bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transfer learning between preclinical models and human tumors identifies conserved NK cell activation signature in anti-CTLA-4 responsive tumors
Authors: Davis-Marcisak Emily F. , Fitzgerald Allison A. , Kessler Michael D. , Danilova Ludmila , Jaffee Elizabeth M. , Zaidi Neeha , Weiner Louis M. , Fertig Elana J. 
Published: June 2020

AbstractBackgroundTumor response to therapy is affected by both the cell types and the cell states present in the tumor microenvironment. This is true for many cancer treatments, including notably immune checkpoint inhibitors (ICIs). While it is well-established that ICIs promote T cell activation, their broader impact on other intratumoral immune cells is unclear; this information is needed to identify new mechanisms of action and improve ICI efficacy. Many preclinical studies have begun to use single cell analysis to delineate therapeutic responses in individual immune cell types within tumors. One major limitation to this approach is that therapeutic mechanisms identified in preclinical models have failed to fully translate to human disease, restraining efforts to improve ICI efficacy in bench to bedside research.MethodWe previously developed a computational transfer learning approach to identify shared biology between independent high-throughput single-cell RNA sequencing (scRNA-seq) datasets. In the present study, we test this framework’s ability to identify conserved and clinically relevant transcriptional changes in complex tumor scRNA-seq data and further expand its application beyond comparison of scRNA-seq datasets into comparison of scRNA-seq datasets with additional data types such as bulk RNA-seq and mass cytometry.ResultsWe found a conserved signature of NK cell activation in anti-CTLA-4 responsive mice and human tumors. In human melanoma, we found that the NK cell activation signature correlates with longer overall survival and is predictive of anti-CTLA-4 (ipilimumab) response. Additional molecular approaches to confirm the computational findings demonstrated that human NK cells express CTLA-4 and bind anti-CTLA-4 independent of the antibody binding receptor (FcR), and that similar to T cells, CTLA-4 expression by NK cells is modified by cytokine-mediated and target cell-mediated NK cell activation.ConclusionsThese data demonstrate the ability of our transfer learning approach to identify cell state transitions conserved in preclinical models and human tumors. This approach can be adapted to explore many immuno-oncology questions, enhancing bench to bedside research and enabling better understanding and treatment of disease.Graphical Abstract
TMEM106BandCPOXare genetic determinants of cerebrospinal fluid Alzheimer’s disease biomarker levels
Authors: Hong Shengjun , Dobricic Valerija , Bos Isabelle , Vos Stephanie J. B. , Prokopenko Dmitry , Tijms Betty M. , Andreasson Ulf , Blennow Kaj , Vandenberghe Rik , Gabel Silvy , Scheltens Philip , Teunissen Charlotte E. , Engelborghs Sebastiaan , Frisoni Giovanni , Blin Olivier , Richardson Jill C. , Bordet Regis , Lleó Alberto , Alcolea Daniel , Popp Julius , Clark Christopher , Peyratout Gwendoline , Martinez-Lage Pablo , Tainta Mikel , Dobson Richard J. B. , Legido-Quigley Cristina , Sleegers Kristel , Van Broeckhoven Christine , Tanzi Rudolph E , ten Kate Mara , Wittig Michael , Franke Andre , Barkhof Frederik , Lovestone Simon , Streffer Johannes , Zetterberg Henrik , Visser Pieter Jelle , Bertram Lars 
Published: June 2020

AbstractBackgroundNeurofilament light (NF-L), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are utilized as biomarkers for Alzheimer’s disease (AD), to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. Here we performed genome-wide association study (GWAS) analyses using all three biomarkers as outcome.MethodsDNA and cerebrospinal fluid (CSF) samples originated from the European Medical Information Framework AD Multimodal Biomarker Discovery (EMIF-AD MBD) study. Overlapping genotype/phenotype data were available for n=671 (NF-L), 677 (YKL-40), and 672 (Ng) individuals. GWAS analyses applied linear regression models adjusting for relevant covariates.FindingsWe identify novel genome-wide significant associations with markers inTMEM106Band CSF levels of NF-L. Additional novel signals were observed with DNA variants inCPOXand CSF levels of YKL-40. Lastly, we confirmed previous work suggesting that YKL-40 levels are regulated bycisprotein quantitative trait loci (pQTL) inCHI3L1.InterpretationOur study provides important new insights into the genetic architecture underlying inter-individual variation in all three tested AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.
Alteration of Substrate-Induced Conformational Changes ofEscherichia coliMelibiose Permease by Mutating Arg149
Authors: Lin Yibin 
Published: June 2020

