Biomusings - English

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In your mouth there are billions of bacterial cells growing on your teeth and below your gumline. These microbes are not each alone in a little bubble, but instead interact with each other and with your human cells. Undoubtedly, these interactions are important; they determine the behavior of individual microbes, the composition of your oral microbiome, and ultimately, the development of cavities and gum disease. But conceptualizing these interactions and understanding their impact is a huge challenge.

As a microbial ecologist, there is no way I can study the millions of possible pairwise interactions among the hundreds of species in our oral microbiota, not to mention any higher-level interactions. Instead, I must prioritize my efforts. I assume that due to the spatial structure of the oral microbiota, not all of these possible interactions are actually occurring. Further, many of the interactions likely have only a minuscule impact on the overall behavior of the community, the evolution of the microbes involved, or the progression of diseases. Yet, this still leaves an unknown, and possibly vast number of important interactions to try to understand, leading me to ponder basic, unanswered questions. When and how do microbes within a community interact? And potentially even more importantly, when and how do those interactions matter?

These questions are not unique to the oral microbiota. A better understanding of microbial interactions is important across environments, from our microbiome, to soil and plant-associated communities, to the microbes driving carbon cycling in our oceans. There is a basic science value in understanding these important, complex communities. Additionally, scientists hypothesize that altering key interactions can allow us to shift our communities, for example from a diseased state to a healthy state. To determine whether this is actually feasible requires researchers to identify the key interactions and to understand the implications of microbial interactions on both ecological and evolutionary timescales. Thus, while I research the oral microbiota because of its importance to human health, ease of study, and interesting spatial and temporal community dynamics, I hope that my work has broad relevance across systems.

Recently, in collaboration with my co-authors, I studied the interactions formed by the oral pathogen Aggregatibacter actinomycetemcomitans (Aa) in pairwise coinfection with 25 different microbes in a mouse abscess infection model. Each of these 25 microbes altered the set of essential genes that Aa needed to survive in the abscess, implying that each microbe interacted with Aa in some manner. Further, we found that each interaction altered Aa’s essential genes in a unique way.



Many of the interactions in these experiments with Aa were indirect, likely mediated through the mouse and its immune system. This type of interaction, transmitted through a eukaryotic host, the environment, or other members of the microbial community, are undoubtedly pervasive. For example, a number of publications have emphasized the role that pH can play in shaping soil communities. Microbes, including transient community members, can alter the pH, impacting a community’s composition and function, and even its antibiotic resistance.

In my coinfection experiments, other interactions may have been directly between Aa and the coinfecting microbe, mediated through processes such as cross-feeding, exploitative competition, or chemical warfare. Yet, it is difficult to know when the microbes were directly interacting. Microbes must be near each other, on a micron scale, to be able to interact through many known mechanisms. Microbial communication via diffusible molecules or cell-cell contact usually requires relatively close quarters between the cells, as does cross-feeding or contact-dependent warfare. However, if microbes are not close by, does that mean they simply thrive in different microenvironments and do not interact? Or does that mean they once were in close proximity, but then distanced due to negative interactions?

Interactions within a microbial community are complex, and defining each of them is not trivial. Thoughtful experimental designs and detailed analyses continue to uncover fascinating interactions within communities that are key to human health or ecosystem functioning. Moreover, we are still discovering new mechanisms that microbes use to compete or cooperate. This work is critical as it informs our understanding of how interactions can alter microbial community ecology, evolutionary trajectories, and even ecosystem-level processes.



Specifically, recent work across environments has illuminated the importance of microbe-microbe interactions in shaping the ecology of communities. By definition, interactions are key to community ecology, influencing aspects ranging from a community’s composition and function, to its stability, to the ability of organisms to invade.


Understanding ecological interactions can also help explain how low abundance species have an over-sized impact. Low abundance microbes known as keystone pathogens are thought to be able to shift a community from a healthy to a diseased state. The classic exemplar in the oral microbiome is Porphyromonas gingivalis. It is thought that P. gingivalis alters the local immune environment, providing opportunities for other pathogenic bacteria to thrive. More broadly, the idea of keystone species is important in microbial communities for conceptualizing how low abundance organisms can shift the ecology of a community through direct or indirect interactions.

