bluuebunny/crossref_metadata_2025 huggingface dataset:
Protein Language Model Identifies Disordered, Conserved Motifs Driving Phase Separation
Authors: Zhang Yumeng , Zheng Jared , Zhang Bin
Published: December 2024

Intrinsically disordered regions (IDRs) play a critical role in phase separation and are essential for the formation of membraneless organelles (MLOs). Mutations within IDRs can disrupt their multivalent interaction networks, altering phase behavior and contributing to various diseases. Therefore, examining the evolutionary fitness of IDRs provides valuable insights into the relationship between protein sequences and phase separation. In this study, we utilized the ESM2 protein language model to map the fitness landscape of IDRs. Our findings reveal that IDRs, particularly those actively participating in phase separation, contain conserved amino acids. This conservation is evident through mutational constraints predicted by ESM2 and supported by direct analyses of multiple sequence alignments. These conserved, disordered amino acids include residues traditionally identified as “stickers” as well as “spacers” and frequently form continuous sequence motifs. The strong conservation, combined with their critical role in phase separation, suggests that these motifs act as functional units under evolutionary selection to support stable MLO formation. Our findings underscore the insights into phase separation’s molecular grammar made possible through evolutionary analysis enabled by protein language models.
Within-Family GWAS does not Ameliorate the Decline in Prediction Accuracy across Populations
Authors: Zhang Luyin , Conley Dalton
Published: December 2024

As polygenic prediction extends beyond the research domain to involve clinical applications, the urgency of solving the “portability problem” becomes amplified—that is, the fact that polygenic indices (PGI) constructed based on discovery analysis in one population (typically of exclusively continental European descent) predict poorly in other populations. In the present paper we test whether population differences in genetic nurture, assortative mating, or population stratification contribute to the fact that polygenic indices constructed based on GWAS results from European-descent samples predict more poorly in admixed populations with Native American and African ancestry. We do this by comparing the rates of decline in prediction accuracy of classical-GWAS-based PGIs versus within-family-based PGIs, each estimated in a population of European descent, as they are deployed in two samples of Latino Americans and African Americans. Within-family GWAS putatively eliminates the effects of parental genetic nurture, assortative mating, and population stratification; thus, we can determine whether without those confounding factors in the PGI construction, the relative prediction accuracy in the out-groups is ameliorated. Results show that relative prediction accuracy is not improved, suggesting that the differences across groups can be almost entirely explained by variation in genetic architecture (i.e. allele frequencies and short-range LD) rather than the aforementioned factors. Additional analysis of the impact of genetic architecture on the decline in prediction accuracy supports this conclusion. Future researchers should test within-family analysis at the prediction rather than the discovery stage.
A novel Mannan-specific chimeric antigen receptor M-CAR redirects T cells to interact withCandidaspp. hyphae andRhizopus oryzaespores
Authors: Guimarães Júlia Garcia , de Campos Gabriela Yamazaki , Machado Michele Procópio , Oliveira Brito Patrícia Kellen Martins , dos Reis Thaila Fernanda , Goldman Gustavo Henrique , Palma Patricia Vianna Bonini , de Campos Fraga-Silva Thais Fernanda , Cavallini Daniela Cardoso Umbelino , Venturini James , da Silva Thiago Aparecido
Published: December 2024

Invasive fungal infections (IFIs) are responsible for elevated rates of morbidity and mortality, causing around of 1.5 million deaths annually worldwide. One of the main causative agents of IFIs isCandida albicans, and non-albicansCandidaspecies have emerged as a spreading global public health concernment. Furthermore, COVID-19 has contributed to a boost in the incidence of IFIs, such as mucormycosis, in whichRhizopus oryzaeis the most prevalent causative agent. The effector host immune response against IFIs depends on the activity of T cells, which are susceptible to the regulatory effects triggered by fungal virulence factors. The fungal cell wall plays a crucial role as a virulence factor, and its remodeling compromises the development of a specific T-cell response. The redirection of Jurkat T cells to targetCandidaspp. by recognizing targets expressed on the fungal cell wall can be facilitated using chimeric antigen receptor (CAR) technology. This study generated an M-CAR that contains an scFv with specificity to α-1,6 mannose backbone of fungal mannan, and the expression of M-CAR on the surface of modified Jurkat cells triggered a strong activation againstCandida albicans(hyphae form),Candida tropicalis(hyphae form),Candida parapsilosis(pseudohyphal form), andCandida glabrata(yeast form). Moreover, M-CAR Jurkat cells recognizedRhizopus oryzaespores, which induced high expression of cell activation markers. Thus, a novel Mannan-specific CAR enabled strong signal transduction in modified Jurkat cells in the presence ofCandidaspp. orR. oryzae.
Midfacial retrusion and loss of facial appendages caused by mutation of Pax9 in zebrafish
Authors: Paudel Sandhya , McLeod Sarah , Gjorcheska Stefani , Barske Lindsey
Published: December 2024

