pwc-archive/papers-with-abstracts huggingface dataset:
Mesoporous Silica Nanoparticles-based Smart Nanocarriers for Targeted Drug Delivery in Colorectal Cancer Therapy
Authors: Rochelle A. Mann, Md. Emran Hossen, Alexander David McGuire Withrow, Jack Thomas Burton, Sean M Blythe, Camryn Grace Evett
Date: 2024-09-27 00:00:00
Tasks: Specificity

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, highlighting the urgent need for advanced therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to improve the specificity and efficacy of anticancer treatments. This review examines three cutting-edge mesoporous silica nanoparticle (MSN)-based drug delivery to introduce novel CRC therapy, each utilizing unique functionalization strategies for targeted drug release. The first system, hyaluronidase-responsive MSN-HA/DOX, employs biotin-modified hyaluronic acid to facilitate dual-stimulus drug release in the tumor microenvironment, exhibiting enhanced in vivo tumor inhibition. The DOX/SLN-PEG-Biotin utilizes polyethylene glycol and biotin to improve drug stability and target biotin-overexpressing CRC cells, demonstrating superior anti-cancer efficacy in vitro and in vivo. Lastly, galactosylated chitosan-functionalized MSNs enable targeted delivery through asialoglycoprotein receptors, providing controlled drug release and strong therapeutic potential. Collectively, these systems highlight the advancements in nanoparticle functionalization for CRC treatment, offering a pathway to overcome the limitations of conventional chemotherapy. Further research is required to translate these innovations into clinical practice, ensuring safety and scalability.
Advanced Targeted Drug Delivery for Colon Cancer Using Pristine and Surface-modified Hydroxyapatite Nanoparticles: Synthesis, Characterization, and pH-Responsive Release
Authors: Alexander David McGuire Withrow, Sean M Blythe, Jack Thomas Burton, Camryn Grace Evett
Date: 2024-09-26 00:00:00

Hydroxyapatite nanoparticles (HANs) have emerged as a promising candidate for targeted drug delivery in the treatment of colon cancer, a disease that remains one of the leading causes of cancer-related deaths globally. In this study, we synthesized and characterized HANs for their potential use as drug delivery vehicle for anticancer drugs specifically targeting colon cancer treatment. Through various in vitro assays, including drug loading efficiency, release kinetics, we demonstrated that HANs exhibit efficient drug loading capacity, and sustained release behavior, which could collectively contribute to enhanced therapeutic outcomes against colon cancer cells. The findings highlight the potential of HANs to serve as a versatile platform for the targeted drug delivery of chemotherapeutic agents, offering a novel approach that could improve treatment efficacy while minimizing side effects due to biocompatibility of the particles. Future research should focus on in vivo studies and clinical translation to further validate the potential of HANs in cancer therapy as drug carrier.
DiffPaSS -- High-performance differentiable pairing of protein sequences using soft scores
Authors: Umberto Lupo, Damiano Sgarbossa, Martina Milighetti, Anne-Florence Bitbol
Date: 2024-09-24 00:00:00

Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due to their common evolutionary history, and feature correlations in amino acid usage due to the need to maintain complementary interaction interfaces. Thus, the problem of finding interacting pairs can be formulated as searching for a pairing of sequences that maximizes a sequence similarity or a coevolution score. Several methods have been developed to address this problem, applying different approximate optimization methods to different scores. We introduce DiffPaSS, a differentiable framework for flexible, fast, and hyperparameter-free optimization for pairing interacting biological sequences, which can be applied to a wide variety of scores. We apply it to a benchmark prokaryotic dataset, using mutual information and neighbor graph alignment scores. DiffPaSS outperforms existing algorithms for optimizing the same scores. We demonstrate the usefulness of our paired alignments for the prediction of protein complex structure. DiffPaSS does not require sequences to be aligned, and we also apply it to non-aligned sequences from T cell receptors.
Oxysterols in Vascular Cells and Role in Atherosclerosis
Authors: Celine Luquain-Costaz, Isabelle Delton
Date: 2024-09-24 00:00:00

