bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving gut virome comparisons using predicted phage host information
Authors: Shamash Michael , Sinha Anshul , Maurice Corinne F. 
Published: October 2024

AbstractThe human gut virome is predominantly made up of bacteriophages (phages), viruses that infect bacteria. Metagenomic studies have revealed that phages in the gut are highly individual specific and dynamic. These features make it challenging to perform meaningful cross-study comparisons. While several taxonomy frameworks exist to group phages and improve these comparisons, these strategies provide little insight into the potential effects phages have on their bacterial hosts. Here, we propose the use of predicted phage host families (PHFs) as a functionally relevant, higher rank unit of phage taxonomy to improve these cross-study analyses. We first show that bioinformatic predictions of phage hosts are accurate at the host family level by measuring their concordance to Hi-C sequencing-based predictions in human and mouse fecal samples. Next, using phage host family predictions, we determined that PHFs reduce intra- and interindividual ecological distances compared to viral contigs in a previously published cohort of 10 healthy individuals, while simultaneously improving longitudinal virome stability. Lastly, by reanalyzing a previously published metagenomics dataset with > 1,000 samples, we determined that PHFs are prevalent across individuals and can aid in the detection of inflammatory bowel disease-specific virome signatures. Overall, our analyses support the use of predicted phage hosts in reducing between-sample distances and providing a biologically relevant framework for making between-sample virome comparisons.
ProFit-1D for quantifying J-difference edited data at 3T
Authors: Chan Kimberly L. , Borbath Tamas , Sherlock Sydney , Maher Elizabeth A. , Patel Toral R. , Henning Anke 
Published: October 2024

AbstractReproducible and accurate fitting of the magnetic resonance spectrum is critical for estimating metabolite concentrations. We have previously developed a fitting software called ProFit-1D which was shown to fit 9.4T semi-LASER data from the human brain with high accuracy and precision. In this study, we adapted ProFit-1D to fit J-difference edited spectra acquired at a clinical field strength of 3T and to assessed its performance in simulated and in vivo data. ProFit-1D was adapted to fit J-difference edited data with alterations to the fitting range to exclude the 1.3 ppm lipid resonance, starting T2relaxation constants, initial fit parameters, and adaptive spectral baseline determination. The accuracy of ProFit-1D was systematically evaluated on simulated GABA-edited and 2-hydroxyglutarate-edited (2HG-edited) data with different types of in vivo parameter variations and compared to that of LCModel and Gannet, two software commonly used to fit J-difference edited data. The precision of ProFit-1D was also evaluated in GABA-edited spectra acquired in vivo in the occipital cortex (OCC) and medial prefrontal cortex (mPFC) of healthy participants at 3T using subsets of averages and compared to that of LCModel and Gannet. The 2HG fit error was also evaluated for ProFit-1D in 2HG-edited spectra acquired in glioma patients and compared to that of LCModel. Overall, it was found that ProFit-1D generally produced fits with low parameter fit errors across a variety of parameter variations. GABA, glutamate plus glutamine (Glx), and 2HG levels were also more accurately estimated with ProFit-1D than with LCModel and Gannet across different spectral disturbances and simulated concentrations. ProFit-1D was found to be as precise as LCModel and more precise than Gannet in estimating GABA and Glx. 2HG fit errors were 45% lower with ProFit-1D than with LCModel. Thus, ProFit-1D was found to produce high-quality fits to J- difference edited data with high accuracy and precision.
AI driven approaches in Nanobody Epitope Prediction: Are We There Yet?
Authors: Eshak Floriane , Goupil-Lamy Anne 
Published: October 2024

