bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian Least-Squares Supertrees (BLeSS): Flexible Inference of Large Time-Calibrated Phylogenies
Authors: Černý David , Slater Graham J. 
Published: December 2024

AbstractTime-calibrated phylogenies are key to macroevolutionary hypothesis testing and parameter inference, but their estimation is difficult when the number of tips is large. Despite its attractive properties, the joint Bayesian inference of topology and divergence times remains computationally prohibitive for large supermatrices. Historically, supertrees represented a popular alternative to supermatrix-based phylogenetic methods, but most of the existing supertree techniques do not accommodate branch lengths or topological uncertainty, rendering them unfit to supply input for modern comparative methods. Here, we present Bayesian Least-Squares Supertrees (BLeSS), a new approach that takes a profile of time trees with partially overlapping leaf sets as its input, and returns the joint posterior distribution of supertree topologies and divergence times as its output. Building upon the earlier exponential error model and average consensus techniques, BLeSS transforms the profile into path-length distance matrices, computes their arithmetic average, and uses MCMC to sample time-calibrated supertrees according to their least-squares fit to the average distance matrix. We provide a fast, flexible, and validated implementation of BLeSS in the programRevBayes, and test its performance using a comprehensive set of simulations. We show that the method performs well across a wide range of conditions, including variation in missing data treatment and the steepness of the error function. Finally, we apply BLeSS to an empirical dataset comprising 33 time trees for 260 species of carnivorans, illustrating its ability to recover well-supported clades and plausible node ages, and discuss how the method can best be used in practice, outlining possible extensions and performance boosts.
Small RNAs regulation and genomic harmony: insights into allopolyploid evolution in marsh orchids (Dactylorhiza)
Authors: Eriksson Mimmi C. , Thornton Matthew , Trucchi Emiliano , Wolfe Thomas M. , Balao Francisco , Hedrén Mikael , Paun Ovidiu 
Published: December 2024

SUMMARYHybridization and polyploidy are prevalent drivers of speciation, with novel ecological properties potentially arising, among other mechanisms, through changes in gene regulation by small RNAs (smRNAs), linked to transposable element (TE) dynamics. With a common garden set-up, we comparatively investigated smRNA abundance in two young, but widely distributed, ecologically divergent sibling allotetraploid marsh orchids (Dactylorhiza majalisandD. traunsteineri) and their diploid parents. Despite independent origins, the allopolyploids appear to share a substantial portion of smRNA targeting, with transgressive smRNA targeting consistently overexpressed in both, related to key genes regulating transcription, cell division, and biotic and abiotic stress responses. TE-targeting smRNAs also display shared patterns between the sibling allopolyploids, with 20-23 nt smRNAs following the maternal and smaller genome, whereas 24 nt smRNAs targeting typically resembling the level of the paternal and larger genome. However, differences between the allopolyploids are also evident, with the older allopolyploidD. majalisoften showing higher regulation by smRNAs, appearing more focused on fine-tuning gene copy regulation, whereas its younger siblingD. traunsteineriexhibits stronger non-additive expression, more prominently reflecting an apparent ongoing resolution of post-polyploidization meiotic/mitotic challenges. These findings highlight shared and species-specific smRNA dynamics, revealing how allopolyploids balance genomic instability and adaptive regulation during their evolutionary trajectories. In this system, the youngerD. traunsteineriseems to prioritize stabilizing its genome, while the olderD. majalisshifted towards optimizing gene expression. Together, this study emphasizes the role of smRNAs in facilitating ecological novelty and speciation during post-polyploidization evolution, providing insights into molecular mechanisms and adaptive evolution.
Boosting of CAR-T cells with rhabdovirus is limited by type I interferon and rapid contraction
Authors: Burchett Rebecca , Morris Claire G. , Ishak Mira , Cummings Derek , Baker Christopher L. , Marius Ricardo , Kazdhan Natasha , Silvestri Christopher M. , Bell John C. , Lichty Brian D. , Walsh Scott R. , Wan Yonghong , Hammill Joanne A. , Bramson Jonathan L. 
Published: December 2024