AbstractFourier transform infrared difference spectroscopy and fluorescence spectroscopic techniques have been used to obtain information about substrate-induced structural changes of the melibiose permease mutant R149C, compared with the Cys-less, which were reconstituted into liposomes. ATR-FTIR evidences show that Na+-induced difference spectra of R149C and Cys-less are similar. However, Na+induces some new peaks for R149C mutant permease. This means that replacement of Arg-149 by Cys may affect the structure of MelB, and then affect the binding of Na+. Melibiose-induced difference spectra of R149C in the presence of Na+show some peaks in the amide I region not seen in Cys-less, corresponding to turns, β-sheets, α-helix changes. This suggests that R149C mutant permease undergo some different secondary structure changes compared to Cys-less mutant permease, when binding melibiose. Comparison of the permease intrinsic fluorescence variations of R149C and Cys-less indicate that there are similar substrate binding properties between R149C and Cys-less. When analyzing the effects of different sugars it appears that the R149C mutant is more sensitive to the sugar. All these data indicate that replacement of Arg-149 by Cys will affect Na+and sugar binding, and enhance the selectivity and sensitivity to sugars.
UPMaBoSS: a novel framework for dynamic cell population modeling
Authors: Stoll Gautier , Naldi Aurélien , Noël Vincent , Viara Eric , Barillot Emmanuel , Kroemer Guido , Thieffry Denis , Calzone Laurence 
Published: June 2020

AbstractOne of the aims of mathematical modeling is to understand and simulate the effects of biological perturbations and suggest ways to intervene and reestablish proper cell functioning. However, it remains a challenge, especially when considering the dynamics at the level of a cell population, with cells dying, dividing and interacting. Here, we introduce a novel framework for the dynamical modelling of cell populations packaged into a dedicated tool, UPMaBoSS. We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks, and add a novel layer to account for cell interactions and population dynamics. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. This appoach can be applied to diverse models of cellular networks, for example to study the impact of ligand release or drug treatments on cell fate decisions, such as commitment to proliferation, differentiation, apoptosis, etc. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time.To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within a new release of the CoLoMoTo Docker image, which contains all required software and the example models.
Implementation of Genomic Variant Calling: A Novel Approach
Authors: Kumar Ambarish , Bangash Ali Haider 
Published: June 2020

AbstractGenomics has emerged as one of the major sources of big data. The task of augmenting data-driven challenges into bioinformatics can be met using technologies of parallel and distributed computing. GATK4 tools for genomic variants detection are enabled for high-performance computing platforms – SPARK Map Reduce framework. GATK4+WDL+CROMWELL+SPARK+DOCKER is proposed as the way forward in achieving automation, reproducibility, reusability, customization, portability and scalability. SPARK-based tools perform equally well in genomic variants detection with that of standard implementation of GATK4 tools over a command-line interface. Implementation of workflows over cloud-based high-performance computing platforms will enhance usability and will be a way forward in community research and infrastructure development for genomic variant discovery.
Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise Response
Authors: Amsalem Oren , King James , Reimann Michael , Ramaswamy Srikanth , Muller Eilif , Markram Henry , Nelken Israel , Segev Idan 
Published: June 2020

