bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification
Authors: He Fei , Fei Ruixin , Gao Mingyue , Su Li , Zhang Xinyu , Xu Dong 
Published: January 2024

AbstractSingle-cell sequencing transformed biology and medicine, providing an unprecedented high-resolution view at the cellular level. However, the vast variability inherent in single-cell sequencing data impedes its utility for in-depth downstream analysis. Inspired by the foundation models in natural language processing, recent advancements have led to the development of single-cell Large Language Models (scLLMs). These models are designed to discern universal patterns across diverse single-cell datasets, thereby enhancing the signal-to-noise ratio. Despite their potential, multiple studies indicate existing scLLMs do not perform well in zero-short settings, highlighting a pressing need for more effective adaptation techniques. This research proposes several adaptation techniques for scLLMs by preserving the original model parameters while selectively updating newly introduced tensors. This approach aims to overcome the limitations associated with traditional fine-tuning practices, such as catastrophic forgetting and computational inefficiencies. We introduce two Parameter-Efficient Fine-Tuning (PEFT) strategies specifically tailored to refine scLLMs for cell type identification. Our investigations utilizing scGPT demonstrate that PEFT can enhance performance, with the added benefit of up to a 90% reduction in parameter training compared to conventional fine-tuning methodologies. This work paves the way for a new direction in leveraging single-cell models with greater efficiency and efficacy in single-cell biology.
Harnessing AlphaFold to reveal hERG channel conformational state secrets
Authors: Ngo Khoa , Yang Pei-Chi , Yarov-Yarovoy Vladimir , Clancy Colleen E. , Vorobyov Igor 
Published: January 2024

AbstractTo design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been resolution of discrete conformational states of transmembrane ion channel proteins. An example is Kv11.1 (hERG), comprising the primary cardiac repolarizing current,Ikr. hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. However, the structural details of multiple conformational states have remained elusive. Here, we guided AlphaFold2 to predict plausible hERG inactivated and closed conformations, obtaining results consistent with multiple available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions in good agreement with experimental results, revealing that most drugs bind more effectively in the inactivated state and are trapped in the closed state. Molecular dynamics simulations demonstrated ion conduction for an open but not AlphaFold2 predicted inactivated state that aligned with earlier studies. Finally, we identified key molecular determinants of state transitions by analyzing interaction networks across closed, open, and inactivated states in agreement with earlier mutagenesis studies. Here, we demonstrate a readily generalizable application of AlphaFold2 as an effective and robust method to predict discrete protein conformations, reconcile seemingly disparate data and identify novel linkages from structure to function.
Epigenomics coverage data extraction and aggregation in R with tidyCoverage
Authors: Serizay Jacques , Koszul Romain 
Published: January 2024

AbstractSummaryThetidyCoverageR package provides a framework for intuitive investigation of collections of genomic tracks over genomic features, relying on the principle of tidy data manipulation. It defines two data structures,CoverageExperimentandAggregatedCoverageclasses, directly extending theSummarizedExperimentfundamental class, and introduces a principled approach to exploring genome-wide data. This infrastructure facilitates the extraction and manipulation of genomic coverage track data across individual or multiple sets of thousands of genomic loci. This allows the end user to rapidly visualize track coverage at individual genomic loci or aggregated coverage profiles over sets of genomic loci.tidyCoverageseamlessly combines with the existing Bioconductor ecosystem to accelerate the integration of genome-wide track data in epigenomic analysis workflows.tidyCoverageemerges as a valuable tool, contributing to the advancement of epigenomics research by promoting consistency, reproducibility, and accessibility in data analysis.Availability and implementationtidyCoverageis an R package freely available from Bioconductor ≥ 3.19 (https://www.bioconductor.org/packages/tidyCoverage) for R ≥ 4.4. The software is distributed under the MIT License and is accompanied by example files and data.Contactjacques.serizay@pasteur.frSupplementary informationAdditional documentation is available fromhttps://js2264.github.io/tidyCoverage/andhttps://js2264.github.io/tidyCoverage/articles/tidyCoverage.html.
For antibody sequence generative modeling, mixture models may be all you need
Authors: Parkinson Jonathan , Wang Wei 
Published: January 2024

ABSTRACTAntibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity. In this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing >400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins. SAM can humanize sequences, generate new sequences, and score sequences for humanness. It is both fast and fully interpretable. Our results highlight the importance of using simple models as baselines for protein engineering tasks. We additionally introduce a new tool for numbering antibody sequences which is orders of magnitude faster than existing tools in the literature. Both these tools are available athttps://github.com/Wang-lab-UCSD/AntPack.
Comprehensive Hierarchical Classification of Transposable Elements based on Deep Learning
Authors: Qi Yang , Chen Yiqi , Wu Yingfu , Li Yanyan , Gao Meihong , Zhang Fuhao , Liao Xingyu , Shang Xuequn 
Published: January 2024

AbstractTransposable elements (TEs) are DNA sequences capable of translocating within a genome. They constitute a substantial portion of eukaryotic genomes and play significant roles in genome evolution and gene regulation. The correct classification of these repetitive elements is essential to investigate their potential impact on genomes. Despite the existence of several tools for TE classification, they often neglect the importance of simultaneously utilizing global and local information for TE-type identification, resulting in suboptimal performance. Furthermore, these tools are not user-friendly due to the complex installation processes and numerous dependencies. In this study, we introduced a novel framework, CREATE, which leverages the strengths ofConvolutional andRecurrent Neural NEtworks, combined withAttention mechanisms, for efficientTEclassification. Given the tree-like structure of TE groups, we separately trained nine models within the class hierarchy. Benchmarking experiments showed that CREATE significantly outperformed other TE classification tools. The source code and demo data for CREATE are available athttps://github.com/yangqi-cs/CREATE. To facilitate TE annotation for researchers, we have developed a web platform, named WebDLTE, based on the CREATE framework. This platform employs GPU-accelerated pre-trained deep learning models for real-time TE classification and offers the most comprehensive collection of TEs for download. The web interface can be accessed athttps://www.webdlte.nwpu.edu.cn.
Adaptive and pleiotropic effects of evolution in synonymous sugar environments
Authors: Ahlawat Neetika , Venkataraman Pavithra , Brajesh Raman Gulab , Saini Supreet 
Published: January 2024

AbstractAdaptation to an environment is enabled by the accumulation of beneficial mutations. When adapted populations are shifted to other environments, the byproduct or pleiotropic fitness effects of these mutations can be wide-ranged. Since there exists no molecular framework to quantify relatedness of environments, predicting pleiotropic effects based on adaptation has been challenging. In this work, we ask if evolution in highly similar environments elicits correlated adaptive and pleiotropic responses. We evolve replicate populations ofEscherichia coliin non-stressful environments that contain either a mixture of glucose and galactose, lactose, or melibiose as the source of carbon. We term these similar sugars as “synonymous”, since lactose and melibiose are disaccharides made up of glucose and galactose. Therefore, the evolution environments differed only in the way carbon was presented to the bacterial population. After 300 generations of evolution, we see that the adaptive responses of these populations are not predictable. We investigate the pleiotropic effects of adaptation in a range of non-synonymous environments, and show that despite uncorrelated adaptive changes, the nature of pleiotropic effects is largely predictable based on the fitness of the ancestor in the non-home environments. Overall, our results highlight how subtle changes in the environment can alter adaptation, but despite sequence-level variations, pleiotropy is qualitatively predictable.Lay SummaryIn nature, evolution in “similar” environments is believed to elicit identical responses. For example, the arctic fox and ptarmigan, which are two unrelated species living in the arctic, have evolved to turn white in the winters. They did not evolve this ability because they from the common ancestor, but because the environment favoured this trait. In this work, we ask what happens to evolving populations if there are minute changes in the environment, and what are the consequences of adapting in these environments that are “almost identical”, or as we call them, “synonymous”.We evolve replicate populations of the bacteriaE. coliin three synonymous environments, and quantify their ability to grow in both synonymous and non-synonymous environments. We see that evolution does not proceed in an identical fashion in these populations, and that each environment favours a different trait. However, interestingly, in non-synonymous environments, these three sets of populations perform almost identically, and their growth is qualitatively predictable.Our results show that even simple and subtle changes in the environment can act as drivers of biodiversity.
Streamlining Intersectoral Provision of Real-World Health Data: A Service Platform for Improved Clinical Research and Patient Care
Authors: Hoffmann Katja , Nesterow Igor , Peng Yuan , Henke Elisa , Barnett Daniela , Klengel Cigdem , Gruhl Mirko , Bartos Martin , Nüßler Frank , Gebler Richard , Grummt Sophia , Seim Anne , Bathelt Franziska , Reinecke Ines , Wolfien Markus , Weidner Jens , Sedlmayr Martin 
Published: January 2024

AbstractIntroductionObtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities — including hospitals, outpatient clinics, and physician practices — the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites.MethodsWe investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term.ResultsWe have developed the pre-built packages “ResearchData-to-FHIR”, “FHIR-to-OMOP” and “Addons”, which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use.ConclusionOur development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.
Elucidating Molecular Networks Underpinning Heterogeneity in Parkinson’s Disease Progression Across Clinical Manifestation Spectrum
Authors: Zhou Manqi , Ke Alison , Wang Xingbo , Chen Kun , Wang Fei , Su Chang 
Published: January 2024

AbstractIn this study, we applied statistical and machine learning techniques to identify molecular mechanisms underlying the heterogeneity in individual Parkinson’s Disease (PD) progression. Leveraging data from the Parkinson’s Progression Markers Initiative (PPMI) cohort, we analyzed genetic and clinical data for patients with PD, focusing on traits including motor symptoms, non-motor symptoms, and biomarkers. Our method identified significant single-nucleotide polymorphisms (SNPs) associated with each PD trait, revealing key genetic factors and their impact on disease progression. Furthermore, through network medicine approaches, we delineated disease modules, uncovering unique gene clusters and their roles in PD pathology. The integration of pathway enrichment analysis further enhanced our understanding of the functional implications of these genetic variations, notably highlighting the significance of cellular stress response and protein aggregation pathways in PD. Overall, our findings offer a comprehensive view of the genetic landscape of PD progression, highlighting the potential of personalized medicine in managing this complex disease.
Specific amygdala and hippocampal subfield volumes in social anxiety disorder and their relation to clinical characteristics – an international mega-analysis
Authors: Ntwatwa Ziphozihle , Spreckelmeyer Jule M. , Bas-Hoogendam Janna Marie , van Honk Jack , Mufford Mary M. , Boraxbekk Carl-Johan , Fouche Jean-Paul , Frick Andreas , Furmark Tomas , Klumpp Heide , Lochner Christine , Phan K Luan , Månsson Kristoffer N.T. , Pannekoek J. Nienke , Peterburs Jutta , Roelofs Karin , Roos Annerine , Straube Thomas , van Steenbergen Henk , Tol Marie-José Van , Veltman Dick J. , van der Wee Nic J.A. , Stein Dan J. , Ipser Jonathan C. , Groenewold Nynke A. 
Published: January 2024

AbstractSocial anxiety disorder (SAD) has been associated with alterations in amygdala and hippocampal volume but there is mixed evidence for the direction of volumetric alterations. Additionally, little is known about the involvement of the distinct subfields in the pathophysiology of SAD. Volumetric data from a large multi-centre sample of 107 adult individuals with SAD and 140 healthy controls (HCs) was segmented using FreeSurfer to produce 9 amygdala and 12 hippocampal subfield volumes. Volumes were compared between groups using linear mixed-effects models adjusted for age, age-squared, sex, site and whole amygdala and hippocampal volumes. Subgroup analyses examined subfield volumes in relation to comorbid anxiety disorder, and comorbid major depressive disorder (MDD), psychotropic medication status, and symptom severity. In the full sample, SAD was associated with smaller amygdala volumes in the basal (d=-0.32, pFDR=0.022), accessory basal (d=-0.42, pFDR=0.005) and corticoamygdaloid transition area (d=-0.37, pFDR=0.014), and larger hippocampal volume in the CA3 (d=0.34, pFDR=0.024), CA4 (d=0.44, pFDR=0.007), dentate gyrus (d=0.35, pFDR=0.022) and molecular layer (d=0.28, pFDR= 0.033), compared to HCs. SAD without comorbid anxiety, in addition, demonstrated smaller lateral amygdala (d=-0.30, pFDR=0.037) and hippocampal amygdala transition area (d=-0.33, pFDR=0.027) relative to HCs. In SAD without comorbid MDD, only the smaller accessory basal amygdala remained significant (d=-0.41, pFDR=0.017). No association was found between subfield volume and medication status or symptom severity. In conclusion, we observed distinct patterns of volumetric differences across specific amygdala and hippocampal subfields, regions that are associated with sensory information processing, threat evaluation and fear generalization. These findings suggest a possible disruption in information flow between the amygdala and hippocampal formation for fear processing in SAD.
Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens
Authors: Jiang Longda , Dalgarno Carol , Papalexi Efthymia , Mascio Isabella , Wessels Hans-Hermann , Yun Huiyoung , Iremadze Nika , Lithwick-Yanai Gila , Lipson Doron , Satija Rahul 
Published: January 2024

