bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding the Utility of Endocardial Electrocardiographic Imaging in Epi-Endocardial Mapping of 3D Reentrant Circuits
Authors: Toloubidokhti Maryam , Gharbia Omar A , Parkosa Adityo , Trayanova Natalia , Hadjis Alexios , Tung Roderick , Nazarian Saman , Sapp John L. , Wang Linwei 
Published: March 2024

AbstractBackgroundStudies of VT mechanisms are largely based on a 2D portrait of reentrant circuits on one surface of the heart. This oversimplifies the 3D circuit that involves the depth of the myocardium. Simultaneous epicardial and endocardial (epi-endo) mapping was shown to facilitate a 3D delineation of VT circuits, which is however difficult via invasive mapping.ObjectiveThis study investigates the capability of noninvasive epicardial-endocardial electrocardiographic imaging (ECGI) to elucidate the 3D construct of VT circuits, emphasizing the differentiation of epicardial, endocardial, and intramural circuits and to determine the proximity of mid-wall exits to the epicardial or endocardial surfaces.Methods120-lead ECGs of VT in combination with subject-specific heart-torso geometry are used to compute unipolar electrograms (CEGM) on ventricular epicardium and endocardia. Activation isochrones are constructed, and the percentage of activation within VT cycle length is calculated on each surface. This classifies VT circuits into 2D (surface only), uniform transmural, nonuniform transmural, and mid-myocardial (focal on surfaces). Furthermore, the endocardial breakthrough time was accurately measured using Laplacian eigenmaps, and by correlating the delay time of the epi-endo breakthroughs, the relative distance of a mid-wall exit to the epicardium or the endocardium surfaces was identified.ResultsWe analyzed 23 simulated and in-vivo VT circuits on post-infarction porcine hearts. In simulated circuits, ECGI classified 21% as 2D and 78% as 3D: 82.6% of these were correctly classified. The relative timing between epicardial and endocardial breakthroughs was correctly captured across all cases. In in-vivo circuits, ECGI classified 25% as 2D and 75% as 3D: in all cases, circuit exits and entrances were consistent with potential critical isthmus delineated from combined LGE-MRI and catheter mapping data.ConclusionsECGI epi-endo mapping has the potential for fast delineation of 3D VT circuits, which may augment detailed catheter mapping for VT ablation.
Post-GWAS functional analyses ofCNTNAP5suggests its role in glaucomatous neurodegeneration
Authors: Chakraborty Sudipta , Sarma Jyotishman , Roy Shantanu Saha , Mitra Sukanya , Bagchi Sayani , Das Sankhadip , Saha Sreemoyee , Mahapatra Surajit , Bhattacharjee Samsiddhi , Maulik Mahua , Acharya Moulinath 
Published: March 2024

AbstractPrimary angle closure glaucoma (PACG) affects more than 20 million people worldwide, with an increased prevalence in south-east Asia. In a prior haplotype-based GWAS, we identified a novelCNTNAP5genic region, significantly associated with PACG. In the current study, we have extended our perception ofCNTNAP5involvement in glaucomatous neurodegeneration in a zebrafish model, through investigating phenotypic consequences pertinent to retinal degeneration upon knockdown of cntnap5 by translation-blocking morpholinos. While cntnap5 knockdown was successfully validated using an antibody, immunofluorescence followed by western blot analyses in cntnap5-morphant (MO) zebrafish revealed increased expression of acetylated tubulin indicative of perturbed cytoarchitecture of retinal layers. Moreover, significant loss of Nissl substance is observed in the neuro-retinal layers of cntnap5-MO zebrafish eye, indicating neurodegeneration. Additionally, in spontaneous movement behavioural analysis, cntnap5-MO zebrafish have a significantly lower average distance traversed in light phase compared to mismatch-controls, whereas no significant difference was observed in the dark phase, corroborating with vision loss in the cntnap5-MO zebrafish. This study provides the first direct functional evidence of a putative role ofCNTNAP5in visual neurodegeneration.
CD40 is an immune checkpoint regulator that potentiates myocardial inflammation through activation and expansion of CCR2+macrophages and CD8 T-cells
Authors: Jimenez Jesus , Amrute Junedh , Ma Pan , Wang Xiaoran , Dai Raymond , Lavine Kory J. 
Published: March 2024