ABSTRACTNanobodies have emerged as a versatile class of biologics with promising therapeutic applications, driving the need for robust tools to predict their epitopes, a critical step for in silico affinity maturation and epitope-targeted design. While molecular docking has long been employed for epitope identification, it requires substantial expertise. With the advent of AI driven tools, epitope identification has become more accessible to a broader community increasing the risk of models’ misinterpretation. In this study, we critically evaluate the nanobody epitope prediction performance of two leading models: AlphaFold3 and AlphaFold2-Multimer (v.2.3.2), highlighting their strengths and limitations. Our analysis revealed that the overall success rate remains below 50% for both tools, with AlphaFold3 achieving a modest overall improvement. Interestingly, a significant improvement in AlphaFold3’s performance was observed within a specific nanobody class. To address this discrepancy, we explored factors influencing epitope identification, demonstrating that accuracy heavily depends on CDR3 characteristics, such as its 3D spatial conformation and length, which drive binding interactions with the antigen. Additionally, we assessed the robustness of AlphaFold3’s confidence metrics, highlighting their potential for broader applications. Finally, we evaluated different strategies aimed at improving prediction success rate. This study can be extended to assess the accuracy of emerging deep learning models adopting a similar approach to AlphaFold3.
Translocations can drive expression changes of multiple genes in regulons covering entire chromosome arms
Authors: Oncins Anna , Zaurin Roser , Toukabri Houyem , Quililan Kimberly , Hernández Mora José R , Karpinska Magdalena A , Wernersson Erik , Smith Alastair , Bianchi Agostina , Albinati Leone , Rivero Andrea , Serra François , Gómez Raúl , López Cristina , Beà Sílvia , Halidi Nadia , Valencia Alfonso , Bienko Magda , Oudelaar A Marieke , Beekman Renée 
Published: October 2024

AbstractTranslocations have largely been implicated in tumor development. However, beyond the consequences of aberrant gene expression near the breakpoint, their effects remain unexplored. In this work, we characterize the interplay between translocations, chromatin organization and gene expression using mantle cell lymphoma (MCL) as a model. We show thatin vitroinduced MCL-associated translocations can drive transcriptional changes at entire chromosome arms affecting multiple genes in a regulon-like fashion. Additionally, overexpressed genes in MCL patients are enriched in the exact same genomic regions, further underlining its potential relevance for lymphomagenesis. Moreover, we demonstrate a clear link between the translocation-induced transcriptional alterations and genome organization, with genes most susceptible to change expression residing in pre-existing long-range interacting loops spanning 50 megabases. The translocation places the strong immunoglobulin enhancer into this loop, allowing the spread of its regulatory potential over the entire affected chromosome arm. Finally, we show that translocation-induced effects mainly represent expression enhancement of genes already active prior to translocation formation, highlighting the importance of the epigenetic state of the cell in which this initial hit occurs. In summary, we show that translocations can induce ample, simultaneous gene expression changes affecting entire chromosome arms, representing an important new mechanism for tumorigenesis.
Towards a greener AlphaFold2 protocol for antibody-antigen modeling: Insights from CAPRI Round 55
Authors: Savaş Büşra , Yılmazbilek İrem , Özsan Atakan , Karaca Ezgi 
Published: October 2024

ABSTRACTIn the 55thround of CAPRI, we used enhanced AlphaFold2 (AF2) sampling and data-driven docking. Our AF2 protocol relies on Wallner’s massive sampling approach, which combines different AF2 versions and sampling parameters to produce thousands of models per target. For T231 (an antibody peptide complex) and T232 (PP2A:TIPRL complex), we employed a 50-fold reduced MinnieFold sampling and a custom ranking approach, leading to a top-ranking medium prediction in both cases. For T233 and T234 (two antibody bound MHC I complexes), we followed data-driven docking, which did not lead to an acceptable model. Our post-CAPRI55 analysis showed that if we would have used our MinnieFold approach on T233 and T234, we could have submitted a medium-quality model for T233 as well. In the scoring challenge, we utilized the scoring function of FoldX, which was effective in selecting acceptable models for T231 and medium quality models for T232. Our success, especially in predicting and ranking a medium quality model for T231 and potentially for T233 underscores the feasibility of green and accurate enhanced AF2 sampling in antibody complex prediction.
NPF4.1 imports embryo-derived GA4to the endosperm to promote seed germination
Authors: Sirlin-Josserand Mathilde , Sakvarelidze-Achard Lali , Pflieger David , Davière Jean-Michel , Achard Patrick 
Published: October 2024

