bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimating the relationship between fitness and metabolic rate: which rate should we use?
Authors: Cameron Hayley , Marshall Dustin 
Published: January 2024

As physiologists seek to better understand how and why metabolism varies, they have focused on how metabolic rate covaries with fitness—that is, selection. Evolutionary biologists have developed a sophisticated framework for exploring selection, but there are particular challenges associated with estimating selection on metabolic rate owing to its allometric relationship with body mass. Most researchers estimate selection on mass and absolute metabolic rate; or selection on mass and mass-independent metabolic rate (MIMR)—the residuals generated from a nonlinear regression. These approaches are sometimes treated as synonymous: their coefficients are often interpreted in the same way. Here, we show that these approaches are not equivalent because absolute metabolic rate and MIMR are different traits. We also show that it is difficult to make sound biological inferences about selection on absolute metabolic rate because its causal relationship with mass is enigmatic. By contrast, MIMR requires less-desirable statistical practices (i.e. residuals as a predictor), but provides clearer causal pathways. Moreover, we argue that estimates of selection on MIMR have more meaningful interpretations for physiologists interested in the drivers of variation in metabolic allometry.
This article is part of the theme issue ‘The evolutionary significance of variation in metabolic rates’.
Metabolic scaling as an emergent outcome of variation in metabolic rate
Authors: Naug Dhruba 
Published: January 2024

The allometric scaling of metabolic rate and what drives it are major questions in biology with a long history. Since the metabolic rate at any level of biological organization is an emergent property of its lower-level constituents, it is an outcome of the intrinsic heterogeneity among these units and the interactions among them. However, the influence of lower-level heterogeneity on system-level metabolic rate is difficult to investigate, given the tightly integrated body plan of unitary organisms. In this context, social insects such as honeybees can serve as important model systems because unlike unitary organisms, these superorganisms can be taken apart and reassembled in different configurations to study metabolic rate and its various drivers at different levels of organization. This commentary discusses the background of such an approach and how combining it with artificial selection to generate heterogeneity in metabolic rate with an analytical framework to parse out the different mechanisms that contribute to the effects of heterogeneity can contribute to the various models of metabolic scaling. Finally, the absence of the typical allometric scaling relationship among different species of honeybees is discussed as an important prospect for deciphering the role of top-down ecological factors on metabolic scaling.
This article is part of the theme issue ‘The evolutionary significance of variation in metabolic rates’.
Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR
Authors: Wang Lin , Dong Wenjuan , Yin Zheng , Sheng Jianting , Ezeana Chika F. , Yang Li , Yu Xiaohui , Wong Solomon SY , Wan Zhihao , Danforth Rebecca L. , Han Kun , Gao Dingcheng , Wong Stephen T. C. 
Published: January 2024

AbstractSingle cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model’s attributes, reconstructs cellular lineage pathways, shedding light on the cell’s evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.
Gene-environment correlation: The role of family environment in academic development
Authors: Zhou Quan , Gidziela Agnieszka , Allegrini Andrea G. , Cheesman Rosa , Wertz Jasmin , Maxwell Jessye , Plomin Robert , Rimfeld Kaili , Malanchini Margherita 
Published: January 2024

AbstractAcademic achievement is partly heritable and highly polygenic. However, genetic effects on academic achievement are not independent of environmental processes. We investigated whether aspects of the family environment mediated genetic effects on academic achievement across development. Our sample included 5,151 children who participated in the Twins Early Development Study, as well as their parents and teachers. Data on academic achievement and family environments (parenting, home environments, and geocoded indices of neighbourhood characteristics) were available at ages 7, 9, 12 and 16. We computed educational attainment polygenic scores (PGS), and further separated genetic effects into cognitive and noncognitive PGS. Three core findings emerged. First, aspects of the family environment, but not the wider neighbourhood context, consistently mediated the PGS effects on achievement across development –accounting for up to 34.3% of the total effect. Family characteristics mattered beyond socio-economic status. Second, family environments were more robustly linked to noncognitive PGS effects on academic achievement than cognitive PGS effects. Third, when we investigated whether environmental mediation effects could also be observed when considering differences between siblings, adjusting for family fixed effects, we found that environmental mediation was nearly exclusively observed between families. This is consistent with the proposition that family environmental contexts contribute to academic development via passive gene-environment correlation processes, or genetic nurture. Our results show how parents tend to shape environments that foster their children’s academic development partly based on their own genetic disposition, particularly towards noncognitive skills, rather than responding to each child’s genetic disposition.
Agonist efficacy at the β2AR is driven by agonist-induced differences in receptor affinity for the Gsprotein, not ligand binding kinetics
Authors: Harwood Clare R. , Sykes David A. , Redfern-Nichols Theo , Ladds Graham , Briddon Stephen J. , Veprintsev Dmitry B. 
Published: January 2024