AbstractRhabdovirus vaccines that encode tumour-associated antigens are potent boosting agents for adoptively transferred tumour-specific T cells. Employing rhabdovirus vaccines to boost adoptively transferred T cells relies ona prioriknowledge of tumour epitopes, isolation of matched epitope-specific T cells, and a personalized vaccine, which limit clinical feasibility. Here, we investigated a universal strategy for boosting transferred tumour-specific T cells where boosting is provided through a chimeric antigen receptor (CAR) that is paired with a vesicular stomatitis virus (VSV) vaccine encoding the CAR-target. Boosting CAR-engineered tumour-specific T cells with paired VSV vaccines was associated with robust T cell expansion and delayed tumour progression in syngeneic models. CAR-T cell expansion and anti-tumour function was enhanced by blocking IFNAR1. However, vaccine-boosted CAR-T cells rapidly contracted and antigen-positive tumours re-emerged. In contrast, when the same T cells were boosted with VSV encoding antigen that stimulates through the TCR, the adoptively transferred T cells displayed improved persistence, tumour-specific endogenous cells expanded in parallel, and tumour cells carrying the antigen target were completely eradicated. Our findings underscore the need for further research into CAR-mediated boosting, how this differs mechanistically from TCR-mediated boosting, and the importance of engaging endogenous tumour-reactive T cells to achieve long-term tumour control.
Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data
Authors: Ercelen Defne , Caggiano Christa , Border Richard , Sankararaman Sriram , Mangul Serghei , Zaitlen Noah , Thompson Michael 
Published: December 2024

ABSTRACTUpwards of 40% of reads in sequencing datasets may be unmapped and discarded by standard protocols. Recent work has shown the utility of re-analyzing these unmapped reads to construct meaningful features, such as immune diversity repertoires or copy number variation in mtDNA and rDNA. While previous analyses of these features have produced significant correlations with diverse traits, they have generally been limited to analyses of RNA-sequencing data in phenotype-specific cohorts. Here, we explore whether associations can be identified using population-scale, whole-exome sequencing data in the UK BioBank. Using recently developed tools, we constructed multiple features including T-cell receptor diversity metrics, microbial load, and mtDNA and rDNA copy numbers for nearly 50,000 individuals in the UK BioBank. We first verify the validity of our method by showing that GWAS on these constructed traits results in replication of associations from studies in which the phenotypes were explicitly measured. Next, across several GWAS, we identified 21 novel independent significant loci in 11 genes, most of them in genes implicated in the innate immune response. Finally, we further analyzed the read-constructed features by establishing correlations to other population-level biobank traits such as immune disorders, metabolic disorders, neuropsychiatric disorders, and blood cell counts. Our results suggest that existing tools for feature construction from unmapped reads can offer novel information at the population level, and that these features can be used to establish novel genetic associations.
Automated Cell Type Annotation with Reference Cluster Mapping
Authors: Galanti Valerio , Shi Lingting , Azizi Elham , Liu Yining , Blumberg Andrew J. 
Published: December 2024

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies have revolutionized our understanding of cellular heterogeneity. However, the characterization of scRNA-seq datasets remains challenging. We introduce a novel computational method that significantly enhances the annotation of scRNA clusters of a query dataset using established datasets as references. RefCM leverages optimal transport to measure the similarity in gene expression distributions between clusters and solves an integer program to optimally link the query and reference datasets based on this metric. Our algorithm produces more accurate cross-technology, cross-tissue, and cross-species mappings than any other currently available method. We demonstrate the efficacy of our method on a variety of benchmark datasets, showcasing its robustness and applicability across diverse biological contexts.
Detection of eco-evolutionary dynamics in communities using Joint Species Distribution Models
Authors: Pantel Jelena H. , Hermann Ruben J. 
Published: December 2024

