Browse Articles

Homoploid hybridization adds clarity to the origins of octoploid strawberries

The evolutionary histories of many polyploid plant species are difficult to resolve due to a complex interplay of hybridization, incomplete lineage sorting, and missing diploid progenitors. In the case of octoploid strawberry with four subgenomes designated ABCD, the identities of the diploid progenitors for subgenomes C and D have been subject to much debate. By integrating new sequencing data from North American diploids with reticulate phylogeny and admixture analyses, we uncovered introgression from an extinct or unsampled species in the clade of Fragaria viridis , Fragaria nipponica , and Fragaria nilgerrensis into the donor of subgenome A of octoploid Fragaria prior to its divergence from F. vesca subsp . bracteata . We also detected an introgression event from F. iinumae into an ancestor of F. nipponica and F. nilgerrensis. Using an LTR-age-distribution-based approach, we estimate that the octoploid and its intermediate hexaploid and tetraploid ancestors emerged approximately 0.8, 2, and 3 million years ago, respectively. These results provide an explanation for previous reports of F. viridis and F. nipponica as donors of the C and D subgenomes and suggest a greater role than previously thought for homoploid hybridization in the diploid progenitors of octoploid strawberry. The integrated set of approaches used here can help advance polyploid genome analysis in other species where hybridization and incomplete lineage sorting obscure evolutionary relationships.

Tumor-promoting UBR4 coordinates impaired mitophagy–associated senescence and lung adenocarcinoma pathogenesis

Cellular senescence, an irreversible cell cycle arrest, plays a pivotal role in development, aging, and tumor suppression. However, the fundamental pathway coordinating senescence and neoplastic transformation remains unclear. Here, we describe the tumorigenic involvement of ubiquitin protein ligase E3 component n-recognin 4 (UBR4), an E3 ubiquitin ligase of the N-degron pathway, in lung adenocarcinoma (LUAD). Public genome databases revealed high UBR4 expression in LUAD patients, associated with a dysregulated cell cycle and impaired mitochondrial homeostasis. UBR4 knockout (ΔUBR4) in A549 lung cancer cells induced cellular senescence with defective mitochondria. Restoration of UBR4 or antioxidant treatment reversed the ΔUBR4 phenotypes caused by impaired mitophagy. Mitochondrial stress exacerbated mitochondrial dysfunction in ΔUBR4 cells, contributing to diverse cellular phenotypes. Additionally, ΔUBR4 cells exhibited substantially slow tumor growth in mouse xenograft models. In LUAD patients, UBR4 levels correlated with tumor stage, mitophagy markers, and poor survival. These findings suggest a tumor-promoting function of UBR4 in LUAD by regulating mitochondrial quality control. Further research into the pharmacological inhibition of UBR4 could open promising avenues for developing effective antitumor therapies targeting LUAD.

Reactivation of an embryonic cardiac neural crest transcriptional profile during zebrafish heart regeneration

During vertebrate development, the heart primarily arises from mesoderm, with crucial contributions from cardiac neural crest (CdNC) cells that migrate to the heart and form a variety of cardiovascular derivatives. Here, by integrating bulk and single cell RNA-seq with ATAC-seq, we identify a gene regulatory subcircuit specific to migratory cardiac crest cells composed of key transcription factors egr1, sox9a, tfap2a, and ets1. Notably, we show that cells expressing the canonical neural crest gene sox10 are essential for proper cardiac regeneration in adult zebrafish. Furthermore, expression of all transcription factors from the migratory cardiac crest gene subcircuit are reactivated after injury at the wound edge. Together, our results uncover a developmental gene regulatory network that is important for CdNC fate determination, with key factors of the program reexpressed during regeneration.

