bluuebunny/crossref_metadata_2025_split huggingface dataset:
drMD: Molecular Dynamics for Experimentalists
Authors: Shrimpton-Phoenix Eugene , Notari Evangelia , Wood Christopher W. 
Published: November 2024

AbstractMolecular dynamics (MD) simulations can be used by protein scientists to investigate a wide array of biologically relevant properties such as the effects of mutations on a protein’s structure and activity, or probing intermolecular interactions with small molecule substrates or other macromolecules. Within the world of computational structural biology, several programs have become popular for running these simulations, but each of these programs requires a significant time investment from the researcher to run even simple simulations. Even after learning how to run and analyse simulations, many elements remain a “black box.” This greatly limits the accessibility of molecular dynamics simulations for non-experts.Here we present drMD, an automated pipeline for running molecular dynamics simulations using the OpenMM molecular mechanics toolkit. We have created drMD with non-experts in computational biology in mind. The drMD codebase has several functions that automatically handle routine procedures associated with running molecular dynamics simulations. This greatly reduces the expertise required to run MD simulations. We have also introduced a series of quality-of-life features to make the process of running MD simulations both easier and more pleasant. Finally, drMD explains the steps it is taking interactively and, where useful, provides relevant references so the user can learn more. All these features make drMD an effective tool for learning molecular dynamics while running publication-quality simulations.drMD is open source and can be found on GitHub:https://github.com/wells-wood-research/drMD.
Environmentally Relevant Lead Exposure Impacts Gene Expression in SH-SY5Y Cells Throughout Neuronal Differentiation
Authors: Morgan Rachel K. , Tapaswi Anagha , Polemi Katelyn M. , Tolrud Elizabeth C. , Bakulski Kelly M. , Svoboda Laurie K. , Dolinoy Dana C. , Colacino Justin A. 
Published: November 2024

AbstractLead (Pb) causes learning and memory impairments, but the molecular effects of continuous, environmentally relevant levels of exposure on key neurodevelopmental processes are not fully characterized. Here we examine the effects of a range of environmentally relevant Pb concentrations (0.16μM, 1.26μM, and 10μM Pb) relative to control on neural differentiation in the SH-SY5Y cell model. Pb exposure began on differentiation day 5 and was continuous for remaining days, after which we assessed the transcriptome via RNA sequencing at several time points. The bulk of detected changes in gene expression occurred with the 10μM Pb condition. Interestingly, changes associated with the lower two exposures were differentiation stage-specific, with aberrant expression of several genes (e.g.,COL3A1,HMOX1, andCCL2) observed during differentiation on days 9, 12, and 15 in both the 0.16μM and 1.26μM Pb conditions, and which disappeared by the time differentiation concluded on day 18. We observed six co-expression clusters of genes during differentiation, and 10uM Pb significantly perturbed two clusters, one involved in cell cycling and DNA repair and the other in protein synthesis. Benchmark concentration analysis identified many genes affected by levels of Pb at or below the current US standard (3.5μg/dL) and these genes were enriched for pathways including stress responses, DNA repair, misfolded protein response, mitosis, and neurotransmitter production. This work highlights potential new mechanisms by which environmentally relevant concentrations of Pb impact gene expression throughout neural differentiation and result in long-lasting implications for neural health and cognition.
To nest or not to nest: environmental cues for olive ridley mass nesting events in Odisha, India
Authors: Srikanthan Prahalad , Pusapati Chandana , Verma Akash , Dhal Gagan , Manoharakrishnan Muralidharan , Shanker Kartik 
Published: November 2024

