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Biology

Evolutionary biologists put the ‘manosphere’ on notice The Medical … – The Medical Republic

Evolutionary scholars want you to stop mis-using their concepts. Those theories dont mean what you think they mean.

There are perfectly legitimate reasons for taking a deep interest in the evolutionary psychology of human female mating strategies. Perhaps youre an anthropology student, a historical novelist, a psychologist, or a reality TV producer.

But some have nefarious intentions, according to research published in the journal of Evolutionary Human Sciences.

[E]volutionary hypotheses on female mating strategies are routinely invoked among the antifeminist online communities, collectively known as the manosphere, the authors from Kent, UK, and Lille, France, warn.

And frankly, theyre sick of it.

Evolutionary scholars might be surprised to see sexist worldviews reinforced by the dual mating strategy and sexy son hypotheses, or by the latest research on the ovulatory cycle, they write.

The manosphere has its own version of evolutionary psychology, mingling cutting-edge scientific theories and hypotheses with personal narratives, sexual double standards and misogynistic beliefs. After analysing this phenomenon, this article suggests ways to mitigate it.

Of course, internet misogynists are not the first to manipulate evolutionary approaches to human behaviour for their own ends.

Everyone likes to claim Darwin for their own 19th century feminists used sexual selection through female choice to bolster the campaign for womens autonomy, and survival of the fittest is an unshakeable ideological pillar for fiscal libertarians, the authors point out, so the manosphere cant be accused of originality. But that doesnt mean it shouldnt be taken seriously.

These (mis)understandings of evolutionary psychology (EP) should be extremely concerning to those working in the field because legitimate scientific hypotheses are routinely used to justify disdain towards women, they say.

And theyre looking at you, incels (involuntary celibates), the Red Pill (TRP), Pickup-Artists (PUAs), Mens Rights Activists (MRAs), and Men Going Their Own Way (MGTOW).

The Back Page will refrain from providing links, for obvious reasons. Muck up your own search history and leave us out of it thank you very much.

You really dont have to go there though, because the authors bravely delved into three decades of manosphere online discourse (from 1993-2022). And they found that, yep, evolutionary psychology was pretty popular on those pages, mainly because of the obsession with sex.

Because evolutionary psychology is, well, huge, the authors had to narrow it down, concentrating specifically on references to female mating strategies (just as quite a lot of the manosphere does too).

Theories on female mating strategies have grown in number since the 1970s. Its a veritable smorgasbord of ideas. Macaques and bonobos have really shown us the way when it comes to realising that not all women are coy or lacking in eagerness to mate, nor necessarily very selective and sometimes monogamous but sometimes not. Females can, you know, have varied behaviours according to circumstances. This is all very good for biological sciences and for feminism, say the authors.

Given this legacy, evolutionary scholars might be quite surprised to see evolutionary research on female mating strategies appropriated in misogynistic ways, the authors say.

The dual mating hypothesis is particularly popular, they found, and the basis of a lot of Ah-ha moments like this one from an MGTOW thread on the social media platform Reddit:

Its 2019, we all know the secret females have been hiding for over a million years now. DUAL MATING STRATEGY. F&*k the alphas [alpha males], suck resources and attention from all others.

And another on the same platform from a Red Pill thread:

There is an observed dualistic mating strategy observed in primates and anecdotally in humans. Women have two motives for using sex. Primal: in an intimate reproductive urge to obtain genes from a partner. Passion and horniness. Transactional: in a survivalist exchange to obtain resources from a partner. Female Bonobos will trade sex for food, and women will marry rich men they are not sexually attracted to.

In this world, there is no grey area of hypotheses, just the hard world of facts, along with citations that dont in fact back up the argument, the researchers found. And, oh yes, its deliberate.

People do not consciously act in their genes best interests. Yet, the use of the term strategy in the evolutionary literature misleadingly reinforces that impression, the authors say.

Hence the conclusion that feminism is, you guessed it, a sexual strategy.

Other theories-as-facts are around extra pair mating, used to assert that all women are cheaters because its a biological drive (and your girlfriend definitely will, especially if youre not an alpha male).

Our analysis revealed a lot of subtle and not-so-subtle shifts occur between EP and its manosphere version, the authors write.

Mostly gone are the marks of hypothesising. So are the precautions about using the genes eye view shortcut, or about the conditional nature of instincts. The timeline also changes: while academic hypotheses dwell on the aggregate behaviour of our ancestors over millennia, their manosphere versions are more unclear on that aspect.

Of course, this is coupled with a total absence of discussion on male sexuality and its evolutionary underpinnings.

Evolutionary biology scholars cant do much to stop their work being misinterpreted in hateful ways, the authors admit.

But they can make it harder to do, starting with ditching the morally loaded terms cuckold and infidelity and promiscuity. Language matters, the authors stress.

Secondly, you might think you dont need to state the obvious in a scientific paper, because your readers are colleagues who know all about the ultimate/proximate distinction and that behaviours from the distant past are not necessarily still with us.

What our analysis reveals however, is that these articles are also routinely read, shared and discussed by online communities. Moreover, in abstracts, titles and conclusions, academic publishing also encourages the communication of results in very definite terms, the authors point out.

Finally, go on, engage, call it out. Write a paper like this one, they say.

Ultimately, this might not contribute to mitigating the prevalence of EP in manosphere communities after all, EP is a rich and blossoming discipline.

However, it would at least make it harder for serious scholarship to get assimilated by the general public to reactionary and misogynistic discourse.

Evolutionary Human Sciences (2023), online 30 August

Send story ideas to cate@medicalrepublic.com.au. You never know wholl end up reading them

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Evolutionary biologists put the 'manosphere' on notice The Medical ... - The Medical Republic

Biology

Unearthing how a carnivorous fungus traps and digests worms – EurekAlert

image:

Glowing traps.

Credit: Hung-Che Lin (CC-BY 4.0, https://creativecommons.org/licenses/by/4.0/)

A new analysis sheds light on the molecular processes involved when a carnivorous species of fungus known as Arthrobotrys oligospora senses, traps and consumes a worm. Hung-Che Lin of Academia Sinica in Taipei, Taiwan, and colleagues present these findings November 21st in the open access journal PLOS Biology.

A. oligospora usually derives its nutrients from decaying organic matter, but starvation and the presence of nearby worms can prompt it to form traps to capture and consume worms. A. oligospora is just one of many species of fungi that can trap and eat very small animals. Prior research has illuminated some of the biology behind this predator-prey relationship (such as certain genes involved in A. oligospora trap formation) but for the most part, the molecular details of the process have remained unclear.

To boost understanding, Lin and colleagues performed a series of lab experiments investigating the genes and processes involved at various stages of A. oligospora predation on a nematode worm species called Caenorhabditis elegans. Much of this analysis relied on a technique known as RNAseq, which provided information on the level of activity of different A. oligospora genes at different points in time. This research surfaced several biological processes that appear to play key roles in A. oligospora predation.

When A. oligospora first senses a worm, the findings suggest, DNA replication and the production of ribosomes (structures that build proteins in a cell) both increase. Next, the activity increases of many genes that encode proteins that appear to assist in the formation and function of traps, such as secreted worm-adhesive proteins and a newly identified family of proteins dubbed trap enriched proteins (TEP).

Finally, after A. oligospora has extended filamentous structures known as hyphae into a worm to digest it, the activity is boosted of genes coding for a variety of enzymes known as proteasesin particular, a group known as metalloproteases. Proteases break down other proteins, so these findings suggest that A. oligospora uses proteases to aid in worm digestion.

These findings could serve as a foundation for future research into the molecular mechanisms involved in A. oligospora predation and other fungal predator-prey interactions.

The authors add, Our comprehensive transcriptomics and functional analyses highlight the role of increased DNA replication, translation, and secretion in trap development and efficacy. Furthermore, a gene family that is largely expanded in the genomes of nematode-trapping fungi were found to be enriched in traps and critical for trap adhesion to nematodes. These results furthered our understanding of the key processes required for fungal carnivory.

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In your coverage, please use this URL to provide access to the freely available paper in PLOS Biology: http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002400

Citation: Lin H-C, de Ulzurrun GV-D, Chen S-A, Yang C-T, Tay RJ, Iizuka T, et al. (2023) Key processes required for the different stages of fungal carnivory by a nematode-trapping fungus. PLoS Biol 21(11): e3002400. https://doi.org/10.1371/journal.pbio.3002400

Author Countries: Taiwan, United States

Funding: Funding for this work was provided by the Academia Sinica Investigator Award AS-IA-111-L02 and the Ministry of Science and Technology MOST grant 110-2311-B-001-047-MY3 to Y.-P.H. Computing was also supported by a research allocation from NSF XSEDE (TG-MCB190010) to E.M.S. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Experimental study

Cells

Competing interests: The authors have declared that no competing interests exist.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Unearthing how a carnivorous fungus traps and digests worms - EurekAlert

Biology

Space Biology Research Wraps Up Crew’s Work Week – NASA Blogs

The suns first rays begin illuminating Earths atmosphere as the space station orbited 260 miles above the central United States.

