bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biomechanical regulation of cell shapes promotes branching morphogenesis of the ureteric bud epithelium
Authors: Kurtzeborn Kristen , Iaroshenko Vladislav , Zárybnický Tomáš , Koivula Julia , Anttonen Heidi , Brigdewater Darren , Krishnan Ramaswamy , Chen Ping , Kuure Satu 
Published: March 2024

AbstractBackgroundBranching morphogenesis orchestrates organogenesis in many tissues including kidney, where ureteric bud branching determines kidney size and nephron number. Defects in branching morphogenesis result in congenital renal anomalies which manifest as deviations in size, function, and nephron number thus critically compromising the lifelong renal functional capacity established during development. Advances in the genetic and molecular understanding of ureteric bud branching regulation have proved insufficient to improve prognosis of congenital renal defects. Thus, we addressed mechanisms regulating three-dimensional (3D) ureteric bud epithelial cell morphology and cell shape changes during novel branch initiation to uncover the contributions of cellular mechanics on cellular functions and tissue organization in normal and branching-compromised bud tips.MethodsWe explored epithelial cell behavior at all scales by utilizing a combination of mouse genetics and a custom machine-learning segmentation pipeline in MATLAB. Ureteric bud epithelial cell shapes and sizes were quantified in 3D wholemount kidneys. A combination with live imaging of fluorescently labelled UB cells, traction force microscopy, and primary UB cells were used to determine how basic cellular features and niche biomechanics contribute to complex novel branch point determination in the process that aims at gaining optimal growth and epithelial density in a limited space.ResultsMachine learning-based segmentation of tip epithelia identified geometrical round-to-elliptical transformation as a key cell shape change facilitating shifts in growth direction that enable propitious branching complexity. Cell shape and molecular analyses in branching-compromised epithelia demonstrated a failure to condense cell size and conformation. Analysis of branching-compromised ureteric bud derived epithelial cells demonstrated disrupted E-CADHERIN and PAXILLIN mediated adhesive forces and defective cytoskeletal dynamics as detected by fluorescent labelling of actin in primary ureteric bud epithelial cells. Branching-compromised ureteric bud epithelial cells showed wrinkled nuclear shapes and alterations in MYH9-based microtubule organization, which suggest a stiff cellular niche with disturbed sensing of and response to biomechanical cues.ConclusionsOur results indicate that the adhesive forces within the epithelium and towards the niche composed of nephron progenitors must dynamically fluctuate to allow complexity in arborization during new branch formation. The data collectively propose a model where epithelial cell crowding in tandem with stretching transforms individual cells into elliptical and elongated shapes. This creates local curvatures that drive new branch formation during the ampulla-to-asymmetric ampulla transition of ureteric bud.
CHANGE-seq-BE enables simultaneously sensitive and unbiasedin vitroprofiling of base editor genome-wide activity
Authors: Lazzarotto Cicera R. , Katta Varun , Li Yichao , Urbina Elizabeth , Lee GaHyun , Tsai Shengdar Q. 
Published: March 2024

AbstractBase editors (BE) enable programmable conversion of nucleotides in genomic DNA without double-stranded breaks and have substantial promise to become new transformative genome editing medicines. Sensitive and unbiased detection of base editor off-target effects is important for identifying safety risks unique to base editors and translation to human therapeutics, as well as accurate use in life sciences research. However, current methods for understanding the global activities of base editors have limitations in terms of sensitivity or bias. Here we present CHANGE-seq-BE, a novel method to directly assess the off-target profile of base editors that is simultaneously sensitive and unbiased. CHANGE-seq-BE is based on the principle of selective sequencing of adenine base editor modified genomic DNAin vitro, and provides an accessible, rapid, and comprehensive method for identifying genome-wide off-target mutations of base editors.
The human genetic variant rs6190 unveils Foxc1 and Arid5a as novel pro-metabolic targets of the glucocorticoid receptor in muscle
Authors: Prabakaran Ashok Daniel , Chung Hyun-Jy , McFarland Kevin , Govindarajan Thirupugal , Soussi Fadoua El Abdellaoui , Durumutla Hima Bindu , Villa Chiara , Piczer Kevin , Latimer Hannah , Werbrich Cole , Akinborewa Olukunle , Horning Robert , Quattrocelli Mattia 
Published: March 2024

