bluuebunny/crossref_metadata_2025_split huggingface dataset:
Alkalinity modulates a unique suite of genes to recalibrate growth and pH homeostasis
Authors: Bailey Mitylene , Hsieh En-Jung , Tsai Huei-Hsuan , Ravindran Arya , Schmidt Wolfgang 
Published: December 2022

AbstractAlkaline soils pose a conglomerate of constraints to plants, restricting the growth and fitness of non-adapted species in habitats with low active proton concentrations. To thrive under such conditions, plants have to compensate for a potential increase in cytosolic pH and restricted softening of the cell wall to invigorate cell elongation in a proton-depleted environment. To discern mechanisms that aid in the adaptation to external pH, we grew plants on media with pH values ranging from 4.5 to 8.5. Growth was severely restricted at pH 4.5 and above pH 6.5, and associated with decreasing chlorophyll levels at alkaline pH. Bicarbonate treatment worsened plant performance, suggesting effects that differ from those exerted by pH as such. Transcriptional profiling of roots subjected to short-term transfer from optimal (pH 5.5) to alkaline (pH 7.5) media unveiled a large set of differentially expressed genes that were partially congruent with genes affected by low pH, bicarbonate and nitrate, but showed only a very small overlap with genes responsive to the availability of iron. Further analysis of selected genes disclosed pronounced responsiveness of their expression over a wide range of external pH values. Alkalinity altered the expression of various proton/anion co-transporters, possibly to recalibrate cellular proton homeostasis. Co-expression analysis of pH-responsive genes identified a module of genes encoding proteins with putative functions in the regulation of root growth, which appears to be conserved in plants subjected to low pH or bicarbonate. Our analysis provides an inventory of pH-sensitive genes and allows comprehensive insights into processes that are orchestrated by external pH.
Measurement of expression from a limited number of genes is sufficient to predict flowering time in maize
Authors: Torres-Rodríguez J. Vladimir , Sun Guangchao , Mural Ravi V. , Schnable James c. 
Published: December 2022

AbstractChanging patterns of weather and climate are limiting breeders’ ability to conduct trials in the same environments in which their released varieties will be grown 7-10 years later. Flowering time plays a crucial role in determining regional adaptation, and mismatch between flowering time and environment can substantially impair yield. Different approaches based on genetic markers or gene expression can be used to predict flowering time before conducting large scale field evaluation and phenotyping. The more accurate prediction of a trait using genetic markers could be hindered due to all the intermediate steps (i.e. transcription, translation, epigenetic modification, and epistasis among others) connecting the trait and their genetic basics. The use of some intermediate steps as predictors could improve the accuracy of the model. Here, we are using two public gene expression (RNA-Seq) data-sets from 14-day-old-maize-seedling roots and whole-seedling tissue at v1 stage (10 day after planting) for which flowering data (days to anthesis and days to silking expressed in growing degree days) and genetic markers were also available to test the predictability of flowering time. In total, 20 different combinations between phenotypic and gene expression data-sets were evaluated. To explore prediction accuracy a random forest model was trained with the expression values of 44,303 gene models hosted in the current B73 maize reference version 5 and then the feature importance was scored based on the decrease in root mean squared error. Later several random forest models with different subsets of the most important features (genes) were trained, and this process was repeated ten times. Results from these analyses show a curve in the prediction accuracy, with an increase in the prediction accuracy as the top most important genes were added. The maximum accuracy was attained when 500 genes for whole-seedling and 100 genes for root gene expression data were used in the analysis, and thereafter adding more genes lead to a decrease in the prediction accuracy. The highest prediction accuracy using the top-most important genes was higher than that of using randomly selected whole-genome 400,000 SNPs. Finally, we described the genes controlling flowering time by looking at the most important genes in the Random forest model with the expression data from all genes. We further found MADS-transcription factor 69 (Mads69) using whole-seedling gene expression and the MADS-transcription factor 67 (Mads67) using root gene expression data, both genes previously described with effect on flowering time. Here, we aim to demonstrate the potential of selecting and using the expression of most informative genes to predict a complex trait, also to demonstrate the robustness and limitations of this analysis by using phenotypic data-sets from different environments.
Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours
Authors: Warne David J. , Varghese Abhishek , Browning Alexander P. , Krell Mario M. , Drovandi Christopher , Hu Wenbiao , Mira Antonietta , Mengersen Kerrie , Jenner Adrianne L. 
Published: December 2022

