bluuebunny/crossref_metadata_2025_split huggingface dataset:
Role of the complement system in Long COVID
Authors: Farztdinov Vadim , Zühlke Boris , Sotzny Franziska , Steinbeis Fridolin , Seifert Martina , Kedor Claudia , Wittke Kirsten , Tober-Lau Pinkus , Zoller Thomas , Textoris-Taube Kathrin , Ludwig Daniela , Dierks Clemens , Bierbaum Dominik , Sander Leif Erik , Hanitsch Leif G , Witzenrath Martin , Kurth Florian , Mülleder Michael , Scheibenbogen Carmen , Ralser Markus 
Published: March 2024

AbstractLong COVID, or Post-Acute COVID Syndrome (PACS), may develop following SARS-CoV-2 infection, posing a substantial burden to society. Recently, PACS has been linked to a persistent activation of the complement system (CS), offering hope for both a diagnostic tool and targeted therapy. However, our findings indicate that, after adjusting proteomics data for age, body mass index and sex imbalances, the evidence of complement system activation disappears. Furthermore, proteomic analysis of two orthogonal cohorts—one addressing PACS following severe acute phase and another after a mild acute phase—fails to support the notion of persistent CS activation. Instead, we identify a proteomic signature indicative of either ongoing infections or sustained immune activation similar to that observed in acute COVID-19, particularly within the mild-PACS cohort.
(Re-)modelling of the disease and mortality burden of the 1918-1920 influenza pandemic in Zurich, Switzerland
Authors: Ziegler Ella , Matthes Katarina L. , Middelkamp Peter W. , Schünemann Verena , Althaus Christian L. , Rühli Frank , Staub Kaspar 
Published: March 2024

AbstractBackgroundOur study aims to enhance future pandemic preparedness by integrating lessons from historical pandemics, focusing on the multidimensional analysis of past outbreaks. It addresses the gap in existing modelling studies by combining various pandemic parameters in a comprehensive setting. Using Zurich as a case study, we seek a deeper understanding of pandemic dynamics to inform future scenarios.Data and methodsWe use newly digitized weekly aggregated epidemic/pandemic time series (incidence, hospitalisations, mortality and sickness absences from work) to re-model the 1918-1920 pandemic in Zurich and investigate how different parameters correspond, how transmissibility changed during the different waves, and how public health interventions were associated with changes in these pandemic parameters.ResultsIn general, the various time series show a good temporal correspondence, but differences in their expression can also be observed. The first wave in the summer of 1918 did lead to illness, absence from work and hospitalisations, but to a lesser extent to increased mortality. In contrast, the second, longest and strongest wave in the autumn/winter of 1918 also led to greatly increased (excess) mortality in addition to the burden of illness. The later wave in the first months of 1920 was again associated with an increase in all pandemic parameters. Furthermore, we can see that public health measures such as bans on gatherings and school closures were associated with a decrease in the course of the pandemic, while the lifting or non-compliance with these measures was associated with an increase of reported cases.DiscussionOur study emphasizes the need to analyse a pandemic’s disease burden comprehensively, beyond mortality. It highlights the importance of considering incidence, hospitalizations, and work absences as distinct but related aspects of disease impact. This approach reveals the nuanced dynamics of a pandemic, especially crucial during multi-wave outbreaks.
Reconciling founder variant multiplicity of HIV-1 infection with the rate of CD4+decline
Authors: Baxter James , Arenas Ch. Julián Villabona , Thompson Robin N. , Hué Stéphane , Regoes Roland R. , Kouyos Roger D. , Günthard Huldrych F. , Albert Jan , Brown Andrew Leigh , Atkins Katherine E. 
Published: March 2024

