bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting regional somatic mutation rates using DNA motifs
Authors: Liu Cong , Wang Zengmiao , Wang Jun , Liu Chengyu , Wang Mengchi , Ngo Vu , Wang Wei 
Published: August 2022

AbstractHow the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-specific regions can accurately predict cancer types. (The code is available from https://github.com/Wang-lab-UCSD/SomaticMutation)Significance StatementThe relationship between DNA motifs and somatic mutation rates in cancers is not fully understood, especially at high resolution. Here we developed an interpretable neural network model to successfully predict somatic mutation rates using DNA motifs in 13 diverse cancers and identified the most informative motifs. Furthermore, we showed that the genomic regions with significant higher mutation rates than the predicted values can be used for cancer classification.
Effect of contact pressure on porcine postmortem brain tissue impedance
Authors: Poßner Lucas , Wilhelmy Florian , Pliquett Uwe , Knösche Thomas R. , Weise Konstantin , Lindner Dirk 
Published: August 2022

AbstractIn this experimental study we demonstrate the influence of contact pressure on porcine postmortem brain tissue impedance using a movable electrode array and a load cell. We show that the variation of the contact pressure between the tissue and the measurement probe leads to a coefficient of variation in the measured impedance of under 3%. Its influence can therefore be neglected. Further, we fit the measured impedances to an equivalent circuit model and compare the resistance as well as the cell density of grey and white matter brain tissue based on the model parameters.
Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models
Authors: Si Yunda , Yan Chengfei 
Published: August 2022

AbstractThe knowledge of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions (PPIs). However, accurately identifying the tens of contacting ones from hundreds of thousands of inter-protein residue pairs is extremely challenging, and performances of the state-of-the-art inter-protein contact prediction methods are still quite limited. In this study, we developed a deep learning method for inter-protein contact prediction, referred to as DRN-1D2D_Inter. Specifically, we employed pretrained protein language models to generate structural information enriched input features to residual networks formed by dimensional hybrid residual blocks to perform inter-protein contact prediction. Extensively benchmarked DRN-1D2D_Inter on multiple datasets including both heteromeric PPIs and homomeric PPIs, we show DRN-1D2D_Inter consistently and significantly outperformed two state-of-the-art inter-protein contact prediction methods including GLINTER and DeepHomo, although both the latter two methods leveraged native structures of interacting proteins in the prediction, and DRN-1D2D_Inter made the prediction purely from sequences.
Phase behaviour of C18-N-acyl sphingolipids, the prevalent species in human brain
Authors: González-Ramírez Emilio J. , Etxaniz Asier , Alonso Alicia , M. Goñi Félix 
Published: August 2022

ABSTRACTLipidomic analysis of the N-acyl components of sphingolipids in different mammalian tissues had revealed that brain tissue differed from all the other samples in that SM contained mainly C18:0 and C24:1 N-acyl chains, and that the most abundant Cer species was C18:0. Only in the nervous system was C18:0 found in sizable proportions. The high levels of C18:0 and C16:0, respectively in brain and non-brain SM, were important because SM is by far the most abundant sphingolipid in the plasma membrane. In view of these observations, the present paper is devoted to a comparative study of the properties of C16:0 and C18:0 sphingolipids (SM and Cer) pure and in mixtures of increasing complexities, using differential scanning calorimetry, confocal microscopy of giant unilamellar vesicles, and correlative fluorescence microscopy and atomic force microscopy of supported lipid bilayers. Membrane rigidity was measured by force spectroscopy. It was found that in mixtures containing dioleoyl phosphatidylcholine, sphingomyelin and cholesterol, i.e. representing the lipids predominant in the outer monolayer of cell membranes, lateral inhomogeneities occurred, with the formation of rigid domains within a continuous fluid phase. Inclusion of saturated Cer in the system was always found to increase the rigidity of the segregated domains. C18:0-based sphingolipids exhibit hydrocarbon chain-length asymmetry, and some singularities observed with this N-acyl chain, e.g. complex calorimetric endotherms, could be attributed to this property. Moreover, C18:0-based sphingolipids, that are typical of the excitable cells, were less miscible with the fluid phase than their C16:0 counterparts. The results could be interpreted as suggesting that the predominance of C18:0 Cer in the nervous system would contribute to the tightness of its plasma membranes, thus facilitating maintenance of the ion gradients.
TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding
Authors: Lim Tian-Yeh , Wilde Blake R. , Thomas Mallory L. , Murphy Kristin E. , Vahrenkamp Jeffery M. , Conway Megan E. , Varley Katherine E. , Gertz Jason , Ayer Donald E. 
Published: August 2022