Loss of dentition has occurred repeatedly throughout vertebrate evolution. Cyprinid fish, including zebrafish, form teeth only deep within the pharynx, not on their oral jaws. However, zebrafish still robustly express transcription factors associated with mammalian tooth development in the neural crest-derived mesenchyme surrounding the mouth. We investigated whether this expression is vestigial or whether these factors contribute to the formation of non-tooth mesenchymal structures in the oral region, using Pax9 as a test case. Zebrafish homozygous for two differentpax9mutant alleles develop the normal complement of pharyngeal teeth but fail to form the premaxilla bone, most of the maxilla, and nasal and maxillary barbels. Lack of most of the upper jaw complex does not preclude effective feeding in the laboratory environment. We observed a significant deficit ofsp7:EGFP+osteoblasts and adjacentalx4a:DsRed+condensing mesenchyme where the maxilla forms, and no accumulation of either in the premaxillary domain. These phenotypes are not presaged by major disruptions in early facial patterning, Wnt signaling, proliferation, or cell death; however, loss of a small population of Wnt-responsive cells around the maxilla correlates with its stalled growth in mutants. We conclude that Pax9 is not unequivocally required for all vertebrate tooth development, but instead may be broadly involved in the development of a variety of organs forming through mesenchymal condensation around the mouth.
Collateral sensitivity and cross resistance in six species of bacteria exposed to six classes of antibiotics
Authors: Wang Xinyu , Nong Luyuan , Schaar Gioia , Jonker Martijs , de leeuw Wim , ter Kuile Benno H.
Published: December 2024

De novoresistance can be developed in bacteria because of exposure to sublethal concentrations of antibiotics. Once the strain has become resistant to an initial antibiotic, this can cause cross-resistance or collateral sensitivity to a second antimicrobial. Specific collateral sensitivity is rarely conserved across species because the mechanisms triggered in different microorganisms to resist antibiotics are often different. In this study, we explored which collateral sensitivity or cross resistance networks are present in six species of bacteria with inducedde novo-resistance. These six species were induced to become resistant to amoxicillin/cefepime, enrofloxacin, kanamycin, tetracycline, erythromycin and chloramphenicol(1). After that, the collateral sensitivity and the cross-resistance networks were evaluated by measuring increase or decrease of MIC of thirteen antibiotics that are often used in the clinic. Collateral sensitivity for kanamycin occurred in five species of strains resistant to chloramphenicol and tetracycline and for β-lactam in strains of five species resistant to kanamycin. Further genetic analysis clarified thatfusAconsistently mutated in five species of bacteria madede novoresistant against kanamycin, suggesting thatfusAoperates in parallel with the other mechanisms related to antimicrobial resistance Based on considerations of resistance, a treatment protocol starting with chloramphenicol/tetracycline, followed by kanamycin and ending with amoxicillin may eliminate bacteria that have developed resistance against the initial treatment.
Multi-Omics Mapping of Gut Microbiota’s Role in Progesterone Metabolism
Authors: Brandon-Mong Guo-Jie , Chen Mei-Jou , Gicana Ronnie G , Wang Hurng-Yi , Li Chi-Ying , Wu Tien-Yu , Chou Chia-Hung , Tu Yi-An , Chang Yu-Yuan , Lai Yi-Li , Li Po-Ting , Tseng Yu-Lin , Hsiao Tsun-Hsien , Chiang Yin-Ru
Published: December 2024

Background

The female reproductive hormone, progesterone, is crucial in regulating the menstrual cycle and maintaining pregnancy. Despite known correlations between gut microbiota, sex hormones, and host phenotypes, the metabolic fates of progesterone in the human gut remain unclear.

Results

Here, an integrated multi-omics approach was employed to investigate gut microbiota-mediated progesterone metabolism. Fresh fecal samples were collected from infertile women with progesterone treatment (n = 14). Progesterone (1 mM) was amended into fecal cultures to enrich progesterone-metabolizing gut microbes, and the key players were identified through culturomics-based metagenomics analysis. Microbial functions were predicted by tracking temporal changes in metabolite profiles and microbial abundances. The prevalence of progesterone metabolism genes among gut microbiota was explored through functional genomics analysis. Pregnanolone was identified as the primary microbial metabolite of gut progesterone. Members of Enterobacteriaceae and Veillonellaceae were identified as crucial pregnanolone producers; progesterone metabolism genes were abundant in these bacterial families. Moreover, these bacterial families showed significant abundance differences among healthy women (n = 19) and women with miscarriages (n = 41).

Conclusions

This study provides potent therapeutic targets for improving progesterone bioavailability and demonstrates stereoselective production of neurosteroids using characterized bacterial strains and their corresponding metabolic pathways.

Highlights

The microorganisms in the human gut can convert progesterone into several neurologically active steroids.

Progesterone is primarily converted into pregnanolone by the gut microbiota in women with infertility.

Members of the bacterial families Enterobacteriaceae and Veillonellaceae play key roles in the pregnanolone production.

Genes involved in pregnanolone production are prevalent in the genomes of Enterobacteriaceae and Veillonellaceae.

The abundances of Enterobacteriaceae and Veillonellaceae differ significantly between women who experienced healthy pregnancies and those who experienced miscarriages.
Utilising Nanopore direct RNA sequencing of blood from patients with sepsis for discovery of co- and post-transcriptional disease biomarkers
Authors: He Jingni , Ganesamoorthy Devika , Chang Jessie J-Y , Zhang Josh , Trevor Sharon L , Gibbons Kristen S , McPherson Stephen J , Kling Jessica C. , Schlapbach Luregn J , Blumenthal Antje ,   the RAPIDS Study Group, Coin Lachlan JM
Published: December 2024

Background

RNA sequencing of whole blood has been increasingly employed to find transcriptomic signatures of disease states. These studies traditionally utilize short-read sequencing of cDNA, missing important aspects of RNA expression such as differential isoform abundance and poly(A) tail length variation.

