bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multiband EEG signature decoded using machine learning for predicting rTMS treatment response in major depression
Authors: Arteaga Alexander , Tong Xiaoyu , Zhao Kanhao , Carlisle Nancy B. , Oathes Desmond J. , Fonzo Gregory A. , Keller Corey J. , Zhang Yu 
Published: September 2024

AbstractMajor depressive disorder (MDD) is a global health challenge with high prevalence. Further, many diagnosed with MDD are treatment resistant to traditional antidepressants. Repetitive transcranial magnetic stimulation (rTMS) offers promise as an alternative solution, but identifying objective biomarkers for predicting treatment response remains underexplored. Electroencephalographic (EEG) recordings are a cost-effective neuroimaging approach, but traditional EEG analysis methods often do not consider patient-specific variations and fail to capture complex neuronal dynamics. To address this, we propose a data-driven approach combining iterated masking empirical mode decomposition (itEMD) and sparse Bayesian learning (SBL). Our results demonstrated significant prediction of rTMS outcomes using this approach (Protocol 1: r=0.40, p<0.01; Protocol 2: r=0.26, p<0.05). From the decomposition, we obtained three key oscillations: IMF-Alpha, IMF-Beta, and the remaining residue. We also identified key spatial patterns associated with treatment outcomes for two rTMS protocols: for Protocol 1 (10Hz left DLPFC), important areas include the left frontal and parietal regions, while for Protocol 2 (1Hz right DLPFC), the left and frontal, left parietal regions are crucial. Additionally, our exploratory analysis found few significant correlations between oscillation specific predictive features and personality measures. This study highlights the potential of machine learning-driven EEG analysis for personalized MDD treatment prediction, offering a pathway for improved patient outcomes.
Building a literature knowledge base towards transparent biomedical AI
Authors: Huang Yuanhao , Han Zhaowei , Luo Xin , Luo Xuteng , Gao Yijia , Zhao Meiqi , Tang Feitong , Wang Yiqun , Chen Jiyu , Li Chengfan , Lu Xinyu , Jiao Tiancheng , Qiu Jiahao , Deng Feiyang , Guan Lingxiao , Shang Haohong , Feng Fan , Vu Thi Hong Ha , Bate Thomas , Xue Dongxiang , Cartailler Jean-Philippe , Stitzel Michael , Chen Shuibing , Brissova Marcela , Parker Stephen , Liu Jie 
Published: September 2024

AbstractAs artificial intelligence (AI) continues to advance and scale up in biomedical research, concerns about AI’s trustworthiness and transparency have grown. There is a critical need to systematically bring accurate and relevant biomedical knowledge into AI applications for transparency and provenance. Knowledge graphs have emerged as a powerful tool that integrates heterogeneous knowledge by explicitly describing biomedical knowledge as entities and relationships between entities. However, PubMed, the largest and most comprehensive repository of biomedical knowledge, exists primarily as unstructured text and is under utilized for advanced machine learning tasks. To address the challenge, we developed LiteralGraph, a computational framework to extract biomedical terms and relationships from PubMed literature into a unified knowledge graph. Using this framework, we established the Genomic Literature Knowledge Base (GLKB), which consolidates 14,634,427 biomedical relationships between 3,276,336 biomedical terms from over 33 million PubMed abstracts and nine well-established biomedical repositories. The database is coupled with RESTful APIs and a user-friendly web interface that makes it accessible to researchers for various usages. We demonstrated the broad utility of GLKB towards transparent AI in three distinct application scenarios. In the LLM grounding scenario, we developed a Retrieval Augmented Generation (RAG) agent to reduce LLM hallucination in biomedical question answering. In the hypothesis generation scenario, we elucidated the potential functions of RFX6 in type 2 diabetes (T2D) using the vast evidence from PubMed articles. In the machine learning scenario, we utilized GLKB to provide semantic knowledge in predictive tasks and scientific fact-checking.
ALMS1KO rat: a new model of metabolic syndrome with spontaneous hypertension
Authors: Jaykumar Ankita B. , Monu Sumit R. , Mendez Mariela , Rhaleb Nour-Eddine , Ortiz Pablo A. 
Published: September 2024