AbstractThe nervous system is notorious for its strong response evoked by a surprising sensory input, but the biophysical and anatomical underpinnings of this phenomenon are only partially understood. Here we utilizedin-silicoexperiments of a biologically-detailed model of a neocortical microcircuit to study stimulus specific adaptation (SSA) in the auditory cortex, whereby the neuronal response adapts significantly for a repeated (“expected”) tone but not for a rare (“surprise”) tone. SSA experiments were mimicked by stimulating tonotopically-mapped thalamo-cortical afferents projecting to the microcircuit; the activity of these afferents was modeled based on ourin-vivorecordings from individual thalamic neurons. The modeled microcircuit expressed naturally many experimentally-observed properties of SSA, suggesting that SSA is a general property of neocortical microcircuits. By systematically modulating circuit parameters, we found that key features of SSA depended on synergistic effects of synaptic depression, spike frequency adaptation and recurrent network connectivity. The relative contribution of each of these mechanisms in shaping SSA was explored, additional SSA-related experimental results were explained and new experiments for further studying SSA were suggested.
RapidMycobacterium tuberculosisspoligotyping from uncorrected long reads using Galru
Authors: Page Andrew J. , Alikhan Nabil-Fareed , Strinden Michael , Le Viet Thanh , Skvortsov Timofey 
Published: June 2020

AbstractSpoligotyping ofMycobacterium tuberculosisprovides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of aMycobacterium tuberculosissample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available fromhttps://github.com/quadram-institute-bioscience/galruunder the GPLv3 open source licence.
Deep learning approach for automated cancer detection and tumor proportion score estimation of PD-L1 expression in lung adenocarcinoma
Authors: Wu J. , Liu C. , Liu X. , Sun W. , Li L. , Zhang Y. , Zhang J. , Wang H. , Liu X. , Yang X. , Huang X. , Lin D. , Ling S. 
Published: June 2020

AbstractBackgroundThis study proposed a computational method to detect the cancer areas and calculate the tumor proportion score (TPS) of PD-L1 immunohistochemistry (IHC) expression for lung adenocarcinoma based on deep learning and transfer learning.Patients and methodsPD-L1 22C3 and SP142 IHC slides of lung adenocarcinoma samples on digitized whole-slide images (WSI) database were employed. We build a deep convolutional neural network (DCNN) to automatically segment cancer regions. TPS was calculated based on segmented areas and then compared with the interpretations of pathologists.ResultsWe trained a DCNN model based on 22C3 dataset and fine-tuned it with SP142 dataset. We obtain a robust performance on cancer region detection on both datasets, with a sensitivity of 93.36% (22C3) and 92.80% (SP142) and a specificity of 93.97% (22C3) and 89.25% (SP142). With all the coefficient of determinations larger than 0.9 and Fleiss’ and Cohen’s Kappa larger than 0.8 (between mean or median of pathologists and TPS calculated by our method), we also found out the strong correlation between the TPS estimated by our computational method and estimation from multiple pathologists’ interpretations of 22C3 and SP142 respectively.ConclusionWe provide an AI method to efficiently predict cancer region and calculate TPS in PD-L1 IHC slide of lung adenocarcinoma on two different antibodies. It demonstrates the potential of using deep learning methods to conveniently access PD-L1 IHC status. In the future, we will further validate the AI tool for automated scoring PD-L1 in large volume samples.
High temperatures drive offspring mortality in a cooperatively breeding bird
Authors: Bourne Amanda R. , Cunningham Susan J. , Spottiswoode Claire N. , Ridley Amanda R. 
Published: June 2020

AbstractAn improved understanding of life history responses to current environmental variability is required to predict species-specific responses to anthopogenic climate change. Previous research has suggested that cooperation in social groups may buffer individuals against some of the negative effects of unpredictable climates. We use a 15-year dataset on a cooperative-breeding arid-zone bird, the southern pied babbler Turdoides bicolor, to test i) whether environmental conditions and group size correlate with survival of young during three development stages (egg, nestling, fledgling), and ii) whether group size mitigates the impacts of adverse environmental conditions on reproductive success. Exposure to high mean daily maximum temperatures (mean Tmax) during early development was associated with reduced survival probabilities of young in all three development stages. No young survived when mean Tmax > 38°C across all group sizes. Low reproductive success at high temperatures has broad implications for recruitment and population persistence in avian communities given the rapid pace of advancing climate change. That impacts of high temperatures were not moderated by group size, a somewhat unexpected result given prevailing theories around the influence of environmental uncertainty on the evolution of cooperation, suggests that cooperative breeding strategies are unlikely to be advantageous in the face of rapid anthropogenic climate change.
Cognitive tests that identify high risk of conversion to dementia in Parkinson’s disease
Authors: Myall D.J. , Horne K-L. , MacAskill M.R. , Livingston L. , Pitcher T.L. , Melzer T.R. , Geurtsen G.J. , Anderson T.J , Dalrymple-Alford J.C. 
Published: June 2020

