bluuebunny/crossref_metadata_2025_split huggingface dataset:
A role for the S4-domain containing protein YlmH in ribosome-associated quality control inBacillus subtilis
Authors: Takada Hiraku , Paternoga Helge , Fujiwara Keigo , Nakamoto Jose A. , Park Esther N. , Dimitrova-Paternoga Lyudmila , Beckert Bertrand , Saarma Merilin , Tenson Tanel , Buskirk Allen R. , Atkinson Gemma C. , Chiba Shinobu , Wilson Daniel N. , Hauryliuk Vasili 
Published: March 2024

AbstractRibosomes trapped on mRNAs during protein synthesis need to be rescued for the cell to survive. The most ubiquitous bacterial ribosome rescue pathway is trans-translation mediated by tmRNA and SmpB. Genetic inactivation of trans-translation can be lethal, unless the ribosomes are rescued by ArfA or ArfB alternative rescue factors or the ribosome-associated quality control (RQC) system, which inB. subtilisinvolves MutS2, RqcH, RqcP and Pth. Using transposon sequencing in a trans-translation-incompetentB. subtilisstrain we identify a poorly characterized S4-domain-containing protein YlmH as a novel potential RQC factor. Cryo-EM structures reveal that YlmH binds peptidyl-tRNA-50S complexes in an position analogous to that of S4-domain-containing RqcP, and that, similarly to RqcP, YlmH can co-habit with RqcH. Consistently, we show that YlmH can assume the role of RqcP in RQC in facilitating the addition of polyalanine tails to the truncated nascent polypeptides. While inB. subtilisthe function of YlmH is redundant with RqcP, our taxonomic analysis reveals that in multiple bacterial phyla RqcP is absent, while YlmH and RqcH are present, suggesting that in these species the YlmH plays a central role in the RQC.
A resource to enable chemical biology and drug discovery of WDR Proteins
Authors: Ackloo Suzanne , Li Fengling , Szewczyk Magda , Seitova Almagul , Loppnau Peter , Zeng Hong , Xu Jin , Ahmad Shabbir , Arnautova Yelena A , Baghaie AJ , Beldar Serap , Bolotokova Albina , Centrella Paolo A , Chau Irene , Clark Matthew A , Cuozzo John W , Dehghani-Tafti Saba , Disch Jeremy S , Dong Aiping , Dumas Antoine , Feng Jianwen A. , Ghiabi Pegah , Gibson Elisa , Gilmer Justin , Goldman Brian , Green Stuart R , Guié Marie-Aude , Guilinger John P , Harms Nathan , Herasymenko Oleksandra , Houliston Scott , Hutchinson Ashley , Kearnes Steven , Keefe Anthony D , Kimani Serah W , Kramer Trevor , Kutera Maria , Kwak Haejin A , Lento Cristina , Li Yanjun , Liu Jenny , Loup Joachim , Machado Raquel AC , Mulhern Christopher J , Perveen Sumera , Righetto Germanna L , Riley Patrick , Shrestha Suman , Sigel Eric A , Silva Madhushika , Sintchak Michael D. , Slakman Belinda L , Taylor Rhys D , Thompson James , Torng Wen , Underkoffler Carl , von Rechenberg Moritz , Watson Ian , Wilson Derek J , Wolf Esther , Yadav Manisha , Yazdi Aliakbar K , Zhang Junyi , Zhang Ying , Santhakumar Vijayaratnam , Edwards Aled M , Barsyte-Lovejoy Dalia , Schapira Matthieu , Brown Peter J , Halabelian Levon , Arrowsmith Cheryl H 
Published: March 2024

