bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inhibition of acid ceramidase as a therapeutic strategy for Niemann-Pick C disease
Authors: Haynes Isabelle , Ward Simon E. , Jones D. Heulyn , Lloyd-Evans Emyr , Waller-Evans Helen 
Published: November 2024

AbstractLysosomal storage disorders (LSDs) are a group of individually rare diseases that as a group constitute the most common forms of childhood neurodegeneration. These life limiting and life shortening diseases have the largest economic burden per patient of any rare disease with the majority being orphan diseases with no disease modifying therapy. Whilst there is significant development in the area of gene therapies for LSDs, these can only be used to treat individual diseases, are expensive, and in recent times many companies have abandoned their gene therapy pipelines. Small molecules on the other hand can be developed to target common mechanisms of pathogenesis or common central disease phenotypes providing therapies that can treat multiple diseases. In this study we report acid ceramidase as a novel therapeutic target for treating Niemann-Pick type C (NPC) disease. Using siRNA, and the literature tool compound carmofur, we observed a reduction in lysosomal area and reduced accumulation of the lipids cholesterol and LBPA in an NPC cellular model. These lipids are known to accumulate as secondary storage molecules across many lysosomal diseases, particularly primary sphingolipid storage disorders where lyso-sphingolipids are known to accumulate via the action of acid ceramidase. As acid ceramidase has also been shown to be a therapeutic target for Krabbe disease, our findings indicate that this is a novel target for the potential treatment of multiple LSDs with primary or secondary storage of sphingolipids.
Semaglutide-induced weight loss improves mitochondrial energy efficiency in skeletal muscle
Authors: Choi Ran Hee , Karasawa Takuya , Meza Cesar A. , Maschek J. Alan , Manuel Allison , Nikolova Linda S. , Fisher-Wellmen Kelsey H. , Cox James E. , Chaix Amandine , Funai Katsuhiko 
Published: November 2024

AbstractObjectiveGlucagon-like peptide 1 receptor agonists (e.g. semaglutide) potently induce weight loss and thereby reducing obesity-related complications. However, weight regain occurs when treatment is discontinued. An increase in skeletal muscle oxidative phosphorylation (OXPHOS) efficiency upon diet-mediated weight loss has been described, which may contribute to reduced systemic energy expenditure and weight regain. We set out to determine the unknown effect of semaglutide on muscle OXPHOS efficiency.MethodsC57BL/6J mice were fed a high-fat diet for 12 weeks before receiving semaglutide or vehicle for 1 or 3 weeks. The rate of ATP production and O2consumption were measured by a high-resolution respirometry and fluorometry to determine OXPHOS efficiency in skeletal muscle at these 2 timepoints.ResultsSemaglutide treatment led to significant reductions in fat and lean mass. Semaglutide improved skeletal muscle OXPHOS efficiency, measured as ATP produced per O2consumed (P/O) in permeabilized muscle fibers. Mitochondrial proteomic analysis revealed changes restricted to two proteins linked to complex III assembly (Lyrm7 and Ttc1, p <0.05 without multiple corrections) without substantial changes in the abundance of OXPHOS subunits.ConclusionsThese data indicate that weight loss with semaglutide treatment increases skeletal muscle mitochondrial efficiency. Future studies could test whether it contributes to weight regain.
Metabolic reprogramming and synergistic cytotoxicity of genistein and chemotherapy in human breast cancer cells
Authors: Tobón-Cornejo Sandra , Vargas-Castillo Ariana , Juarez Mandy , Acevedo-Carabantes Joshua Ayork , Noriega Lilia G. , Granados-Portillo Omar , Chávez-Blanco Alma , Morales-Bárcenas Rocío , Torres Nimbe , Tovar Armando R. , Schcolnik-Cabrera Alejandro 
Published: November 2024

