bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chlamydomonas cells transition through distinct Fe nutrition stages within 48 h of transfer to Fe-free medium
Authors: Liu Helen W. , Urzica Eugen I. , Gallaher Sean D. , Blaby-Haas Crysten E. , Iwai Masakazu , Schmollinger Stefan , Merchant Sabeeha S. 
Published: February 2024

AbstractLow iron (Fe) bioavailability can limit the biosynthesis of Fe-containing proteins, which are especially abundant in photosynthetic organisms, thus negatively affecting global primary productivity. Understanding cellular coping mechanisms under Fe limitation is therefore of great interest. We surveyed the temporal responses of Chlamydomonas (Chlamydomonas reinhardtii) cells transitioning from an Fe-rich to an Fe-free medium to document their short- and long-term adjustments. While slower growth, chlorosis and lower photosynthetic parameters are evident only after one or more days in Fe-free medium, the abundance of some transcripts, such as those for genes encoding transporters and enzymes involved in Fe assimilation, change within minutes, before changes in intracellular Fe content are noticeable, suggestive of a sensitive mechanism for sensing Fe. Promoter reporter constructs indicate a transcriptional component to this immediate primary response. With acetate provided as a source of reduced carbon, transcripts encoding respiratory components are maintained relative to transcripts encoding components of photosynthesis and tetrapyrrole biosynthesis, indicating metabolic prioritization of respiration over photosynthesis. In contrast to the loss of chlorophyll, carotenoid content is maintained under Fe limitation despite a decrease in the transcripts for carotenoid biosynthesis genes, indicating carotenoid stability. These changes occur more slowly, only after the intracellular Fe quota responds, indicating a phased response in Chlamydomonas, involving both primary and secondary responses during acclimation to poor Fe nutrition.
TSC22D,WNKandNRBPgene families exhibit functional buffering and evolved with Metazoa for macromolecular crowd sensing
Authors: Xiao Yu-Xi , Lee Seon Yong , Aguilera-Uribe Magali , Samson Reuben , Au Aaron , Khanna Yukti , Liu Zetao , Cheng Ran , Aulakh Kamaldeep , Wei Jiarun , Farias Adrian Granda , Reilly Taylor , Habsid Andrea , Brown Kevin R. , Chan Katherine , Mero Patricia , Huang Jie Qi , Billmann Maximilian , Rahman Mahfuzur , Myers Chad , Andrews Brenda J. , Youn Ji-Young , Yip Christopher M. , Rotin Daniela , Derry W. Brent , Forman-Kay Julie D. , Moses Alan M. , Pritišanac Iva , Gingras Anne-Claude , Moffat Jason 
Published: February 2024

SUMMARYThe ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. Myriad redundancies have evolved across all facets of osmosensing in metazoans, including among water and ion transporters, regulators of cellular morphology, and macromolecular crowding sensors, hampering efforts to gain a clear understanding of how cells respond to rapid water loss. In this study, we harness the power of gene co-essentiality analysis and genome-scale CRISPR-Cas9 screening to identify an unappreciated relationship betweenTSC22D2,WNK1andNRBP1in regulating cell volume homeostasis. Each of these genes have paralogs and are functionally buffered for macromolecular crowd sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK and NRBP family members physically associate into cytoplasmic biocondensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans reveals thatTSC22Dgenes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT (NRBPbinding region withTSC22D), and this co-evolution is concomitant with rapid IDR length expansion in WNK family kinases. Our study identifies functions for unrecognized components of the cell volume sensing machinery and reveals thatTSC22D,WNKandNRBPgenes evolved as cytoplasmic crowding sensors in metazoans to co-regulate rapid cell volume changes in response to osmolarity.
Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline
Authors: Mitchell Joshua M. , Chi Yuanye , Thapa Maheshwor , Pang Zhiqiang , Xia Jianguo , Li Shuzhao 
Published: February 2024