Atherosclerosis is a major cardiovascular complication of diseases associated with elevated oxidative stress such as type 2 diabetes and metabolic syndrome. In these situations, low density lipoproteins (LDL) undergo oxidation. Oxidized LDL display proatherogenic activities through multiple and complex mechanisms which lead to dysfunctions of vascular cells (endothelial cells, smooth muscle cells and macrophages). Oxidized LDL are enriched in oxidized products of cholesterol called oxysterols formed either by autoxidation, enzymatically, or by both mechanisms. Several oxysterols have been shown to accumulate in atheroma plaques and to play a key role in atherogenesis. Depending on the type of oxysterols, various biological effects are exerted on vascular cells to regulate the formation of macrophage foam cells, endothelial integrity, adhesion and transmigration of monocytes, plaque progression and instability. Most of these effects are linked to the ability of oxysterols to induce cellular oxidative stress and cytotoxicity mainly through apoptosis and proinflammatory mediators. Like for excess cholesterol, high density lipoproteins (HDL) can exert antiatherogenic activity stimulating the efflux of oxysterols that have accumulated in foamy macrophages.
Higher-Order Message Passing for Glycan Representation Learning
Authors: Roman Joeres, Daniel Bojar
Date: 2024-09-20 00:00:00
Tasks: Diversity, Graph Classification, Link Prediction, Representation Learning

Glycans are the most complex biological sequence, with monosaccharides forming extended, non-linear sequences. As post-translational modifications, they modulate protein structure, function, and interactions. Due to their diversity and complexity, predictive models of glycan properties and functions are still insufficient. Graph Neural Networks (GNNs) are deep learning models designed to process and analyze graph-structured data. These architectures leverage the connectivity and relational information in graphs to learn effective representations of nodes, edges, and entire graphs. Iteratively aggregating information from neighboring nodes, GNNs capture complex patterns within graph data, making them particularly well-suited for tasks such as link prediction or graph classification across domains. This work presents a new model architecture based on combinatorial complexes and higher-order message passing to extract features from glycan structures into a latent space representation. The architecture is evaluated on an improved GlycanML benchmark suite, establishing a new state-of-the-art performance. We envision that these improvements will spur further advances in computational glycosciences and reveal the roles of glycans in biology.
Natural fumigation as a mechanism for volatile transport between flower organs
Authors: Benoît Boachon, Joseph Lynch, Shaunak Ray, Jing Yuan, Kristian Mark P. Caldo, Robert Junker, Sharon A. Kessler, John A. Morgan, Natalia Dudareva
Date: 2024-09-20 00:00:00

Plants synthesize volatile organic compounds (VOCs) to attract pollinators and beneficial microorganisms, to defend themselves against herbivores and pathogens and for plant-plant communication. Generally, accumulation and emission of VOCs occur from the tissue of their biosynthesis. However, using biochemical and reverse genetic approaches, we demonstrate a new physiological phenomenon: inter-organ aerial transport of VOCs via natural fumigation. Before petunia flowers open, a tube-specific terpene synthase produces sesquiterpenes, which are released inside the buds and then accumulate in the stigma, potentially defending the developing stigma from pathogens. These VOCs also affect reproductive organ development and seed yield, which is a previously unknown function for terpenoid compounds. could serve as a mechanism to coordinate the timing of pistil development with petal development in order to insure that the stigma is receptive when the flowers are most likely to attract pollinators. Further studies are required to assess whether natural fumigation is conserved in flowering plants, to uncover the mechanisms involved, and to determine its evolutionary advantage in plant reproduction.
Discrimination vs. Generation: The Machine Learning Dichotomy for Dopaminergic Hit Discovery
Authors: Temitope Sobodu, Adeshina Yusuf, Dan Kiel, Dong Kong
Date: 2024-09-19 00:00:00
Tasks: Drug Discovery

