bluuebunny/crossref_metadata_2025 huggingface dataset:
SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models
Authors: de Almeida Bernardo P , Dalla-Torre Hugo , Richard Guillaume , Blum Christopher , Hexemer Lorenz , Gelard Maxence , Mendoza-Revilla Javier , Pandey Priyanka , Laurent Stefan , Lopez Marie , Laterre Alexandre , Lang Maren , Sahin Ugur , Beguir Karim , Pierrot Thomas
Published: March 2024

Foundation models have achieved remarkable success in several fields such as natural language processing, computer vision and more recently biology. DNA foundation models in particular are emerging as a promising approach for genomics. However, so far no model has delivered granular, nucleotide-level predictions across a wide range of genomic and regulatory elements, limiting their practical usefulness. In this paper, we build on our previous work on the Nucleotide Transformer (NT) to develop a segmentation model, SegmentNT, that processes input DNA sequences up to 30kb-long to predict 14 different classes of genomic elements at single nucleotide resolution. By utilizing pre-trained weights from NT, SegmentNT surpasses the performance of several ablation models, including convolution networks with one-hot encoded nucleotide sequences and models trained from scratch. SegmentNT can process multiple sequence lengths with zero-shot generalization for sequences of up to 50kb. We show improved performance on the detection of splice sites throughout the genome and demonstrate strong nucleotide-level precision. Because it evaluates all gene elements simultaneously, SegmentNT can predict the impact of sequence variants not only on splice site changes but also on exon and intron rearrangements in transcript isoforms. Finally, we show that a SegmentNT model trained on human genomic elements can generalize to elements of different human and plant species and that a trained multispecies SegmentNT model achieves stronger generalization for all genic elements on unseen species. In summary, SegmentNT demonstrates that DNA foundation models can tackle complex, granular tasks in genomics at a single-nucleotide resolution. SegmentNT can be easily extended to additional genomic elements and species, thus representing a new paradigm on how we analyze and interpret DNA. We make our SegmentNT-30kb human and multispecies models available on our github repository in Jax and HuggingFace space in Pytorch.
Predicting absolute protein folding stability using generative models
Authors: Cagiada Matteo , Ovchinnikov Sergey , Lindorff-Larsen Kresten
Published: March 2024

While 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.
Emergent time scales of epistasis in protein evolution
Authors: Di Bari Leonardo , Bisardi Matteo , Cotogno Sabrina , Weigt Martin , Zamponi Francesco
Published: March 2024

We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. The model quantitatively reproduces epistatic phenomena such as contingency and entrenchment, as well as the loss of predictability in protein evolution observed in deep mutational scanning experiments of distant homologs. It thereby deepens our understanding of the interplay between mutation and selection in shaping protein diversity and novel functions, allows one to statistically forecast evolution, and challenges the prevailing independent-site models of protein evolution, which are unable to capture the fundamental importance of epistasis.
MetagenomicKG: a knowledge graph for metagenomic applications
Authors: Ma Chunyu , Liu Shaopeng , Koslicki David
Published: March 2024

Motivation

The 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.

Results

We 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 Implementation

The 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.

Contact

dmk333@psu.edu

Supplementary information

available atBioinformaticsonline.
Protein Language Models Expose Viral Mimicry and Immune Escape
Authors: Ofer Dan , Linial Michal
Published: March 2024

Motivation

Viruses 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.

Results

We 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 implementation

Data and results available inhttps://github.com/ddofer/ProteinHumVir.

Contact

michall@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

mRNA 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.
Data Imbalance in Drug Response Prediction - Multi-Objective Optimization Approach in Deep Learning Setting
Authors: Narykov Oleksandr , Zhu Yitan , Brettin Thomas , Evrard Yvonne A. , Partin Alexander , Xia Fangfang , Shukla Maulik , Vasanthakumari Priyanka , Doroshow James H. , Stevens Rick L.
Published: March 2024

Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly exper-iments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning (ML) models that attempt to reason over complex data space of small compounds and biological characteris-tics of tumors. However, the data depth is still lacking compared to computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and increase the generalizability of the DRP mod-els, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy (MOORLE) loss function and plug-ging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make sugges-tions for further potential application of the work to promote equitable outcomes in the healthcare field. Availability: https://github.com/AlexandrNP/MOORLE Contact: onarykov@anl.gov
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

Introduction

Elexacaftor, 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.

