bluuebunny/crossref_metadata_2025 huggingface dataset:
A Variational Graph Partitioning Approach to Modeling Protein Liquid-liquid Phase Separation
Authors: Wang Gaoyuan , Warrell Jonathan H , Zheng Suchen , Gerstein Mark
Published: January 2024

Graph Neural Network (GNN)s have emerged as a powerful general-purpose tool for representation learning across many domains. Their efficacy often depends on having an optimal underlying graph for prediction. In many cases, the most relevant information comes from specific subgraphs. In this work, we introduce a novel GNN architecture, called Graph Partitioned GNN (GP-GNN), designed to partition graphs during the prediction process, thereby focusing attention on subgraphs containing the most relevant information. Our approach jointly learns task-dependent graph partitions and representations, making it particularly effective for predictive tasks where critical features may reside within initially unidentified subgraphs. Protein Liquid-Liquid Phase Separation (LLPS) is an important physical problem well-suited to our architecture, primarily because protein sub-domains called intrinsically disordered regions (IDR)s are known to play a crucial role in the phase separation process. LLPS plays an essential role in cellular processes and is known to be associated with various diseases (e.g., neurodegenerative diseases and cancer). However, our ability to accurately predict which proteins undergo LLPS remains limited. In this study, we demonstrate how GP-GNN can be utilized to accurately predict LLPS by partitioning protein graphs into task-relevant subgraphs, such as those highlighting IDRs. Our model not only achieves state-of-the-art accuracy in predicting LLPS for both regulator and scaffold proteins but also offers valuable biological insights, that can be used to guide further downstream investigations. Notably, upon examining subgraphs identified by the GP-GNN, we show these are consistent with annotated IDRs.
AFM-RL: Large Protein Complex Docking Using AlphaFold-Multimer and Reinforcement Learning
Authors: Aderinwale Tunde , Jahandideh Rashidedin , Zhang Zicong , Zhao Bowen , Xiong Yi , Kihara Daisuke
Published: January 2024

Various biological processes in living cells are carried out by protein complexes, whose interactions can span across multiple protein structures. To understand the molecular mechanisms of such processes, it is crucial to know the quaternary structures of these complexes. Although the structures of many protein complexes have been determined through biophysical experiments, there are still many important complex structures that are yet to be determined, particularly for large complexes with multiple chains. To supplement experimental structure determination, many computational protein docking methods have been developed, but most are limited to two chains, and few are designed for three chains or more. We have previously developed a method, RL-MLZerD, for multiple protein docking, which was applied to complexes with three to five chains. Here, we expand the ability of this method to predict the structures of large protein complexes with six to twenty chains. We use AlphaFold-Multimer (AFM) to predict pairwise models and then assemble them using our reinforcement learning framework. Our new method, AFM-RL, can predict a diverse set of pairwise models, which aids the RL assembly steps for large protein complexes. Additionally, AFM-RL demonstrates improved modeling performance when compared to existing methods for large protein complex docking.
Resistance patterns in drug-adapted cancer cell lines reflect the complex evolution in clinical tumours
Authors: Grimsley Helen E. , Antczak Magdalena , Reddin Ian G. , McLaughlin Katie-May , Nist Andrea , Mernberger Marco , Stiewe Thorsten , Fenton Tim R. , Speidel Daniel , Harper-Wynne Catherine , Cox Karina , Cinatl Jindrich , Wass Mark N. , Garrett Michelle D. , Michaelis Martin
Published: January 2024

Background

Here, we introduce a novel set of triple-negative breast cancer (TNBC) cell lines consisting of MDA-MB-468, HCC38, and HCC1806 and their sublines adapted to cisplatin, doxorubicin, eribulin, paclitaxel, gemcitabine, or 5-fluorouracil.

Methods

The cell lines were characterized by whole exome sequencing and the determination of drug-response profiles. Moreover, genes harbouring resistance-associated mutations were investigated using TCGA data for potential clinical relevance.

Result

Sequencing combined with TCGA-derived patient data resulted in the identification of 682 biomarker candidates in the pan-cancer analysis. Thirty-five genes were considered the most promising candidates because they harboured resistance-associated variants in at least two resistant sublines, and their expression correlated with TNBC patient survival. Exome sequencing and response profiles to cytotoxic drugs and DNA damage response inhibitors identified revealed remarkably little overlap between the resistant sublines, suggesting that each resistance formation process follows a unique route. All of the drug-resistant TNBC sublines remained sensitive or even displayed collateral sensitivity to a range of tested compounds. Cross-resistance levels were lowest for the CHK2 inhibitor CCT241533, the PLK1 inhibitor SBE13, and the RAD51 recombinase inhibitor B02, suggesting that CHK2, PLK1, and RAD51 are potential drug targets for therapy-refractory TNBC.