Bacteria, brain aging, and gravity-sensing cells were the main research subjects aboard the International Space Station on Friday. The seven Expedition 70 crew members also worked on computers, communications gear, and life support maintenance to wrap up the work week.

NASA Flight Engineer Loral OHara explored how microorganisms grow in microgravity, the potential damage they cause to spacecraft, and ways to disinfect the harmful bacteria. She inoculated microbe samples inside the Life Science Glovebox that will be compared to uninoculated samples. The NASA-sponsored Bacteria Adhesion and Corrosion study takes place in the Kibo laboratory module and aims to keep space crews and humans on Earth healthy.

Commander Andreas Mogensen from ESA (European Space Agency) viewed cell samples under a microscope for the Cerebral Ageing experiment. The study looks at brain cell-like samples to understand accelerated aging symptoms seen in patients on Earth and observed in astronauts on long-term space missions.

Astronaut Satoshi Furukawa from JAXA (Japan Aerospace Exploration Agency) prepared different cell samples for observation inside the Confocal Microscope then closed out the Cell Gravisensing biology Investigation. Earlier in the day, he swapped hard drives on a laptop computer then assisted OHara continuing to unpack the SpaceX Dragon cargo spacecraft.

NASA Flight Engineer Jasmin Moghbeli spent her day in the Harmony module configuring a variety of NASA and Roscosmos hardware. She first calibrated an ultrasonic inspection device that uses high-frequency sound waves to analyze materials, Afterward, Moghbeli checked space-to-ground, VHF, and inter-module communication systems.

Roscosmos cosmonaut Oleg Kononenko researched 3D printing techniques to learn how to manufacture tools and supplies in space and reduce dependence on cargo missions from Earth. Cosmonaut Nikolai Chub spent his day on life support and electronics maintenance. Cosmonaut Konstantin Borisov configured Soyuz crew ship and Progress resupply ship laptop computers then continued his photographic analysis of the stations Roscosmos modules.

Learn more about station activities by following thespace station blog,@space_stationand@ISS_Researchon X, as well as theISS FacebookandISS Instagramaccounts.

Get weekly video highlights at:https://roundupreads.jsc.nasa.gov/videoupdate/

Get the latest from NASA delivered every week. Subscribe here:www.nasa.gov/subscribe

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Space Biology Research Wraps Up Crew's Work Week - NASA Blogs

Biology

Whither Queer Biology? On Richard O. Prum’s Performance All the Way Down – lareviewofbooks

INTERDISCIPLINARY WORK across the humanities and sciences is inherently tricky and fraught. While the body mostly lives within the biological sciences, its key descriptive categorieslike sex/gender, natural/unnatural, and health/pathologyare not inevitable scientific or quasiscientific categories but are very much shaped by particular histories, including patriarchal and Eurocentric ones, and doubtless plenty of others we cant even name yet. One enduring problem is that in translating colonial politics onto the natural world from the 17th century on, bio-superiority was naturalized and scientized through and in the language of biology. Politics, in other words, became invisible within biology. A central project of feminism is to reveal the politics, but this is no easy task: scientists claim they leave cultural biases behind when they enter their laboratory or field site. Their critics show that this is not true, but the showing, as it were, is a veritable process of whack-a-molea scientific study is refuted only for another to reclaim so-called categorical difference. Despite the now-long list of tarnished studiesracial differences in skull size, sex differences in scientific ability, correlations between finger size and homosexuality, IQ differences across nationsthe obsession with category-creating biological differences continues unabated. During the first two years of the COVID-19 pandemic, for instance, all kinds of claims about racial differences in the incidence, morbidity and mortality rates of COVID-19 were made. These differences were couched in the scientific language of comorbidity rather than, as they ought to have been, in that of economic inequality or unequal access to health care.

Adults, its now clear, are not preformed as the preformationists, as they were called in the 18th century, believed. We now know they emerge from complex interrelations (entanglements, feedback loops, interactions, intra-actions) between genes and environment, nature and nurture; there is no being but always a becoming. In gender scholar Angela Willeys evocative framing, life opens up biopossibilities, complexly mediated capacities that cannot be relegated to a pure biology or nature but which embody socially salient traits and differences. Characteristics such as height, weight, hair length, and texture are, for example, shaped by natural and cultural factors. Likewise, categories such as sex, gender, race, class, nationality, and behavioral designations such as temperament, intelligence, abilities, and wealth exist within histories of science and politics. How we define these categories themselves constitute biopolitical projects. Biology and society are not separate entities. Some scientists have incorporated this knowledgebut have done so by entirely enfolding the social realm into their disciplines. In particular, versions of sociobiology popularized by E. O. Wilson in 1975 attempt to explain human sociality through biology: humans are the product not only of individual human biology but also of a collective social biology. Rather than nature and culture, everything here is nature and natural. Its critics argued, quite rightly in my view, that the early sociobiology looked a lot like the old scienceit rationalized an unjust and hierarchical world as natural. A second set of responses have come from feminist, queer, critical race, intersex, trans, and disability rights scholars who refuse to cede biology to the sciences, instead insisting that any meaning-making must be centered on both nature and cultureor, more precisely, on biology, culture, and political contexts. In critiquing the biological determinism of science, they have advocated for new epistemologies, methodologies, and methods that bring the ands in the sentences above, in all their entangled complexity, to the fore. Neither response, sociobiological or feminist et al., is homogeneous or without internal debate.

This is where evolutionary ornithologist Richard Pruma professor of ornithology at Yale University, and author of The Evolution of Beauty: How Darwins Forgotten Theory of Mate Choice Shapes the Animal Worldand Us (2017)picks up in his new book Performance All the Way Down: Genes, Development, and Sexual Difference. He combines the two strainssociobiological and critical feministto propose that biology, and sex in particular, is a choreographed performance not just in terms of behaviors or the organism as a whole but all the way down to genes, cells, tissues, and hormones. I am reminded of Charis Thompsons evocative framing of Making Parents: The Ontological Choreography of Reproductive Technologies (2005). Splicing these two strains into one theory makes for provocative readingIll unpack its problematic nature later in this essay.

The point here is that something called science manufactures exceptions, and society pathologizes these exceptions in an endless feedback loop. Similarly, no single gene, hormone, or behavior can claim to be the root of all sexual difference. Moving beyond the human species to animal and plant worlds, the amount of variation explodes. Many organisms have a chromosomal structure that is contrary to ours. In birds, females are ZW and males ZZ. In some organisms, like turtles, for instance, temperature can help determine sex. Hundreds of fish species regularly change sex as adults (and then sometimes revert). In some lizards, females are parthenogenetic (eggs develop into embryos without fertilization). Biologists Joan Roughgarden (in Evolutions Rainbow: Diversity, Gender, and Sexuality in Nature and People) and Bruce Bagemihl (in Biological Exuberance: Animal Homosexuality and Natural Diversity) chronicle the astonishing diversity of queer possibilities. Over 85 percent of flowering plants do not have distinct male and female partsyet even here we are obsessed with binary sex: we label flowers male and female. If flowers arent binary, then we mark flower structures as male and female. These binary categories then unfold within biology into sexual scripts, featuring Darwins coy females and aggressive males. Male and female arent innocent terms, howeverthey can generate misogynist scripts. In short, despite resplendent diversity, science has created a monotonous world modeled on Victorian gentlemen and ladies! As Prum summarizes, the deviations in sexual development are not failures or aberrations but evidence that the individual sexual binary does not exist. Its easy to agree with Prums analysis thus far.

It gets more complicated though. Prum builds on this critique to create a grand unifying theory. The book cover features a blue-and-pink chromosome bathed in pastel shades of pink and blue. Prum is not refusing a binaryrather, he is refusing simple bio-determinism in favor of something more interesting that draws in part on Judith Butlers theory of gender as performance. In a nutshell, gender, they argue, is socially constituted through everyday speech acts and communication styles that are performative. Gender is a performance, a process, a doing by an individual in a social world. Prums extrapolation: Performance is a playing out of an historically derived role in a social context (an enactment of Darwinian evolution), and a becoming through doing, which provides a role for individual action and thus for the agency of organisms. Here he also draws inspiration from feminist philosopher Karen Barad and their epistemology of intra-action and agential realism. Refusing the distinction between matter and discourse, agency is not enacted by inter-actions between discrete organisms, Barad argues, but rather through intra-actions: the dynamism of forces in which all things constantly exchange, diffract, influence, and work inseparably. In agential realism, both matter and discourse are mutually constitutive.

Prum does not challenge or critique feminist or queer theory, but instead folds them wholesale into biological theories of evolution and animal behavior. That would be fine up to a pointbut he misunderstands their core ideas. He insists, for instance, on core differences between sex categories as vital for evolution. Sex, he argues, is relational and dyadic within populations: i.e., there are two sexes even though individuals may not be one or the other. And while critical of sex difference research, he nonetheless finds the notion of sexual difference productive; it is productive to study individual sexual variation in the absence of a priori binary categories of individual sex. In his theorization, a phenotype, such as the birds brilliant plumage or mating dances, is a performative enactment by an individual organism. Hormones dont cause effectsrather, individual bodies employ hormones to enact desirable phenotypes. Rather than adhering to a story where hormone levels are biologically determined and produce particular behaviors, hormones themselves are open to social and evolutionary forces. In this way, his theory of performance helps outline a choreography of molecular, cellular, and organismal intra-actions.