AbstractTo standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.Author SummaryAll life processes involve the consumption, creation, and interconversion of metabolites. Metabolomics is the comprehensive study of these small molecules, often using mass spectrometry, to provide critical information of health and disease. Automated processing of such metabolomics data is desired, especially for the bioinformatics community with familiar tools and infrastructures. Despite of Python’s popularity in bioinformatics and machine learning, the Python ecosystem in computational metabolomics still misses a complete data pipeline. We have developed an end-to-end computational metabolomics data processing pipeline, based on the raw data preprocessor Asari [1]. Our pipeline takes experimental data in .mzML or .raw format and outputs annotated feature tables for subsequent biological interpretation. We demonstrate the application of this pipeline to multiple metabolomics and lipidomics datasets. Accompanying the pipeline, we have designed a set of reusable data structures, released as the MetDataModel package, which shall promote more consistent terminology and software interoperability in this area.
Knockdown resistance (kdr) Associated organochlorine Resistance in mosquito-borne diseases (Culex quinquefasciatus): Systematic study of reviews and meta-analysis
Authors: Abbasi Ebrahim , Daliri Salman 
Published: February 2024

AbstractIntroductionCulex quinquefasciatusis one of the most important carriers of human pathogens. The use of insecticides is one of the most important methods of combating this vector. But the genetic resistance created inCulex quinquefasciatushas led to disruption in the fight against this pest. As a result, it is necessary to know the level of resistance in order to fight this vector. Based on this, the present study was conducted with the aim of investigating the prevalence of kdr resistance inCulex quinquefasciatusagainst organochlorine insecticides in the world.MethodsThis study was conducted by systematic review and meta-analysis on the prevalence of kdr resistance and mortality rate inCulex quinquefasciatusagainst organochlorine insecticides in the world. Based on this, during the search in the scientific databases Web of Science, PubMed, Scopus, biooan.org, Embase, ProQuest and Google scholar without time limit until the end of November 2023, all related articles were extracted and analyzed. Statistical analysis of the data was done using fixed and random effects model in meta-analysis,I2index, Cochran’s test and meta-regression by STATA version 17 software.ResultsSeventy articles were included in the meta-analysis process. Based on the findings, the prevalence of Kdr inCulex quinquefasciatusagainst organochlorine insecticide was estimated at 63.1%. Also, the mortality rate against the insecticide deltamethrin was 46%, DDT 18.5%, permethrin 42.6%, malathion 54.4% and lambdacyhalothrin 53%.ConclusionMore than half of Cx. quinquefasciatus had Kdr. This vector was relatively resistant to DDT and permethrin insecticides and sensitive to malathion, deltamethrin and lambdacyhalothrin. As a result, it is necessary to use effective insecticides to fight this vector in order to prevent the increase of resistance to other insecticides.
HairSplitter: haplotype assembly from long, noisy reads
Authors: Faure Roland , Lavenier Dominique , Flot Jean-François 
Published: February 2024

AbstractMotivationLong-read assemblers face challenges in discerning closely related viral or bacterial strains, often collapsing similar strains into a single sequence. This limitation has been hampering metagenome analysis, as diverse strains may harbor crucial functional distinctions.ResultsWe introduce a novel software, HairSplitter, designed to retrieve strains from a partially or totally collapsed assembly and long reads. The method uses a custom variant-calling process to operate with erroneous long reads and introduces a new read binning algorithm to recover an a priori unknown number of strains. On noisy long reads, HairSplitter recovers more strains while being faster than state-of-the-art tools, both in the cases of viruses and bacteria.AvailabilityHairSplitter is freely available on GitHub atgithub.com/RolandFaure/HairSplitter.Contactroland.faure@irisa.fr
Data mining antibody sequences for database searching in bottom-up proteomics
Authors: Trinh Xuan-Tung , Freitag Rebecca , Krawczyk Konrad , Schwämmle Veit 
Published: February 2024

