long, M., VanKuren, N. W., Chen, S. and Vibranovski, M. D. (2013). New gene evolution: little did we know. Annual review of genetics 47, 307-333.

Loomis, W. F. (2014). Cell signaling during development of Dictyostelium. Developmental biology 391, 1-16.

Strassmann, J. E., Zhu, Y. and Queller, D. C. (2000). Altruism and social cheating in the social amoeba Dictyostelium discoideum. Nature 408, 965-967.

Ujvari, B., Gatenby, R. A. and Thomas, F. (2016). Transmissible cancers, are they more common than thought? Evolutionary applications 9, 633-634.

On teaching developmental biology in the 21st century (a biofundamentalist perspective)

On teaching developmental biology and trying to decide where to start: differentiation

Having considered the content of courses in chemistry [1] and  biology [2, 3], and preparing to teach developmental biology for the first time, I find myself reflecting on how such courses might be better organized.  In my department, developmental biology (DEVO) has returned after a hiatus as the final capstone course in our required course sequence, and so offers an opportunity within which to examine what students have mastered as they head into their more specialized (personal) educational choices.  Rather than describe the design of the course that I will be teaching, since at this point I am not completely sure what will emerge, what I intend to do (in a series of posts) is to describe, topic by topic, the progression of key concepts, the observations upon which they are based, and the logic behind their inclusion.

Modern developmental biology emerged during the mid-1800s from comparative embryology [4] and was shaped by the new cell theory (the continuity of life and the fact that all organisms are composed of cells and their products) and the ability of cells to differentiate, that is, to adopt different structures and behaviors [5].  Evolutionary theory was also key.  The role of genetic variation based on mutations and selection, in the generation of divergent species from common ancestors, explained why a single, inter-connected Linnaean (hierarchical) classification system (the phylogenic tree of life →) of organisms was possible and suggested that developmental mechanisms were related to similar processes found in their various ancestors. 

So then, what exactly are the primary concepts behind developmental biology and how do they emerge from evolutionary, cell, and molecular biology?  The concept of “development” applies to any process characterized by directional changes over time.  The simplest such process would involve the progress from the end of one cell division event to the beginning of the next; cell division events provide a convenient benchmark.  In asexual species, the process is clonal, a single parent gives rise to a genetically identical (except for the occurrence of new mutations) offspring. Often there is little distinction between parent and offspring.  In sexual species, a dramatic and unambiguous benchmark involves the generation of a new and genetically distinct organism.  This “birth” event is marked by the fusion of two gametes (fertilization) to form a new diploid organism.  Typically gametes are produced by a complex cellular differentiation process (gametogenesis), ending with meiosis and the formation of haploid cells.  In multicellular organisms, it is often the case that a specific lineage of cells (which reproduce asexually), known as the germ line, produce the gametes.  The rest of the organism, the cells that do not produce gametes, is known as the soma, composed of somatic cells.   Cellular continuity remains, however, since gametes are living (albeit haploid) cells.  

It is common for the gametes that fuse to be of two different types, termed oocyte and sperm.  The larger, and generally immotile gamete type is called an oocyte and an individual that produces oocytes is termed female. The smaller, and generally motile gamete type is called a sperm; individuals that produces sperm are termed male. Where a single organism can produce both oocytes and sperm, either at the same time or sequentially, they are referred to as hermaphrodites (named after Greek Gods, the male Hermes and the female Aphrodite). Oocytes and sperm are specialized cells; their formation involves the differential expression of genes and the specific molecular mechanisms that generate the features characteristic of the two cell types.  The fusion of gametes, fertilization,  leads to a zygote, a diploid cell that (usually) develops into a new, sexually mature organism.    

An important feature of the process of fertilization is that it requires a level of social interaction, the two fusing cells (gametes) must recognize and fuse with one another.  The organisms that produce these gametes must cooperate; they need to produce gametes at the appropriate time and deliver them in such a way that they can find and recognize each other and avoid “inappropriate” interactions”.  The specificity of such interactions underlie the reproductive isolation that distinguishes one species from another.  The development of reproductive isolation emerges as an ancestral population of organisms diverges to form one or more new species.  As we will see, social interactions, and subsequent evolutionary effects, are common in the biological world.  

The cellular and molecular aspects of development involve the processes by which cells grow, replicate their genetic material (DNA replication), divide to form distinct parent-offspring or similar sibling cells, and may alter their morphology (shape), internal organization, motility, and other behaviors, such as the synthesis and secretion of various molecules, and how these cells respond to molecules released by other cells.  Developmental processes involve the expression and the control of all of these processes.

