bluuebunny/crossref_metadata_2025_split huggingface dataset:
Targeting Reductive Metabolic Shifts by T315I Mutation in BCR-ABL Myeloid Leukemia for Therapy
Authors: Huang Chang-Yu , Chung Yin-Hsuan , Wu Sheng-Yang , Wang Hsin-Yang , Lin Zhi-Yu , Yang Tsung-Jung , Feng Jim-Ming , Hu Chun-Mei , Chang Zee-Fen 
Published: March 2022

AbstractT315I mutation of Bcr-Abl in chronic myeloid leukemia (CML) leads to therapeutic resistance. It is known that Bcr-Abl transformation causes ROS-induced DNA damages and replication stress, which can be exploited for anti-nucleotide therapy. We developed a small compound, JMF4073, which inhibited pyrimidylate kinases and selectively eliminated Bcr-Abl-transformed, but not untransformed myeloid cells, due to dTTP exhaustion and ROS-induced replication stress. However, T315I-Bcr-Abl-transformed cells were less vulnerable to JMF4073 because of higher dTTP pool and low replication stress. Unlike WT-Bcr-Abl-transformed cells, T315I-Bcr-Abl cells lacked Sirt1- regulated OXPHOS with increased glutamine flux to reductive carboxylation in TCA cycle and glutathione synthesis. Blocking mitochondrial pyruvate carrier (MPC) by UK-5099 reduced NADH and glutathione levels with replication stress induction, thereby converting T315I-Bcr-Abl cells sensitive to JMF4073 with dTTP and dCTP depletion. The combination of JMF4073 with UK-5099 showed in vivo eradication of T315I-Bcr-Abl-CML. These data reveal that T315I mutation causes reductive metabolic shifts in Bcr-Abl-CML, and demonstrate the therapeutic option by co-targeting MPC and pyrimidylate kinases.
Screening of Relevant Metabolism-Disrupting Chemicals on Pancreatic β-Cells: Evaluation of Murine and Human in Vitro Models
Authors: Al-Abdulla Ruba , Ferrero Hilda , Soriano Sergi , Boronat-Belda Talía , Alonso-Magdalena Paloma 
Published: March 2022

AbstractEndocrine-disrupting chemicals (EDCs) are chemical substances that can interfere with the normal function of the endocrine system. EDCs are ubiquitous and can be found in a variety of consumer products such as food packaging materials, personal care and household products, plastic additives, and flame retardants. Over the last decade, the impact of EDCs on human health has been widely acknowledged as they have been associated with different endocrine diseases. Among them, a subset called metabolism-disrupting chemicals (MDCs) are able to promote metabolic changes that can lead to the development of metabolic disorders such as diabetes, obesity, hepatic steatosis, and metabolic syndrome, among others. Despite this, today, there are still no definitive and standardized in vitro tools to support the metabolic risk assessment of existing and emerging MDCs for regulatory purposes. Here, we evaluated two different pancreatic cell-based in vitro systems, the murine pancreatic β-cell line MIN6 as well as the human pancreatic β-cell line EndoC- βH1. Both were challenged with a range of relevant concentrations of seven well-known EDCs (bisphenol-A (BPA), bisphenol-S (BPS), bisphenol-F (BPF), perfluorooctanesulfonic acid (PFOS), di(2-ethylhexyl) phthalate (DEHP), cadmium chloride (CdCl2) and dichlorodiphenyldichloroethylene (DDE)). The screening revealed that most of the tested chemicals have detectable deleterious effects on glucose-stimulated insulin release, insulin content, electrical activity, gene expression, and/or viability. Our data provide new molecular information on the direct effects of the selected chemicals on key aspects of pancreatic β-cell function such as the stimulus-secretion coupling and ion channel activity. In addition, we found that, in general, the sensitivity and responses were comparable to those from other in vivo studies reported in the literature. Overall, our results suggest that both systems can serve as effective tools for rapid screening of potential MDC effects on pancreatic β-cell physiology as well for deciphering and better understanding the molecular mechanisms that underlie their action.
Development of in vitro test methods in a model of human pancreatic β-cells to identify metabolism disrupting chemicals with diabetogenic activity
Authors: Dos Santos Reinaldo Sousa , Medina-Gali Regla María , Babiloni-Chust Ignacio , Marroqui Laura , Nadal Angel 
Published: March 2022