AbstractALMS1 is a protein initially associated with Alström syndrome. This is a rare human disorder characterized by metabolic dysfunction, hypertension, obesity and hyperinsulinemia. In addition,ALMS1gene was linked to hypertension status in a multipoint linkage population analysis. However, the mechanisms by which ALMS1 contributes to the development of obesity, insulin resistance and other metabolic disturbances are unknown. To study the role of ALMS1 in blood pressure regulation and renal function we previously generated an ALMS1 knockout rat model, where we found these rats are hypertensive. In this study, we further characterized theALMS1knockout rat, and found that they exhibit most characteristics of metabolic syndrome including hypertension and higher body weight by 10-12 weeks of age. In contrast, obesity, hyperinsulinemia and vascular dysfunction manifested at around 14-16 weeks of age. Interestingly,ALMS1KO rats developed hyperleptinemia prior to the development of obesity rapidly after weaning by 7 weeks of age, suggesting an early role for ALMS1 in the hormonal control of leptin. We also found that femaleALMS1KO rats develop severe metabolic syndrome with hypertension similar to their male counterparts, lacking any protection often associated with better cardiovascular outcomes. Therefore, ALMS1 is an essential gene for sex-and age-dependent metabolic function. TheALMS1knockout rat provides an invaluable pre-clinical animal model that recapitulates most symptoms present in patients and allows the study of new drugs and mechanisms that cause metabolic syndrome.
A Novel Machine Learning Based Framework for Developing Composite Digital Biomarkers of Disease Progression
Authors: Zhai Song , Liaw Andy , Shen Judong , Xu Yuting , Svetnik Vladimir , FitzGerald James J. , Antoniades Chrystalina A. , Holder Dan , Dockendorf Marissa F. , Ren Jie , Baumgartner Richard 
Published: September 2024

AbstractBackgroundCurrent methods of measuring disease progression of neurodegenerative disorders, including Parkinson’s disease (PD), largely rely on composite clinical rating scales, which are prone to subjective biases and lack the sensitivity to detect progression signals in a timely manner. Digital health technology (DHT)-derived measures offer potential solutions to provide objective, precise, and sensitive measures that address these limitations. However, the complexity of DHT datasets and the potential to derive numerous digital features that were not previously possible to measure pose challenges, including in selection of the most important digital features and construction of composite digital biomarkers.MethodsWe present a comprehensive machine learning based framework to construct composite digital biomarkers for progression tracking. This framework consists of a marginal (univariate) digital feature screening, a univariate association test, digital feature selection, and subsequent construction of composite (multivariate) digital disease progression biomarkers using Penalized Generalized Estimating Equations (PGEE). As an illustrative example, we applied this framework to data collected from a PD longitudinal observational study. The data consisted of OpalTMsensor-based movement measurements and MDS-UPDRS Part III scores collected at 3-month intervals for 2 years in 30 PD and 10 healthy control participants.ResultsIn our illustrative example, 77 out of 235 digital features from the study passed univariate feature screening, with 11 features selected by PGEE to include in construction of the composite digital measure. Compared to MDS-UPDRS Part III, the composite digital measure exhibited a smoother and more significant increasing trend over time in PD groups with less variability, indicating improved ability for tracking disease progression. This digital composite measure also demonstrated the ability to classify between de novo PD and healthy control groups.ConclusionMeasures from DHTs show promise in tracking neurodegenerative disease progression with increased sensitivity and reduced variability as compared to traditional clinical scores. Herein, we present a novel framework and methodology to construct composite digital measure of disease progression from high-dimensional DHT datasets, which may have utility in accelerating the development and application of composite digital biomarkers in drug development.
A Novel Adhesin ofB. pertussisis Key to Colonisation of Epithelial Cells
Authors: Gollan Michael , Gestal Monica C , Parrish Katelyn M , Harvill Eric T , Preston Andrew , MacArthur Iain 
Published: September 2024

