bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between Serum Biomarker Profile and Real-World Evidence of Disability in Multiple Sclerosis
Authors: Zhu Wen , Chen Chenyi , Zhang Lili , Hoyt Tammy , Walker Elizabeth , Venkatesh Shruthi , Zhang Fujun , Qureshi Ferhan , Foley John F. , Xia Zongqi 
Published: October 2022

AbstractObjectiveBiomarkers could inform disease worsening and severity in people with MS (pwMS). Few studies have examined blood biomarkers informative of patient-reported outcome (PRO) of disability in pwMS. In this study we examine the associations between serum protein biomarker profiles and patient-reported disability in pwMS.MethodsThis cross-sectional study included adults with a neurologist-confirmed diagnosis of MS from the University of Pittsburgh Medical Center (Pittsburgh, PA) and the Rocky Mountain MS Clinic (Salt Lake City, Utah) between 2017 and 2020. For exposure, we included 19 serum protein biomarkers potentially associated with MS inflammatory disease activity and 7 key clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying treatment, and time interval between sample collection and closest PRO assessment). Using 6 machine learning approaches (Least Absolute Shrinkage and Selection Operator [LASSO] regression, Random Forest [RF], XGBoost, Support-Vector Machines [SVM], stacking ensemble learning, and stacking classification algorithm), we examined model performance in predicting Patient Determined Disease Steps (PDDS) as the primary outcome. We assessed model prediction of Patient-Reported Outcomes Measurement Information System (PROMIS) physical function in a subgroup. We reported model performance using the held-out testing set.ResultsWe included 431 unique participants (mean age 49 years, 81% women, 94% non-Hispanic White). Using binary outcomes, models comprising both routine clinical factors and the 19 proteins as features consistently outperformed base models (containing clinical features alone or clinical features plus single protein) in predicting severe (PDDS≥4, PROMIS<35) versus mild/moderate (PDDS<4, PROMIS≥35) disability for all machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS=0.91, AUCPROMIS=0.90). Using ordinal/continuous outcomes, LASSO models with combined clinical factors and 19 proteins as features (R2PDDS=0.31, R2PROMIS=0.35) again outperformed base models. The four LASSO models (PDDS, PROMIS; both binary and ordinal/continuous) with combined clinical and protein features shared 2 clinical features (disease subtype, disease duration) and 4 protein biomarkers (CDCP1, IL-12B, NEFL, PRTG).ConclusionsSerum protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus individual protein biomarker, reaching clinically actionable performance.
Metabolic consequences of various fruit-based diets in a generalist insect species
Authors: Olazcuaga Laure , Baltenweck Raymonde , Leménager Nicolas , Maia-Grondard Alessandra , Claudel Patricia , Hugueney Philippe , Foucaud Julien 
Published: October 2022

AbstractMost phytophagous insect species exhibit a limited diet breadth and specialize on few or a single host plant. In contrast, some species display a remarkably large diet breadth, with host plants spanning several families and many species. It is unclear, however, whether this phylogenetic generalism is supported by a generic metabolic use of common host chemical compounds (‘metabolic generalism’) or alternatively by distinct uses of diet-specific compounds (‘multi-host metabolic specialism’)? Here, we simultaneously investigated the metabolomes of fruit diets and of individuals of a generalist phytophagous species,Drosophila suzukii, that developed on them. The direct comparison of metabolomes of diets and consumers enabled us to disentangle the metabolic fate of common and rarer dietary compounds. We showed that the consumption of biochemically dissimilar diets resulted in a canalized, generic response from generalist individuals, consistent with the metabolic generalism hypothesis. We also showed that many diet-specific metabolites, such as related to the particular color, odor or taste of diets, were not metabolized, and rather accumulated in consumer individuals, even when probably detrimental to fitness. As a result, while individuals were mostly similar across diets, the detection of their particular diet was straightforward. Our study thus supports the view that dietary generalism may emerge from a passive, opportunistic use of various resources, contrary to more widespread views of an active role of adaptation in this process. Such a passive stance towards dietary chemicals, probably costly in the short term, might favor the later evolution of new diet specializations.
Multi-Omic Analysis ofTyrophagus putrescentiaeReveals Insights into the Allergen Complexity of Storage Mites
Authors: Wan Angel Tsz-Yau , Xiong Qing , Xiao Xiaojun , Ao Kelvin Fu-Kiu , Jang Seok Woo , Wong Brian Shing-Hei , Wang Mingqiang , Cao Qin , Fung Cathy Sin-Hang , Chew Fook-Tim , Sun Baoqing , Ngai Sai Ming , Leung Ting-Fan , Jeong Kyoung Yong , Liu Xiaoyu , Tsui Stephen Kwok-Wing 
Published: October 2022

