bluuebunny/crossref_metadata_2025_split huggingface dataset:
Human cytomegalovirus infection coopts chromatin organization to diminish TEAD1 transcription factor activity
Authors: Sayeed Khund , Parameswaran Sreeja , Beucler Matthew J. , Edsall Lee E. , VonHandorf Andrew , Crowther Audrey , Donmez Omer , Hass Matthew , Richards Scott , Forney Carmy , Wright Jay , Leong Merrin Man Long , Murray-Nerger Laura A. , Gewurz Ben E. , Kaufman Kenneth M. , Harley John B. , Zhao Bo , Miller William E. , Kottyan Leah C. , Weirauch Matthew T. 
Published: April 2024

AbstractHuman cytomegalovirus (HCMV) infects up to 80% of the world’s population. Here, we show that HCMV infection leads to widespread changes in human chromatin accessibility and chromatin looping, with hundreds of thousands of genomic regions affected 48 hours after infection. Integrative analyses reveal HCMV-induced perturbation of Hippo signaling through drastic reduction of TEAD1 transcription factor activity. We confirm extensive concordant loss of TEAD1 binding, active H3K27ac histone marks, and chromatin looping interactions upon infection. Our data position TEAD1 at the top of a hierarchy involving multiple altered important developmental pathways. HCMV infection reduces TEAD1 activity through four distinct mechanisms: closing of TEAD1-bound chromatin, reduction of YAP1 and phosphorylated YAP1 levels, reduction of TEAD1 transcript and protein levels, and alteration ofTEAD1exon-6 usage. Altered TEAD1-based mechanisms are highly enriched at genetic risk loci associated with eye and ear development, providing mechanistic insight into HCMV’s established roles in these processes.
Inferring residue level hydrogen deuterium exchange with ReX
Authors: Crook Oliver M. , Gittens Nathan , Chung Chun-wa , Deane Charlotte M. 
Published: April 2024

AbstractHydrogen-Deuterium Exchange Mass-Spectrometry (HDX-MS) has emerged as a powerful technique to explore the conformational dynamics of proteins and protein complexes in solution. The bottom-up approach to MS uses peptides to represent an average of residues, leading to reduced resolution of deuterium exchange and complicates the interpretation of the data. Here, we introduce ReX, a method to infer residue-level uptake patterns leveraging the overlap in peptides, the temporal component of the data and the correlation along the sequence dimension. This approach infers statistical significance for individual residues by treating HDX-MS as a multiple change-point problem. By fitting our model in a Bayesian non-parametric framework, we perform parameter number inference, differential HDX confidence assessments, and uncertainty estimation for temporal kinetics. Benchmarking against existing methods using a three-way proteolytic digestion experiment shows our method’s superior performance at predicting unseen HDX data. Moreover, it aligns HDX-MS with the reporting standards of other structural methods by providing global and local resolution metrics. Using ReX, we analyze the differential flexibility of BRD4’s two Bromodomains in the presence of I-BET151 and quantify the conformational variations induced by a panel of seventeen small molecules on LXRα. Our analysis reveals distinct residue-level HDX signatures for ligands with varied functional outcomes, highlighting the potential of this characterisation to inform mode of action analysis.
Early-life stress alters postnatal chromatin development in the nucleus accumbens
Authors: Rashford Rebekah L. , DeBerardine Michael , Kim Hye Ji J. , Arzouni Nibal , Hirschfield Laura W. , Oke Oluwamayowa M. , Bennett Shannon N. , Peña Catherine Jensen 
Published: April 2024