Methods

We used Oxford Nanopore Technologies long-read sequencing to sequence native mRNA extracted from whole blood from 12 patients with suspected bacterial and viral sepsis, and compared with results from matching Illumina short-read cDNA sequencing data. Additionally, we explored poly(A) tail length variation, novel transcript identification and differential transcript usage.

Results

The correlation of gene count data between Illumina cDNA and Nanopore RNA-sequencing strongly depended on the choice of analysis pipeline;NanoCountfor Nanopore andKallistofor Illumina data yielded the highest mean Pearson’s correlation of 0.93 at gene level and 0.74 at transcript isoform level. We identified 18 genes significantly differentially polyadenylated and 4 genes with significant differential transcript usage between bacterial and viral infection. Gene ontology gene set enrichment analysis of poly(A) tail length revealed enrichment of long tails in signal transduction and short tails in oxidoreductase molecular functions. Additionally, we detected 594 non-artifactual novel transcript isoforms, including 9 novel isoforms for Immunoglobulin lambda like polypeptide 5 (IGLL5).

Conclusions

Nanopore RNA- and Illumina cDNA-gene counts are strongly correlated, indicating that both platforms are suitable for discovery and validation of gene count biomarkers. Nanopore direct RNA-seq provides additional advantages by uncovering additional post- and co-transcriptional biomarkers, such as poly(A) tail length variation and transcript isoform usage.
Social isolation and mortality risk in late-stage older Japanese: A longitudinal study of community-dwelling residents from 2020 to 2024
Authors: Okawa Yukari
Published: December 2024

Social isolation presents a high risk for mortality. Evidence is limited, however, for older adults, especially in Japan, a country whose life expectancy at birth is the longest in the world. This retrospective longitudinal study included homebound Japanese citizens aged ≥75 years who underwent voluntary checkups conducted by Zentsuji City, Kagawa Prefecture, Japan from 2000 to 2024. The relationship between social isolation and mortality was assessed using the Cox proportional hazards model. Social isolation was defined as having no regular contact with family or friends, and survival information was confirmed by the city’s database as of 1 July, 2024. Of the 3,366 participants (male: 42.4%), 3,024 (male: 42.6%) remained in the final cohort. At study entry, socially isolated participants tended to be older and have fewer exercising habits, worse self-rated health status, worse life satisfaction, and no family members in the same household with them. The mean follow-up time was 2.47 years, during which 9.1% of the participants died. Mortality risk was 2.37 times higher (95% confidence interval [CI]: 1.61–3.48) for socially isolated individuals than for those who were not socially isolated, after controlling for covariates such as physical status, self-rated health, lifestyle, life satisfaction, and sharing a household with family members. The results showed a consistent trend, even after excluding participants with short follow-up periods. Consequently, when evaluating mortality risks for older adults, it is important to consider whether they are socially isolated.
Opinions of the UK general public in using artificial intelligence and “opt-out” models of consent in medical research
Authors: Heseltine-Carp William , Thurston Mark , Allen Michael , Browning Daniel , Courtman Megan , Kasabe Aishwarya , Ifeachor Emmanuel , Mullin Stephen
Published: December 2024

Background

Due to its complexity, Artificial Intelligence often requires large, confidential clinical datasets. 20-30% of the general public remain sceptical of Artificial Intelligence in healthcare due to concerns of data security, patient-practitioner communication, and commercialisation of data/models to third parties. A better understanding of public concerns of Artificial Intelligence is therefore needed, especially in the context of stroke research.

Aims

We aimed to evaluate the opinion of patients and the public in acquiring large clinical datasets using an “opt-out” consent model, in order to train an AI-based tool to predict the future risk of stroke from routine healthcare data. This was in the context of our project ABSTRACT, a UK Medical Research Council study which aims to use AI to predict future risk of stroke from routine hospital data.

Methods

Opinions were gathered from those with lived experience of stroke/TIA, caregivers, and the general public through an online survey, semi-structured focus groups, and 1:1 interviews. Participants were asked about their perceived importance of the project, the acceptability of handling deidentified routine healthcare data without explicit consent, and the acceptability of acquiring these data via an opt-out model of consent model by members within and outside of the routine clinical care team.

Results

Of the 83 that participated, 34% of which had a history of stroke/TIA. Nearly all (99%) supported the project’s aims in using AI to predict stroke risk, acquiring data via an opt-out consent model, and the handling of pseudonymized data by members within and outside of the routine clinical care team.

Conclusion

Both the general public and those with lived experience of stroke/TIA are generally supportive of using large, de-identified medical datasets to train AI models for stroke risk prediction under an opt-out consent model, provided the research is transparent, ethically sound, and beneficial to public health.
Addressing inequalities in vaccination coverage among children aged 12 to 23 months in ten Sub-Saharan African countries: Insights from DHS and MIS Data (2017-2022)
Authors: Xavier Sancho Pedro , Mahoche Manuel , Rondó Patrícia Helen , da Silva Ageo Mário , Flores-Ortiz Renzo , Victor Audêncio
Published: December 2024

Introduction

Vaccination is one of the most effective public health interventions for preventing and controlling infectious diseases, particularly in low- and middle-income countries. This study analyzed disparities in vaccination coverage among children aged 12 to 23 months in ten Sub-Saharan African (SSA) countries.