Virtual screening plays a pivotal role in early drug discovery, traditionally dominated by physics-based methods. While these approaches offer detailed insights, they are often hindered by high computational costs, limited sampling, and forcefield inaccuracies. Advances in Machine Learning (Ml)and Deep Learning (DL) present resource-efficient alternatives, with approaches like predictive geometric ML (EQUIBIND) and generative geometric ML (DIFFDOCK)showing promise in enhancing both efficiency and predictive capability. Here, we compare these two strategies, retrospectively and prospectively, for identifying novel agonists targeting the dopamine D2 receptor. To complement DIFFDOCK's dual functionality in protein-ligand conformer generation and confidence estimation, we adopted a complementary atom-type-based confidence model for EQUIBIND. This pipeline, termed the discriminative model, integrates a featurization step and an XGBoost classifier to differentiate between active and inactive ligands. The top-ranked compounds from both models were evaluated using an ultrafast dopaminergic biosensor assay, dLight. Our results demonstrate that the generative model achieved a higher hit rate, notably leading to the discovery of Compound 1, a nanomolar dopamine D2 receptor agonist with a novel scaffold.
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design
Authors: Yannick Vogt, Mehdi Naouar, Maria Kalweit, Christoph Cornelius Miething, Justus Duyster, Joschka Boedecker, Gabriel Kalweit
Date: 2024-09-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Antibodies offer great potential for the treatment of various diseases. However, the discovery of therapeutic antibodies through traditional wet lab methods is expensive and time-consuming. The use of generative models in designing antibodies therefore holds great promise, as it can reduce the time and resources required. Recently, the class of diffusion models has gained considerable traction for their ability to synthesize diverse and high-quality samples. In their basic form, however, they lack mechanisms to optimize for specific properties, such as binding affinity to an antigen. In contrast, the class of offline Reinforcement Learning (RL) methods has demonstrated strong performance in navigating large search spaces, including scenarios where frequent real-world interaction, such as interaction with a wet lab, is impractical. Our novel method, BetterBodies, which combines Variational Autoencoders (VAEs) with RL guided latent diffusion, is able to generate novel sets of antibody CDRH3 sequences from different data distributions. Using the Absolut! simulator, we demonstrate the improved affinity of our novel sequences to the SARS-CoV spike receptor-binding domain. Furthermore, we reflect biophysical properties in the VAE latent space using a contrastive loss and add a novel Q-function based filtering to enhance the affinity of generated sequences. In conclusion, methods such as ours have the potential to have great implications for real-world biological sequence design, where the generation of novel high-affinity binders is a cost-intensive endeavor.
Long-range gene expression prediction with token alignment of large language model
Authors: Edouardo Honig, Huixin Zhan, Ying Nian Wu, Zijun Frank Zhang
Date: 2024-10-02 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Large Language Model

Gene expression is a cellular process that plays a fundamental role in human phenotypical variations and diseases. Despite advances of deep learning models for gene expression prediction, recent benchmarks have revealed their inability to learn distal regulatory grammar. Here, we address this challenge by leveraging a pretrained large language model to enhance gene expression prediction. We introduce Genetic sequence Token Alignment (GTA), which aligns genetic sequence features with natural language tokens, allowing for symbolic reasoning of genomic sequence features via the frozen language model. This cross-modal adaptation learns the regulatory grammar and allows us to further incorporate gene-specific human annotations as prompts, enabling in-context learning that is not possible with existing models. Trained on lymphoblastoid cells, GTA was evaluated on cells from the Geuvadis consortium and outperforms state-of-the-art models such as Enformer, achieving a Spearman correlation of 0.65, a 10\% improvement. Additionally, GTA offers improved interpretation of long-range interactions through the identification of the most meaningful sections of the input genetic context. GTA represents a powerful and novel cross-modal approach to gene expression prediction by utilizing a pretrained language model, in a paradigm shift from conventional gene expression models trained only on sequence data.
Global Constraint Principle for Microbial Growth Law
Authors: Jumpei F. Yamagishi, Tetsuhiro S. Hatakeyama
Date: 2024-09-19 00:00:00
Tasks: model