AbstractWhen effective vaccines are available, vaccination programs are typically one of the best defences against the spread of an infectious disease. Unfortunately, vaccination rates may be suboptimal for a prolonged duration as a result of slow uptake of vaccines by the public. Key factors driving slow vaccination uptake can be a complex interaction of vaccine roll-out policies and logistics, and vaccine hesitancy behaviours potentially caused by an inflated sense of risk in adverse reactions in some populations or community complacency in communities that have not yet experienced a large outbreak. In the recent COVID-19 pandemic, public health responses around the world began to include vaccination programs from late 2020 to early 2021 with an aim of relaxing non-pharmaceutical interventions such as lockdowns and travel restrictions. For many jurisdictions there have been challenges in getting vaccination rates high enough to enable the relaxation of restrictions based on non-pharmaceutical interventions. A key concern during this time was vaccine hestitancy behaviours potentially caused by vaccine safety concerns fuelled by misinformation and community complacency in jurisdictions that had seen very low COVID-19 case numbers throughout 2020, such as Australia and New Zealand. We develop a novel stochastic epidemiological model of COVID-19 transmission that incorporates changes in population behaviour relating to responses based on non-pharmaceutical interventions and community vaccine uptake as functions of the reported COVID-19 cases, deaths, and vaccination rates. Through a simulation study, we develop a Bayesian analysis approach to demonstrate that different factors inhibiting the uptake of vaccines by the population can be isolated despite key model parameters being subject to substantial uncertainty. In particular, we are able to identify the presence of vaccine hesitancy in a population using reported case, death and vaccination count data alone. Furthermore, our approach provides insight as to whether the dominant concerns driving hesitancy are related to vaccine safety or complacency. While our simulation study is inspired by the COVID-19 pandemic, our tools and techniques are general and could be enable vaccination programs of various infectious diseases to be adapted rapidly in response to community behaviours moving forward into the future.
ReIGNITE RT Boost: An international study testing the accuracy and feasibility of using restriction spectrum imaging (RSI) MRI to guide intraprostatic tumor target volume for radiotherapy boost
Authors: Lui Asona J. , Kallis Karoline , Zhong Allison Y. , Hussain Troy S. , Conlin Christopher , Digma Leonardino A. , Phan Nikki , Mathews Ian T. , Do Deondre D. , Rojo Mariluz , Karunamuni Roshan , Kuperman Joshua , Dale Anders M. , Rakow-Penner Rebecca , Hahn Michael E. , Seibert Tyler M. 
Published: December 2022

AbstractIn a phase III randomized trial, adding a radiation boost to visible tumor(s) on MRI improved prostate cancer disease-free and metastasis-free survival without additional toxicity. However, radiation oncologists’ ability to identify prostate tumors is critical and represents a major barrier to widely adopting intraprostatic tumor radiotherapy boost for patients. We previously developed a quantitative diffusion MRI biomarker for prostate cancer, called the Restriction Spectrum Imaging restriction score (RSIrs), that has been shown to improve radiologist identification of clinically significant prostate cancer.42 radiation oncologists (participants) from multiple, international institutions contoured prostate tumors on 40 patient cases using standard MRI with or without RSIrs map, producing 1646 target volumes. Use of RSIrs maps significantly improved all evaluated accuracy metrics, including participants’ percent overlap with consensus expert target volume (73% vs. 42%, p<0.001). A mixed effects model confirmed that RSIrs maps were the main variable driving the improvement in all metrics. System Usability Scores indicated RSIrs maps significantly improved the contouring experience (72 vs. 58, p<0.002). The expert-defined tumor was completely missed 158 times on standard MRI alone and only 19 times with RSIrs maps. RSIrs maps improve the accuracy of target delineation for prostate tumor boost.Patient SummaryAdding an extra boost of radiation to tumor(s) visible on MRI has been shown to prevent cancer recurrence and cancer spread beyond the prostate without adding additional side effects; however, drawing the prostate tumor on MRI is difficult, and most radiation oncologists have not been trained to do this. We have developed an advanced MRI technique (RSIrs maps) that increases tumor visibility. We found that RSIrs maps improve radiation oncologists’ accuracy in targeting prostate tumors.
NAD+ biosynthesis as a collateral lethality target for precision oncology
Authors: Muller Florian L. , Deng Pingna , Lin Yu-Hsi , Satani Nikunj , Hammoudi Naima , Mohapatra Gayatry , Asara John M. , DePinho Ronald A. 
Published: December 2022

