Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques 1Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation timescales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.
HP1 oligomerization compensates for low-affinity H3K9me recognition and provides a tunable mechanism for heterochromatin-specific localization HP1 proteins bind with low affinity but high specificity to histone H3 lysine 9 methylation (H3K9me), forming transcriptionally inactive genomic compartments referred to as heterochromatin. How HP1 proteins traverse a complex and crowded chromatin landscape on the millisecond timescale and yet recognize H3K9me with high specificity remains paradoxical. Here, we visualize the single-molecule dynamics of an HP1 homolog, the fission yeast Swi6, in its native chromatin environment. By analyzing the motions of individual Swi6 molecules, we identify mobility states that map to discrete biochemical intermediates. Using mutants that perturb Swi6 H3K9me recognition, oligomerization, or nucleic acid binding, we parse the mechanism by which each biochemical property affects protein dynamics. We find that rather than enhancing chromatin binding, nucleic acid interactions, compete with and titrates Swi6 away from heterochromatin. However, as few as four tandem Swi6 chromodomains are necessary and sufficient to restore H3K9me-dependent localization. Our studies propose propose that HP1 oligomerization stabilizes higher-order protein configurations of a defined stoichiometry that facilitates high-specificity H3K9me recognition and outcompetes the inhibitory effects of nucleic acid-binding.
Polycystins are upregulated in fibrotic kidneys and promote renal fibrosis Autosomal polycystic kidney disease (ADPKD) is caused by mutations in PKD1or PKD2 gene, which encodes polycystin-1 or polycystin-2 respectively. Paradoxically, polycystins are up-regulated in human ADPKD kidneys and overexpression of polycystin-1 even induced renal cyst formation in mice, suggesting that up-regulation of polycystin is pathogenic. In this study, we determined the role of polycystin in renal fibrosis and explored its underlying mechanisms. We found that the expression of polycystin-1 or polycystin-2 was positively correlated with the expression of collagen-I in the kidney of three different fibrotic mouse models. Pharmaceutical inhibition or genetic knockout of polycystin-2 reduced the expression of pro-fibrotic markers in fibrotic kidneys of two animal models. Moreover, conditional knockout of Pkd1 gene attenuated renal fibrosis in two mouse models. Enhancer of Zeste homolog 2 (EZH2) is a methyltransferase which plays important role in renal fibrosis. We found that EZH2 was positively correlated with the expression of polycystins in fibrotic kidneys. Conditional knockout of the Ezh2 attenuated the anti-fibrotic responses induced by Pkd1 deletion in fibrotic kidneys.

In conclusion, polycystins are up-regulated in injured kidneys and polycystins promote renal fibrosis through up-regulation of EZH2 in fibrotic kidneys, suggesting that renal fibrosis in polycystic kidneys is not the direct effect of PKD gene mutations.