AbstractBackgroundPeople with Parkinson’s disease who meet criteria for mild cognitive impairment are at increased risk of dementia. It is not known which tests are more effective than others for identifying the risk of dementia.MethodsAt baseline, we assessed performance on 21 neuropsychological test measures spanning five cognitive domains in a prospective longitudinal study of 196 non-demented people with Parkinson’s. Elastic net logistic regression was used to identify a pair of tests from each cognitive domain that best predicted conversion to dementia over a four year period. The optimal tests most predictive of dementia were also determined when mild cognitive impairment was derived from a logistic-regression classifier that used all 21 measures simultaneously.ResultsWith two tests per domain, the resulting mild cognitive impairment group (N=87/196) captured 44 of 51 individuals who converted to PDD; the out-of-sample relative risk of PDD was 8.0 (95% CI [4.3, 24]), similar to that achieved with the full battery (N=102/196, capturing 45/51, relative risk = 6.9). When selecting tests regardless of domain, there was strong evidence for three tests: Trail Making part B (Executive), Map Search (Attention), and CVLT-II word list acquisition (Episodic Memory). The logistic-regression classifier achieved an out-of-sample AUC of 0.90 [0.84, 0.96] and a relative risk of 12 [6, 39].ConclusionsAn abbreviated selection of neuropsychological tests can identify non-demented patients who have a high relative risk of progression to PDD.
ON THE UNCERTAINTY ABOUT HERD IMMUNITY LEVELS REQUIRED TO STOP COVID-19 EPIDEMICS
Authors: Gianola Daniel 
Published: June 2020

AbstractCOVID-19 evolved into a pandemic in 2020 affecting more than 150 countries. Given the absence of a vaccine, discussion has taken place on the strategy of allowing the virus to spread in a population, to increase population “herd immunity”. Knowledge of the minimum proportion of a population required to have recovered from COVID-19 infection in order to attain “herd” immunity, Pcrit, is important for formulating epidemiological policy. A method for measuring uncertainty about Pcrit based on a widely used package, EpiEstim, is derived. The procedure is illustrated using data from twelve countries at two early times during the COVID-19 epidemic. It is shown that simple plug-in measures of confidence on estimates of Pcrit are misleading, but that a full characterization of statistical uncertainty can be derived from EpiEstim, which reports percentiles only. Because of the important levels of uncertainty, it is risky to design epidemiological policy based on guidance provided by a single point estimate.
A Highly Sensitive Fluorogenic Assay for the Detection of Nephrotoxin-Induced Oxidative Stress in Live Cells and Renal Tissue
Authors: Mukherjee Kamalika , Chio Tak Ian , Gu Han , Sackett Dan L. , Bane Susan L. , Sever Sanja 
Published: June 2020

AbstractA common manifestation of drug toxicity is kidney injury or nephrotoxicity. Nephrotoxicity frequently leads to termination of clinical trials and drug withdrawals, which jeopardize biomedical progress. Efficient preclinical screening platforms capable of detecting mild signs of kidney damage, which may elicit considerable toxic responsein vivo,are essential. A common manifestation of chemical toxicity is oxidative modification of cellular biomolecules. Therefore, we have developed a facile biomolecule carbonyl detection assay that well surpasses the sensitivity of the standard assays in identifying modest forms of renal injury. Using a novel fluorogenic sensor, TFCH, we have demonstrated the applicability of the assay in live kidney cells and in renal tissue. This robust assay can help inform preclinical decisions to recall unsafe drug candidates. Application of this assay in identifying and analyzing diverse pathologies is envisioned.
Comprehensive evaluation of deconvolution methods for human brain gene expression
Authors: Sutton Gavin J , Poppe Daniel , Simmons Rebecca K , Walsh Kieran , Nawaz Urwah , Lister Ryan , Gagnon-Bartsch Johann A , Voineagu Irina 
Published: June 2020