AbstractSea turtles of the genusLepidochelystake part in synchronised nesting events, known asarribadas, where a large number of females emerge simultaneously from the sea to nest on the beach. Despite some evidence for the role of environmental factors, there is considerable uncertainty about both the factors that influence mass-nesting at an annual scale as well as the onset of individual events. We used a multi-decadal dataset from two olive ridley mass nesting beaches in Odisha, India, to identify the environmental factors associated with inter-annual nesting variation, nesting seasonality and the onset of arribadas. Interestingly, the two sites showed different patterns in inter-annual patterns associated with the non-occurrence of arribadas, despite being only 250 km apart. The nesting season was consistently during the dry and cooler part of the year. Arribada start dates were non-uniformly distributed across the lunar phase in Gahirmatha with most arribadas occurring during the first and third quarters. Fast Southern Winds characterised the period before and during the arribada at both sites. Machine learning models (XGBoost) identified the most important parameters for the onset of mass nesting and accurately predicted more than 75% of the start dates. Lunar phase, mean sea level pressure and wind speed and direction were important parameters at both sites. Differences in parameter importance between sites suggest that mass nesting is influenced by the interaction of several environmental parameters, along with site-specific characteristics. Insights into the triggers of mass nesting can thus provide guidance for site-specific management efforts.
Predicting the pathway involvement of metabolites annotated in the MetaCyc knowledgebase
Authors: Huckvale Erik D. , Moseley Hunter N.B. 
Published: November 2024

AbstractThe associations of metabolites with biochemical pathways are highly useful information for interpreting molecular datasets generated in biological and biomedical research. However, such pathway annotations are sparse in most molecular datasets, limiting their utility for pathway level interpretation. To address these shortcomings, several past publications have presented machine learning models for predicting the pathway association of small biomolecule (metabolite and zenobiotic) using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG). But other similar knowledgebases exist, for example MetaCyc, which has more compound entries and pathway definitions than KEGG. As a logical next step, we trained and evaluated multilayer perceptron models on compound entries and pathway annotations obtained from MetaCyc. From the models trained on this dataset, we observed a mean Matthews correlation coefficient (MCC) of 0.845 with 0.0101 standard deviation, compared to a mean MCC of 0.847 with 0.0098 standard deviation for the KEGG dataset. These performance results are pragmatically the same, demonstrating that MetaCyc pathways can be effectively predicted at the current state-of-the-art performance level.Author summaryMany thousands of different molecules play important roles in the processes of life. To generally handle the complexity of life, biological and biomedical researchers typically organize the molecular parts and pieces of biological processes into pathways of biomolecules and their myriad of molecular interactions. While the role of large macromolecules like proteins are well characterized within these pathways, the role of small biomolecules are not as comprehensively known. To close this knowledge gap, several machine learning models have been trained on data from a knowledgebase known as the Kyoto Encyclopedia of Genes and Genomes (KEGG) to predict which pathways a small biomolecule is associated with. More data generally improves these machine learning models. So in this work, we used the MetaCyc knowledgebase to increase the amount of data available by about ten-fold and then trained new machine learning models that demonstrate comparable prediction performance to models trained on KEGG, but covering 8-fold more pathways defined in MetaCyc vs KEGG.
Assessing the readiness of Oxford Nanopore sequencing for clinical genomics applications
Authors: Arres Judith , Elavalli Santosh , Behl Shalini , Matias Sanchez Daniel , Ali Ayesha Al , Ahmed Yehia Abdelaziz Saad Abdelrahman , Attia Azza , Minas Cyla , Pariyachery Sharika , Ahmed Shariq , Aldhuhoori Fatmah , Thulasidharan Nitu , Katagi Gurunath , Soliman Omar , Purohit Shilp , Kusuma Vinay , Cardoso Thyago , Paulin Luis F , Sanio Philippe , Mafofo Joseph , Wu Haiguo , Zvereff Val , El-Khani Albarah , Marzooqi Fahed Al , Magalhães Tiago R , Sedlazeck Fritz , Quilez Javier 
Published: November 2024