ABSTRACTRecent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but learning causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single cell sequencing (i.e. Perturb-seq) to systematically identify the targets of signaling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signaling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. Finally, we demonstrate how our Perturb-seq derived gene lists can be used to precisely infer changes in signaling pathway activation for in-vivo and in-situ samples. Our work enhances our understanding of signaling regulators and their targets, and lays a computational framework towards the data-driven inference of an ‘atlas’ of perturbation signatures.
Mechanism of bacterial predation via ixotrophy
Authors: Lien Yun-Wei , Amendola Davide , Lee Kang Soo , Bartlau Nina , Xu Jingwei , Furusawa Go , Polz Martin F. , Stocker Roman , Weiss Gregor L. , Pilhofer Martin 
Published: January 2024

AbstractPredation allows bacteria to access alternative substrates in low-nutrient conditions. Ixotrophy has been proposed as a predatory lifestyle of multicellular filamentous bacteria in aquatic environments; however, the molecular mechanism remains unknown.Here we uncover by a multidisciplinary approach that ixotrophy requires the interplay of multiple cellular machineries and a regulatory mechanism. Attacker-prey contacts are established by gliding motility and extracellular grappling hook-like structures that bind prey flagella. Cryo-electron microscopy identifies the grappling hooks as a heptameric assembly of a Type 9 Secretion System substrate. Cryo-electron tomography and functional assays show that killing is mediated by puncturing of the prey cell using a Type 6 Secretion System, possibly triggered by extracellular antennae. Single-cell analyses with stable isotope-labeled prey demonstrate that prey components are taken up by the attacker. Depending on nutrient availability, ixotrophy is switched off by endogenous Insertion Sequence Elements and re-activated through their excision. A marine metagenomic time series provides evidence for coupled dynamics of ixotrophic bacteria and their prey.Our study reveals the complex mechanism of a conserved microbial predatory lifestyle and indicates the need for its regulation in conditions where the expression of costly pathways is dispensable.
Nutrient Abundance Signals the Changing of the Seasons by Phosphorylating PER2
Authors: Levine Daniel C. , Reeh Rasmus H. , McMahon Thomas , Mandrup-Poulsen Thomas , Fu Ying-Hui , Ptáček Louis J. 
Published: January 2024

SUMMARYThe circadian clock synchronizes metabolic and behavioral cycles with the rotation of the Earth by integrating environmental cues, such as light. Nutrient content also regulates the clock, though how and why this environmental signal affects the clock remains incompletely understood. Here, we elucidate a role for nutrient in regulating circadian alignment to seasonal photoperiods. High fat diet (HFD) promoted entrainment to a summer light cycle and inhibited entrainment to a winter light cycle by phosphorylating PER2 on serine 662. PER2-S662 phospho-mimetic mutant mice were incapable of entraining to a winter photoperiod, while PER2-S662 phospho-null mutant mice were incapable of entraining to a summer photoperiod, even in the presence of HFD. Multi-omic experimentation in conjunction with isocaloric hydrogenated-fat feeding, revealed a role for polyunsaturated fatty acids in nutrient-dependent seasonal entrainment. Altogether, we identify the mechanism whereby nutrient content shifts circadian rhythms to anticipate seasonal photoperiods in which that nutrient state predominates.HIGHLIGHTSHigh fat diet promotes entrainment to summer but inhibits entrainment to winter.Calorie restriction promotes entrainment to winter but inhibits entrainment to summer.PER2-S662 phosphorylation is required for nutritional regulation of seasonal circadian entrainment.Dietary polyunsaturated fatty acids regulate seasonal circadian entrainment.