tbuckley/cabot-search huggingface dataset:
Frontotemporal dementia-like disease progression elicited by seeded aggregation and spread of FUS
Authors: Sonia Vazquez-Sanchez, Britt Tilkin, Fatima Gasset-Rosa, Sitao Zhang, Diana Piol, Melissa McAlonis-Downes, Jonathan Artates, Noe Govea-Perez, Yana Verresen, Lin Guo, Don W. Cleveland, James Shorter, Sandrine Da Cruz
Journal: Molecular Neurodegeneration (2024)

Abstract RNA binding proteins have emerged as central players in the mechanisms of many neurodegenerative diseases. In particular, a proteinopathy of fu sed in s arcoma (FUS) is present in some instances of familial Amyotrophic lateral sclerosis (ALS) and about 10% of sporadic Frontotemporal lobar degeneration (FTLD). Here we establish that focal injection of sonicated human FUS fibrils into brains of mice in which ALS-linked mutant or wild-type human FUS replaces endogenous mouse FUS is sufficient to induce focal cytoplasmic mislocalization and aggregation of mutant and wild-type FUS which with time spreads to distal regions of the brain. Human FUS fibril-induced FUS aggregation in the mouse brain of humanized FUS mice is accelerated by an ALS-causing FUS mutant relative to wild-type human FUS. Injection of sonicated human FUS fibrils does not induce FUS aggregation and subsequent spreading after injection into naïve mouse brains containing only mouse FUS, indicating a species barrier to human FUS aggregation and its prion-like spread. Fibril-induced human FUS aggregates recapitulate pathological features of FTLD including increased detergent insolubility of FUS and TAF15 and amyloid-like, cytoplasmic deposits of FUS that accumulate ubiquitin and p62, but not TDP-43. Finally, injection of sonicated FUS fibrils is shown to exacerbate age-dependent cognitive and behavioral deficits from mutant human FUS expression. Thus, focal seeded aggregation of FUS and further propagation through prion-like spread elicits FUS-proteinopathy and FTLD-like disease progression.
Self-administered mindfulness interventions reduce stress in a large, randomized controlled multi-site study
Authors: Alessandro Sparacio, Hans IJzerman, Ivan Ropovik, Filippo Giorgini, Christoph Spiessens, Bert N. Uchino, Joshua Landvatter, Tracey Tacana, Sandra J. Diller, Jaye L. Derrick, Joahana Segundo, Jace D. Pierce, Robert M. Ross, Zoë Francis, Amanda LaBoucane, Christine Ma-Kellams, Maire B. Ford, Kathleen Schmidt, Celia C. Wong, Wendy C. Higgins, Bryant M. Stone, Samantha K. Stanley, Gianni Ribeiro, Paul T. Fuglestad, Valerie Jaklin, Andrea Kübler, Philipp Ziebell, Crystal L. Jewell, Yulia Kovas, Mahnoosh Allahghadri, Charlotte Fransham, Michael F. Baranski, Hannah Burgess, Annika B. E. Benz, Maysa DeSousa, Catherine E. Nylin, Janae C. Brooks, Caitlyn M. Goldsmith, Jessica M. Benson, Siobhán M. Griffin, Stephen Dunne, William E. Davis, Tam J. Watermeyer, William B. Meese, Jennifer L. Howell, Laurel Standiford Reyes, Megan G. Strickland, Sally S. Dickerson, Samantha Pescatore, Shayna Skakoon-Sparling, Zachary I. Wunder, Martin V. Day, Shawna Brenton, Audrey H. Linden, Christopher E. Hawk, Léan V. O’Brien, Tenzin Urgyen, Jennifer S. McDonald, Kim Lien van der Schans, Heidi Blocker, Caroline Ng Tseung-Wong, Gabriela M. Jiga-Boy
Journal: Nature Human Behaviour (2024)

Abstract Mindfulness witnessed a substantial popularity surge in the past decade, especially as digitally self-administered interventions became available at relatively low costs. Yet, it is uncertain whether they effectively help reduce stress. In a preregistered (OSF https://doi.org/10.17605/OSF.IO/UF4JZ ; retrospective registration at ClinicalTrials.gov NCT06308744 ) multi-site study ( n sites = 37, n participants = 2,239, 70.4% women, M age = 22.4, s.d. age = 10.1, all fluent English speakers), we experimentally tested whether four single, standalone mindfulness exercises effectively reduced stress, using Bayesian mixed-effects models. All exercises proved to be more efficacious than the active control. We observed a mean difference of 0.27 ( d = −0.56; 95% confidence interval, −0.43 to −0.69) between the control condition ( M = 1.95, s.d. = 0.50) and the condition with the largest stress reduction (body scan: M = 1.68, s.d. = 0.46). Our findings suggest that mindfulness may be beneficial for reducing self-reported short-term stress for English speakers from higher-income countries.
Deep-learning-enabled antibiotic discovery through molecular de-extinction
Authors: Fangping Wan, Marcelo D. T. Torres, Jacqueline Peng, Cesar de la Fuente-Nunez
Journal: Nature Biomedical Engineering (2024)