SignificanceMutations of polycystins cause autosomal polycystic kidney disease (ADPKD) which is associated with increased renal fibrosis in cystic kidneys. However, the role of polycystin in renal fibrosis remains unclear. Here, we showed that the expression of polycystins is positively correlated with the development of renal fibrosis in injured kidneys. Pharmaceutical inhibition or genetic deletion of polycystin-2 attenuated renal fibrosis in fibrotic kidneys. Moreover, genetic deletion of polycystin-1 inhibited renal fibrosis in two different animal models. Finally, we showed that ablation of the methyltransferase EZH2 mitigated the anti-fibrotic effect of polycystin-1 deletion in fibrotic kidneys. We conclude that polycystin promotes through EZH2 and renal fibrosis in cystic kidney is not the direct effect of PKD gene mutations. Embracing imperfection: machine-assisted invertebrate classification in difficult datasets O_LIDespite growing concerns over the health of global invertebrate diversity, terrestrial invertebrate monitoring efforts remain poorly geographically distributed. Machine-assisted classification has been proposed as a potential solution to quickly gather large amounts of data; however, previous studies have often used unrealistic or idealized datasets to train their models.
C_LIO_LIIn this study, we describe a practical methodology for including machine learning in ecological data acquisition pipelines. Here we train and test machine learning algorithms to classify over 56,000 bulk terrestrial invertebrate specimens from morphometric data and contextual metadata. All vouchered specimens were collected in pitfall traps by the National Ecological Observatory Network (NEON) at 27 locations across the United States in 2016. Specimens were photographed, and morphometric data was extracted as feature vectors using ImageJ. Issues stemming from inconsistent taxonomic label specificity were resolved by making classifications at the lowest identified taxonomic level (LITL). Taxa with too few specimens to be included in the training dataset were classified by the model using zero-shot classification.
C_LIO_LIWhen classifying specimens that were known and seen by our models, we reached an accuracy of 72.7% using extreme gradient boosting (XGBoost) at the LITL. Models that were trained without contextual metadata underperformed models with contextual metadata by an average of 7.2%. We also classified invertebrate taxa that were unknown to the model using zero-shot classification, with an accuracy of 39.4%, resulting in an overall accuracy of 71.5% across the entire NEON dataset.
C_LIO_LIThe general methodology outlined here represents a realistic application of machine learning as a tool for ecological studies. Hierarchical and LITL classifications allow for flexible taxonomic specificity at the input and output layers. These methods also help address the  long tail problem of underrepresented taxa missed by machine learning models. Finally, we encourage researchers to consider more than just morphometric data when training their models, as we have shown that the inclusion of contextual metadata can provide significant improvements to accuracy.
C_LI Differential impact of mutations in cell cycle checkpoint kinases on breast cancer presentation and prognosis. Cell cycle checkpoint kinases ATM/Chk2 and ATR/Chk1 are important tumor suppressors that are activated by DNA damage and other stressors to arrest replication. Germline variants in ATM and CHEK2 associate with increased incidence of estrogen receptor positive (ER+)/HER2- breast cancer and some evidence suggests that somatic inactivation of ATM/Chk2 induces therapy resistance in ER+/HER2- breast cancer patients. However, the relative contributions of germline and somatic inactivation of each of the four cell cycle checkpoint kinases to ER+/HER2- and ER-/PR-/HER2- (or triple negative breast cancer; TNBC) breast cancer incidence and outcomes has not yet been systematically assessed. Here, we comprehensively analyze associations between mutations in each cell cycle checkpoint kinase gene with age at diagnosis, tumor characteristics, and patient survival outcomes using six breast cancer datasets. Overall, we find that of all the cell cycle checkpoint kinases, CHEK2 (p=0.001) is the only gene that is preferentially mutated in ER+/HER2- breast cancer samples relative to TNBCs. CHEK2 mutations also associate with younger age at diagnosis of metastatic ER+/HER2- breast cancer (median age 41.5 years compared to 50 years for CHEK2 wildtype patients, p=0.03) that is highly PR positive (10% increase in PR positivity in metastatic vs primary CHEK2 mutant tumors, as opposed to a 25% decrease in PR positivity in CHEK2 wildtype tumors, p=0.03). These associations appear to be driven by germline variants, rather than somatic mutations. Whether germline or somatic, deleterious (truncating, frameshift or splice site) mutations in CHEK2 associate with rapid disease progression in response to standard first line endocrine therapy when compared to tumors with wildtype CHEK2 in two independent datasets (HR=6.15, p=0.01). Conversely, neither ATM nor ATR mutations are enriched in ER+/HER2- breast cancers relative to TNBC. However, co-incident mutation of ATR and TP53 is 2-fold enriched in this subset (p=0.002), and further enriched in metastatic (3.3%) vs primary (1%) ER+/HER2- breast cancers (p=0.002). This co-incident mutation of ATR and TP53 associates with decreased PR positivity in primary ER+/HER2- breast cancer diagnoses (p<0.0001) and significantly worse metastasis-free survival than mutations in either ATR or TP53 alone in two independent datasets (HR=2.01, p=0.007). This association is not specific to ER+/HER2- breast cancer but is observable in ER- breast cancer patients as well. These results provide the first systematic analysis of the contribution of germline and somatic mutations in cell cycle checkpoint kinases to tumor characteristics affecting patient prognosis and treatment response. They demonstrate a unique role for CHK2 in ER signaling and response to endocrine therapy, and a more universal role for ATR in combination with TP53 in metastatic progression across breast cancer subtypes. These findings provide rationale for streamlined testing of checkpoint kinase mutations as prognostic markers of metastatic potential and/or predictive markers of responsiveness to endocrine treatment in breast cancer patients. Hepatic MIR20B promotes nonalcoholic fatty liver disease by suppressing PPARA BackgroundNon-alcoholic fatty liver disease (NAFLD) is characterized by excessive lipid accumulation and imbalances in lipid metabolism in the liver. Although nuclear receptors (NRs) play a crucial role in hepatic lipid metabolism, the underlying mechanisms of NR regulation in NAFLD remain largely unclear.