AbstractMass spectrometry (MS)-based proteomics allows identifying and quantifying thousands of proteins but suffers from challenges when measuring human antibodies due to their vast variety. The mainly used bottom-up proteomics approaches rely on database searches that compare experimental values of peptides and their fragments to theoretical values derived from protein sequences in a database. While the human body can produce millions of distinct antibodies, the current databases for human antibodies such as UniProtKB/Swiss-Prot are limited to only 1095 sequences (as of 2024 Jan). This limitation may hinder the identification of new antibodies using mass spectrometry. Therefore, extending the database for mass spectrometry is an important task for discovering new antibodies. Recent genomic studies have compiled millions of human antibody sequences publicly accessible through the Observed Antibody Space (OAS) database. However, this data has yet to be exploited to confirm the presence of these antibodies. In this study, we adopted this extensive collection of antibody sequences for conducting efficient database searches in publicly available proteomics data with a focus on the SARS-CoV-2 disease. Thirty million heavy antibody sequences from 146 SARS-CoV-2 patients in the OAS database were digestedin silicoto obtain 18 million unique peptides. These peptides were then used to create new databases for bottom-up proteomics. We used those databases for searching new antibody peptides in publicly available SARS-CoV-2 human plasma samples in the Proteomics Identification Database (PRIDE). This approach avoids false positives in antibody peptide identification as confirmed by searching against negative controls (brain samples) and employing different database sizes. We show that the found sequences provide valuable information to distinguish diseased from healthy and expect that the newly discovered antibody peptides can be further employed to develop therapeutic antibodies. The method will be broadly applicable to find characteristic antibodies for other diseases.
Characterization of a new lacrimal gland cell line in 2D and 3D cell culture models
Authors: Gleixner Sophie , Zahn Ingrid , Dietrich Jana , Singh Swati , Drobny Alice , Schneider Yanni , Schwendner Raphael , Socher Eileen , Blavet Nicolas , Bräuer Lars , Gostian Antoniu-Oreste , Balk Matthias , Schulze-Tanzil Gundula , Günther Claudia , Paulsen Friedrich , Arnold Philipp 
Published: February 2024

AbstractThe lacrimal gland produces the tear film’s aqueous component, which moistens and nourishes the ocular surface to maintain eye health. Reduced production of this component leads to dry eye disease, which affects over 250 million people worldwide. Despite the impact on patients, the availability of primary human material to study underlying disease mechanisms is severely limited and there is no cell model available for human lacrimal gland epithelial cells. After insertion of an SV40 antigen into primary human lacrimal gland epithelial cells, we selected, expanded, and characterized three epithelial cell clones from a female lacrimal gland donor. We show their epithelial character at genomic (PCR and RNAseq) and protein (immunofluorescence) levels and grow these cells in a 3D cell spheroid model. Here, we report the development of an immortalized human lacrimal gland epithelial cell line that improves accessibility to study the molecular pathogenesis mechanisms of dry eye disease and link them to causal treatments. We show the expression of typical lacrimal gland epithelial cell marker genes (e.g. PAX6, FOXC1, AQP5, CSTB, and CST6) and describe the feasibility of the cells to form 2D cell sheets and 3D cell spheroids.We successfully established immortalized human lacrimal gland cells with epithelial character. In the future, the integration of these cells into larger studies holds great potential for advancing our understanding of dry eye disease and its underlying cellular mechanisms.
UBA1-CDK16: A Sex-Specific Chimeric RNA and Its Role in Immune Sexual Dimorphism
Authors: Shi Xinrui , Facemire Loryn , Singh Sandeep , Kumar Shailesh , Cornelison Robert , Liang Chen , Qin Fujun , Liu Aiqun , Lin Shitong , Tang Yue , Elfman Justin , Manley Thomas , Bullock Timothy , Haverstick Doris M. , Wu Peng , Li Hui 
Published: February 2024