ABSTRACTLong-read sequencing (LRS) technologies, namely Oxford Nanopore Technologies (ONT) and Pacific Biosciences, have emerged as promising solutions to overcome the limitations of short-read sequencing (SRS). Nevertheless, the still higher sequencing error rates compared to SRS, need for customized pipelines, rapidly updating software and incipient scalability are proving the adoption of the ONT for standard clinical practice to be challenging. Here we assess the performance of ONT (R9 and R10 chemistries) in comparison to Illumina and MGI across 17 well-characterized reference samples with 11 clinical variants representing 9 different genetic diseases. To enable this, we have implemented a production-ready pipeline including SNV, INDEL, STR, SV and CNV detection together with reporting key summary metrics to ensure high quality of data at production sequencing level. Our results show high accuracy of ONT across SNV (F-score >0.975) and SV, but still several weaknesses across INDEL (F-score<0.80). However, we highlight that ONT accurately detected all four pathogenic INDELs as well as the performance improvement in exons and with the newer R10 chemistry. We further demonstrated the importance of long-reads to detect clinical-impacting variants such as aFMR1pathogenic expansion, often misclassified by SRS as premutation range. Some issues remain as long-read analysis reported the wrong CNV genotype in one of the medical case samples. Remarkably, our multi-platform analysis and Sanger validation discovered a 1-bp error in the Coriell annotation for a cystic fibrosis causing INDEL in GM07829. Overall, this work highlights the readiness of ONT for clinical applications and large-scale operations.
A synthetic data generation pipeline to reproducibly mirror high-resolution multi-variable peptidomics and real-patient clinical data
Authors: Jaimes Campos Mayra Alejandra , Kabić Stipe , Latosinska Agnieszka , Anicic Ena , Siwy Justyna , Dragušica Vinko , Rupprecht Harald , Catanese Lorenzo , Keller Felix , Perco Paul , Gomez- Gomez Enrique , Beige Joachim , Vlahou Antonia , Mischak Harald , Vukelić Davorin , Križan Tomislav , Frantzi Maria 
Published: November 2024

AbstractGenerating high quality, real-world clinical and molecular datasets is challenging, costly and time intensive. Consequently, such data should be shared with the scientific community, which however carries the risk of privacy breaches. The latter limitation hinders the scientific community’s ability to freely share and access high resolution and high quality data, which are essential especially in the context of personalised medicine. In this study, we present an algorithm based on Gaussian copulas to generate synthetic data that retain associations within high dimensional (peptidomics) datasets. For this purpose, 3,881 datasets from 10 cohorts were employed, containing clinical, demographic, molecular (> 21,500 peptide) variables, and outcome data for individuals with a kidney or a heart failure event. High dimensional copulas were developed to portray the distribution matrix between the clinical and peptidomics data in the dataset, and based on these distributions, a data matrix of 2,000 synthetic patients was developed. Synthetic data maintained the capacity to reproducibly correlate the peptidomics data with the clinical variables. Consequently, correlation of the rho-values of individual peptides with eGFR between the synthetic and the real-patient datasets was highly similar, both at the single peptide level (rho = 0.885, p < 2.2e-308) and after classification with machine learning models (rhosynthetic= -0.394, p = 5.21e-127; rhoreal= -0.396, p = 4.64e-67). External validation was performed, using independent multi-centric datasets (n = 2,964) of individuals with chronic kidney disease (CKD, defined as eGFR < 60 mL/min/1.73m²) or those with normal kidney function (eGFR > 90 mL/min/1.73m²). Similarly, the association of the rho-values of single peptides with eGFR between the synthetic and the external validation datasets was significantly reproduced (rho = 0.569, p = 1.8e-218). Subsequent development of classifiers by using the synthetic data matrices, resulted in highly predictive values in external real-patient datasets (AUC values of 0.803 and 0.867 for HF and CKD, respectively), demonstrating robustness of the developed method in the generation of synthetic patient data. The proposed pipeline represents a solution for high-dimensional sharing while maintaining patient confidentiality.
Overcoming the widespread flaws in the annotation of vertebrate selenoprotein genes in public databases
Authors: Ticó Max , Sullivan Emerson , Guigó Roderic , Mariotti Marco 
Published: November 2024