MethodsUsing network analysis and RNA-seq to determine the correlation between NRs and microRNA in human NAFLD patients, we revealed that MIR20B specifically targets PPARA. MIR20B mimic and anti-MIR20B were administered to human HepG2 and Huh-7 cells and mouse primary hepatocytes as well as high fat diet (HFD)- or methionine-deficient diet (MCD)-fed mice to verify the specific function of MIR20B in NAFLD. We tested the inhibition of the therapeutic effect of a PPAR agonist, fenofibrate, by MIR20B and the synergic effect of combination of fenofibrate with anti-MIR20B.

ResultsWe revealed that MIR20B specifically targets PPARA through miRNA regulatory network analysis of nuclear receptor genes in NAFLD. The expression of MIR20B was upregulated in free fatty acid (FA)- treated hepatocytes and the livers of both obesity-induced mice and NAFLD patients. Overexpression of MIR20B significantly increased hepatic lipid accumulation and triglyceride levels. Furthermore, MIR20B significantly reduced FA oxidation and mitochondrial biogenesis by targeting PPARA. In MIR20B-introduced mice, the effect of fenofibrate to ameliorate hepatic steatosis was significantly suppressed. Finally, inhibition of MIR20B significantly increased FA oxidation and uptake, resulting in improved insulin sensitivity and a decrease in NAFLD progression. Moreover, combination of fenofibrate and anti-MIR20B exhibited the synergic effect on improvement of NAFLD in MCD-fed mice.

ConclusionsTaken together, our results demonstrate that the novel MIR20B targets PPARA, plays a significant role in hepatic lipid metabolism, and present an opportunity for the development of novel therapeutics for NAFLD.

FundingThis research was funded by Korea Mouse Phenotyping Project (2016M3A9D5A01952411), the National Research Foundation of Korea (NRF) grant funded by the Korea government (2020R1F1A1061267, 2018R1A5A1024340, NRF-2021R1I1A2041463, 2020R1I1A1A01074940), and the Future-leading Project Research Fund (1.210034.01) of UNIST. Novel Feline Papillomavirus Isolate P20 Assembled from Metagenomic Data Isolated from the Human Skin of a House Cat Owner A feline papillomavirus was assembled from metagenomic sequencing data collected from the human skin of a house cat owner. This circular papillomavirus strain P20 is 8069 bp in length, has a GC content of 54.38%, and displays genome organization typical of feline papillomaviruses with six annotated protein coding regions. The genome exhibits approximately 75% sequence similarity to other feline papillomavirus genomes. Force-dependent remodeling of cytoplasmic ZO-1 condensates contributes to cell-cell adhesion Although the physiological importance of biomolecular condensates is widely recognized, how it is controlled in time and space during development is largely unknown. Here we show that a tight junction protein ZO-1 forms cytoplasmic condensates in the trophectoderm (TE) of the mouse embryo before E4.0. These disappear via dissolution, and ZO-1 accumulates at the cell junction as the blastocyst cavity grows and internal pressure on TE cells increases. In contrast, this dissolution was less evident in TE cells attached to the inner cell mass, as they receive weaker tensile forces. Furthermore, analyses using MDCK cells have demonstrated that the ZO-1 condensates are generated and maintained by liquid-liquid phase separation. Our study also highlights that the dynamics of these condensates depends on the physical environment via an interaction between ZO-1 and F-actin. We propose that the force-dependent regulation of ZO-1 condensation contributes to establishing robust cell-cell adhesion during early development. The Temporal Statistics of Musical Rhythm across Western Genres: An Amplitude Modulation Phase Hierarchy Model Statistical learning by the human brain plays a core role in the development of cognitive systems like language and music. Both music and speech have structured inherent rhythms, however the acoustic sources of these