AbstractThe genetic determinants of the glucocorticoid receptor (GR) metabolic action remain largely unelucidated. This is a compelling gap in knowledge for the GR single nucleotide polymorphism (SNP) rs6190 (p.R23K), which has been associated in humans with enhanced metabolic health but whose mechanism of action remains completely unknown. We generated transgenic knock-in mice genocopying this polymorphism to elucidate how the mutant GR impacts metabolism. Compared to non-mutant littermates, mutant mice showed increased insulin sensitivity on regular chow and high-fat diet, blunting the diet-induced adverse effects on adiposity and exercise intolerance. Overlay of RNA-seq and ChIP-seq profiling in skeletal muscle revealed increased transactivation ofFoxc1andArid5Agenes by the mutant GR. Using myotropic adeno-associated viruses for in vivo overexpression or knockdown in muscle, we found thatFoxc1was required and sufficient for normal expression levels of insulin response pathway genesInsrandIrs1, promoting muscle insulin sensitivity. In parallel,Arid5awas required and sufficient to transcriptionally repress the lipid uptake genesC 36andFabp4, reducing muscle triacylglycerol accumulation. Moreover, the Foxc1 and Arid5a programs in muscle were divergently changed by glucocorticoid regimens with opposite metabolic outcomes in muscle. Finally, we found a direct human relevance for our mechanism of SNP action in the UK Biobank and All of Us datasets, where the rs6190 SNP correlated with pro-metabolic changes in BMI, lean mass, strength and glucose control according to zygosity. Collectively, our study leveraged a human nuclear receptor coding variant to unveil novel epigenetic regulators of muscle metabolism.Graphical abstract
Current forest carbon offset buffer pools do not adequately insure against disturbance-driven carbon losses
Authors: Anderegg William R. L. , Trugman Anna T. , Vargas German , Wu Chao , Yang Linqing 
Published: March 2024

AbstractNature-based climate solutions in Earth’s forests could strengthen the land carbon sink and contribute to climate mitigation, but must adequately account for climate risks to the durability of carbon storage. Forest carbon offset protocols use a ‘buffer pool’ to insure against disturbance risks that may compromise durability. However, current buffer pool tools and allocations are not based on existing scientific data or models. Here, we use a tropical forest stand biomass model and an extensive set of long-term tropical forest plots to test whether current buffer pools are adequate to insure against observed disturbance regimes. We find that forest age and disturbance regime both influence necessary buffer pool sizes. In the vast majority of disturbance scenarios, current buffer pools are substantially smaller than required by carbon cycle science. Buffer pool estimates urgently need to be updated based on rigorous, open scientific datasets for nature-based climate solutions to succeed.Plain Language SummaryForests could contribute to climate mitigation through conservation and restoration activities. Carbon offsets are a widespread pathway to fund these nature-based climate solutions in forests, but must account for the risks to durability that forests face in a changing climate. Current carbon offset protocols have a buffer pool to insure against risk in different disturbance regimes, but the buffer pool contributions have not been tested with observed disturbance regimes and rigorous models. We tested these contributions using widespread tropical forest plot data and a carbon cycle model and find that the current buffer pool contributions are generally not adequate for most disturbance regimes. Our results highlight that better datasets, models, and tools are urgently needed in forest carbon offset protocols.Key pointsNature-based climate solutions in forests face substantial and rising climate risks to durabilityCarbon offsets use a buffer pool to insure against disturbance, which is not currently based on rigorous evidenceOur results reveal current carbon offset protocols do not have an adequate buffer pool for most tropical forest disturbance regimes
Timing of standard chow exposure determines the variability of mouse phenotypic outcomes and gut microbiota profile
Authors: Knuth Megan M. , Campos Carolina Vieira , Smith Kirsten , Hutchins Elizabeth K. , Lewis Shantae , York Mary , Coghill Lyndon M. , Franklin Craig , MacFarlane Amanda , Ericsson Aaron C. , Magnuson Terry , Ideraabdullah Folami 
Published: March 2024