AbstractNovel immune checkpoint therapeutics including CD40 agonists have tremendous promise to elicit antitumor responses in patients resistant to current therapies. Conventional immune checkpoint inhibitors (PD-1/PD-L1, CTLA-4 antagonists) are associated with serious adverse cardiac events including life-threatening myocarditis. However, little is known regarding the potential for CD40 agonists to trigger myocardial inflammation or myocarditis. Here, we leveraged genetic mouse models, single cell sequencing, and cell depletion studies to demonstrate that an anti-CD40 agonist antibody reshapes the cardiac immune landscape through activation of CCR2+macrophages and subsequent recruitment of effector memory CD8 T-cells. We identify a positive feedback loop between CCR2+macrophages and CD8 T-cells driven by IL12b, TNF, and IFN-γ signaling that promotes myocardial inflammation and show that prior exposure to CD40 agonists sensitizes the heart to secondary insults and accelerates LV remodeling. Collectively, these findings highlight the potential for CD40 agonists to promote myocardial inflammation and potentiate heart failure pathogenesis.
A new framework for evaluating model out-of-distribution for the biochemical domain
Authors: Fernández-Díaz Raúl , Hoang Thanh Lam , Lopez Vanessa , Shields Denis C. 
Published: March 2024

ABSTRACTQuantifying model generalization to out-of-distribution data has been a longstanding challenge in machine learning. Addressing this issue is crucial for leveraging machine learning in scientific discovery, where models must generalize to new molecules or materials. Current methods typically split data into train and test sets using various criteria — temporal, sequence identity, scaffold, or random cross-validation — before evaluating model performance. However, with so many splitting criteria available, existing approaches offer limited guidance on selecting the most appropriate one, and they do not provide mechanisms for incorporating prior knowledge about the target deployment distribution(s).To tackle this problem, we have developed a novel metric, AU-GOOD, which quantifies expected model performance under conditions of increasing dissimilarity between train and test sets, while also accounting for prior knowledge about the target deployment distribution(s), when available. This metric is broadly applicable to biochemical entities, including proteins, small molecules, nucleic acids, or cells; as long as a relevant similarity function is defined for them. Recognizing the wide range of similarity functions used in biochemistry, we propose criteria to guide the selection of the most appropriate metric for partitioning. We also introduce a new partitioning algorithm that generates more challenging test sets, and we propose statistical methods for comparing models based on AU-GOOD.Finally, we demonstrate the insights that can be gained from this framework by applying it to two different use cases: developing predictors for pharmaceutical properties of small molecules, and using protein language models as embeddings to build biophysical property predictors.
Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations
Authors: Garcia-Abadillo Julian , Adunola Paul , Aguilar Fernando S. , Trujillo-Montenegro Jhon Henry , Riascos John Jaime , Persa Reyna , Isidro y Sanchez Julio , Jarquín Diego 
Published: March 2024

AbstractSugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane’s yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6 × 186 = 1,116 phenotypes). Our study employed three predictive models, including environment and genotype as main effects, as well as the G×E interaction to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA (ρ= 0.611) and for TCH (ρ= 0.341) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72 = 12 × 6) being the most convenient cost-benefit combination.
The transcription factor ATML1 maintains giant cell identity by inducing synthesis of its own long-chain fatty acid-containing ligands
Authors: Vadde Batthula Vijaya Lakshmi , Russell Nicholas J. , Bagde Saket Rahul , Askey Bryce , Saint-Antoine Michael , Brownfield Bryce , Mughal Salaiha , Apprill Lauren E. , Khosla Aashima , Clark Frances K. , Schwarz Erich M. , Alseekh Saleh , Fernie Alisdair R. , Singh Abhyudai , Schrick Kathrin , Fromme J. Christopher , Skirycz Aleksandra , Formosa-Jordan Pau , Roeder Adrienne H. K. 
Published: March 2024