The book is not actually interdisciplinary but rather multidisciplinary. Two bodies of worknamely biological work on sex on the one hand and feminist and queer work on the otherare juxtaposed, quite literally in separate chapters and appendices. The chapters on feminist and queer theory cite key texts and explain important concepts. These are not simplistic or simplified but elaborated with care and some depth. Yet, reading about them was an odd experience for me. For much of my academic life, Ive bemoaned the refusal of most scientists (I myself am trained as a scientist) to take feminist and queer theory seriously. And Ive noticed that when scientists do discover for themselves the complexity of sex and gender, they rarely credit feminist scholarship. So, kudos to Richard Prum for reading this work, citing it, and recommending it to his peers. And yet, the book makes me uneasy. It is as though someone took an advanced course in feminist and queer theory but entirely skipped Feminism or Queer Studies 101! For all its grand theory and terminology, the book is entirely devoid of politics or a theory of power, which is after all the raison dtre of feminist and queer theories. This obliviousness to politics leads to strange conclusionslike arguing that a feminist notion of gender/sex is precisely congruent with Richard Dawkinss concept of extended phenotype! To glibly claim feminist congruence with a figure decidedly hostile to feminism (and vice versa) is jarring, to say the least. The claim demands at least some exegesis. Similarly, he frames his work as extending Butler to Lewis Thomass The Lives of a Cell: Notes of a Biology Watcher (1974); he squishes Barad and Bruno Latour together as though they were analogous, and extends Foucault unproblematically to theories of the evolution of sexual reproduction itself. These easy analogies between the two fields may be unnerving for readers conversant with both. One is likely to get whiplash.

Interdisciplinarity is difficult because knowledge formation is organic and accumulates over time, becoming ever more nuanced, debated, and detour-prone. Knowledge is deeply contextual within fields; words and terminology aim to be precise and must be understood within disciplinary histories. Practicing feminist and queer theory isnt just about using the right words but also about understanding and engaging with power and politics. Much can be lost in translation, and here it most certainly is. Ultimately, for a book engaging with queer theory, it is decidedly unqueer! For example, after the extensive critique of binary sex/gender, Prum holds on to the two categories male and female, but in capitalswhich, he claims, makes it easier for him to discuss reproductive biology. But wouldnt talking about reproduction without Males and Females be productively queer? For example, consider intersexuality. If we supportas we absolutely shouldthe end of unwanted genital surgeries, then we ought to be able to deal with the continuum of sex, and to do so without othering and stigmatizing nonbinary individuals. Many countries have indeed begun to offer options beyond male and female in their passports, and social media platforms have embraced a plethora of genders. Workplaces allow individuals to claim their own pronouns (or refuse them). Scholars have proposed models of sex as a continuum. With respect to plants, Madelaine Bartlett and I propose dispensing with the terms altogether, focusing instead on reproductive structures. Yet, despite the proliferation of queer life and possibilities in the world, and despite his own critique of binary sex at an individual level, Prum insists on its significance at higher levels, and especially so for those embodied features that have evolved for reproductive function. This means that, in his book, the whole apparatus of reproductive heteronormativitygender, heterosexuality, family, marriage, monogamylooms large but unaddressed. In short, Prum offers a theory of biological performances of intra-actions, yet ahistoricized and depoliticized.

Most importantly, the invocation of sex/gender binaries as universal invariably risks reproducing whiteness-related assumptions. For example, many women athletes, especially Black women, are routinely read as masculine. Femininity remains resolutely in the province of delicate whiteness. In many countries, like India, light skin personifies beauty. This is a colonial aesthetic, and a colonial legacy. Colonial hierarchies extended the dehumanized status of animals to nonwhite colonial subjects. In short, Prum may cite work on intersectionality and claim to be an ornithologist for intersectionality, but there is evidence to the contrary in his insistence on sex/gender as productive.

Also, by centering reproduction at more meta levels than that of the individual, Prum is able to do something he is clearly keen to do: embrace theories of sexual selection, which also means embracing a gamut of phenomena that some feminists have long critiqued because they are implicated in histories of biological determinism. Those histories, it should be noted, have wrought systematic pain and suffering onto gendered, raced, and colonized bodies deemed marginal. Eugenics is possible because science identified some bodies as unworthy of lifee.g., the purportedly feeble-minded, degenerate, perverse, deformed, and promiscuous, as well as epileptics, criminals, alcoholics, paupers, and so on. Eugenicsthe late-19th-century science of good genes, along with the statistical methods developed by Francis Galtonprovided the scientific rationalization for 20th-century sterilization laws in the United States, which then inspired the Holocaust in Europe. Eugenic logics live on in this century, most dramatically in IVF contexts, in who is encouraged to reproduce (wealthy, white), and in who is discouraged or even coerced into long-term contraception or sterilization (people of color and the poor). Some fetuses, as we know, are preferentially terminated. According to the logic of bio-determinism, they cant be dismantled or undone in the population at large except through the extermination of their germ- or bloodlines. Biology becomes destiny rather than ideology.

On a slightly different note, I find Prums easy extension of human phenomena to plants and animals troubling. For example, he advocates the use of the word rape in nonhuman organisms because the term forced copulation in biology has allowed scientists to avoid the recognition that sexual violence is, to paraphrase [journalist and activist Susan] Brownmiller, against the will of the ducks. For decades, feminists have insisted that we understand rape as an exercise in power rather than a biological imperative. Men rape not because of biological programming but because of male supremacy. Human rape is not an evolved feature of [] evolutionary histor[y].

In many ways, Prum proposes a queer sociobiology in much the way that some feminists have posited a feminist sociobiology. Both formulations are oxymorons. I dont think we need to biologize everything in the world to explain human politics. We dont need to argue that feminism, civil rights movements, antisexual violence, and antisexual harassment are biological responses to oppression. We dont have to wait for our biologies to evolve a countermovement to oppressive histories and actions. We need the language of politics, power, and resistance, not the language of biology. Otherwise, we are left with a scientized humanities, with everything in the world engulfed by a sociobiology that portrays us as the victims of our own biology and evolutionary history (written by and for a colonial and patriarchal science).

Second, we might consider that, within the walls of science, a distinguished senior scientist like Prum can embark on this work, but can a graduate student or junior feminist or queer scientist? Will their work be published in scientific journals? Will their work be read as science, credited in decisions regarding promotion and tenure? No, of course not.

Third, while Prum does cite and summarize the role of race in theories of feminist and queer studies, race as a concept drops off repeatedly in his book. He pays a great deal of lip service to intersectionality in feminist and queer studies, but he fails to realize that these fields have not yet fully integrated race or colonialism. In his book, the natural world, for example, is gendered but rarely raced or colonized. Recent work by Bndicte Boisseron, Zakiyyah Iman Jackson, and Harlan Weaver demonstrates just how much race grounds colonial thinking. Animality and race are transposed not only onto some human subjects but also onto animal and plant worlds. Theorizing gender through the history of colonialism might have helped Prum destabilize the whiteness of a universal sex/gender system.

Finally, there is much work in feminist and queer studies that destabilizes sex and gender and decenters reproduction. Why not do more with horizontal and lateral inheritance, where genetic material moves across organismsas when bacteria and viruses move genetic material across speciesrather than vertically from parent to offspring? While Prum does mention this in passing, he foregrounds vertical inheritance and sexual reproduction as the bedrock of evolution. Its too bad. Decentering reproduction would have been delightfully queer. Similarly, theories of symbiogenesis queer individualist stories of evolution. Feminist, queer, and trans biologies stress multiplicity in order to open up biopossibilities. Rather than extending human biology to animals and plants, why not go the other way? As Myra Hird argues, given that most plants are intersex, fungi have multiple sexes, other species are transex, and bacteria are devoid of sexual difference altogether, we ought to ground theories of sex/gender in the world that defies sexual logics. Maybe then we can rethink the human beyond its sexual formations.

Whither queer biology? In the early days of institutionalized feminism, scholars debated whether they were working for their own obsolescence. If all disciplines embraced feminist work, then might there be no need for feminist departments? In his conclusion, Prum writes that once the biological sciences more thoroughly engage with queer studies in their research and teaching, they will cease to be queer. Not true! As disciplines engage with feminist work, feminist and queer work doesnt stand still; it continues to chart new ground, unmoored from the silos of disciplines. Feminist and queer ideas are not static, in other words, but are relational and themselves evolving. By the time biologists embrace this version of queer theory, the fields will have moved on. We dont need a momentary exchange but, rather, continual scrutinization of the unequal distribution of power across disciplines. And we need to broker new practices of collaboration that open up heretofore unthought-of choreographies of interdisciplinarity.