rhythms are debated. Theoretically, rhythm structures in both systems may be related to a novel set of acoustic statistics embedded in the amplitude envelope, statistics originally revealed by modelling childrens nursery rhymes. Here we apply similar modelling to explore whether the amplitude modulation (AM) timescales underlying rhythm in music match those in child-directed speech (CDS). Utilising AM-driven phase hierarchy modelling previously applied to infant-directed speech (IDS), adult-directed speech (ADS) and CDS, we test whether the physical stimulus characteristics that yield speech rhythm in IDS and CDS describe rhythm in music. Two models were applied. One utilized a low-dimensional representation of the auditory signal adjusted for known mechanisms of the human cochlear, and the second utilized probabilistic amplitude demodulation, estimating the modulator (envelope) and carriers using Bayesian inference. Both models revealed a similar hierarchically-nested temporal modulation structure across Western musical genres and instruments. Core bands of AM and spectral patterning matched prior analyses of IDS and CDS, and music showed strong phase dependence between slower bands of AMs, again matching IDS and CDS. This phase dependence is critical to the perception of rhythm. Control analyses modelling other natural sounds (wind, rain, storms, rivers) did not show similar temporal modulation structures and phase dependencies. We conclude that acoustic rhythm in language and music has a shared statistical basis. Hidden dynamics of human proteasome autoregulation during protein degradation The cellular functions are executed by macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving conformational continuum of important complexes at atomic level remains a key challenge in the methodology of computational biophysics. Here we introduce a deep-learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy reconstructions of nonequilibrium conformational continuum. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. Applications of this methodology to six experimental datasets demonstrated its generality in breaking resolution barrier of visualizing dynamic components of functional complexes, in choreographing continuous inter-subunit motions and in exploring their  hidden conformational space. Importantly, AlphaCryo4D identified 64 conformers of the substrate-bound human 26S proteasome and reconstituted single-nucleotide-exchange dynamics of proteasomal AAA-ATPase motor during translocation initiation, which together illuminates a grand hierarchical allostery for proteasome autoregulation. Propionate ameliorates diabetes-induced neurological dysfunction through regulating the PI3K/Akt/eNOS signaling pathway A large body of research has established diabetes-related cognitive deterioration, sometimes known as "diabetic encephalopathy." Current evidence supports that oxidative stress, neuronal apoptosis, and cerebral microcirculation weakness are associated with cognition deficits induced by diabetes. The present study explores the effect of propionate on neurological deficits, cerebral blood flow, and oxidative stress in diabetic mice. Propionate can markedly improve neurological function, which is correlated with its capabilities of stimulating nitric oxide (NO) production, increasing cerebral microcirculation, suppressing oxidative stress and reducing neuron loss in the hippocampus. In addition, the results of Western Blotting indicated that the brain-protective function of propionate in streptozocin (STZ)-induced insulin-dependent diabetes mellitus (IDDM) mice is related to phosphoinositide 3-kinase (PI3K)/serine-threonine protein kinase (Akt)/endothelial nitric oxide synthase(eNOS) signaling pathway. In a diabetic mouse model, propionate reduces cerebral microcirculation, hippocampus apoptosis, and neurological impairment. Thus, propionate, now employed as a food preservative, may also help slow diabetes-induced cognitive loss.