AbstractIntroductionThe β2-adrenoceptor (β2AR) is a class A G protein-coupled receptor (GPCR). It is therapeutically relevant in asthma, whereby β2AR agonists relieve bronchoconstriction. The β2AR is a prototypical GPCR for structural and biophysical studies. However, the molecular basis of agonist efficacy at the β2AR is not understood. We hypothesized that the kinetics of ligand binding and GPCR-G protein interactions could play a role in ligand efficacy. We characterised the molecular pharmacology of a range of β2AR agonists and examined the correlation between ligand and mini-Gsbinding kinetics and efficacy.MethodsWe used a Time-resolved Fluorescence Resonance Energy Transfer (TR-FRET) based competition ligand binding assay to measure the affinity and residence times of a range of β2AR agonists binding to the human β2AR. TR-FRET between Lumi4-Tb3+on the N terminus of the β2AR and fluorescent CA200693 (S)-propranolol-green was measured using a PHERAstar FSX. The ability of these β2AR agonists to activate the heterotrimeric Gsprotein was measured using the CASE Gsprotein biosensor. This assay senses a reduction in NanoBRET between the nano-luciferase (nLuc) donor on the Gα subunit and Venus acceptor on the Gψ, on receptor activation, quantified using the operational model of agonism. NanoBRET was also used to measure interactions between DDM solubilised β2AR-nLuc and purified Venus-mini-Gs. A large excess of unlabelled mini-Gswas used to dissociate the β2AR-nLuc: Venus-mini-Gscomplex.ResultsCharacterisation of the molecular pharmacology of seven β2AR agonists showed a broad range of ligand binding affinities (pKi= 4.4 ± 0.09 to 9.2 ± 0.08) and kinetics parameters. There was no correlation between ligand residence times and their ability (logτ) to activate the Gsprotein (R2=0.26, p=0.29). However, there were statistically significant differences in the association rate (kon (fast)) (3.36±0.64×105to 9.19± 0.42×105) and affinity (Kd) values of mini-Gsbinding to the agonist-β2AR complex (pKd=6.0 to 6.7). Both an increase in ligand driven mini-Gskon(fast)rate and associated increase in mini-GspKdfor the receptor, were moderately correlated with efficacy (logτ) (R2=0.58 and R2=0.50 respectively).ConclusionsThese data support a model in which agonists of increased efficacy cause the β2AR to adopt a conformation that is more likely to recruit G protein. Conversely, these data did not support a role for agonist binding kinetics in the molecular basis of efficacy.
eMOSAIC: Multi-modal Out-of-distribution Uncertainty Quantification Streamlines Large-scale Polypharmacology
Authors: Badkul Amitesh , Xie Li , Zhang Shuo , Xie Lei 
Published: January 2024

AbstractPolypharmacology has emerged as a new paradigm to discover novel therapeutics for unmet medical needs. Accurate, reliable and scalable predictions of protein-ligand binding affinity across multiple proteins are essential for polypharmacology. Machine learning is a promising tool for multi-target binding affinity predictions, often formulated as a multi-modal regression problem. Despite considerable efforts, three challenges remain: out-of-distribution (OOD) generalizations for compounds with new chemical scaffolds, uncertainty quantification of OOD predictions, and scalability to billions of compounds, which structure-based methods fail to achieve. To address aforementioned challenges, we propose a new model-agnostic anomaly detection-based uncertainty quantification method,embeddingMahalanobisOutlierScoring andAnomalyIdentification viaClustering (eMOSAIC). eMOSAIC uniquely quantifies distribution similarities or differences between the multi-modal representation of known cases and that of a new unseen one. We apply eMOSAIC to a multi-modal deep neural network model for multi-target ligand binding affinity predictions, leveraging a pre-trained strucrture-informed large protein language model. We extensively validate eMOSAIC in OOD settings, showing that it significantly outperforms state-of-the-art sequence-based deep learning and structure-based protein-ligand docking (PLD) methods by a large margin as well as existing uncertainty quantification methods. This finding highlights eMOSAIC’s potential for real-world polypharmacology and other applications.
Cooperation between Nodal and FGF signals regulates zebrafish cardiac cell migration and heart morphogenesis
Authors: Gonzalez Vanessa , Grant Meagan G. , Suzuki Makoto , Christophers Briana , Williams Jessica Rowland , Burdine Rebecca D. 
Published: January 2024