Methods

A cross-sectional study using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) collected between 2017 and 2022 in ten Sub-Saharan African countries. The primary outcome was full vaccination coverage. Logistic regression models were applied to identify factors associated with the outcome.

Results

Full vaccination coverage rates varied significantly across countries, with Gambia presenting the highest rate (86.4%) and Guinea the lowest (21.2%). Factors associated with full vaccination coverage included place of residence, maternal education and age, economic status, place of delivery, possession of a health card, and attendance at prenatal care services.

Conclusion

The study highlighted significant disparities in vaccination coverage among children in Sub-Saharan Africa, influenced by sociodemographic and economic factors. Investing in maternal education, improving economic conditions, and strengthening healthcare infrastructure are essential measures to reduce these inequalities.
Establishing a relationship between iron-based blood measures and structural brain changes using neural networks in UK Biobank
Authors: Sammet Joshua , Alhasani Kholod , Chung Ejin , Cheung Jedidiah , Liu Qiang , Patel Raihaan , Suri Sana , Nevado-Holgado Alejo , Winchester Laura
Published: December 2024

Background

Disturbance of iron homeostasis in both the brain and blood is linked to cognitive impairment and neurodegenerative diseases. Investigation of the associations between changes in blood measurements and the accompanying structural changes in the brain, represented by susceptibility-weighted imaging, would generate evidence of a shared physiopathology between modalities, however linear approaches using derived imaging measures have not detected this relationship.

Method

Brain SWI of 4436 participants from UK Biobank at the first imaging visit were used to build a convolutional neural network (CNN) predicting the haemoglobin level. Haemoglobin concentration was grouped in three sets percentiles creating prediction classes. Attention maps were extracted from the CNN using Integrated Gradients to identify which brain regions allowed the CNN to correctly classify haemoglobin.

Results

The model predicted haemoglobin level >99% accuracy for three classes and >85% accuracy for ten classes. Using attention maps to understand the model’s decision process, we identified regions of interest for the 3-class model. Four out of the nine regions were located in the cerebral cortex. Changes in the right thalamus and the white matter tracts between the temporal gyrus, putamen, hippocampus, and thalamus regions were associated to high haemoglobin class.

Conclusion

Our model accurately predicted haemoglobin levels from SWI suggesting an association between the iron measures. Furthermore, it was possible to identify brain areas used by the CNN for decision-making. Using this integrated model of iron modalities will give a more comprehensive view of iron homeostasis and the potential associations with neurodegenerative diseases such as dementia.
APOE Genotype and Statin Response: Evidence from the UK Biobank Baseline Assessment and Linked Mortality Data
Authors: Asiimwe Innocent G. , Jorgensen Andrea L. , Pirmohamed Munir ,   Multimorbidity Mechanism and Therapeutic Research Collaborative
Published: December 2024

Introduction

APOEgenotype may influence response to statin therapy. We examined the relationship betweenAPOEgenotype, statin use, lipid biomarkers and mortality using data from the UK Biobank.

Methods

UK Biobank baseline assessment data and linked mortality records (389,843–452,189 participants) were analysed. Linear regression and Cox proportional hazards models assessed associations betweenAPOEgenotype, statin use, and lipid biomarkers (Apolipoprotein A, Apolipoprotein B, HDL cholesterol [HDLC], LDL cholesterol [LDLC], Lipoprotein A, Total Cholesterol, Triglycerides) as well as mortality, adjusting for clinical and genetic covariates.

Results

Significant interactions betweenAPOEgenotype and statin use were observed for most lipid biomarkers at the Bonferroni-adjusted threshold (P< 0.007), including Apolipoprotein A (P= 0.0065), Apolipoprotein B (P< 2.00e-16), LDLC, Total Cholesterol, and Triglycerides (allP< 2.00e-16), and HDLC (P= 0.0001). Lipoprotein A was not significant (P= 0.104). Population-level trends did not always translate to individual outcomes; for example, statin-treatedε4ε4carriers showed significant LDLC reductions but their LDLC levels remained higher than those of untreatedε2ε2individuals.APOEgenotype was significantly associated with all-cause death (trendP< 2.00e-16) and cardiovascular-related death (P= 1.55e-10). Theε4ε4genotype had the highest risk, with respective hazard ratios of 1.51 (95% CI: 1.41– 1.62) and 1.54 (1.33–1.77). However, theAPOE:statin use interaction was not significant.

Conclusion

TheAPOEgenotype influences lipid biomarker levels, with statin use associated with favourable changes across all genotypes. The magnitude of these changes depends on both the APOE genotype and baseline lipid levels.
Sleep architecture correlates with neurological and neurobehavioral short- and mid-term outcome in a sample of very preterm infants
Authors: Uccella Sara , Marazzotta Valentina , Preiti Deborah , Battaglini Marcella , Burlando Gaia , Roascio Monica , Rossi Andrea , Arnulfo Gabriele , Ramenghi Luca Antonio , Nobili Lino
Published: December 2024

Newborns spend most of their time sleeping. This activity fosters neurodevelopment. Prematurity, defined by birth occurring prior the 37thweek of gestation, disrupts normal brainin-uteroprogramming, with long-lasting consequences that carry a high social burden. Sleep alterations may contribute to these sequelae. In this pilot study we aimed to describe the 24-hours distribution of sleep states among very preterm infants (VPI), and to correlate it with neurobehavioral assessment up to 6 months of corrected age (CA). Secondly, we aimed to assess if the presence of a brain lesion detected at MRI could affect sleep duration, architecture, and quality.

