bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic design of gene regulatory mechanisms for spatial pattern formation
Authors: Mousavi Reza , Lobo Daniel 
Published: July 2023

AbstractSynthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms—including the number of genes necessary for the formation of the target pattern—we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.
A comprehensive experimental comparison between federated and centralized learning
Authors: Garst Swier , Dekker Julian , Reinders Marcel 
Published: July 2023

AbstractPurposeFederated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be quite complicated due to privacy and legal concerns of the data. In such cases, federated learning has the potential to vastly speed up the research cycle. Although federated and central learning have been compared from a theoretical perspective, an extensive experimental comparison of performances and learning behavior still lacks.MethodsWe have performed a comprehensive experimental comparison between federated and centralized learning. We evaluated various classifiers on various datasets exploring influences of different sample distributions as well as different class distributions across the clients.ResultsThe results show similar performances under a wide variety of settings between the federated and central learning strategies. Federated learning is able to deal with various imbalances in the data distributions. It is sensitive to batch effects between different datasets when they coincide with location, similar as with central learning, but this setting might go unobserved more easily.ConclusionFederated learning seems robust to various challenges such as skewed data distributions, high data dimensionality, multiclass problems and complex models. Taken together, the insights from our comparison gives much promise for applying federated learning as an alternative to sharing data.
Ribosome subunit attrition and activation of the p53–MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition
Authors: Howard Gregory C. , Wang Jing , Rose Kristie Lindsey , Jones Camden , Patel Purvi , Tsui Tina , Florian Andrea C. , Vlach Logan , Lorey Shelly L. , Grieb Brian C. , Smith Brianna N. , Slota Macey J. , Reynolds Elizabeth M. , Goswami Soumita , Savona Michael R. , Mason Frank M. , Lee Taekyu , Fesik Stephen W. , Liu Qi , Tansey William P. 
Published: July 2023

SummaryThe chromatin-associated protein WD Repeat Domain 5 (WDR5) is a promising target for cancer drug discovery, with most efforts blocking an arginine-binding cavity on the protein called the "WIN" site that tethers WDR5 to chromatin. WIN site inhibitors (WINi) are active against multiple cancer cell types in vitro, the most notable of which are those derived from MLL-rearranged (MLLr) leukemias. Peptidomimetic WINi were originally proposed to inhibit MLLr cells via dysregulation of genes connected to hematopoietic stem cell expansion. Our discovery and interrogation of small molecule WIN site inhibitors, however, revealed that they act in MLLr cell lines to suppress ribosome protein gene (RPG) transcription, induce nucleolar stress, and activate p53. Because there is no precedent for an anti-cancer strategy that specifically targets RPG expression, we took an integrated multi-omics approach to further interrogate the mechanism of action of WINi in MLLr cancer cells. We show that WINi induce depletion of the stock of ribosomes, accompanied by a broad yet modest translational choke and changes in alternative mRNA splicing that inactivate the p53 antagonist MDM4. We also show that WINi are synergistic with agents including venetoclax and BET-bromodomain inhibitors. Together, these studies reinforce the concept that WINi are a novel type of ribosome-directed anti-cancer therapy and provide a resource to support their clinical implementation in MLLr leukemias and other malignancies.
Soft robotics-enabled large animal model of HFpEF hemodynamics for device testing
Authors: Rosalia Luca , Ozturk Caglar , Wang Sophie X. , Quevedo-Moreno Diego , Saeed Mossab Y. , Mauskapf Adam , Roche Ellen T. 
Published: July 2023