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Whither Queer Biology? On Richard O. Prum's Performance All the Way Down - lareviewofbooks

Biology

An Argument from Ignorance? – Discovery Institute

Image source: Discovery Institute Press.

Editors note: We are delighted to welcome the new and greatly expanded second edition ofThe Design Inference, by William Dembski and Winston Ewert. The following is excerpted from the Introduction.

Tacitly in the first edition ofThe Design Inferenceand explicitly in its sequel,No Free Lunch, I argued that natural selection and random variation could not create the sort of complexity we see in living things. My approach in applying the design inference to biology was to piggyback on the work of design biologists such as Douglas Axe and Michael Behe. They had identified certain subcellular systems (e.g., bacterial flagella and beta-lactamase enzymes) that proved highly resistant to Darwinian explanations.

Our joint task was to put plausible numbers to these systems so that even factoring in Darwinian natural selection, the probability of these systems arising was exceedingly small. Note that the specification of these systems, as in their exhibiting the right sort of pattern for a design inference, was never in question. The issue was always whether the probabilities were small enough. In using specified improbability to draw a design inference for biology, I therefore needed to argue that the probabilities for Darwinian processes producing certain biological systems, such as those identified by Axe and Behe, were indeed small.

As far as Darwinists were concerned, however, all attempts to show such biological systems to be vastly improbable were misguided and irrelevant. Any design inferences meant to defeat Darwinian evolution were, according to them, arguments from ignorance. For them, unidentified Darwinian pathways could never be decisively ruled out, so their mere possibility invalidated any design inference applied to biological evolution. In short, no calculated improbability could ever convince the Darwinian critics that the probabilities were actually small.

It didnt matter that Darwinists were ignorant of any detailed evidence for such Darwinian pathways, and thus had no counter-probabilities to offer. It was enough for them merely to gesture at the possibility of such pathways, as though raising a possibility could itself constitute evidence for an argument from improbability. To ID proponents critical of Darwins theory, the argument-from-ignorance objection seemed to apply more aptly to the Darwinists themselves for positing unsubstantiated Darwinian pathways that offered no nuts and bolts, no nitty-gritty, just hand-waving.

No matter. For Darwinists to refute ID, they merely needed to postulate unidentified, and perhaps forever unidentifiable, indirect Darwinian pathways in which structure and function coevolved and led to the complex biological features in question. Brown University biologist Kenneth Miller led the way. Michael Behe had defined a system (biological or otherwise) to beirreducibly complexif its function was lost by removing key parts. He argued that such systems resisted Darwinian explanations. Miller countered that Behes concept of irreducible complexity was ill-conceived because removing parts from, or otherwise simplifying, a biological system could always yield a system with a different function. To convinced Darwinists like Miller, design in biology was therefore a nonstarter. Darwinian pathways to all complex biological systems had to exist, and any inability to find them simply reflected the imperfection of our biological knowledge, not any imperfection in Darwins theory.

Richard Dawkins, better than anyone, has publicly championed the dogma that Darwinian pathways can and must always exist for any biological system. In a 1990s television interview he memorably took Behe to task for claiming that irreducibly complex biochemical machines, of the sort Behe popularized inDarwins Black Box, were beyond the reach of Darwinian processes. Dawkins charged Behe with being lazy (yes, he used that very word) for seeing in the irreducible complexity of these machines a reason to conclude design, and thus to rule out any further effort to discover how Darwinian processes could have formed, say, a bacterial flagellum. That is, instead of concluding that these systems were designed by a real intelligence, Behe should get back into the lab and redouble his efforts to discover how Darwinian evolution could have produced them apart from design.

The reaction of the ID community to Dawkinss laziness challenge was that he might just as well have recommended to physicists that they keep trying to construct a perpetual motion machine. Yet why did one task seem futile (constructing a perpetual motion machine) but not the other (discovering Darwinian pathways to irreducibly complex biochemical machines)? Physicists had the second law of thermodynamics to rule out the charge of laziness. Thats why Dawkins would never have said to a physicist, Youre just being lazy for giving up on inventing a machine that can run itself forever.

Even so, Dawkinss laziness challenge was and remains misguided because Behes skepticism is based not on ignorance but on careful study of the obstacles that Darwinian evolution must overcome and its consistent failure to do so. To seal the deal, however, the ID research community still needed something like the second law for biology. We found it in thelaw of conservation of information. This law logically completes the design inference. Well address this law in the epilogue.

See the article here:

An Argument from Ignorance? - Discovery Institute

Biology

The Uniquely Buoyant Biology of the Cuttlefish – Laughing Squid

Stephanie SammannofReal Scienceexamined the unique biology of the cuttlefish, noting its mesmerizing strobing bioluminescence, remarkable intelligence, and singular buoyancy.

A major component of getting around the ocean is having some kind of buoyancy system cuttlefish have an utterly unique buoyancy mechanism the cuddle bone. cuttlebone has high porosity and high permeability,meaning it can resist changes in shape when being acted on by a physical force.

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The Uniquely Buoyant Biology of the Cuttlefish - Laughing Squid

Biology

Scientists Shed New Light on the Dark Matter of Cellular Biology – SciTechDaily

Researchers have created a novel fluorogenic probe to illuminate the interactions between sugars and proteins, crucial for understanding various biological processes and diseases.

Researchers at the University of Montreals Chemistry Department have created an innovative fluorogenic probe for analyzing interactions between sugars and proteins, two families of biomolecules essential to life.

The findings by professorSamy Cecioni and his students, which open the door to a wide range of applications, were recently published in theprestigious European journalAngewandte Chemie.

Sugar is omnipresent in our lives, present in almost all the foods we eat. But the importance of these simple carbohydrates extends far beyond tasty desserts. Sugars are vital to virtually all biological processes in living organisms and there is a vast diversity of naturally occurring sugar molecules.

All of the cells that make up living organisms are covered in a layer of sugar-based molecules known as glycans, said Cecioni. Sugars are therefore on the front line of almost all physiological processes and play a fundamental role in maintaining health and preventing disease.

Fluorogenic lock and key graphic. Credit: Cecioni Lab

For a long time, he added, scientists believed that the complex sugars found on the surface of cells were simply decorative. But we now know that these sugars interact with many other types of molecules, in particular with lectins, a large family of proteins.

Like sugars, lectins are found in all living organisms. These proteins have the unique ability to recognize and temporarily attach themselves to sugars. Such interactions occur in many biological processes, such as during the immune response triggered by an infection.

Lectins are attracting a lot of attention these days. This is because scientists have discovered that the phenomenon of lectins sticking to sugars plays a key role in the appearance of numerous diseases.

The more we study the interactions between sugars and lectins, the more we realize how important they are in disease processes, said Cecioni. Studies have shown how such interactions are involved in bacteria colonizing our lungs, viruses invading our cells, even cancer cells tricking our immune system into thinking theyre healthy cells.

There are still many missing pieces in the puzzle of how interactions between sugars and lectins unfold because they are so difficult to study. This is because these interactions are transient and weak, making detection a real challenge.

Two of Cecionis students, masters candidate Ccile Bousch and Ph.D. candidate Brandon Vreulz, had the idea of using light to detect these interactions. The three researchers set to work to create a sort of chemical probe capable of freezing the meeting between sugar and lectin and making it visible through fluorescence.

The interaction between sugar and lectin can be described using a lock and key relationship, where the key is the sugar and the lock is the lectin. Chemists have already created molecules capable of blocking this lock-and-key interaction, and can now to identify exactly what sugars are binding to lectins of high interest to human health.

Our idea was to label sugar molecules with a chromophore, a chemical that gives a molecule its color, explained Cecioni. The chromophore is actually fluorogenic, which means that it can become fluorescent if the binding of sugar with the lectin is efficiently captured. Scientists can then study the mechanisms underlying these interactions and the disturbances that can arise.

Cecioni and his students are confident their technique can be used with other types of molecules. It may even be possible to control the color of new fluorescently labeled probes that are created.

By making it possible to visualize interactions between molecules, this discovery is giving researchers a valuable new tool for studying biological interactions, many of which are critical to human health.