Conserving the beauty of the world’s reef fish assemblages

On shallow rocky and coral reefs, cultural and recreational values, like aesthetics, are critical aspects of Nature’s Contributions to People (NCP) that support human well-being and provide billions of dollars in tourism revenue. Quantifying the aesthetic value of reef ecosystems and uncovering the conditions that enhance it could support NCP-based management. Here, we combine a global dataset of reef fish surveys, species-level aesthetic values, and causal modeling to assess the global status and drivers of reef fish assemblage aesthetic value. We find that aesthetic value is inherently linked to species richness, displaying a latitudinal gradient with peaks in the tropics, but varies strongly with the presence of exceptionally beautiful or less-beautiful species. Sea surface temperature, primary productivity, human gravity, and protection status are the strongest drivers of assemblage-level aesthetic value. Protection against human impacts consistently enhances aesthetic value by boosting taxonomic and phylogenetic diversity, and this effect is greatest in species-rich, tropical ecoregions. Economic development has little influence, indicating that low-income countries are not constrained from maintaining beautiful fish assemblages. Our results therefore suggest that marine protected areas (MPAs) can support multiple NCPs simultaneously, particularly in developing tropical countries. While we highlight the effectiveness of MPAs, given the low level of marine protection globally and the sensitivity of aesthetic value to environmental conditions, the beauty of the world’s reefs appears severely threatened. Aesthetic value should be immediately integrated into reef conservation and management plans.

Estimating the extent and sources of model uncertainty in political science

Assessing model uncertainty is crucial to quantitative political science. Yet, most available sensitivity analyses focus only on a few modeling choices, most notably the covariate space, while neglecting to jointly consider several equally important modeling choices simultaneously. In this article, we combine the exhaustive and systematic method of the Extreme Bounds Analysis with the more multidimensional logic underpinning the multiverse approach to develop an approach to sensitivity analyses. This allows us to systematically assess the degree and sources of model uncertainty across multiple dimensions, including the control set, fixed effect structures, SE types, sample selection, and dependent variable operationalization. We then apply this method to four prominent topics in political science: democratization, institutional trust, public good provision, and welfare state generosity. Results from over 3.6 bn estimates reveal widespread model uncertainty, not just in terms of the statistical significance of the effects, but also their direction, with most independent variables yielding a substantive share of statistically significant positive and negative coefficients depending on model specification. We compare the strengths and weaknesses of three distinct approaches to estimating the relative importance of different model specification choices: nearest 1-neighbor; logistic; and deep learning. All three approaches reveal that the impact of the covariate space is relatively modest compared to the impact of sample selection and dependent variable operationalization. We conclude that model uncertainty stems more from sampling and measurement than conditioning and discuss the methodological implications for how to assess model uncertainty in the social sciences.

Enigmatic carbon isotopic variability in the oceanic upper mantle

Unraveling the origin(s) of carbon on Earth has remained challenging, not only because of the multiple isotopic fractionation episodes that may have occurred during planet formation processes but also because the end point of these processes, the current isotopic value of Earth’s deep carbon reservoirs remains poorly constrained. Here, we present carbon isotopic measurements on rare undegassed mid-ocean ridge basalts from the Pacific, Atlantic, and Arctic Oceans that have preserved the isotopic signature of their mantle source. We find that Earth’s present-day convecting upper mantle has variable δ 13 C value from ~−10 to −4‰, significantly different from the δ 13 C value of peridotitic diamonds and with the highest values being restricted to the Atlantic. Evidence for significant mantle heterogeneity contrasts with previous assumptions and its origin remains puzzling being uncorrelated with geochemical markers associated with either subduction and surficial recycling processes or lower mantle contributions. The data do not preclude other causes such as primordial mantle heterogeneity. We suggest that the δ 13 C value of the bulk silicate Earth may need to be revised.

