bluuebunny/crossref_metadata_2025_split huggingface dataset:
Protein Dynamics Underlies Strong Temperature Dependence of Heat Receptors
Authors: Mugo Andrew Njagi , Chou Ryan , Qin Feng 
Published: November 2024

AbstractIon channels are generally allosteric proteins, involving specialized stimulus sensor domains conformationally linked to the gate to drive channel opening. Temperature receptors are a group of ion channels from the transient receptor potential (TRP) family. They exhibit an unprecedentedly strong temperature dependence and are responsible for temperature sensing in mammals. Despite intensive studies, however, the nature of the temperature sensor domain in these channels remains elusive. By direct calorimetry of TRPV1 proteins, we have recently provided a proof of principle that temperature sensing by ion channels may diverge from the conventional allosterity theory; rather it is intimately linked to inherent thermal instability of channel proteins. Here we tackle the generality of the hypothesis and provide key molecular evidences on the coupling of thermal transitions in the channels. We show that while wild-type channels possess a single concerted thermal transition peak, the chimera, in which strong temperature dependence becomes disrupted, results in multi-transition peaks, and the activation enthalpies are accordingly reduced. The data show that the coupling with protein unfolding drives up the energy barrier of activation, leading to a strong temperature dependence of opening. Furthermore, we pinpoint the proximal N-terminus of the channels as a linchpin in coalescing different parts of the channels into concerted activation. Thus, we suggest that coupled interaction networks in proteins underlie the strong temperature dependence of temperature receptors.SignificanceDecoding receptor mechanisms requires understanding receptor activation at molecular levels. Whereas structural studies can unravel critical residues participating in activation, functional measurements are ultimately needed to pinpoint their mechanistic roles. Temperature receptors are gateways to thermosensation and pain. Despite intensive studies, how they detect temperature remains elusive. Here, by directly measuring heat flow in the most temperature-sensitive, high-threshold noxious heat receptor TRPV2, we show that channel activation is accompanied with a heat uptake sufficient to induce protein unfolding. We present molecular evidence that heat activation and unfolding are coupled, and propose a new mechanism based on concerted activation of different parts of channels to drive up temperature sensitivity. Our findings provide a mechanistic framework for understanding thermal biological processes.
Unified Analysis of Multi-order Tensors for Integrative Molecular Profiling
Authors: De Azevedo Kevin , Buettner Florian 
Published: November 2024

AbstractIn recent years, the exponential growth of high-dimensional, multi-modal molecular data has created both opportunities and challenges in personalized medicine. While existing approaches like matrix decomposition and neural network-based embeddings have been used to analyze such data, they have limitations in interpretability, handling missing values, and treating features across modalities as unrelated. To address these challenges, we present MUSIC (MUltiview BayeSIan Tensor DeComposition), a novel framework for probabilistic multi-view tensor decomposition that can integrate collections of tensors of different orders. MUSIC combines the strengths of group factor analysis and tensor decomposition through a Bayesian approach with structured sparsity priors. The framework offers several key advantages: (1) fast model training using variational inference, (2) inference of interpretable embeddings via structured sparsity, (3) efficient handling of missing values, and (4) flexible combination of tensors of different orders. We demonstrate MUSIC’s effectiveness on both simulated data and real-world applications, including drug response analysis in CLL patients and multi-modal single-cell data analysis in leukemia patients. Our results show that MUSIC can reveal interpretable multi-modal patterns capturing structured variation across patients, cell types, and modalities that are associated with disease states and can be explained through cell type- and modality-specific pathway activities.
Transcriptome Assembly at Single-Cell Resolution with Beaver
Authors: Shi Qian , Zhang Qimin , Shao Mingfu 
Published: November 2024