Ten VPI were assessed at 32 weeks PMA with a 24-hours video-polysomnography and received a neurobehavioral examination at the time of the recording, at term equivalent age (TEA), and at 6 months CA. Analysis of sleep stages distribution and transitions, and power spectra were conducted.

Total sleep time and amount of quiet sleep positively correlated with neurological, and neurobehavioral assessment at 32 weeks PMA, at TEA, and at 6 months CA, while Sleep Onset Active Sleep (SOAS) had a negative association. Infants carrying brain lesions showed lower Total Sleep time accompanied by a higher prevalence of AS+SOAS and showed a gradient for higher power of posterior slow activity (slow δ and δ) during SOAS in the left hemisphere posterior regions.

Understanding sleep dynamics among preterm infants might provide future therapeutic/management strategies, which need to encompass sleep care.
Prioritizing Parkinson’s disease risk genes in genome-wide association loci
Authors: Lange Lara M. , Cerquera-Cleves Catalina , Schipper Marijn , Panagiotaropoulou Georgia , Braun Alice , Kraft Julia , Awasthi Swapnil , Bell Nathaniel , Posthuma Danielle , Ripke Stephan , Blauwendraat Cornelis , Heilbron Karl
Published: December 2024

Recent advancements in Parkinson’s disease (PD) drug development have been significantly driven by genetic research. Importantly, drugs supported by genetic evidence are more likely to be approved. While genome-wide association studies (GWAS) are a powerful tool to nominate genomic regions associated with certain traits or diseases, pinpointing the causal biologically relevant gene is often challenging. Our aim was to prioritize genes underlying PD GWAS signals.

The polygenic priority score (PoPS) is a similarity-based gene prioritization method that integrates genome-wide information from MAGMA gene-level association tests and more than 57,000 gene-level features, including gene expression, biological pathways, and protein-protein interactions. We applied PoPS to data from the largest published PD GWAS in East Asian- and European-ancestries.

We identified 120 independent associations withP< 5×10−8and prioritized 46 PD genes across these loci based on their PoPS scores, distance to the GWAS signal, and presence of non-synonymous variants in the credible set. Alongside well-established PD genes (e.g., TMEM175andVPS13C), some of which are targeted in ongoing clinical trials (i.e.,SNCA,LRRK2, andGBA1), we prioritized genes with a plausible mechanistic link to PD pathogenesis (e.g., RIT2, BAG3, andSCARB2). Many of these genes hold potential for drug repurposing or novel therapeutic developments for PD (i.e., FYN, DYRK1A, NOD2, CTSB, SV2C,andITPKB). Additionally, we prioritized potentially druggable genes that are relatively unexplored in PD (XPO1, PIK3CA, EP300, MAP4K4, CAMK2D, NCOR1,andWDR43).

We prioritized a high-confidence list of genes with strong links to PD pathogenesis that may represent our next-best candidates for disease-modifying therapeutics. We hope our findings stimulate further investigations and preclinical work to facilitate PD drug development programs.
Basal forebrain volume and concurrent hallucinations and mild cognitive impairment in Parkinson’s disease
Authors: Adamo Sabrina M. , Mutreja Vaishali , Statucka Marta , Kaya Batu , Schmitz Taylor W. , Ruocco Anthony C. , Cohn Melanie
Published: December 2024

Background

Visual hallucinations (VH) and mild cognitive impairment (MCI) often co-occur in Parkinson’s Disease (PD). Each symptom in isolation is associated with cholinergic basal forebrain (BF) atrophy. However, it is unclear whether BF integrity relates to one symptom preferentially or to their co-occurrence, and whether this relationship is specific to the nucleus basalis of Meynert (Ch4 & Ch4p) or extends to other BF nuclei (Ch1-2 & Ch3).

Objectives

To bridge the literature on MCI, VH, and the BF by examining associations between total and subregional BF volumes with VH, MCI, and concurrent symptomology.

Methods

We used clinical and structural MRI data in PD patients (n=296) with or without VH (n=75; n=221), and with intact cognition or MCI (n=115; n=181), evaluated prior to deep brain stimulation. One-way ANCOVAs were run for each region (total BF, Ch1-2, Ch3, Ch4, and Ch4p). The independent variable was either cognitive status, VH status, or a combined factor with three levels (Intact Cognition-noVH, MCI-noVH, and MCI-VH), and covariates included intracranial volume, age, levodopa-equivalent dose, and dopamine agonist therapy (yes/no).

Results

We demonstrate main effects of MCI on BF, Ch4, and Ch1-2volumes, but not of VH. Subgroup analyses show reduced BF and Ch4 volumes in patients with both symptoms (d=0.44) relative to patients with neither.

Conclusions

We show dissociations within the cholinergic system in PD, where reduced volume of different BF nuclei relates to MCI only versus the combination of symptoms. We provide support for the “cholinergic phenotype”.
Social and Polygenic Risk Factors for Time to Comorbid Diagnoses in Individuals with Substance Use Disorders: A Phenome-Wide Survival Analysis
Authors: Barr Peter B. , Neale Zoe E. , Bigdeli Tim B. , Chatzinakos Chris , Harvey Philip D. , Peterson Roseann E. , Meyers Jacquelyn L.
Published: December 2024

Importance

Persons with substance use disorders (SUD) often suffer from additional comorbidities, including psychiatric conditions and physical health problems. Researchers have explored this overlap in electronic health records (EHR) using phenome wide association studies (PheWAS) to characterize how different indicators are related to all conditions in an individual’s EHR. However, analyses have been largely cross-sectional in nature.