ABSTRACTSelenocysteine (Sec) is a non-canonical amino acid incorporated into selenoproteins, oxidoreductase enzymes carrying essential roles in redox homeostasis. Sec insertion is performed in response to the UGA codon, normally interpreted as a stop codon, but recoded in selenoprotein mRNAs. Owing to the dual function of UGA, the identification of selenoprotein genes poses a challenge.We show here that the vertebrate selenoprotein genes are widely misannotated in the major public databases. In Ensembl, considered the gold standard of genomic annotation, our analysis shows that only ∼10% of selenoprotein genes are well annotated; ∼10% have no annotation at all, and ∼80% have flawed annotations which lack the Sec-encoding UGA. Only model organisms have correct selenoprotein annotations in Ensembl, ascribed to manual curation. At NCBI, ∼50% of selenoproteins are misannotated, mostly corresponding to families with C-terminal Sec residues.We argue that selenoproteins must be correctly annotated in public database and that must occur via automated pipelines, to keep the pace with genome sequencing. To facilitate this task, we present a new version of Selenoprofiles, an homology based tool for prediction of selenoproteins that can be easily deployed and produce correct predictions with accuracy comparable to manual curation.
cOmicsArt – a customizable Omics Analysis and reporting tool
Authors: Seep Lea , Jost Paul Jonas , Lisowski Clivia , Huang Hao , Grein Stephan , Hermannsdottir Hildigunnur , Kuellmer Katharina , Fromme Tobias , Klingenspor Martin , Mass Elvira , Kurts Christian , Hasenauer Jan 
Published: November 2024

AbstractMotivationThe availability of bulk-omic data is steadily increasing, necessitating collaborative efforts between experimental and computational researchers. While software tools with graphical user interfaces (GUIs) enable rapid and interactive data assessment, they are limited to pre-implemented methods, often requiring transitions to custom code for further adjustments. However, most available tools lack GUI-independent reproducibility such as direct integration with R, resulting in very limited support for transition.ResultsWe introduce the customizable Omics Analysis and reporting tool – cOmicsArt. cOmicsArt aims to enhance collaboration through integration of GUI-based analysis with R. The GUI allows researchers to perform user-friendly exploratory and statistical analyses with interactive visualizations and automatic documentation. Downloadable R scripts and results ensure reproducibility and seamless integration with R, supporting both novice and experienced programmers by enabling easy customizations and serving as a foundation for more advanced analyses. This versatility also allows for usage in educational settings guiding students from GUI-based analysis to R Code.AvailabilitycOmicsArt is freely available athttps://shiny.iaas.uni-bonn.de/cOmicsArt/User documentation is available athttps://icb-dcm.github.io/cOmicsArt/Source code is available on GitHubhttps://github.com/ICB-DCM/cOmicsArtA docker image can be retrieved fromhttps://hub.docker.com/r/pauljonasjost/comicsart/tagsA snapshot upon publication can be found on Zenodo:https://zenodo.org/records/13740904A screen recording of cOmicsArt is available at:https://www.youtube.com/watch?v=pTGjtIYQOakpContactjan.hasenauer@uni-bonn.deSupplementary informationSupplementary data are available online.
Comprehensive Unbiased Analysis of Vascular Tissue Changes in Accelerated Atherosclerosis Using High-Resolution Ultrasound combined with Photoacoustic Imaging
Authors: de Jong Alwin , Grasso Valeria , van Dijk Kayleigh , Sluiter Thijs. J. , Quax Paul. H.A. , Jose Jithin , de Vries Margreet R. 
Published: November 2024