Abstractc-Myc protooncogene places a demand on glucose uptake to drive glucose-dependent biosynthetic pathways. To achieve this demand, c-Myc protein (Myc henceforth) drives the expression of glucose transporters and represses the expression of Thioredoxin Interacting Protein (TXNIP), which is a potent negative regulator of glucose uptake. A Mychigh/TXNIPlow gene signature is clinically significant as it correlates with poor clinical prognosis in Triple-Negative Breast Cancer (TNBC) but not in other subtypes of breast cancer. To better understand how TXNIP function contributes to the aggressive behavior of TNBC, we generated TXNIP null MDA-MB-231 (231:TKO) cells for our study. We show here that TXNIP loss drives a transcriptional program that resembles those driven by Myc and increases global Myc genome occupancy. TXNIP loss allows Myc to invade the promoters and enhancers of target genes that are potentially relevant to cell transformation. Together, these findings suggest that TXNIP is a broad repressor of Myc genomic binding. The increase in Myc genomic binding in the 231:TKO cells expands the Myc-dependent transcriptome we identified in parental MDA-MB-231 cells. This expansion of Myc-dependent transcription following TXNIP loss occurs without an apparent increase in Myc’s intrinsic capacity to activate transcription and without increasing Myc levels. Together, our findings suggest that TXNIP loss mimics Myc overexpression, connecting Myc genomic binding and transcriptional programs to the metabolic signals that control TXNIP expression.
ALOHA: AI-guided tool for the quantification of venom-induced haemorrhage in mice
Authors: Jenkins Timothy P. , Laprade William Michael , Sánchez Andrés , Tulika Tulika , O’Brien Carol , Sørensen Christoffer V. , Stewart Trenton K. , Fryer Thomas , Laustsen Andreas H. , Gutiérrez José María 
Published: August 2022

AbstractVenom-induced haemorrhage constitutes a severe pathology in snakebite envenomings, especially those inflicted by viperid species. In order to both explore venom compositions accurately, and evaluate the efficacy of viperid antivenoms for the neutralisation of haemorrhagic activity it is essential to have available a precise, quantitative tool for empirically determining venom-induced haemorrhage. Thus, we have built on our prior approach and developed a new AI-guided tool (ALOHA) for the quantification of venom-induced haemorrhage in mice. Using a smartphone, it takes less than a minute to take a photo, upload the image, and receive accurate information on the magnitude of a venom-induced haemorrhagic lesion in mice. This substantially decreases analysis time, reduces human error, and does not require expert haemorrhage analysis skills. Furthermore, its open access web-based graphical user interface makes it easy to use and implement in laboratories across the globe. Together, this will reduce the resources required to preclinically assess and control the quality of antivenoms, whilst also expediting the profiling of hemorrhagic activity in venoms for the wider toxinology community.
Gene expression noise dynamics unveil functional heterogeneity of ageing hematopoietic stem cells
Authors: Rosales-Alvarez Reyna Edith , Rettkowski Jasmin , Herman Josip Stefan , Dumbović Gabrijela , Cabezas-Wallscheid Nina , Grün Dominic 
Published: August 2022