ABSTRACTBreast cancer (BCa) is a heterogeneous disease, initially responsive to hormone therapy but often developing resistance to both hormonal and chemotherapy treatments. Novel therapeutic strategies are needed for drug-resistant BCa. Genistein, a phytoestrogen structurally similar to estrogen, competes with estrogen for receptor binding and exhibits anti-cancer effects. In this study, we investigated the cellular and metabolic impacts of genistein, alone or in combination with chemotherapy, in two human BCa cell lines—one estrogen receptor-positive (ER+) and one estrogen receptor-negative (ER−). We observed a strong synergistic effect on cell viability at low concentrations of genistein and chemotherapy, resulting in reduced clonogenic capacity and impaired cell migration. Genistein alone modulated cellular energy metabolism, notably reducing ATP production in MCF7 (ER+) cells. This metabolic shift was linked to a decreased dependence on fatty acids for energy, coupled with a decrease in the rate-limiting mitochondrial translocase CPT1 required for fatty acid oxidation, alongside with an increase in intracellular fatty acid levels. While the most significant changes occurred in ER+ cells, ER− cells also showed responses to genistein treatment. Collectively, our findings suggest that low dose genistein, in combination with conventional chemotherapy, induces synergistic anti-cancer effects, promoting cellular senescence. This effect may be partly mediated by a reduced reliance on fatty acid metabolism in BCa cells.
Structure Characterization of Bacterial Microcompartment Shells via X-ray Scattering and Coordinate Modeling: Evidence for adventitious capture of cytoplasmic proteins
Authors: Zuo Xiaobing , Jussupow Alexander , Ponomarenko Nina S. , Tefft Nicholas M. , Yadav Neetu Singh , Range Kyleigh L. , Ralston Corie Y. , TerAvest Michaela A. , Sutter Markus , Kerfeld Cheryl A. , Vermaas Josh V. , Feig Michael , Tiede David M. 
Published: November 2024

ABSTRACTBacterial microcompartments (BMCs) are self-assembling, protein shell structures that are widely investigated across a broad range of biological and abiotic chemistry applications. A central challenge in BMC research is the targeted capture of enzymes during shell assembly. While crystallography and cryo-EM techniques have been successful in determining BMC shell structures, there has been only limited success in visualizing the location of BMC-captured enzyme cargo. Here, we demonstrate the opportunity to use small angle X-ray scattering (SAXS) and pair density distribution function (PDDF) measurements combined with quantitative comparison to coordinate structure models as an approach to characterize BMC shell structures in solution conditions directly relevant to biochemical function. Using this approach, we analyzed BMC shells fromHaliangium ochraceumthat were isolated following expression inE. coli. The analysis allowed BMC shell structures and the extent of encapsulated enzyme cargo to be identified. Notably, the results demonstrate that HO-BMC shells adventitiously capture significant amounts of cytoplasmic cargo during assembly inE. coli. Our findings highlight the utility of SAXS/PDDF analysis for evaluating BMC architectures and enzyme encapsulation, offering valuable insights for designing BMC shells as platforms for biological and abiotic catalyst capture within confined environments.
Atomic elementary flux modes explain the steady state flow of metabolites in large-scale flux networks
Authors: Chitpin Justin G. , Perkins Theodore J. 
Published: November 2024

AbstractSteady state fluxes are a measure of cellular activity under metabolic homoeostasis, but understanding how individual substrates are metabolized remains a challenge in large-scale networks. Pathway-based approaches such as elementary flux mode (EFM) analysis are limited to small networks due to the combinatorial explosion of pathways and the ambiguity of decomposing fluxes onto EFMs. Here, we present an alternative approach to explain metabolic fluxes in terms of the steady state flow of their atomic constituents. We refer to these pathways as atomic elementary flux modes (AEFMs) and show that computations involving AEFMs are orders of magnitude faster than standard EFMs. Using our approach, we enumerate carbon and nitrogen AEFMs in five genome-scale metabolic models and compute the AEFM decomposition of fluxes estimated in a HepG2 liver cancer cell line. Our results systematically characterize carbon and nitrogen remodelling and, on the HepG2 network, predict glutamine metabolism through a recently discovered non-canonical TCA cycle.
SMAC: identifying DNA N6-methyladenine (6mA) at the single-molecule level using SMRT CCS data
Authors: Li Haicheng , Niu Junhua , Sheng Yalan , Liu Yifan , Gao Shan 
Published: November 2024