AbstractHeart failure with preserved ejection fraction (HFpEF) is a major challenge in cardiovascular medicine, accounting for approximately 50% of all cases of heart failure. Due to the lack of effective therapies for this condition, the mortality associated with HFpEF remains higher than that of most cancers. Despite the ongoing efforts, no medical device has yet received FDA approval. This is largely due to the lack of an in vivo model of the HFpEF hemodynamics, resulting in the inability to evaluate device effectiveness in vivo prior to clinical trials. Here, we describe the development of a highly tunable porcine model of HFpEF hemodynamics using implantable soft robotic sleeves, where controlled actuation of a left ventricular and an aortic sleeve can recapitulate changes in ventricular compliance and afterload associated with a broad spectrum of HFpEF hemodynamic phenotypes. We demonstrate the feasibility of the proposed model in preclinical testing by evaluating the hemodynamic response of the model post-implantation of an interatrial shunt device, which was found to be consistent with findings from in silico studies and clinical trials. This work addresses several of the limitations associated with previous models of HFpEF, such as their limited hemodynamic fidelity, elevated costs, lengthy development time, and low throughput. By showcasing exceptional versatility and tunability, the proposed platform has the potential to revolutionize the current approach for HFpEF device development and selection, with the goal of improving the quality of life for the 32 million people affected by HFpEF worldwide.
Eyes on nature: Embedded vision cameras for multidisciplinary biodiversity monitoring
Authors: Darras Kevin F.A. , Balle Marcel , Xu Wenxiu , Yan Yang , Zakka Vincent G. , Toledo-Hernández Manuel , Sheng Dong , Lin Wei , Zhang Boyu , Lan Zhenzhong , Fupeng Li , Wanger Thomas C. 
Published: July 2023

AbstractGlobal environmental challenges require comprehensive data to manage and protect biodiversity. Currently, vision-based biodiversity monitoring efforts are mixed, incomplete, human-dependent, and passive. To tackle these issues, we present a portable, modular, low-power device with embedded vision for biodiversity monitoring. Our camera uses interchangeable lenses to resolve barely visible and remote subjects, as well as customisable algorithms for blob detection, region-of-interest classification, and object detection to identify targets. We showcase our system in six case studies from the ethology, landscape ecology, agronomy, pollination ecology, conservation biology, and phenology disciplines. Using the same devices, we discovered bats feeding on durian tree flowers, monitored flying bats and their insect prey, identified nocturnal insect pests in paddy fields, detected bees visiting rapeseed crop flowers, triggered real-time alerts for waterbirds, and tracked flower phenology over months. We measured classification accuracies between 55% and 96% in our field surveys and used them to standardise observations over highly-resolved time scales. The cameras are amenable to situations where automated vision-based monitoring is required off the grid, in natural and agricultural ecosystems, and in particular for quantifying species interactions. Embedded vision devices such as this will help addressing global biodiversity challenges and facilitate a technology-aided global food systems transformation.
Grow now, pay later: when should a bacterium go into debt?
Authors: Lopez Jaime G. , Erez Amir 
Published: July 2023

AbstractMicrobes grow in a wide variety of environments and must balance growth and stress resistance. Despite the prevalence of such trade-offs, understanding of their role in non-steady environments is limited. In this study, we introduce a mathematical model of “growth debt”, where microbes grow rapidly initially, paying later with slower growth or heightened mortality. We first compare our model to a classical chemostat experiment, validating our proposed dynamics and quantifyingEscherichia coli’s stress resistance dynamics. Extending the chemostat theory to include serial-dilution cultures, we derive phase diagrams for the persistence of “debtor” microbes. We find that debtors cannot coexist with non-debtors if “payment” is increased mortality but can coexist if it lowers enzyme affinity. Surprisingly, weak noise considerably extends the persistence of resistance elements, pertinent for antibiotic resistance management. Our microbial debt theory, broadly applicable across many environments, bridges the gap between chemostat and serial dilution systems.TeaserMicrobes can sacrifice future growth for immediate gains, how does this trade-off shape the structure of microbial communities?
Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data
Authors: Lee GaRyoung , Lee Sang Mi , Lee Sungyoung , Jeong Chang Wook , Song Hyojin , Lee Sang Yup , Yun Hongseok , Koh Youngil , Kim Hyun Uk 
Published: July 2023

AbstractBackgroundOncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to a large number of metabolites in a cell and the presence of multiple genes associated with cancer development.ResultsHere we report the development of a computational workflow that predicts metabolite-gene-pathway sets (MGPs). MGPs present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate MGPs. A GEM is a computational model that predicts reaction fluxes at a genome scale, and can be constructed in a cell-specific manner by using omics data (e.g., RNA-seq). The computational workflow is first validated by comparing the resulting metabolite-gene (MG) pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from 17 acute myeloid leukemia samples and 21 renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the MGPs predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting MGPs is also discussed.ConclusionsValidation of the MGP-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting MGPs will help identify novel oncometabolites, and also suggest cancer treatment strategies.
Dynamic Structure-based Pharmacophore Models for Virtual Screening of Small Molecule Libraries Targeting the YB-1
Authors: Oktay Lalehan , Sayyah Ehsan , Durdağı Serdar 
Published: July 2023