AbstractVenous bypass grafts are commonly used to circumvent complex coronary or peripheral artery occlusions. The patency rates, however, are hampered due to accelerated buildup of atherosclerotic lesions in the vein graft wall. Identification of unstable plaques is crucial to guide clinical decision making. In this study, we employ advanced high-resolution ultrasound (US) coupled with spectral photoacoustic imaging (sPAI) to enhance the accurate visualization and analysis of tissue compositionin vivo. By applying unbiased spectral analysis, we investigate the composition and plaque instability in a murine vein graft model.MethodMale hypercholesterolemic ApoE3*Leiden mice and normocholesterolemic C57BL/6 mice underwent vein graft surgery in which a caval vein from a donor mouse was interpositioned into the arterial circulation of a recipient at the sight of the right common carotid artery. US imaging with sPAI was conducted on 7, 14, 21, and 28 days after surgery. Spectral curves from the near-infrared (NIR) I region, spanning 680 to 970nm, were extracted using a data-driven approach. Component discovery and cross-correlation analysis were performed with Matlab, and ImageJ reconstructed the components within 3D images. At the endpoint histological analysis of the vein grafts was performed.ResultsAnalysis of the NIRI region revealed distinct components, with 7 and 10 components tested in the cross-correlation map. Relative abundance values identified melanin, oxidized hemoglobin, deoxygenized hemoglobin, lipids, and collagen. Lipids and collagen spectra accurately identified lipid and collagen-rich tissuesin vivo. The sPAI analysis of of the vein graft wallin vivoresulted in a 8.7% lipids in the vein graft wall compared to 1.8% lipids in the histological analysis at t=28d. For vein grafts from ApoE*3-Leiden mice no differences in the lipid positive area was observed between the sPAI analysis or histological quantification. The percentages collagen present in the vein graft walls from both strains analyzed via sPAI and histological showed comparable results at t=28d.ConclusionOur study demonstrates that sPAI can be utilized for compositional analysis of murine tissue in an unbiased manner. This methodology can be used to enhance our understanding of vein graft dynamics and holds promise to advance non-invasive characterization of vascular diseases to ultimately guide clinical decision making.
Inheritable cell-states shape drug-persister correlations and population dynamics in cancer cells
Authors: Iyer Anton , Granada Adrián E. , Chakrabarti Shaon 
Published: November 2024

AbstractDrug tolerant persisters (DTPs) drive cancer therapy resistance by temporarily evading drug action, allowing multiple routes to eventual permanent resistance. Despite clear evidence for DTPs, the timing of their emergence, proliferative nature, and how their population dynamics arise from measured single-cell kinetics remain poorly understood. Here we use time-lapse microscopy data from two cancer cell lines, integrating single-cell and population measurements, to develop a quantitative description of drug persistence. Contrary to the expectation that increasing levels of genotoxic stress should lead to slower times to division and faster times to death, we observe minor changes in the single-cell intermitotic and death time distributions upon increasing cisplatin concentration. Yet, population decay rates increase 3-fold, suggesting a surprising independence of the overall dynamics from the measured birth and death rates. To explain this phenomenon, we argue that the observed lineage correlations and concentration-dependent decay rates imply cell-state dependent fate choices madebothpre and post-cisplatin as opposed to just post-drug birth/death rate-based competitive fate choices. We demonstrate that these cell-states, present in the ancestors of DTP and sensitive cells, exhibit no difference in cycling speed and are inherited across 2-3 cellular generations. A stochastic model implementing these rules simultaneously recapitulates the observed decay rates and cell-fate correlations, also explaining howpre-drug fate decisions are consistent with barcoding experiments where barcode diversity remains unchanged after drug administration. Our results provide a powerful perspective on drug tolerance based on general arguments, without requiring knowledge of the underlying molecular architecture of the heterogeneous cell states.
Characterisation of the historic demographic decline of the British European polecat population
Authors: Shaw R. , MacPherson J. , Kitchener A. C. , Etherington G. J. , Haerty W. 
Published: November 2024

AbstractThe European polecat (Mustela putorius) has a widespread distribution across many countries of mainland Europe, but is documented to be declining within these ranges. In Britain, direct persecution led to a severe decline of the polecat population during the 19thcentury. Unlike European mainland populations, it is now recovering across much of its former range. The genomic and conservation implications of such a severe demographic decline, followed by the current recovery, have still to be characterised in the European polecat in Britain. Here we carry out population-level whole-genome analyses of 65 polecats from Britain (Wales, England) and the European mainland. Welsh polecats show a unique genetic structure compared to English and European populations. We also reconstructed the demographic history of the Welsh polecat to quantify the magnitude of the bottleneck. Our analyses confirmed the drastic decline of the Welsh polecat’s effective population size, with a severe genetic bottleneck around 40 generations ago (1854-1872). We investigated whether genomic signatures reflected this demographic event and found that Welsh polecats had significantly less genetic diversity than English ones, but not European polecats. Runs of homozygosity and the genetic load present in Welsh polecat genomes also indicated recent historic inbreeding. We show that the increase of the British polecat population could be due to admixture events, but also that the Welsh polecat serves as a distinct genetic population, which could be crucial for the overall conservation of European polecats.