On an evolutionary timescale, a lot of exciting work has recently highlighted the importance of polymicrobial interactions. While studies on the long-term evolution of individual species in isolation have been groundbreaking and critically informative, studies have also shown that evolutionary trajectories differ when organisms are within a community. For example, the presence of a community can slow the rate of evolution of antibiotic resistance, a pressing global challenge. Further, in plant biomass degrading communities, the evolutionary and functional outcomes of focal organisms varied depending on the community diversity. These studies remind us that the community is a critical determinant of microbial evolution. 


“Interactions within a microbial community are complex, and defining each of them is not trivial”


To further complicate matters, interspecies interactions themselves can evolve. This phenomenon has been studied in simple communities where, for instance, obligate mutualists both evolved to have a more stable and productive interaction. Evolution of increased stability has also been observed in simple cross-feeding interactions. However, as mentioned above, the taxonomic and functional diversity of the community itself influences evolutionary trajectories, so it remains unknown how these findings will scale.



These examples demonstrate that interactions are critically important in microbial ecology and evolution. However, in many cases, it is not known which polymicrobial interactions influence the behavior and evolutionary trajectory of microbes. It is an exciting time in microbial ecology as we continue to integrate the chronicling of microbial interactions and the assessment of their impact. Together, this work is promising and important, advancing our understanding of the microbial communities that define our lives.

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When we think of the hallmarks of complex societies and civilization—agriculture, division of labor, architecture, language, and, in the modern era, even democracy—we often attribute these features only to humans. We tend to believe these achievements set us apart from other species and are a testament to our dominance on Earth. The story we often tell students is that “civilization” began approximately 5,000 years ago when simple human societies of hunter-gatherers gave way to more complex human groups that practiced agriculture.

But when we do so, we omit the fact that ant societies in the Amazon basin discovered agriculture some 50 million years earlier, transitioning from life as hunter-gatherers to fungus farmers and that there are countless other species living in complex societies rich with many of the same dynamics and properties we attribute to the pinnacle of human existence. Humans, after all, are animals and the dynamics that govern other animal societies do not suddenly stop at the doorstep of humanity . Therefore, a more ready exchange of ideas between the social and biological sciences will achieve a more complete understanding of social life.

The founders of sociology recognized the importance of looking beyond humans in order to understand society. Auguste Comte, who originally coined the term “sociology”, argued that studying animal societies could yield hints of humanity’s social nature. Fellow founder of the field, Emile Durkheim, drew from Darwin and biological parallels to argue for the “natural laws” of societal evolution that result in division of labor and economic specialization in humans. However, centuries later, biology and sociology remain separate disciplines and research that includes both is not the norm. In my work, I am seeking to bridge the gap between the two sciences through the use of computational modeling.  Computational modeling allows me to ask “what if” scenarios: I simulate societies in which individuals follow a particular set of rules or behaviors, and I get to observe how those rules play out and affect group-level organization. Not only is this helpful in finding possible explanations for patterns we observe in societies, but it can also create new predictions to guide future work (e.g., if individuals all do X, we would expect to see Y).

“The founders of sociology recognized the importance of looking beyond humans in order to understand society.”

To see how broadening the study of societies beyond humanity could bring new buzz to our understanding of societies, consider the species who Aristotle once declared joined humans in the realm of “political animals”: the honeybee. Millennia later, more rigorous studies have revealed the sophisticated, and yes perhaps political, inner workings of a hive.  Occasionally, honeybees must move their hive to a new location, which triggers a collective decision-making process. Scouts fly out from the colony in different directions in search of suitable new places for the hive. After locating a site, a scout returns to the hive and begins dancing in order tell others about the site. The angle of her dance tells others what angle they must fly from the hive to find the new site, the length of ground she takes to perform her dance tells them the distance they must fly, and the number of times she performs the dance tells how good of a site she thinks it is. Dancing scouts pique the interest of observing hivemates who go and see the site for themselves. Upon their return, if they also find the site to be suitable, they join the original scout in promoting this site through dance. This process typically continues for several days, as factions compete to win over “the votes” of undecided bees. Once there is only one faction left and the dancers are all performing in favor of one site, the colony flies together to the new location.