AbstractThree quarters of new HIV-1 infections are reported to be initiated by a single genetic variant. Infections initiated by multiple variants have been linked with higher recipient set point viral loads (SpVL) and a faster rate of CD4+T cell decline, indicative of a worse clinical prognosis if left untreated. These findings have not been universally replicated, however, and a mechanism through which multiple variants might lead to a worse prognosis is yet to be elucidated. In this study, we first summarised the existing evidence for this ‘dose response’ phenomenon for HIV-1, and quantified how likely we are to observe a true difference in set point viral load between multiple and single variant infections. Next, we considered whether the association between higher SpVL and multiple variant infection could exist in the absence of a causal mechanism. For a fixed diversity, high transmitter SpVL could simultaneously lead to high recipient SpVL through the inheritance of a ‘high virulence’ genotype and a greater probability that recipient infection is initiated by multiple genetic variants. Nonetheless, a high transmitter SpVL also shortens the duration of infection, consequently reducing the likelihood of the higher SpVL individual transmitting and restricting the overall accumulation of viral diversity. We combined data-driven models of transmission, heritability and HIV-1 disease progression to test whether an association between multiple variant infection and clinical progression is expected. First, we found that we are unlikely to record a significant difference in SpVL between multiple and single variant infections, at frequencies of multiple variant infections consistent with empirical observations. Second, we found that we would not expect multiple variant infections to lead to higher SpVL or faster CD4+T cell decline without a causal mechanism. Specifically, the probability that infection is initiated by multiple variants is greatest at the highest transmitter SpVLs, yet the relationship between transmitter and recipient SpVL is relatively weak. This finding supports the hypothesis that a within-patient causal mechanism is required to explain the association of multiple variant infection with higher viral loads and faster CD4+T cell decline. Further investigation into events happening during and just after transmission are required to enhance our understanding of this association.
Common variants at 22q12.2 are associated with susceptibility to Tuberculosis
Authors: Chang Xuling , Li Zheng , Thai Phan Vuong Khac , Ha Dang Thi Minh , Thuong Nguyen Thuy Thuong , Silcocks Matthew , Chee Cynthia Bin Eng , Nhu Nguyen Thi Quynh , Heng Chew-Kiat , Teo Yik Ying , Yuan Jian-Min , Koh Woon-Puay , Caws Maxine , Khor Chiea Chuen , Dorajoo Rajkumar , Dunstan Sarah J 
Published: March 2024

AbstractTuberculosis (TB) continues to be a leading cause of morbidity and mortality worldwide. Past genome-wide association studies (GWAS) have explored TB susceptibility across various ethnic groups, yet a significant portion of TB heritability remains unexplained. In this study, we conducted GWAS in the Singapore Chinese and Vietnamese, followed by a comprehensive meta-analysis incorporating independent East Asian data, and identified a novel pulmonary TB (PTB) susceptibility locus at 22q12.2 [rs6006426, OR(95%Cl)=1.097(1.066, 1.130),Pmeta=3.31×10-10]. Our lead SNP was found to affect the expression ofSF3A1in various immune-related cells (Pranging from 1.48×10-9to 6.17×10-18). Furthermore, a significant association was observed between rs6006426 and cigarette smoking (P<0.044). When exploring the interplay between genetic marker, smoking and TB, our findings indicated that smoking status significantly mediated the effect of rs6006426 on PTB (βindirect-effect=-0.004,Pindirect-effect=0.020). Our findings offer novel insights into the genetic factors underlying TB and reveals new avenues for understanding its etiology.
Predicting absolute protein folding stability using generative models
Authors: Cagiada Matteo , Ovchinnikov Sergey , Lindorff-Larsen Kresten 
Published: March 2024

AbstractWhile there has been substantial progress in our ability to predict changes in protein stability due to amino acid substitutions, progress has been slower in methods to predict the absolute stability of a protein. Here we show how a generative model for protein sequence can be leveraged to predict absolute protein stability. We benchmark our predictions across a broad set of proteins and find a mean error of 1.5 kcal/mol and a correlation coefficient of 0.7 for the absolute stability across a range of natural, small–medium sized proteins up to ca. 150 amino acid residues. We analyse current limitations and future directions including how such model may be useful for predicting conformational free energies. Our approach is simple to use and freely available via an online implementation.
MetagenomicKG: a knowledge graph for metagenomic applications
Authors: Ma Chunyu , Liu Shaopeng , Koslicki David 
Published: March 2024

AbstractMotivationThe sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities and their interrelations into a cohesive network. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored.ResultsWe presentMetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis.MetagenomicKGintegrates taxonomic, functional, and pathogenesis-related information from widely used databases, and further links these with established biomedical knowledge graphs to expand biological connections. Through several use cases, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction.Availability and ImplementationThe source code and technical details for constructing theMetagenomicKGand reproducing all analyses are available at Github:https://github.com/KoslickiLab/MetagenomicKG. We also host a Neo4j instance:http://mkg.cse.psu.edu:7474for accessing and querying this graph.Contactdmk333@psu.eduSupplementary informationavailable atBioinformaticsonline.
Protein Language Models Expose Viral Mimicry and Immune Escape
Authors: Ofer Dan , Linial Michal 
Published: March 2024