AbstractDuring development, cells not only adopt specialized identities but also maintain those identities. Endoreduplication is thought to maintain cell identity. High concentrations of ARABIDOPSIS THALIANA MERISTEM LAYER1 (ATML1) specify giant cell identity and induce endoreduplication in sepals. How different concentrations of ATML1 can specify different identities remains unclear. Here, we show that high concentrations of ATML1 induce the biosynthesis of both long-chain and very long-chain fatty acids (LCFAs/VLCFAs), and these fatty acids are required for the maintenance of giant cell identity. Inhibition of VLCFA biosynthesis causes endoreduplicated giant cells to resume division and lose their identity, indicating that endoreduplication is not sufficient to maintain cell identity. Structural predictions suggest that LCFA-containing lipids bind to the START domain 2 of ATML1, causing ATML1 dimerization and its auto-activation. Our data and modeling imply that ATML1 induces biosynthesis of its own lipid ligands in a positive feedback loop, shedding light on the intricate network dynamics that specify and maintain giant cell identity.Teaser:Endoreduplicated cells inArabidopsis thalianasepals divide and de-differentiate in the absence of VLCFA biosynthesis.
EHMT2 Inactivation in Pancreatic Epithelial Cells Shapes the Transcriptional Landscape and Inflammation Response of the Whole Pancreas
Authors: Pollin Gareth , Mathison Angela J. , de Assuncao Thiago M. , Thomas Anju , Zeighami Lida , Salmonson Ann , Liu Hongfei , Urrutia Guillermo , Vankayala Pallavi , Pandol Stephen J , Zimmermann Michael T. , Iovanna Juan , Jin Victor X. , Urrutia Raul , Lomberk Gwen 
Published: March 2024

ABSTRACTThe Euchromatic Histone Methyl Transferase Protein 2 (EHMT2), also known as G9a, deposits transcriptionally repressive chromatin marks that play pivotal roles in the maturation and homeostasis of multiple organs. Recently, we have shown that EHMT2 inactivation alters growth and immune gene expression networks, antagonizing KRAS-mediated pancreatic cancer initiation and promotion. Here, we elucidate the essential role of EHMT2 in maintaining a transcriptional landscape that protects organs from inflammation. Comparative RNA-seq studies between normal postnatal and young adult pancreatic tissue fromEHMT2conditional knockout animals (EHMT2fl/fl) targeted to the exocrine pancreatic epithelial cells (Pdx1-CreandP48Cre/+), reveal alterations in gene expression networks in the whole organ related to injury-inflammation-repair, suggesting an increased predisposition to damage. Thus, we induced an inflammation repair response in theEHMT2fl/flpancreas and used a data science-based approach to integrate RNA-seq-derived pathways and networks, deconvolution digital cytology, and spatial transcriptomics. We also analyzed the tissue response to damage at the morphological, biochemical, and molecular pathology levels. TheEHMT2fl/flpancreas displays an enhanced injury-inflammation-repair response, offering insights into fundamental molecular and cellular mechanisms involved in this process. More importantly, these data show that conditional EHMT2 inactivation in exocrine cells reprograms the local environment to recruit mesenchymal and immunological cells needed to mount an increased inflammatory response. Mechanistically, this response is an enhanced injury-inflammation-repair reaction with a small contribution of specific EHMT2-regulated transcripts. Thus, this new knowledge extends the mechanisms underlying the role of the EHMT2-mediated pathway in suppressing pancreatic cancer initiation and modulating inflammatory pancreatic diseases.
Development of the 12-Base Short Dimeric Myogenetic Oli-godeoxynucleotide That Induces Myogenic Differentiation
Authors: Umezawa Koji , Ikeda Rena , Sakamoto Taiichi , Enomoto Yuya , Nihashi Yuma , Shinji Sayaka , Shimosato Takeshi , Kagami Hiroshi , Takaya Tomohide 
Published: March 2024