This form of unanimous direct democracy does not always work smoothly, however, providing a cautionary tale for democratic societies. Perhaps familiar to those living under modern American politics, sometimes two sites manage to gain equally sized, equally fervent groups of dancers arguing in their favor. This deadlock—or, in the parlance of political science, opinion polarization—among the group can have devastating consequences. Thomas Seeley, the Cornell biologist who is responsible for much of these fascinating observations of honeybees, once observed a hive so locked in indecision between two sites that it literally split into two separate groups that flew to two separate sites. Making matters worse, the queen was somehow lost in the chaotic separation of the colony factions. In losing the queen, who had the sole responsibility of producing new offspring for the hive, the colony lost its future too. After a fruitless search for the queen, the two opposing factions eventually dissolved entirely and scattered with the winds, thus ending the colony’s existence all together.

Despite its grim description, the splitting of a hive is an extremely rare occurrence thanks to the social dynamics employed by the bees. Biologists have found that deadlock is avoided in hives because the individuals reporting in favor of a site frequently shift back to “undecided”. This prevents bees from being locked in support for one site and instead ensures that all bees have more of a chance to switch their support over time. Why do bees drop their support for sites over time? The scouts reporting in favor of a specific site seek out scouts reporting in favor of other sites, and upon finding one, release a signal that causes their competitor to stop reporting that site—a process called cross-inhibition. Reflecting on our own modern democracies, such a process would be akin to supporters of a political candidate reaching out to supporters of another candidate and convincing them to drop their support (although not necessarily pick up the support of another candidate right away). This social dynamic ultimately prevents a society from reaching a deadlock of even sized fractions, as shown by the hundreds of thousands of years that bees have been using this dynamic to successfully make group decisions.

But just as animal societies can provide insights to our own social dynamics, insights from human society may be equally valuable to biology. For hundreds of years, the social sciences have been documenting social dynamics found in our species; perhaps it is time we consider whether some of these concepts might apply to the other complex societies on Earth. To illustrate this potential, consider ants, who typically live in large, impersonal societies of thousands of individuals. The first half of my dissertation used computational models to study how division of labor—specialization by individuals on certain tasks, such that they each fulfill the role of “forager” or “nurse”, for example—emerges in collectives, particularly social insect colonies, purely through self-organization. Despite us labeling the egg layer of a colony “the queen”, she actually exhibits no control over the workers in the colony. Instead, colonies are organized very democratically, typically with individuals following local cues to make decisions about what job they should do to help the colony.

I became interested in how social interactions between individuals might influence which tasks an individual ends up specializing in. In reading how other modelers have thought about how social interactions in groups, I came across a body of literature in the intersection of sociology and political sciences concerned with political polarization. Sociological modelers showed that if individuals tend to become similar in opinion to those they interact with and tend to interact with those who share similar opinions, a feedback loop forms that ultimately ends with the group split into two groups holding extreme and opposing opinions. Given that political polarization causes individuals to act predictably—e.g., only vote for one party or issue position—it seemed to be a potential behavioral parallel to division of labor, where individuals act predictably and by specializing on one or a few tasks.

We borrowed this general dynamic—a combination of social influence and bias towards interacting with those who are similar—and put it in a model of division of labor we knew explained ant behavior well. We found that this feedback loop caused division of labor to emerge, even when individuals were all initially identical, leading to individuals that became so specialized that they each only performed one task. What’s more, “polarized” social networks emerged as a result of these social dynamics, such that individuals performing the same task all closely associated with each other and tended not to interact with those performing other tasks. This interaction pattern actually resembles what is really seen in ant social networks, and therefore the social dynamic we tested could help fill a gap in our understanding of how social network structure and division of labor are intertwined in social insect colonies. More broadly, however, our results suggested that political polarization and division of labor may be driven by the same process. Given how ubiquitous social interactions are in societies, this opens up the question of whether this same process may be organizing societies in other ways as well.