AbstractDNA modifications, such as N6-methyladenine (6mA), play important roles in various processes in eukaryotes. Single molecule, real-time (SMRT) sequencing enables the direct detection of DNA modifications without requiring special sample preparation. However, most SMRT-based studies of 6mA rely on ensemble-level consensus by combining multiple reads covering the same genomic position, which misses the single-molecule heterogeneity. While recent methods have aimed at single-molecule level detection of 6mA, limitations in sequencing platforms, resolution, accuracy, and usability restrict their application in comprehensive epigenetic studies. Here, we present SMAC (Single Molecule 6mA analysis of CCS reads), a novel framework for accurately detecting 6mA at the single-molecule level using SMRT CCS data from the Sequel II system. It is an automated method that streamlines the entire workflow by packaging both existing software and built-in script, with support for user-defined parameters to allow easy adaptation for various studies. This algorithm utilizes the statistical distribution characteristics of enzyme kinetic indicators to identify 6mA of each DNA molecule, rather than relying on a fixed cutoff, which significantly improves accuracy at the single-nucleotide and single-molecule level. SMAC is a powerful new tool that enables de novo detection of 6mA and empowers investigation of its functions in modulating physiological processes.
€ Harnessing Protein Language Model for Structure-Based Discovery of Highly Efficient and Robust PET Hydrolases
Authors: Wu Banghao , Zhong Bozitao , Zheng Lirong , Huang Runye , Jiang Shifeng , Li Mingchen , Tan Pan , Hong Liang 
Published: November 2024

AbstractPlastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, prompting extensive research into enzymatic biodegradation. Existing PET hydrolases are limited to a narrow sequence space and demonstrate insufficient performance for biodegradation. This study introduces a novel discovery pipeline that combines protein language models (PLMs) with a structural representation tree to identify enzymes based on structural similarity. Using the crystal structure ofIsPETase as a template, we employed PLMs and a representation tree to efficiently search and cluster target proteins. Screening of candidate proteins was further refined using PLM-based assessments of solubility and thermal stability. Biochemical experiments showed that 14 of 34 candidates exhibited PET degradation activity across a temperature range of 30–60 °C. Notably, we identified a PET hydrolase -KbPETase, which possesses a melting temperature 32 °C higher than that ofIsPETase and exhibits the highest PET degradation activity within 30–50 °C compared to other wild-type PETases.KbPETase also shows higher catalytic efficiency than FastPETase. X-ray crystallography and molecular dynamics simulations further revealed thatKbPETase has a conserved catalytic domain and enhanced intramolecular interactions. This work develops a novel deep learning approach to discover natural PETases with enhanced functions.
A Rapid, Cost-Effective Method for High-Yield DNA Purification from PCR Products and Agarose Gels
Authors: Fatima Susheel , Javed Maliha , Zahra Hijab , Hussain Irfan , Mian Afsar Ali 
Published: November 2024

AbstractNucleic acid purification is essential for molecular biology workflows, enabling successful downstream applications like cloning, sequencing, and PCR amplification. While commercial kits are widely used for DNA extraction from PCR products and agarose gels, their high costs can burden resource-limited laboratories. To provide a viable alternative, we developed an optimized, cost-effective in-house protocol for high-yield DNA purification. This study evaluates the performance of our protocol for plasmid DNA and PCR products, comparing it with commercial kits from Qiagen, Thermo Fisher, and WizBio in terms of cost, time, DNA concentration, and purity. Results from gel electrophoresis demonstrated that the in-house protocol significantly enhances PCR product clarity and reduces background smearing, yielding high-purity DNA compatible with sensitive applications. Restriction-digested plasmid samples showed successful ligation and transformation in E. coli (DH5α), with Sanger sequencing chromatogram further confirming the sequence integrity of the purified DNA. Our findings highlight the in-house protocol as a cost-effective, efficient, and reliable alternative to commercial kits, delivering high-quality DNA suitable for various molecular applications. This method offers an accessible and practical solution for laboratories seeking to optimize DNA purification under budget constraints.
Peripheral Immune pattern in a genetic cohort of p.A53T alpha-synuclein Parkinson’s disease
Authors: Koros Christos , Simitsi Athina-Maria , Papagiannakis Nikolaos , Antonelou Roubina , Bougea Anastasia , Papadimitriou Dimitra , Pachi Ioanna , Beratis Ion , Kontaxopoulou Dionysia , Fragkiadaki Stella , Sfikas Evangelos , Alefanti Ioanna , Chrysovitsanou Chrysa , Angelopoulou Efthalia , Bregianni Marianna , Lourentzos Konstantinos , Constantinides Vasilios C. , Velonakis Georgios , Prassopoulos Vasilios , Bonakis Anastasios , Potagas Constantin , Papageorgiou Sokratis G. , Stamelou Maria , Stefanis Leonidas 
Published: November 2024