AbstractProtein class-focused drug discovery has a long and successful history in pharmaceutical research, yet most members of druggable protein families remain unliganded, often for practical reasons. Here we combined experiment and computation to enable discovery of ligands for WD40 repeat (WDR) proteins, one of the largest human protein families. This resource includes expression clones, purification protocols, and a comprehensive assessment of the druggability for hundreds of WDR proteins. We solved 21 high resolution crystal structures, and have made available a suite of biophysical, biochemical, and cellular assays to facilitate the discovery and characterization of small molecule ligands. To this end, we use the resource in a hit-finding pilot involving DNA-encoded library (DEL) selection followed by machine learning (ML). This led to the discovery of first-in-class, drug-like ligands for 9 of 20 targets. This result demonstrates the broad ligandability of WDRs. This extensive resource of reagents and knowledge will enable further discovery of chemical tools and potential therapeutics for this important class of proteins.
Long-term Metabolic Side Effects of Second-Generation Antipsychotics in Chinese Patients with Schizophrenia: A Within-Subject Approach with modelling of dosage effects
Authors: Wong Kenneth C.Y. , Leung Perry B.M. , Lee Benedict K.W. , Sham Pak C. , Lui Simon S.Y. , So Hon-Cheong 
Published: March 2024

AbstractBackgroundSecond-generation antipsychotics (SGAs) are commonly used to treat schizophrenia (SCZ), but SGAs may differ in the severity of side effects. Previous observational studies had limitations like failing to account for confounding factors and short follow-up periods. This study compared the long-term metabolic and anthropometric side effects of seven second-generation antipsychotics (SGAs) in a Chinese schizophrenia population, using a within-subject approach to reduce risk of confounding.MethodsLongitudinal data on SGA prescriptions, concomitant medications, fasting blood glucose, lipid profiles, and BMI were collected from 767 schizophrenia patients, with follow-up up to 18.7 years (median ∼6.2 years). Linear mixed models estimated the effects of SGAs, as binary predictors and by dosage, on metabolic profiles.ResultsWhen considering SGAs as binary predictors, clozapine and olanzapine were associated with the most substantial worsening of lipid profiles and BMI, while clozapine alone showed a significant increase in blood glucose. Amisulpride, paliperidone, and quetiapine worsened lipid profiles and increased BMI. Conversely, aripiprazole improved lipid profiles but slightly increased BMI. Examining dosage effects showed consistent results overall. At minimum effective doses, clozapine had the most severe metabolic side effects, followed by olanzapine. Risperidone and aripiprazole had the least metabolic impact, with aripiprazole significantly lowering lipids.ConclusionsThis study clarified the long-term, dose-dependent metabolic and anthropometric effects of different SGAs in Chinese schizophrenia patients. Our findings may inform clinicians and SCZ patients of SGA choices.
Effects of maternal exercise on infant mesenchymal stem cell mitochondrial function, insulin action, and body composition in early infancy
Authors: Jevtovic Filip , Zheng Donghai , Claiborne Alex , Biagioni Ericka M. , Wisseman Breanna L. , Krassovskaia Polina M. , Collier David N. , Isler Christy , DeVente James E. , Neufer P. Darrell , Houmard Joseph A. , May Linda E. 
Published: March 2024

AbstractObjectiveRates of pediatric obesity are continuously rising and are likely to translate into a high incidence of metabolic disease later in life. Maternal exercise (ME) has been established as a useful non-pharmacological intervention to improve infant metabolic health; however, mechanistic insight behind these adaptations remains mostly confined to animal models. Infant mesenchymal stem cells (MSCs) give rise to infant tissues (e.g., skeletal muscle), and remain involved in mature tissue maintenance. Importantly, these cells maintain metabolic characteristics of an offspring donor and provide a model for the investigation of mechanisms behind infant metabolic health improvements.MethodsWe used undifferentiated MSC to investigate if ME affects infant MSC mitochondrial function and insulin action, and if these adaptations are associated with lower infant adiposity.ResultsWe found that infants from exercising mothers have improvements in MSC insulin signaling are related to higher MSC respiration and fat oxidation, and expression and activation of energy-sensing and redox-sensitive proteins. Further, we found that infants exposed to exercise in utero were seemingly leaner at 1-month of age, with a significant inverse correlation between infant MSC respiration and infant adiposity at 6-months of age.ConclusionThese data suggest that infants from exercising mothers are relatively leaner and this is associated with higher infant MSC mitochondrial respiration, fat use, and insulin action.
Enhancing COVID-19 Case Forecasting in the United States: A Comparative Analysis of ARIMA, SARIMA, and RNN Models with Grid Search Optimization
Authors: Nichols Samira , Abolmaali Saina 
Published: March 2024