We should ultimately consider the exchange of ideas between biology and social science to be a two-way street, as there are still many more areas where the combination of both fields could unearth a wealth of new insights. Not only do ants and people have similar social networks, so too do dolphins, fish, chimps, and birds (where research shows individuals form cliques based on a range of factors including age, body size, and personality, among other traits). Could these animal social networks help us understand how we organize ourselves? Could sociology and psychology shed light on why animals may tend to associate with others like themselves? Elsewhere, a new field of study is beginning to explore how architecture and space influence the behavior of their inhabitants, with work spanning from examining office buildings to ant nests.

The current state of science—replete with the computing power to run complex behavioral simulations, algorithms to track the behavior of thousands of individuals at once in bee hives and the internet alike, and new ways to access research from outside our discipline—seems poised to offer a new era of collaboration among behavioral scientists. While the structure of the scientific enterprise may force us to more closely associate with those in the same field as us, I hope we find ways to reach across disciplinary boundaries. Ultimately, to find a common thread in the social life of not only all people, but all social species on Earth, we will need to see the bigger picture.

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Most of us grow up learning that cheating is bad. We should not steal. We should not lie. And above all: we should not exploit the groups we are part of, whether this is our family, our circle of friends, or even humanity itself. The implication is that cooperation and trust would be driven to extinction if such behavior were to spread – leading the downfall of not only the group, but also the individual cheats themselves [Kant sends his regards].

As many economists have pointed out, there are measures, largely through institutional design, that human populations can take to prevent a cultural spread of cheating. But what can non-human groups of organisms do against the spread of cheaters: that is, individuals that —unlike cooperators within the group— do not contribute to the collective good? Indeed, how is it possible at all that multicellular organisms such as ourselves evolved, if we are nothing but a highly integrated and cooperative group of individual cells? It turns out that the answer may force us to see cheats not as the doom, but rather as the savior of cooperation!

While economists and philosophers have often wondered how cooperation among humans is possible (see Veit 2019), biologists were primarily concerned with a different question: if cheaters benefit from their actions at the expense of others and thus will always do better than cooperators, then how is it possible that cooperation can not only evolve but also persist for an extended period of time. Shouldn’t evolution eventually eliminate all cooperators in a population in favor of cheats? The traditional answer here has been similar to that of economics – to find mechanisms or explore the conditions that aid the persistence of cooperators (see for instance Michod 1999). In much of traditional work in biology, cheats were seen as an unquestionable evil. Economic tools such as evolutionary game theory were used, often successfully, in biology (see Veit 2019). But through this process biologists inherited the agential explanations common in economics – making them blind to the potentially positive roles of ‘cheats’.

Evolutionary biologist Paul Rainey, in a number of influential publications with colleagues (2003, 2007, 2010, 2014), argued that this view is too narrow – that indeed, it misrepresents the importance of conflict within evolution. Through an exciting experiment on a possible mechanism for the evolution of multicellularity, that was eventually published in Nature (see Hammerschmidt et al. 2014), they argued that cheats —while initially undermining cooperation— could eventually restore cooperation and, indeed, even strengthen it within the group.

They argue that the problem for the evolution of multicellularity is that cooperating cells are often bound together. They cannot disperse when cheats arrive. Cheats, therefore, take over the population (due to their higher replication rate) and eventually come to dominate the population —leading to the eventual downfall of all.

Genuine multicellular organisms, however, are more than just a group of cooperating cells. They are integrated in special ways, such that they depend on each other, show division of labor, and have special cells (germ cells) that are responsible for the reproduction of the individual.