Reference: Fluorogenic Photo-Crosslinking of Glycan-Binding Protein Recognition Using a Fluorinated Azido-Coumarin Fucoside by Ccile Bousch, Brandon Vreulz, Kartikey Kansal, Ali El-Husseini and Samy Cecioni, 17 October 2023,Angewandte Chemie International Edition. DOI: 10.1002/anie.202314248

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Scientists Shed New Light on the Dark Matter of Cellular Biology - SciTechDaily

Biology

Pan-cancer landscape of epigenetic factor expression predicts tumor outcome | Communications Biology – Nature.com

Tumors from 24 adult tissue types separate into two distinct clusters based on expression of epifactor genes

We investigated whether tumors from each of the 24 adult cancer types in the TCGA repository (Fig.1a and Supplementary Data1)16 would separate into well-defined subgroups based on the expression patterns of 720 epifactor genes from the Epifactors database23 (Supplementary Data2). These epifactors encode proteins involved in the addition, removal, and recognition of DNA methylation and histone marks, and chromatin remodeling (Fig.1b, top panel,Supplementary Data2). The majority of the epifactor genes (556 out of 720) are not known to be genetically altered in cancer tissues (Fig.1b, bottom panel and Supplementary Data2)24,25. We clustered the patient tumors from each cancer type using the non-negative matrix factorization (NMF) algorithmbased on the epifactor genes with the most variable expression among the patient tumors (Fig.1c and Supplementary Data 1)26,27. With NMF clustering, a reduced representation of the gene expression data is generated that delineates a subset of genes that are important for separating the samples into clusters. For each of the 24 cancer types evaluated independently, separating the tumors into two clusters resulted in the best solution based on three measures of cluster validation (Supplementary Data1, Supplementary Fig.1, and Supplementary Data3). The two clusters for each cancer type were characterized by a set of signature top NMF genes with distinct expression patterns for the tumors in the two clusters (Supplementary Data4). As an example, for breast cancer, two distinct tumor clusters were observed (Fig.1d and Supplementary Fig.2a), and the PAM50 breast cancer subtypes28,29 were non-randomly distributed between the two BRCA epifactor expression-based clusters (Supplementary Fig.2b), consistent with a previous study showing different epigenetic characteristics for the PAM50 subtypes30.

a Tissue of origin for the 24 adult cancer types from TCGA included in the clustering analysis based on epifactor expression. Tissue locations are labeled with TCGA abbreviations. Sex-specific tissue locations are shown in purple for female (left panel) and blue for male (center panel). The full names for each cancer type are provided (right panel). b Functional categories for the 720 epifactor genes included in this study (top panel). This list of epifactors genes was obtained from the manually curated Epifactors database generated by Medvedeva et al.23. The bottom panel shows the overlap of these epifactor genes with the genetically altered cancer genes cataloged in either the COSMIC24 or OncokB25 databases. c The NMF-based clustering26,27 analysis workflow is provided. Raw RNA-seq counts for all of the genes in each patients tumor for a specific cancer type were normalized using the DESeq267 R package. The most variable epigenetic genes (var_epi) were selected based on a cancer type-specific standard deviation cutoff. This dimensionally reduced counts matrix (patient x var_epi) was used as an input to the NMF R program27. d PCA plot showing the two clusters (red and cyan) of the BRCA patient tumors (depicted as dots) as determined by the NMF method. The variances explained by principal components (PC) 1 (x axis) and PC2 (y axis) are plotted. e Heatmap showing the fraction of the top NMF genes for the cancer type in the corresponding column that overlaps with the top NMF genes for the cancer type in the corresponding row. Darker colors indicate a higher fraction of overlap. The rows and columns are hierarchically clustered. f Heatmap describing the most frequent top NMF genes (rows, genes ranked in decreasing order of frequency) across the 24 cancer types (columns). a and b were created using Biorender. Supporting information for this figure can be found in Supplementary Figs.1 and 2, and Supplementary Data14.

The number of top NMF genes across the 24 cancer types ranged from 76 genes for LGG to 9 genes for CRC, with a median of 43 genes (Supplementary Fig.2c). A pan-cancer map based on the expression patterns of the top NMF genes from all tumor types showed that the tumors group largely based on their tissues of origin, and, to some extent, tissue proximity (Supplementary Fig.2d). For example, KIRC and KIRP, two types of kidney cancer, were found near each other, and LGG and GBM, two types of brain cancer, were also adjacent to each other in this low-dimensional representation.

There was a high overlap among the top NMF genes in the ACC (carcinoma of the adrenal glands that sit atop each kidney), BRCA, LIHC (liver), LUAD (lung), STAD (stomach), LGG (brain), UCEC (uterus), and SARC (soft tissues and bone) cancer types (Fig.1e). A strong overlap was also observed among the top NMF genes in the KIRC (kidney), KIRP (kidney) and PAAD (pancreas) tumors (Fig.1e). SATB1 was the gene most frequently represented as a top NMF gene and was a signature gene for 12 cancer types (Fig.1f). SATB1 mediates chromatin organization by acting as a landing platform for chromatin remodeling proteins31. When the top NMF genes were assigned to one of 19 protein complex groups23, epifactors belonging to histone acetyltransferase (HAT) complexes were significantly enriched (P <0.05)among the top NMF genes for seven cancer types (LIHC, GBM, UCEC, BRCA, LUAD, KIRP, and PAAD).

To determine whether patients in the two clusters developed from expression levels of epifactors differ with regard to their clinical outcomes, we compared the progression-free interval (PFI), disease-specific survival (DSS), and overall survival of the patients in the two clusters for each cancer type. Cox regression was used to adjust for the effects of age and sex of the patients in each cluster, unless otherwise mentioned. The clusters from 10 out of 24 cancer types (ACC, CRC, KIRC, KIRP, LGG, LIHC, LUAD, PRAD, STAD, and UCEC) had significant differences in clinical outcome (P<0.05) for at least one of the three metrics (PFI, DSS, and overall survival) (Fig.2a).

a Heatmap showing the significance (P value from multivariate Cox regression analysis; adjusted for age and sex) of the difference in the clinical outcome (PFI, DSS, and overall survival) between the two epifactor expression-based tumor clusters for each of the 24 cancer types. The grey color indicates that the difference in clinical outcome between the two clusters is not significant. bf KaplanMeier plots comparing the progression-free intervals of the two NMF-derived clusters for the five-cancer group that show significant differences in clinical outcome for the three metrics PFI, DSS, and overall survival. Significance was determined with the log-rank MantelCox test. The cluster with poor outcome is designated (in superscript) poor, while the cluster with better outcome is designated better. The number of patients (n) in each cluster is shown. b ACCpoor n=40, ACCbetter n=31. c KIRCpoor n=61, KIRCbetter n=108. d LGGpoor n=107, LGGbetter n=146, e LIHCpoor n=70, LIHCbetter n=72, f LUADpoor n=20, LUADbetter n=49. g Heatmap showing the significance (P value; two-tailed Fishers exact test) of the difference in clinical metrics (pathologic M, pathologic T, pathologic N, stage, and grade) between the epifactor expression-derived clusters for the five-cancer group. hl Barplots of the clinical characteristics for instances (shown in g) in which the two clusters significantly differ. m, n Composition of epifactor expression-derived clusters for ACC (m) and LGG (n) with regard to established TCGA subtypes. o Classification of the epifactor expression-derived clusters for the five-cancer group based on established immunologic subtypes from ref. 33 with the following prognostic order (worst to best): C4~C6>C2~C1>C3~C5. Data for the clinical metrics were obtained from cBioPortal for Cancer Genomics60. Supporting information for this figure can be found in Supplementary Figs.38 and Supplementary Data5.

For the cancer types in the five-cancer group (ACC, KIRC, LGG, LIHC, and LUAD), the two clusters (Supplementary Figs.3 and 4) significantly differed in clinical outcome for all three metrics (Fig.2bf and Supplementary Fig.5). Consistent with the differences in outcome, the poor outcome ACCpoor cluster was composed of tumors with higher cancer stage (TNM stages 3 and 4), larger size (T3 and T4), and greater likelihood of lymph node spread (N1), compared to tumors in the better outcome ACCbetter cluster (Fig.2gj). The poor outcome LGGpoor and LIHCpoor tumors also included a significantly higher fraction of patients with grade 3 tumors than the tumors in the LGGbetter and LIHCbetter clusters, respectively (Fig.2g, k, l). When the clinical outcome differences between the NMF clusters were adjusted for stage and grade, all three metrics were still significant, except that for LGG and LIHC, significance was observed for 2 out of 3 metrics (Supplementary Fig6a). The distributions of the patients races and ethnicities did not differ between the clusters (Supplementary Fig6bg and Supplementary Data1) except for LIHCpoor, which had a higher fraction (~twofold) of Asian patients than LIHCbetter.

The prognostic efficacy of epifactor expression-based clusters was better than grade or epithelial-to-mesenchymal transition (EMT) for the five-cancer group (Supplementary Fig.7ad). Tumor grade was effective in predicting the outcome for just 1 or 2 cancer types, out of the five, across the three outcome metrics, while EMT could predict outcome for 24 cancer types across the three outcome metrics (Supplementary Fig.7ad). The two tumor clusters were not significantly different for EMT for ACC, LGG, LIHC, or LUAD (Supplementary Fig.7e). For KIRC, we did observe a significant difference (P=0.0015, two-tailed MannWhitney test) in the EMT scores between the two clusters, however, the tumors in the poor outcome cluster had a lower EMT score (median = 0.0750) compared to the tumors in the better outcome cluster (median = 0.17), the opposite of the expectation that a more mesenchymal phenotype would be associated with a worse prognosis32.