AbstractEmerging single-cell RNA sequencing techniques (scRNA-seq) has enabled the study of cellular transcriptome heterogeneity, yet accurate reconstruction of full-length transcripts at single-cell resolution remains challenging due to high dropout rates and sparse coverage. While meta-assembly approaches offer promising solutions by integrating information across multiple cells, current methods struggle to balance consensus assembly with cell-specific transcriptional signatures. Here, we present Beaver, a cell-specific transcript assembler designed for short-read scRNA-seq data. Beaver implements a transcript fragment graph to organize individual assemblies and designs an efficient dynamic programming algorithm that searches for candidate full-length transcripts from the graph. Beaver in-corporates two random forest models trained on 51 meticulously engineered features that accurately estimate the likelihood of each candidate transcript being expressed in individual cells. Our experiments, performed using both real and simulated Smart-seq3 scRNA-seq data, firmly show that Beaver substantially outperforms existing meta-assemblers and single-sample assemblers. At the same level of sensitivity, Beaver achieved 32.0%-64.6%, 13.5%-36.6%, and 9.8%-36.3% higher precision in average compared to meta-assemblers Aletsch, TransMeta, and PsiCLASS, respectively, with similar improvements over single-sample assemblers Scallop2 (10.1%-43.6%) and StringTie2 (24.3%-67.0%). Beaver is freely available athttps://github.com/Shao-Group/beaver. Scripts that reproduce the experimental results of this manuscript are available athttps://github.com/Shao-Group/beaver-test.
Single Cell Transcriptomics Reveal Genomic Indicators of Nonalcoholic Fatty Liver Disease
Authors: Thakur Madhavendra 
Published: November 2024

AbstractNonalcoholic fatty liver disease (NAFLD) affects roughly 25% of people globally. This paper seeks to use single cell RNA sequencing (scRNA-seq) to identify genomic markers for the identification of NAFLD and the evaluation of therapeutic methodsin silico. Publicly available scRNA-seq datasets of livers of both control and NAFLD mice were analyzed in R using the Seurat package for scRNA-seq analysis. Datasets were clustered as needed using the Seurat standard preprocessing workflow, and the zonation of hepatocyte clusters was identified based on established marker genes. When examining hepatocyte reprogramming during NAFLD, trends were seen in the zonation of hepatocytes expressing fructose metabolism pathways such as Aldob, Slc2a5 and Khk. In one dataset, healthy livers showed elevated expression of fructose metabolism pathways in central hepatocytes, while NAFLD livers exhibited this expression predominantly in portal hepatocytes. Conversely, in a second dataset, this zonation pattern was reversed, with central hepatocytes in NAFLD livers showing higher fructose metabolism expression compared to portal regions. This consistent observation of a zonation switch across datasets strengthens our understanding of hepatocyte reprogramming during NAFLD and fibrosis. Further, the direction of this switching may provide insights into fibrosis etiology, potentially reflecting differences in diet and metabolic stressors that contribute to fibrosis progression. It could also prove valuable inin silicoevaluations of therapies for NAFLD by identifying liver fibrosis.
Influence of metal ions on the isothermal self-assembly of DNA nanostructures
Authors: Rodriguez Arlin , Madhanagopal Bharath Raj , Sarkar Kahini , Nowzari Zohreh , Mathivanan Johnsi , Talbot Hannah , Morya Vinod , Halvorsen Ken , Vangaveti Sweta , Berglund J. Andrew , Chandrasekaran Arun Richard 
Published: November 2024

AbstractDNA nanostructures are typically assembled by thermal annealing in buffers containing magnesium. We demonstrate the assembly of DNA nanostructures at constant temperatures ranging from 4 °C to 50 °C in solutions containing different metal ions. The choice of metal ions and the assembly temperature influence the isothermal assembly of several DNA motifs and designed three-dimensional DNA crystals. Molecular dynamics simulations show more fluctuations of the DNA structure in select monovalent ions (Na+and K+) compared to divalent ions (Mg2+and Ca2+). A key highlight is the successful assembly of DNA motifs in nickel-containing buffer at temperatures below 40 °C, otherwise unachievable at higher temperatures, or using an annealing protocol. DNA nanostructures isothermally assembled in different ions do not affect the viability of fibroblasts, myoblasts, and myotubes and or the immune response in myoblasts. The use of ions other than the typically-used magnesium holds key potential in biological and materials science applications that require minimal amounts of magnesium.
Efficient Finite-Difference Estimation of Second-Order Parametric Sensitivities for Stochastic Discrete Biochemical Systems
Authors: Jabeen Fauzia , Ilie Silvana 
Published: November 2024