Conclusions

We present novel preclinical models of acquired drug resistance in TNBC and many novel candidate biomarkers for further investigation. The finding that each cancer cell line adaptation process follows an unpredictable route reflects recent findings on cancer cell evolution in patients, supporting the relevance of drug-adapted cancer cell lines as preclinical models of acquired resistance.
Toxicological evaluation of Vanillin Flavor in E-Liquid Aerosols on Endothelial Cell Function: Findings from the Replica Project
Authors: Emma R. , Sun A. , Partsinevelos K. , Rust S. , Volarevic V. , Lesmana R. , Giordano A. , Goenawan H. , Barliana M. I. , Arsenijevic A. , Kastratovic N. , Markovic V. , Spasic B. , Distefano A. , Orlando L. , Carota G. , Polosa R. , Caruso M. , Li Volti G.
Published: January 2024

Background

There are challenges that require collaboration among researchers to ensure that tobacco harm reduction strategies are evidence-based. One key challenge is evaluating the safety of flavors used in electronic cigarettes (e-cigarettes). While many flavorings are approved as food additives or deemed “generally recognized as safe” (GRAS) for ingestion, this does not guarantee their safety when inhaled. In this context, the international research group Replica replicated a study conducted by Fetterman and colleagues in 2018, investigating the effects of aerosolized vanillin - one of the most popular flavors in vaping - on vascular endothelium when vaporized by an electronic cigarette.

Methods

We used Aspire Zelos 3 e-cigarette and prepared e-liquids containing propylene glycol, vegetable glycerin and vanillin. The e-liquids were vaporized under two settings - regular (1 ohm coil using wattage control mode at 14 watts) and sub-ohm (0.3 ohm coil using temperature control mode at 200 °C) – using a vaping machine, following the standardized puffing regime, ISO20768:2018. The vapor was then collected into a trapping solution to prepare aqueous extracts for the treatment of human aortic endothelial cells. We evaluated cytotoxicity, oxidative stress, nitric oxide bioavailability, and inflammation addressing some gaps reported in the original study.

Results

We observed some harmful effects, mostly attributable to ethanol, used to dilute vanillin in the original work by Fetterman, but no harmful effects on cell viability, their ability to produce nitric oxide, or oxidative stress from vanillin. Furthermore, no pro-inflammatory effects of vanillin were observed in terms of ICAM-1 and IL-6 gene expression.

Conclusions

Our results confirm the endothelial cell dysfunction observed in the original paper, but clarify that these effects are mainly attributable to ethanol and not to vaporized vanillin. These findings suggest that vanillin could be a safer flavoring agent for e-cigarette, without causing adverse effects on the cardiovascular system.
Walk This Way: Modeling Foraging Ant Dynamics in Multiple Food Source Environments
Authors: Hartman Sean , Ryan Shawn D. , Karamched Bhargav R.
Published: January 2024

Foraging for resources is an essential process for the daily life of an ant colony. What makes this process so fascinating is the self-organization of ants into trails using chemical pheromone in the absence of direct communication. Here we present a stochastic lattice model that captures essential features of foraging ant dynamics inspired by recent agent-based models while forgoing more detailed interactions that may not be essential to trail formation. Nevertheless, our model’s results coincide with those presented in more sophisticated theoretical models and experiment. Furthermore, it captures the phenomenon of multiple trail formation in environments with multiple food sources. This latter phenomenon is not described well by other more detailed models. An additional feature of this approach is the ability to derive a corresponding macroscopic PDE from the stochastic lattice model which can be described via first principle interactions and is amenable to analysis. Linear stability analysis of this PDE reveals the key biophysical parameters that give rise to trail formation. We also highlight universal features of the modeling framework that this simple formation may allow it to be used to study complex systems beyond ants.
Rbbp8nl contributes to resistance to anticancer immunotherapy by creating a non-inflamed tumor microenvironment and attenuating CD8+ T cell infiltration
Authors: Yi Zhenglin , Liu Jinhui , Deng Hao , Hu Jiao , Zu Xiongbing
Published: January 2024

Immune checkpoint blockade (ICB) monotherapy has limited efficacy, and it is crucial to explore predictive markers for the efficacy of immunotherapy and its use as new therapeutic targets or sensitization targets in combination with immunotherapy. Pan-cancer research using RNA sequencing data from The Cancer Genome Atlas (TCGA) revealed that RBBP8NL is particularly overexpressed in the tumor microenvironment (TME) of different malignancies. Additionally, there is a negative correlation between RBBP8NL and immune modulators, T cell inflammatory scores, immunological checkpoints, cancer immune cycle, and tumor-infiltrating immune cells (TIICs), so it can be inferred that RBBP8NL shapes the non-inflammatory TME in BLCA. BLCA patients with high RBBP8NL expression have a low response rate to immunotherapy, and they are more likely to experience hyperprogression of the illness. Interestingly, anti-RBBP8NL with immunotherapy could work better together than they do separately. According to the results of the multi-omics analysis, RBBP8NL inhibits the recruitment of cytotoxic lymphocytes. Furthermore, RBBP8NL’s predictive significance for immunotherapy success has been confirmed across several immunotherapy cohorts. In summary, RBBP8NL is a crucial component of the TME and a developing target for binding to ICB as well as a biomarker to direct precision medicine.
Community-aware explanations in knowledge graphs with XP-GNN
Authors: Mora Andrés Martínez , Polychronopoulos Dimitris , Ughetto Michaël , Nilsson Sebastian
Published: January 2024