ABSTRACTGenomic deletion of tumor suppressor genes (TSG) often encompasses neighboring genes which may be members of multi-gene families encoding cell essential functions. These genomic events create targetable cancer-specific vulnerabilities, termed “collateral lethality” as illustrated by homozygous deletion of the glycolytic geneENO1, which sensitizes glioblastoma (GBM) cells to inhibition of its paralogENO2. Here, we sought to generalize the concept by validating a second multi-gene family in an unrelated metabolic pathway. Nicotinamide-nucleotide adenylyltransferase (NMNAT) is an essential and (unlike the more extensively studied NAMPT) non-bypassable step in NAD biosynthesis encoded by three paralogs, one of which,NMNAT1, is homozygously deleted as part of the 1p36 tumor suppressor locus in TCGA data of GBM, Cholangiocarcinoma and Hepatocellular carcinoma (with near zero expression of found in several other malignancies). In a glioma cell line (Gli56) with homozygous deletion ofNMNAT1expressing NMNAT2 and NMNAT3, shRNA-mediated knockdown of NMNAT2 is selectively toxic to Gli56NMNAT1-deleted but not ectopically rescued cells. As NMNAT1 and NMNAT2 are predominantly localized in the nucleus and cytosol, respectively, these data suggest a functionally common pool of cytosolic and nuclear NAD+. Inducible shRNA-mediated extinction of NMNAT2 decreases NAD+ levels and selectively killsNMNAT1-deleted, but notNMNAT1-rescued cellsin vitroand eradicates intracranial tumorsin vivo. Thus, collateral lethality is a generalizable framework for the development of new classes of targeted agents with an informed clinical development path in cancer.Statement of significanceAn ongoing challenge in precision oncology is the translation of genomic data into actionable therapeutic opportunities with clear clinical benefit. We have demonstrated that genes homozygously deleted by virtue of chromosomal proximity to major tumor suppressor genes can confer cancer-specific vulnerabilities, termed “collateral lethality”. While our previous work validated one such collateral lethality target in glycolysis, we now provide empiricalin vitroandin vivoevidence that this concept applies to another deleted gene governing an altogether distinct biochemical pathway. The generalization of collateral lethality may expand the spectrum of molecular targets in cancer with a genomically-informed path for accurate clinical development.
Spatial transcriptome profiling ofin vitro3D tumouroids to study tumour-stroma interactions
Authors: Bakkalci Deniz , Al-Badri Georgina , Yang Wei , Nam Andy , Liang Yan , Khurram Syed Ali , Heavey Susan , Fedele Stefano , Cheema Umber 
Published: December 2022

AbstractBioengineering facets of the tumour microenvironment (TME) are essential in 3D tissue models to accurately recapitulate tumour progression. Stromal cells are key components of the TME and their incorporation into 3D biomimetic bioengineered tumour-stroma models is essential to be able to mimic the TME. By engineering tumouroids with distinct tumour and stromal compartments, it has been possible to identify how gene expression is altered by the presence of different stromal cells using spatial transcriptomics. Ameloblastoma is a benign epithelial tumour of the jawbone and in engineered multi-compartment tumouroids increased expression of oncogenes was found where osteoblasts (bone stroma) were present. Engineering a gingival fibroblast stroma resulted in increased matrix remodelling genes in the ameloblastoma tumour. This study provides evidence to show the stromal specific effect on tumour behaviour and illustrates the importance of engineering biologically relevant stroma for engineered tumour models. Our novel results show that an engineered fibroblast stroma causes the upregulation of matrix remodelling genes in ameloblastoma which directly correlates to measured invasion in the model. In contrast the presence of an osteoblast/bone stroma increases the expression of oncogenes by ameloblastoma cells.
First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa
Authors: Turon Gemma , Hlozek Jason , Woodland John G. , Chibale Kelly , Duran-Frigola Miquel 
Published: December 2022