Objective

To characterize whether various social and genetic risk factors are associated with time to comorbid diagnoses in electronic health records (EHR) after the first diagnosis of SUD.

Design

Leveraging those with EHR and whole-genome sequencing data in All of Us (N = 287,012), we explored whether social determinants of health are associated with lifetime risk of SUD. Next, within those with a diagnosed SUD (N = 17,460), we examined whether polygenic scores (PGS) were associated with time to comorbid diagnoses performing a phenome-wide survival analysis.

Setting

Participating health care organizations across the United States.

Participants

Participants in the All of Us Research Program with available EHR and genomic data,

Exposures

Social determinants of health and polygenic scores (PGS) for psychiatric and substance use disorders,

Main Outcomes and Measures

Phecodes for diagnoses derived from International Statistical Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification, codes from EHR.

Results

Multiple social and demographic risk factors were associated with lifetime SUD diagnosis. Most strikingly, those reporting an annual income <$10K had 4.5 times the odds of having an SUD diagnosis compared to those reporting $100-$150K annually (OR = 4.48, 95% CI = 4.01, 5.01). PGSs for alcohol use disorders, schizophrenia, and post-traumatic stress disorder were associated with time to their respective diagnoses (HRAUD= 1.10, 95% CI = 1.06, 1.14; HRSCZ= 1.13, 95% CI = 1.06, 1.20; HRPTSD= 1.15, 95% CI = 1.08, 1.22). A PGS for ever-smoking was associated with time to subsequent smoking related comorbidities and additional SUD diagnoses HRSMOK= 1.6 to 1.16).

Conclusions and Relevance

Social determinants, especially those related to income have profound associations with lifetime SUD risk. Additionally, PGS may include information related to outcomes above and beyond lifetime risk, including timing and severity.
Impact of race, sex and age on the probability of pancreatic cancer among patients with newly diagnosed diabetes
Authors: Afghani Elham , Lau Bryan , Becker Laura , Goggins Michael G , Klein Alison P.
Published: December 2024

Background

Pancreatic cancer diagnoses are frequently preceded by a new diabetes diagnosis. Screening individuals newly diagnosed with diabetes could enable earlier pancreatic cancer detection. We sought to estimate the risk of pancreatic cancer by age, sex, race and time since diabetes diagnosis.

Methods

Johns Hopkins Medicine conducted this de-identified claims-based cohort study using the Optum Labs Data Warehouse (OLDW). Enrollees from 1/2008–9/2018 were identified as non-diabetic or newly diagnosed diabetics and time to pancreatic cancer analysis was conducted using a flexible Weibull model. Diabetes and cancer were defined using ICD-9/10 codes.

Results

Our risk set included 4,732,313 individuals (424,129 newly diabetic) in 5,844,934 enrollment periods. Individuals with newly diagnosed diabetes were at an increased hazard ratio (HR) of pancreatic cancer but this effect waned over time. The HR of pancreatic cancer following a diabetes diagnosis was higher in younger individuals and varied by race (lower HR in non-White individuals) (p<0.01, main effects and interactions). Thus, the probability of pancreatic cancer following a diabetes diagnosis was dependent on age, race, and sex. For example: the 1-year probability of pancreatic cancer in a white male aged 75 was 0.45% (95%CI 0.41%-0.49%) if they were newly diagnosed with diabetes and 0.090% (95%CI 0.084%-0.096%) fi they were free of diabetes. In contrast, risk was lower if they were age 55 at 0.15% (new-diabetic, 95%CI 0.13%-0.16) and 0.022% (diabetes free, 95%CI 0.020%-0.023%). The HR of pancreatic cancer for individuals with newly diagnosed diabetes compared to those free of diabetes was highest 1 month after diagnosis (HR=9.6 and 14.7 for a 75 and 55 year old while male, respectively) but decreased in the following months, with a ∼39% reduction in HR from 1- to-3 months, ∼17% from 3 -to-6 months, and ∼14% from 6 month-to -1 year (p<0.01).

Conclusions

Consideration of the age-race-sex specific probability of pancreatic cancer and time since diabetes diagnosis is necessary to when evaluating the risk of pancreatic cancer following a diabetes diagnosis.
Assessing the impact of variation in diagnostic coding among the three countries in the UK Biobank
Authors: Clifton Lei , Liu Wenyu , Collister Jennifer A , Littlejohns Thomas J , Goldacre Raphael R , Allen Naomi , Hunter David J
Published: December 2024

1.

Background

The UK Biobank (UKB) study has linked hospital inpatient data collected from England, Scotland, and Wales, which use different clinical coding systems to record health outcomes. Scotland records up to 6 different diagnostic codes for one inpatient episode, compared with up to 20 in England, and 14 in Wales. We assessed the impact of the variations in diagnostic coding among countries on observed disease incidence rates.

Methods

We examined the number of diagnoses coded by each country, and then compared the incidence of three diseases between countries: Parkinson’s disease (PD), type 2 diabetes (T2D), and dementia. We constructed Cox models for each disease, adjusting for “country”.