AbstractThe COVID-19 pandemic has resulted in a substantial number of fatalities in the United States since its onset in January 2020. In an effort to mitigate the spread of this highly infectious disease, a range of measures, including social distancing, mask-wearing, lockdowns, and vaccination campaigns, have been implemented. However, despite these extensive efforts, the persistent transmission of the virus can be attributed to a combination of vaccine hesitancy among certain individuals and the emergence of new viral strains. To effectively manage the ongoing pandemic, healthcare providers and government officials rely on infectious disease modeling to anticipate and secure the necessary resources. Accurate short-term case number forecasting is of paramount importance for healthcare systems.Since the beginning of the pandemic, numerous models have been employed to forecast the number of confirmed cases. In this study, we undertake a comparative analysis of six time-series techniques: Simple Moving Average (SMA), Exponentially Weighted Moving Average (EWMA), Holt-Winters Double Exponential Smoothing Additive (HWDESA), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Recurrent Neural Network (RNN), with regard to their modeling and forecasting capabilities. SMA, EWMA, and HWDESA were employed for predictive modeling, while the ARIMA, SARIMA, and RNN models were utilized for case number forecasting. A comprehensive grid search was carried out to determine the optimal parameter combinations for both the ARIMA and SARIMA models. Our research findings demonstrate that the Holt-Winters Double Exponential model outperforms both the Exponentially Weighted Moving Average and Simple Moving Average in predicting the number of cases. On the other hand, the RNN model surpasses conventional time-series models such as ARIMA and SARIMA in terms of its forecasting accuracy. The finding of this study emphasizes the importance of accurately predicting the number of COVID-19 cases, given the substantial loss of lives caused by both the virus itself and the societal responses to it. Equipping healthcare managers with precise tools like Recurrent Neural Networks (RNNs) can enable them to forecast future cases more accurately and enhance their preparedness for effective response.
Trends in fluoroquinolone prescribing in UK primary and secondary care between 2019 and 2023
Authors: Hamilton Fergus , Darley Elizabeth , MacGowan Alasdair 
Published: March 2024

IntroductionFluoroquinolones are important, widely used antibiotics but have associations with a significant number of adverse outcomes. A recent (January 2024) decision by the UK drug regulator, the Medicines and Health Regulatory Authority (MHRA), restricted systemic use of these antibiotics to when only absolutely necessary with immediate eSect. One stated reason for the ban was the failure of previous guidance (2019, 2023), to impact prescribing, with the 2023 MHRA Drug Safety Update stating there had been “no change in prescribing” of fluoroquinolones in relation to guidance.MethodsWe evaluated the trend in prescribing of fluoroquinolones and comparator antibiotics using national data for all primary care practices in England from 2019 to 2023. We calculated the percent change in prescribing of fluoroquinolones using linear regression, comparing with other antibacterials Analyses were then performed at the integrated care board (ICB) level. We also performed a similar analysis on secondary care prescribing and included hospital inpatient stay data.ResultsIn primary care, there was a clear negative trend in fluoroquinolone (particularly ciprofloxacin) item dispensing, with a 4.2% reduction in items dispended per year, 95% confidence interval, CI (−5.2%; −3.3%, p = 6 x 10-13). This occurred despite no change in overall antibacterial prescription (+2% −0.56%; +4.6%, p = 0.12) and no decrease in comparator antibiotics. These occurred across nearly all (97/101) ICBs. Secondary care data showed stable prescription of fluoroquinolones, but other comparator antibiotics increased, leading to relatively fewer prescriptions compared to other agents.ConclusionsThere was a marked reduction in fluoroquinolone prescribing in primary care in England in both absolute terms and relative to other antibiotics between 2019 to 2023. Relative reductions have occurred in secondary care.FundingWellcome Trust (222894/Z/21/Z)
Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes
Authors: Kim Dongin , Han Seokjin , Nam Seong-Hyeuk , Kim Tae Yong 
Published: March 2024