AbstractWe present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool to train small-molecule activity prediction models. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets, ranging from whole-cell growth inhibition assays to drug metabolism properties. The tool has been implemented end-to-end at the Holistic Drug Discovery and Development (H3D) Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. We have exploited in-house data collected from over a decade of drug discovery research in malaria and tuberculosis and built models to predict the outcomes of 15 key checkpoint assays. We subsequently deployed these models as a virtual screening cascade at an organisational scale to increase the hit rate of current experimental assays. We show how computational profiling of compounds, prior to synthesis and experimental testing, can increase the rate of progression by up to 40%. Moreover, we demonstrate that the approach can be applied to prioritise small molecules within a chemical series and to assess the likelihood of success of novel chemotypes, promoting efficient usage of limited experimental resources. This project is part of a first-of-its-kind collaboration between the H3D Centre, a research centre operating in a low-resource setting, and the Ersilia Open Source Initiative, a young tech non-profit devoted to building data science capacity in the Global South.
The ratio of adaptive to innate immune cells differs between genders and associates with improved prognosis and response to immunotherapy
Authors: Ahrenfeldt J , Christensen D. S. , Østergaard A. B. , Kisistók J. , Sokac M. , Birkbak N. J. 
Published: December 2022

AbstractImmunotherapy has revolutionised cancer treatment. However, not all cancer patients benefit, and current stratification strategies based primarily on PD1 status and mutation burden is far from perfect. We hypothesised that high activation of an innate response relative to the adaptive response may prevent proper tumour neoantigen identification and decrease the specific anticancer response, both in the presence and absence of immunotherapy. To investigate this, we obtained transcriptomic data from three large publicly available cancer datasets, the Cancer Genome Atlas (TCGA), the Hartwig Medical Foundation (HMF), and a recently published cohort of metastatic bladder cancer patients treated with immunotherapy.To analyse immune infiltration into bulk tumours, we developed an RNAseq-based model based on previously published definitions to estimate the overall level of infiltrating innate and adaptive immune cells from bulk tumour RNAseq data. From these, the adaptive-to-innate immune ratio (A/I ratio) was defined.A meta-analysis of 32 cancer types from TCGA overall showed improved overall survival in patients with an A/I ratio above median (Hazard ratio (HR) females 0.73, HR males 0.86, P < 0.05). Of particular interest, we found that the association was different for males and females for eight cancer types, demonstrating a gender bias in the relative balance of the infiltration of innate and adaptive immune cells. For patients with metastatic disease, we found that responders to immunotherapy had a significantly higher A/I ratio than non-responders in HMF (P = 0.036) and a significantly higher ratio in complete responders in a separate metastatic bladder cancer dataset (P = 0.022).Overall, the adaptive-to-innate immune ratio seems to define separate states of immune activation, likely linked to fundamental immunological reactions to cancer. This ratio was associated with improved prognosis and improved response to immunotherapy, demonstrating potential relevance to patient stratification. Furthermore, by demonstrating a significant difference between males and females that associates with response, we highlight an important gender bias which likely has direct clinical relevance.
Bimodal 1/fnoise and anticorrelation between DNA-Water and DNA-Ion energy fluctuations
Authors: Mukherjee Saumyak , Mondal Sayantan , Bagchi Biman 
Published: December 2022