Unlike cooperators, cheats in ancestral populations, due to their lack of cell-glue production (i.e. the common good) can plausibly detach themselves from others. If they are able to produce daughter cells that stick together and cooperate, either by a genetic switch or by a mutation in the daughter cells, then a new group organism can be formed. This is a possible key that solves multiple problems for biologists at the same time: cooperation of individual cells and a mechanism of reproduction of the entire group. We get a life cycle and a new level of selection at which selection can occur. Prior to Paul Rainey, however, no one had come up with the simple hypothesis that ‘cheats’ could play the role of propagules, i.e. function as the reproductive system of the group. Why is that?

The answer lies in what some consider a pernicious way of thinking, common in biology. As already mentioned, biologists were eager to apply the tools of economics to problems in biology. Was this a mistake that eventually led to the introduction of agential thinking (Godfrey-Smith 2009) into biology? I would argue that this gets the causation backwards. Humans love agential explanations and have become addicted to narratives involving agents, goals, and purposes (see Rosenberg 2011 & Veit 2018). We constantly describe the behavior of our fellow humans as goal-oriented, and speculate about others’ hidden beliefs and desires. This theory of mind, we are all equipped with and use in our daily lives, has been called ‘folk psychology’ and criticized by many as scientifically misguided. Despite being used by humans for thousands of years, there seems to be little if any improvement in how we predict and explain the behavior of others within the last hundreds of years (in ordinary life).

“How is it possible at all that multicellular organisms such as ourselves evolved, if we are nothing but a highly integrated and cooperative group of individual cells?”

What justification do we have in biology for treating organisms, genes, and nature as agents? Biologists often argue that this agential way of thinking is merely metaphorical. It helps us to come up with new hypothesis and guide our thinking. Indeed, I have defended this way of thinking myself in a joint publication with Daniel Dennett (famous for his defense of what he calls ‘the intentional stance’), and other ‘Dennettian’ philosophers (Veit et al. forthcoming). But recognition of a useful role for such a mode of thinking cannot be an unlimited ‘get out of jail’ card. As Rainey’s work shows, this way of thinking can not only go wrong, but it can also stop us from considering alternative hypotheses, something that the heuristic defense of agential thinking is all about. Instead, we should embrace a model pluralism (see Veit 2019) that helps us to generate new hypotheses, without taking the model or heuristic ALL too seriously. By embracing a variety of different models, we can leave it to the empirical results from experiments to decide where our theoretical investigations should take us next.

In the end, however, we may have to choose between accepting that ‘cheats’ need not always be bad, or give up on the intentional language altogether.

If this raised your interest, you might be interested in the paper this brief entry in Biomusings is based on: “Evolution of multicellularity: cheating done right” (Veit 2019).

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Is Lamarckism back? Current discussions, specifically in the field of epigenetics, seem to suggest that this is the case. Scientists as well as science popularizers describe numerous instances of the inheritance of acquired traits as the vindication of Lamarck. What does it mean, however, to qualify as “Lamarckian” inheritance? And why are we so tempted to connect recent discoveries to the ideas of a natural philosopher, who wrote an ill-received book, the Philosophie Zoologique, more than 200 years ago?

In what follows, I will address several questions. What does it mean for Lamarck to be back? Are all cases of the inheritance of acquired traits cases of Lamarckism? What is problematic about calling a phenomenon Lamarckian? And, finally, if it is problematic, why use the word at all?

In recent years, I tried to push back against a very sloppy use of the term “Lamarckian”. Far too often are instances of the inheritance of acquired traits broadly characterized as Lamarckian. However, this is a mistake, as Lamarck did not invent the idea of the inheritance of acquired traits. What was new with Lamarck is that he proposed a mechanism: if a specific faculty of an organism is used extensively, it will get augmented, and this augmentation can be passed on to subsequent generations. Similarly, if a faculty gets disused, it will get reduced, and this reduction can be passed on to subsequent generations. I call this mechanistic underpinning the “use / disuse paradigm”.

While there are many different instances of the inheritance of acquired traits, I believe that only those instances operated by a “use/disuse” process ought to be called Lamarckian. This often requires a “molecularized” interpretation of the terms “use”, “disuse”, “augmentation”, “reduction” and “faculty”.  That is, we need to establish correspondences between these terms and molecular entities and mechanisms. 