Graphical Abstract

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/464493v2_ufig1.gif" ALT="Figure 1">
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org.highwire.dtl.DTLVardef@81fa09org.highwire.dtl.DTLVardef@1469451org.highwire.dtl.DTLVardef@b37fa1org.highwire.dtl.DTLVardef@8037e7_HPS_FORMAT_FIGEXP  M_FIG C_FIG HighlightsO_LIPropionate ameliorates diabetic-induced depression-like behavior, spatial learning and memory deficits.
C_LIO_LIPropionate increases cerebral microcirculation by enhancing NO production in diabetic mice.
C_LIO_LIPropionate suppresses oxidative stress and reduces neuron loss in the hippocampus.
C_LIO_LIThe brain-protective function of propionate is related to PI3K/Akt/eNOS signaling pathway.
C_LI Non-Newtonian Blood Rheology Impacts Left Atrial Stasis in Patient-Specific Simulations The lack of mechanically effective contraction of the left atrium (LA) during atrial fibrillation (AF) disturbs blood flow, increasing the risk of thrombosis and ischemic stroke. Thrombosis is most likely in the left atrial appendage (LAA), a small narrow sac where blood is prone to stagnate. Slow flow promotes the formation of erythrocyte aggregates in the LAA, also known as rouleaux, causing viscosity gradients that are usually disregarded in patient-specific simulations. To evaluate these non-Newtonian effects, we built atrial models derived from 4D computed tomography scans of patients and carried out computational fluid dynamics simulations using the Carreau-Yasuda constitutive relation. We examined six patients, three of whom had AF and LAA thrombosis or a history of transient ischemic attacks (TIAs). We modeled the effects of hematocrit and rouleaux formation kinetics by varying the parameterization of the Carreau-Yasuda relation and modulating non-Newtonian viscosity changes based on residence time. Comparing non-Newtonian and Newtonian simulations indicates that slow flow in the LAA increases blood viscosity, altering secondary swirling flows and intensifying blood stasis. While some of these effects can be subtle when examined using instantaneous metrics like shear rate or kinetic energy, they are manifested in the blood residence time, which accumulates over multiple heartbeats. Our data also reveal that LAA blood stasis worsens when hematocrit increases, offering a potential new mechanism for the clinically reported correlation between hematocrit and stroke incidence. In summary, we submit that hematocrit-dependent non-Newtonian blood rheology should be considered in calculating patient-specific blood stasis indices by computational fluid dynamics. The snoGloBe interaction predictor reveals a broad spectrum of C/D snoRNA RNA targets Box C/D small nucleolar RNAs (snoRNAs) are a conserved class of RNA known for their role in guiding ribosomal RNA 2-O-ribose methylation. Recently, C/D snoRNAs were also implicated in regulating the expression of non-ribosomal genes through different modes of binding. Large scale RNA-RNA interaction datasets detect many snoRNAs binding messenger RNA, but are limited by specific experimental conditions. To enable a more comprehensive study of C/D snoRNA interactions, we created snoGloBe, a human C/D snoRNA interaction predictor based on a gradient boosting classifier. SnoGloBe considers the target type, position and sequence of the interactions, enabling it to outperform existing predictors. Interestingly, for specific snoRNAs, snoGloBe identifies strong enrichment of interactions near gene expression regulatory elements including splice sites. Abundance and splicing of predicted targets were altered upon the knockdown of their associated snoRNA. Strikingly, the predicted snoRNA interactions often overlap with the binding sites of functionally related RNA binding proteins, reinforcing their role in gene expression regulation. SnoGloBe is also an excellent tool for discovering viral RNA targets, as shown by its capacity to identify snoRNAs targeting the heavily methylated SARS-CoV-2 RNA. Overall, snoGloBe is capable of identifying experimentally validated binding sites and predicting novel sites with shared regulatory function. G protein-coupled estrogen receptor activation by Bisphenol-A disrupts protection from apoptosis conferred by estrogen receptors ERα and ERβ in pancreatic beta cells 17{beta}-estradiol protects pancreatic {beta}-cells from apoptosis via the estrogen receptors ER, ER{beta} and GPER. Conversely, the endocrine disruptor Bisphenol-A (BPA), which exerts multiple effects in this cell type via the same estrogen receptors, increased basal apoptosis. The molecular initiated events that trigger these opposite actions have yet to be identified. We demonstrated that combined genetic downregulation and pharmacological blockade of each estrogen receptor increased apoptosis to a different extent. The increase in apoptosis induced by BPA was diminished by the pharmacological blockade or the genetic silencing of GPER, and it was partially reproduced by the GPER agonist G1. BPA and G1-induced apoptosis were abolished upon pharmacological inhibition, silencing of ER and ER{beta}, or in dispersed islet cells from ER{beta} knockout (BERKO) mice. Yet, the ER and ER{beta} agonists, PPT and DPN, respectively, had no effect on beta cell viability. To exert their biological actions, ER and ER{beta} form homodimers and heterodimers. Molecular dynamic simulations together with proximity ligand assay and coimmunoprecipitation experiments indicated that the interaction of BPA with ER and ER{beta} as well as the GPER activation by G1 decreased ER{beta} heterodimers. We propose that ER{beta} heterodimers play an antiapoptotic role in beta cells and that BPA- and G1-induced decrease in ER{beta} heterodimers leads to beta cell apoptosis. Unveiling how different estrogenic chemicals affect the crosstalk among estrogen receptors should help to identify diabetogenic endocrine disruptors.