AbstractGibberellins (GAs) are plant hormones essential for seed germination. In non-dormant seeds, humidity and light induce a fast increase in GA levels, which in turn enhances the growth potential of the embryo and weakening of the surrounding endosperm. While it is admitted that the tissue-specific distribution of GAs must be finely regulated in germinating seeds, it remains unclear where the bioactive forms are synthetized and how they are transported to their site of action. Here we demonstrate that bioactive GAs are mainly produced in the hypocotyl of the embryo and are transported in the endosperm, a few hours from the onset of imbibition, to induce the expression of cell wall loosening genes such asEXPANSIN2. We further show that NITRATE TRANSPORTER1/PEPTIDE TRANSPORTER FAMILY (NPF) NPF4.1, previously identified as a cellular GA influx transporter, is localized in the plasma membrane of endosperm cells and contributes in the transport of embryo-derived GA4into the endosperm. Accordingly,npf4.1mutant seeds are hypersensitive to the GA biosynthesis inhibitor paclobutrazol in germination assay, and the phenotype is suppressed by application of exogenous bioactive GAs. Finally, we show that two other GA transporters, NPF3.1 and NPF2.13, are expressed in the embryo of germinating seeds and may modulate cellular GA levels in elongating cells of the radicle and cotyledons.
Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs
Authors: Uçar Talip , Malherbe Cedric , Gonzalez Ferran 
Published: October 2024

AbstractGenerative models trained on antibody sequences and structures have shown great potential in advancing machine learning-assisted antibody engineering and drug discovery. Current state-of-the-art models are primarily evaluated using two categories of in silico metrics: sequence-based metrics, such as amino acid recovery (AAR), and structure-based metrics, including root-mean-square deviation (RMSD), predicted alignment error (pAE), and interface predicted template modeling (ipTM). While metrics such as pAE and ipTM have been shown to be useful filters for experimental success, there is no evidence that they are suitable for ranking, particularly for antibody sequence designs. Furthermore, no reliable sequence-based metric for ranking has been established. In this work, using real-world experimental data from seven diverse datasets, we extensively benchmark a range of generative models, including LLM-style, diffusion-based, and graph-based models. We show that log-likelihood scores from these generative models correlate well with experimentally measured binding affinities, suggesting that log-likelihood can serve as a reliable metric for ranking antibody sequence designs. Additionally, we scale up one of the diffusion-based models by training it on a large and diverse synthetic dataset, significantly enhancing its ability to predict and score binding affinities. Our implementation is available at:https://github.com/AstraZeneca/DiffAbXL
Water immunity overrides stomatal immunity in plant resistance toPseudomonas syringae
Authors: Kemppinen Jasmin , Pollmeier Maximillian , Ehonen Sanna , Brosché Mikael , Sierla Maija 
Published: October 2024

AbstractStomata play crucial roles in the multilayered defense system against pathogens. Upon pathogen perception, stomata close promptly, establishing the first line of defense known as stomatal immunity. The bacterial pathogenPseudomonas syringae(Pst) exploits open stomata for entry, however, it can also induce stomatal closure at post-invasive stages to enhance apoplastic hydration. This creates a favorable environment forPstproliferation, evident as water-soaked lesions on leaves. During the post-invasive stages ofPstinfection, plants deploy a second layer of stomatal defense by reopening their stomata, a process termed water immunity. To evaluate the relative importance of stomatal versus water immunity, we utilized a diverse set of Arabidopsis (Arabidopsis thaliana) mutants with impaired stomatal function and monitored bacterial growth, stomatal behavior, and water-soaking capacity afterPstpv.tomatoDC3000 infection. Most mutants with constitutively open stomata and disrupted stomatal closure were more resistant againstPstthan wild-type plants. Also, while some stomatal mutants displayed similar stomatal behavior at the initial steps of defense, their disease outcomes were the opposite, suggesting that stomatal immunity is not the determining factor in disease resistance. Instead, we discovered that the water-soaking capacity, associated with stomatal status at later stages of infection, i.e., water immunity, dictates the disease outcome. Our results show that water immunity can override the lack of stomatal immunity in plant resistance toPst. We also address previous discrepancies in literature showing contradicting results for pathogen growth on stomatal mutants, highlighting the challenges in dissecting stomatal effects on plant resistance.
Complete Reference Genome and Pangenome Expand Biologically Relevant Information for Genome-Wide DNA Methylation Analysis Using Short-Read Sequencing and Array Data
Authors: Dong Zheng (Joe) , Whitehead Joanne , Fu Maggie , MacIsaac Julia L. , Rehkopf David H. , Rosero-Bixby Luis , Kobor Michael S. , Korthauer Keegan 
Published: October 2024