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.
Association between gut microbiota and CpG island methylator phenotype in colorectal cancer
Authors: Pyoung Hwa Park, Kelsey Keith, Gennaro Calendo, Jaroslav Jelinek, Jozef Madzo, Raad Z. Gharaibeh, Jayashri Ghosh, Carmen Sapienza, Christian Jobin, Jean-Pierre J. Issa
Journal: Gut Microbes (2024)

The intestinal microbiota is an important environmental factor implicated in CRC development. Intriguingly, modulation of DNA methylation by gut microbiota has been reported in preclinical models, although the relationship between tumor-infiltrating bacteria and CIMP status is currently unexplored. In this study, we investigated tumor-associated bacteria in 203 CRC tumor cases and validated the findings using The Cancer Genome Atlas datasets. We assessed the abundance of Bacteroides fragilis, Escherichia coli, Fusobacterium nucleatum, and Klebsiella pneumoniae through qPCR analysis and observed enrichment of all four bacterial species in CRC samples. Notably, except for E. coli, all exhibited significant enrichment in cases of CIMP. This enrichment was primarily driven by a subset of cases distinguished by high levels of these bacteria, which we labeled as "Superhigh". The bacterial Superhigh status showed a significant association with CIMP (odds ratio 3.1, p-value = 0.013) and with MLH1 methylation (odds ratio 4.2, p-value = 0.0025). In TCGA CRC cases (393 tumor and 45 adj. normal), bacterial taxa information was extracted from non-human whole exome sequencing reads, and the bacterial Superhigh status was similarly associated with CIMP (odds ratio 2.9, p < 0.001) and MLH1 methylation (odds ratio 3.5, p < 0.001). Finally, 16S ribosomal RNA gene sequencing revealed high enrichment of Bergeyella spp. C. concisus, and F. canifelinum in CIMP-Positive tumor cases. Our findings highlight that specific bacterial taxa may influence DNA methylation, particularly in CpG islands, and contribute to the development and progression of CIMP in colorectal cancer.
TRIM35 Monoubiquitinates H2B in Cardiac Cells, Implications for Heart Failure
Authors: Maria Areli Lorenzana-Carrillo, Saymon Tejay, Joseph Nanoa, Guocheng Huang, Yongsheng Liu, Alois Haromy, Yuan Yuan Zhao, Michelle Mendiola Pla, Dawn E. Bowles, Adam Kinnaird, Evangelos D. Michelakis, Gopinath Sutendra
Journal: Circulation Research (2024)

The tumor suppressor and proapoptotic transcription factor P53 is induced (and activated) in several forms of heart failure, including cardiotoxicity and dilated cardiomyopathy; however, the precise mechanism that coordinates its induction with accessibility to its transcriptional promoter sites remains unresolved, especially in the setting of mature terminally differentiated (nonreplicative) cardiomyocytes.
Machine learning integrative approaches to advance computational immunology
Authors: Fabiola Curion, Fabian J. Theis
Journal: Genome Medicine (2024)

Abstract The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components—transcriptome, proteome, chromatin, epigenetic modifications and metabolites—within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple “omics” data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records
Authors: Jessica Perry, Jennifer A. Brody, Christine Fong, Jacob E. Sunshine, Vikas N. O’Reilly-Shah, Michael R. Sayre, Thomas D. Rea, Noah Simon, Ali Shojaie, Nona Sotoodehnia, Neal A. Chatterjee
Journal: Circulation (2024)