An ancient origin of the naked grains of maize

Adaptation to novel environments requires genetic variation, but whether adaptation typically acts upon preexisting genetic variation or must wait for new mutations remains a fundamental question in evolutionary biology. Selection during domestication has been long used as a model to understand evolutionary processes, providing information not only on the phenotypes selected but also, in many cases, an understanding of the causal loci. For each of the causal loci that have been identified in maize, the selected allele can be found segregating in natural populations, consistent with their origin as standing genetic variation. The sole exception to this pattern is the well-characterized domestication locus tga1 ( teosinte glume architecture1 ), which has long been thought to be an example of selection on a de novo mutation. Here, we use a large dataset of maize and teosinte genomes to reconstruct the origin and evolutionary history of tga1 . We first estimated the age of tga1-maize using a genealogy-based method, finding that the allele arose approximately 42,000 to 49,000 y ago, predating the beginning of maize domestication. We also identify tga1-maize in teosinte populations, indicating that the allele can survive in the wild. Finally, we compare observed patterns of haplotype structure and mutational age distributions near tga1 with simulations, finding that patterns near tga1 in maize better resemble those generated under simulated selective sweeps on standing variation. These multiple lines of evidence suggest that maize domestication likely drew upon standing genetic variation at tga1 and cement the importance of standing variation in driving adaptation during domestication.

Sp140L functions as a herpesvirus restriction factor suppressing viral transcription and activating interferon-stimulated genes

Herpesviruses, including Epstein–Barr virus (EBV) – a human oncogenic virus and essential trigger of multiple sclerosis – must bypass host DNA-sensing mechanisms to establish lifelong, latent infection. Therefore, herpesviruses encode viral proteins to disrupt key host factors involved in DNA sensing and viral restriction. The first viral latency protein expressed, EBNA-LP, is essential for transformation of naïve B cells and establishment of viral gene expression, yet its role in evading host defenses remains unclear. Using single-cell RNA sequencing of EBNA-LP Knockout (LPKO)-infected B cells, we reveal an antiviral response landscape implicating the “speckled proteins” as key cellular restriction factors countered by EBNA-LP. Specifically, loss of Sp100 or the primate-specific Sp140L reverses the restriction of LPKO, suppresses a subset of canonically interferon-stimulated genes, and restores transcription of essential latent viral genes and cellular proliferation. Notably, we also identify Sp140L as a restriction target of the herpesvirus saimiri ORF3 protein, implying a role for Sp140L in immunity to other diverse DNA viruses. This study reveals Sp140L as a restriction factor that we propose links sensing and transcriptional suppression of viral DNA to an Interferon-independent innate immune response, likely relevant to all nuclear DNA viruses.

Linking pregnancy- and birth-related risk factors to a multivariate fusion of child cortical structure

Pregnancy- and birth-related factors affect offspring brain development, emphasizing the importance of early life exposures. While most previous studies have focused on a few variables in isolation, here we investigated associations between a broad range of pregnancy- and birth-related variables and multivariate cortical brain MRI features. Our sample consisted of 8,396 children aged 8.9 to 11.1 y from the Adolescent Brain Cognitive Development Study. Through multiple correspondence analysis and factor analysis of mixed data, we distilled numerous pregnancy and birth variables into four overarching dimensions; maternal pregnancy complications, maternal substance use, low birth weight and prematurity, and newborn birth complications. Vertex-wise measures of cortical thickness (CT), surface area (SA), and curvature were fused using linked independent component analysis. Linear mixed-effects models showed that maternal pregnancy complications and low birth weight and prematurity were associated with smaller global SA. Additionally, low birth weight and prematurity was associated with complex regional cortical patterns reflecting bidirectional variations in both SA and CT. Newborn birth complications showed multivariate patterns reflecting smaller occipital- and larger temporal area, bidirectional frontal area variations, and reduced CT across the cortex. Maternal substance use showed no associations with child cortical structure. By employing a multifactorial and multivariate morphometric fusion approach, we connected complications during pregnancy and fetal size and prematurity to global SA and specific regional signatures across child cortical MRI features.