ABSTRACTEarly-life stress sensitizes individuals to subsequent stressors to increase lifetime risk for psychiatric disorders. Within the nucleus accumbens (NAc) — a key limbic brain region — early-life stress sensitizes both cellular and transcriptional response to later stress. However, the molecular mechanisms linking initial activation of neurons by early-life stress with continued stress sensitivity across the lifespan are poorly understood. Using a combination of activity-dependent cellular tagging and ATAC-sequencing across postnatal development, we find that early-life stress initially opens chromatin in stress-activated cells and that chromatin opening predicts gene expression response to adult stress, suggesting epigenetic priming as a mechanism of stress sensitization. Moreover, early-life stress accelerates both chromatin development within these activated cells, and H3K4me1 deposition broadly in NAc — a post-translational histone modification associated with open chromatin and epigenetic priming. By adulthood, we observe chromatin remodeling throughout the NAc, indicating that the effects of stress are long-lasting and propagate across development into the broader cell population. Lastly, through viral-mediated epigenome editing and behavioral quantification, we find that deposition of H3K4me1 in NAc during early postnatal development is sufficient to mimic early-life stress and prime hypersensitivity to subsequent stress. Together, our results show that a molecular memory of early-life stress is encoded at an epigenetic level through changes in chromatin architecture. This constitutes a novel biological mechanism by which early-life stress programs lifelong stress sensitivity.
A framework for quality control in quantitative proteomics
Authors: Tsantilas Kristine A. , Merrihew Gennifer E. , Robbins Julia E. , Johnson Richard S. , Park Jea , Plubell Deanna L. , Canterbury Jesse D. , Huang Eric , Riffle Michael , Sharma Vagisha , MacLean Brendan X. , Eckels Josh , Wu Christine C. , Bereman Michael S. , Spencer Sandra E. , Hoofnagle Andrew N. , MacCoss Michael J. 
Published: April 2024

AbstractA thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifierPXD051318.
Ripened plant-based cheese analogs in Europe: nutritional and microbial profiles
Authors: Jaeger Isabela , Köhn Cecília R. , Evans Joshua D. , Frazzon Jeverson , Renault Pierre , Kothe Caroline Isabel 
Published: April 2024

AbstractPlant-based cheese analogs have emerged as a novel global market trend driven by sustainability concerns for our planet. This study examines eleven soft ripened plant-based cheese analogs produced in Europe, primarily with bloomy rinds and cashew nuts as the main ingredient. First, we focused on exploring the macronutrients and salt content stated on the labels, as well a detailed fatty acid analysis of the samples. Compared to dairy cheeses, plant-based cheeses share similarities in lipid content, but their fatty acid profiles diverge significantly, with higher ratio of mono- and polyunsaturated fatty acids such as oleic and linoleic acids. We also investigated the microbiota of these analog products, employing a culture-dependent and -independent approaches. We identified a variety of microorganisms in the plant-based cheeses, withLactococcus lactisandLeuconostoc mesenteroidesbeing the dominant bacterial species, andGeotrichum candidumandPenicillium camembertithe dominant fungal species. Most of the species characterized are similar to those present in dairy cheeses, suggesting that they have been inoculated as culture starters to contribute to the sensorial acceptance of plant-based cheeses. However, we also identify several species that are possibly intrinsic to plant matrices or originate from the production environment, such asPediococcus pentosaceusandEnterococcusspp. This coexistence of typical dairy-associated organisms with plant associated species highlights the potential microbial dynamics inherent in the production of plant-based cheese. These findings will contribute to a better understanding of plant-based cheese alternatives, enable the development of sustainable products, and pave the way for future research exploring the use of plant-based substrates in the production of cheese analogues.
Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types
Authors: Nelli Filippo , Ge Zongyuan , Blackall Linda , Taheriashtiani Negar , Henry Rebekah , Brumley Douglas R. , Grace Michael , Jex Aaron , Burch Michael , Lin Tsair-Fuh , Bertelkamp Cheryl , Willis Anusuya , Gao Li , Schmidt Jonathan , Crosbie Nicholas D. , Zamyadi Arash 
Published: April 2024