AbstractThere is a need to develop identification tests for Metabolism Disrupting Chemicals (MDCs) with diabetogenic activity. Here we used the human EndoC-βH1 β-cell line, the rat β-cell line INS-1E and dispersed mouse islet cells to assess the effects of endocrine disruptors on cell viability and glucose-stimulated insulin secretion (GSIS). We tested six chemicals at concentrations within human exposure (from 0.1 pM to 1 μM). Bisphenol-A (BPA) and tributyltin (TBT) were used as controls while four other chemicals, namely perfluorooctanoic acid (PFOA), triphenylphosphate (TPP), triclosan (TCS) and dichlorodiphenyldichloroethylene (DDE), were used as “unknowns”. Regarding cell viability, BPA and TBT increased cell death as previously observed. Their mode of action involved the activation of estrogen receptors and PPARγ, respectively. ROS production was a consistent key event in BPA- and TBT-treated cells. None of the other MDCs tested modified viability or ROS production. Concerning GSIS, TBT increased insulin secretion while BPA produced no effects. PFOA decreased GSIS, suggesting that this chemical could be a “new” diabetogenic agent. Our results indicate that the EndoC-βH1 cell line is a suitable human β-cell model for testing diabetogenic MDCs. Optimization of the test methods proposed here could be incorporated into tier protocols for the identification of MDCs.
Human CD4+/CD8α+ regulatory T cells induced by Faecalibacterium prausnitzii protect against intestinal inflammation
Authors: Touch Sothea , Godefroy Emmanuelle , Rolhion Nathalie , Danne Camille , Oeuvray Cyriane , Straube Marjolène , Galbert Chloé , Brot Loïc , Salgueiro Iria Alonso , Chadi Sead , Ledent Tatiana , Chatel Jean-Marc , Langella Philippe , Jotereau Francine , Altare Frédéric , Sokol Harry 
Published: March 2022

ABSTRACTFaecalibacterium prausnitzii (F. prausnitzii), a dominant bacterium of the human microbiota, is decreased in patients with inflammatory bowel diseases (IBD) and exhibits anti-inflammatory effects. In human, colonic lamina propria contains IL-10-positive, Foxp3-negative regulatory T cells (Treg) characterized by a double expression of CD4 and CD8α (DP8α) and a specificity for F. prausnitzii. This Treg subset is decreased in IBD. The in vivo effect of DP8α cells has not been evaluated yet. Here, using a humanized model of NOD.Prkcscid IL2rγ-/- (NSG) immunodeficient mouse strain that expresses human leucocyte antigen D-related 4 (HLA-DR4) but not murine class II (NSG-Ab° DR4), we demonstrated a protective effect of DP8α Tregs combined with F. prausnitzii administration in a colitis model. In a cohort of patients with IBD, we showed an independent association between the frequency of circulating DP8α cells and disease activity. Finally, we pointed out a positive correlation between F. prausnitzii-specific DP8α Tregs and the amount of F. prausnitzii in fecal microbiota in healthy individuals and patients with ileal Crohn’s disease.
Structural architecture and brain network efficiency links polygenic scores to intelligence
Authors: Genç Erhan , Metzen Dorothea , Fraenz Christoph , Schlüter Caroline , Voelkle Manuel C. , Arning Larissa , Streit Fabian , Nguyen Huu Phuc , Güntürkün Onur , Ocklenburg Sebastian , Kumsta Robert 
Published: March 2022

AbstractIntelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.
Rethinking the Methods and Algorithms for Inner Speech Decoding - and Making them Reproducible
Authors: Simistira Liwicki Foteini , Gupta Vibha , Saini Rajkumar , De Kanjar , Liwicki Marcus 
Published: March 2022

AbstractThis study focuses on the automatic decoding of inner speech using noninvasive methods, such as electroencephalography (EEG)). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to decode nonvoiced spoken words by using various brain-computer interfaces. The main shortcomings of existing work are reproducibility and the availability of data and code. In this work, we investigate various methods (using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTM)) for the detection task of 5 vowels and 6 words on a publicly available EEG dataset. The main contributions of this work are (1) subject dependent vs. subject-independent approaches, (2) the effect of different preprocessing steps (Independent Component Analysis (ICA), down-sampling and filtering), and (3) word classification (where we achieve state-of-the-art performance on a publicly available dataset). Overall we achieve a performance accuracy of 35.20% and 29.21% when classifying 5 vowels and 6 words respectively in a publicly available dataset, using our tuned iSpeech-CNN architecture. All of our code and processed data are publicly available to ensure reproducibility. As such, this work contributes to a deeper understanding and reproducibility of experiments in the area of inner speech detection.
N1-methylpseudouridine-incorporated mRNA enhances exogenous protein expression and suppresses immunogenicity in primary human fibroblast-like synoviocytes
Authors: Mokuda Sho , Watanabe Hirofumi , Kohno Hiroki , Ishitoku Michinori , Araki Kei , Hirata Shintaro , Sugiyama Eiji 
Published: March 2022

AbstractStudies conducted using murine arthritis models have indicated that the use of in vitro-transcribed messenger RNA (IVT mRNA) is an effective therapeutic approach for joint diseases. However, the use of IVT mRNA in human synovial cells has not been widely studied. Recently, the outbreak of the novel coronavirus disease has accelerated the development of innovative mRNA vaccines such as those containing a modified nucleic acid, N1-methylpseudouridine-5′-triphosphate (m1ψ). IVT mRNA is an attractive tool for biological experiments and drug discovery. To verify the protein expression of IVT mRNA in vitro, primary cultured human fibroblast-like synoviocytes (FLS) were transfected with enhanced green fluorescent protein (EGFP) mRNA with or without m1ψ incorporation. EGFP was detected using western blotting and fluorescence microscopy. A multiplex assay was performed to comprehensively understand IVT mRNA-induced immunogenicity. FLS transfected EGFP mRNA containing m1ψ generated higher levels of EGFP than unmodified EGFP mRNA or control RNAs. The multiplex assay of the FLS culture supernatant revealed that concentrations of IL-6, TNF-α, and CXCL10 were upregulated by unmodified EGFP mRNA, whereas they were suppressed by EGFP mRNA with m1ψ. Overall, m1ψ incorporation enhanced protein expression and decreased cytokine expressions in primary cultured FLS. The findings may contribute to arthritis research.
Suppression of transposon mobilization by m6A-mediated RNA sequestration in stress granules
Authors: Fan Wenwen , Wang Ling , Lei Zhen , Chu Jie , Cho Jungnam 
Published: March 2022

AbstractTransposon is a mobile and ubiquitous DNA that can vastly causes genomic alterations. In plants, it is well documented that transposon mobilization is strongly repressed by DNA methylation; however, the roles of RNA methylation in transposon control remain unknown. Here we suggest that transposon RNA is marked by m6A RNA methylation and is sequestered in stress granule (SG) in m6A-dependent manner. Intriguingly, a SG-localized AtALKBH9B selectively demethylates a heat-activated retroelement Onsen, and thereby releases from spatial confinement allowing for its mobilization. In addition, we show evidence that m6A RNA methylation contributes to transpositional suppression by inhibiting the virus-like particles assembly and extrachromosomal DNA production. In summary, this study unveils a hidden role for m6A in the suppression of transposon mobility and provides an insight into how transposon counteracts the host’s epitranscriptomic control by hitchhiking RNA demethylase.
Wolbachia wAlbB inhibit dengue and Zika infection in the mosquito Aedes aegypti with an Australian background
Authors: Hugo Leon E. , Rašić Gordana , Maynard Andrew J. , Ambrose Luke , Liddington Catherine , Thomas Callum J. E. , Nath Nisa S. , Graham Melissa , Winterford Clay , Wimalasiri-Yapa B. M. C. Randika , Xi Zhiyong , Beebe Nigel W. , Devine Gregor J. 
Published: March 2022