HighlightsO_LIPharmacological blockade and gene silencing of estrogen receptors ER, ER{beta} and GPER indicate that they are antiapoptotic in basal conditions.
C_LIO_LIGPER activation by G1 and BPA triggered apoptosis via a crosstalk with ER and ER{beta}.
C_LIO_LIBPA interaction with ER and ER{beta} as well as GPER activation decreased ER{beta} heterodimers, which was associated to increased apoptosis.
C_LIO_LIThis pathway represents a novel molecular initiating event underlying the pro-apoptotic effect of BPA
C_LIO_LIThe EndoC-{beta}H1 cell line may be a valid model of human {beta}-cells for identifying diabetogenic pollutants.
C_LI Integrative Modelling of Signalling Network Dynamics Identifies Cell Type-selective Therapeutic Strategies for FGFR4-driven Cancers Oncogenic FGFR4 signalling represents a potential therapeutic target in many cancer types, including triple negative breast cancer (TNBC) and hepatocellular carcinoma (HCC). However, resistance to single-agent therapy directed at FGFR4 remains a major challenge, prompting the need to identify more effective combinatorial therapeutic strategies. Here, we integrated computational network modelling and experimental validation to characterise dynamic reprogramming of the FGFR4 signalling network in TNBC following FGFR4 kinase inhibition. We found that AKT, which signals downstream of FGFR4, displayed a rapid and potent reactivation following FGFR4 targeting. Through model-based simulation and systematic prediction of the effect of co-targeting specific network nodes, we predicted, and validated experimentally, strong synergism of co-targeting FGFR4 and particular ErbB kinases or AKT, but not the upstream kinase PI3K. Further, incorporation of protein expression data from hundreds of cancer cell types enabled us to adapt our model to other diverse cellular contexts, leading to the prediction that while AKT rebound occurs frequently, it is not a general phenomenon. Instead, ERK is reactivated in a subset of cell types, including the FGFR4-driven HCC cell line Hep3B. This was subsequently corroborated, and moreover, co-targeting FGFR4 and MEK in Hep3B cells markedly enhanced inhibition of cell proliferation. Overall, these findings provide novel insights into the dynamics of drug-induced network remodelling in cancer cells, highlight the impact of protein expression heterogeneity on network response to targeted therapy and identify candidate cell type-selective combination treatments for FGFR4-driven cancer. Population differences in the length and early-life dynamics of telomeres among European pied flycatchers Telomere length and shortening rate are increasingly used as biomarkers for long-term costs in ecological and evolutionary studies because of their relationships with survival and fitness. Both early-life conditions and growth, and later-life stressors can create variation in telomere shortening rate. Studies on between- population telomere length and dynamics are scarce, despite the expectation that populations exposed to varying environmental constraints would present divergent telomere length patterns. The pied flycatcher (Ficedula hypoleuca) is a passerine bird breeding across Eurasia (from Spain to western Siberia) and migrating through the Iberian Peninsula to spend the non-breeding period in sub-Saharan Africa. Thus, different populations show marked differences in migration distance. We studied the large-scale variation of telomere length and early-life dynamics in the pied flycatcher by comparing six European populations across a north- south gradient (Finland, Estonia, England, and Spain) predicting negative effect of migration distance on adult telomere length, and of nestling growth on nestling telomere dynamics. There were clear population differences in telomere length, with English birds from mid-latitudes having the longest telomeres. Telomere length did not thus show consistent latitudinal variation and was not linearly linked to differences in migration distance. Early-life telomere shortening rate tended to vary between populations. Fast growth was associated with shorter telomeres in the early life, but faster nestling growth affected telomeres more negatively in northern than southern populations. While the sources of between-population differences in telomere-related biology remain to be more intensively studied, our study illustrates the need to expand telomere studies at the between-population level. Trade-offs in biosensor optimization for dynamic pathway engineering Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design and calibration of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature, and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy and pharmaceutical sectors. Naming of human diseases on the wrong side of history BackgroundIn the medical sphere, understanding naming conventions strengthen the integrity of naming human diseases remains nominal rather than substantial yet. Since the current nosology-based standard for human diseases could not offer a one-size-fits-all corrective mechanism, many idiomatic but flawed names frequently appear in scientific literature and news outlets at the cost of sociocultural impacts.

ObjectiveWe attempt to examine the ethical oversights of current naming practices and propose heuristic rationales and approaches to determine a pithy name instead of an inopportune nosology.

MethodsFirst, we examined the compiled global online news volumes and emotional tones on some inopportune nosology like German measles, Middle Eastern Respiratory Syndrome, Spanish flu, Hong Kong flu, and Huntingtons disease in the wake of COVID-19. Second, we prototypically scrutinize the lexical dynamics and pathological differentials of German measles and common synonyms by leveraging the capacity of the Google Books Ngram Corpus. Third, we demonstrated the empirical approaches to curate an exclusive substitute for an anachronistic nosology German measles based on deep learning models and post-hoc explanations.

ResultsThe infodemiological study shows that the public informed the offensive names with extremely negative tones in textual and visual narratives. The findings of the historiographical study indicate that many synonyms of German measles did not survive, while German measles became an anachronistic usage, and rubella has taken the dominant place since 1994. The PubMedBERT model could identify rubella as a potential substitution for German measles with the highest semantic similarity. The results of the semantic drift experiments further indicate that rubella tends to survive during the ebb and flow of semantic drift.

ConclusionsOur findings indicate that the nosological evolution of anachronistic names could result in sociocultural impacts without a corrective mechanism. To mitigate such impacts, we introduce some ethical principles for formulating an improved naming scheme. Based on deep learning models and post-hoc explanations, our illustrated experiments could provide hallmark references to the remedial mechanism of naming practices and pertinent credit allocations. Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes IntroductionIncreasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare genetic variants. Sparseness leads to maximum likelihood estimators (MLEs) of log-OR parameters that are biased away from their null value of zero and tests with inflated type 1 errors. Different penalized-likelihood methods have been developed to mitigate sparse-data bias. We study penalized logistic regression using a class of log-F priors indexed by a shrinkage parameter m to shrink the biased MLE towards zero.