AbstractIn drug discovery, ligand-based techniques offer rapid screening, whereas structure-based approaches provide deeper insights but are time-consuming. Hybrid methods like structure-based pharmacophore models combine advantages for accurate screening of large ligand libraries. However, there are substantial limits to build structure-based pharmacophore models. Static models relying on a single co-crystallized structure or docking pose often fall short in capturing the dynamic nature of binding interactions. In this study, we present dynamic structure-based pharmacophore models, aimed at better representing physiological conditions and addressing these challenges. The urgent need for improved cancer treatment has led to the search for new chemotherapeutic strategies. Y box binding protein 1 (YB-1) is a multifunctional protein associated with tumor progression and treatment resistance in various cancers. For the first time in the literature, our study utilizes a known small molecule YB-1 inhibitor (SU056) bound to the active regions of the RNA-binding sites to develop dynamic structure-based pharmacophores. These models were then used in the screening of large ligand libraries.
An improved epigenetic counter to track mitotic age in normal and precancerous tissues
Authors: Zhu Tianyu , Tong Huige , Du Zhaozhen , Beck Stephan , Teschendorff Andrew E. 
Published: July 2023

AbstractThe cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC’s mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.
A Hypothesis-Based Hop Microbiology Laboratory Module Testing the Plausibility of the Mythical Origin of the India Pale Ale (IPA)
Authors: Pineda Julio Molina , Scholes Amanda N. , Lewis Jeffrey A. 
Published: July 2023

AbstractAs one of the most famous fermented drinks in the world, beer is an especially relatable topic for microbiology courses. Here, we describe a short and easily adaptable module based on the antibacterial properties of hops used in brewing. By the 15thcentury, beer recipes included hops (the flower of theHumulus lupulusplant) as a bittering agent and antimicrobial. By the 19thcentury, the highly-hopped Indian Pale Ale (IPA) became popular, and a modern myth has emerged that IPAs were invented to survive long ocean voyages such as from Britain to India. With that myth in mind, we designed a hypothesis-driven microbiology lab module that tests the plausibility of this brewing myth— namely that highly-hopped beers possess enough antibacterial activity to prevent spoilage, while lowly-hopped beers do not. The overall design of the module is to test the antimicrobial properties of hops using petri plates containing varying concentrations of hop extract. The module includes hypothesis generation and testing related to bacterial physiology and morphology (hops are not equally effective against gram-positive and gram-negative bacteria), and to mechanisms of antimicrobial resistance (as beer spoilage bacteria have repeatedly evolved hop resistance). Pre and post assessment showed that students made significant gains in the learning objectives for the module, which encourages critical thinking and hypothesis testing by linking microbial physiology and antimicrobial resistance to an important and topical real-world application.
Neuronal NPR-15 modulates molecular and behavioral immune responses via the amphid sensory neuron-intestinal axis inC. elegans
Authors: Otarigho Benson , Butts Anna Frances , Aballay Alejandro 
Published: July 2023

AbstractThe survival of hosts during infections relies on their ability to mount effective molecular and behavioral immune responses. Despite extensive research on these defense strategies in various species, including the model organismCaenorhabditis elegans, the neural mechanisms underlying their interaction remain poorly understood. Previous studies have highlighted the role of neural G protein-coupled receptors (GPCRs) in regulating both immunity and pathogen avoidance, which is particularly dependent on aerotaxis. To address this knowledge gap, we conducted a screen of mutants in neuropeptide receptor family genes. We found that loss-of-function mutations innpr-15activated immunity while suppressing pathogen avoidance behavior. Through further analysis, NPR-15 was found to regulate immunity by modulating the activity of key transcription factors, namely GATA/ELT-2 and TFEB/HLH-30. Surprisingly, the lack of pathogen avoidance ofnpr-15mutant animals was not influenced by oxygen levels. Moreover, our studies revealed that the amphid sensory neuron ASJ is involved in mediating the immune and behavioral responses orchestrated by NPR-15. Additionally, NPR-15 was found to regulate avoidance behavior via the TRPM gene, GON-2, which may sense the intestinal distension caused by bacterial colonization to elicit pathogen avoidance. Our study contributes to a broader understanding of host defense strategies and mechanisms underlining the interaction between molecular and behavioral immune responses.
Spatial profiling of ovarian clear cell carcinoma reveals immune-hot features
Authors: Tai Ya-Ting , Lin Wei-Chou , Wang Duncan Yi-Te , Ye Jieru , Tan Tuan Zea , Wei Lin-Hung , Huang Ruby Yun-Ju 
Published: July 2023