Methods

Participants 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.

Results

Of 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.

Conclusions

ETI use in adolescents in a real-world setting led to sustained improvements in health outcomes, consistent with those seen in open trial extension studies

What 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

Background

Chronic 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.

Methods

In 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.

Results

Our 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.

Conclusions

Machine 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

Successively 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

Ancient 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.
Revisiting the activity of two poly(vinyl chloride)- and polyethylene-degrading enzymes
Authors: Stepnov Anton A. , Lopez-Tavera Esteban , Klauer Ross , Lincoln Clarissa L. , Chowreddy Ravindra R. , Beckham Gregg T. , Eijsink Vincent G. H. , Solomon Kevin , Blenner Mark , Vaaje-Kolstad Gustav
Published: March 2024

Biocatalytic degradation of non-hydrolyzable plastics is a rapidly growing field of research, driven by the global accumulation of waste. Enzymes capable of cleaving the carbon-carbon bonds in synthetic polymers are highly sought-after as they may provide tools for environmentally friendly plastic recycling. Despite some reports of oxidative enzymes acting on non-hydrolyzable plastics, including polyethylene or poly(vinyl chloride), the notion that these materials are susceptible to efficient enzymatic degradation remains controversial, partly driven by a general lack of studies independently reproducing previous observations. We attempted to replicate two recent studies reporting that deconstruction of polyethylene and poly(vinyl chloride) can be achieved using an insect hexamerin fromGalleria mellonella(so-called “Ceres”) or a bacterial catalase-peroxidase fromKlebsiella sp., respectively. Reproducing previously described experiments with the recombinant proteins, we did not observe any activity on plastics using multiple reaction conditions and multiple substrate types. Digging deeper into the discrepancies between the previous data and our observations, we show how and why the original experimental results may have been misinterpreted, leading to the erroneous claim that enzymatic deconstruction of polyethylene and poly(vinyl chloride) had occurred. Our results should lead to caution when interpreting the growing amount of literature claiming enzymatic degradation of non-hydrolyzable plastics.
Imputation of ancient canid genomes reveals inbreeding history over the past 10,000 years
Authors: Bougiouri Katia , Aninta Sabhrina Gita , Charlton Sophy , Harris Alex , Carmagnini Alberto , Piličiauskienė Giedrė , Feuerborn Tatiana R. , Scarsbrook Lachie , Tabadda Kristina , Blaževičius Povilas , Parker Heidi G. , Gopalakrishnan Shyam , Larson Greger , Ostrander Elaine A. , Irving-Pease Evan K. , Frantz Laurent A.F. , Racimo Fernando
Published: March 2024

The multi-millenia long history between dogs and humans has placed them at the forefront of archeological and genomic research. Despite ongoing efforts including the analysis of ancient dog and wolf genomes, many questions remain regarding their geographic and temporal origins, and the microevolutionary processes that led to the diversity of breeds today. Although ancient genomes provide valuable information, their use is hindered by low depth of coverage and post-mortem damage, which inhibits confident genotype calling. In the present study, we assess how genotype imputation of ancient dog and wolf genomes, utilising a large reference panel, can improve the resolution provided by ancient datasets. Imputation accuracy was evaluated by down-sampling high coverage dog and wolf genomes to 0.05-2x coverage and comparing concordance between imputed and high coverage genotypes. We measured the impact of imputation on principal component analyses and runs of homozygosity. Our findings show high (R2>0.9) imputation accuracy for dogs with coverage as low as 0.5x and for wolves as low as 1.0x. We then imputed a dataset of 90 ancient dog and wolf genomes, to assess changes in inbreeding during the last 10,000 years of dog evolution. Ancient dog and wolf populations generally exhibited lower inbreeding levels than present-day individuals. Interestingly, regions with low ROH density maintained across ancient and present-day samples were significantly associated with genes related to olfaction and immune response. Our study indicates that imputing ancient canine genomes is a viable strategy that allows for the use of analytical methods previously limited to high-quality genetic data.
Population structure and genetic diversity of the Critically Endangered bowmouth guitarfish (Rhina ancylostomus) in the Northwest Indian Ocean
Authors: Kipperman Marja J. , Jabado Rima W. , Haque Alifa Banthe , Fernando Daniel , Anjani P.A.D.L , Spaet Julia L.Y. , Humble Emily
Published: March 2024