AbstractAlgae and cyanobacteria are microorganisms found in almost all fresh and marine waters, where they can pose environmental and public health risks when they grow excessively and produce blooms. Accurate identification and quantification of these microorganisms are vital for ecological research, water quality monitoring, and public health safety. However, traditional methods of manually counting and morphologically identifying these microorganisms are time-consuming and prone to human error. Application of the machine learning-driven Fast Segment Anything Model (FastSAM), an image segmentation model, automates and potentially enhances the accuracy and efficiency of cell identification and enumeration from microscopic images. We assessed FastSAM for algal cell image segmentation, and three clustering evaluation metrics. Segmentation of microscopic images of algal and cyanobacterial cells in water and treated wastewater samples using the Convolutional Neural Network based FastSAM algorithm demonstrated benefits and challenges of this machine learning-driven image processing. Notably, the pre-trained algorithm segmented entire elements in all microscopic images used in this study. Depending on the shape, 50-100% similarity was observed between machine-based segmentation and manual validation of all segmented elements, with 100% of single cells being correctly segmented by FastSAM. The performance of clustering metrics varied between 57-94% with the Spectral Angle Mapper achieving the most accurate performance, 84-94%, compared to the manually chosen clustering benchmarks. Cyanobacterial and algal communities are biologically diverse and have ecological significance. The application of image clustering techniques in studying their cell shapes marks an important advancement in microbial ecology and environmental monitoring. As technology progresses, these methods will become increasingly utilised to decipher the complex roles that algae and cyanobacteria play in our ecosystems supporting mitigation and public health protection measures.
AutoGaitA – Automated Gait Analysis in Python
Authors: Hosseini M. , Klein I. , Kuzu T. D. , Semmler C. , Wunderle V. , Mardare V. , Galvao A. , Haustein M. , Büschges A. , Grefkes C. , Korotkova T. , Fink G. R. , Weiss P. H. , Gatto G. , Daun S. 
Published: April 2024

AbstractAnimals change their location in space by means of walking, running, swimming, or flying, a series of rhythmic motor behaviours that together are defined as locomotion. Individual types of locomotion require a unique coordinated pattern of muscle contractions that can be inferred by the location of joints across the body. Implementations of recent advancements in machine learning (ML), such as DeepLabCut and Simi Shape, have automated the tracking of body posture, even in markerless subjects. Despite ML algorithms alleviating the tracking effort, making sense of the tracked points still requires substantial amounts of manual labour and lacks standardisation across research labs. To this end, we developedAutoGaitA(Automated Gait Analysis), an open-source Python toolbox designed to automate the analysis of locomotion by normalising the step cycle, extracting meaningful features from the tracked coordinates (e.g. angles, velocity, acceleration) and allowing intra- and inter-animal comparisons. Here, we employedAutoGaitAin a series of proof of principles experiments to show age-dependent changes in locomotion in flies, mice and humans, age-dependent changes in adaptation mechanisms in mice, and to compare the key features of walking across species.AutoGaitA’s adaptability to any kind of motor behaviour and any species of interest makes it a valuable tool for the motor community to standardise the analysis of rhythmic behaviours across genotypes, disease states and species.
AutoGDC: A Python Package for DNA Methylation and Transcription Meta-Analyses
Authors: Brown Chase Alan , Wren Jonathan D. 
Published: April 2024

Abstract0.1MotivationThe Genomic Data Commons is a powerful resource which facilitates the exploration of molecular alterations across various diseases. However, utilizing this resource for meta-analysis requires many different tools to query, download, organize, and analyze the data. In order to facilitate a more rapid, simple means of analyzing DNA methylation and RNA sequencing datasets from the GDC we developedautogdc, a python package that integrates data curation and preprocessing with meta-analysis functionality into one simplified bioinformatic pipeline.0.2Availability and ImplementationTheautogdcpython package is available under the GPLv3 license at along with several examples of typical use-case scenarios in the form of a jupyter notebook. The data is all originally provided by the GDC, and is therefore available under the NIH Genomic Data Sharing (GDS) and NCI GDS policies.
Potential Opportunities of Modeling Bioavailability for Monoclonal Antibodies: An Overview of mAbs and the current challenges of mAb development
Authors: Kohli Aryan , Fayaz Omar , Jensen Alexandra Y. , Chung Christian , Korban Christopher 
Published: April 2024