AbstractBackgroundThe storage miteTyrophagus putrescentiaeis one of the major mites causing allergies in Chinese and Korean populations, but its allergen profile in incomplete when compared with that of house dust mites. Multiple genome-based methods have been introduced into the allergen study of mites and have enabled a better understanding of these medically important organisms.ObjectiveWe sought to reveal a comprehensive allergen profile ofTyrophagus putrescentiaeand advance the allergen study of storage mites.MethodsBased on a high-quality assembled and annotated genome, anin silicoanalysis was performed by searching reference sequences to identify allergens. Immunoassay ELISA assessed the allergenicities of recombinant proteins. MALDI-TOF mass spectrometry identified the IgE-binding proteins. Comparative genomics analysis was employed for the important allergen gene families.ResultsA complete allergen profile ofTyrophagus putrescentiaewas revealed, including thirty-seven allergen groups (up to Tyr p 42). Among them, five novel allergens were verified using the sera of allergy patients. Massive allergen homologs were identified as the result of gene duplications in genome evolution. Proteomic identification again revealed a wide range of allergen homologs. In the NPC2 family and GSTs, comparative analysis shed light on the expansion and diversification of the allergen groups.ConclusionUsing multi-omic approaches, the comprehensive allergen profile including massive homologs was disclosed inTyrophagus putrescentiae, which revealed the allergen complexity of the storage mite and could ultimately facilitate the component-resolved diagnosis.
SARS-CoV-2 nsp3-4 suffice to form a pore shaping replication organelles
Authors: Zimmermann Liv , Zhao Xiaohan , Makroczyova Jana , Wachsmuth-Melm Moritz , Prasad Vibhu , Bartenschlager Ralf , Chlanda Petr 
Published: October 2022

AbstractCoronavirus replication is associated with the remodeling of cellular membranes resulting in the formation of double-membrane vesicles (DMVs). Recently, a pore spanning DMV was identified as a putative portal for viral RNA transcription and replication products providing a novel target for antiviral intervention. However, the exact components and the structure of the SARS-CoV-2 pore remain to be determined. Here, we investigate the structure of DMV pores byin situcryo-electron tomography combined with subtomogram averaging. We reveal non-structural proteins (nsp) 3 and 4 as minimal components forming a DMV spanning pore and show that nsp3 Ubl1-Ubl2 domains are critical for inducing membrane curvature and DMV formation. Altogether, SARS-CoV-2 nsp3-4 has a dual role by driving the biogenesis of replication organelles and forming DMV-spanning replicopores.One-Sentence SummaryBiogenesis of SARS-CoV-2 replication organelles is driven by nsp3-4 constituting the double-membrane vesicle spanning pore.
A systems approach evaluating the impact of SARS-CoV-2 variant of concern mutations on CD8+ T cell responses
Authors: Buckley Paul R. , Lee Chloe H. , Antanaviciute Agne , Simmons Alison , Koohy Hashem 
Published: October 2022