Essentially all changes in cellular behavior are associated with changes in the activities of biological molecules and the expression of genes, initiated in response to various external signaling events – fertilization itself is such a signal.  These signals set off a cascade of regulatory interactions, often leading to multiple “cell types”, specialized for specific functions (such as muscle contraction, neural and/or hormonal signaling, nutrient transport, processing, and synthesis,  etc.).  For specific parts of the organism, external or internal signals can result in a short term “adaptive” response (such as sweating or panting in response to increased internal body temperature), after which the system returns to its original state, or in the case of developing systems, to new states, characterized by stable changes in gene expression, cellular morphology, and behavior.    

Development in bacteria (and other unicellular organisms):  In most unicellular organisms, the cell division process is reasonably uneventful, the cells produced are similar to the original cell – but not always.  A well studied example is the bacterium Caulobacter crescentus (and related species) [link][link].  In cases such as this, the process of growth  leads to phenotypically different daughters.  While it makes no sense to talk about a beginning (given the continuity of life after the appearance of the last universal common ancestor or LUCA), we can start with a “swarmer” cell, characterized by the presence of a motile flagellum (a molecular machine driven by coupled chemical reactions – see past blogpost] that drives motility [figure modified from 6 ]. 

A swarmer will eventually settle down, loose the flagellum, and replace it with a specialized structure (a holdfast) designed to anchor the cell to a solid substrate.  As the organism grows, the holdfast develops a stalk that lifts the cell away from the substrate.  As growth continues, the end of the cell opposite the holdfast begins to differentiate (becomes different) from the holdfast end of the cell – it begins the process leading to the assembly of a new flagellar apparatus.  When reproduction (cell growth, DNA replication, and cell division) occurs, a swarmer cell is released and can swim away and colonize another area, or settle nearby.  The holdfast-anchored cell continues to grow, producing new swarmers.  This process is based on the inherent asymmetry of the system – the holdfast end of the cell is molecularly distinct from the flagellar end [see 7].

The process of swarmer cell formation in Caulobacter is an example of what we will term deterministic phenotypic switching.  Cells can also exploit molecular level noise (stochastic processes) that influence gene expression to generate phenotypic heterogeneity, different behaviors expressed by genetically identical cells within the same environment [see 8, 9].  Molecular noise arises from the random nature of molecular movements and the rather small (compared to macroscopic systems) numbers of most molecules within a cell.  Most cells contain one or two copies of any particular gene, and a similarly small number of molecular sequences involved in their regulation [10].  Which molecules are bound to which regulatory sequence, and for how long, is governed by inter-molecular surface interactions and thermally driven collisions, and is inherently noisy.  There are strategies that can suppress but not eliminate such noise [see 11].  As dramatically illustrated by Elowitz  and colleagues [8](), molecular level noise can produce cells with different phenotypes.  Similar processes are active in eukaryotes (including humans), and can lead to the expression of one of the two copies of a gene (mono-allelic expression) present in a diploid organism.  This can lead to effects such as haploinsufficiency and selective (evolutionary) lineage effects if the two alleles are not identical [12, 13]. Such phenotypic heterogeneity among what are often genetically identical cells is a topic that is rarely discussed (as far as I can discern) in introductory cell, molecular, or developmental biology courses [past blogpost].

The ability to switch phenotypes can be a valuable trait if an organism’s environment is subject to significant changes.  As an example, when the environment gets hostile, some bacterial cells transition from a rapidly dividing to a slow or non-dividing state.  Such “spores” can differentiate so as to render them highly resistant to dehydration and other stresses.  If changes in environment are very rapid, a population can protect itself by continually having some cells (stochastically) differentiating into spores, while others continue to divide rapidly. Only a few individuals (spores) need to survive a catastrophic environmental change to quickly re-establish the population.

Dying for others – social interactions between “unicellular” organisms:  Many students might not predict that one bacterial cell would “sacrifice” itself for the well being of others, but in fact there are a number of examples of this type of self-sacrificing behavior, known as programmed cell death, which is often a stochastic process.  An interesting example is provided by cellular specialization for photosynthesis or nitrogen fixation in cyanobacteria [see 9].  These two functions require mutually exclusive cellular environments to occur, in particular the molecular oxygen (O2) released by photosynthesis inhibits the process of nitrogen fixation.  Nevertheless, both are required for optimal growth.  The solution?  some cells differentiate into what are known as heterocysts, cells committed to nitrogen fixation ( a heterocyst in Anabaena spiroides, adapted from link), while most ”vegetative” cells continue with photosynthesis.  Heterocysts cannot divide, and eventually die – they sacrifice themselves for the benefit of their neighbors, the vegetative cells, cells that can reproduce.