2AbstractBiodiversity at the metacommunity scale is typically influenced by a number of environmental, spatial, biotic, and stochastic factors. At the same time, these factors impact the evolution of individual species, as sites present different local selection pressures and connectivity can impact gene flow and genetic drift. Identifying the relative impacts of environmental, spatial, biotic, and other drivers on community composition across spatial and temporal scales has been greatly facilitated by joint species distribution models, but these models have yet to consider the impact of microevolution on community composition. We used Heirarchical Models of Species Communities (HMSC) to analyze simulated data of an populations and communities, including in a large evolving metacommunity model, to establish whether HMSC can sufficiently quantify the contribution of phenotypic evolution for metacommunity composition. The models successfully partitioned variance contributed by environmental, spatial, and evolving phenotypic drivers, and also estimated site-and year-specific covariance. We also applied the HMSC with trait evolution model to an existing dataset studying trait change and community dynamics in an experimental aquatic plant system. The study of eco-evolutionary dynamics may require data that reflects numerous complex, interacting processes and it is necessary to have flexible, generalized statistical models to analyze this data. JSDM models such as HMSC present one promising path for analysis of eco-evolutionary dynamics in multi-species communities.
Dynamic Control of Argonautes by a Rapidly Evolving Immunological Switch
Authors: Ewe Chee Kiang , Teichman Guy , Knott Maximilian M L , Anava Sarit , Gingold Hila , Sarmiento Mario Bardan , Troemel Emily , Rechavi Oded 
Published: December 2024

AbstractSmall RNAs, coupled with Argonuate proteins (AGOs), regulate diverse biological processes, including immunity against nucleic acid parasites.C. eleganspossesses an expanded repertoire of at least 19 AGOs functioning in an intricate gene regulatory network. Despite their crucial roles, little is known about the regulation of AGOs, and whether their expression levels, tissue specificity, and functions change in response to genetic perturbations or environmental triggers. Here, we report that PALS-22, a member of an unusually expanded protein family inC. elegans, acts as a negative regulator of antiviral RNAi involving the RIG-I homolog. The loss ofpals-22leads to enhanced silencing of transgenes and endogenous dsRNAs. We found that PALS-22 normally suppresses the expression of two AGOs, VSRA-1 and SAGO-2, which are activated by bZIP transcription factor ZIP-1. Whenpals-22is eliminated,vsra-1andsago-2are upregulated. These AGOs in turn play key roles in defense against foreign genetic elements and intracellular pathogens, respectively. Surprisingly, while inpals-22mutants immune genes functioning in the intracellular pathogen response (IPR) are upregulated, removing SAGO-2 or the RNA-dependent RNA polymerase RRF-3 in these mutants leads to the downregulation of these genes. This observation contrasts with the typical gene-silencing role of siRNAs. Finally, by analyzingC. eleganswild isolates and lab reference strains, we demonstrate that PALS-22 regulates the expression of several germline AGOs, affecting germline mortality and transgenerational epigenetic inheritance. In summary, PALS-22 is a key genetic node that balances the trade-off between immunity and germline health by modulating the functions of different AGOs, thereby shaping the outputs of the RNAi machinery and the dynamics of epigenetic inheritance.
Systems biology-enabled targeting of NF-κΒ and BCL2 overcomes microenvironment-mediated BH3-mimetic resistance in DLBCL
Authors: Vareli Aimilia , Narayanan Haripriya Vaidehi , Clark Heather , Jayawant Eleanor , Zhou Hui , Liu Yi , Stott Lauren , Simoes Fabio , Hoffmann Alexander , Pepper Andrea , Pepper Chris , Mitchell Simon 
Published: December 2024

AbstractIn Diffuse Large B-cell Lymphoma (DLBCL), elevated anti-apoptotic BCL2-family proteins (e.g., MCL1, BCL2, BCLXL) and NF-κB subunits (RelA, RelB, cRel) confer poor prognosis. Heterogeneous expression, regulatory complexity, and redundancy offsetting the inhibition of individual proteins, complicate the assignment of targeted therapy. We combined flow cytometry ‘fingerprinting’, immunofluorescence imaging, and computational modeling to identify therapeutic vulnerabilities in DLBCL. The combined workflow predicted selective responses to BCL2 inhibition (venetoclax) and non-canonical NF-κB inhibition (Amgen16). Within the U2932 cell line we identified distinct resistance mechanisms to BCL2 inhibition in cellular sub-populations recapitulating intratumoral heterogeneity. Co-cultures with CD40L-expressing stromal cells, mimicking the tumor microenvironment (TME), induced resistance to BCL2 and BCLXL targeting BH3-mimetics via cell-type specific upregulation of BCLXL or MCL1. Computational models, validated experimentally, showed that basal NF-κB activation determined whether CD40 activation drove BH3-mimetic resistance through upregulation of RelB and BCLXL, or cRel and MCL1. High basal NF-κB activity could be overcome by inhibiting BTK to resensitize cells to BH3-mimetics in CD40L co-culture. Importantly, non-canonical NF-κB inhibition overcame heterogeneous compensatory BCL2 upregulation, restoring sensitivity to both BCL2- and BCLXL-targeting BH3-mimetics. Combined molecular fingerprinting and computational modelling provides a strategy for the precision use of BH3-mimetics and NF-κB inhibitors in DLBCL.
Intraspecific differences in alkaline tolerance in brook stickleback (Culaea inconstans) inhabiting neutral and alkaline lakes
Authors: Zimmer Alex M. , Bernard Charles , Giacomin Marina , Dion-Côté Anne-Marie , Goss Greg G. , Glover Chris N. 
Published: December 2024