MethodsWe propose a two-step approach to the analysis of a genetic association study: first, a set of variants that show evidence of association with the trait is used to estimate m; and second, the estimated m is used for log-F -penalized logistic regression analyses of all variants using data augmentation with standard software. Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation.

ResultsWe evaluate the statistical properties of our proposed two-step method and compared its performance to other shrinkage methods by a simulation study. Our simulation studies suggest that the proposed log-F -penalized approach has lower bias and mean squared error than other methods considered. We also illustrate the approach on data from a study of genetic associations with "super senior" cases and middle aged controls.

Discussion/ConclusionWe have proposed a method for single rare variant analysis with binary phenotypes by logistic regression penalized by log-F priors. Our method has the advantage of being easily extended to correct for confounding due to population structure and genetic relatedness through a data augmentation approach. Single cell profiling of functionally cured Chronic Hepatitis B patients reveals the emergence of activated innate and an altered adaptive immune response in the intra-hepatic environment Hepatitis B surface antigen (HBsAg) loss or functional cure (FC), is considered the desirable therapeutic outcome for chronic hepatitis B (CHB) patients. However, the immuno-pathological biomarkers and underlying mechanisms remain unclear. Here we present a comprehensive single cell-transcriptomic atlas together with immune-phenotyping of disease-associated cell states (DACS) isolated from intra-hepatic tissue and matched PBMCs of either CHB or FC patients. We find that the intra-hepatic environment displays specific cell identities and molecular signatures that are distinct from PBMCs. FC is associated with emergence of an altered adaptive immune response marked by CD4 cytotoxic T lymphocytes (CD4-CTLs), and an activated innate response represented by liver-resident natural killer (LR-NK) cells. Overall, these findings provide novel insights into immuno-pathological cell states associated with FC that could serve as prognostic biomarkers. Foraging with friends, moving with anyone: effects of the social environment on movement-integrated habitat selection O_LIMovement links the distribution of habitats with the social environment of animals using those habitats; yet integrating movement, habitat selection, and socioecology remains an opportunity for further study.
C_LIO_LIHere, our objective was to disentangle the roles of habitat selection and social association as drivers of collective movement in a gregarious ungulate. To accomplish this objective, we (1) assessed whether socially familiar individuals form discrete social communities and whether social communities have high spatial, but not necessarily temporal, overlap; and (2) we modelled the relationship between collective movement and selection of foraging habitats using socially informed integrated step selection analysis.
C_LIO_LIWe used social network analysis to assign individuals to social communities and determine short and long-term social preference among individuals. Using integrated step selection functions (iSSF), we then modelled the effect of social processes, i.e., nearest neighbour distance and social preference, and movement behaviour on patterns of habitat selection.
C_LIO_LIBased on assignment of individuals to social communities and home range overlap analyses, individuals assorted into discrete social communities, and these communities had high spatial overlap. By unifying social network analysis with iSSF, we identified movement-dependent social association, where individuals foraged with more familiar individuals, but moved collectively with any between foraging patches.
C_LIO_LIOur study demonstrates that social behaviour and space use are inter-related based on spatial overlap of social communities and movement-dependent habitat selection. Movement, habitat selection, and social behaviour are linked in theory. Here, we put these concepts into practice to demonstrate that movement is the glue connecting individual habitat selection to the social environment.
C_LI Bioactive constituents of Verbena officinalis regulate killing efficiency of primary human natural killer cells by accelerating killing processes BackgroundNatural killer (NK) cells play a key role in eliminating tumorigenic and pathogen-infected cells. Verbena officinalis (V. officinalis) has been used as a medical plant in traditional and modern medicine, exhibiting anti-tumor and anti-inflammation activity, but its role in NK cells remains unclear.

PurposeThe impact of bioactive constituents of V. officinalis on immune responses remains largely elusive. In this work we investigated the potential targets of V. officinalis and focused on killing efficiency and related functions of NK cells regulated by bioactive constituents of V. officinalis.

Study design/methodsWe used primary human NK cells from peripheral blood mononuclear cells. Potential regulatory roles of selected compounds in NK killing function-related genes were first analyzed by network pharmacology approaches. Potential engagement between compounds and NK receptors was examined by molecular docking assay. Killing efficiency of NK cells was determined with real-time killing assay and live-cell imaging. Proliferation was examined by CFSE staining. Expression of cytotoxic proteins was analyzed using flow cytometry. Lytic granule release was quantified by CD107a degranulation assay. Contact time required for killing and determination of serial killers were analyzed using live cell imaging results.