Monod's law is a widely accepted phenomenology for bacterial growth. Since it has the same functional form as the Michaelis--Menten equation for enzyme kinetics, cell growth is often considered to be locally constrained by a single reaction. In contrast, this paper shows that a global constraint principle of resource allocation to metabolic processes can well describe the nature of cell growth. This concept is a generalization of Liebig's law, a growth law for higher organisms, and explains the dependence of microbial growth on the availability of multiple nutrients, in contrast to Monod's law.
Quantitative effects of the stress response to DNA damage in the cell size control of Escherichia coli
Authors: Ignacio Madrid Canales, James Broughton, Sylvie Méléard, Meriem El Karoui
Date: 2024-09-17 00:00:00

In Escherichia coli the response to DNA damage shows strong cell-to-cell-heterogenity. This results in a random delay in cell division and asymmetrical binary fission of single cells, which can compromise the size homeostasis of the population. To quantify the effect of the heterogeneous response to genotoxic stress (called SOS response in E. coli) on the growth of the bacterial population, we propose a flexible time-continuous parametric model of individual-based population dynamics. We construct a stochastic model based on the "adder" size-control mechanism, extended to incorporate the dynamics of the SOS response and its effect on cell division. The model is fitted to individual lineage data obtained in a 'mother machine' microfluidic device. We show that the heterogeneity of the SOS response can bias the observed division rate. In particular, we show that the adder division rate is decreased by SOS induction and that this perturbative effect is stronger in fast-growing conditions.
The Mitochondrial Genome of Cathaya argyrophylla Reaches 18.99 Mb: Analysis of Super-Large Mitochondrial Genomes in Pinaceae
Authors: Kerui Huang, Wenbo Xu, Haoliang Hu, XiaoLong Jiang, Lei Sun, Wenyan Zhao, Binbin Long, Shaogang Fan, Zhibo Zhou, Ping Mo, Xiaocheng Jiang, Jianhong Tian, Aihua Deng, Peng Xie, Yun Wang
Date: 2024-10-09 00:00:00

Mitochondrial genomes in the Pinaceae family are notable for their large size and structural complexity. In this study, we sequenced and analyzed the mitochondrial genome of Cathaya argyrophylla, an endangered and endemic Pinaceae species, uncovering a genome size of 18.99 Mb, meaning the largest mitochondrial genome reported to date. To investigate the mechanisms behind this exceptional size, we conducted comparative analyses with other Pinaceae species possessing both large and small mitochondrial genomes, as well as with other gymnosperms. We focused on repeat sequences, transposable element activity, RNA editing events, chloroplast-derived sequence transfers (mtpts), and sequence homology with nuclear genomes. Our findings indicate that while Cathaya argyrophylla and other extremely large Pinaceae mitochondrial genomes contain substantial amounts of repeat sequences and show increased activity of LINEs and LTR retrotransposons, these factors alone do not fully account for the genome expansion. Notably, we observed a significant incorporation of chloroplast-derived sequences in Cathaya argyrophylla and other large mitochondrial genomes, suggesting that extensive plastid-to-mitochondrial DNA transfer may play a crucial role in genome enlargement. Additionally, large mitochondrial genomes exhibited distinct patterns of RNA editing and limited similarity with nuclear genomes compared to smaller genomes. These results suggest that the massive mitochondrial genomes in Pinaceae are likely the result of multiple contributing factors, including repeat sequences, transposon activity, and extensive plastid sequence incorporation. Our study enhances the understanding of mitochondrial genome evolution in plants and provides valuable genetic information for the conservation and study of Cathaya argyrophylla.
Annotation of protein-coding genes in 49 diatom genomes from the Bacillariophyta clade
Authors: Natalia Nenasheva, Clara Pitzschel, Cynthia N. Webster, Alex Hart, Jill L. Wegrzyn, Mia M. Bengtsson, Katharina J. Hoff
Date: 2024-10-07 00:00:00