We asked whether the clinical differences between the clusters might reflect differences in the purities of the tumors in the two clusters. There was a significant difference in purity only for ACC and LGG (P<0.05, two-tailed MannWhitney test) (Supplementary Fig.8a), and all three clinical outcomes (PFI, DSS, and overall survival) were still significantly different for both ACC (P=0.002, P=0.006, and P=0.001; Cox regression) and LGG (P=0.008, P=0.007, and P=0.02) after adjusting for tumor purity. The differences in stromal fraction were significantly different for ACC and LGG clusters (Supplementary Fig.8b), but the clinical outcome differences between the NMF clusters for ACC (P=0.0003, P=0.0006, and P=0.0003; Cox regression) and LGG (P=0.0006, P=0.001, and P=0.003) were still significant after adjusting for stromal fraction. The ACC and KIRC clusters had different levels of immune infiltration (Supplementary Fig.8c), but the clinical outcome differences between the NMF clusters for ACC (P=0.007, P=0.015, and P=0.002; Cox regression) and KIRC (P=0.003, P=0.005, and P=0.0007) were still significant after adjusting for leukocyte infiltration.

We compared the epifactor expression-derived clusters with established TCGA subtypes for the five-cancer group33. None of the clusters were composed of only a single TCGA-defined tumor subtype. But, for each of the clusters, there was at least one TCGA subtype that was overrepresented (Fig.2m, n, Supplementary Fig.8df and Supplementary Data5). For example, for ACC and LGG (Fig.2m, n), there was a greater representation of some DNA methylation-based TCGA subtype(s) in the poor outcome cluster compared with the better outcome cluster, and vice versa. These results indicate that previously reported TCGA subtypes may have epigenetic features that contribute to their distinctive characteristics. Our epifactor expression-derived clusters also contained tumors with significantly different compositions of immunologic subtypes (see Methods)33 (Fig.2o and Supplementary Data5). Epifactor expression-derived clusters with poor clinical outcomes were enriched in immunological subtypes associated with poor prognosis (such as C4) and/or depleted of the subtypes associated with better outcome (such as C3 and C5), consistent with epifactor expression in cancer cells affecting the immune response to the tumor.

We performed a detailed analysis to determine the significant differences (adjusted P value<0.05, BenjaminiHochberg method) in the frequencies (fraction of affected patient tumors) of mutations and copy number alternations (CNAs) between the two clusters for these five cancer types (Supplementary Data4). For ACC, there were no significant differences in the mutation or CNA frequencies between the clusters for any gene (epifactor or non-epifactor). For KIRC, the two clusters were different in terms of CNA frequencies for six epifactor and 431 non-epifactor genes. None of these six epifactors (NPM1, UIMC1, NSD1, HDAC3, DND1, and TAF7) were assigned as cluster-defining top NMF genes. No differences in mutational frequencies between the clusters were observed for any gene. For LGG, only three non-epifactor and zero epifactor genes had significant mutational frequency differences, while three epifactor and 58 non-epifactor genes had CNA frequency differences, between the two clusters. The three epifactors (PRMT8, CHD4, and ING4) with CNA frequency differences were not part of the cluster-defining top NMF gene group. For LIHC, none of the genes had a difference in mutational frequencies between the two clusters. Twenty epifactor genes including one top NMF gene (TONSL), and 790 non-epifactor genes had significant differences in CNA frequencies between the two clusters. The CNA in TONSL affected <16% of patient tumors in both the clusters suggesting that this CNA is unlikely to have a major effect on the observed differences in patient outcome. For LUAD, only TP53, an epifactor gene, displayed differences in mutational frequencies between the two clusters (P=0.006; 68% in LUADpoor vs. 21% in LUADbetter, two-tailed Fishers exact test and adjusted for multiple hypothesis correction). None of the genes (epifactor or non-epifactor) showed differences in CNA frequencies between the two clusters. After adjusting for TP53 mutations, all three clinical outcomes were still significantly different for the two LUAD clusters. These results suggest that the differences in expression levels of signature epifactor genes for the poor and better outcome clusters were unlikely to exclusively reflect mutations or CNAs.

We performed weighted correlation network analysis (WGCNA)34 to identify the gene ontology (GO) terms35 associated with gene groups (modules) with similar patterns of expression as the top NMF epifactor genes of poor outcome or better outcome clusters (Fig.3a, b, Supplementary Fig.9ac and Supplementary Data6). The GO terms for the modules related to poor outcome clusters were enriched for cell cycle genes (dark orange module, ACC; midnight blue module, LGG; grey60 module, LIHC; and green module, LUAD) and developmental genes (turquoise module, LIHC), indicating that differences in proliferation rate or stem-like features36 may contribute to the clinical differences observed between the clusters. The protein-protein interaction (PPI) networks formed from the top NMF epifactors were significantly enriched (P<0.05) compared to background for all the five cancer types (Supplementary Data6, Fig.3c, d and Supplementary Fig.9df). The top NMF epifactors belonging to the cell cycle-related modules formed tight, well-connected PPI networks indicating a possible coordinated mechanism of action37.

a, b GO terms (bar labels on the right) associated with gene modules containing top NMF genes for ACC (a) and LIHC (b). Modules were generated using the WGCNA analysis tool34 applied to co-expressed genes. Only modules containing at least five top NMF genes were considered. Modules in which top NMF genes are associated with poor outcome are shown in blue, and modules for better outcome are shown in purple. Adjusted P values (Padj) were obtained by applying BenjaminiHochberg multiple test correction to the unadjusted P values in a module. Only the top representative GO terms related to biological process or molecular function were considered for each gene module. c, d PPI networks were generated for the encoded proteins of the top NMF genes for the ACC (c) and LIHC (d) cancer types. The top NMF genes in the networks (nodes; shown as circles) are colored based on the modules in which they reside. Top NMF genes that were not assigned to a module with 5 or more top NMF genes were depicted as white circles (no color fill). The thickness of a line (edge) connecting two top NMF genes (nodes) indicates the confidence level of the protein-protein interaction prediction between those two top NMF genes. Supporting information for this figure can be found in Supplementary Fig.9 and Supplementary Data6 and 7.

PCA plots based on array-based DNA methylation levels for the five cancer types13 revealed that DNA methylation captures some of the differences between the clusters developed based on epifactor gene expression, but that DNA methylation alone provides significantly less separation between the two tumor clusters than can be achieved by analyzing data from all epifactor genes (Supplementary Fig.10).

DNA methylation factors tend to be expressed at higher levels in tumors with poor outcome (red color in the heatmapinSupplementary Fig.11a). This is true for the epifactors that are directly involved in de novo DNA methylation (DNMT3A and DNMT3B) or in the maintenance of DNA methylation (DNMT1 and UHRF1)13. For each tumor type, we determined the number of hypermethylated and hypomethylated loci in the poor outcome cluster compared to the better outcome cluster (Supplementary Fig.11b and Supplementary Data7). The pattern of differential methylation between the clusters varied across the five cancer types with more hypermethylation events in the tumors in poor outcome clusters for ACC and LIHC, while the reverse was true for LGG. We determined for all hypermethylated and hypomethylated sites linked to genic regions, whether the closest gene was upregulated or downregulated in the poor outcome cluster (Supplementary Fig.11cg). The relationship between DNA methylation state and gene expression levels was significant (Fishers exact test) for all cancer types except KIRC, suggesting an impact of DNA methylation levels on downstream gene regulation.

To complement our clustering analysis based on patterns detected among all of the epifactors, we performed a systematic analysis of the prognostic value of the expression levels of each of the variable epifactor genes (see Methods) considered individually across the 24 cancer types (Supplementary Data8). The fraction of prognostic epifactors (out of the total number of variable epifactors) varied across the cancer types and ranged from 77% for KIRC to 0.4% for TGCT (Fig.4a), with a median of 21%. The prognostic direction of a gene was not always the same across the cancer types (Supplementary Data8) and the expression levels of these prognostic genes among the tumors were not consistently associated with mutations or CNAs that could explain the expression level differences associated with patient outcome (Supplementary Data8). Among the 24 cancer types, the fractions of prognostic epifactors and non-epifactors were highly correlated (Supplementary Fig.12a, b), but on average, the fraction of prognostic genes was higher for epifactor genes than for non-epifactor genes (P=0.015, Wilcoxon matched-pairs signed rank test) (Supplementary Fig.12c). The fraction of variable epifactors that are prognostic among tumor types had a weak negative correlation that did not reach statistical significance (P>0.05) with either the total number of mutations (Supplementary Fig.13a) or the total number of copy number alterations (CNAs) (Supplementary Fig.13b).

a Number of prognostic epifactor genes for each of the 24 cancer types. Prognostic genes were identified based on a significant difference in PFI outcome between patient tumors with high and low expression levels of the gene. p values were adjusted for age and sex of patients and for multiple hypothesis testing (BenjaminiHochberg method). b Circos plot showing the epifactor genes that are most frequently prognostic across cancer types. The lines connect the genes and the cancer types in which they were determined to be prognostic. Red lines indicate the five-cancer group (ACC, KIRC, LGG, LIHC, and LUAD) and blue lines indicate other cancer types. c Forest plot showing the hazard ratio and 95% confidence interval (CI) for the most significant prognostic gene (gene symbols in parentheses) for each of the 24 cancer types. Hazard ratios lower than one indicate that higher expression of the gene is associated with poorer PFI. d Heatmap for the enrichment of protein complexes among the prognostic genes for the 24 cancer types. White rectangles indicate no significant enrichment. Significance was determined using permutation tests. e Barplot showing the fraction of top NMF genes that are prognostic for the 24 cancer types. f Heatmap indicating the prognostic status of the most frequent top NMF genes across the five cancer types. g KaplanMeier plots for the most significant prognostic SWI/SNF genes for ACC and LIHC. Significance was determined with a log-rank MantelCox test and the number of patient tumors (n) in each group are provided. ACCSMARCD1,high n=20, ACCSMARCD1,low n=51, LIHCARID1A,high n=19, LIHCARID1A,low n=123, LIHCCHAF1B,high n=60, LIHCCHAF1B,low n=82. h Bar plots indicating the effect of meta-PCNA correction on the number of cancer types for which a gene is prognostic for the top NMF genes that were included in WGCNA-derived gene modules for ACC (left and center) and LIHC (right). i Prognostic status for genes that remain prognostic after the meta-PCNA correction among the cancer types. Supporting information for this figure can be found in Supplementary Data8.