The brain computes dynamic facial movements for emotion categorization using a third pathway

Emerging theories in cognitive neuroscience propose a third brain pathway dedicated to processing biological motion, alongside the established ventral and dorsal pathways. However, its role in computing dynamic social signals for behavior remains uncharted. Here, participants (N = 10) actively categorized dynamic facial expressions synthesized by a generative model and displayed on different face identities—as “happy,” “surprise,” “fear,” “anger,” “disgust,” “sad”—while we recorded their MEG responses. Using representational interaction measures that link facial features with MEG activity and categorization behavior, we identified within each participant a functional social pathway extending from the occipital cortex to the superior temporal gyrus. This pathway selectively represents, communicates, and integrates facial movements that are essential for the behavioral categorization of emotion, while task-irrelevant identity features are filtered out in the occipital cortex. Our findings uncover how the third pathway selectively computes complex dynamic social signals for emotion categorization in individual participants, offering computational insights into the dynamics of neural activity.

ATF6 enables pathogen infection in ticks by inducing <i>stomatin</i> and altering cholesterol dynamics

How tick-borne pathogens interact with their hosts has been primarily studied in vertebrates where disease is observed. Comparatively less is known about pathogen interactions within the tick. Here, we report that Ixodes scapularis ticks infected with either Anaplasma phagocytophilum (causative agent of anaplasmosis) or Borrelia burgdorferi (causative agent of Lyme disease) show activation of the ATF6 branch of the unfolded protein response (UPR). Disabling ATF6 functionally restricts pathogen survival in ticks. When stimulated, ATF6 functions as a transcription factor, but is the least understood out of the three UPR pathways. To interrogate the Ixodes ATF6 transcriptional network, we developed a custom R script to query tick promoter sequences. This revealed stomatin as a potential gene target, which has roles in lipid homeostasis and vesical transport. Ixodes stomatin was experimentally validated as a bona fide ATF6-regulated gene through luciferase reporter assays, pharmacological activators, RNA interference transcriptional repression, and immunofluorescence microscopy. Silencing stomatin decreased A. phagocytophilum colonization in Ixodes and disrupted cholesterol dynamics in tick cells. Furthermore, blocking stomatin restricted cholesterol availability to the bacterium, thereby inhibiting growth and survival. Taken together, we have identified the Ixodes ATF6 pathway as a contributor to vector competence through Stomatin-regulated cholesterol homeostasis. Moreover, our custom, web-based transcription factor binding site search tool “ArthroQuest” revealed that the ATF6-regulated nature of stomatin is unique to blood-feeding arthropods. Collectively, these findings highlight the importance of studying fundamental processes in nonmodel organisms.

The oncogene SLC35F2 is a high-specificity transporter for the micronutrients queuine and queuosine

The nucleobase queuine (q) and its nucleoside queuosine (Q) are micronutrients derived from bacteria that are acquired from the gut microbiome and/or diet in humans. Following cellular uptake, Q is incorporated at the wobble base (position 34) of tRNAs that decode histidine, tyrosine, aspartate, and asparagine codons, which is important for efficient translation. Early studies suggested that cytosolic uptake of queuine is mediated by a selective transporter that is regulated by mitogenic signals, but the identity of this transporter has remained elusive. Here, through a cross-species bioinformatic search and genetic validation, we have identified the solute carrier family member SLC35F2 as a unique transporter for both queuine and queuosine in Schizosaccharomyces pombe and Trypanosoma brucei . Furthermore, gene disruption in human HeLa cells revealed that SLC35F2 is the sole transporter for queuosine (K m 174 nM) and a high-affinity transporter for the queuine nucleobase (K m 67 nM), with the additional presence of second low-affinity queuine transporter (K m 259 nM). Ectopic expression of labeled SLC35F2 reveals localization to the cell membrane and Golgi apparatus via immunofluorescence. Competition uptake studies show that SLC35F2 is not a general transporter for other canonical ribonucleobases or ribonucleosides but selectively imports q and Q. The identification of SLC35F2, an oncogene, as the transporter of both q and Q advances our understanding of how intracellular levels of queuine and queuosine are regulated and how their deficiency contributes to a variety of pathophysiological conditions, including neurological disorders and cancer.