Diatoms, a major group of microalgae, play a critical role in global carbon cycling and primary production. Despite their ecological significance, comprehensive genomic resources for diatoms are limited. To address this, we have annotated previously unannotated genome assemblies of 49 diatom species. Genome assemblies were obtained from NCBI Datasets and processed for repeat elements using RepeatModeler2 and RepeatMasker. For gene prediction, BRAKER2 was employed in the absence of transcriptomic data, while BRAKER3 was utilized when transcriptome short read data were available from the Sequence Read Archive. The quality of genome assemblies and predicted protein sets was evaluated using BUSCO, ensuring high-quality genomic resources. Functional annotation was performed using EnTAP, providing insights into the biological roles of the predicted proteins. Our study enhances the genomic toolkit available for diatoms, facilitating future research in diatom biology, ecology, and evolution.
Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification
Authors: Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed
Date: 2024-10-05 00:00:00
Tasks: Cancer Classification, Data Integration, feature selection, Graph Attention, Graph Neural Network

Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration poses significant challenges due to the high dimensionality, data complexity, and distinct characteristics of various omics types. This study addresses these challenges and evaluates three graph neural network architectures for multi-omics (MO) integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN) for classifying 31 cancer types and normal tissues. To address the high-dimensionality of multi-omics data, we employed LASSO (Least Absolute Shrinkage and Selection Operator) regression for feature selection, leading to the creation of LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOTGN models. Graph structures for the networks were constructed using gene correlation matrices and protein-protein interaction networks for multi-omics integration of messenger-RNA, micro-RNA, and DNA methylation data. Such data integration enables the networks to dynamically focus on important relationships between biological entities, improving both model performance and interpretability. Among the models, LASSO-MOGAT with a correlation-based graph structure achieved state-of-the-art accuracy (95.9%) and outperformed the LASSO-MOGCN and LASSO-MOTGN models in terms of precision, recall, and F1-score. Our findings demonstrate that integrating multi-omics data in graph-based architectures enhances cancer classification performance by uncovering distinct molecular patterns that contribute to a better understanding of cancer biology and potential biomarkers for disease progression.
FlexLMM: a Nextflow linear mixed model framework for GWAS
Authors: Saul Pierotti, Tomas Fitzgerald, Ewan Birney
Date: 2024-10-02 00:00:00

Summary: Linear mixed models are a commonly used statistical approach in genome-wide association studies when population structure is present. However, naive permutations to empirically estimate the null distribution of a statistic of interest are not appropriate in the presence of population structure, because the samples are not exchangeable with each other. For this reason we developed FlexLMM, a Nextflow pipeline that runs linear mixed models while allowing for flexibility in the definition of the exact statistical model to be used. FlexLMM can also be used to set a significance threshold via permutations, thanks to a two-step process where the population structure is first regressed out, and only then are the permutations performed. We envision this pipeline will be particularly useful for researchers working on multi-parental crosses among inbred lines of model organisms or farm animals and plants. Availability and implementation: The source code and documentation for the FlexLMM is available at https://github.com/birneylab/flexlmm.
Objectively Evaluating the Reliability of Cell Type Annotation Using LLM-Based Strategies
Authors: Wenjin Ye, Yuanchen Ma, Junkai Xiang, Hongjie Liang, Tao Wang, Qiuling Xiang, Andy Peng Xiang, Wu Song, Weiqiang Li, Weijun Huang
Date: 2024-09-24 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Reliability in cell type annotation is challenging in single-cell RNA-sequencing data analysis because both expert-driven and automated methods can be biased or constrained by their training data, especially for novel or rare cell types. Although large language models (LLMs) are useful, our evaluation found that only a few matched expert annotations due to biased data sources and inflexible training inputs. To overcome these limitations, we developed the LICT (Large language model-based Identifier for Cell Types) software package using a multi-model fusion and "talk-to-machine" strategy. Tested across various single-cell RNA sequencing datasets, our approach significantly improved annotation reliability, especially in datasets with low cellular heterogeneity. Notably, we established objective criteria to assess annotation reliability using the "talk-to-machine" approach, which addresses discrepancies between our annotations and expert ones, enabling reliable evaluation even without reference data. This strategy enhances annotation credibility and sets the stage for advancing future LLM-based cell type annotation methods.