The top ten most frequent prognostic epifactor genes across the cancer types (Fig.4b) were involved in chromatin remodeling (DPF1 and TOP2A) and in depositing and reading histone modifications including histone phosphorylation, methylation, and deubiquitination (AURKA, BUB1, CDK1, CHEK1, GSG2, MSH6, SMYD2, and USP49). Out of these, AURKA, TOP2A, CDK1, and BUB1 were also included in the list of most frequent top NMF genes across the 24 cancer types (Fig.1f). For the most significant prognostic gene for each of the 24 cancer types, high expression of the prognostic gene was associated with poor outcome for 15 cancer types (hazard ratio <1), while high expression of the prognostic gene was associated with better outcome for nine cancer types (hazard ratio >1) (Fig.4c). Genes associated with the HAT or chromatin remodeling (SWI/SNF or ISWI) complexes were significantly overrepresented among the prognostic genes (P<0.05) in 11 or more cancer types (Fig.4d).

For each epifactor, we determined the number of cancer types in which high expression (N) or low expression (M) of that epifactor was associated with poor outcome. Positive prognostic residuals (N-M>0) were more frequent than negative prognostic residuals, indicating that high expression of epifactors was more often associated with poor outcome for the epifactors overall (all groups in Supplementary Fig.14a, b) and for subsets of epifactors associated with DNA modification (n=25), histone modifications (n=487), and chromatin remodeling (n=124) (Supplementary Fig.14a, b). We observed a similar trend among epifactors that are histone writers (n=147), erasers (n=57), and readers (n=80) (Supplementary Fig.14c, d). When we further divided the histone writers based upon the specific histone mark they deposit, the writers that catalyze histone acetylation (n=33), but not those that catalyze methylation (n=47), phosphorylation (n=36), or ubiquitination (n=28), had more negative prognostic residuals than positive residuals, indicating that high expression of histone acetylases is associated with a more favorable prognosis (Supplementary Fig.14e, f).

A higher fraction of the top NMF genes for the five-cancer group were prognostic for outcome as compared with other cancer types (Fig.4e, f). Further, the prognostic direction of these frequent top NMF genes was consistent across the five-cancer group, with the exception of PPARGC1A (Fig.4f). The frequent top NMF genes ASF1B, ATAD2, BUB1, CDK1, CHAF1A, HJURP, PBK, and TOP2A (Fig.4f) that were signature genes for the poor outcome cluster in ACC, LGG, LIHC, and LUAD (Supplementary Fig.4 and Supplementary Data4) were also significantly associated with poor outcome when expressed at high levels for those same cancer types (Fig.4f). The prognostic genes for ACC and LIHC had a shared enrichment for SWI/SNF chromatin remodeler genes (Fig.4d) with SMARCD1 in ACC, and ARID1A and CHAF1B in LIHC, being the most significantly prognostic SWI/SNF genes (P<0.0001) in these two cancer types (Fig.4g). The top NMF epifactors were more likely to be predictive of outcome than SWI/SNF epifactors, overall (Supplementary Fig.15a, b).

In their investigation of genes that predict breast cancer outcome, Venet et al. found that the prognostic value of the majority of signature genes was eliminated when they adjusted for the expression levels of a meta-PCNA signature that removed the confounding effects of cell proliferation38,39. After adjusting for the meta-PCNA signature, in addition to age and sex, we found that the expression levels of some prognostic epifactor genes were no longer associated with clinical outcome (Supplementary Data8). Analysis of the instances in which an epifactor gene was proliferation-independent (prognostic even after meta-PCNA correction) or proliferation-dependent (not prognostic after meta-PCNA correction) revealed that the top NMF genes belonging to a co-expression module (Fig.3a, b and Supplementary Fig.9ac) with a highly enriched cell cycle GO term (such as the dark orange module of ACC) were more affected by meta-PCNA correction compared to top NMF genes in modules highly enriched for other GO terms such as autophagy (the blue module of ACC) or development (the turquoise module of LIHC) (Fig.4h). The frequently prognostic epifactor genes that were also unaffected by the meta-PCNA correction included those involved in histone modifications (KDM4B, KAT6A, MBTD1, MTA1, and PHF1), histone binding (BRD3, MBTD1, and PHF1), and chromatin organization and remodeling (MTA1) (Fig.4i).

We asked whether pan-cancer epigenetic features can be used to develop a predictor for patient outcome for the five-cancer group. To achieve this, we used the Cox-nnet artificial neural network (ANN) framework by Ching et al.40. The Cox-nnet model consists of an input layer, a hidden layer with 143 nodes, and a final Cox-regression layer that outputs the prognostic index (PI), equivalent to the log hazards ratio (Fig.5a). Patients from the combined cohort of the five cancer types were randomly split (80:20) into training and test sets. For the model trained on the epifactor expression data, age and sex of the patients in the 5-cancer group, the clinical outcomes (PFI) for the high PI and low PI groups of the test set were significantly different (P<0.0002) (Fig.5b), indicating that the trained model was able to successfully predict the likely clinical outcome for patients that were not included in the training set. As the top NMF epifactor genes of KIRC showed less overlap with the remaining four cancer types (Figs.1e and 4f), we also trained a Cox-nnet model based only on the other four cancer types (ACC, LGG, LIHC, and LUAD). With this 4-cancer-type model, the log-rank P value for the test set was highly significant (P<0.0001) (Fig.5c). The model trained only on KIRC did not result in groups with a significant difference in outcome (P=0.19) (Fig.5d).

a A Cox-nnet model40 was used as a framework for predicting patient outcomes. The patient cohort was randomly split (80:20) into training and test sets. The model was trained on input features consisting of the expression values of the 720 epifactor genes, and the age and sex of the patients in the training set. The model consisted of an input layer that accepts the input features and is fully connected to a hidden layer. The output of the nodes of the hidden layer was fed to a cox-regression layer. The final output of the model was the log hazard ratios of the patients (prognostic index, PI). To evaluate the performance of the model, the test set patients were divided into high PI and low PI groups based on the median PI of the patients. The clinical outcomes between these two groups were compared using the log-rank MantelCox test (KaplanMeier method). Created using Biorender. bd KaplanMeier plots evaluating the performance of the model. b Results when the model was trained and tested on patients from the 5-cancer group (ACC, KIRC, LGG, LIHC, and LUAD). High PI n=71, Low PI n=70. c Results when the model was trained and tested on four cancer types (ACC, LGG, LIHC, and LUAD). High PI n=55, Low PI n=56. d Results for a model trained and tested on only KIRC. High PI n=17, Low PI n=17. e Prognostic status of the top 20 input features (left panel) ranked on the basis of their importance in the Cox-nnet machine learning (ML) model for the five cancer types is shown. A heatmap indicating which of the top 20 features from the left panel are also top NMF genes across the five cancer types is shown on the right. Only the features that are a top NMF gene for at least one cancer type are shown. f Same as (e), but for the four cancer type model. KIRC was not included in the Cox-nnet model, but is included in these heatmaps for comparison. Supporting information for this figure can be found in Supplementary Data9.

Most of the top 20 important features for clinical outcome from the pan-cancer model (Supplementary Data9) were individually prognostic (P<0.05; Supplementary Data8) with higher expression of these features associated with poor outcome (Fig.5e, f, left panels). About half of these important features (10 out of 20 for the 5-cancer model and 11 out of 20 for the 4-cancer model) were top NMF genes in at least one of the 5 cancer types (Fig.5e, f, right panels).