ResultsResults from network pharmacology approaches and molecular docking analyses predicted that the five bioactive compounds (acteoside, apigenin, kaempferol, verbenalin and hastatoside) from V. officinalis can regulated various key genes essential for NK effector functions and can bind to NK inhibitory receptors as well as activating receptors, indicating their potential role in regulating NK killing capacity. Analyses of NK killing efficiency revealed that out of these five compounds verbenalin exhibited an encouraging potency to enhance NK killing efficiency. Further investigation showed that verbenalin did not affect proliferation, expression of cytotoxic proteins, or lytic granule degranulation, but rather reduced contact time required for killing, enhanced total killing events per NK cell, and elevated the fraction of killing competent NK cells.

ConclusionsIn this work, we have identified verbenalin as the most potent bioactive constituent from V. officinalis to enhance NK cell killing efficiency via accelerating killing processes and converting non-killers to killing competent NK cells without affecting the lytic granule pathway. Actin isovariant ACT7 controls root meristem development in Arabidopsis through modulating auxin and ethylene responses Meristem is the most functionally dynamic part in a plant body. The shaping of the meristem requires constant cell division and cell elongation, which are influenced by hormones and cell cytoskeletal component, actin. Although the roles of hormones in modulating meristem development have been extensively studied, the role of actin in this process is still elusive. Using the single and double mutants of the vegetative class actin, we demonstrate that actin isovariant ACT7 plays an important role in root meristem development. In absence of ACT7, but not ACT8 and ACT2, depolymerization of actin was observed. Consistently, act7 mutant showed reduced cell division, cell elongation, and meristem length. Intracellular distribution and trafficking of auxin transport proteins in the actin mutants revealed that ACT7 specifically functions in root meristem to facilitate the trafficking of auxin efflux carriers PIN1 and PIN2, and consequently the transport of auxin. Compared with act7, act7act8 double mutant shows slightly enhanced phenotypic response and altered intracellular trafficking. The altered distribution of auxin in act7 and act7act8 affects the response of the roots to ethylene but not to cytokinin. Collectively, our results suggest that ACT7 dependent auxin-ethylene response plays a key role in controlling Arabidopsis root meristem development. Evolutionary rates of testes-expressed genes differ in monogamous and promiscuous Peromyscus species Reproductive proteins, including those expressed in the testes, are among the fastest evolving proteins across the tree of life. Sexual selection on traits involved in sperm competition is thought to be a primary driver of testes gene evolution and is expected to differ between promiscuous and monogamous species due to intense competition between males to fertilize females in promiscuous lineages and lack thereof in monogamous ones. Here, we employ the rodent genus Peromyscus as a model to explore differences in evolutionary rates between testes-expressed genes in monogamous and promiscuous species. We find candidate genes that may be associated with increased sperm production in promiscuous species and gene ontology categories that show patterns of molecular convergence associated with phenotypic convergence in independently evolved monogamous species. Overall, our results highlight the possible molecular consequences of differences in mating system, likely due to differences in selective pressures. Mouse frontal cortex mediates additive multisensory decisions To interpret the world and make accurate perceptual decisions, the brain must combine information across sensory modalities. For instance, it must combine vision and hearing to localize objects based on their image and sound. Probability theory suggests that evidence from multiple independent cues should be combined additively, and several studies support this hypothesis1-14. However, other studies suggest that mammals, particularly mice, can exhibit sensory bias during audiovisual integration15-19; furthermore, the cortical substrates of multisensory integration remain uncertain20. Here we show that to localize a stimulus, mice combine auditory and visual spatial cues additively, a computation supported by additive multisensory integration in frontal cortex. We developed an audiovisual localization task where mice turn a wheel to indicate the joint position of an image and a sound. Scanning optogenetic inactivation21-24 during stimulus presentation showed that visual cortex contributes unisensory information whereas frontal cortex contributes multisensory information to the mouses decision. Neuropixels recordings of >10,000 neurons indicated that neural activity in frontal area MOs (secondary motor cortex) reflects an additive combination of visual and auditory signals. An accumulator model2,25,26 applied to the sensory representations of MOs neurons reproduced both the observed choices and reaction times. Sensory signals in MOs were specific to trained mice, and indeed the same accumulator applied to naive mice failed to perform the task. These results suggest that frontal cortex adapts through learning to integrate information across sensory cortices, providing a signal that is then transformed into a binary decision by a downstream accumulator.