AbstractBackgroundThe new complete telomere-to-telomere human genome assembly, T2T-CHM13, and the first draft of the human pangenome reference provide unique opportunities to update the reference genome for epigenetics investigations and clinical research. However, it is largely unclear how these reference genome updates may impact DNA methylation (DNAm) analysis.ResultsCompared to the previous GRCh38 assembly, we found an average increase of 7.4% (range 5.4%–9.9% across samples and sequencing methods) in the number of CpGs genome-wide using T2T-CHM13 with data from four commonly used short-read sequencing DNAm profiling methods. The increase in number of CpGs facilitated discovery of 88 new differentially methylated CpGs within cancer driver genes in an epigenome-wide association study (EWAS) of colon cancer. Further, by aligning probe sequences from the commonly used and recently released Illumina DNAm arrays to T2T-CHM13 and GRCh38, we showed the enhanced utility of T2T-CHM13 for evaluation of potential probe cross-reactivity (i.e., where probes match multiple regions) and mismatch (i.e., where probes do not perfectly match the target region), resulting in the identification of new and more reproducible sets of unambiguous probes (i.e., probes uniquely mapping to the target region) (HM450K,n= 430,719; EPIC,n= 777,491; EPICv2,n= 859,216). In EWASs of 24 cancer types, an average of 945 additional differentially methylated CpG sites were identified in the new unambiguous probe set rather than in the GRCh38-based unambiguous probe set, with enrichments in cancer driver genes and cancer signaling pathways. Moreover, the pangenome called 4.5% more CpGs on average in short-read sequencing data than T2T-CHM13 and identified cross-population and population-specific unambiguous probes in DNAm arrays, owing to its improved representation of genetic diversity. These additional CpGs were overlapped with the promoters and gene bodies of various biologically and medically relevant genes and pangenome-based unambiguous probes can potentially facilitate the discovery of DNAm alterations in more than 200 cancer driver genes in each cancer type.ConclusionsUse of T2T-CHM13 and pangenome references can benefit epigenome-wide association studies by including CpGs previously unobserved in short-read sequencing data and by improving the identification of unambiguous probes for DNAm arrays, thus expanding biologically relevant information. This study highlights the practical applications of T2T-CHM13 and pangenome for genome biology and provides a basis for expansion of epigenetics investigations.
TP53minigene analysis of 161 sequence changes provides evidence for role of spatial constraint and regulatory elements on variant-induced splicing impact
Authors: Canson Daffodil M. , Llinares-Burguet Inés , Fortuno Cristina , Sanoguera-Miralles Lara , Bueno-Martínez Elena , de la Hoya Miguel , Spurdle Amanda B. , Velasco-Sampedro Eladio A. 
Published: October 2024

ABSTRACTGermlineTP53genetic variants that disrupt splicing are implicated in hereditary cancer predisposition, while somatic variants contribute to tumorigenesis. We investigated the role ofTP53splicing regulatory elements (SREs), including G-runs that act as intronic splicing enhancers, using exons 3 and 6 and their downstream introns as models. Minigene microdeletion assays revealed four SRE-rich intervals: c.573_598, c.618_641, c.653_669 and c.672+14_672+36. A diagnostically reported deletion c.655_670del, overlapping an SRE-rich interval, induced an in-frame transcript Δ(E6q21) from new donor site usage. Within intron 6, deletion of at least four G-runs led to 100% aberrant transcript expression. Additionally, assay results suggested a donor-to-branchpoint distance cutoff of <50 nt for complete splicing aberration due to spatial constraint, and >75 nt for low risk of splicing abnormality. Overall, splicing data for 134 single nucleotide variants (SNVs) and 27 deletions inTP53demonstrated that SRE-disrupting SNVs have weak splicing impact (up to 26% exon skipping), while deletions spanning multiple SREs can have profound splicing effects. Results also provide more data to inform splicing impact prediction for intronic deletions that shorten intron size.
Heterogeneity in ligand-bound TRPV1: A comparison of methods in cryo-EM and molecular dynamics simulation
Authors: Astore Miro A. , Blackwell Robert , Silva-Sánchez David , Cossio Pilar , Hanson Sonya M. 
Published: October 2024