AbstractGene expression measurements, similar to DNA methylation and proteomic measurements, are influenced by the cellular composition of the sample analysed. Deconvolution of bulk transcriptome data aims to estimate the cellular composition of a sample from its gene expression data, which in turn can be used to correct for composition differences across samples. Although a multitude of deconvolution methods have been developed, it is unclear whether their performance is consistent across tissues with different complexities of cellular composition. The human brain is unique in its transcriptomic diversity, expressing the highest diversity of alternative splicing isoforms and non-coding RNAs. It comprises a complex mixture of cell-types including transcriptionally similar sub-types of neurons, which undergo gene expression changes in response to neuronal activity. However, a comprehensive assessment of the accuracy of transcriptome deconvolution methods on human brain data is currently lacking.Here we carry out the first comprehensive comparative evaluation of the accuracy of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with transcriptome data from human pancreas and heart.We evaluate 8 transcriptome deconvolution approaches, covering all main classes: 4 partial deconvolution methods, each applied with 9 different cell-type signatures, 2 enrichment methods, and 2 complete deconvolution methods. We test the accuracy of cell-type estimates usingin silicomixtures of single-cell RNA-seq data, mixtures of neuronal and glial RNA, as well as nearly 2,000 human brain samples.Our results bring several important insights into the performance of transcriptome deconvolution:(a)We find that cell-type signature data has a stronger impact on brain deconvolution accuracy than the choice of method.(b)We demonstrate that biological factors influencing brain cell-type signature data (e.g.brain region,in vitrocell culturing), have stronger effects on the deconvolution outcome than technical factors (e.g.RNA sequencing platform).(c)We find that partial deconvolution methods outperform complete deconvolution methods on human brain data. To facilitate wider implementation of correction for cellular composition, we develop a webtool that implements the best performing methods, and is available athttps://voineagulab.shinyapps.io/BrainDeconvShiny/.
An enormousParis polyphyllagenome sheds light on genome size evolution and polyphyllin biogenesis
Authors: Li Jing , Lv Meiqi , Du Lei , Yunga A , Hao Shijie , Zhang Yaolei , Zhang Xingwang , Guo Lidong , Gao Xiaoyang , Deng Li , Zhang Xuan , Shi Chengcheng , Guo Fei , Liu Ruxin , Fang Bo , Su Qixuan , Hu Xiang , Su Xiaoshan , Lin Liang , Liu Qun , Wang Yuehu , Qin Yating , Zhang Wenwei , Li Shengying , Liu Changning , Li Heng 
Published: June 2020

AbstractThe monocot family Melanthiaceae with varying genome sizes in a range of 230-fold is an ideal model to study the genome size fluctuation in plants. Its family memberParisgenus demonstrates an evolutionary trend of bearing huge genomes characterized by an average c-value of 49.22 pg. Here, we report a 70.18 Gb genome assembly out of the 82.55 Gb genome ofParis polyphyllavar. yunnanensis (PPY), which represents the biggest sequenced genome to date. We annotate 69.53% repetitive sequences in this genome and 62.50% of which are long-terminal repeat (LTR) transposable elements. Further evolution analysis indicates that the giant genome likely results from the joint effect of common and species-specific expansion of different LTR superfamilies, which might contribute to the environment adaptation after speciation. Moreover, we identify the candidate pathway genes for the biogenesis of polyphyllins, the PPY-specific medicinal saponins, by complementary approaches including genome mining, comprehensive analysis of 31 next-generation RNA-seq data and 55.23 Gb single-molecule circular consensus sequencing (CCS) RNA-seq reads, and correlation of the transcriptome and phytochemical data of five different tissues at four growth stages. This study not only provides significant insights into plant genome size evolution, but also paves the way for the following polyphyllin synthetic biology.