To further test and validate our findings on the prognostic role of epifactors, we used independent, publicly available datasets for KIRC, LGG, and LUAD (Supplementary Data9). For each cancer type, we assigned the tumors in this validation cohort to either poor outcome or better outcome groups (Supplementary Fig.16ag and Supplementary Data9) based on the expression pattern of the top NMF epifactor markers that we determined based on the original datasets (Supplementary Fig.4). In the case of KIRC and LUAD, we observed significant clinical differences (P<0.05; Cox regression, adjusted for age and sex) between the two groups of tumors (Supplementary Fig.16bg), while for LGG, the difference was nearly significant (P=0.071) (Supplementary Fig.16a). There was also a significant overlap (P<0.05, based on the hypergeometric distribution) between the epifactors that were individually prognostic for the validation and primary datasets (Supplementary Fig.16h and Supplementary Data9) for KIRC (P=0.019), LGG (P=0.0003), and LUAD (P=0.014). These results demonstrate that expression levels of these epifactors, together or individually, have a robust capacity to classify tumors based on clinical outcomes.

Mutation frequencies are estimated to be 14 times lower in pediatric than adult cancers41. In one detailed genomic study, for 10% of pediatric tumors, no underlying, cancer-promoting mutation or structural copy number variant could be identified41. From this perspective, pediatric tumors have the potential to be more epigenetically driven than adult tumors. To compare our findingson epifactors in adult tumors with pediatric cancers, we obtained genomic and clinical data for pediatric tumors from four high-risk cancer types (neuroblastoma (NBL), osteosarcoma (OS), acute myleoid leukemia (AML), and Wilms tumor (WT)) in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets (Fig.6a). These four pediatric cancer types are difficult to treat and originate in different tissues and cells within the body: immature nerve cells of the sympathetic nervous system (NBL); bone (OS); immature white blood cells of the bone marrow (AML); and kidney (WT).

a Schematic depicting the four high-risk and hard-to-treat pediatric cancer types (NBL, OS, AML, and WT) from the TARGET program in this study. These cancer types originate in brain (NBL), bone (OS), immature white blood cells (AML), and kidney (WT) in children and adolescents. These pediatric tumors were compared with 24 adult cancer types (depicted in Fig.1a) from TCGA. b Two epigenetic expression-based clusters showed significantly different survival outcomes for NBL and OS (KaplanMeier survival plots). NBLpoor n=68, NBLbetter n=37. OSpoor n=40, OSbetter n=28. c Heatmap showing the fraction of cluster-defining top NMF genes for four pediatric cancer types (columns) that overlap with the top NMF genes of the 24 adult cancer types (rows). d Heatmap showing status of the most frequent pediatric top NMF genes (rows) as top NMF genes for the different pediatric and adult cancer types. e Enrichment of different protein complexes in the top NMF genes of the four pediatric cancer types. Only the significantly enriched complexes (purple) are shown. Significance was calculated using the two-tailed Fishers exact test. f Barplot indicating the GO terms for three different gene modules for NBL obtained from the WGCNA analysis. These modules included at least 11 top NMF genes that were either all upregulated in the poor outcome or better outcome clusters obtained from the NMF algorithm. g PPI network generated using the top NMF genes of NBL. The genes are color-coded based on their associated WGCNA modules (shown in f). Thicker edges (connecting lines between the genes (nodes)) indicate higher degree of PPI between the two genes connected by the edge. h KaplanMeier survival plots for the most prognostic gene for NBL (RUVBL2) and OS (PRDM12). NBLRUVBL2,high n=16, NBLRUVBL2,low n=89. OSPRDM12,high n=13, OSPRDM12,low n=55. i Heatmap indicating the prognostic value of the most frequent pediatric prognostic genes (rows) across the pediatric and adult cancer types (columns). White indicates the gene is not prognostic; green indicates that low expression is associated with a poor outcome; pink indicates that high expression is associated with poor outcome. For (b, h), the P values from the log-rank MantelCox and the number of patients in each cluster (n) are indicated. Supporting information for this figure can be found in Supplementary Fig.7 and Supplementary Data10 and 11.

NMF clustering based on expression levels of epifactors in the pediatric patient tumors resulted in two clusters for each of the four pediatric cancer types (Supplementary Data10). The two clusters were significantly different in overall survival (corrected for age and sex, Cox regression) for NBL (P=0.024) and OS (P=0.032) (Fig.6b and Supplementary Fig.17ad), but not for AML (P=0.092) and WT (P=0.693). The top NMF genes for the four pediatric cancer types (81 top NMF genes for NBL; 34 for OS; 27 for AML; and 31 for WT) (Supplementary Data10) overlapped to different degrees (ranging from 0 to 44%) with the top NMF genes of the 24 adult cancer types from TCGA (Fig.6c). Out of the 21 genes that were a top NMF gene in at least two pediatric cancer types (Fig.6d), seven genes (ASF1B, AURKB, SMARCD3, TONSL, UBE2T, ZBTB7C, and ZNHIT1) were shared with the 27 most frequent top NMF genes in adult cancers (Fig.1f). Across the 24 adult cancer types, LIHC and OV had the most overlap of 8 genes between their top NMF genes and the frequent pediatric top NMF genes (Fig.6d, right heatmap). The top NMF genes of the pediatric cancer types were enriched for genes related to SWI/SNF (WT and OS), HMT (WT), MLL (WT), and HAT (NBL) protein complexes23 (Fig.6e). Similar to our findings for the adult cancer types, the signature top NMF genes for the poor outcome clusters of both NBL and OS (Fig.6b) correlated in expression with cell cycle genes in the green modules of NBL (Fig.6f) and OS (Supplementary Fig.17e). Also similar to our findings in adult tumors, the top NMF genes included in the green module for NBL formed a well-connected PPI interaction network (Fig.6g).

Out of 720 epifactors, 51 genes were prognostic for overall survival in NBL, 98 genes in OS, and 97 genes in AML (Supplementary Data11). None of the epifactor genes were prognostic for overall survival in WT. KaplanMeier plots for the most significantly prognostic genes for NBL (RUVBL2) and OS (PRDM12) are shown in Fig.6h. The prognostic value did not change after the meta-PCNA correction for any of the pediatric prognostic genes (Supplementary Data11). Twenty-four epifactor genes were prognostic in at least 2 of 3 pediatric cancer types (Fig.6i, left heatmap). The prognostic value (and direction) of these 24 epifactor genes varied across the adult cancer types (Fig.6i, right heatmap) with the most overlap observed for ACC, KIRC, and LGG, three members of the 5-cancer group.

Given our observation that tumors were associated with one epifactor expression-based cluster or another, we asked whether the individual cells within a tumor would display a gene expression profile related to one of the two clusters. Expression-based composite scores derived from the signature genes for each of the two epifactor expression-based clusters (Supplementary Fig.3c) were mapped to each cancer cell in a two-patient, single-cell RNA-seq dataset for LGG42 (see Methods). Individual cancer cells were assigned to one of the four different groups: LGGpoor, LGGbetter, LGGpoor+LGGbetter (mixed group with characteristics of both clusters), and none (not scoring high for genes enriched in either cluster) (Fig.7b). Both of the sequenced LGG tumors contained cells from all four groups in different proportions (Fig.7a, b). The signature top NMF genes of LGG were differentially expressed in cells in the four groups (Fig.7c). In a similar analysis of a pediatric single-cell dataset for NBL43 (Fig.7d), individual cells contained patterns of NBLpoor, NBLbetter, both NBLpoor and NBLbetter, and neither (Fig.7e, f), thus, supporting these epifactors as possible determinants of cellular states.

a UMAP plot of the LGG tumor cells color-coded (grey or black) based on the tumor sample from which they originate (LGG-03 or -04). b UMAP plot showing the assignment of tumor cells to four different groups based on the expression levels of the signature genes for the poor outcome (LGGlow) and better outcome (LGGhigh) tumor clusters for LGG. c Dot plot depicting the expression levels and the percent of cells expressing the signature genes for LGGlow and LGGhigh tumor clusters across the four groups determined using single-cell analysis. Only the genes that are differentially expressed across the four different groups are used for the plot. df Same plots as (ac), respectively, but for NBL tumor cells.

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Pan-cancer landscape of epigenetic factor expression predicts tumor outcome | Communications Biology - Nature.com

Biology

Lifetime achievement award in marine biology for UH professor … – University of Hawaii

Professor Mark Hixon received the Western Society of Naturalists Lifetime Achievement Award from Professor Brian Tissot in Monterey, California.

Professor Mark Hixon of the University of Hawaii at Mnoa School of Life Sciences has been honored with a lifetime achievement award from the Western Society of Naturalists, the oldest professional marine biological society in western North America.

Hixon, the Hsiao Endowed Professor of Marine Biology, received the award at the annual meeting of the society in Monterey, California, in November 2023. He teaches courses in marine biology, climate disruption and science communication, and is known internationally for his research and public outreach on understanding coral reef ecosystems and their conservation.

Im honored and humbled to be welcomed among the ranks of my scientific heroes, said Hixon.

Dr. Hixon blends a rare combination of excellence in teaching and mentoring, creative and rigorous research that is widely cited, and outstanding service to his community and the world, said nominator Professor Brian Tissot of Humboldt State University.

Former recipients of the award include eminent marine biologists Joseph Connell of the University of California at Santa Barbara, and Robert Paine of the University of Washington.

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Lifetime achievement award in marine biology for UH professor ... - University of Hawaii