AbstractCryogenic electron microscopy (cryo-EM) has emerged as a powerful method for resolving the atomistic details of cellular components. In recent years, several computational methods have been developed to study the heterogeneity of molecules in single-particle cryo-EM. In this study, we analyzed a publicly available single-particle dataset of TRPV1 using five of these methods: 3D Flexible Refinement, 3D Variability Analysis, cryoDRGN, ManifoldEM, and Bayesian ensemble reweighting. Beyond what we initially expected, we have found that this dataset contains significant heterogeneity— indicating that single particle datasets likely contain a rich spectrum of biologically relevant states. Further, we have found that different methods are best suited to studying different kinds of heterogeneity, with some methods being more sensitive to either compositional or conformational heterogeneity. We also apply a combination of Bayesian ensemble reweighting and molecular dynamics as supporting evidence for the presence of these rarer states within the sample. Finally, we developed a quantitative metric based on the analysis of the singular value decomposition and power spectra to compare the resulting volumes from each method. This work represents a detailed view of the variable outcomes of different heterogeneity methods used to analyze a single real dataset and presents a pathway to a deeper understanding of the biology of complex macromolecules like the TRPV1 ion channel.
Sounds and Sights in Sequence Learning: Can Accessory Auditory Cues Enhance Motor Task Performance?
Authors: Leow Li-Ann , Nguyen An , Corti Emily , Marinovic Welber 
Published: October 2024

AbstractMotor sequence learning, or the ability to learn and remember sequences of actions, such as the sequence of actions required to tie one’s shoelaces, is ubiquitous to everyday life. Contemporary research on motor sequence learning has been largely unimodal, ignoring the possibility that our nervous system might benefit from sensory inputs from multiple modalities. In this study, we investigated the properties of motor sequence learning in response to auditory-visual stimuli. We found that sequence learning with auditory-visual stimuli showed a hallmark feature of traditional unimodal sequence learning tasks: sensitivity to stimulus timing, where lengthier interstimulus intervals of 500 ms improved sequence learning compared to briefer interstimulus intervals of 200 ms. Consistent with previous findings, we also found that auditory-visual stimuli improved learning compared to a unimodal visual-only condition. Furthermore, the informativeness of the auditory stimuli was important, as auditory stimuli which predicted the location of visual cues improved sequence learning compared to uninformative auditory stimuli which did not predict the location of the visual cues. Our findings suggest a potential utility of leveraging audio-visual stimuli in sequence learning interventions to enhance skill acquisition in education and rehabilitation contexts.
Wildland Fire-Related Smoke PM2.5and Cardiovascular Disease ED Visits in the Western United States
Authors: Li Linzi , Wang Wenhao , Chang Howard H. , Alonso Alvaro , Liu Yang 
Published: October 2024

AbstractBackgroundThe impact of short-term exposure to fine particulate matter (PM2.5) due to wildland fire smoke on the risk of cardiovascular disease (CVD) remains unclear. We investigated the association between short-term exposure to wildfire smoke PM2.5and Emergency Department (ED) visits for acute CVD in the Western United States from 2007 to 2018.MethodsED visits for primary or secondary diagnoses of atrial fibrillation (AF), acute myocardial infarction (AMI), heart failure (HF), stroke, and total CVD were obtained from hospital associations or state health departments in California, Arizona, Nevada, Oregon, and Utah. ED visits included those that were subsequently hospitalized. Daily smoke, non-smoke, and total PM2.5were estimated using a satellite-driven multi-stage model with a high resolution of 1 km. The data were aggregated to the zip code level and a case-crossover study design was employed. Temperature, relative humidity, and day of the year were included as covariates.ResultsWe analyzed 49,759,958 ED visits for primary or secondary CVD diagnoses, which included 6,808,839 (13.7%) AFs, 1,222,053 (2.5%) AMIs, 7,194,474 (14.5%) HFs, and 808,396 (1.6%) strokes. Over the study period from 2007-01-01 to 2018-12-31, the mean smoke PM2.5was 1.27 (Q1: 0, Q3: 1.29) µg/m3. A 10 µg/m3increase in smoke PM2.5was associated with a minuscule decreased risk for AF (OR 0.994, 95% CI 0.991-0.997), HF (OR 0.995, 95% CI 0.992-0.998), and CVD (OR 0.9997, 95% CI 0.996-0.998), but not for AMI and stroke. Adjusting for non-smoke PM2.5did not alter these associations. A 10 µg/m3increase in total PM2.5was linked to a small increased risk for all outcomes except stroke (OR for CVD 1.006, 95% CI 1.006-1.007). Associations were similar across sex and age groups.ConclusionWe identified an unexpected slight lower risk of CVD ED visits associated with short-term wildfire smoke PM2.5exposure. Whether these findings are due to methodological issues, behavioral changes, or other factors requires further investigation.