AbstractBiochemical reaction systems in a cell exhibit a stochastic behaviour, owing to the unpredictable nature of the molecular interactions. The fluctuations at the molecular level may lead to a different behaviour than that predicted by the deterministic model of the reaction rate equations, when some reacting species have low population numbers. As a result, stochastic models are vital to accurately describe the system dynamics. Sensitivity analysis is an important method for studying the influence of the variations in various parameters on the output of a biochemical model. We propose a finite-difference strategy for approximating second-order parametric sensitivities for stochastic discrete models of biochemically reacting systems. This strategy utilizes adaptive tau-leaping schemes and coupling of the perturbed and nominal processes for an efficient sensitivity estimation. The advantages of the new technique are demonstrated through its application to several biochemical system models with practical significance.
“FGFR4 p.Gly388Arg Mutations in PBMCs of LAM Patients: A Potential Systemic Driver of Neoplastic-like Behaviour”
Authors: Koc-Gunel Sinem , Ryan Amy L. , Winter Melanie , Wagner T.O.F. 
Published: November 2024

AbstractLymphangioleiomyomatosis (LAM) is a rare, progressive lung disease with neoplastic traits, primarily driven by mutations in the TSC2 gene, which lead to hyperactivation of the mTOR pathway and uncontrolled cell growth. However, additional mutations may contribute to disease progression. In this study, we investigate the potential role of co-mutations, focusing on the FGFR4 p.Gly388Arg polymorphism, which has previously been associated with aggressive cancer progression, as a possible co-driver in LAM. Peripheral blood mononuclear cells (PBMCs) were isolated from seven sporadic LAM patients and analyzed using Next-Generation Sequencing (NGS) to identify tumorigenic mutations. The FGFR4 p.Gly388Arg variant was identified in four patients, with allelic frequencies ranging from 49% to 99%. The highest frequency was observed in a patient with severe bullous lung disease, who ultimately required lung transplantation. Our analysis revealed a strong positive correlation (r = 0.85, p = 0.15) between FGFR4 allelic frequency and lung function decline (FEV1%), as well as a moderate positive correlation (r = 0.55, p = 0.20) between FGFR4 mutation status (presence vs. absence) and FEV1% decline. Although these correlations did not reach statistical significance, the trends suggest that FGFR4 mutations may contribute to disease progression in LAM. These findings indicate that FGFR4 mutations could play a role in the systemic nature of LAM, potentially exacerbating disease severity. Further research is needed to evaluate FGFR4 as a biomarker and therapeutic target in conjunction with mTOR inhibitors for the treatment of LAM.
Predictors of functional outcome following acute ischemic stroke secondary to M1 MCA/ICA occlusion in the extended time window
Authors: Constantakis John , Steiner Quinn , Reher Thomas , Choi Timothy , Holangel Fauzia , Zhao Qianqian , Bennett Nicole , Nair Veena A. , Adelman Eric , Prabhakaran Vivek , Aagard-Kienitz Beverly , Famakin Bolanle 
Published: November 2024

AbstractIntroductionA validated clinical decision tool predictive of favorable functional outcomes following endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) remains elusive. We performed a retrospective case series of patients at our regional Comprehensive Stroke Center, over a 4-year period, who have undergone EVT to elucidate patient characteristics and factors associated with a favorable functional outcome after EVT.MethodsWe reviewed all cases of EVT at our institution from 2/2018 - 2/2022 in the extended time window from 6 – 24 hours. Demographic, clinical, imaging, and procedure co-variates were included. A favorable clinical outcome was defined as a modified Rankin scale or 0-2. We included patients with M1 or ICA occlusion treated with EVT within 6-24 hours after symptom onset. We used a univariate and multivariate logistic regression analysis to identify patient factors associated with a favorable clinical outcome at 90 days.ResultsOur analysis demonstrates that higher recanalization score based on the mTICI scale (2B-3) was a strong indicator of favorable outcome per both our multivariate and univariate analysis (OR 4.11; CI 1.10 – 15.31; p 0.035). Our data also showed signal that the younger age (p0.013), lower baseline NIHSS (p0.043), shorter hospital length of stay (LOS) (p0.030), and absence of pre-existing hypertension (p0.026) may also be a predictor of favorable outcome per our univariate analysis.ConclusionPatients without pre-existing hypertension had more favorable outcomes following EVT in the expanded time window. This is consistent with other multicenter data in the expanded time window that demonstrates greater odds of a poor outcome with elevated pre-, peri-, and post-endovascular treatment blood pressure. Our data also demonstrates mTICI score is a strong predictor of favorable outcome even when controlled for other variables. Other factors that may indicate a favorable outcome include younger age, lower baseline NIHSS, and shorter hospital LOS.
Stemness prediction models reveal glioma aggressiveness and therapeutic targets in gliomas
Authors: Simões Renan L.S. , Marção Maycon , Santos Emerson de Souza , Uyemura Sergio Akira , Malta Tathiane M. 
Published: November 2024