ABSTRACTBiological control of mosquito vectors using the insect-specific bacteria Wolbachia is an emerging strategy for the management of human arboviral diseases. We recently described the development of a strain of Ae. aegypti infected with the Wolbachia strain wAlbB (referred to as the wAlbB2-F4 strain) through simple backcrossing of wild type Australian mosquitoes with a wAlbB infected Ae. aegypti strain from the USA. Field releases of male wAlbB2-F4 mosquitoes resulted in the successful suppression of a wild population of mosquitoes in the trial sites by exploiting the strains Wolbachia-induced cytoplasmic incompatibility. We now demonstrate that the strain is resistant to infection by dengue and Zika viruses and is genetically similar to endemic Queensland populations. There was a fourfold reduction in the proportion of wAlbB2-F4 mosquitoes that became infected following a blood meal containing dengue 2 virus (16.7%) compared to wild type mosquitoes (69.2%) and a 6-7 fold reduction in the proportion of wAlbB2-F4 mosquitoes producing virus in saliva following a blood meal containing an epidemic strain of Zika virus (8.7% in comparison to 58.3% in wild type mosquitoes). Restriction-site Associated DNA (RAD) sequencing revealed that wAlbB2-F4 mosquitoes have > 98% Australian ancestry, confirming the successful introduction of the wAlbB2 infection into the Australian genomic background through backcrossing. Genotypic and phenotypic analyses showed the wAlbB2-F4 strain retains the insecticide susceptibility phenotype and genotype of the Australian mosquitoes. We demonstrate that the Wolbachia wAlbB2-F4, in addition to being suitable for suppression programs, can be effective in population replacement programs given its high inhibition of virus infection in mosquitoes. The ease at which a target mosquito population can be transfected with wAlbB2, while retaining genotypes and phenotypes of the target population, shows the robustness of this strain as a biocontrol agent against the Ae. aegypti mosquito itself as well as the pathogens it transmits.IMPORTANCEEpidemics of arthopod-borne virus (arbovirus) diseases affect millions of people and are becoming more frequent and widespread. A successful strategy to control these diseases is by infecting mosquito populations with benign, insect-specific Wolbachia bacteria that render mosquitoes refractory to infection with pathogenic arboviruses. Here we show that a strain of the major mosquito vector Ae. aegypti that was infected with Wolbachia following a simple back-cross mating procedure is refractory to infection with dengue and Zika viruses. Importantly, the genetic background of the strain is equivalent to the target population, which is important for persistence of the strain and regulatory approval.
Identifying competition phenotypes in synthetic biochemical circuits
Authors: Al-Radhawi M. Ali , Del Vecchio Domitilla , Sontag Eduardo D. 
Published: March 2022

AbstractSynthetic gene circuits require cellular resources, which are often limited. This leads to competition for resources by different genes, which alter a synthetic genetic circuit’s behavior. However, the manner in which competition impacts behavior depends on the identity of the “bottleneck” resource which might be difficult to discern from input-output data. In this paper, we aim at classifying the mathematical structures of resource competition in biochemical circuits. We find that some competition structures can be distinguished by their response to different competitors or resource levels. Specifically, we show that some response curves are always linear, convex, or concave. Furthermore, high levels of certain resources protect the behavior from low competition, while others do not. We also show that competition phenotypes respond differently to various interventions. Such differences can be used to eliminate candidate competition mechanisms when constructing models based on given data. On the other hand, we show that different networks can display mathematically equivalent competition phenotypes.
Fat tails and the need to disclose distribution parameters of qEEG databases
Authors: Wood Guilherme , Willmes Klaus , Koten Jan Willem , Kober Silvia Erika 
Published: March 2022