Results

England appears to have the highest risk (hazard ratio, HR) for all three diseases, while Scotland appears to have the lowest risk: HR [95% CI] = 0.62 [0.54, 0.72] for PD, 0.49 [0.45, 0.54] for T2D, and 0.88 [0.78, 0.99] for dementia.

Conclusions

The observed incidence of these diseases and the estimated effect of “country” in Cox models are likely influenced by the clinical coding variations among countries. Researchers need to be aware of this and account for these variations in their analyses.

2.

Key messages in a box

What is already known on this topic:

There are variations in the clinical coding systems in hospitals among the three countries in UKB.

These variations could impact research investigating geographic differences in disease incidence. What this study adds:

We assessed the impact of the variations in diagnostic coding among the three countries in UKB.

We showed that the observed disease incidence and the estimated effect of “country” are likely influenced by these variations.

How this study might affect research, practice or policy:

Researchers need to understand the provenance of data and account for the different diagnostic coding and other geographic variation specific to each of three countries, in order to reach robust conclusions that are not influenced by artefacts.
In silicocomparison of novel proteins from Plasmodium Falciparum as candidate drug targets against malaria
Authors: Giannetta Surya Angela Merici , Austin Felicia , Parikesit Arli Aditya
Published: December 2024

Malaria is a parasitic disease with a persistent prevalence in multiple parts of the world.Plasmodium falciparum, the deadliest human-infecting strain, has been a rising concern as the multi-faceted life-cycle poses a challenge in developing anti-malarial treatments while the effectiveness of present drugs begins to decline. As such, various research has been done to identify new drug targets leveraging modern computational biology techniques, such as proteomics. The objective of this study is to evaluate potential drug targets by comparing novel proteins inP. falciparumas reported in recent literature (2018-2022) based on their structural analysis of their 3D models along with the druggability properties and protein-protein interactions. A list of 22 proteins were acquired from 48 literature, in which only 7 can be considered as a favorable candidate protein drug target due to their non-human homologous nature, ERRAT score, residues percentage in favourable spot in Ramachandran plot, and high druggability score. Following an examination of protein-protein interactions, it is found that among the evaluated proteins only PF3D7_1438600 exhibits a sufficiently robust network, highlighting it to be the most promising candidate for drug targeting. This study demonstrates the valuable role of proteomics in uncovering potential drug targets and highlights its significance in driving emerging research efforts.
PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies
Authors: Perez Manolo Fernandez , Gascuel Olivier
Published: December 2024

Phylodynamics and diversification studies using complex evolutionary models can be challenging, especially with traditional likelihood-based approaches. As an alternative, likelihood-free simulation-based approaches have been proposed due to their ability to incorporate complex models and scenarios. Here, we propose a new simulation-based deep learning (DL) method capable of analyzing large datasets and accurately estimating parameter values for birth-death models in both phylodynamics and diversification studies. Our approach involves encoding trees by extracting a vector of local features for all nodes of the input phylogeny. We also developed a dedicated convolutional neural network architecture called PhyloCNN. Using simulations, we compared the accuracy of PhyloCNN when using feature vectors with a variable number of generations to describe the local context of nodes and leaves. The number of generations had a greater impact when considering smaller training sets, with a broader context showing higher accuracy, especially for complex evolutionary models. Compared to other recently developed DL approaches, PhyloCNN showed higher or similar accuracies for all parameters when used with training sets one or two orders of magnitude smaller (10,000 to 100,000 simulated training trees, instead of millions). We applied PhyloCNN with compelling results to two real-world phylodynamics and diversification datasets, related to HIV superspreaders in Zurich and to primates and their ecological role as seed dispersers. The high accuracy and computational efficiency of our method opens new possibilities for phylodynamics and diversification studies that need to account for idiosyncratic phylogenetic histories with specific parameter spaces and sampling scenarios not considered in more general approaches.
Genome-enabled stilbenoid gene cluster discovery inPleuropterus multiflorus
Authors: Jiang Jinzhu , Liu Xianju , Wang Yi , Liang Zhenchang , Liu An
Published: December 2024

Stilbenoids, a class of compounds with the stilbene skeleton as part of plant secondary metabolism, are renowned for their diverse health benefits. Among them, 2,3,5,4′-Tetrahydroxystilbene-2-O-β-D-glucoside (THSG), specifically abundant in the famous medicinal plantPleuropterus multiflorus, exhibits significant pharmacological properties. A selfing progeny ofP. multifloruscommercial variety ‘Jinwufugui No.1’, which contains high THSG content, was used for genome sequencing. Through the combination of weighted correlation network analysis, genome mining, and enzymatic characterization studies, we identified an unpredicted biosynthetic gene cluster responsible for THSG biosynthesis. This cluster includes a stilbene synthase (PmSTS1), a flavin-containing monooxygenase (PmFMO3), and a UDP-glycosyltransferase (UGT72B90). Our findings suggest this stilbenoid gene cluster is formed by transposable element-mediated gene duplications and neofunctionalization, and regulated by MYB and NAC type transcription factors. Notably, PmFMO3, as the first identified stilbenoid-2-hydroxylase, was confirmed that it can modify the other stilbenoids without the A ring modifications. The novel type of gene cluster, in which FMO serves as the key decorating enzyme, will guide the identification of more biosynthetic gene clusters in plants. In addition, the characterization of PmFMO3 extends our understanding of the FMO gene function in plants, and provides an important catalytic bioparts for the heterologous synthesis of polyhydroxylated stilbenoids.
GABA, Glutamate dynamics and BOLD observed during cognitive processing in psychosis patients with hallucinatory traits
Authors: Craven Alexander R. , Dwyer Gerard , Ersland Lars , Kazimierczak Katarzyna , Lilleskare Lin , Noeske Ralph , Sandøy Lydia Brunvoll , Johnsen Erik , Hugdahl Kenneth
Published: December 2024