AbstractRNA processing mechanisms, such as alternative splicing and RNA editing, have been recognized as critical means to expand the transcriptome. Chimeric RNAs formed by intergenic splicing provide another potential layer of RNA diversification. By analyzing a large set of RNA-Seq data and validating results in over 1,200 blood samples, we identifiedUBA1-CDK16, a female-specific chimeric transcript. Intriguingly, both parental genes, are expressed in males and females. Mechanistically,UBA1-CDK16is produced by cis-splicing between the two adjacent X-linked genes, originating from the inactive X chromosome. A female-specific chromatin loop, formed between the junction sites, facilitates the alternative splicing of its readthrough precursor. This unique chimeric transcript exhibits evolutionary conservation, evolving to be female-specific from non-human primates to humans. Furthermore, our investigation reveals thatUBA1-CDK16is enriched in the myeloid lineage and plays a regulatory role in myeloid differentiation. Notably, female COVID-19 patients who tested negative for this chimeric transcript displayed higher counts of neutrophils, highlighting its potential role in disease pathogenesis. These findings support the notion that chimeric RNAs represent a new repertoire of transcripts that can be regulated independently from the parental genes, and a new class of RNA variance with potential implications in sexual dimorphism and immune responses.
TheTofumutation restores female fertility toDrosophilawith a nullBEAFmutation
Authors: McKowen J. Keller , Dassanayake Maheshi , Hart Craig M. 
Published: February 2024

ABSTRACTCompensatory mutations offer clues to deciphering the role of a particular protein in cellular processes. Here we investigate an unknown compensatory mutation, present in theBEAFNP6377fly line, that provides sufficient rescue of the defective ovary phenotype caused by nullBEAFalleles to allow maintenance of fly stocks lacking the chromatin domain insulator proteins Boundary Element-Associated Factors BEAF-32A and BEAF-32B. We call this mutationTofu. We employ both classical genetics and genomic sequencing to attempt to identify the mutation. We find evidence that points to a mutation in a predicted Polycomb response element upstream of theribbongene, which may lead to aberrantribexpression.
Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia
Authors: Andersen Onofre Kelsey F. , Delaquis Erik , Newby Jonathan C. , de Haan Stef , Le Thuy Cu Thi , Minato Nami , Legg James P. , Cuellar Wilmer J. , Alcalá Briseño Ricardo I. , Garrett Karen A. 
Published: February 2024

AbstractCONTEXTEffective seed systems must both distribute high-performing varieties efficiently and slow or stop the spread of pathogens and pests. Epidemics increasingly threaten crops around the world, endangering the incomes and livelihoods of smallholder farmers. Responding to these food and economic security challenges requires stakeholders to act quickly and decisively during the early stages of invasions, typically with very limited resources. The recent introduction of cassava mosaic virus into southeast Asia threatens cassava production in the region.OBJECTIVESOur goal in this study is to provide a decision-support framework for efficient management of healthy seed systems, applied to cassava mosaic disease. The specific objectives are to (1) evaluate disease risk in disease-free parts of Cambodia, Lao PDR, Thailand, and Vietnam by integrating disease occurrence, climate, topology, and land use, using machine learning; (2) incorporate this predicted environmental risk with seed exchange survey data and whitefly spread in the landscape to model epidemic spread in a network meta-population model; and (3) use scenario analysis to identify candidate regions to be prioritized in seed system management.RESULTS AND CONCLUSIONSThe analyses allow stakeholders to evaluate strategy options for allocating their resources in the field, guiding the implementation of seed system programs and responses. Fixed rather than adaptive deployment of clean seed produced more favorable outcomes in this model, as did prioritization of a higher number of districts through the deployment of smaller volumes of clean seed.SIGNIFICANCEThe decision-support framework presented here can be applied widely to seed systems challenged by the dual goals of distributing seed efficiently and reducing disease risk. Data-driven approaches support evidence-based identification of optimized surveillance and mitigation areas in an iterative fashion, providing guidance early in an epidemic, and revising them as data accrue over time.
PRS-Net: Interpretable polygenic risk scores via geometric learning
Authors: Li Han , Zeng Jianyang , Snyder Michael P. , Zhang Sai 
Published: February 2024