Machine learning applications for the drug discovery pipeline have exponentially increased in the last few years. An example of these applications is the biological Knowledge Graph. These graphs represent biological entities and the relations between them based on existing knowledge. Graph machine learning models such as Graph Neural Networks can be applied on top of knowledge graphs to support the development of novel therapeutics. Nevertheless, Graph Neural Networks present an improved performance at the expense of complexity, becoming difficult to explain their decisions. State-of-the-art explanation algorithms for Graph Neural Networks focus on determining the most relevant subgraphs involved in their decision-making while considering graph elements (nodes and edges) as independent entities and ignoring any communities these graphs could present. We explore in this work the idea that graph community structure in biological Knowledge Graphs could provide a better grasp of the decision-making of Graph Neural Networks. For that purpose, we introduceXP-GNN, a novel explanation technique for Graph Neural Networks in Knowledge Graphs. XP-GNN exploits the communities of nodes or edges in graphs to refine their explanations, inspired bycooperative game theory. We characterize XP-GNN in a basic example and in terms of scalability and stability. In two relevant use cases for the drug discovery pipeline, XP-GNN provides more relevant explanations than previous techniques, being evaluated quantitatively and by domain experts. At the same time, XP-GNN presents limitations on scalability and stability, which we will address.

ACM Reference Format

Andrés Martínez Mora, Dimitris Polychronopoulos, Michaël Ughetto, and Sebastian Nilsson. 2024. Community-aware explanations in knowledge graphs with XP-GNN. InProceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 21 pages.https://doi.org/10.1145/nnnnnnn.nnnnnnn

This work has been funded by AstraZeneca AB, Mölndal, Sweden and AstraZeneca Cambridge. Unfortunately, due to proprietary reasons from AstraZeneca AB, the data used in this work cannot be shared.
Mammalian SWI/SNF complex activity regulates POU2F3 and constitutes a targetable dependency in small cell lung cancer
Authors: Duplaquet Leslie , So Kevin , Ying Alexander W. , Li Xinyue , Li Yixiang , Qiu Xintao , Li Rong , Singh Shilpa , Wu Xiaoli S. , Liu Qi , Qi Jun , Somerville Tim D.D. , Heiling Hillary , Mazzola Emanuele , Lee Yenarae , Zoller Thomas , Vakoc Christopher R. , Doench John G. , Forrester William C. , Abrams Tinya , Long Henry W. , Niederst Matthew J. , Kadoch Cigall , Oser Matthew G.
Published: January 2024

Small cell lung cancers (SCLC) are comprised of heterogeneous subtypes marked by lineage-specific transcription factors, including ASCL1, NEUROD1, and POU2F3. POU2F3-positive SCLC, ∼12% of all cases, are uniquely dependent on POU2F3 itself; as such, approaches to attenuate POU2F3 expression may represent new therapeutic opportunities. Here using genome-scale screens for regulators of POU2F3 expression and SCLC proliferation, we define mSWI/SNF complexes, including non-canonical BAF (ncBAF) complexes, as top dependencies specific to POU2F3-positive SCLC. Notably, clinical-grade pharmacologic mSWI/SNF inhibition attenuates proliferation of all POU2F3-positive SCLCs, while disruption of ncBAF via BRD9 degradation is uniquely effective in pure non-neuroendocrine POU2F3-SCLCs. mSWI/SNF maintains accessibility over gene loci central to POU2F3-mediated gene regulatory networks. Finally, chemical targeting of SMARCA4/2 mSWI/SNF ATPases and BRD9 decrease POU2F3-SCLC tumor growth and increase survivalin vivo. Taken together, these results characterize mSWI/SNF-mediated global governance of the POU2F3 oncogenic program and suggest mSWI/SNF inhibition as a therapeutic strategy for SCLC.
Turnover rates of human muscle proteins in vivo reported in fractional, mole and absolute units
Authors: Stansfield Ben N. , Barrett Jennifer S. , Bennett Samuel , Stead Connor A. , Pugh Jamie , Shepherd Sam O. , Strauss Juliette A. , Louis Julien , Close Graeme L. , Lisboa Paulo J. , Burniston Jatin G.
Published: January 2024

Protein fractional turnover rates (FTR) represent measurements of flux through a protein pool, i.e. net abundance (ABD) of the protein. If protein abundance is not measured or is different between experimental conditions the interpretation of FTR data may be confounded. This project investigates the consequences of reporting turnover rates of human muscle proteinsin vivoin mole and absolute units (that incorporate protein abundance data) compared to fractional (%/d) data that ignore protein abundance. Three physically active males (21 ± 1 years) were recruited and underwent a 12-d protocol of daily deuterium oxide (D2O) consumption and biopsies of vastus lateralis on days 8 and 12. Protein abundances were normalised to yeast alcohol dehydrogenase, added during sample preparation, and FTR was calculated from time-dependent changes in peptide mass isotopomer profiles. FTR and abundance data (fmol/ μg protein) were combined to calculate mole turnover rates (MTR; fmol/ μg protein/ d) and absolute turnover rates (ATR; ng/ μg protein/ d). Abundance data were collected for 1,772 proteins and FTR data were calculated from 3,944 peptides representing 935 proteins (average 3 peptides per protein). The median (M), lower- (Q1) and upper-quartile (Q3) values for protein FTR (%/d) were M = 4.3, Q1 = 2.52, Q3 = 7.84. Our analyses suggest MTR data is preferred over FTR, particularly for studies on multiprotein complexes, wherein MTR takes account of potential differences amongst the molecular weight of the component subunits. ATR data may be preferred over MTR and FTR, particularly when comparing samples with different abundance profiles.
Information Dynamics of the Heart and Respiration Rates: a Novel Venue for Digital Phenotyping in Humans
Authors: Keshmiri Soheil , Tomonaga Sutashu , Mizutani Haruo , Doya Kenji
Published: January 2024