AbstractMotivationViruses elude the immune system through molecular mimicry, adopting biophysical characteristics of their host. We adapt protein language models (PLMs) to differentiate between human and viral proteins. Understanding where the immune system and our models make mistakes could reveal viral immune escape mechanisms.ResultsWe applied pretrained deep-learning PLMs to predict viral from human proteins. Our predictors show state-of-the-art results with AUC of 99.7%. We use interpretable error analysis models to characterize viral escapers. Altogether, mistakes account for 3.9% of the sequences with viral proteins being disproportionally misclassified. Analysis of external variables, including taxonomy and functional annotations, indicated that errors typically involve proteins with low immunogenic potential, viruses specific to human hosts, and those using reverse-transcriptase enzymes for their replication. Viral families causing chronic infections and immune evasion are further enriched and their protein mimicry potential is discussed. We provide insights into viral adaptation strategies and highlight the combined potential of PLMs and explainable AI in uncovering mechanisms of viral immune escape, contributing to vaccine design and antiviral research.Availability and implementationData and results available inhttps://github.com/ddofer/ProteinHumVir.Contactmichall@cc.huji.ac.il
Development of an engineered extracellular vesicles-based vaccine platform for combined delivery of mRNA and protein to induce functional immunity
Authors: Luo Xin , McAndrews Kathleen M. , Arian Kent A. , Morse Sami J. , Boeker Viktoria , Kumbhar Shreyasee V. , Hu Yingying , Mahadevan Krishnan K. , Church Kaira A. , Chitta Sriram , Ryujin Nicolas T. , Hensel Janine , Dai Jianli , Dowlatshahi Dara P. , Sugimoto Hikaru , Kirtley Michelle L. , LeBleu Valerie S. , Shalapour Shabnam , Simmons Joe H. , Kalluri Raghu 
Published: March 2024

AbstractmRNA incorporated in lipid nanoparticles (LNPs) became a new class of vaccine modality for induction of immunity against COVID-19 and ushered in a new era in vaccine development. Here, we report a novel, easy-to-execute, and cost effective engineered extracellular vesicles (EVs)-based combined mRNA and protein vaccine platform (EVX-M+Pvaccine) and explore its utility in proof-of-concept immunity studies in the settings of cancer and infectious disease. As a first example, we engineered EVs to contain ovalbumin mRNA and protein (EVOvaM+P) to serve as cancer vaccine against ovalbumin-expressing melanoma tumors. EVOvaM+Padministration to mice with established melanoma tumors resulted in tumor regression associated with effective humoral and adaptive immune responses. As a second example, we generated engineered EVs, natural nanoparticle carriers shed by all cells, that contain mRNA and protein Spike (S) protein to serve as a combined mRNA and protein vaccine (EVSpikeM+Pvaccine) against SARS-CoV-2 infection. EVSpikeM+Pvaccine administration in mice and baboons elicited robust production of neutralizing IgG antibodies against RBD (receptor binding domain) of S protein and S protein specific T cell responses. Our proof-of-concept study describes a new platform with an ability for rapid development of combination mRNA and protein vaccines employing EVs for deployment against cancer and other diseases.
Real-world impact of Elexacaftor-Tezacaftor-Ivacaftor treatment in young people with Cystic Fibrosis: A longitudinal study
Authors: Connett GJ , Maguire S , Larcombe TC , Scanlan N , Shinde SS , Muthukumarana T , Bevan A , Keogh RH , Legg JP 
Published: March 2024

AbstractIntroductionElexacaftor, Tezacaftor, Ivacaftor (ETI) became available in the UK in August 2020 to treat people with Cystic Fibrosis (CF) aged > 12 years. We report a real-world study of clinical outcomes in young people treated with ETI at our CF centre within the first two years of its availability.MethodsParticipants aged 12-17 were identified within our clinic, with demographic data supplemented by the UK CF registry. Comprehensive outcome data spanning two years pre- and two years post-initiation of CFTR modulators were compiled from various local sources, including patient records, medication delivery logs, and clinical notes.ResultsOf the 62 patients started on ETI (32 male, mean age 13.3 years), most (76%) were homozygous for the F508del mutation. Three discontinuations occurred: one pregnancy, two related to side effects. Adherence was high (Proportion of Days covered >90% both years). Following ETI initiation there was a significant increase in mean FEV1% (+11.7 units; 95% CI 7.4 – 15.6), sustained throughout the two-year treatment period. There was no association between baseline lung function and the degree of improvement or rate of decline post-treatment. Improvements were similar for all treatable genotypes. There was a small increase in BMI z-score at four months of treatment, returning to baseline by 24 months. There was a marked reduction in the need for intravenous antibiotics.ConclusionsETI use in adolescents in a real-world setting led to sustained improvements in health outcomes, consistent with those seen in open trial extension studiesWhat is already known on this topic -Clinical trials have demonstrated the efficacy of the highly effective CFTR modulator ETI in improving health outcomes for CF patients. However, there is a significant gap in understanding its real-world impact, particularly in young patients where adherence to optimise long-term outcomes is crucial.What this study adds -ETI provides sustained real-world benefits in young people with CF, including better lung function and reduced need for intravenous antibiotic treatment. High adherence likely plays a role.How this study might affect research, practice or policy -These findings support the widespread adoption of ETI in eligible CF patients and emphasise the need for further research to assess its long-term benefits and optimal integration into CF treatment protocols.
Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements
Authors: Metherall Brady , Berryman Anna K. , Brennan Georgia S. 
Published: March 2024

AbstractBackgroundChronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored.MethodsIn this study, we focus on CKD classification and creatinine prediction using three sets of features; at-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. Using 10-fold cross-validation, we calculate metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and mean squared error.ResultsOur results reveal RF achieves superior accuracy (92.5%) in at-home CKD classification over ANNs (82.9%). ANNs achieve a higher TPR (92.0%) but a lower TNR (67.9%) compared with RFs (90.0% and 95.8%, respectively). For monitoring and laboratory features, both methods achieve accuracies exceeding 98%. The R2 score for creatinine regression is approximately 0.3 higher with laboratory features than at-home features. Feature importance analysis identifies key clinical variables hemoglobin and blood urea, and key comorbidities hypertension and diabetes mellitus, in agreement with previous studies.ConclusionsMachine learning models, particularly RFs, exhibit promise in CKD diagnosis and highlight significant features in CKD detection. Moreover, such models may assist in screening a general population using at-home features—potentially increasing early detection of CKD, thus improving patient care and offering hope for a more effective approach to managing this prevalent health condition.
A Protein Language Model for Exploring Viral Fitness Landscapes
Authors: Ito Jumpei , Strange Adam , Liu Wei , Joas Gustav , Lytras Spyros ,   The Genotype to Phenotype Japan (G2P-Japan) Consortium, Sato Kei 
Published: March 2024

AbstractSuccessively emerging SARS-CoV-2 variants lead to repeated epidemic surges through escalated spreading potential (i.e., fitness). Modeling genotype–fitness relationship enables us to pinpoint the mutations boosting viral fitness and flag high-risk variants immediately after their detection. Here, we introduce CoVFit, a protein language model able to predict the fitness of variants based solely on their spike protein sequences. CoVFit was trained with genotype–fitness data derived from viral genome surveillance and functional mutation data related to immune evasion. When limited to only data available before the emergence of XBB, CoVFit successfully predicted the higher fitness of the XBB lineage. Fully-trained CoVFit identified 549 fitness elevation events throughout SARS-CoV-2 evolution until late 2023. Furthermore, a CoVFit-based simulation was able to predict the higher fitness of JN.1 subvariants before their detection. Our study provides both insight into the SARS-CoV-2 fitness landscape and a novel tool potentially transforming viral genome surveillance.
High-resolution genomic ancestry reveals mobility in early medieval Europe
Authors: Speidel Leo , Silva Marina , Booth Thomas , Raffield Ben , Anastasiadou Kyriaki , Barrington Christopher , Götherström Anders , Heather Peter , Skoglund Pontus 
Published: March 2024

AbstractAncient DNA has unlocked new genetic histories and shed light on archaeological and historical questions, but many known and unknown historical events have remained below detection thresholds because subtle ancestry changes are challenging to reconstruct. Methods based on sharing of haplotypes1,2and rare variants3,4can improve power, but are not explicitly temporal and have not been adopted in unbiased ancestry models. Here, we developTwigstats, a new approach of time-stratified ancestry analysis that can improve statistical power by an order of magnitude by focusing on coalescences in recent times, while remaining unbiased by population-specific drift. We apply this framework to 1,151 available ancient genomes, focussing on northern and central Europe in the historical period, and show that it allows modelling of individual-level ancestry using preceding genomes and provides previously unavailable resolution to detect broader ancestry transformations. In the first half of the first millennium ∼1-500 CE (Common Era), we observe an expansion of Scandinavian-related ancestry across western, central, and southern Europe. However, in the second half of the millennium ∼500-1000 CE, ancestry patterns suggest the regional disappearance or substantial admixture of these ancestries in multiple regions. Within Scandinavia itself, we document a major ancestry influx by ∼800 CE, when a large proportion of Viking Age individuals carried ancestry from groups related to continental Europe. This primarily affected southern Scandinavia, and was differentially represented in the western and eastern directions of the wider Viking world. We infer detailed ancestry portraits integrated with historical, archaeological, and stable isotope evidence, documenting mobility at an individual level. Overall, our results are consistent with substantial mobility in Europe in the early historical period, and suggest that time-stratified ancestry analysis can provide a new lens for genetic history.