AbstractDespite effective vaccines to protect against Whooping cough, or pertussis, the disease is resurgent in many countries. A switch from acellular to whole-cell vaccines has resulted in waning protective immunity, likely contributing to increases in infection prevalence, underlining the need to better understandB. pertussisvirulence. As a respiratory pathogen,B. pertussiscolonises the upper respiratory tract utilising an array of adhesins, four of which (FHA, pertactin, Fim2/3) are included in the acellular vaccine. In this study, we identified two Bvg regulated genes that are upregulated during virulence conditions and thus potentially involved in pathogenesis.bp1251andbp1252encode orphan toxin B subunits, with homology to AB toxin B subunits. Mutation ofbp1251andbp1252reduced thein vitroadherence ofB. pertussisto A549 and BEAS-2B alveolar and bronchial epithelial-like cells. In a murine model of infection,bp1251andbp1252 mutant strains were recovered from the nasal cavity and lungs at lower levels than WT.In vitrono effect of mutation ofbp1251orbp1252on cell invasion or toxicity was found suggesting that these proteins do not form part of a toxin. Given their homology to B subunits of AB toxins, and their role in colonisation, we propose that BP1251 and BP1252 are novel adhesins. Our data suggests that these proteins play a significant role inBordetellainfection and have the potential to further the understanding ofB. pertussispathogenesis.
Genetic and microbial analysis of invasiveness forEscherichia colistrains associated with inflammatory bowel disease
Authors: Kim Jungyeon , Zhang Jing , Kinch Lisa , Shen Jinhui , Field Sydney , Klapproth Jan-Michael , Forsberg Kevin J. , Harris-Tryon Tamia , Orth Kim , Cong Qian , Ni Josephine 
Published: September 2024

AbstractBackground & AimsThe adherent-invasiveEscherichia coli(AIEC) pathotype is implicated in inflammatory bowel disease (IBD) pathogenesis. AIEC strains are currently defined by phenotypic measurement of their pathogenicity, including invasion of epithelial cells. This broad definition, combined with the genetic diversity of AIEC across IBD patients, has complicated the identification of virulence determinants. We sought to quantify the invasion phenotype of clinical isolates from IBD patients and identify the genetic basis for their invasion into epithelial cells.MethodsA pangenome with core and accessory genes (genotype) was assembled using whole genome sequencing of 168E. colisamples isolated from 13 IBD patients. A modified assay for invasion of epithelial cells (phenotype) was established with consideration of antibiotic resistance phenotypes. Isolate genotype was correlated to invasiveness phenotype to identify genetic factors that co-segregate with invasion.ResultsPangenome-wide comparisons ofE. coliclinical isolates identified accessory genes that can co-segregate with invasion phenotype. These correlations found the acquisition of antibiotic resistance genes in clinical isolates compromised the traditional gentamicin protection assays used to quantify invasion. Therefore, an alternate assay, based on amikacin resistance, identified genes co-segregating with invasion. These genes encode an arylsulfatase, a glycoside hydrolase, and genetic islands carrying propanediol utilization and sulfoquinovose metabolism pathways.ConclusionsThis study highlights the importance of incorporating antibiotic resistance screening for invasion assays used in AIEC identification. Accurately screened invasion phenotypes identified accessory genome elements amongE. coliIBD isolates that correlate with their ability to invade epithelial cells. These results help explain why single genetic markers for the AIEC phylotype are challenging to identify.SynopsisWe established a pangenome ofEscherichia coliisolates from inflammatory bowel disease patients using whole genome sequencing. The genotypes were correlated with a newly developed measurement of invasion phenotype to identify co-segregating genes.
TNFSF13 insufficiency disrupts human colonic epithelial cell-mediated B cell differentiation
Authors: Ma Xianghui , Dawany Noor , Kondo Ayano , Maurer Kelly , Karakasheva Tatiana , Shraim Rawan , Williams Patrick A. , Parham Louis R. , Simon Lauren A. , Danan Charles H. , Conrad Maire A. , Piccoli David A. , Devoto Marcella , Sullivan Kathleen E. , Kaestner Klaus H. , Kelsen Judith R. , Hamilton Kathryn E. 
Published: September 2024