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans’ physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans’ digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (up to 6 days, 4.63±1.52) HR and RR of 89 freely-behaving human subjects (Female: 39, age 57.28±5.67, Male: 50, age 58.48±6.32) and analyzed their HR and RR dynamics using linear models and information theoretic measures. While the predictability by linear autoregressive (AR) showed correlation with subjects’ age, an information theoretic measure of predictability, active information storage (AIS), captured these correlations more clearly. Furthermore, analysis of the information flow between HR and RR by transfer entropy (i.e.,HR → RRandRR → HR) revealed thatRR → HRis correlated with alcohol consumption and exercise habits. Thus we propose the AIS of HR and the transfer entropyRR → HRas two-dimensional biomarkers of cardiorespiratory physiology for digital phenotyping. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals.
PRRGO: A Tool for Visualizing and Mapping Globally Expressed Genes in Public Gene Expression Omnibus RNA-Sequencing Studies to PageRank-scored Gene Ontology Terms
Authors: Solano Luis E. , D’Sa Nicholas M. , Nikolaidis Nikolas
Published: January 2024

We herein report PageRankeR Gene Ontology (PRRGO), a downloadable web application that can integrate differentially expressed gene (DEG) data from the gene expression omnibus (GEO) GEO2R web tool with the gene ontology (GO) database [1]. Unlike existing tools, PRRGO computes the PageRank for the entire GO network and can generate both interactive GO networks on the web interface and comma-separated values (CSV) files containing the DEG statistics categorized by GO term. These hierarchical and tabular GO-DEG data are especially conducive to hypothesis generation and overlap studies with the use of PageRank data, which can provide a metric of GO term centrality. We verified the tool for accuracy and reliability across nine independent heat shock (HS) studies for which the RNA-seq data was publicly available on GEO and found that the tool produced increasing concordance between study DEGs, GO terms, and select HS-specific GO terms.
Molecular characterization, localization, and physiological roles of ITP and ITP-L in the mosquito,Aedes aegypti
Authors: Sajadi Farwa , Paluzzi Jean-Paul
Published: January 2024

The insect ion transport peptide (ITP) and its alternatively spliced variant, ITP-like peptide (ITP-L), belong to the crustacean hyperglycemic hormone family of peptides and are widely conserved among insect species. While limited, studies have characterized the ITP/ITP-L signaling system within insects, and putative functions including regulation of ion and fluid transport, ovarian maturation, and thirst/excretion have been proposed. Herein, we aimed to molecularly investigateItpandItp-lexpression profiles in the mosquitoAedes aegypti,examine peptide immunolocalization and distribution within the adult central nervous system, and elucidate physiological roles for these neuropeptides. Transcript expression profiles of bothAedaeItpandAedaeItp-lrevealed distinct enrichment patterns in adults, withAedaeItpexpressed in the brain andAedaeItp-lexpression predominantly within the abdominal ganglia. Immunohistochemical analysis within the central nervous system revealed expression ofAedaeITP peptide in a number of cells in the brain and in the terminal ganglion. Comparatively,AedaeITP-L peptide was localized solely within the pre-terminal abdominal ganglia of the central nervous system. Interestingly, prolonged desiccation stress caused upregulation ofAedaeItpandAedaeItp-llevels in adult mosquitoes, suggesting possible functional roles in water conservation and feeding-related activities. RNAi-mediated knockdown ofAedaeItpcaused an increase in urine excretion, while knockdown of bothAedaeItpandAedaeItp-lreduced blood feeding and egg-laying in females as well as hindered egg viability, suggesting roles in reproductive physiology and behaviour. Altogether, this study identifiesAedaeITP andAedaeITP-L as key pleiotropic hormones, regulating various critical physiological processes in the disease vector,A. aegypti.
scaDA: A Novel Statistical Method for Differential Analysis of Single-Cell Chromatin Accessibility Sequencing Data
Authors: Zhao Fengdi , Ma Xin , Yao Bing , Chen Li
Published: January 2024

Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named “scaDA”, which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer ‘s disease (AD) study, regions which are most enriched in GO terms related to neurogenesis, the clinical phenotype of AD, and SNPs identified in AD-associated GWAS.