AbstractIntroductionOCCC has high incidence in Asia with frequent occurrence at early stage but without sufficient data on molecular stratification for high-risk patients. Recently, immune-hot features have been proposed as an indicator for poor prognosis for early-stage OCCC. Specific patterns of intra-tumoral heterogeneity (ITH) associated with immune-hot features need to be defined.MethodsFormalin-fixed paraffine embedded (FFPE) tumor sections from 10 early-stage OCCC patients were included. Digital Spatial Profiling (DSP) of 18 protein targets was conducted by using the nanoString GeoMx system to profile selected regions of interest (ROIs) based on the reference H&E staining morphology. Areas of illumination (AOIs) were defined according to ROI segmentation by the fluorescence signals of visualization markers pan-cytokeratin (PanCK), CD45, or DNA.ResultsUnsupervised hierarchical clustering of 252 AOIs from 229 ROIs showed that PanCK segments expressed different combinations of immune markers suggestive of immune mimicry features. Three immune-hot clusters were identified: granzyme B high (C1-a), immune signal high (C1-b) and immune-like cells (C1-c); two immune-cold clusters were identified: fibronectin-high (C2-a) and signal-cold (C2-b). Immune cells around C1-b and C1-c PanCK+ AOIs were tumor infiltrating immune cells (TIIs) with higher expression of CD68, while those around C1-a, C2-a and C2-b PanCK+ AOIs were non-TIIs with higher expression of SMA. C1-c and C2-a PanCK+ AOIs were associated with OCCC recurrence. TIIs had higher frequencies in C1-b and C1-c PanCK+ AOIs and were associated with OCCC recurrence. Correlating with morphology, tumor samples with recurrence showed higher frequency of papillary pattern. Plus, ROIs with papillary pattern had extremely high frequency of PanCK segments of C1-c feature, higher frequency of TIIs, and macrophage lineage immune mimicry with high intensity of CD68.ConclusionsSpatial profiling of early-stage OCCC tumors revealed that immune mimicry of tumor cells, the presence of TIIs, and papillary pattern in morphology were associated with recurrence.
MethyLasso: a segmentation approach to analyze DNA methylation patterns and identify differentially methylation regions from whole-genome datasets
Authors: Balaramane Delphine , Spill Yannick G. , Weber Michaël , Bardet Anaïs Flore 
Published: July 2023

ABSTRACTDNA methylation is an epigenetic mark involved in the regulation of gene expression and patterns of DNA methylation anticorrelates with chromatin accessibility and transcription factor binding. DNA methylation can be profiled at the single cytosine resolution in the whole genome and has been performed in many cell types and conditions. Computational approaches are then essential to study DNA methylation patterns in a single condition or capture dynamic changes of DNA methylation levels across conditions. Towards this goal, we developed MethyLasso, a new approach based on the segmentation of DNA methylation data, that enables the identification of low-methylated regions (LMRs), unmethylated regions (UMRs), DNA methylation valleys (DMVs) and partially methylated domains (PMDs) in a single condition as well as differentially methylated regions (DMRs) between two conditions. We performed a rigorous benchmarking comparing existing approaches by evaluating the number, size, level of DNA methylation, boundaries, CpG content and coverage of the regions using several real datasets as well as the sensitivity and precision of the approaches using simulated data and show that MethyLasso performs best overall. MethyLasso is freely available athttps://github.com/abardet/methylasso.
ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data
Authors: Teng Pang-ning , Schaaf Joshua P. , Abulez Tamara , Hood Brian L. , Wilson Katlin N. , Litzi Tracy J. , Mitchell David , Conrads Kelly A. , Hunt Allison L. , Olowu Victoria , Oliver Julie , Park Fred S. , Edwards Marshé , Chiang AiChun , Raj-Kumar Praveen-Kumar , Tarney Christopher M. , Darcy Kathleen M. , Phippen Neil T. , Maxwell G. Larry , Conrads Thomas P. , Bateman Nicholas W. 
Published: July 2023

SummaryNumerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms, ESTIMATE and ConsensusTME(r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomics data are available at (https://lmdomics.org/ProteoMixture/.