BACKGROUND: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk. METHODS: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system. RESULTS: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80–0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified. CONCLUSIONS: In a population-based case–control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity.
DOPA-decarboxylase is elevated in CSF, but not plasma, in prodromal and de novo Parkinson’s disease
Authors: Ellen Appleton, Shervin Khosousi, Michael Ta, Michael Nalls, Andrew B. Singleton, Andrea Sturchio, Ioanna Markaki, Wojciech Paslawski, Hirotaka Iwaki, Per Svenningsson
Journal: Translational Neurodegeneration (2024)
Genome-wide enhancer RNA profiling adds molecular links between genetic variation and human cancers
Authors: Yi-Min Cai, Ze-Qun Lu, Bin Li, Jin-Yu Huang, Ming Zhang, Can Chen, Lin-Yun Fan, Qian-Ying Ma, Chun-Yi He, Shuo-Ni Chen, Yuan Jiang, Yan-Min Li, Cai-Bo Ning, Fu-Wei Zhang, Wen-Zhuo Wang, Yi-Zhuo Liu, Heng Zhang, Meng Jin, Xiao-Yang Wang, Jin-Xin Han, Zhen Xiong, Ming Cai, Chao-Qun Huang, Xiao-Jun Yang, Xu Zhu, Ying Zhu, Xiao-Ping Miao, Shao-Kai Zhang, Yong-Chang Wei, Jian-Bo Tian
Journal: Military Medical Research (2024)

Dysregulation of enhancer transcription occurs in multiple cancers. Enhancer RNAs (eRNAs) are transcribed products from enhancers that play critical roles in transcriptional control. Characterizing the genetic basis of eRNA expression may elucidate the molecular mechanisms underlying cancers. Initially, a comprehensive analysis of eRNA quantitative trait loci (eRNAQTLs) was performed in The Cancer Genome Atlas (TCGA), and functional features were characterized using multi-omics data. To establish the first eRNAQTL profiles for colorectal cancer (CRC) in China, epigenomic data were used to define active enhancers, which were subsequently integrated with transcription and genotyping data from 154 paired CRC samples. Finally, large-scale case-control studies (34,585 cases and 69,544 controls) were conducted along with multipronged experiments to investigate the potential mechanisms by which candidate eRNAQTLs affect CRC risk. A total of 300,112 eRNAQTLs were identified across 30 different cancer types, which exert their influence on eRNA transcription by modulating chromatin status, binding affinity to transcription factors and RNA-binding proteins. These eRNAQTLs were found to be significantly enriched in cancer risk loci, explaining a substantial proportion of cancer heritability. Additionally, tumor-specific eRNAQTLs exhibited high responsiveness to the development of cancer. Moreover, the target genes of these eRNAs were associated with dysregulated signaling pathways and immune cell infiltration in cancer, highlighting their potential as therapeutic targets. Furthermore, multiple ethnic population studies have confirmed that an eRNAQTL rs3094296-T variant decreases the risk of CRC in populations from China (OR = 0.91, 95%CI 0.88-0.95, P = 2.92 × 10-7) and Europe (OR = 0.92, 95%CI 0.88-0.95, P = 4.61 × 10-6). Mechanistically, rs3094296 had an allele-specific effect on the transcription of the eRNA ENSR00000155786, which functioned as a transcriptional activator promoting the expression of its target gene SENP7. These two genes synergistically suppressed tumor cell proliferation. Our curated list of variants, genes, and drugs has been made available in CancereRNAQTL ( http://canernaqtl.whu.edu.cn/#/ ) to serve as an informative resource for advancing this field. Our findings underscore the significance of eRNAQTLs in transcriptional regulation and disease heritability, pinpointing the potential of eRNA-based therapeutic strategies in cancers.
Association of Occupational Dysfunction and Hospital Admissions With Different Polygenic Profiles in Bipolar Disorder
Authors: Lina Jonsson, Elin Hörbeck, Amedeo Primerano, Jie Song, Robert Karlsson, Erik Smedler, Katherine Gordon-Smith, Lisa Jones, Nicholas Craddock, Ian Jones, Patrick F. Sullivan, Erik Pålsson, Arianna Di Florio, Timea Sparding, Mikael Landén
Journal: American Journal of Psychiatry (2024)