Coupling between the fluctuations of DNA and its surroundings consisting of water and ions in solution remains poorly understood and relatively less investigated as compared to proteins. Here, with the help of molecular dynamics simulations and statistical mechanical analyses, we explore the dynamical coupling between DNA, water, and counterions through correlations among respective energy fluctuations in both double (ds-) and single-stranded (ss-) DNA solutions. Fluctuations in the collective DNA-Water and DNA-Ion interaction energies are found to be strongly anti-correlated across all the systems. The fluctuations of DNA self-energy, however, are weakly coupled to DNA-water and DNA-ion interactions in ds-DNA. An enhancement of the DNA-water coupling is observed in ss-DNA, where the system is less rigid. All the interaction energies exhibit 1/fnoise in their energy power spectra with surprisingly prominent bimodality in the DNA-water and DNA-ion fluctuations. The nature of the energy spectra appears to be indifferent to the relative rigidity of the DNA. We discuss the role of the observed correlations in ion-water motions on a DNA duplex in the experimentally observed anomalous slow dielectric relaxation and solvation dynamics, and in furthering our understanding of the DNA energy landscape.TOC GRAPHIC
MIDAS: a deep generative model for mosaic integration and knowledge transfer of single-cell multimodal data
Authors: He Zhen , Chen Yaowen , Hu Shuofeng , An Sijing , Shi Junfeng , Liu Runyan , Zhou Jiahao , Dong Guohua , Shi Jinhui , Zhao Jiaxin , Wang Jing , Zhu Yuan , Ou-Yang Le , Bo Xiaochen , Ying Xiaomin 
Published: December 2022

AbstractRapidly developing single-cell multi-omics sequencing technologies generate increasingly large bodies of multimodal data. Integrating multimodal data from different sequencing technologies,i.e. mosaic data, permits larger-scale investigation with more modalities and can help to better reveal cellular heterogeneity. However, mosaic integration involves major challenges, particularly regarding modality alignment and batch effect removal. Here we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation, and batch correction of mosaic data by employing self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to other methods and reliability by evaluating its performance in full trimodal integration and various mosaic tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells (PBMCs), and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis, and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS.
Hydrogel mechanics regulate fibroblast DNA methylation and chromatin condensation
Authors: Sumey Jenna L. , Johnston Peyton C. , Harrell Abigail M. , Caliari Steven R. 
Published: December 2022

AbstractCellular mechanotransduction plays a central role in fibroblast activation during fibrotic disease progression, leading to increased tissue stiffness and reduced organ function. While the role of epigenetics in disease mechanotransduction has begun to be appreciated, there is little known about how substrate mechanics, particularly the timing of mechanical inputs, regulate epigenetic changes such as DNA methylation and chromatin reorganization during fibroblast activation. In this work, we engineered a hyaluronic acid hydrogel platform with independently tunable stiffness and viscoelasticity to model normal (storage modulus, G’ ∼ 0.5 kPa, loss modulus, G’’ ∼ 0.05 kPa) to increasingly fibrotic (G’ ∼ 3.5 and 8 kPa, G’’ ∼ 0.05 kPa) lung mechanics. Human lung fibroblasts exhibited increased spreading and nuclear localization of myocardin-related transcription factor A (MRTF-A) with increasing substrate stiffness within 1 day, with these trends holding steady for longer cultures. However, fibroblasts displayed time-dependent changes in global DNA methylation and chromatin organization. Fibroblasts initially displayed increased DNA methylation and chromatin decondensation on stiffer hydrogels, but both of these measures decreased with longer culture times. To investigate how culture time affected the responsiveness of fibroblast nuclear remodeling to mechanical signals, we engineered hydrogels amenable toin situsecondary crosslinking, enabling a transition from a compliant substrate mimicking normal tissue to a stiffer substrate resembling fibrotic tissue. When stiffening was initiated after only 1 day of culture, fibroblasts rapidly responded and displayed increased DNA methylation and chromatin decondensation, similar to fibroblasts on static stiffer hydrogels. Conversely, when fibroblasts experienced later stiffening at day 7, they showed no changes in DNA methylation and chromatin condensation, suggesting the induction of a persistent fibroblast phenotype. These results highlight the time-dependent nuclear changes associated with fibroblast activation in response to dynamic mechanical perturbations and may provide mechanisms to target for controlling fibroblast activation.