“Lamarck did not invent the idea of the inheritance of acquired traits.”

Inheritance systems involving competition are candidate examples for a “use/disuse” process. Imagine scenarios in which a specific resource is limited. This resource mediates the amplification and persistence of specific molecular entities throughout generations. Not all molecular entities that compete for the limited resource will achieve to interact with it. Interacting with a limited resource by an entity means its “use” and results in amplification and augmentation of the respective entity in subsequent generations. Failing to interact means “disuse” and results in the reduction of the respective entity in subsequent generations. I have recently identified two examples of such use / disuse governed inheritance systems: small RNA inheritance in the roundworm C. elegans and the CRISPR/Cas system in Bacteria.

So far, I have answered two of the four questions:

What does it mean for Lamarck to be back? – The identification of use / disuse governed inheritance systems!

Are all cases of the inheritance of acquired traits a case of Lamarckism? – No, it makes sense to narrow this term down to denote only use / disuse governed processes!

The next question we need to address is what is problematic about using the term “Lamarckian”. One could argue that the operationalization I provided is fairly innocent. It is however important to keep in mind that terms have a history, and this is certainly true for the term “Lamarckian”. One issue that is tied to its history is “teleology”. Part of Lamarck’s position was the idea that organisms have an innate tendency to evolve towards a definite goal, such as increasing complexity. Additionally, the concept of the organism is strongly associated with an idea of agency. Thus, in a Lamarckian framework, the organism is envisioned to strive towards greater complexity.

Both teleology, and the organism as an actor are not necessarily concepts that are part of the discourse around the inheritance of acquired traits. Use of the term “Lamarckian” might nevertheless unconsciously reintroduce these concepts.

In the last paragraph I examined what might go wrong when we use the term “Lamarckian”. The last thing to do here is to argue why we should still call some instances of inheritance “Lamarckian”. Many terms constantly used by biologists have a long-standing history, and had different meanings, and different connotations throughout history.  This is true for concepts such as “organism”, “mechanism” or “the gene”, to name only a few examples. Nevertheless, practice proves that it is possible to use these terms successfully, and, at best, be mindful of their history. Also, we can use such knowledge to understand preconceptions and prevailing forms of representation of these concepts. As we cannot escape the history of the terms we use, we need to be mindful of the way we use them. Explaining the intended connotation is better than trying to keep biology “sterile” of its own history.

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Microorganisms, including viruses, bacteria, Archaea, and some fungi, are ubiquitous, occupying diverse niches and ecosystems. They associate stably or transiently with all macrobes on Earth, and this interplay sometimes gives rise to very close and intimate connections. In fact, some microorganisms carry the genes that encode very important phenotypic traits within their hosts (animals and plants), some of which are required for activities as important as nutrition, development, protection against parasitoids, thermal tolerance, among others.

In light of these findings, a group of biologists and philosophers has recently coined the concepts of “holobiont” and “hologenome”. Holobiont refers to the ecological entity of a macrobial host plus the set of associated microorganisms that compose its microbiome. Hologenome refers to the collection of genes of this ecological unit (host genome plus genes of the microbiome). The goal of this essay is to explain the biological implications of the hologenome perspective. I will show why, if biologists want to have a complete picture of the evolutionary process, they should pay more attention to the evolutionary effects of the microbiome on animal and plant evolution.

The hologenome concept reconceptualizes hosts as communities or ecosystems of genetically diverse organisms. Resultantly, variation in the holobiont compositions (either at the level of the host, at the level of the microbiome, or at both levels) can lead to neutral or selective changes in hologenome composition over multiple generations. A key point of the hologenome concept is that response to selection may not just occur at the host genomic level, but at the microbial or host-microbial levels, thus claiming that the holobiont is a level (or unit) of selection in evolution.