The bowmouth guitarfish (Rhina ancylostomus) is a unique and relatively understudied species of wedgefish with a distribution spanning the Indo-Pacific Oceans. Due to targeted and bycatch fisheries, this species is experiencing serious declines across its range. It is now considered among the most threatened species of elasmobranch. Despite this, species-specific management is limited, particularly around primary fishing hotspots. This is in part due to knowing very little about fundamental population processes. Here, we combine mitochondrial and single nucleotide polymorphism (SNP) data to carry out the first genetic assessment ofR. ancylostomusacross the Northwest Indian Ocean. We find evidence for population structure across the species’ northwest range that is best explained by dispersal constraints and therefore emphasises the importance of habitat connectivity. We also uncover a cline in genetic variation from east to west of the sampling distribution and identify regions in potential need of heightened protection. In doing so, our study provides baseline information to support conservation management ofR. ancylostomusand highlights the value of genomic data in elasmobranch conservation.
Welding PROxAb Shuttles: A modular approach for generating bispecific antibodies via site-specific protein-protein conjugation
Authors: Lehmann Tanja , Schneider Hendrik , Tonillo Jason , Schanz Jennifer , Schwarz Daniel , Schröter Christian , Kolmar Harald , Hecht Stefan , Anderl Jan , Rasche Nicolas , Rieker Marcel , Dickgiesser Stephan
Published: March 2024

Targeted protein degradation is an innovative therapeutic strategy to selectively eliminate disease-causing proteins. Exemplified by proteolysis-targeting chimeras (PROTACs), it has shown promise in overcoming drug resistance and targeting previously undruggable proteins. However, PROTACs face challenges such as low oral bioavailability and limited selectivity. The recently published PROxAb Shuttle technology offers a solution enabling targeted delivery of PROTACs using antibodies fused with PROTAC-binding domains derived from camelid single-domain antibodies (VHHs). Here, a modular approach to quickly generate PROxAb Shuttles by enzymatically coupling PROTAC-binding VHHs to off- the-shelf antibodies was developed. The resulting conjugates retained their target binding and internalization properties and incubation with BRD4-targeting PROTACs resulted in formation of defined PROxAb-PROTAC complexes. These complexes selectively induced degradation of the BRD4 protein, resulting in cytotoxicity specifically in cells expressing the antibody’s target. The chemoenzymatic approach described here provides a versatile and efficient solution for generating antibody-VHH conjugates for targeted protein degradation applications but could also be used to combine antibody and VHH binders to generate bispecific antibodies for further applications.
A numbers game: Mosquito-based arbovirus surveillance in two distinct geographic regions of Latin America
Authors: Mojica Jacqueline , Arévalo Valentina , Juarez Jose G. , Galarza Ximena , Gonzalez Karla , Carrazco Andrés , Suazo Harold , Harris Eva , Coloma Josefina , Ponce Patricio , Balmaseda Angel , Cevallos Varsovia
Published: March 2024

Aedesmosquitoes, as vectors of medically important arthropod-borne viruses (arboviruses), constitute a major public health threat that requires entomological and epidemiological surveillance to guide vector control programs to prevent and reduce disease transmission. In this study, we present the collaborative effort of one year of mosquito-based arbovirus surveillance in two geographically distinct regions of Latin America (Nicaragua and Ecuador). Adult female mosquitoes were collected using backpack aspirators in over 2,800 randomly selected households (Nicaragua, Ecuador) and 100 key sites (Nicaragua) from eight distinct communities (Nicaragua: 2, Ecuador: 6). A total of 1,358 mosquito female pools were processed for RNA extraction and viral RNA detection using real-time RT-PCR. Ten positive dengue virus (DENV) pools were detected (3 in Nicaragua and 7 in Ecuador), all of which were found during the rainy season and matched the serotypes found in humans (Nicaragua: DENV-1 and DENV-4; Ecuador: DENV-2). Infection rates ranged from 1.13 to 23.13, with the Nicaraguan communities having the lowest infection rates. Our results demonstrate the feasibility of detecting DENV-infectedAedesmosquitoes in low-resource settings and underscore the need for targeted mosquito arbovirus sampling and testing, providing valuable insights for future surveillance programs in the Latin American region.
Dysregulation of cell state dynamics during early stages of serous endometrial carcinogenesis
Authors: Flesken-Nikitin Andrea , Pirtz Matalin G. , Ashe Christopher S. , Ellenson Lora H. , Cosgrove Benjamin D. , Nikitin Alexander Yu.
Published: March 2024