AbstractStandard chow diet contributes to reproducibility in animal model experiments since chows differ in nutrient composition, which can independently influence phenotypes. However, there is little evidence of the role of timing in the extent of variability caused by chow exposure. Here, we measured the impact of diet (5V5M, 5V0G, 2920X, and 5058) and timing of exposure (adult exposure (AE), lifetime exposure (LE), and developmental exposure (DE)) on growth & development, metabolic health indicators, and gut bacterial microbiota profiles across genetically identical C57BL6/J mice. Diet drove differences in macro-and micronutrient intake for all exposure models. AE had no effect on measured outcomes. However, LE mice exhibited significant sex-dependent diet effects on growth, body weight, and body composition. LE effects were mostly absent in the DE model, where mice were exposed to chow differences from conception to weaning. Both AE and LE models exhibited similar diet-driven beta diversity profiles for the gut bacterial microbiota, with 5058 diet driving the most distinct profile. Diet-induced beta diversity profiles were sex-dependent for LE mice. Compared to AE, LE drove 9X more diet-driven differentially abundant genera, majority of which were the result of inverse effects of 2920X and 5058. Our findings demonstrate that lifetime exposure to different chow diets has the greatest impact on reproducibility of experimental measures that are common components of preclinical mouse model studies. Importantly, weaning DE mice onto a uniform diet is likely an effective way to reduce unwanted phenotypic variability among experimental models.
Type I interferon drives a cellular state inert to TCR-stimulation and could impede effective T-cell differentiation in cancer
Authors: Corvino Dillon , Batstone Martin , Hughes Brett G.M , Kempchen Tim , Ng Susanna S , Salim Nazhifah , Schneppenheim Franziska , Rommel Denise , Kumar Ananthi , Pearson Sally , Madore Jason , Koufariotis Lambross T. , Steinheuer Lisa Maria , Pathirana Dilan , Thurley Kevin , Hölzel Michael , Borcherding Nicholas , Braun Matthias , Bald Tobias 
Published: March 2024

AbstractHead and neck squamous cell carcinoma (HNSCC) arises from the mucosal epithelium of the oral cavity, pharynx, or larynx and is linked to exposure to classical carcinogens and human papillomavirus (HPV) infection. Due to molecular, immunological, and clinical disparities between HPV+ and HPV-HNSCC, they are recognized as distinct cancer types. While immune checkpoint inhibition (ICI) has demonstrated efficacy in recurrent/metastatic HNSCC, response variability persists irrespective of HPV status. To gain insights into the CD8+ T-cell landscape of HPV-HNSCC, we performed multimodal sequencing (RNA and TCR) of CD8+ tumor-infiltrating lymphocytes (TILs) from treatment-naïve HPV-HNSCC patients. Additionally, we subjected cells toex vivoTCR-stimulation, facilitating the tracing of clonal transcriptomic responses. Our analysis revealed a subset of CD8+ TILs highly enriched for interferon-stimulated genes (ISG), which were found to be clonally related to a subset of granzyme K (GZMK)-expressing cells. Trajectory inference suggests ISG transition via GZMK cells towards terminal effector states. However, unlike GZMK cells, which rapidly an effector-like phenotype in response to TCR stimulation, ISG cells remain transcriptionally inert. Consequently, ISG cells may impede effective T-cell differentiation within the TME. Although, the functional consequences of ISG cells are poorly understood, we revealed that they possess receptors and ligands enabling cell-cell communication networks with key TME immunomodulators such as dendritic cells. Additionally, ISG cells were found to be a core feature across various tumor entities and were specifically enriched within tumor tissue. Thus, our findings illuminate the complexity of T-cell heterogeneity in HPV-HNSCC and reveal an overlooked population of IFN-stimulated CD8+ TILs. Further exploration of their functional significance may offer insights into therapeutic strategies for HPV-HNSCC and other cancer types.
Development of a Two-Leaf Photosynthetic Model Sensitive to Chlorophyll and Its Coupling with a Wheat Growth Model
Authors: Tang Yining , Wang Jiacheng , Hong Yifan , Guo Caili , Zheng Hengbiao , Yao Xia , Cheng Tao , Zhu Yan , Cao Weixing , Tian Yongchao 
Published: March 2024