Author summary

Understanding the cis-regulatory elements that control the fundamental gene regulatory process is important to basic biology. scATAC-seq data offers an unprecedented opportunity to investigate chromatin accessibility on the single-cell level and explore cell heterogeneity to reveal the dynamic changes of cis-regulatory elements among different cell types. To understand the dynamic change of gene regulation using scATAC-seq data, differential chromatin (DA) analysis, which is one of the most fundamental analyses for scATAC-seq data, can enable the identification of differentially accessible regions between cell types or between multiple conditions. Subsequently, DA analysis has many applications such as identifying cell type-specific chromatin accessible regions to reveal the cell type-specific gene regulatory program, assessing disease-associated changes in chromatin accessibility to detect potential biomarkers, and linking differentially accessible regions to differentially expressed genes for building a comprehensive gene regulatory map. This paper proposes a novel statistical method named “scaDA” to improve the detection of differentially accessible regions by performing differential distribution analysis. scaDA is believed to benefit the research community of single-cell genomics.
Reproducing within-reef variability in coral dynamics with a metacommunity modelling framework
Authors: Cresswell Anna K , Haller-Bull Vanessa , Gonzalez-Rivero Manuel , Gilmour James P , Bozec Yves-Marie , Barneche Diego R , Robson Barbara , Anthony Ken , Doropoulos Christopher , Roelfsema Chris , Lyons Mitchell , Mumby Peter J , Condie Scott , Lago Veronique , Ortiz Juan-Carlos
Published: January 2024

Reef systems span spatial scales from 10s to 100s and even 1000s of kilometres, with substantial spatial variability across these scales. Managing and predicting the future of coral reefs requires insights into reef functioning at all spatial scales. However, investigations of reef functioning often consider individual reefs as the smallest unit (10s of kilometres), despite substantial spatiotemporal variability occurring within-reefs (100s of meters). We developedC∼scape,a coral metacommunity modelling framework that integrates the demography of corals with population-level responses to physical and environmental spatial layers, to simulate a mosaic of interacting coral communities across a heterogenous seascape. Coral communities are linked using biophysical connectivity modelling. Coral community growth is modelled with a logistic growth model, with the intrinsic growth parameter determined from taxa-specific Integral Projection Models to incorporate demographic mechanisms. Site-specific coral habitat parameters, derived from satellite-based geomorphic and benthic habitat maps, define the maximum coral cover and are used to modulate community growth spatially and temporally as a function of the available space suitable for corals. These parameters are a proxy for the many interacting physical and environmental factors — e.g., depth, light, wave exposure, temperature, and substrate type — that drive within-reef variability in coral demography. Using a case study from the Great Barrier Reef, we show that modulating community growth using site-specific habitat parameters enables more accurate hindcasts of coral cover dynamics, while overlooking within-reef variability may lead to misleading conclusions about metacommunity dynamics. More generally,C∼scapeprovides a valuable framework for predicting spatiotemporal dynamics of coral communities within and between reefs, offering a mechanistic approach to test a range of management and restoration options.
Scale-up of 99DOTS for tuberculosis treatment supervision in Uganda: An interrupted time series analysis
Authors: Crowder Rebecca , Nakasendwa Suzan , Kityamuwesi Alex , Musoke Muhammad , Nannozi Joyce , Waswa Joseph , Sanyu Agnes Nakate , Lamunu Maureen , Twinamasiko Amon , Tinka Lynn Kunihira , Oyuku Denis , Babirye Diana , Berger Christopher , Thompson Ryan , Turyahabwe Stavia , Dowdy David , Katamba Achilles , Cattamanchi Adithya , Kiwanuka Noah
Published: January 2024

Background

Digital adherence technologies like 99DOTS are being scaled-up for tuberculosis (TB) treatment despite limited evidence of their effectiveness and concerns about accessibility. We evaluated the reach and effectiveness of 99DOTS during its scale-up in Uganda.

Methods

We included all adults initiating drug-susceptible pulmonary TB treatment between August 2019 and June 2021 at 12 99DOTS-naïve health facilities (n=3,526). Using an interrupted time series design, we compared the proportion with treatment success (primary outcome) and enrolled on 99DOTS in the 9 months before and 12 months after implementing an ‘enhanced 99DOTS’ intervention. This included providing low-cost phones to people with TB when needed, task shifting monitoring and support to community health workers, and automated task lists. Treatment data were derived from routine TB treatment registers.

Results

The proportion enrolled on 99DOTS post-intervention was high (87.2%) and had a slight upward trend (proportion ratio 1.01, 95% CI 1.00-1.01). Treatment completion remained similar across periods (89.8% post-intervention vs. 87.1% pre-intervention). There was no immediate level or slope change in treatment success following the intervention.

Conclusions

With appropriate implementation supports, 99DOTS can have high uptake without negatively impacting treatment outcomes. Equity in access should be prioritized during implementation
Machine Learning-Based Prediction of Pediatric Ulcerative Colitis Treatment Response using Diagnostic Histopathology
Authors: Liu Xiaoxuan , Walters Thomas , Siddiqui Iram , Lopez-Nunez Oscar , Prasath Surya , Denson Lee A ,   PROTECT consortium, Dhaliwal Jasbir
Published: January 2024

Background and Aims

We previously reported clinical features associated with outcomes in pediatric ulcerative colitis (UC). Here we developed a histopathology model to predict corticosteroid-free remission (CSFR) on mesalamine therapy alone.