Many but not all persons with bipolar disorder require hospital care because of severe mood episodes. Likewise, some but not all patients experience long-term occupational dysfunction that extends beyond acute mood episodes. It is not known whether these dissimilar outcomes of bipolar disorder are driven by different polygenic profiles. Here, polygenic scores (PGSs) for major psychiatric disorders and educational attainment were assessed for associations with occupational functioning and psychiatric hospital admissions in bipolar disorder.
Cortical and Subcortical Brain Alterations in Specific Phobia and Its Animal and Blood-Injection-Injury Subtypes: A Mega-Analysis From the ENIGMA Anxiety Working Group
Authors: Kevin Hilbert, Ole Jonas Boeken, Till Langhammer, Nynke A. Groenewold, Janna Marie Bas-Hoogendam, Moji Aghajani, André Zugman, Fredrik Åhs, Volker Arolt, Katja Beesdo-Baum, Johannes Björkstrand, Jennifer U. Blackford, Laura Blanco-Hinojo, Joscha Böhnlein, Robin Bülow, Marta Cano, Narcis Cardoner, Xavier Caseras, Udo Dannlowski, Katharina Domschke, Lydia Fehm, Brandee Feola, Mats Fredrikson, Liesbet Goossens, Hans J. Grabe, Dominik Grotegerd, Raquel E. Gur, Alfons O. Hamm, Anita Harrewijn, Ingmar Heinig, Martin J. Herrmann, David Hofmann, Andrea P. Jackowski, Andreas Jansen, Antonia N. Kaczkurkin, Merel Kindt, Ellen N. Kingsley, Tilo Kircher, Anna L. Klahn, Katja Koelkebeck, Axel Krug, Harald Kugel, Bart Larsen, Elisabeth J. Leehr, Lieselotte Leonhardt, Martin Lotze, Jürgen Margraf, Jarosław Michałowski, Markus Muehlhan, Igor Nenadić, Pedro M. Pan, Paul Pauli, Wenceslao Peñate, Andre Pittig, Jens Plag, Jesus Pujol, Jan Richter, Francisco L. Rivero, Giovanni A. Salum, Theodore D. Satterthwaite, Axel Schäfer, Judith Schäfer, Anne Schienle, Silvia Schneider, Elisabeth Schrammen, Koen Schruers, Stefan M. Schulz, Esther Seidl, Rudolf M. Stark, Frederike Stein, Benjamin Straube, Thomas Straube, Andreas Ströhle, Boris Suchan, Sophia I. Thomopoulos, Carlos Ventura-Bort, Renee Visser, Henry Völzke, Albert Wabnegger, André Wannemüller, Julia Wendt, Julian Wiemer, Hans-Ulrich Wittchen, Katharina Wittfeld, Barry Wright, Yunbo Yang, Anna Zilverstand, Peter Zwanzger, Dick J. Veltman, Anderson M. Winkler, Daniel S. Pine, Neda Jahanshad, Paul M. Thompson, Dan J. Stein, Nic J.A. Van der Wee, Ulrike Lueken
Journal: American Journal of Psychiatry (2024)

Specific phobia is a common anxiety disorder, but the literature on associated brain structure alterations exhibits substantial gaps. The ENIGMA Anxiety Working Group examined brain structure differences between individuals with specific phobias and healthy control subjects as well as between the animal and blood-injection-injury (BII) subtypes of specific phobia. Additionally, the authors investigated associations of brain structure with symptom severity and age (youths vs. adults).
Single Cell RNA Sequencing of Human Eosinophils in Allergic Inflammation in the Esophagus
Authors: Netali Ben-Baruch Morgenstern, Mark Rochman, Michael Kotliar, Julia L M Dunn, Lydia Mack, John Besse, Mia A Natale, Andrea M Klingler, Jennifer M Felton, Julie M Caldwell, Artem Barski, Marc E Rothenberg
Journal: Journal of Allergy and Clinical Immunology (2024)
Integration of new technologies in the multidisciplinary approach to primary liver tumours: The next-generation tumour board
Authors: Jean-Charles Nault, Julien Calderaro, Maxime Ronot
Journal: Journal of Hepatology (2024)

Primary liver tumours, including benign liver tumours, hepatocellular carcinoma and cholangiocarcinoma, present a multifaceted challenge, necessitating a collaborative approach, as evidenced by the role of the multidisciplinary tumour board (MDTB). The approach to managing primary liver tumours gathers specialized teams, including surgeons, radiologists, oncologists, pathologists, hepatologists, and radiation oncologists, to propose individualized treatment plans. The evolving landscape of primary liver cancer treatment introduces complexities, particularly with the expanding array of systemic and locoregional therapies, alongside the potential integration of molecular biology and artificial intelligence (AI) into MDTB in the future.Precision medicine demands collaboration across disciplines, challenging traditional frameworks. The next decade anticipates the convergence of AI, molecular biology, pathology, and advanced imaging, requiring adaptability in MDTB structure to incorporate these cutting-edge technologies. Navigating this evolution also requires a focus on enhancing basic, translational, and clinical research, as well as boosting clinical trials through an upgraded use of MDTBs as hubs for scientific collaboration and raising literacy about AI and new technologies. In this review, we will delineate the current unmet needs in the clinical management of primary liver cancers, discuss our perspective on the future role of MDTBs in primary liver cancers ("next generation" MDTBs), and unravel the potential power and limitations of novel technologies that may shape the multidisciplinary care landscape for primary liver cancers in the coming decade.