AbstractA myogenetic oligodeoxynucleotide (myoDN), iSN04 (5’-AGA TTA GGG TGA GGG TGA-3’), is a single-stranded 18-base telomeric DNA that serves as an anti-nucleolin aptamer and induces myogenic differentiation, which is expected to be a nucleic acid drug for the prevention of disease-associated muscle wasting. To improve the drug efficacy and synthesis cost of myoDN, shortening the sequence while maintaining its structure-based function is a major challenge. Here, we report the novel 12-base non-telomeric myoDN, iMyo01 (5’-TTG GGT GGG GAA-3’), which has comparable myogenic activity to iSN04. iMyo01 as well as iSN04 promoted myotube formation of primary-cultured human myoblasts with upregulation of myogenic gene expression. Both iMyo01 and iSN04 interacted with nucleolin, but iMyo01 did not bind to berberine, the isoquinoline alkaloid that stabilizes iSN04. Nuclear magnetic resonance revealed that iMyo01 forms a G-quadruplex structure despite its short sequence. Native polyacrylamide gel electrophoresis and computational molecular dynamics simulation indicated that iMyo01 forms a homodimer to generate a G-quadru-plex. These results provide new insights into the aptamer truncation technology that preserves aptamer conformation and bioactivity for the development of efficient nucleic acid drugs.Key ContributionThis study reports the structure-based shortening of a myogenetic oligodeox-ynucleotide, iSN04, as an anti-nucleolin aptamer that induces myogenesis. The shortening technology of aptamers while maintaining their conformation and activity improves their potency of drug function and synthesis cost.
Comprehensive detection and characterization of human druggable pockets through novel binding site descriptors
Authors: Comajuncosa-Creus Arnau , Jorba Guillem , Barril Xavier , Aloy Patrick 
Published: March 2024

AbstractDruggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, strategies to derive pocket descriptors are scarce and usually exhibit limited applicability. Here, we present PocketVec, a novel approach to generate pocket descriptors for any protein binding site of interest through the inverse virtual screening of lead-like molecules. We assess the performance of our descriptors in a variety of scenarios, showing that it is on par with the best available methodologies, while overcoming some important limitations. In parallel, we systematically search for druggable pockets in the folded human proteome, using experimentally determined protein structures and AlphaFold2 models, identifying over 32,000 binding sites in more than 20,000 protein domains. Finally, we derive PocketVec descriptors for each small molecule binding site and run an all-against-all similarity search, exploring over 1.2 billion pairwise comparisons. We show how PocketVec descriptors facilitate the identification of druggable pocket similarities not revealed by structure- or sequence-based comparisons. Indeed, our analyses unveil dense clusters of similar pockets in distinct proteins for which no inhibitor has yet been crystalized, opening the door to strategies to prioritize the development of chemical probes to cover the druggable space.
Machine learning-based prediction of fish acute mortality: Implementation, interpretation, and regulatory relevance
Authors: Gasser Lilian , Schür Christoph , Perez-Cruz Fernando , Schirmer Kristin , Baity-Jesi Marco 
Published: March 2024