AbstractIntroductionPrevious research has shown that inflammatory immune biomarkers including peripheral white blood cell subpopulations differ between Parkinson’s disease (PD) patients and healthy controls (HC), with idiopathic PD exhibiting higher neutrophil to lymphocyte ratio (NLR). The aim of our present report was to assess the peripheral immune profile in patients or asymptomatic carriers harboring the p.A53T alpha-synuclein (SNCA) mutation.MethodsData regarding 31 p.A53T SNCA PD patients, 9 asymptomatic mutation carriers and 194 HCs were obtained from the database of the Parkinson’s Progression Markers Initiative (PPMI). Focus was placed on peripheral immune blood cells subpopulations and clinical/imaging parameters during the initial study assessment.ResultsNLR, Absolute Neutrophil cell count and Neutrophil to total Leukocytes ratio were increased in the p.A53T SNCA PD group as compared to HCs [2,77 vs 2,18 (p<0.001), 4,32×10^3 cells/μL vs 3,67x 10^3 cells/μL (p=0.001), 65,67% vs 59,55% (p<0.001) respectively]. Differences in NLR were mainly driven by the male patient subgroup. The absolute Lymphocyte cell count showed a trend towards being decreased in p.A53T PD, and Lymphocyte to total leukocytes ratio was lower in p.A53T SNCA cohort as compared to HC [26,16% vs 30,02% (p=0.001)]. Monocyte to total Leukocytes ratio was lower in p.A53T PD 5,49% vs 6,74% (p=0.002). Finally, we observed a positive correlation between the absolute Lymphocyte count and the mean putamen DATSCAN signal. Asymptomatic carriers did not differ statistically from p.A53T SNCA PD or HC regarding leucocyte subpopulations counts.DiscussionOur current study provides evidence of a specific pattern of peripheral immune response in the p.A53T SNCA PD group which aligns well with literature data in idiopathic and other genetic PD forms. Furthermore, given former evidence that alpha-synuclein represents an immune target in PD, we can speculate a putative underlying inflammatory pathway in this archetypal form of genetic synucleinopathy.
Leveraging Deep Learning of Chest Radiograph Images to Identify Individuals at High Risk for Chronic Obstructive Pulmonary Disease
Authors: Doroodgar Jorshery Saman , Chandra Jay , Walia Anika S. , Stumiolo Audra , Corey Kristin , Zekavat Seyedeh Maryam , Zinzuwadia Aniket N. , Patel; Krisha , Short Sarah , Mega Jessica L. , Plowman R. Scooter , Pagidipati Neha , Sullivan Shannon S. , Mahaffey Kenneth W. , Shah Svati H. , Hernandez Adrian F. , Christiani David , Aerts Hugo J.W.L. , Weiss Jakob , Lu Michael T. , Raghu Vineet K. 
Published: November 2024