Serous endometrial carcinoma (SEC) constitutes about 10% of endometrial carcinomas and is one of the most aggressive and lethal types of uterine cancer. Due to the rapid progression of SEC, early detection of this disease is of utmost importance. However, molecular and cellular dynamics during the pre-dysplastic stage of this disease remain largely unknown. Here, we provide a comprehensive census of cell types and their states for normal, pre-dysplastic, and dysplastic endometrium in a mouse model of SEC. This model is associated with inactivation of tumor suppressor genesTrp53andRb1, whose pathways are altered frequently in SEC. We report that pre-dysplastic changes are characterized by an expanded and increasingly diverse immature luminal epithelial cell populations. Consistent with transcriptome changes, cells expressing the luminal epithelial marker TROP2 begin to substitute FOXA2+ cells in the glandular epithelium. These changes are associated with a reduction in number and strength of predicted interactions between epithelial and stromal endometrial cells. By using a multi-level approach combining single-cell and spatial transcriptomics paired with screening for clinically relevant genes in human endometrial carcinoma, we identified a panel of 44 genes suitable for further testing of their validity as early diagnostic and prognostic markers. Among these genes are known markers of human SEC, such as CDKN2A,and novel markers, such asOAS2 and OASL,members of 2-5A synthetase family that is essential for the innate immune response. In summary, our results suggest an important role of the luminal epithelium in SEC pathogenesis, highlight aberrant cell-cell interactions in pre-dysplastic stages, and provide a new platform for comparative identification and characterization of novel, clinically relevant prognostic and diagnostic markers and potential therapeutic modalities.
Machine learning methods to track dynamic facial function in facial palsy
Authors: Rao Akshita A. , Greene Jacqueline J. , Coleman Todd P.
Published: March 2024

For patients with facial paralysis, the wait for return of facial function and resulting vision risk from poor eye closure, difficulty speaking and eating from flaccid oral sphincter muscles, as well as psychological morbidity from the inability to smile or express emotions through facial movement can be devastating. There are limited methods to assess ongoing facial nerve regeneration: clinicians rely on subjective descriptions, imprecise scales, and static photographs to evaluate facial functional recovery, and thus facial nerve regeneration remains poorly understood. We propose a more precise evaluation of dynamic facial function through video-based machine learning analysis to facilitate a better understanding of the sometimes subtle onset of facial nerve recovery and improve guidance for facial reanimation surgery.

Methods

We here present machine learning methods employing likelihood ratio tests, optimal transport theory, and Mahalanobis distances to: 1) assess the use of defined facial landmarks for binary classification of different facial palsy types; identify regions of asymmetry and potential paralysis during specific facial cues; and 3) quantify palsy severity and map it directly to widely used clinical scores, offering clinicians an objective way to assess facial nerve function.

Results

Our work presents promising results of utilizing videos, rather than static photographs, to provide robust quantitative analyses of dynamic properties for various facial movements without requiring manual assessment.

Significance

The long-term potential of this project is to enable clinicians to have more accurate and timely information to make decisions for facial reanimation surgery which will have drastic consequences on the quality of life for affected patients.
sSocial Determinants of Health and Cumulative Incidence of Mortality among U.S. Adults without Major Chronic Diseases
Authors: Claudel Sophie E. , Verma Ashish
Published: March 2024

Background

Social determinants of health (SDOH) are widely known to contribute to poor health outcomes and premature mortality among individuals with prevalent diseases. Understanding the impact of SDOH on mortality among adults without major chronic diseases may inform public policy in the United States (U.S.).