AbstractCrop growth models (CGMs) commonly simulates the response of photosynthetic rate to nitrogen (N) dynamic by calculating critical N concentration. However, critical N concentration makes it hard to describe the physiological effect of N dynamic on photosynthesis. Meanwhile, the effect of diffuse light on photosynthesis was limited in previous studies. In this study, we introduced a Two-leaf Photosynthetic Model Sensitive to Chlorophyll Content (TPMSCC) and coupled it with the crop growth model (WheatGrow) to enhance our understanding of how N dynamic and diffuse light affects photosynthesis. By coupling the Farquhar-von Caemmerer-Berry (FvCB) C3photosynthesis model with a canopy radiative transfer model (PROSAIL), TPMSCC simulated the light interception of direct and diffuse light, and employed leaf chlorophyll content (LCC) to simulate the light absorption and electron transfer rate of leaves. Result showed that TPMSCC well simulated the light absorption of the wheat canopy and found the canopy photosynthetic rate benefited from the increase of diffuse radiation fraction (DRF) except for when the condition of a dense canopy at a high solar zenith angle was present. Research found the logarithmic and linear relationships of LCC to the initial light use efficiency (ɑ) and the maximum photosynthetic rate (Amax), respectively, which followed the field measurements. Additionally, the optimized WheatGrow model outperforms its predecessor in describing the response of N application rate on photosynthesis.
Establishing the foundations for a data-centric AI approach for virtual drug screening through a systematic assessment of the properties of chemical data
Authors: Chong Allen , Phua Ser-Xian , Xiao Yunzhi , Ng Woon Yee , Li Hoi Yeung , Goh Wilson Wen Bin 
Published: March 2024

SummaryResearchers have adopted model-centric artificial intelligence (AI) approaches in cheminformatics by using newer, more sophisticated AI methods to take advantage of growing chemical libraries. It has been shown that complex deep learning methods outperform conventional machine learning (ML) methods in QSAR and ligand-based virtual screening1–3but such approaches generally lack explanability. Hence, instead of developing more sophisticated AI methods (i.e., pursuing a model-centric approach), we wanted to explore the potential of a data-centric AI paradigm for virtual screening. A data-centric AI is an intelligent system that would automatically identify the right type of data to collect, clean and curate for later use by a predictive AI and this is required given the large volumes of chemical data that exist in chemical databases – PubChem alone has over 100 million unique compounds. However, a systematic assessment of the attributes and properties of suitable data is needed. We show here that it is not the result of deficiencies in current AI algorithms but rather, poor understanding and erroneous use of chemical data that ultimately leads to poor predictive performance. Using a new benchmark dataset of BRAF ligands that we developed, we show that our best performing predictive model can achieve an unprecedented accuracy of 99% with a conventional ML algorithm (SVM) using a merged molecular representation (Extended + ECFP6 fingerprints), far surpassing past performances of virtual screening platforms using sophisticated deep learning methods. Thus, we demonstrate that it is not necessary to resort to the use of sophisticated deep learning algorithms for virtual screening because conventional ML can perform exceptionally well if given the right data and representation. We also show that the common use of decoys for training leads to high false positive rates and its use for testing will result in an over-optimistic estimation of a model’s predictive performance. Another common practice in virtual screening is defining compounds that are above a certain pharmacological threshold as inactives. Here, we show that the use of these so-called inactive compounds lowers a model’s sensitivity/recall. Considering that some target proteins have a limited number of known ligands, we wanted to also observe how the size and composition of the training data impact predictive performance. We found that an imbalance training dataset where inactives outnumber actives led to a decrease in recall but an increase in precision, regardless of the model or molecular representation used; and overall, we observed a decrease in the model’s accuracy. We highlight in this study some of the considerations that one needs to take into account in future development of data-centric AI for CADD.
Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex
Authors: Warrington Oliver , Graedel Nadine N. , Callaghan Martina F. , Kok Peter 
Published: March 2024