AbstractT cell recognition of SARS-CoV-2 antigens after vaccination and/or natural infection has played a central role in resolving SARS-CoV-2 infections and generating adaptive immune memory. However, the clinical impact of SARS-CoV-2-specific T cell responses is variable and the mechanisms underlying T cell interaction with target antigens are not fully understood. This is especially true given the virus’ rapid evolution, which leads to new variants with immune escape capacity. In this study, we used the Omicron variant as a model organism and took a systems approach to evaluate the impact of mutations on CD8+ T cell immunogenicity. We computed an ‘immunogenicity potential’ score for each SARS-CoV-2 peptide antigen from the ancestral strain and Omicron, capturing both antigen presentation and T cell recognition probabilities. By comparing ancestral vs. Omicron immunogenicity scores, we reveal a divergent and heterogeneous landscape of impact for CD8+ T cell recognition of mutated targets in Omicron variants. While T cell recognition of Omicron peptides is broadly preserved, we observed mutated peptides with deteriorated immunogenicity that may assist breakthrough infection in some individuals. We then combined our scoring scheme with anin-silicomutagenesis, to characterise the position- and residue-specific theoretical mutational impact on immunogenicity. While we predict many escape trajectories from the theoretical landscape of substitutions, our study suggests that Omicron mutations in T cell epitopes did not develop under cell-mediated pressure. Our study provides a generalisable platform for fostering a deeper understanding of existing and novel variant impact on antigen-specific vaccine- and/or infection-induced T cell immunity.
In vitropersistence level reflectsin vivoantibiotic survival of naturalPseudomonas aeruginosaisolates in a murine lung infection model
Authors: Verstraete Laure , Aizawa Juliana , Govaerts Matthias , De Vooght Linda , Michiels Jan , Van den Bergh Bram , Cos Paul 
Published: October 2022

AbstractNowadays, clinicians are more and more confronted with the limitations of antibiotics to completely cure bacterial infections in patients. It has long been assumed that only antibiotic resistance plays a pivotal role in this. Indeed, the worldwide emergence of antibiotic resistance is considered as one of the major health threats of the 21stcentury. However, the presence of persister cells also has a significant influence on treatment outcomes. These antibiotic-tolerant cells are present in every bacterial population and are the result of the phenotypic switching of normal, antibiotic-sensitive cells. Persister cells complicate current antibiotic therapies and contribute to the development of resistance. In the past, extensive research has been performed to investigate persistence in laboratory settings, however, antibiotic tolerance in conditions that mimic the clinical setting is still poorly understood. In this study, we have optimized a mouse model for lung infections of the opportunistic pathogenPseudomonas aeruginosa. In this model, mice are intratracheally infected withP. aeruginosaembedded in seaweed alginate beads and subsequently treated with tobramycin via nasal droplets. A strain panel of 18P. aeruginosaisolates originating from environmental, human and animal clinical sources was selected to assess survival in the animal model. These survival levels were positively correlated with the survival levels determined via time-kill assays which is a common method to study persistence in the laboratory. We showed that both survival levels are comparable and thus that the classical persister assays are indicative for antibiotic tolerance in a clinical setting. The optimized animal model also allows us to test potential antipersister molecules and study persistence.ImportanceThe importance of targeting persister cells in antibiotic therapies becomes more evident as these antibiotic-tolerant cells underlie relapsing infections and resistance development. Here, we studied persistence in a clinically relevant pathogen,Pseudomonas aeruginosa. It is one of the six ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, P. aeruginosa, Enterobacterspp.) that are considered as a major health threat.P. aeruginosais mostly known for causing chronic lung infections in cystic fibrosis patients. We mimicked these lung infections in a mouse model to study persistence in more clinical conditions. We showed that the survival levels of naturalP. aeruginosaisolates in this model are positively correlated with the survival levels measured in classical persistence assays. These results not only validate the use of our current techniques to study persistence, but also open opportunities to study new persistence mechanisms or evaluate new antipersister compoundsin vivo.
Plant spatial aggregation modulates the interplay between plant competition and pollinator attraction with contrasting outcomes of plant fitness
Authors: Hurtado María , Godoy Oscar , Bartomeus Ignasi 
Published: October 2022