Daily briefing: How to make America healthy — the real problems and how to fix them

Gut sulfide metabolism modulates behavior and brain bioenergetics

The host–microbiome interface is rich in metabolite exchanges and exquisitely sensitive to diet. Hydrogen sulfide (H 2 S) is present at high concentrations at this interface and is a product of both microbial and host metabolism. The mitochondrial enzyme, sulfide quinone oxidoreductase (SQOR), couples H 2 S detoxification to oxidative phosphorylation; its inherited deficiency presents as Leigh disease. Since an estimated two-thirds of systemic H 2 S metabolism originates in the gut, it raises questions as to whether impaired sulfide clearance in this compartment contributes to disease and whether it can be modulated by dietary sulfur content. In this study, we report that SQOR deficiency confined to murine intestinal epithelial cells perturbs colon bioenergetics that is reversed by antibiotics, revealing a significant local contribution of microbial H 2 S to host physiology. We also find that a 2.5-fold higher methionine intake, mimicking the difference between animal and plant proteins, synergizes with intestinal SQOR deficiency to adversely impact colon architecture and alter microbiome composition. In serum, increased thiosulfate, a biomarker of H 2 S oxidation, reveals that intestinal SQOR deficiency combined with higher dietary methionine affects sulfide metabolism globally and perturbs energy metabolism as indicated by higher ketone bodies. The mice exhibit lower exploratory locomotor activity while brain MRI reveals an atypical reduction in ventricular volume, which is associated with lower aquaporin 1 that is important for cerebrospinal fluid secretion. Our study reveals the dynamic interaction between dietary sulfur intake and sulfide metabolism at the host–microbe interface, impacting gut health, and the potential for lower dietary methionine intake to modulate pathology.

Terrestrial locomotion of microscopic robots enabled by 3D nanomembranes with nonreciprocal shape morphing

Microscopic robots exhibit efficient locomotion in liquids by leveraging fluid dynamics and chemical reactions to generate force asymmetry, thereby enabling critical applications in photonics and biomedicine. However, achieving controllable locomotion of such robots on terrestrial surfaces remains challenging because fluctuating adhesion on nonideal surfaces disrupts the necessary asymmetry for propulsion. Here, we present a microscopic robot composed of three-dimensional nanomembranes, which navigate diverse terrestrial surfaces with omnidirectional motion. We propose a general mechanism employing nonreciprocal shape morphing to generate stable asymmetric forces on surfaces. This nonreciprocal shape morphing is realized through a laser-actuated vanadium dioxide nanomembrane, leveraging the material's inherent hysteresis properties. We demonstrate that these robots can be fabricated in various shapes, ranging from simple square structures to bioinspired "bipedal" helical designs, enabling them to directionally navigate challenging surfaces such as paper, leaves, sand, and vertical walls. Furthermore, their omnidirectional motion facilitates applications in microassembly and microelectronic circuit integration. Additionally, we developed an artificial intelligence control algorithm based on reinforcement learning, enabling these robots to autonomously follow complex trajectories, such as tracing the phrase "hello world". Our study lays a theoretical and technological foundation for microscopic robots with terrestrial locomotion and paves a way for microscopic robots capable of operating on surfaces for advanced nanophotonic, microelectronic, and biomedical applications.

Single antisense oligonucleotides correct diverse splicing mutations in hotspot exons

Mutations that impact splicing play a significant role in disease etiology but are not fully understood. To characterize the impact of exonic variants on splicing in 71 clinically actionable disease genes in asymptomatic people, we analyzed 32,112 exonic mutations from ClinVar and Geisinger MyCode using a minigene reporter assay. We identify 1,733 splice-disrupting mutations, with the most extreme variants likely being deleterious. We report that these variants are not distributed evenly across exons but are mostly concentrated in the ~8% of exons that are most susceptible to splicing mutations (i.e., hotspot exons). We demonstrate how multiple, splice-disrupting mutations in these exons can be reverted by the same ASOs targeting the splice sites of either their upstream or downstream flanking exons. This finding supports the feasibility of developing single therapeutic ASOs that could revert all splice-altering variants localized to a particular exon.