SUMMARYBackgroundThis study assessed whether deep learning applied to routine outpatient chest X-rays (CXRs) can identify individuals at high risk for incident chronic obstructive pulmonary disease (COPD).MethodsUsing cancer screening trial data, we previously developed a convolutional neural network (CXR-Lung-Risk) to predict lung-related mortality from a CXR image. In this study, we externally validated CXR-Lung-Risk to predict incident COPD from routine CXRs. We identified outpatients without lung cancer, COPD, or emphysema who had a CXR taken from 2013-2014 at a Mass General Brigham site in Boston, Massachusetts. The primary outcome was 6-year incident COPD. Discrimination was assessed using AUC compared to the TargetCOPD clinical risk score. All analyses were stratified by smoking status. A secondary analysis was conducted in the Project Baseline Health Study (PBHS) to test associations between CXR-Lung-Risk with pulmonary function and protein abundance.FindingsThe primary analysis consisted of 12,550 ever-smokers (mean age 62·4±6·8 years, 48.9% male, 12.4% rate of 6-year COPD) and 15,298 never-smokers (mean age 63·0±8·1 years, 42.8% male, 3.8% rate of 6-year COPD). CXR-Lung-Risk had additive predictive value beyond the TargetCOPD score for 6-year incident COPD in both ever-smokers (CXR-Lung-Risk + TargetCOPD AUC: 0·73 [95% CI: 0·72-0·74] vs. TargetCOPD alone AUC: 0·66 [0·65-0·68], p<0·01) and never-smokers (CXR-Lung-Risk + TargetCOPD AUC: 0·70 [0·67-0·72] vs. TargetCOPD AUC: 0·60 [0·57-0·62], p<0·01). In secondary analyses of 2,097 individuals in the PBHS, CXR-Lung-Risk was associated with worse pulmonary function and with abundance of SCGB3A2 (secretoglobin family 3A member 2) and LYZ (lysozyme), proteins involved in pulmonary physiology.InterpretationIn external validation, a deep learning model applied to a routine CXR image identified individuals at high risk for incident COPD, beyond known risk factors.FundingThe Project Baseline Health Study and this analysis were funded by Verily Life Sciences, San Francisco, California.ClinicalTrials.gov IdentifierNCT03154346
Pre-frontal cortical activity during gait is altered in pre-manifest and early spinocerebellar ataxia
Authors: Mancini Martina , Silva-Batista Carla , Shah Vrutangkumar V. , Horak Fay B. , Carlson-Kuhta Patricia , Safarpour Delaram , Gomez Christopher M. 
Published: November 2024

AbstractBackgroundSpinocerebellar ataxia (SCA) is a degenerative cerebellar disease, causing progressive impairment of gait and balance in adults. To identify the ideal subjects for disease-modifying therapies it is critical to identify biomarkers for the earliest stages of SCA.ObjectiveWe investigated whether prefrontal cortex activity is increased during walking in in early SCA or in pre-manifest SCA compared to healthy control subjects.MethodsSixteen participants with genetically determined SCA and 15 age-matched healthy controls participated in the study. The SARA was administered by a movement disorders specialist before the gait assessment. An 8-channel, mobile, fNIRS, with two reference channels, was used to record changes in oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin within the PFC. Participants walked for 2-minutes at a comfortable pace while wearing wireless, inertial sensors to derive gait characteristics.ResultsOf the 16 individuals with SCA, 9 were classified as pre-manifest (SARA<3) and 7 as early SCA (SARA<10). PFC activity (HbO2) while walking was greater than controls of similar age in people with SCA. Increased PFC activity was also present even in the pre-manifest stage of SCA. Increase in PFC activity was related to worse gait (double-support time and toe-out angle).ConclusionsPFC activity is increased in pre-manifest SCA, even when clinical scores are normal in the pre-manifest stage of the disease, and may serve as a biomarker that precedes onset of clinical disease. Increased PFC activity is consistent less automatic, cortical control of gait to compensate for impaired automatic, cerebellar control, even in early stages of ataxia.
LLMs for analyzing open text in global health surveys: why children are not accessing vaccine services in DRC
Authors: Burstein Roy , Mafuta Eric , Proctor Joshua L. 
Published: November 2024

AbstractThis study evaluates the use of large language models (LLMs) to analyze free-text responses from large-scale global health surveys, using data from the Enquête de Couverture Vaccinale (ECV) household coverage surveys from 2020, 2021, 2022, and 2023 as a case study. We tested several LLM approaches varying from zero-shot and few-shot prompting, fine-tuning, and a natural language processing approach using semantic embeddings to analyze responses on reasons caregivers did not vaccinate their children. Performance ranged from 61.5% to 96% based on testing against a curated benchmarking dataset drawn from the ECV surveys, with accuracy improving when LLM models were fine-tuned or provided examples for few-shot learning. We show that even with as few as 20–100 examples, LLMs can achieve high accuracy in categorizing free-text responses. This approach offers significant opportunities for reanalyzing existing datasets and designing surveys with more open-ended questions, providing a scalable, cost-effective solution for global health organizations. Despite challenges with closed-source models and computational costs, the study underscores LLMs’ potential to enhance data analysis and inform global health policy.