The process by which the death of an individual can contribute resources that can be used to insure or enhance the survival and reproduction of surrounding individuals is an inherently social process, and is subject of social evolutionary mechanisms [14, 15][past blogpost].  Social behaviors can be selected for because the organism’s neighbors, the beneficiaries of their self-sacrifice are likely to be closely (clonally) related to themselves.  One result of the social behavior is, at the population level, an increase in one aspect of evolutionary fitness,  termed “inclusive fitness.”  

Such social behaviors can enable a subset of the population to survive various forms of environmental stress (see spore formation above).  An obvious environmental stress involves the impact of viral infection.  Recall that viruses are completely dependent upon the metabolic machinery of the infected cell to replicate. While there are a number of viral strategies, a common one is bacterial lysis – the virus replicates explosively, kills the infected cells, leading to the release of virus into the environment to infect others.  But, what if the infected cell kills itself BEFORE the virus replicates – the dying (self-sacrificing, altruistic) cell “kills” the virus (although viruses are not really alive) and stops the spread of the infection.  Typically such genetically programmed cell death responses are based on a simple two-part system, involving a long lived toxin and a short-lived anti-toxin.  When the cell is stressed, for example early during viral infection, the level of the anti-toxin can fall, leading to the activation of  the toxin. 

Other types of social behavior and community coordination (quorum effects):  Some types of behaviors only make sense when the density of organisms rises above a certain critical level.  For example,  it would make no sense for an Anabaena cell  to differentiate into a heterocyst (see above) if there are no vegetative cells nearby.  Similarly, there are processes in which a behavior of a single bacterial cell, such as the synthesis and secretion of a specific enzyme, a specific import or export machine,  or the construction of a complex, such as a DNA uptake machine, makes no sense in isolation – the secreted molecule will just diffuse away, and so be ineffective, the molecule to be imported (e.g. lactose) or exported (an antibiotic) may not be present, or there may be no free DNA to import.  However, as the concentration (organisms per volume) of bacteria increases, these behaviors can begin to make biological sense – there is DNA to eat or incorporate and the concentration of secreted enzyme can be high enough to degrade the target molecules (so they are inactivated or can be imported as food).   

So how does a bacterium determine whether it has neighbors or whether it wants to join a community of similar organisms?  After all, it does not have eyes to see. The process used is known as quorum sensing.  Each individual synthesizes and secretes a signaling molecule and a receptor protein whose activity is regulated by the binding of the signaling molecule.  Species specificity in signaling molecules and receptors insures that organisms of the same kind are talking to one another and not to other, distinct types of organisms that may be in the environment.   At low signaling molecule concentrations, such as those produced by a single bacterium in isolation, the receptor is not activated, and the cell’s behavior remains unchanged.  However, as the concentration of bacteria increases, the concentration of the signal increases, leading to the activation of the receptor.  Activation of the receptor can have a number of effects, including increased synthesis of the signal and other changes, such as movement in response to signal s- through regulation of flagellar and other motility systems, such a system can lead to the directed migration (aggregation) of cells [see 16].   

In addition to driving the synthesis of a common good (such as a useful extracellular molecule), social interactions can control processes such as  programmed cell death.  When the concentration of related neighbors is high, the programmed death of an individual can be beneficial, it can  lead to release of nutrients,  common goods, including DNA molecules, that can be used by neighbors (relatives)[17, 18] – an increase in the probability of cell death in response to a quorum can increased in a way that increases inclusive fitness.  On the other hand,  if there are few related individuals in the neighborhood, programmed cell death “wastes” these resources, and so is likely to be suppressed (you might be able to generate plausible mechanisms that could control the probability of programmed cell death).     

As we mentioned previously with respect to spore formation, the generation of a certain percentage of “persisters” – individuals that withdraw from active growth and cell division, can enable a population to survive stressful situations, such as the presence of an antibiotic.  On the other hand, generating too many persisters may place the population at a reproductive disadvantage.  Once the antibiotic is gone, the persisters can return into active division. The ability of bacteria to generate persisters is a serious problem in treating people with infections [19].  

Of course, as in any social system, the presumption of cooperation (expending energy to synthesize the signal, sacrificing oneself for others) can open the system to cheaters [blogpost].  All such “altruistic” behaviors are vulnerable to cheaters.(1)  For example, a cheater that avoids programmed cell death (for example due to an inactivating mutation that effects the toxin molecule involved) will come to take over the population.  The downside, for the population, is that if cheaters take over,  the population is less likely to survive the environmental events that the social behavior was evolve to address.  In response to the realities of cheating, social organisms need to adopt various social-validation systems [see 20 as an example]; we see this pattern of social cooperation, cheating, and social defense mechanism throughout the biological world. 