Cryo-EM structures of GnRHR: Foundations for next-generation therapeutics

Gonadotropin-releasing hormone receptor (GnRHR) is critical for reproductive health and a key therapeutic target for endocrine disorders and hormone-responsive cancers. Using high-resolution cryoelectron microscopy, we determined the structures of Sus scrofa and Xenopus laevis GnRHRs bound to mammal GnRH, uncovering conserved and species-specific mechanisms of receptor activation and G protein coupling. The conserved “U”-shaped GnRH conformation mediates high-affinity binding through key interactions with residues such as K 3.32 , Y 6.51 , and Y 6.52 . Species-specific variations in extracellular loops and receptor–ligand contacts fine-tune receptor function, while ligand binding induces structural rearrangements, including N terminus displacement and TM6 rotation, critical for signaling. Structure–activity relationship analysis demonstrates how D-amino acid substitutions in GnRH analogs enhance stability and receptor affinity. Distinct binding modes of agonists and antagonists elucidate mechanisms of ligand-dependent activation and inactivation. These insights lay the groundwork for designing next-generation GnRHR therapeutics with enhanced specificity and efficacy for conditions like endometriosis, prostate cancer, and infertility.

Deep mechanism design: Learning social and economic policies for human benefit

Human society is coordinated by mechanisms that control how prices are agreed, taxes are set, and electoral votes are tallied. The design of robust and effective mechanisms for human benefit is a core problem in the social, economic, and political sciences. Here, we discuss the recent application of modern tools from AI research, including deep neural networks trained with reinforcement learning (RL), to create more desirable mechanisms for people. We review the application of machine learning to design effective auctions, learn optimal tax policies, and discover redistribution policies that win the popular vote among human users. We discuss the challenge of accurately modeling human preferences and the problem of aligning a mechanism to the wishes of a potentially diverse group. We highlight the importance of ensuring that research into “deep mechanism design” is conducted safely and ethically.

Heterogeneity, reinforcement learning, and chaos in population games

Inspired by the challenges at the intersection of Evolutionary Game Theory and Machine Learning, we investigate a class of discrete-time multiagent reinforcement learning (MARL) dynamics in population/nonatomic congestion games, where agents have diverse beliefs and learn at different rates. These congestion games, a well-studied class of potential games, are characterized by individual agents having negligible effects on system performance, strongly aligned incentives, and well-understood advantageous properties of Nash equilibria. Despite the presence of static Nash equilibria, we demonstrate that MARL dynamics with heterogeneous learning rates can deviate from these equilibria, exhibiting instability and even chaotic behavior and resulting in increased social costs. Remarkably, even within these chaotic regimes, we show that the time-averaged macroscopic behavior converges to exact Nash equilibria, thus linking the microscopic dynamic complexity with traditional equilibrium concepts. By employing dynamical systems techniques, we analyze the interaction between individual-level adaptation and population-level outcomes, paving the way for studying heterogeneous learning dynamics in discrete time across more complex game scenarios.

Tabula rasa agents display emergent in-group behavior

Theories on group-bias often posit an internal preparedness to bias one’s cognition to favor the in-group (often envisioned as a product of evolution). In contrast, other theories suggest that group-biases can emerge from nonspecialized cognitive processes. These perspectives have historically been difficult to disambiguate given that observed behavior can often be attributed to innate processes, even when groups are experimentally assigned. Here, we use modern techniques from the field of AI that allow us to ask what group biases can be expected from a learning agent that is a pure blank slate without any intrinsic social biases, and whose lifetime of experiences can be tightly controlled. This is possible because deep reinforcement-learning agents learn to convert raw sensory input (i.e. pixels) to reward-driven action, a unique feature among cognitive models. We find that blank slate agents do develop group biases based on arbitrary group differences (i.e. color). We show that the bias develops as a result of familiarity of experience and depends on the visual patterns becoming associated with reward through interaction. The bias artificial agents display is not a static reflection of the bias in their stream of experiences. In this minimal environment, the bias can be overcome given enough positive experiences, although unlearning the bias takes longer than acquiring it. Further, we show how this style of tabula rasa group behavior model can be used to test fine-grained predictions of psychological theories.