ABSTRACTEcosystem functions such as seed production are the result of a complex interplay between competitive plant-plant interactions and mutualistic pollinator-plant interactions. In this interplay, spatial plant aggregation could work in two different directions: it could increase intra- and interspecific competition, thus reducing seed production; but it could also attract pollinators increasing plant fitness. To shed light on how plant spatial arrangement modulates this balance, we conducted a field study in a Mediterranean annual grassland with three focal plant species with different phenology (Chamaemelum fuscatum(early phenology),Leontodon maroccanus(middle phenology) andPulicaria paludosa(late phenology)) and a diverse guild of pollinators (flies, bees, beetles, and butterflies). All three species showed spatial aggregation of conspecific individuals. Additionally, we found that the two mechanisms were working simultaneously: crowded neighborhoods reduced individual seed production via plant-plant competition, but they also made individual plants more attractive for some pollinator guilds, increasing visitation rates and plant fitness. The balance between these two forces varied depending on the focal species and the spatial scale considered. Therefore, our results indicate that mutualistic interactions not always effectively compensate for competitive interactions in situations of spatial aggregation of flowering plants, at least in our study system. We highlight the importance of explicitly considering the spatial structure at different spatial scales of multitrophic interactions to better understand individual plant fitness and community dynamics.
ACDA: Implementation of an Augmented Drug Synergy Prediction Algorithm
Authors: Domanskyi Sergii , Jocoy Emily L. , Srivastava Anuj , Bult Carol J. 
Published: October 2022

AbstractMotivationDrug synergy prediction is a complex problem typically approached with machine learning techniques using molecular data, pharmacological data, and knowledge of biological-interaction networks. The recently published Cancer Drug Atlas (CDA) uses a logistic regression model to predict a binary synergy outcome in cell-line models by utilizing drug target information, knowledge of genes mutated in each model, and the models’ monotherapy drug sensitivity. However, we observed low performance, 0.33, of the CDA measured by Pearson correlation of predicted versus measured sensitivity when we evaluated datasets from six studies that were not considered during the development of the CDA. Here we describe improvements to the CDA algorithm, the Augmented CDA, that improved performance by 71% and robustness to dataset variations in drug response values.ResultsWe augmented the drug-synergy prediction-modeling approach CDA described in Narayan et al. by applying a random forest regression and optimization via cross-validation hyper-parameter tuning. We benchmarked the performance of our Augmented CDA (ACDA) compared to the original CDA algorithm using datasets from DrugComb, an open-access drug-combination screening data resource. The ACDA’s performance is 71% higher than that of the CDA when trained and validated on the same dataset spanning ten tissues. The ACDA performs marginally better (6% increase) than the CDA when trained on one dataset and validated on another dataset in 22 cases that cover seven tissues. We also compared the performance of ACDA to one of the winners of the DREAM Drug Combination Prediction Challenge (Mikhail Zaslavskiy’s algorithm which we denoted as EN). The performance of EN was smaller than that of the ACDA in 15 out of 19 cases. In addition to data from cell lines, we also trained the ACDA algorithm on Novartis Institutes for BioMedical Research PDX encyclopedia (NIBR PDXE) data and generated sensitivity predictions for the cases where drug-combination tumor-volume measurements were unavailable. Finally, we developed an approach to visualize synergy-prediction data using dendrograms and heatmaps instead of the Voronoi diagrams used in the CDA. The latter has a complex algorithmic realization and no publicly available implementation, whereas the ACDA visualization approach is more transparent and has open access. We implemented and wrapped the ACDA algorithm in an easy-to-use python package available from PyPI.AvailabilityThe source code is available athttps://github.com/TheJacksonLaboratory/drug-synergy, and the software package can be installed directly from PyPI using pip.ContactAnuj.Srivastava@jax.org,Carol.Bult@jax.org
Uncovering transcriptional regulatory network during regeneration for boosting wheat transformation
Authors: Liu Xuemei , Bie Xiaomin , Lin Xuelei , Li Menglu , Wang Hongzhe , Zhang Xiaoyu , Yang Yiman , Zhang Chunyan , Zhang Xiansheng , Xiao Jun 
Published: October 2022