Methods

We conducted a prospective observational study using the National Health and Nutrition Examination Survey data (1999-2018) among 11,413 adults without hypertension, diabetes, hyperlipidemia, severe obesity, chronic kidney disease, cardiovascular disease, chronic respiratory disease, cancer, liver disease, arthritis, hepatitis B or C, human immunodeficiency virus, or pregnancy. We calculated 15-year adjusted cumulative incidences of all-cause mortality by baseline SDOH and described the trends in the prevalence of adverse SDOH.

Results

The mean ±SD age was 34.9±11.2 years. Over a median follow-up of 10.3 years, 275 participants died. The prevalence of low educational attainment decreased over the study period from 19.8% to 12.1%, while the prevalence of food insecurity increased from 11.5% to 23.1%. The 15-year adjusted cumulative incidences of all-cause mortality were 5.6% (95% CI, 2.8-8.5), 5.2% (95%CI, 3.2-7.3), 4.9% (95%CI, 2.7-7.2), 4.0% (95%CI, 2.8-5.2) for participants who had < 100 % of the federal poverty level, below high school education, food insecurity, and were born in the U.S., respectively. In the final adjusted model, a 1-point increase in cumulative SDOH score was associated with 33% increased risk of all-cause mortality.

Conclusions

In this study of community-dwelling U.S. adults without major chronic diseases, we demonstrate a pronounced increase in all-cause mortality associated with adverse SDOH.
Aging-dependent Change in Th17 and Cytokine Response in Multiple Sclerosis
Authors: Zhu Wen , Revu Shankar , Chen Chenyi , Dahl Megan , Ramkumar Archana , Kelly Conor , McGeachy Mandy J , Xia Zongqi
Published: March 2024

Background

Multiple sclerosis (MS) is a chronic autoimmune disease damaging the central nervous system. Diminished inflammatory disease activity (DA) as people with MS (pwMS) age motivated randomized clinical trials assessing disease-modifying therapy (DMT) discontinuation in older pwMS given the concern for risks outweighing benefits. This study aims to examine whether peripheral production of Myelin Basic Protein (MBP)-driven cytokine responses mediate the aging-associated decline in MS inflammatory DA.

Methods

We included the clinical data of 669 adult pwMS between 2017 and 2022 who enrolled in a clinic-based prospective cohort. From a subset of 80 participants, we isolated fresh peripheral blood mononuclear cells (PBMCs) and cultured with 50μg/ml of MBP (or heat-killed Candida) for 24 hours. We assayed cell culture supernatants for interleukin 17 (IL-17) and interferon gamma (IFN-γ) using Enzyme-Linked Immunosorbent Assay and a subset of the supernatant samples using a commercial human cytokine/chemokine array. We examined the associations between age and annualized relapse rate (ARR) as well as between age and MBP-stimulated cytokine production (by cultured PBMC) using covariate-adjusted linear regressions. We performed mediation analyses to determine the extent to which MBP-driven cytokine response drives the association between age and ARR.

Results

Among 669 pwMS (mean age 51.7±12.7 years, 80.7% women, 89.4% non-Hispanic White), ARR declined with age (β=-0.003, p<0.001). Among the subgroup of 80 pwMS whose cultured PBMCs underwent ex vivo MBP stimulation, IL-17 production declined with age in women (β=-0.27, p=0.04) but not men (β=-0.1, p=0.73). MBP-driven IL-17 response partially mediated the association between older age and lower ARR (24.7% in women, 15.3% in men). In exploratory analyses, older pwMS (≥50 years) had marginally lower (IL-4, MCP-2, MCP-3, PDGF-AA, PDGF-AB/BB) and higher (Fractalkine, MDC) concentrations of several cytokines than younger pwMS (<50 years), while certain cytokines (MCP-2, MDC) mediated whereas others negated the effect of age on ARR.