AbstractIn the dynamic field of cancer research, the fusion of genetics and machine learning presents a groundbreaking opportunity to understand the intricate links between genomic variations and cancer phenotypes. Despite the wealth of genetic data, translating it into actionable insights remains a challenge, particularly in the context of complex cellular metabolism. VaMP (Variants,Metabolic Fluxes &Phenotypes), our novel approach, addresses this by integrating neural networks and metabolic models, offering a comprehensive framework for deciphering cancer biology. The developed method leverages a novel end-to-end neural network architecture, integrating genome-scale metabolic models (GSMs) to capture the intricate relationship between genetic variations and cellular phenotypes. VaMP’s encoder maps genetic variants to metabolic fluxes through a series of carefully designed steps utilizing neural network and GSM, while the decoder predicts phenotype probabilities using the differences between input and reference fluxes. The training data comprise mutation information and phenotypes, eliminating the need for explicit metabolic flux data preparation. Validation experiments on five cancer types demonstrate VaMP’s ability to identify significant genes and metabolic signatures. Further utilizing SCREENER and co-occurrence analyses, the assessment reveals VaMP’s capacity to anticipate established gene-disease relationships. The identified metabolic signatures are robustly substantiated by diverse literature grounded in experimental studies. Furthermore, an in-depth exploration of the outcomes from five VaMP models involved the correlation analysis for variants and fluxes to establish connections between significant genes and cancer-causing variants. Overall, VaMP can be used as a promising tool for unraveling the complex interplay between genetic alterations and cancer phenotypes, with implications for understanding disease mechanisms and identifying novel therapeutic targets.
The inflammatory micro-environment induced by targeted CNS radiotherapy is underpinned by disruption of DNA methylation
Authors: Millner TO , Panday P , Xiao Y , Boot JR , Nicholson J , Arpe Z , Stevens P , Rahman N , Zhang X , Mein C , Kitchen N , McEvoy AW , McKintosh E , McKenna G , Paraskevopoulos D , Lewis R , Badodi S , Marino S 
Published: March 2024

AbstractAlthough targeted radiotherapy (RT) is integral to the increasing survival of cancer patients, it has significant side-effects, the cellular and molecular mechanisms of which are not fully understood. During RT epigenetic changes occur in neoplastic tissue, but few studies have assessed these in non-neoplastic tissue and results are highly variable. Using bulk DNA methylation and RNA sequencing as well as spatial transcriptomics (ST) in a unique cohort of patient tissue samples, we show distinct differences in DNA methylation patterns in irradiated brain tissue, whilst ST characterisation identifies specific micro-environmental niches present after irradiation and highlights neuropeptides that could be propagating neuroinflammation. We also show that in a cerebral organoid (CO) model of early changes in neurons after irradiation there are similar DNA methylation alterations and disruption of the DNA methylation machinery, suggesting that early but persistent epigenetic dysregulation plays a role in neurotoxicity. We provide a link between radiotherapy induced neuroinflammation and disruption of DNA methylation for the first time and suggest possible driving mechanisms for this chronic neuroinflammation.
Wolbachiainfection inAedes aegyptidoes not affect its vectorial capacity forDirofilaria immitis
Authors: Shirozu Takahiro , Regilme Maria Angenica F , Ote Manabu , Sasaki Mizuki , Soga Akira , Bochimoto Hiroki , Kawabata Hidenobu , Umemiya-Shirafuji Rika , Kanuka Hirotaka , Fukumoto Shinya 
Published: March 2024

AbstractMosquito-borne diseases such as dengue and filariasis are a growing public health concern in endemic countries. Biological approaches, such as the trans-infection ofWolbachia pipientisin mosquitoes, are an alternative vector control strategy, especially for arthropod-borne viruses such as dengue. In the present study, the effect ofWolbachia(wMel strain) on the vectorial capacity ofAedes aegyptiforDirofilaria immitiswas studied. Our results showed thatWolbachiadoes not affect the phenotype of mosquito survival or the prevalence, number, and molting rate of third-stage larvae in both susceptible and resistant strains ofAe.aegypti. RNA-seq analysis of Malpighian tubules at 2 days post-infection withD. immitisshowed the differentially expressed genes (DEGs) with and without wMel infection. No characteristic immune-related gene expression patterns were observed among the DEGs. No significant change in the amount ofWolbachiawas observed in theAe. aegyptiafterD. immitisinfection. Our results suggest that infection ofD. immitisinAe. aegyptipopulations will not interfere withWolbachia-based vector control strategies in dengue-endemic areas where cases ofD. immitisare present. This study demonstrated the veterinary medical validity of a dengue control program usingWolbachia.
Identification of Southeast AsianAnophelesmosquito species using MALDI-TOF mass spectrometry
Authors: Chaumeau Victor , Piarroux Martine , Kulabkeeree Thithiworada , Sawasdichai Sunisa , Inta Aritsara , Watthanaworawit Wanitda , Nosten François , Piarroux Renaud , Nabet Cécile 
Published: March 2024