Proponents of the hologenome concept assume a multilevel, hierarchical view of the evolutionary process (selection occurs at multiple levels, including the holobiont unit). They accept that the hologenome can be shaped both by selection and neutrality forces (selection is not the only process that determines evolutionary trajectories); thus, the hologenome concept does not necessarily require the cospeciation of the host and its microbes. Their point is that the wide variety of specializations (dietary, behavioural, ecological, etc.) that we see in macrobes may be the result of a selection or a drift process on the host genome and/or on the genetic components of the microbiome, and consequently both levels must be studied.

In my research, I introduced the concepts of “trait-recurrence” or “stability of traits”, to describe this perspective. This terminology puts the emphasis on the genes of the microbiome that transgenerationally give rise to the phenomenon of phenotypic trait-recurrence in the holobiont. The peculiarity of the stability of traits perspective is that it does not require that the bacterial taxa (or genomes) containing the genes with phenotypic effects on the holobiont are transgenerationally preserved: only the relevant genes need to be consistently preserved. This means that what is ultimately important is the preservation of the phenotypic effects, irrespective of the taxa bearing them.

For the stability of traits perspective, thus, the microbiome should not be taken merely as part of the host environment, it must be integrated with it as an “extended genome” or hologenome.

Let me use an example that highlights the importance of the hologenome perspective to properly characterize animal evolution. In a recent research paper about the evolutionary origin of blood-dietary specialization in common vampire bats (order Chiroptera), Dr. Mendoza and her colleagues demonstrated that genes in the host genome as well as genes in the microbiome had been shaped in specific ways to cope with the dietary challenges posed by hematophagy (obligatory blood-sucking diet). Their research was conducted in three stages:

• First, they observed that the genes in the vampire bat genome were insufficient to explain some of their adaptations to hematophagy, including the capacity to deal with blood-borne pathogens, or the adaptation to a protein-based diet.

• Second, they observed that the vampire bat microbiome was functionally very different from the microbiomes of frugivorous, insectivorous, and carnivorous bats, indicating the specificity of the microbiome to hematophagy.

• Third, they proved that part of the diverging functional genes in the vampire microbiome were directly related to hematophagy. Concretely, they played a substantial role in dealing with some of the dietary challenges associated with the blood-sucking lifestyle.

Dr. Mendoza and her collaborators concluded that the microbiome had played a substantial evolutionary role in the evolution of vampire bats. This led them to conclude that some of the genes of the microbiome, despite not being physically integrated into the bat genome, evolved as a part of an “extended bat genome”. 

The main lesson that we can draw from this research is that animal and plant genomes have the capacity to “externalize” part of their genetic resources, rather than incorporating them within their molecular structure. By “externalizing” these resources, an emergent entity–the hologenome–results from the sum of the animal or plant genome, plus the genes coded in its microbiome. This emergent entity can experience selection (and mutation, neutrality, drift, etc.).

Through this process of externalization, complex adaptations in hosts become feasible without the necessity of substantial genomic changes, relying in some cases just on changes in the genetic makeup of their microbiomes. As changes in the genetic compositions of the microbiome can occur (and spread) more rapidly than changes in the host genome, the possibility of hologenomic evolution opens up a completely new avenue for animal and plant evolution, including new ways to cope with sudden environmental challenges like climate change.

“As changes in the genetic compositions of the microbiome can occur (and spread) more rapidly than changes in the host genome, the possibility of hologenomic evolution opens up a completely new avenue for animal and plant evolution...”

The momentum for the hologenome perspective is coming, and it is thus time for biologists to realize that the best way of properly thinking about animal and plant evolution requires one to take into account the important and selectable effects of the microbiome on animal and plant genome evolution.

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I’ve been fascinated with life in the universe since I was a kid. Who hasn’t spent hours thinking of what else is out there, or if we’re alone, or where we come from?

In my case, I also spent a lot of time wondering how different life could be, on the most basic level, and the circumstances that lead to life emerging in the first place. I decided that since we only have one example of life, here on Earth, I should get to know that one first. So, I decided to first study biochemistry and then went on to research the origin of life on Earth through a Ph.D. in prebiotic chemistry. 