Follow-on posts:

footnotes:

1): Such as people who fail to pay their taxes or disclose their tax returns.

literature cited: 

1. Cooper, M.M. and M.W. Klymkowsky, Chemistry, life, the universe, and everything: a new approach to general chemistry, and a model for curriculum reform. Journal of Chemical Education, 2013. 90: 1116-1122.

2. Klymkowsky, M.W., Teaching without a textbook: strategies to focus learning on fundamental concepts and scientific process. CBE Life Sci Educ, 2007. 6: 190-3.

3. Klymkowsky, M.W., J.D. Rentsch, E. Begovic, and M.M. Cooper, The design and transformation of Biofundamentals: a non-survey introductory evolutionary and molecular biology course. LSE Cell Biol Edu, 2016. pii: ar70.

4. Arthur, W., The emerging conceptual framework of evolutionary developmental biology. Nature, 2002. 415:  757.

5. Wilson, E.B., The cell in development and heredity. 1940.

6. Jacobs‐Wagner, C., Regulatory proteins with a sense of direction: cell cycle signalling network in Caulobacter. Molecular microbiology, 2004. 51:7-13.

7. Hughes, V., C. Jiang, and Y. Brun, Caulobacter crescentus. Current biology: CB, 2012. 22:R507.

8. Elowitz, M.B., A.J. Levine, E.D. Siggia, and P.S. Swain, Stochastic gene expression in a single cell. Science, 2002. 297:1183-6.

9. Balázsi, G., A. van Oudenaarden, and J.J. Collins, Cellular decision making and biological noise: from microbes to mammals. Cell, 2011. 144: 910-925.

10. Fedoroff, N. and W. Fontana, Small numbers of big molecules. Science, 2002. 297:1129-1131.

11. Lestas, I., G. Vinnicombe, and J. Paulsson, Fundamental limits on the suppression of molecular fluctuations. Nature, 2010. 467:174-178.

12. Zakharova, I.S., A.I. Shevchenko, and S.M. Zakian, Monoallelic gene expression in mammals. Chromosoma, 2009. 118:279-290.

13. Deng, Q., D. Ramsköld, B. Reinius, and R. Sandberg, Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science, 2014. 343: 193-196.

14. West, S.A., A.S. Griffin, A. Gardner, and S.P. Diggle, Social evolution theory for microorganisms. Nature reviews microbiology, 2006. 4:597.

15. Bourke, A.F.G., Principles of Social Evolution. Oxford series in ecology and evolution. 2011, Oxford: Oxford University Press.

16. Park, S., P.M. Wolanin, E.A. Yuzbashyan, P. Silberzan, J.B. Stock, and R.H. Austin, Motion to form a quorum. Science, 2003. 301:188-188.

17. West, S.A., S.P. Diggle, A. Buckling, A. Gardner, and A.S. Griffin, The social lives of microbes. Annual Review of Ecology, Evolution, and Systematics, 2007: 53-77.

18. Durand, P.M. and G. Ramsey, The Nature of Programmed Cell Death. Biological Theory, 2018:  1-12.

19. Fisher, R.A., B. Gollan, and S. Helaine, Persistent bacterial infections and persister cells. Nature Reviews Microbiology, 2017. 15:453.

20. Queller, D.C., E. Ponte, S. Bozzaro, and J.E. Strassmann, Single-gene greenbeard effects in the social amoeba Dictyostelium discoideum. Science, 2003. 299: 105-106.

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.

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

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. 

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 biology 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 (→) “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 ensure 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 too often serve the role of a hazing ritual, driving many students out of the biological sciences [5], in part I suspect because they often seem irrelevant to students’ interests in the workings of biological systems.[6].  

Nanopore’s sequencer and Wanunu’s article 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 (→).  The Nanopore sequencer works because 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 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.  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., Increased cytoplasm viscosity hampers aggregate polar segregation in Escherichia coli [7].  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 [8], 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 (← adapted from Joshi et al., 2017) 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)[9].  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 controlled by a social (quorum sensing) system (previous post). One of the ways that such organisms determine 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, for example, 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 understandingwhere 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)[10].  The contraction of such a filament has also been found to be used to import DNA into the cell, the first 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)[11]. This molecular machine enables the bacterium to import DNA from the environment, released, perhaps, from a neighbor killed by its spear.  In this system (adapted from Matthey & Bioesch  et al., 2017 →), the double stranded DNA molecule is first transported through the bacterium’s outer membrane (“OM”); 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 can be used 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 th e core premise of biology 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 12].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 and literature cited

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

2. Klymkowsky, M.W. Physics for (molecular) biology students. 2014  

3. Alberts, B., The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell, 1998. 92: 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: p. 1333-1333.

6. 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) general chemistry course sequence.  

7. 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: p. 686-699.

8. 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.

9. 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: p. 267-279.

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

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

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

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!