SummaryVariability of gene expression due to stochasticity of transcription or variation of extrinsic signals, termed biological noise, is a potential driving force of cellular differentiation. While unicellular organisms exploit noise as a bet-hedging strategy, its role during multilineage differentiation of stem cells is underexplored. Utilizing single-cell RNA-sequencing to reconstruct cell state manifolds, we developed VarID2, a method for the quantification of biological noise at single-cell resolution. VarID2 reveals enhanced nuclear versus cytoplasmic noise across cell types of the peripheral blood, and distinct regulatory modes stratified by correlation between noise, expression, and chromatin accessibility. Noise levels are minimal in murine hematopoietic stem cells and increase during both differentiation and ageing. Differential noise identified myeloid-biased Dlk1+ long-term-HSCs in aged mice with enhanced quiescence and self-renewal capacity. VarID2 reveals fundamental properties of noise across cellular compartments, during stem cell differentiation and ageing, and uncovers distinct cellular sub-states invisible to conventional gene expression analysis.
Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold
Authors: Li Ziyao , Liu Xuyang , Chen Weijie , Shen Fan , Bi Hangrui , Ke Guolin , Zhang Linfeng 
Published: August 2022

AbstractRecent breakthroughs on protein structure prediction, namely AlphaFold, have led to unprecedented new possibilities in related areas. However, the lack of training utilities in its current open-source code hinders the community from further developing or adapting the model. Here we present Uni-Fold as a thoroughly open-source platform for developing protein folding models beyond AlphaFold. We reimplemented AlphaFold and AlphaFold-Multimer in the PyTorch framework, and reproduced their from-scratch training processes with equivalent or better accuracy. Based on various optimizations, Uni-Fold achieves about 2.2 times training acceleration compared with AlphaFold under similar hardware configuration. On a benchmark of recently released multimeric protein structures, Uni-Fold outperforms AlphaFold-Multimer by approximately 2% on the TM-Score. Uni-Fold is currently the only open-source repository that supports both training and inference of multimeric protein models. The source code, model parameters, test data, and web server of Uni-Fold are publicly available3.
A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing
Authors: Lengyel Ernst , Li Yan , Weigert Melanie , Zhu Lisha , Eckart Heather , Javellana Melissa , Ackroyd Sarah , Xiao Jason , Olalekan Susan , Glass Dianne , Iyer Shilpa , Bilecz Agnes Julia , Lastra Ricardo , Chen Mengjie , Basu Anindita 
Published: August 2022

SummaryAs part of the Human Cell Atlas initiative, we generated transcriptomic (scRNA-seq; 86,708 cells) and regulatory (scATAC-seq; 59,118 cells) profiles of the normal postmenopausal ovary and fallopian tube (FT) at single-cell resolution. In the FT, 22 cell clusters integrated into 11 cell types, including ciliated and secretory epithelial cells, while the ovary had 17 distinct cell clusters defining 6 major cell types. The dominant cell type in both the postmenopausal ovary and FT was stromal cells, which expressed several genes associated with aging. The fimbrial end of the FT had a significant number and variety of immune cells and active communication with the ovary; the ovary contained mostly stromal cells but few immune cells. The epithelial cells of the normal FT expressed multiple ovarian cancer risk-associated genes (CCDC170, RND3, TACC2, STK33, and ADGB). By integrating paired single-cell transcriptomics and chromatin accessibility data we found that the regulatory landscape of the fimbriae was markedly different from the isthmus and ampulla. Intriguingly, several cell types in the FT had comparable gene expression but different transcriptional regulations. Our single-cell transcriptional and regulatory maps allowed us to disentangle the complex cellular makeup of the postmenopausal FT and ovary and will contribute to a better understanding of gynecologic diseases in menopause.
Is Tau the Initial Pathology in Dopaminergic Nigrostriatal Degeneration? Studies in Parkinsonism and Parkinson’s Disease
Authors: Chu Yaping , Hirst Warren D. , Federoff Howard J. , Harms Ashley S. , Stoessl A. Jon , Kordower Jeffrey H. 
Published: August 2022