AbstractGliomas are complex and heterogeneous primary brain tumors with a high degree of therapeutic resistance, particularly glioblastomas, which carry a poor prognosis. Cellular plasticity and stem cell-like features, or “stemness,” are increasingly recognized as key contributors to tumor progression and treatment resistance. In this study, we introduce two machine-learning-based prediction models designed to assess stemness in glioma samples using bulk gene expression data. One model captures fetal astrocyte characteristics (ASTsi), while the other identifies glioma stem cell traits (GSCsi). ASTsi was notably correlated with poor prognosis in IDHmut gliomas, whereas GSCsi was more indicative of stemness in IDHwt subtypes. Longitudinal data analysis showed that IDHwt and grade IV gliomas exhibit shifts in stemness indices upon recurrence, suggesting a phenotypic change linked to therapy resistance. Additionally, single-cell transcriptomic analysis confirmed that GSCsi can detect stem-like cell subsets in IDHwt gliomas. This approach enhances our understanding of glioma heterogeneity and reveals potential therapeutic targets.
Reducing CETP activity prevents memory decline in an Alzheimer’s disease mouse model
Authors: Phénix Jasmine , Sarty Isabel , Katz Megan S. , Nie Hannah , Kerksiek Anja , Kiss Robert S. , Lütjohann Dieter , Pastor William A. , Poirier Judes , Munter Lisa Marie ,   PREVENT-AD Research Group
Published: November 2024

AbstractEpidemiological studies have shown that lower activity of the cholesteryl ester transfer protein (CETP) correlates with reduced Alzheimer’s disease (AD) risk. While small molecule CETP inhibitors like evacetrapib have previously been assessed for cardiovascular diseases, their involvement in AD has not been investigated. Here, we establish CETP as a novel pharmacological target for AD treatment. Using CETP transgenic mice crossed to a mouse model of amyloidosis and administering evacetrapib, we provide evidence that CETP inhibition maintained memory independent of classic AD markers, increased hippocampal cholesterol, altered plasma lipoproteins, and changed transcription of genes linked to brain barriers. Using proteomic data of cerebrospinal fluid from cognitively unimpaired individuals at risk for AD (the PREVENT-AD cohort), we confirm that our mouse model reflects physiological changes in pre-symptomatic human subjects. We propose the repurposing of CETP inhibitors as an effective therapeutic strategy to delay or prevent cognitive impairment in AD.
Mpox Vaccine Design Through Immunoinformatics and Computational Epitope Prediction
Authors: Rivera-Orellana Sebastián , Ramírez‑Iglesias José R. , Acosta-España Jaime David , Espinosa-Espinosa Jorge , Navarro Juan-Carlos , Herrera-Yela Andrés , López-Cortés Andrés 
Published: November 2024

ABSTRACTThe Mpox virus (Monkeypox virus) poses significant public health risks due to its potential for severe outbreaks in humans. This study presents an innovative vaccine design using bioinformatics to identify epitopes that activate helper T cells (HTLs) via the human leukocyte antigen class II (HLA-II) complex. Starting with 50,040 vaccine candidates, 14 epitopes with the highest HLA-II affinity were selected based on antigenicity, allergenicity, toxicity, stability, and homology. These epitopes were integrated into a multi-epitope vaccine with spacers and adjuvants to enhance the immune response. A 3D model was developed, confirming structural stability and optimal epitope exposure through molecular dynamics simulations. The results indicate that the vaccine can induce robust immune responses, suggesting its potential effectiveness against the Mpox virus. Additionally, population coverage analysis supports its promise as a significant tool for controlling Mpox epidemics and advancing global public health initiatives.
Microbiology and Anthropology: a multidisciplinary approach for estimating time since death during Winter in rural New South Wales, Australia
Authors: Green Hayley , Espedido Bjorn , Ligot Rogine , Jensen Slade O. , Morton Charles Oliver 
Published: November 2024