ABSTRACTCytokines mediating epithelial and immune cell interactions modulate mucosal healing-a process that goes awry with chronic inflammation as in inflammatory bowel disease. TNFSF13 is a cytokine important for B cell maturation and function, but roles for epithelial TNFSF13 and putative contribution to inflammatory bowel disease are poorly understood. We evaluated functional consequences of a novel monoallelicTNFSF13variant using biopsies, tissue-derived colonoids and induced pluripotent stem cell (iPSC)-derived colon organoids.TNFSF13variant colonoids exhibited a >50% reduction in secreted TNFSF13, increased epithelial proliferation, and reduced apoptosis, which was confirmed in iPSC-derived colon organoids. Single cell RNA-sequencing, flow cytometry, and co-immunoprecipitation identified FAS as the predominant colonic epithelial receptor for TNFSF13. Imaging mass cytometry revealed an increase in epithelial-associated B cells inTNFSF13variant colon tissue sections. Finally,TNFSF13variant colonoids co-cultured with memory B cells demonstrated a reduction in the production of IgA+ plasma cells compared to control colonoid co-cultures. Our findings support a role for epithelial TNFSF13 as a regulator of colonic epithelial growth and epithelial crosstalk with B cells.SUMMARYEpithelial TNFSF13 regulates colonic epithelial growth and epithelial-B cell interactions.Graphical Abstract
Environmental sphagnum-associated fungi target thiol homeostasis to compete withMycobacterium tuberculosis
Authors: Malhotra Neha , Oh Sangmi , Finin Peter , Medrano Jessica , Andrews Jenna , Goodwin Michael , Markowitz Tovah E. , Lack Justin , Boshoff Helena I. M. , Barry Clifton Earl 
Published: September 2024

AbstractMycobacterial species in nature are found in abundance in sphagnum peat bogs where they compete for nutrients with a variety of microorganisms including fungi. We screened a collection of fungi isolated from sphagnum bogs by co-culture withMycobacterium tuberculosis(Mtb) to look for inducible expression of antitubercular agents and identified five fungi that produced cidal antitubercular agents upon exposure to liveMtb. Whole genome sequencing of these fungi followed by fungal RNAseq afterMtbexposure allowed us to identify biosynthetic gene clusters induced by co-culture. Three of these fungi induced expression of patulin, one induced citrinin expression and one induced the production of nidulalin A. The biosynthetic gene clusters for patulin and citrinin have been previously described but the genes involved in nidulalin A production have not been described before. All three of these potent electrophiles react with thiols and treatment ofMtbcells with these agents followed byMtbRNAseq showed that these natural products all induce profound thiol stress suggesting a rapid depletion of mycothiol. The induction of thiol-reactive mycotoxins through three different systems in response to exposure toMtbsuggests that fungi have identified this as a highly vulnerable target in a similar microenvironment to that of the caseous human lesion.
The Mitochondrial Ubiquitin Ligase MARCHF5 Cooperates with MCL1 to Inhibit Apoptosis in KSHV-Transformed Primary Effusion Lymphoma Cell Lines
Authors: Viswanathan Prasanth , Bersonda Justine R. , Gill Jackson , Navarro Alyssandra , Farrar Allison C. , Dunham Daniel , Boehme Karl W. , Manzano Mark 
Published: September 2024

ABSTRACTKaposi’s sarcoma-associated herpesvirus (KSHV) causes several malignancies in people with HIV including Kaposi’s sarcoma and primary effusion lymphoma (PEL). We have previously shown that PEL cell lines require myeloid cell leukemia-1 (MCL1) to inhibit apoptosis. MCL1 is an oncogene that is amplified in cancers and causes resistance to chemotherapy regimens. MCL1 is thus an attractive target for drug development. The emerging clinical relevance and therapeutic potential of MCL1 motivated us to study the roles of this oncogene in PEL in depth. Using a systems biology approach, we uncovered an unexpected genetic interaction between MCL1 and MARCHF5 indicating that they function in the same pathway. MARCHF5 is an E3 ubiquitin ligase most known for regulating mitochondrial homeostasis and antiviral signaling, but not apoptosis. We thus investigated how MCL1 and MARCHF5 cooperate to promote PEL cell survival. CRISPR knockout (KO) of MARCHF5 in PEL cell lines resulted in a significant increase in apoptosis despite the presence of MCL1. The anti-apoptotic function of MARCHF5 was dependent on its E3 ligase and dimerization activities. Loss of MARCHF5 or inhibition of the 26S proteasome furthermore stabilized the MCL1 antagonist NOXA without affecting levels of MCL1. Interestingly, NOXA KO provides a fitness advantage to PEL cells suggesting that NOXA is the pro-apoptotic signal that necessitates the anti-apoptotic activities of MCL1 and MARCHF5. Finally, endogenous reciprocal co-immunoprecipitation experiments show that MARCHF5 and NOXA are found in the same protein complex. Our findings thus provide the mechanistic link that underlies the genetic interaction between MCL1 and MARCHF5. We propose that MARCHF5 induces the degradation of the MCL1 antagonist NOXA thus reinforcing the pro-survival role of MCL1 in these tumor cells. This newly appreciated interaction of the MCL1 and MARCHF5 oncogenes may be useful to improve the design of combination therapies for KSHV malignancies.
Mycobacterium abscessuspersistence in the face ofPseudomonas aeruginosaantagonism
Authors: Gupta Rashmi , Schuster Martin , Rohde Kyle H. 
Published: September 2024