AbstractCurrent evidence points to the hippocampus as an essential region coordinating learning and exploiting predictive relationships in the service of perception. However, it remains unclear whether the hippocampus drives the communication of predictions to the sensory cortex or acts as a recipient of predictions from else-where. Here, we collected sub-millimetre 7T fMRI data to investigate neural signals in the medial temporal lobe (MTL). We used layer-specific fMRI to infer the direction of communication between the hippocam-pus and cortex. Specifically, superficial layers of the MTL cortex project to the hippocampus, while deep MTL layers receive feedback projections. Participants performed a task in which auditory cues predicted abstract shapes. Crucially, we omitted the expected shape on 25% of trials, thus isolating the prediction signal from bottom-up input and allowing us to ask: In which direction are predictions communicated between the hippocampus and neocortex? Neural patterns in CA23, pre/parasubiculum and the parahippocampal cortex (PHC) reflected shape-specific predictions. Layer-specific informational connectivity analyses revealed that communication between CA23 and PHC was specific to the deep layers of PHC. These findings are in line with the hippocampus generating predictions through pattern completion in CA23 and feeding these pre-dictions back to the neocortex.
Preservation of an Aging-Associated Mitochondrial Signature in Advanced Human Neuronal Models
Authors: Varghese Nimmy , Szabo Leonora , Cader Zameel , Lejri Imane , Grimm Amandine , Eckert Anne 
Published: March 2024

1.AbstractThis study investigated whether induced pluripotent stem cell-derived neurons (iPSCsNs) and directly converted neurons (iNs) generated from the same cells of origin (human fibroblasts) represent aging-related characteristics on mitochondrial levels. There is still uncertainty regarding the potential for rejuvenation or preservation of an aging-associated donor signature in aged iPSCsNs upon transition through pluripotent states, while direct conversion retains the aging-associated mitochondrial impairments. Surprisingly, both aged neuronal models exhibited age-associated donor phenotypes, including decreased ATP, mitochondrial membrane potential, mitochondrial respiration, NAD+/NADH ratio, and increased radical levels and mitochondrial mass. Besides, a fragmented mitochondrial network was observed in both aged neuronal models. However, unlike aged iNs, aged iPSCsNs did not show a metabolic shift towards anaerobic glycolysis to compensate for the energy deficit. Moreover, the mRNA expression profile significantly differed between aged iPSCsNs and aged iNs. Our study indicates that aged iPSCsNs may experience rejuvenation in certain parameters, such as transcriptomics and the aging-associated glycolytic shift. Nevertheless, aged iPSCsNs can be a valuable tool for studying neuronal aging of mitochondrial parametersin vitroalongside aged iNs.
Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning
Authors: de Jager Marjolein , Kolbeck Pauline J. , Vanderlinden Willem , Lipfert Jan , Filion Laura 
Published: March 2024

ABSTRACTProtein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥1 kbp) are challenging to address experimentally or by all-atom molecular dynamics simulations, making coarse-grained simulations a natural approach. Here we present a simple and generic coarse-grained model for the DNA-protein and protein-protein interactions, and investigate the role of the latter in the protein-induced compaction of DNA. Our approach models the DNA as a discrete worm-like chain. The proteins are treated in the grand-canonical ensemble and the protein-DNA binding strength is taken from experimental measurements. Protein-DNA interactions are modeled as an isotropic binding potential with an imposed binding valency, without specific assumptions about the binding geometry. To systematically and quantitatively classify DNA-protein complexes, we present an unsupervised machine learning pipeline that receives a large set of structural order parameters as input, reduces the dimensionality via principal component analysis, and groups the results using a Gaussian mixture model. We apply our method to recent data on the compaction of viral genome-length DNA by HIV integrase and we find that protein-protein interactions are critical to the formation of looped intermediate structures seen experimentally. Our methodology is broadly applicable to DNA-binding proteins and to protein-induced DNA compaction and provides a systematic and quantitative approach for analyzing their mesoscale complexes.SIGNIFICANCEDNA is central to the storage and transmission of genetic information and is frequently compacted and condensed by interactions with proteins. Their size and dynamic nature make the resulting complexes difficult to probe experimentally and by all-atom simulations. We present a simple coarse-grained model to explore ∼kbp DNA interacting with proteins of defined valency and concentration. Our analysis uses unsupervised learning to define conformational states of the DNA-protein complexes and pathways between them. We apply our simulations and analysis to the compaction of viral genome-length DNA by HIV integrase. We find that protein-protein interactions are critical to account for the experimentally observed intermediates and our simulated complexes are in good agreement with experimental observations.