AbstractExposure to alkaline water (pH > 9.0) is physiologically challenging for fish, yet our understanding of the physiology of alkaline tolerance in fishes is limited to a small number of ihighly specialized species. This study aimed to characterize mechanisms of alkaline tolerance in brook stickleback (Culaea inconstans), a fish species with a broad pH habitat range, including highly alkaline waters such as Buffalo Lake (pH = 9.2) in Alberta, Canada. Stickleback from Buffalo Lake and a neutral reference lake (Buck Lake; pH = 8.2) were collected from the wild and acclimated to common conditions (pH = 8.0) for at least 2 months. Both populations were then exposed to alkaline conditions (pH = 9.5), resulting in a significant decrease in survival (14% by 7 d of exposure) in Buck Lake fish, but no mortality in Buffalo Lake stickleback. In a 4-d exposure to alkaline water, fish from both populations experienced characteristic inhibitions of ammonia excretion followed by subsequent recovery, in conjunction with an accumulation of ammonia within the body. However, no differences were observed between populations. Analysis of tissue Na+and Cl-content showed a more pronounced decrease in Cl-in Buck Lake fish, suggesting that tighter regulation of Cl-homeostasis and/or acid-base balance may be an important feature of alkaline tolerance. RNA-sequencing analysis highlighted large differences in gene expression between the alkaline and neutral lake populations, and in response to alkaline exposure. Few of these changes in the expression involved genes known to be associated with nitrogen, ion, or acid-base balance. These data indicate that alkaline tolerance is higher in brook stickleback resident to an alkaline lake than those sourced from a neutral lake, a trait that may be related to differences in physiological and transcriptomic responses to alkaline exposure.
Cluster Formation and Phase Separation Driven by Mobile Myosin Motors in the Motility Assay
Authors: Slater Brandon , Sciortino Alfredo , Bausch Andreas R. , Kim Taeyoon 
Published: December 2024

ABSTRACTInteractions between F-actin and myosin are critically important for a wide range of biological processes, including cell migration, cytokinesis, and morphogenesis. The motility assay with myosin motors fixed on a surface has been utilized for understanding various phenomena emerging from the interactions between F-actin and myosin. For example, F-actin in the motility assay exhibited distinct collective behaviors when actin concentration was above a critical threshold. Recent studies have performed the myosin motility assay on a lipid bilayer, meaning that myosin motors anchored on the fluid-like membrane have mobility. Interestingly, mobile motors led to very different collective behaviors of F-actin compared to those induced by stationary motors. However, the dynamics and mechanism of the unique collective behaviors have remained elusive. In this study, we employed our cutting-edge computational model to simulate the motility assay with mobile myosin motors. We reproduced the formation of actin clusters observed in experiments and showed that F-actin within clusters exhibits strong polar ordering and leads to phase separation between myosin motors and F-actin. The cluster formation was highly dependent on the average length and concentration of F-actin. Our study provides insights into understanding the collective behaviors of F-actins that could emerge under more physiological conditions.
Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale
Authors: Ellington Caleb N. , Sun Ning , Ho Nicholas , Tao Tianhua , Mahbub Sazan , Li Dian , Zhuang Yonghao , Wang Hongyi , Song Le , Xing Eric P. 
Published: December 2024