Methods

Pre-treatment rectal biopsy slides were digitized in training and validation groups of 292 and 113 pediatric UC patients, respectively. Whole slide images (WSI) underwent pre-processing. Thirteen machine learning (ML) models were trained using 250 histomic features including texture, color, histogram, and nuclei. Feature importance was determined by the Gini index with the classifier re-trained using the top features.

Results

187571 informative patches from 292 training group patients (Male:53%; Age:13y (IQR:11-15); CSFR:41%) were trained on 13 ML classifiers. The best model was random forest (RF). Eighteen optimal histomic features were identified and trained, and the corresponding WSI AUROC was 0.89 (95%CI:0.71, 0.96), accuracy of 90% for CSFR. Features were re-trained on an independent real-world dataset of 113 patients and the model WSI AUROC was 0.85 (95%CI:0.75, 0.95), accuracy of 85%.

Conclusion

Routine histopathology obtained at diagnosis contains histomic features associated with UC treatment response.
Artificial intelligence-enabled reconstruction of the right ventricular pressure curve using the peak pressure value: a proof-of-concept study
Authors: Szijártó Ádám , Nicoara Alina , Podgoreanu Mihai , Tokodi Márton , Fábián Alexandra , Merkely Béla , Sárkány András , Tősér Zoltán , Lakatos Bálint , Kovács Attila
Published: January 2024

Conventional echocardiographic parameters of right ventricular (RV) function are heavily afterload-dependent. Therefore, the incorporation of the RV pressure curve may enable the formulation of new parameters that reflect intrinsic RV function more accurately. Accordingly, we sought to develop an artificial intelligence-based method that can reconstruct the RV pressure curve based on the peak RV pressure.

We invasively acquired RV pressure in 29 heart failure patients before and after the implantation of a left ventricular (LV) assist device. Using these tracings, we trained various machine learning models to reconstruct the RV pressure curve of the entire cardiac cycle solely based on the peak value of the invasively acquired curve. The best-performing model was compared with the performance of two other methods that estimated RV pressure curves based on a reference LV and RV pressure curve, respectively.

Among the evaluated algorithms, the multilayer perceptron (MLP) achieved the best performance with an R2of 0.887 [0.834 – 0.941]. The RV and LV reference curve-based methods achieved R2values of 0.879 [0.815 – 0.943] and 0.636 [0.500 – 0.771], respectively. The MLP and the RV reference curve-based estimation showed good agreement with the invasive RV pressure curves (mean bias: -0.38 mmHg and -0.73 mmHg, respectively), whereas the LV reference curve-based estimation exhibited a high mean bias (+3.93 mmHg).

The proposed method enables the reconstruction of the RV pressure curve, using only the peak value as input. Thus, it may serve as a fundamental component for developing new echocardiographic tools targeting the afterload-adjusted assessment of RV function.

Graphical abstract

Key question. Can artificial intelligence be useful in the reconstruction of the right ventricular pressure curve of the entire cardiac cycle using only the peak value as input?Key findings. Multilayer Perceptron predicted instantaneous pressure values with a balanced, low bias throughout the cardiac cycle, even slightly outperforming the reference curve creation methods.Take-home message. Accurate prediction of the right ventricular pressure curve may enable formulation of new echocardiographic parameters targeting the afterload-adjusted assessment of RV function.

LV: left ventricular, PVC: pulmonary valve closure, PVO: pulmonary valve opening, RV: right ventricular, TVC: tricuspid valve closure, TVO: tricuspid valve opening
Comprehensive Benchmarking of CNN-Based Tumor Segmentation Methods Using Multimodal MRI Data
Authors: Kundal Kavita , Rao K Venkateswara , Majumdar Arunabha , Kumar Neeraj , Kumar Rahul
Published: January 2024

Magnetic resonance imaging (MRI) is become an essential and a frontline technique in the detection of brain tumor. However, manual segmentation of tumor from MRI scans is a time-consuming and labour-intensive process. There is a prevalent trend in employing fully automated methods for accurate tumor segmentation using MRI scans. The precision in brain tumor segmentation is essential for the better diagnosis, treatment and prognosis. This study focuses on benchmarking and evaluating the performance of four widely used convolutional neural network (CNN) based brain tumor segmentation methods CaPTk, 2DVNet, EnsembleUNets, and ResNet50. We used 1251 multimodal MRI scans from the BraTS2021 dataset, which encompasses T1, T2, T1ce, and Flair imaging modalities. We compared the performance of these CNN-based methods against a reference set of previously segmented images obtained with the assistance of radiologists. The evaluation encompasses direct utilization of segmented outputs and also by employing radiomic features. Performance evaluation with direct segments method using Dice Similarity Coefficient score (DSC) and Hausdorff Distance (HD) suggested better performance of EnsembleUNets with DSC and HD of 0.93 and 18 respectively, outperforming the other methods. Comparative analysis using radiomics features also revealed that EnsembleUNets is the most precise segmentation method as compared to CaPTk, 2DVNet, and ResNet50. EnsembleUNets achieved Concordance Correlation Coefficient (CCC), Total Deviation Index (TDI), and Root Mean Square Error (RMSE) of 0.79, 1.14 and 0.53 respectively and outperformed its counterparts. These findings contribute valuable insight into the comparative efficacy of EnsembleUNets, facilitating informed decision for accurate brain tumor segmentation.
Relationship between sleep and progression of Parkinson’s disease – A Mendelian randomization study
Authors: Mahjoub Mahiar , Matar Elie
Published: January 2024