AbstractAccurate determination of the post-mortem interval (PMI) is critical in forensic casework. Most studies conclude that PMI determinations are dependent on local climatic and geographical factors. Despite this, there is little data in an Australian environment outside of entomological studies. In the absence of insect data on or around the remains, alternative methods are required.Anthropological methods of observing and scoring the extent of decomposition at the time of discovery provide only broad estimates of PMI. Microbial succession is responsible for these observable soft tissue changes, particularly in early and late decomposition. The aim of this study was to combine anthropological and microbiological methods to provide data for determining PMI in a temperate Australian climate.Microbial DNA was isolated from skin and cavity swabs and used to perform 16S rRNA metagenomic analysis of pooled DNA samples to allow the choice of target taxa for qPCR. qPCR indicated significant changes in microbial communities with a dominant population ofGammaproteobacteriaat early time points giving way toFirmicutesandBacteroidetesnear the end of the experiment. Expressing qPCR data as a ratio ofLactobacillales/Enterobacteriaceaeprovided data that could be useful in determining early and late decomposition. The genusPsychrobacterwas identified as a good indicator of late decomposition in winter conditions. qPCR analysis, with further refinements, could be part of an accurate quantitative method of determining PMI.
Individualized cortical thickness asymmetry in Autism Spectrum Disorders and Schizophrenia
Authors: Echave Marta Martin , Schnack Hugo G. , Díaz-Caneja Covadonga M. , Pina-Camacho Laura , Janssen Niels , Gordaliza Pedro M. , Kho Kuan H. , Buimer Elizabeth E.L. , van Haren Neeltje E.M. , Kahn René S. , Hulshoff Pol Hilleke E. , Parellada Mara , Arango Celso , Janssen Joost 
Published: November 2024

AbstractIntroductionCortical thickness asymmetry has been proposed as a latent biomarker for Autism Spectrum Disorders (ASD) and schizophrenia (SZ). However, the degree of abnormal asymmetry at the individual level in ASD and SZ remains unclear. To investigate this, we applied normative modeling.MethodsNormative means for the whole brain and regional (160 cortical parcels) cortical thickness asymmetry index (AI) were established using a training set of healthy subjects (n=4,904, 45.15% male, age range: 6-95 years), controlling for age, sex, image quality and scanner. We calculated z-scores to quantify individual deviations from the normative mean in a test set consisting of healthy controls (HCtest, n=526, 40% male), participants with ASD (n=135, 83% male) and SZ (n=287, 81% male). Regional deviance was assessed by counting the number of individuals with significant deviations below (infra-normal, z-score ≤ -1.96) or above (supra-normal, z-score ≥ 1.96) normative means in each parcel. We also evaluated individual deviance by counting the number of regions with significant deviations for each participant. A data-driven multivariate approach was employed to determine whether joint regional deviance was associated with diagnosis.ResultsThere were no differences for deviance of whole brain AI between any of the groups. Distributions of individual deviances overlapped across all 160 regions, with only one superior temporal region in which SZ individuals showed a higher proportion of supra-normal AI values compared to HCtest(HCtest= 1.14%, SZ = 5.92%,χ2 = 15.45, PFDR< 0.05, ω = 0.14). The SZ group also had a higher average number of regions with significant deviations than HCtest(infra-normal: z = -4.21, p < 0.01; supra-normal: z = -4.33, p < 0.01). Multivariate analysis showed no association between inter-regional heterogeneity of AI and diagnosis. Results were consistent when using a higher resolution parcellation, alternative asymmetry calculations, analysis restricted to males, and after controlling for handedness and IQ.ConclusionsOur findings indicate that whole brain, regional and inter-regional variability in cortical thickness AI among those with ASD is entirely accounted for by normative variation. This study challenges the utility of cortical thickness asymmetry as a biomarker for ASD.