ABSTRACTChronic bacterial infections are responsible for significant mortality and morbidity in cystic fibrosis (CF) patients.Pseudomonas aeruginosa(Pa), the dominant CF pathogen, andMycobacterium abscessus(Mab) both cause persistent pulmonary infections that are difficult to treat. Co-infection by both bacterial pathogens leads to severe disease and poor clinical outcomes. To explore understudied interactions between these two CF pathogens, we employed culture- and molecular-based approaches. In a planktonic co-culture model, growth ofPacontinued unimpeded, and it exerted a bacteriostatic effect onMab. Strikingly, exposure ofMabgrown in monoculture to cell-free spent supernatant ofParesulted in a dramatic, dose-dependent bactericidal effect. Initial characterization indicated that this potentPa-derived anti-Mabcidal activity was mediated by a heat-sensitive, protease-insensitive soluble factor of >3kDa size. Further analysis demonstrated that expression of this mycobactericidal factor requires LasR, a central regulator ofPaquorum sensing (QS). In contrast, ΔLasRPawas still able to exert a bacteriostatic effect onMabduring co-culture, pointing to additional LasR-independent factors able to antagonizeMabgrowth. However, the ability ofMabto adapt during co-culture to counter the cidal effects of a LasR regulated factor suggested complex interspecies dynamics. Dual RNAseq analysis ofMab-Paco-cultures revealed significant transcriptional remodeling ofMab, with differential expression of 68% ofMabgenes compared to minimal transcriptional changes inPa. Transcriptome analysis reflected slowedMabgrowth and remodeling of carbon and energy metabolism akin to a non-replicating persister-like phase. A tailoredMabresponse toPawas evident by enhanced transcript levels of genes predicted to interfere with alkylquinolone QS signals or provide protection against respiratory toxins and hydrogen cyanide. This is the first study to provide a transcriptome-level view of genetic responses governing the interplay between two important CF pathogens. This will provide insights into interspecies interaction mechanisms which can be targeted to disrupt their communities in a CF lung to improve patient clinical performance. Moreover, identification of a novel antimicrobial natural product with potent cidal activity againstMabwill provide a chemical biology tool for identifying new drug targets inMab.
Rfam 15: RNA families database in 2025
Authors: Ontiveros Nancy , Cooke Emma , Nawrocki Eric P. , Triebel Sandra , Marz Manja , Rivas Elena , Griffiths-Jones Sam , Petrov Anton I. , Bateman Alex , Sweeney Blake 
Published: September 2024

AbstractThe Rfam database, a widely-used repository of non-coding RNA (ncRNA) families, has undergone significant updates in release 15.0. This paper introduces major improvements, including the expansion of Rfamseq to 26, 106 genomes, a 76% increase, incorporating the latest UniProt reference proteomes and additional viral genomes. Sixty-five RNA families were enhanced using experimentally determined 3D structures, improving the accuracy of consensus secondary structures and annotations. R-scape covariation analysis was used to refine structural predictions in 26 families. Gene Ontology and Sequence Ontology annotations were comprehensively updated, increasing GO term coverage to 75% of families. The release adds 14 new Hepatitis C Virus RNA families and completes microRNA family synchronisation with miRBase, resulting in 1, 603 microRNA families. New data types, including FULL alignments, have been implemented. Integration with APICURON for improved curator attribution and multiple website enhancements further improve user experience. These updates significantly expand Rfam’s coverage and improve annotation quality, reinforcing its critical role in RNA research, genome annotation, and the development of machine learning models. Rfam is freely available athttps://rfam.org.Graphical AbstractRfam has undergone a major update with the release of 15.0. We have increased the number of genomes in our sequence database Rfamseq by 75%, completed the synchronisation with miRBase and improved 65 families using 3D structures.