Background

Sleep disturbances are common in Parkinson’s disease (PD) and growing evidence suggests a bidirectional relationship between sleep disruption and neurodegeneration.

Objectives

To study the causal relationship between sleep and rate of PD progression using two-sample Mendelian randomisation (MR).

Methods

Genetic variants linked to sleep duration and insomnia were analysed within a GWAS combining 12 longitudinal cohorts of patients with PD(n=4093 patients) examining motor and cognitive progression.

Results

Genetic liability to insomnia was associated with greater cognitive decline measured by MMSE. Consistent trends across MR estimates suggested a protective effect of increased sleep duration, and detrimental effect of insomnia on motor decline measured using UPDRS-III. Sensitivity analyses reinforced these relationships. The strength of causality among these associations was limited by heterogeneity and balanced pleiotropy.

Conclusion

Sleep related variables may alter the trajectory of cognitive and motor progression in PD and warrants further study.
A phenome-wide association study of tandem repeat variation in 168,554 individuals from the UK Biobank
Authors: Manigbas Celine A. , Jadhav Bharati , Garg Paras , Shadrina Mariya , Lee William , Martin-Trujillo Alejandro , Sharp Andrew J.
Published: January 2024

Most genetic association studies focus on binary variants. To identify the effects of multi-allelic variation of tandem repeats (TRs) on human traits, we performed direct TR genotyping and phenome-wide association studies in 168,554 individuals from the UK Biobank, identifying 47 TRs showing causal associations with 73 traits. We replicated 23 of 31 (74%) of these causal associations in the All of Us cohort. While this set included several known repeat expansion disorders, novel associations we found were attributable to common polymorphic variation in TR length rather than rare expansions and includee.g.a coding polyhistidine motif inHRCT1influencing risk of hypertension and a poly(CGC) in the 5’UTR ofGNB2influencing heart rate. Causal TRs were strongly enriched for associations with local gene expression and DNA methylation. Our study highlights the contribution of multi-allelic TRs to the “missing heritability” of the human genome.
Diverse competitor B cell responses to a germline-targeting priming immunogen in human Ig loci transgenic mice
Authors: Schiffner Torben , Palser Anne , Jardine Joseph G. , Liguori Alessia , Menis Sergey , Raemisch Sebastian , Hu Xiaozhen , Kulp Daniel W. , Wang Xiaoning , Diedrich Jolene K. , Groschel Bettina , Kalyuzhniy Oleksandr , Tingle Ryan , Le Khoa , Georgeson Erik , Adachi Yumiko , Kubitz Michael , Landais Elise , Yates John R. , Paulson James C. , Sok Devin , Kellam Paul , Schief William R.
Published: January 2024

Broadly neutralizing antibodies (bnAbs) have promise to protect against HIV infection, but induction of bnAbs by immunization is an unsolved vaccine design challenge. Germline-targeting priming immunogens aim to initiate the induction of bnAbs by specifically activating rare bnAb-precursor B cells that can subsequently be matured using suitable heterologous boosting and shepherding immunogens. Several pre-clinical studies, and the IAVI G001 human clinical trial, have demonstrated the ability of a germline-targeting priming immunogen, eOD-GT8 60mer, to induce precursors of the VRC01 class of bnAbs. However, much less is known about B cells induced against other epitopes of the immunogen. Here, we performed unbiased analysis of B cells induced by eOD-GT8 60mers in Intelliselect Transgenic mice (Kymice) that are transgenic for the human Ig loci and produce human-like BCRs. B cells isolated with intact eOD-GT8 60mer nanoparticles showed a large diversity of non-VRC01-class B cells, with 38% unique clonotypes and only 5% of BCRs belonging to public lineages shared among all animals. We found that many competitors recognize epitopes in close proximity to or overlapping with the VRC01 epitope. These results indicate that optimal boosting of VRC01-class bnAb-precursor B cells primed by eOD-GT8 60mer might require a first-boost immunogen that minimizes recognition of competitor B cells, and such competitors isolated from Kymice could serve as valuable reagents for boost development.
Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum
Authors: Madugula Sita Sirisha , Pujar Pranav , Bharani Nammi , Wang Shouyi , Jayasinghe-Arachchige Vindi M. , Pham Tyler , Mashburn Dominic , Artilis Maria , Liu Jin
Published: January 2024