Regulation of chemicals requires knowledge of their toxicological effects on a large number of species, which has traditionally been acquired throughin vivotesting. The recent effort to find alternatives based on machine learning, however, has not focused on guaranteeing transparency, comparability and reproducibility, which makes it difficult to assess advantages and disadvantages of these methods. Also, comparable baseline performances are needed. In this study, we trained regression models on the ADORE “t-F2F” challenge proposed in [Schüret al.,Nature Scientific data, 2023] to predict acute mortality, measured as LC50 (lethal concentration 50), of organic compounds on fishes. We trained LASSO, random forest (RF), XGBoost, Gaussian process (GP) regression models, and found a series of aspects that are stable across models: (i) using mass or molar concentrations does not affect performances; (ii) the performances are only weakly dependent on the molecular representations of the chemicals, but (iii) strongly on how the data is split. Overall, the tree-based models RF and XGBoost performed best and we were able to predict the log10-transformed LC50 with a root mean square error of 0.90, which corresponds to an order of magnitude on the original LC50 scale. On a local level, on the other hand, the models are not able to consistently predict the toxicity of individual chemicals accurately enough. Predictions for single chemicals are mostly influenced by a few chemical properties while taxonomic traits are not captured sufficiently by the models. We discuss technical and conceptual improvements for these challenges to enhance the suitability ofin silicomethods to environmental hazard assessment. Accordingly, this work showcases state-of-the-art models and contributes to the ongoing discussion on regulatory integration.Environmental significanceConventional environmental hazard assessment in its current form will not be able to adapt to the growing need for toxicity testing. Alternative methods, such as toxicity prediction through machine learning, could fulfill that need in an economically and ethically sound manner. Proper implementation, documentation, and the integration into the regulatory process are prerequisites for the usability and acceptance of these models.
Abnormal global longitudinal strain and reduced serum inflammatory markers in cardiac AL amyloidosis patients without significant amyloid fibril deposition
Authors: Edwards Camille V. , Ferri Grace M. , Villegas-Galaviz Josue , Ghosh Sabrina , Bawa Pushpinder Singh , Wang Feiya , Klimtchuk Elena , Ajayi Tinuola B. , Morgan Gareth J. , Prokaeva Tatiana , Staron Andrew , Ruberg Frederick L. , Sanchorawala Vaishali , Giadone Richard M. , Murphy George J. 
Published: March 2024

BackgroundCardiac dysfunction in AL amyloidosis is thought to be partly related to the direct impact of AL LCs on cardiomyocyte function, with the degree of dysfunction at diagnosis as a major determinant of clinical outcomes. Nonetheless, mechanisms underlying LC-induced myocardial toxicity are not well understood.MethodsWe identified gene expression changes correlating with human cardiac cells exposed to a cardiomyopathy-associated κAL LC. We then sought to confirm these findings in a clinical dataset by focusing on clinical parameters associated with the pathways dysregulated at the gene expression level.ResultsUpon exposure to a cardiomyopathy-associated κAL LC, cardiac cells exhibited gene expression changes related to myocardial contractile function and inflammation, leading us to hypothesize that there could be clinically detectable changes in GLS on echocardiogram and serum inflammatory markers in patients. Thus, we identified 29 patients with normal IVSd but abnormal cardiac biomarkers suggestive of LC-induced cardiac dysfunction. These patients display early cardiac biomarker staging, abnormal GLS, and significantly reduced serum inflammatory markers compared to patients with clinically evident amyloid fibril deposition.ConclusionCollectively, our findings highlight early molecular and functional signatures of cardiac AL amyloidosis, with potential impact for developing improved patient biomarkers and novel therapeutics.
Pivotal role of biallelic frequency analysis in identifying copy number alterations using genome-wide methods in tumors with a high level of aneuploidy
Authors: Rymuza Julia , Woroniecka Renata , Grygalewicz Beata , Bujko Mateusz 
Published: March 2024

AbstractChromosome number abnormalities is one of the hallmarks of cancer. DNA copy number alterations (CNA) are studied using various genome-wide methods. In our study we investigated CNA in human pituitary tumors using three platforms CytoSNP-850K microarrays, low-pass whole-genome sequencing (average x7 coverage, LPWGS), and Infinium Methylation EPIC array. Virtual karyotypes based on each dataset were generated using open-source software packages for each sample. Concordant CNA profiles were found for most of tumor. Surprisingly, substantial discrepancies between results from SNP arrays and LPWGS/EPIC arrays were identified in 20% of tumors, for which discrimination of true karyotype was required. B-allelic frequency data from SNP arrays was crucial to adjust normal ploidy level as ultimately verified with FISH. The discrepancy between virtual karyotypes was more pronounced the more CNAs were found. When CNAs covered more than half of genome the level of normal/diploid copy number was incorrectly set with methods, based solely on signal intensity/read-counts coverage. To conclude, CNA analysis with methods such as LPWGS and methylation arrays in highly aneuploid tumors are prone to a bias from improper normal ploidy level setting. These methods are commonly used therefore we aimed to aware the scientific community about this underestimated methodological problem.