AbstractWhile Parkinson’s disease (PD) remains clinically defined by cardinal motor symptoms resulting from nigrostriatal degeneration, it is now appreciated that PD consists of multiple pathologies, but it is unclear which occurs first and which are responsible for the nigrostriatal degeneration. For the past number of years, we have been studying a well-characterized cohort of subjects with motor impairment that we have termed mild motor deficits (MMD). Motor deficits were determined on a modified and validated Unified Parkinson’s Disease Rating Scale III (UPDRS III), but they occur to a degree insufficient to diagnose PD. We consider this population to have prodromal PD. However, in past studies, cases in this cohort had a selection bias as both a clinical syndrome in between no motor deficits and PD, plus nigral Lewy pathology as defined post-mortem, were required for inclusion. Therefore, in this study, we only based inclusion on a clinical phenotype intermediate between no motor impairment and PD. Then, we divided this group further based upon whether or not they had a synucleinopathy. Here we demonstrate that loss of nigral dopaminergic neurons, loss of putamenal dopaminergic innervation, loss of TH-phenotype in the substantia nigra and putamen, and changes in axonal transport occur equally in groups with and without nigral alpha-synuclein aggregates. Indeed, the common feature of these two groups is that both have similar degrees of AT8-expressing phospho-tau, a pathology not seen in the nigrostriatal system of aged-matched controls. These finding were confirmed with early (CP13) and late (PHF1) tau markers. This suggests that the initiation of nigrostriatal dopaminergic neurodegeneration occurs independently of alpha-synuclein aggregation and is likely tau mediated.
Differential processing of decision information in subregions of rodent medial prefrontal cortex
Authors: Diehl Geoffrey W , Redish A David 
Published: August 2022

AbstractDecision-making involves multiple cognitive processes requiring different aspects of information about the situation at hand. The rodent medial prefrontal cortex (mPFC) has been hypothesized to be central to these abilities. Functional studies have sought to link specific processes to specific anatomical subregions, but past studies of mPFC have yielded controversial results, leaving the precise nature of mPFC function unclear. To settle this debate, we recorded from the full dorso-ventral extent of mPFC in each of 8 rats, as they performed a complex economic decision task. These data revealed four distinct functional domains within mPFC that closely mirrored anatomically-identified subregions, including novel evidence to divide prelimbic cortex into dorsal and ventral components. We found that dorsal aspects of mPFC (ACC, dPL) were more involved in processing information about active decisions, while ventral aspects (vPL, IL) were more engaged in motivational factors.Impact StatementDiehl and Redish describe how the rodent medial prefrontal cortex is composed of functionally distinct subregions that process economic decision information differentially along a dorso-ventral gradient.
Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features
Authors: Henderson Margaret , Tarr Michael J. , Wehbe Leila 
Published: August 2022

AbstractRepresentations of visual and semantic information can overlap in human visual cortex, with the same neural populations exhibiting sensitivity to low-level features (orientation, spatial frequency, retinotopic position), and high-level semantic categories (faces, scenes). It has been hypothesized that this relationship between low-level visual and high-level category neural selectivity reflects natural scene statistics, such that neurons in a given category-selective region are tuned for low-level features or spatial positions that are diagnostic of the region’s preferred category. To address the generality of this “natural scene statistics” hypothesis, as well as how well it can account for responses to complex naturalistic images across visual cortex, we performed two complementary analyses. First, across a large set of rich natural scene images, we demonstrated reliable associations between low-level (Gabor) features and high-level semantic dimensions (indoor-outdoor, animacy, real-world size), with these relationships varying spatially across the visual field. Second, we used a large-scale fMRI dataset (the Natural Scenes Dataset) and a voxelwise forward encoding model to estimate the feature and spatial selectivity of neural populations throughout visual cortex. We found that voxels in category-selective visual regions exhibit systematic biases in their feature and spatial selectivity which are consistent with their hypothesized roles in category processing. We further showed that these low-level tuning biases are largely independent of viewed image category. Together, our results are consistent with a framework in which low-level feature selectivity contributes to the computation of high-level semantic category information in the brain.
Novel transient cell clusters provide a possible link between early neural activity and angiogenesis in the neonatal mouse retina
Authors: de Montigny Jean , Thorne Courtney , Bhattacharya Diya , Sertedakis Dimitrios Bousoulas , Krishnamoorthy Vidhyasankar , Rozenblit Fernando , Gollisch Tim , Sernagor Evelyne 
Published: August 2022