AbstractGenetic transformation is important for gene functional study and crop breeding. Though it is available in many plant species, the transformation efficiency in wheat is generally low, which greatly restricts the genetic manipulation in wheat. Here, we use multi-omic analysis strategy to uncover core transcriptional regulatory network (TRN) driving wheat shoot regeneration and identify key factors that boost the transformation efficiency. RNA-seq, ATAC-seq and CUT&Tag were used to profile the transcriptome and chromatin dynamic during regeneration process from immature embryo of wheat variety Fielder. Sequential expression of gene clusters that mediating cell fate transition during regeneration is induced by auxin signaling, in coordination with changes of chromatin accessibility, H3K27me3 and H3K4me3 status. The TRN driving wheat shoot regeneration was built-up and 446 key transcriptional factors (TFs) occupied the core of network were identified, including functionally tested regeneration factors in other species. We further compared the regeneration process between wheat andArabidopsisand found that DNA binding with one finger (DOF) TFs show distinct patterns in two species. Furthermore, we found thatTaDOF5.6(TraesCS6A02G274000) andTaDOF3.4(TraesCS2B02G592600) can significantly improve the transformation efficiency of different wheat varieties. Thus, our data uncovers the molecular regulatory insights for wheat shoot regeneration process and provides potential novel targets for improving transformation efficiency in wheat.
The nanopore sequencing of a Chinese rhesus macaque revealed patterns of methylation, recombination, and selection for structural variations
Authors: Chen Jianhai , Zhong Jie , He Xuefei , Jakovlić Ivan , Zhang Yong , Yang Hao , Chen Younan , Yang Guang , Fan Chuanzhu , Shen Bairong 
Published: October 2022

AbstractRhesus macaques (Macaca mulatta) are the most extensively studied nonhuman primate species for human biomedical modeling. However, little is known about the biological pattern of genome-wide structural variations (SVs) and the evolutionary forces underlying SVs. Here, we conducted genomic sequencing and analyses based on Nanopore long reads and Illumina short reads technology. We called SVs between the two subspecies (China vs. India), using three methods of assembly-based and long-reads-based algorithms. Interestingly, we found significantly more SVs in X-chromosome than in autosomes, consistent with the expectation of the faster-X divergence at the subspecies level. With the fine-scale methylation frequencies and recombination rates, we found duplications with significantly lower methylation frequencies while higher recombination rates than other types of SVs, suggesting a higher level of transcriptional and evolutionary potential for duplications than for other SVs types. A genome-wide scan of selective sweep revealed that over 3% of SVs are under positive selection. Moreover, X chromosome showed significantly higher number of positively selected SVs than do autosomes, suggesting the “faster-X effect” of SVs. Our study revealed a different evolutionary importance for duplications compared with other SVs forms. We also revealed the “faster-X effect” of SVs, which could provide raw material upon which positive selection can further play.
ViReaDB: A user-friendly database for compactly storing viral sequence data and rapidly computing consensus genome sequences
Authors: Moshiri Niema 
Published: October 2022

AbstractMotivationIn viral molecular epidemiology, reconstruction of consensus genomes from sequence data is critical for tracking mutations and variants of concern. However, storage of the raw sequence data can become prohibitively large, and computing consensus genome from sequence data can be slow and requires bioinformatics expertise.ResultsViReaDB is a user-friendly database system for compactly storing viral sequence data and rapidly computing consensus genome sequences. From a dataset of 1 million trimmed mapped SARS-CoV-2 reads, it is able to compute the base counts and the consensus genome in 16 minutes, store the reads alongside the base counts and consensus in 50 MB, and optionally store just the base counts and consensus (without the reads) in 300 KB.AvailabilityViReaDB is freely available on PyPI (https://pypi.org/project/vireadb) and on GitHub (https://github.com/niemasd/ViReaDB) as an open-source Python software project.Contactniema@ucsd.edu