AbstractAsymmetric vertebrate heart development is driven by an intricate sequence of morphogenetic cell movements, the coordination of which requires precise interpretation of signaling cues by heart primordia. Here we show that Nodal functions cooperatively with FGF during heart tube formation and asymmetric placement. Both pathways act as migratory stimuli for cardiac progenitor cells (CPCs), but FGF is dispensable for directing heart tube asymmetry, which is governed by Nodal. We further find that Nodal controls CPC migration by inducing left-right asymmetries in the formation of actin-based protrusions in CPCs. Additionally, we define a developmental window in which FGF signals are required for proper heart looping and show cooperativity between FGF and Nodal in this process. We present evidence FGF may promote heart looping through addition of the secondary heart field. Finally, we demonstrate that loss of FGF signaling affects proper development of the atrioventricular canal (AVC), which likely contributes to abnormal chamber morphologies in FGF-deficient hearts. Together, our data shed insight into how the spatiotemporal dynamics of signaling cues regulate the cellular behaviors underlying organ morphogenesis.Summary statementThis study explores the cooperative and independent roles of Nodal and FGF signaling in generating heart asymmetry.
mRNA initiation and termination are spatially coordinated
Authors: Calvo-Roitberg Ezequiel , Carroll Christine L. , Venev Sergey V. , Kim GyeungYun , Mick Steven T. , Dekker Job , Fiszbein Ana , Pai Athma A. 
Published: January 2024

ABSTRACTThe expression of a precise mRNA transcriptome is crucial for establishing cell identity and function, with dozens of alternative isoforms produced for a single gene sequence. The regulation of mRNA isoform usage occurs by the coordination of co-transcriptional mRNA processing mechanisms across a gene. Decisions involved in mRNA initiation and termination underlie the largest extent of mRNA isoform diversity, but little is known about any relationships between decisions at both ends of mRNA molecules. Here, we systematically profile the joint usage of mRNA transcription start sites (TSSs) and polyadenylation sites (PASs) across tissues and species. Using both short and long read RNA-seq data, we observe that mRNAs preferentially using upstream TSSs also tend to use upstream PASs, and congruently, the usage of downstream sites is similarly paired. This observation suggests that mRNA 5’ end choice may directly influence mRNA 3’ ends. Our results suggest a novel “Positional Initiation-Termination Axis” (PITA), in which the usage of alternative terminal sites are coupled based on the order in which they appear in the genome. PITA isoforms are more likely to encode alternative protein domains and use conserved sites. PITA is strongly associated with the length of genomic features, such that PITA is enriched in longer genes with more area devoted to regions that regulate alternative 5’ or 3’ ends. Strikingly, we found that PITA genes are more likely than non-PITA genes to have multiple, overlapping chromatin structural domains related to pairing of ordinally coupled start and end sites. In turn, PITA coupling is also associated with fast RNA Polymerase II (RNAPII) trafficking across these long gene regions. Our findings indicate that a combination of spatial and kinetic mechanisms couple transcription initiation and mRNA 3’ end decisions based on ordinal position to define the expression mRNA isoforms.
The control of overt and covert attention across two nodes of the attention-control network
Authors: Polosecki Pablo , Steenrod Sara C. , Stemmann Heiko , Freiwald Winrich A. 
Published: January 2024

ABSTRACTAttention is a central cognitive capability whose focus is thought to be directed by a spatial map coding behavioral priority. Here we tested the three defining properties of priority map theory with electrophysiological recordings from two attentional control areas. Both areas, lateral intraparietal area LIP and dorsal posterior inferotemporal cortex PITd, selected behaviorally relevant locations even in the absence of visual stimuli (cognitive sustainment principle). Second, priority signals are thought to arise from the summation of multiple spatial signals (superposition principle). LIP approximated linear summation for visual stimuli, spatial attention, and eye movements, while PITd did not. Third, the same priority signal should guide different behaviors (agnosticity principle). LIP, instead, used separable processing channels for representing attentional focus and eye position, while PITd coded attentional focus only. Thus primate attentional control circuits implement multiple priority maps, whose functional diversity and dimensionality increase the computational capacity of attentional selection.SIGNIFICANCE STATEMENTA central theory of attention poses that the brain computes a priority map to highlight spatial locations of relevance for behavior. Here we tested the hypothesis and its key predictions about how priority signals are assembled and used, through electrophysiological single-unit recordings from two nodes of the attention control network, localized by functional magnetic resonance imaging. We found both areas to highlight locations even in the absence of a stimulus, and that each assembled spatial signals differently and provided spatial information in different forms. These findings force a revision of how and where spatial attention is controlled in the brain.
The sea urchinParacentrotus lividusorients to visual stimuli
Authors: Kirwan John D. , Li Tianshu , Ullrich-Lüter Jack , La Camera Giancarlo , Nilsson Dan-Eric , Arnone Maria Ina 
Published: January 2024