AbstractLanguage models applied to protein sequences have become a panacea, enabling therapeutics development, materials engineering, and core biology research. Despite the successes of protein language models, genome language models remain nascent. Recent studies suggest the bottleneck is data volume or modeling context size, since long-range interactions are widely acknowledged but sparsely annotated. However, it may be the case that even short DNA sequences are modeled poorly by existing approaches, and current models are unable to represent the wide array of functions encoded by DNA. To study this, we develop AIDO.DNA, a pretrained module for DNA representation in an AI-driven Digital Organism [1]. AIDO.DNA is a seven billion parameter encoder-only transformer trained on 10.6 billion nucleotides from a dataset of 796 species. By scaling model size while maintaining a short context length of 4k nucleotides, AIDO.DNA shows substantial improvements across a breadth of supervised, generative, and zero-shot tasks relevant to functional genomics, synthetic biology, and drug development. Notably, AIDO.DNA outperforms prior encoder-only architectureswithoutnew data, suggesting that new scaling laws are needed to achieve computeoptimal DNA language models. Models and code are available through Model-Generator inhttps://github.com/genbio-ai/AIDOand on Hugging Face athttps://huggingface.co/genbio-ai.
RNA-binding proteins control the G2-M checkpoint of the germinal centre B cell
Authors: Salerno Fiamma , Whale Alex J. , Matheson Louise S. , Foster William S. , Stammers Melanie , Bell Sarah E. , Hodson Daniel J. , Linterman Michelle A. , Houseley Jonathan , Turner Martin 
Published: December 2024

AbstractHow germinal centre (GC) B cells undergo rapid cell division while maintaining genome stability is poorly understood. Here, we show that the RNA-binding proteins ZFP36L1 and ZFP36L2 act downstream of antigen-sensing and protect GC B cells from replication stress by controlling a cell cycle-related RNA post-transcriptional regulon. ZFP36L1 and ZFP36L2 safeguard faithful completion of mitosis by restraining the expression of CDK1 and cyclin B1, whilst controlling their activity through regulation of a p21-mediated negative feedback loop. In the absence of ZFP36L1 and ZFP36L2, GC B cells arrest in G2-M and die by apoptosis, resulting in curtailed GC responses. This is associated with stalling of the DNA replication fork at active replication initiation zones, which causes replication stress and increased activity of the ATR/CHK1 DNA damage response. Our findings reveal that gene regulation by RNA-binding proteins is essential for a functional G2-M checkpoint to operate in GC B cells.
A negative binomial latent factor model for paired microbiome sequencing data
Authors: Kim Hyotae , Siddiqui Nazema , Karstens Lisa , Ma Li 
Published: December 2024

AbstractMotivationMicrobiome compositional data are often collected from several body sites and exhibit dependency among them. Analyzing microbial compositions from different sites jointly allows for effective borrowing of information by exploiting the underlying cross-site correlation, which can lead to more effective statistical analysis, especially when the sample size at one or both sites is limited. To this end, we introduce a joint model for microbiome compositions at two (or more) sites within the same subjects. Our model incorporates (i) latent factors shared across two body sites to explain the common subject effects and to serve as the source of correlation between the two sites; and (ii) mixtures of latent factors to allow heterogeneity among the samples in their level of cross-site association. The model is illustrated with synthetic data and we apply it in a case study involving samples of the urinary and vaginal microbiome collected from women.ResultsSimulation studies show how common subject effects influence regression analysis results; a stronger association between two sites in the data causes a greater degree of bias in the analysis. The model with latent factors mitigates the bias present in the model without latent factors, whereas the two models perform comparably for the data set without paired associations. In a case study involving samples collected from a study on the female urogenital microbiome with aging (e.g., the UMICRO study), our model leads to the detection of covariate associations of the vaginal and urinary microbiome composition that are otherwise not statistically significant under a similar regression model applied to the two sites separately. Our model also enables prediction of the microbial abundance at one site based on observations from another site. We also consider a model extension that allows the clustering of subjects (samples) and cluster-specific levels of paired association. Under the extended modeling framework, the clusters can be classified according to their association strengths.