AbstractWith a growing market size, and a large variety of applications, monoclonal antibody technology adoption and clinical usage is at an all-time high. This review article seeks to explore 10 monoclonal antibodies (mAbs) and their mechanism of action, specifically their pharmacodynamic (PD) and pharmacokinetic (PK) properties, and use a machine learning model with various parameters to assess whether the mAb has adequate bioavailability when delivered subcutaneously. This is an investigation of drug optimization and patient outcomes when transitioning from traditional IV administrations to subcutaneous injections. The machine learning model is an extension based on a paper by Han Lou and Michael Hageman,Machine Learning Attempts for Predicting Human Subcutaneous Bioavailability of Monoclonal Antibodies, where they took 10 mAbs and analyzed 45 different features. To further extend this paper, we took an additional 10 monoclonal antibodies that were delivered subcutaneously, and took into account their dosage concentration as an extension to traditional PK properties. By including additional mAbs and dosage, a more sophisticated model can be produced with high scalability to deep learning modalities.
Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence
Authors: Oropeza-Valdez Juan José , Padron-Manrique Cristian , Vázquez-Jiménez Aarón , Soberon Xavier , Resendis-Antonio Osbaldo 
Published: April 2024

AbstractThe COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection’s long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. By integrating ML with SHAP (SHapley Additive exPlanations) values, we aimed to uncover metabolomic signatures and identify potential biomarkers for these conditions. Our analysis included a cohort of 142 COVID-19, 48 Post-COVID-19 samples and 38 CONTROL patients, with 111 identified metabolites. Traditional analysis methods like PCA and PLS-DA were compared with advanced ML techniques to discern metabolic changes. Notably, XGBoost models, enhanced by SHAP for explainability, outperformed traditional methods, demonstrating superior predictive performance and providing different insights into the metabolic basis of the disease’s progression and its aftermath, the analysis revealed several metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. This study highlights the potential of integrating ML and XAI in metabolomics research.
ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention
Authors: Li Mingchen , Tan Pan , Ma Xinzhu , Zhong Bozitao , Yu Huiqun , Zhou Ziyi , Ouyang Wanli , Zhou Bingxin , Hong Liang , Tan Yang 
Published: April 2024

AbstractProtein language models (PLMs) have shown remarkable capabilities in various protein function prediction tasks. However, while protein function is intricately tied to structure, most existing PLMs do not incorporate protein structure information. To address this issue, we introduce ProSST, a Transformer-based protein language model that seamlessly integrates both protein sequences and structures. ProSST incorporates a structure quantization module and a Transformer architecture with disentangled attention. The structure quantization module translates a 3D protein structure into a sequence of discrete tokens by first serializing the protein structure into residue-level local structures and then embeds them into dense vector space. These vectors are then quantized into discrete structure tokens by a pre-trained clustering model. These tokens serve as an effective protein structure representation. Furthermore, ProSST explicitly learns the relationship between protein residue token sequences and structure token sequences through the sequence-structure disentangled attention. We pre-train ProSST on millions of protein structures using a masked language model objective, enabling it to learn comprehensive contextual representations of proteins. To evaluate the proposed ProSST, we conduct extensive experiments on the zero-shot mutation effect prediction and several supervised downstream tasks, where ProSST achieves the state-of-the-art performance among all baselines. Our code and pretrained models are publicly available2.
Recurrent Interneuron Connectivity does not Support Synchrony in a Biophysical Dentate Gyrus Model
Authors: Müller-Komorowska Daniel , Fujishige Temma , Fukai Tomoki 
Published: April 2024