I went into my Ph.D. thinking that the molecules that our biology is built on, and the chemistry that our life uses in general, were born of complete chance, and that life could have easily chosen a very different path. I came out of it with a slightly different point of view. To be clear, I still think that when we find life elsewhere, it will be based on completely different biochemistry; there is no reason to think that the biomolecules we use are the only ones that can fulfill that role. However, perhaps there weren’t as many potential chemical avenues for biology to take on the early Earth. Let me explain how my research changed my opinion on this.

I did my Ph.D. at University College London with Matthew Powner, and in our lab, one of the main interests was in understanding how the building blocks of ribonucleic acid (RNA) could be assembled from simple molecules that were (most likely) around on the early Earth. In particular, we wanted to understand under what reaction conditions the products we were interested in formed selectively, without too much of all the other very similar molecules that could form. One of the reactions I was performing involved producing potential precursors to ribonucleotides that already contain the sugar ring structure of the final nucleotide. This sugar moiety could be one of four orientations (diastereomers): xylose, arabinose, lyxose, and ribose (as you’ll recognize, the R in RNA).

Early on, I was struck by how consistently, in the reactions I was performing, the precursor containing the ribose orientation was preferred. Under a slew of reaction conditions, such as changing pH, starting material, temperature, etc, the ribose-containing molecule was the main product. Turns out, there is a reason that our life uses ribonucleic acids, and not lyxonucleic acids or others: chemistry. For example, and without going into too much (chemistry) detail, ribose-containing molecules in this case are more stable and less strained than other conformations, making it the preferred outcome of the reaction.

This made me wonder about what other features of our biochemistry were not due to just chance. After my PhD, I had the chance to work with Jim Cleaves at the Earth Life Science Institute in Tokyo, doing a short computational biology project there with a large international team. The project sought to understand why we use the 20 amino acids (canonical amino acids) that we do in biology, when there are loads of different ones possible. We looked at how well our 20 amino acids covered the chemical space (here defined as charge, hydrophobicity and mass ranges) compared to 20 other amino acids (selected at random from a large pool of possible amino acids). As it turns out, the set of canonical amino acids covers the space better: more evenly and more broadly (most of the time). The same thing happens when we look at subsets of those 20 amino acids vs smaller sets of random amino acids. This could explain why our life uses these 20 amino acids specifically, they’re better at what they do than other sets of amino acids.

Ok, so perhaps the biochemistry we use here on Earth isn’t completely random, and perhaps there is a (chemical) reason why we use the biomolecules that we do, to some extent. But what about life elsewhere? What if we started with completely different conditions, different molecules - what patterns would be the same? What would change? Are there any things that are universal in all possible biologies? And more importantly, how can we find those fundamental laws of biology when we only have one example to work with?

These are the questions that the astrobiological community are asking, and questions that I am trying to chip away at. I’ve now let go of lab work, and am using another powerful tool to try and work on these problems: modelling. Computational and theoretical modelling allow us to open up new spaces and, for example, “wind back the clock” again and again, and see how things can happen differently, or similarly, throughout the experiments. Are some things path-dependent and just due to chance? Or are some things conserved throughout repeated experiments or even through different experiments? Of course, we don’t have any way of knowing exactly what to input at the beginning of our models to replicate the conditions of the origin of life (here or elsewhere). But we can simplify, propose hypotheses for certain phenomena and test them out.

Right now, I am using these tools to try to understand if we can tell the difference between polymers that are made ‘abiotically’ (randomly, with no selection) versus some that are made through biology. The polymers I’m working with aren’t sophisticated computational chemical models resembling DNA or proteins, but rather strings of zeros and ones. Despite the extreme simplicity of the model, we can still get some very interesting insights into how different ways of making the polymers, and different dynamics of the environment, influence the final population in a distinct and detectable way. 

The answers we gain from these kinds of models help us understand better what we are looking for when we look for life elsewhere, because ultimately, we don’t know. We don’t know how different life could be in different environments and we don’t really know what to look for. But by trying to understand the universal patterns in our life, hopefully we can understand a bit more about life in general, and ultimately help guide the search for life in the universe.