AbstractDeveloping neurons become spontaneously active while growing blood vessels begin to irrigate their surroundings. However, surprising little is known about early interactions between neural activity and angiogenesis. In the neonatal mouse retina, spontaneous waves of impulses sweep across the ganglion cell layer (GCL), just underneath the growing superficial vascular plexus. We discovered clusters of transient auto-fluorescent cells in the GCL, forming an annulus that co-localizes with the frontline of the growing plexus. Blood vessel density is highest within cluster areas, suggesting their involvement in angiogenesis. Once the clusters and blood vessels reach the retinal periphery by the end of the first postnatal week, the clusters disappear, eliminated by microglial phagocytosis. Electrical imaging suggests that they have their own electrophysiological signature. Blocking Pannexin1 (PANX1) hemi-channels with probenecid blocks the waves and the fluorescent clusters disappear following prolonged exposure to the drug. Spontaneous waves’ initiation points follow a developmental center-to-periphery progression similar to the cluster cells. We suggest that these transient cells are specialized, hyperactive neurons residing in the GCL. They generate spontaneous activity hotspots, thereby triggering waves through purinergic paracrine signaling via PANX1 hemi-channels. The strong activity generated around these hotspots triggers angiogenesis, attracting new blood vessels that provide local oxygen supply. Signaling through PANX-1 attracts microglia that establish contact with these cells, eventually leading to their elimination by phagocytosis. These cluster cells may provide the first evidence that specialized transient neuronal populations guide angiogenesis in the developing CNS through neural activity.
Bayesian Multidimensional Nominal Response Model for Observer Study of Radiologists
Authors: Nishio Mizuho , Kobayashi Daigo , Matsuo Hidetoshi , Urase Yasuyo , Nishioka Eiko , Murakami Takamichi 
Published: August 2022

AbstractPurposeThis study proposes a Bayesian multidimensional nominal response model (MD-NRM) to statistically analyze the nominal response of multiclass classifications.Materials and methodsFirst, for MD-NRM, we extended the conventional nominal response model to achieve stable convergence of the Bayesian nominal response model and utilized multidimensional ability parameters. We then applied MD-NRM to a 3-class classification problem, where radiologists visually evaluated chest X-ray images and selected their diagnosis from one of the three classes. The classification problem consisted of 150 cases, and each of the six radiologists selected their diagnosis based on a visual evaluation of the images. Consequently, 900 (= 150×6) nominal responses were obtained. In MD-NRM, we assumed that the responses were determined by the softmax function, the ability of radiologists, and the difficulty of images. In addition, we assumed that the multidimensional ability of one radiologist were represented by a 3×3 matrix. The latent parameters of the MD-NRM (ability parameters of radiologists and difficulty parameters of images) were estimated from the 900 responses. To implement Bayesian MD-NRM and estimate the latent parameters, a probabilistic programming language (Stan, version 2.21.0) was used.ResultsFor all parameters, the Rhat values were less than 1.10. This indicates that the latent parameters of the MD-NRM converged successfully.ConclusionThe results show that it is possible to estimate the latent parameters (ability and difficulty parameters) of the MD-NRM using Stan. Our code for the implementation of the MD-NRM is available as open source.Short AbstractTo statistically analyze the nominal response of multiclass classifications, this study proposes a Bayesian multidimensional nominal response model (MD-NRM). With MD-NRM, it is possible to statistically analyze the nominal response of multiclass classifications obtained by radiologists.