AbstractPolygenic risk score (PRS) serves as a valuable tool for predicting the genetic risk of complex human diseases for individuals, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. We present PRS-Net, an interpretable deep learning-based framework designed to effectively model the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genomewide PRS at the single-gene resolution, and then it encapsulates gene-gene interactions for genetic risk prediction leveraging a graph neural network, thereby enabling the characterization of biological nonlinearity underlying complex diseases. An attentive readout module is specifically introduced into the framework to facilitate model interpretation and biological discovery. Through extensive tests across multiple complex diseases, PRS-Net consistently outperforms baseline PRS methods, showcasing its superior performance on disease prediction. Moreover, the interpretability of PRS-Net has been demonstrated by the identification of genes and gene-gene interactions that significantly influence the risk of Alzheimer’s disease and multiple sclerosis. In summary, PRS-Net provides a potent tool for parallel genetic risk prediction and biological discovery for complex diseases.
pipesnake: Generalized software for the assembly and analysis of phylogenomic datasets from conserved genomic loci
Authors: Brennan Ian G. , Singhal Sonal , Al Bkhetan Ziad 
Published: February 2024

AbstractMotivationPhylogenetics has moved into the era of genomics, incorporating enormous volumes of data to study questions at both shallow and deep scales. With this increase in information, phyloge-neticists need new tools and skills to manipulate and analyze these data. To facilitate these tasks and encourage reproducibility, the community is increasingly moving towards automated workflows.ResultsHere we present pipesnake, a phylogenomics pipeline written in Nextflow for the processing, assembly, and phylogenetic estimation of genomic data from short-read sequences. pipesnake is an easy to use and efficient software package designed for this next era in phylogenetics.AvailabilityThe quick brown fox jumps over the lazy dog.Contactexample@example.orgSupplementary informationpipesnake is written in Nextflow and freely available fromhttps://github.com/AusARG/pipesnakealongside comprehensive documentation and a wiki/tutorial. The pipeline is containerized and deployable via Docker, Singularity, and Conda making it easy to run on different compute infrastructures.
High and low frequency anterior nucleus of thalamus deep brain stimulation: Impact on memory and mood in five patients with treatment resistant temporal lobe epilepsy
Authors: Marks Victoria S. , Balzekas Irena , Grimm Jessica A. , Richner Thomas J. , Sladky Vladimir , Mivalt Filip , Gregg Nicholas M. , Lundstrom Brian N. , Miller Kai J. , Joseph Boney , Gompel Jamie Van , Brinkmann Benjamin , Croarkin Paul , Alden Eva C. , Kremen Vaclav , Kucewicz Michal , Worrell Gregory A 
Published: February 2024

AbstractHigh frequency anterior nucleus of the thalamus deep brain stimulation (ANT DBS) is an established therapy for treatment resistant focal epilepsies. Although high frequency-ANT DBS is well tolerated, patients are rarely seizure free and the efficacy of other DBS parameters and their impact on comorbidities of epilepsy such as depression and memory dysfunction remain unclear. The purpose of this study was to assess the impact of low vs high frequency ANT DBS on verbal memory and self-reported anxiety and depression symptoms. Five patients with treatment resistant temporal lobe epilepsy were implanted with an investigational brain stimulation and sensing device capable of ANT DBS and ambulatory intracranial electroencephalographic (iEEG) monitoring, enabling long-term detection of electrographic seizures. While patients received therapeutic high frequency (100 and 145 Hz continuous and cycling) and low frequency (2 and 7 Hz continuous) stimulation, they completed weekly free recall verbal memory tasks and thrice weekly self-reports of anxiety and depression symptom severity. Mixed effects models were then used to evaluate associations between memory scores, anxiety and depression self-reports, seizure counts, and stimulation frequency. Memory score was significantly associated with stimulation frequency, with higher free recall verbal memory scores during low frequency ANT DBS. Self-reported anxiety and depression symptom severity was not significantly associated with stimulation frequency. These findings suggest the choice of ANT DBS stimulation parameter may impact patients’ cognitive function, independently of its impact on seizure rates.