AbstractMalaria control in South-East Asia remains a challenge, underscoring the importance of accurately identifying malaria mosquitoes to understand transmission dynamics and improve vector control. Traditional methods such as morphological identification require extensive training and cannot distinguish between sibling species, while molecular approaches are costly for extensive screening. Matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid and cost-effective tool forAnophelesspecies identification, yet its current use is limited to few specialized laboratories. This study aimed to develop and validate an online reference database for MALDI-TOF MS identification of Southeast AsianAnophelesspecies. The database, constructed using the in-house data analysis pipeline MSI2 (Sorbonne University), comprised 2046 head mass spectra from 209 specimens collected at the Thailand-Myanmar border. Molecular identification via COI and ITS2 DNA barcodes enabled the identification of 20 sensu stricto species and 5 sibling species complexes. The high quality of the mass spectra was demonstrated by a MSI2 median score (min-max) of 61.62 (15.94-77.55) for correct answers, using the best result of four technical replicates of a test panel. Applying an identification threshold of 45, 93.9% (201/214) of the specimens were identified, with 98.5% (198/201) consistency with the molecular taxonomic assignment. In conclusion, MALDI-TOF MS holds promise for malaria mosquito identification and can be scaled up for entomological surveillance in Southeast Asia. The free online sharing of our database on the MSI2 platform represents an important step towards the broader use of MALDI-TOF MS in malaria vector surveillance.Author summaryMosquito-borne diseases like malaria are on the rise globally, and climate change may exacerbate this global threat. Accurate identification ofAnophelesmosquitoes, the malaria vectors, is crucial for understanding and controlling the disease. Unfortunately, morphological identification methods require extensive training and molecular methods can be time-consuming, especially when analyzing large samples. In this study, we established a reference database for identifying 25 species of Southeast AsianAnophelesusing mass spectrometry, a rapid method based on protein fingerprinting. Using a test panel, we demonstrated the effectiveness of this innovative approach in identifying Southeast AsianAnophelesvectors. Importantly, the online sharing of our database marks an important step towards wider application of the tool, thereby contributing to the global effort to combat malaria.
Cryo-EM Structure of the R388 plasmid conjugative pilus reveals a helical polymer characterised by an unusual pilin/phospholipid binary complex
Authors: Vadakkepat Abhinav K. , Xue Songlin , Redzej Adam , Smith Terry K. , Ho Brian , Waksman Gabriel 
Published: March 2024

AbstractBacterial conjugation is a process by which DNA is transferred unidirectionally from a donor cell to a recipient cell. It is the main means by which antibiotics resistance genes spread among bacterial populations. It is crucially dependent upon the elaboration of an extracellular appendage, termed “pilus”, by a large double-membrane spanning secretion system termed conjugative “type IV secretion system”. Here we present the structure of the conjugative pilus encoded by the R388 plasmid. We demonstrate that, as opposed to all conjugative pili produced so far for cryo-EM structure determination, that encoded by the R388 plasmid is greatly stimulated by the presence of recipient cells. Comparison of its cryo-EM structure with existing conjugative pilus structures highlights a number of important differences between the R388 pilus structure and that of its homologues, the most prominent being the highly distinctive conformation of its bound lipid.
Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows
Authors: Kwon Yumi , Woo Jongmin , Yu Fengchao , Williams Sarah M. , Markillie Lye Meng , Moore Ronald J. , Nakayasu Ernesto S. , Chen Jing , Campbell-Thompson Martha , Mathews Clayton E. , Nesvizhskii Alexey I. , Qian Wei-Jun , Zhu Ying 
Published: March 2024

AbstractMultiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.