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations like large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In the current study, we aim to elucidate the unique protein attributes associated with Cas9 and Cas12 families and identify the features that distinguish each family from the other. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,495 features) encoding various physiochemical, topological, constitutional, and coevolutionary information of Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and Non-Cas proteins. All the models were evaluated rigorously on the test and independent datasets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 95% and 97% on their respective independent datasets, while the multiclass classifier achieved a high F1 score of 0.97. We observed that Quasi-sequence-order descriptors like Schneider-lag descriptors and Composition descriptors like charge, volume, and polarizability are essential for the Cas12 family. More interestingly, we discovered that Amino Acid Composition descriptors, especially the Tripeptide Composition (TPC) descriptors, are important for the Cas9 family. Four of the identified important descriptors of Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all the Cas9 proteins and were located within different catalytically important domains of the Cas9 protein structure. Among these four tripeptides, tripeptides DHI and HHA are well-known to be involved in the DNA cleavage activity of the Cas9 protein. We therefore propose the the other two tripeptides, PWN and PYY, may also be essential for the Cas9 family. Our identified important descriptors enhanced the understanding of the catalytic mechanisms of Cas9 and Cas12 proteins and provide valuable insights into design of novel Cas systems to achieve enhanced gene-editing properties.
Integrative Omics Uncovers Low Tumorous Magnesium Content as A Driver Factor of Colorectal Cancer
Authors: Zhang Rou , Hu Meng , Liu Yu , Li Wanmeng , Xu Zhiqiang , He Siyu , Lu Ying , Gong Yanqiu , Wang Xiuxuan , Hai Shan , Li Shuangqing , Qi Shiqian , Li Yuan , Shu Yang , Du Dan , Zhang Huiyuan , Xu Heng , Zhou Zongguang , Lei Peng , Chen Hai-Ning , Dai Lunzhi
Published: January 2024

Magnesium (Mg) deficiency is associated with increased risk and malignancy of colorectal cancer (CRC), yet the underlying mechanisms remain elusive. Here we used genomic, proteomic, and phosphoproteomic data to elucidate the impact of Mg deficiency on CRC. Genomic analysis identified 160 genes with higher mutation frequencies in Low-Mg tumors, including key driver genes such asKMT2CandERBB3. Unexpectedly, initiation driver genes of CRC, such asTP53andAPC, displayed higher mutation frequencies in High-Mg tumors. Additionally, proteomics and phosphoproteomics indicated that low tumorous Mg content may activate epithelial-mesenchymal transition (EMT) by modulating inflammation or remodeling the phosphoproteome of cancer cells. Notably, we observed a negative correlation between the phosphorylation of DBN1 at S142 (DBN1S142p) and Mg content. A mutation in S142 to D (DBN1S142D) mimicking DBN1S142pupregulated MMP2 and enhanced cell migration, while treatment with MgCl2reduced DBN1S142p, thereby reversing this phenotype. Mechanistically, Mg2+attenuated the DBN1-ACTN4 interaction by decreasing DBN1S142p, which, in turn, enhanced the binding of ACTN4 to F-actin and promoted F-actin polymerization, ultimately reducing MMP2 expression. These findings shed new light on the crucial role of Mg deficiency in CRC progression and suggest that Mg supplementation may offer a promising preventive and therapeutic strategy for CRC.
The modified activity of prolyl 4 hydroxylases (P4Hs) reveals the effect of arabinogalactan proteins (AGPs) on changes in the cell wall during the tomato ripening process
Authors: Kutyrieva-Nowak Nataliia , Leszczuk Agata , Ezzat Lamia , Kaloudas Dimitris , Zając Adrian , Szymańska-Chargot Monika , Skrzypek Tomasz , Krokida Afroditi , Mekkaoui Khansa , Lampropoulou Evangelia , Kalaitzis Panagiotis , Zdunek Artur
Published: January 2024

Arabinogalactan proteins (AGPs) are proteoglycans with an unusual molecular structure characterised by the presence of a protein part and carbohydrate chains. Their specific properties at different stages of the fruit ripening programme make AGPs unique markers of this process. An important function of AGPs is to co-form an amorphous extracellular matrix in the cell wall-plasma membrane continuum; thus, changes in the structure of these molecules can determine the presence and distribution of other components. The aim of the current work was to characterise the molecular structure and localisation of AGPs during the fruit ripening process in transgenic lines with silencing and overexpression ofSlP4H3genes. The objective was accomplished through comprehensive and comparativein situandex situanalyses of AGPs from the fruit of transgenic lines and wild-type plants at specific stages of ripening. The experiment showed that changes in P4H3 activity affected the content of AGPs and the progress in their modifications in the ongoing ripening process. The analysis of the transgenic lines confirmed the presence of AGPs with high molecular weights (120–60 kDa) at all the examined stages, but a changed pattern of the molecular features of AGPs was found in the last ripening stages, compared to WT. In addition to the AGP molecular changes, morphological modifications of fruit tissue and alterations in the spatio-temporal pattern of AGP distribution at the subcellular level were detected in the transgenic lines with the progression of the ripening process. The work highlights the irreversible impact of AGPs and their alterations on the fruit cell wall assembly and changes in AGPs associated with the progression of the ripening process.

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