The perception of a voice in the absence of an external auditory source – an auditory verbal hallucination – is a characteristic symptom of schizophrenia. To better understand this phenomenon requires integration of findings across behavioural, functional, and neurochemical levels. We address this with a locally adapted MEGA-PRESS sequence incorporating interleaved unsuppressed water acquisitions, allowing concurrent assessment of behaviour, blood-oxygenation-level-dependent (BOLD) functional changes, Glutamate+Glutamine (Glx), and GABA, synchronised with a cognitive (flanker) task. We acquired data from the anterior cingulate cortex (ACC) of 51 patients with psychosis (predominantly schizophrenia spectrum disorder) and hallucinations, matched to healthy controls. Consistent with the notion of an excitatory/inhibitory imbalance, we hypothesized differential effects for Glx and GABA between groups, and aberrant dynamics in response to task. Results showed impaired task performance, lower baseline Glx and positive association between Glx and BOLD in patients, contrasting a negative correlation in healthy controls. Task-related increases in Glx were observed in both groups, with no significant difference between groups. No significant effects were observed for GABA. These findings suggest that a putative excitatory/inhibitory imbalance affecting inhibitory control in the ACC is primarily observed as tonic, baseline glutamate differences, rather than GABAergic effects or aberrant dynamics in relation to a task.

Highlights

In-vivo, GABA-edited functional1H-MRS data were collected from 51 patients with hallucinations and a similar number of matched healthy controls

Reduced Glutamate+Glutamine (Glx) levels were observed in the patient group.

BOLD association to baseline Glutamate+Glutamine (Glx) differed between patients and controls

Robust task-related increases in measured Glx were observed in the Anterior Cingulate Cortex (ACC)

Task-related changes in measured Glx did not differ between patients and controls
Enzymatic epimerization of monoterpene indole alkaloids in Kratom
Authors: McDonald Allwin , Nakamura Yoko , Schotte Carsten , Lau Kin , Alam Ryan , Lopes Adriana A. , Buell C. Robin , O’Connor Sarah
Published: December 2024

Monoterpene indole alkaloids (MIAs) are a large, structurally diverse class of bioactive natural products. These compounds are biosynthetically derived from a stereoselective Pictet-Spengler condensation that generates a tetrahydro-β-carboline scaffold characterized by a 3Sstereocenter. However, a subset of MIAs contain a non-canonical 3Rstereocenter. Herein, we report the basis for 3R-MIA biosynthesis inMitragyna speciosa(Kratom). We discover the presence of the iminium species, 20S-3-dehydrocorynantheidine, which led us to hypothesize that isomerization of 3Sto 3Roccurs by oxidation and stereoselective reduction downstream of the initial Pictet-Spengler condensation. Isotopologue feeding experiments implicated young leaves and stems as the sites for pathway biosynthesis, facilitating the identification of an oxidase/reductase pair that catalyzes this epimerization. This enzyme pair has broad substrate specificity, suggesting that the oxidase and reductase may be responsible for the formation of many 3R-MIAs and downstream spirooxindole alkaloids in Kratom. These enzymes allow biocatalytic access to a range of previously inaccessible pharmacologically active compounds.
RNA virus discovery sheds light on the virome of a major vineyard pest, the European grapevine moth (Lobesia botrana)
Authors: Debat Humberto , Gomez-Talquenca Sebastian , Bejerman Nicolas
Published: December 2024

The European grapevine moth (Lobesia botrana) poses a significant threat to vineyards worldwide, causing extensive economic losses. While its ecological interactions and control strategies are well-studied, its associated viral diversity remains unexplored. Here, we employ high-throughput sequencing data mining to comprehensively characterize theL. botranavirome, revealing novel and diverse RNA viruses. We characterized four new viral members belonging to distinct families, with evolutionary cues of cypoviruses (Reoviridae), sobemo-like viruses (Solemoviridae), phasmaviruses (Phasmaviridae), and carmotetraviruses (Carmotetraviridae). Phylogenetic analysis of the cypoviruses places them within the genus in affinity with other moth viruses, suggesting a potential ecological roles and host range within Lepidoptera. The bi-segmented and highly divergent sobemo-like virus showed a distinctive evolutionary trajectory of its encoding proteins at the periphery of recently reported invertebrate Sobelivirales. Notably, the presence of a novel phasmavirus, typically associated with mosquitoes, expands the known host range and diversity of this family to moths. Furthermore, the identification of a carmotetravirus branching in the same cluster of providence virus, a lepidopteran virus which replicates in plants, raises questions regarding the biological significance of this moth virus and grapevine. We further explored viral sequences in several publicly available transcriptomic datasets of the moth, indicating potential prevalence across distinct conditions. These results underscore the existence of a complex virome withinL. botranaand lays the foundation for future studies investigating the ecological roles, evolutionary dynamics, and potential biocontrol applications of these viruses on theL. botrana—vineyard ecosystem.