Conclusion

Diminished peripheral IL-17 response as a potential biological mechanism underlying the aging-dependent decline in MS inflammatory DA warrants further investigation.
Exploring plasma cell motility and extracellular matrix protein biomarkers for primary progressive multiple sclerosis: A pilot study
Authors: Lackey Elijah , Reinke Stephanie , Luo Sheng , Laskowitz Daniel , Eckstein Christopher , Gregory Simon G
Published: March 2024

Primary progressive multiple sclerosis is associated with neurodegeneration and chronic inflammation, and results in the accumulation of gradual disability. This pilot study investigated 92 plasma proteins using proximal extension assay to identify MS subtype-specific biomarkers with a focus on predicting primary progressive MS. We analyzed samples from 66 MS patients (22 relapsing-remitting, 22 secondary progressive, and 22 primary progressive) and 22 controls. ANOVA identified five proteins (ACAN, TMSB10, BST1, CLEC11A, MYOC) with p < 0.05 for differentiating phenotypes of MS, four of which have been previously implicated in MS pathophysiology. However, after correcting for multiple comparisons no individual proteins remained statistically significant. Logistic regression and support vector models using these 5 proteins for predicting primary progressive, in one-vs all-models, against other MS phenotypes and controls were of low accuracy (0.69 and 0.68, respectively). While not immediately translatable, these results lay the groundwork for future studies into MS progression biomarkers.
Tumor and pan-tumor diversity and heterogeneity of cancer tissue microbiomes: a medical ecology analysis across 32 cancer types
Authors: Ma Zhanshan (Sam)
Published: March 2024

Diversity and heterogeneity are hallmarks of any ecosystems including cancer ecosystems. Tumor heterogeneities have been a hot spot in cancer research because of their critical roles in promoting clonal evolution and metastasis of cancer cells and in influencing cancer progression and therapy efficacy. Cancer tissue microbiome as part of tumor microenvironment can influence tumor heterogeneities both directly and indirectly through their “intimate” intracellular and intercellular interactions with human cells including tumor, immune and normal cells. From an ecological perspective, the relationship between tumor microenvironment and tumor heterogeneity is not unlike that between habitat heterogeneity and community heterogeneity. That is, their heterogeneities should be interwoven with each other, and then the net effects of the microbiomes on cancer development, malignant progression, and therapy responses can be either promotive or suppressive depending on the so-termed immune-oncology-microbiome axis or trio. The objective of this study is to assess and interpret the heterogeneity and often conflated diversity of tumor microbiomes. Our findings, from reanalyzing a big microbiome dataset originally distilled from the TCGA (The Cancer Genome Atlas) database by Pooreet al. (2020, Nature) including 16555 tumor microbiome samples from the primary tumor (PT), solid tissue normal (SN) and blood derived normal (B) of 32 cancer types, include: (i) The tumor microbiome heterogeneity (diversity) cancer relationship HCR (DCR),i.e.,the heterogeneity (diversity) differences between PTvs.SN (B) are only significantly in approximately 10%-40% depending on the cancer types. (ii) The pan-tumor HCR (DCR),i.e.,microbiome heterogeneity/diversity differences of same tissue type (e.g., PT) across cancer types (e.g., lung vs. breast cancers) are approximately twice the range of previous tumor-HCR (DCR) (i.e., 30%-80% for pan-tumorvs. 10%-40% of tumor scale). In both tumor and pan-tumor scales, the heterogeneity differences ranges are wider than the diversity ranges. (iii) The NSR values range between 0.4 and 0.8 and in 75% cases NSR>0.5, suggesting that tumor selection plays a dominant role than stochastic drifts in shaping microbiome diversity/heterogeneity patterns. Furthermore, the NSR values are significantly different between PT and NT (B) in 50%-100% (mostly 70%-80%) cases across 32 cancer types, further confirming that it should be the tumor growth that is largely responsible for the dominance of selection forces. Finally, we postulate that the HCR (DCR) should be dynamic with tumor types, progression, microbial taxa, host genomics and physiology, therapy and diets.
Pathways and barriers to becoming physician-scientists for first-generation individuals
Authors: Christophers Briana , Macedo Briana , Weng Jessica , Granovetter Michael C. , Kumar Rachit , Smith Chynna , Andersen Olaf S. , Boothroyd Catharine
Published: March 2024

Introduction

Physician-scientists are uniquely positioned to contribute translational research that will impact patient care and our understanding of disease. Having a diverse cadre of physician-scientists is critical to ensuring that the biomedical research enterprise explores the breadth of problems affecting the nation’s health. The National Institutes of Health has identified diversity, including educational background, to be important for the biomedical workforce. In 2020, less than ten percent of MD-PhD program matriculants were the first in their families to pursue higher education (first-generation) despite the majority of the US population having less than a Bachelor’s degree. Little is known about the specific challenges that first-generation students face, which makes it challenging to address this gap in matriculation.