AbstractThough lacking eyes, some sea urchins can see: Several species exhibit resolving vision, as distinct from mere light detection. How and where light is captured in the eyeless sea urchins, and how this information is integrated to elicit visual behaviour, remains a fascinating enigma. We assessed the spatial resolution of the sea urchinParacentrotus lividusin laboratory experiments using fifty adults from the Bay of Naples. This keystone species is an important grazer of the NE Atlantic and Mediterranean and a model system to study development.We carried out behavioural trials in which individuals were placed in a submerged cylindrical arena to determine if they orient towards a visual stimulus on the arena wall, under diffuse, downwelling light. We adopted a novel isoluminant stimulus, necessitating vision of a given resolving power around the horizon to be detected. We tested individuals at five stimulus widths, including a uniform control. Animals oriented (upon clearing an obstacle) only to the widest stimuli (45 deg and above). This acuity may suffice for tasks such as finding nearby shelters or distant patches of habitat.We modelled the visual and neuronal processes to recapitulate these responses inP. lividus, by fine-tuning the model of Li et al. (2022), as applied to the sea urchinDiadema africanum. While these species differ morphologically, the model robustly predicts angular sensitivity in keeping with the behavioural experiments. We find thatP. lividus(and likely many Echinacea) possesses coarse spatial vision and that the neurosensory model applies broadly to sea urchins.
Rabphilin-3A negatively regulates neuropeptide release, through its SNAP25 interaction
Authors: Abramian Adlin , Hoogstraaten Rein I. , Murphy Fiona H. , mcDaniel Kathryn F. , Toonen Ruud F. , Verhage Matthijs 
Published: January 2024

AbstractNeuropeptides and neurotrophins are stored in and released from dense-core vesicles (DCVs). While DCVs and synaptic vesicles (SVs) share fundamental SNARE/SM proteins for exocytosis, a detailed understanding of DCV exocytosis remains elusive. We recently identified the RAB3-RIM1 pathway to be essential for DCV-, but not SV exocytosis, highlighting a significant distinction between the SV- and DCV secretory pathways. Whether RIM1 is the only RAB3 effector that is essential for DCV exocytosis is currently unknown. In this study, we show that rabphilin-3A (RPH3A), a known downstream effector of RAB3A, is a negative regulator of DCV exocytosis. Using live-cell imaging at single vesicle resolution with RPH3A-deficient hippocampal neurons, we show that DCV exocytosis increased 3-fold in the absence of RPH3A. RAB3A-binding deficient RPH3A lost its punctate distribution, but still restored DCV exocytosis to WT levels when re-expressed. SNAP25-binding deficient RPH3A did not rescue DCV exocytosis. In addition, we show that RPH3A did not travel with DCVs, but remained stationary at pre-synapses. RPH3A null neurons also had longer neurites, which was partly restored when ablating all regulated secretion with tetanus neurotoxin. Taken together, these results show that RPH3A negatively regulates DCV exocytosis, potentially also affecting neuron size. Furthermore, RAB3A interaction is required for the synaptic enrichment of RPH3A, but not for limiting DCV exocytosis. Instead, the interaction of RPH3A with SNAP25 is relevant for inhibiting DCV exocytosis.
Sequence clustering confounds AlphaFold2
Authors: Schafer Joseph W. , Chakravarty Devlina , Chen Ethan A. , Porter Lauren L. 
Published: January 2024

AbstractThough typically associated with a single folded state, some globular proteins remodel their secondary and/or tertiary structures in response to cellular stimuli. AlphaFold21(AF2) readily generates one dominant protein structure for these fold-switching (a.k.a. metamorphic) proteins2, but it often fails to predict their alternative experimentally observed structures3,4. Wayment-Steele, et al. steered AF2 to predict alternative structures of a few metamorphic proteins using a method they call AF-cluster5. However, their Paper lacks some essential controls needed to assess AF-cluster’s reliability. We find that these controls show AF-cluster to be a poor predictor of metamorphic proteins. First, closer examination of the Paper’s results reveals that random sequence sampling outperforms sequence clustering, challenging the claim that AF-cluster works by “deconvolving conflicting sets of couplings.” Further, we observe that AF-cluster mistakes some single-folding KaiB homologs for fold switchers, a critical flaw bound to mislead users. Finally, proper error analysis reveals that AF-cluster predicts many correct structures with low confidence and some experimentally unobserved conformations with confidences similar to experimentally observed ones. For these reasons, we suggest using ColabFold6-based random sequence sampling7–augmented by other predictive approaches–as a more accurate and less computationally intense alternative to AF-cluster.