AbstractSynchronous activity of neuronal networks is found in many brain areas and correlates with cognition and behavior. Gamma synchrony is particularly strong in the dentate gyrus, which is thought to process contextual information in the hippocampus. Several network mechanisms for synchrony generation have been proposed and studied computationally. One such mechanism relies solely on recurrent inhibitory interneuron connectivity, but it requires a large enough number of synapses. Here, we incorporate connectivity data of the dentate gyrus into a biophysical computational model to test its ability to generate synchronous activity. We find that recurrent interneuron connectivity is insufficient to induce a synchronous network state. This applies to an interneuron ring network and the broader dentate gyrus circuitry. In the asynchronous state, recurrent interneuron connectivity can have small synchronizing effects but can also desynchronize the network for specific types of synaptic input. Our results show that synchronizing mechanisms relying solely on interneurons are unlikely to be biologically plausible in the dentate gyrus.
Assessment of the level of pollution based on soil andTilia × europaealeaves
Authors: Simon Edina , Bin Ismail Haziq , Sipos Bianka , Abriha-Molnár Vanda Éva , Abriha Dávid , Tőzsér Dávid , Sajtos Zsófi , Boluda Rafael , Roca-Pérez Luis 
Published: April 2024

AbstractAnthropic activities related to urbanization release pollutants, including potentially toxic elements, into the atmosphere and these are eventually deposited in soils, water, infrastructures, vegetation. Urban soil and plant leaves are widely used as ecological indicators to assess the effect of urbanization. This study aimed to evaluate the impact of urbanization based on the elemental concentration of soil and leaves ofTilia × europaeafrom urban, suburban, and rural areas along an urbanization gradient in Debrecen, Hungary. Using the ICP-OES technique, we measured the concentration of Ba, Cd, Co, Cr, Cu, Ni, Pb, Sr, and Zn and based on the measured concentration, bioaccumulation factor (BAF) was calculated. The highest concentration of all elements was found in soil samples from urban areas, with an increasing tendency along the urbanization gradient. A significant difference was found along the urbanization gradient only for Cr based on the plant leaves.T. × europaeashowed bioaccumulation capacity for Cr and Sr. Our results suggest that urbanization has a remarkable effect on the elemental concentration of soil, which is a perfect ecological indicator. At the same time, we concluded that theT. × europaeawas not sensitive enough to indicate the effect of urbanization.
Beyond oscillations - A novel feature space for characterizing brain states
Authors: Balestrieri Elio , Chalas Nikos , Stier Christina , Fehring Jana , Ávila Cristina Gil , Dannlowski Udo , Ploner Markus , Gross Joachim 
Published: April 2024

AbstractOur moment-to-moment conscious experience is paced by transitions between states, each one corresponding to a change in the electromagnetic brain activity. One consolidated analytical choice is to characterize these changes in the frequency domain, such that the transition from one state to the other corresponds to a difference in the strength of oscillatory power, often in pre-defined, theory-driven frequency bands of interest. Today, the huge leap in available computational power allows us to explore new ways to characterize electromagnetic brain activity and its changes.Here we leveraged an innovative set of features on an MEG dataset with 29 human participants, to test how these features described some of those state transitions known to elicit prominent changes in the frequency spectrum, such as eyes-closed vs eyes-open resting-state or the occurrence of visual stimulation. We then compared the informativeness of multiple sets of features by submitting them to a multivariate classifier (SVM).We found that the new features outperformed traditional ones in generalizing states classification across participants. Moreover, some of these new features yielded systematically better decoding accuracy than the power in canonical frequency bands that has been often considered a landmark in defining these state changes. Critically, we replicated these findings, after pre-registration, in an independent EEG dataset (N=210).In conclusion, the present work highlights the importance of a full characterization of the state changes in the electromagnetic brain activity, which takes into account also other dimensions of the signal on top of its description in theory-driven frequency bands of interest.