Methods

This qualitative study used a phenomenological approach to examine the experiences of first-generation individuals, from the applicant stage to the early-career stage. We conducted semi-structured interviews with 41 participants and analyzed responses in accordance with a networked ecological systems theory.

Results

The interviews revealed that first-generation individuals put together a patchwork of support. Whereas many MD-PhD trainees struggle at some point in their training, first-generation individuals tend to lack a support system that may provide proactive advice and prepare them for milestones. Interviews shared a common sentiment of isolation due to both a lack of social capital within medicine and academia, as well as a growing disconnect from their families and community.

Discussion

Key interventions that would support first-generation students include greater access to high-quality information about the pathway, tailored mentorship throughout training, and more financial resources at the application stage. Trainees and early career physician-scientists seek more flexibility, opportunities for finding community, financial guidance and options, and mentorship around building their careers.
The somatic mutation landscape of normal gastric epithelium
Authors: Coorens Tim H.H. , Collord Grace , Jung Hyungchul , Wang Yichen , Moore Luiza , Hooks Yvette , Mahbubani Krishnaa , Saeb-Parsy Kourosh , Campbell Peter J. , Martincorena Iñigo , Leung Suet Yi , Stratton Michael R.
Published: March 2024

The landscapes of somatic mutation in normal cells inform on the processes of mutation and selection operative throughout life, permitting insight into normal ageing and the earliest stages of cancer development. Here, by whole-genome sequencing of 238 microdissections from 30 individuals, including 18 with gastric cancer, we elucidate the developmental trajectories of normal and malignant gastric epithelium. We find that gastric glands are units of monoclonal cell populations which accrue ∼28 somatic single nucleotide variants per year, predominantly attributable to endogenous mutational processes. In individuals with gastric cancer, glands often show elevated mutation burdens due to acceleration of mutational processes linked to proliferation and oxidative damage. These hypermutant glands were primarily detected in the gastric antrum and were mostly associated with chronic inflammation and intestinal metaplasia, known cancer risk factors. Unusually for normal cells, gastric epithelial cells often carry recurrent trisomies of specific chromosomes, which are highly enriched in a subset of individuals. Surveying approximately 8,000 gastric glands by targeted sequencing, we found somatic driver mutations in a distinctive repertoire of known cancer genes, includingARID1A, CTNNB1, KDM6AandARID1B. Their prevalence increases with age to occupy approximately 5% of the gastric epithelial lining by age 60 years. Our findings provide insights into the intrinsic and extrinsic influences on somatic evolution in the gastric epithelium, in healthy, precancerous and malignant states.
Transfer and Capture of Envelope Protein Receptor-Binding Domains in the Retroviruses
Authors: Hötzel Isidro
Published: March 2024

The surface subunit (SU) of the envelope protein (Env) or retroviruses is highly variable due to adaptation to different hosts during their long evolutionary history. Several exogenous and endogenous retroviral gamma-like Env have a receptor-binding domain (RBD) in the amino-terminal region of SU that folds independently from the carboxyterminal SU C-domain. Two structurally distinct RBD classes have been described, one that adopts a modified immunoglobulin (Ig)-like domain fold and a second, in the Env of RDR interference group retroviruses, with a distinct β-sheet fold. Here, the distribution of different RBD classes among exogenous and endogenous gammatype Env was determined by phylogenetic analyses of Env and structural modeling of retroviral SU with AlphaFold2. The patterns of RBD distribution indicate multiple RBD transfer events in the retroviruses. In addition, SU structural modeling identified an endogenous alpharetroviral-like Env in mammalian species with an amino-terminal RBD. This RDB has a typical IgV domain fold closely related both structurally and in sequence to the mammalian signal regulatory proteins (SIRP) α, β and γ, indicating, for the first time, acquisition of an RBD from a mammalian gene by the retroviruses. The results described herein indicate intragenicenvrecombination between retroviruses and between retroviruses and their hosts as a major factor in the evolution of retroviral Env.