AbstractNeurometry (a.k.a. quantitative EEG or qEEG) is a popular method to assess clinically relevant abnormalities in the electroencephalogram. Neurometry is based on norm values for the distribution of specific EEG parameters and believed to show good psychometric properties such as test-retest reliability. Many psychometric properties only hold under the Gaussian distribution and become problematic when distributions are fat-tailed. EEG signals are typically fat-tailed and do not show fast convergence to a Gaussian distribution. To circumvent this property of EEG, log-transformations have frequently, but not always been employed. In Monte Carlo simulations, we investigated the impact of fat-tails (i.e. deviations from Gaussian) on the cut-off criteria and changeability of what in neurometry is termed “abnormal EEG”. Even slight deviations from the Gaussian distribution as measured by skewness and kurtosis lead to large inflation in the number of false positive qEEG findings. The more stringent the cutoff value adopted, the larger the inflation. Moreover, “abnormal EEG” seems to recover spontaneously at rates not compatible with the alleged test-retest reliability of qEEG. Alternative methods should be employed to determine cut-off values for diagnostics purposes, since a large number of false positive results emerge even when slight deviations from the Gaussian distribution are present. We argue that distribution properties of qEEG databases should be disclosed in much more detail by commercial providers to avoid questionable research practices and promote diagnostic transparency. We provide recommendations for the improvement of psychometric properties of existing qEEG databases.
COMMUTE: communication-efficient transfer learning for multi-site risk prediction
Authors: Gu Tian , Lee Phil H , Duan Rui 
Published: March 2022

AbstractObjectivesWe propose a communication-efficient transfer learning approach (COMMUTE) that efficiently and effectively incorporates multi-site healthcare data for training risk prediction models in a target population of interest, accounting for challenges including population heterogeneity and data sharing constraints across sites.MethodsWe first train population-specific source models locally within each institution. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in some sites, COMMUTE requests only the locally trained models from these sites, with which, COMMUTE generates heterogeneity-adjusted synthetic data for training the target model. We evaluate COMMUTE via extensive simulation studies and an application to multi-site data from the electronic Medical Records and Genomics (eMERGE) Network to predict extreme obesity.ResultsSimulation studies show that COMMUTE outperforms methods without adjusting for population heterogeneity and methods trained in a single population over a broad spectrum of settings. Using eMERGE data, COMMUTE achieves an area under the receiver operating characteristic curve (AUC) around 0.80, which outperforms other benchmark methods with AUC ranging from 0.51 to 0.70.ConclusionCOMMUTE improves the risk prediction in the target population and safeguards against negative transfer when some source populations are highly different from the target. In a federated setting, it is highly communication efficient as it only requires each site to share model parameter estimates once, and no iterative communication or higher-order terms are needed.
IsarPipeline: Combining MMseqs2 and PSI-BLAST to Quickly Generate Extensive Protein Sequence Alignment Profiles
Authors: Arab Issar 
Published: March 2022

AbstractMany of the machine learning (ML) models used in the field of bioinformatics and computational biology to predict either function or structure of proteins rely on the evolutionary information as summarized in multiple-sequence alignments (MSAs) or the resulting position-specific scoring matrices (PSSMs), as generated by PSI-BLAST. The current procedure used in protein structure and function prediction is computationally exhaustive and time-consuming. The main issue relies on the PSI-BLAST software being forced to load the current database of sequences (about 220 GB) in batches and search for similar sequence alignments to a query sequence. This leads to an average runtime of about 40-60 min for a medium-sized (450 Amino Acids) query protein. This average runtime is strictly dependent on the hardware used to run the software. The issue is becoming more problematic since the bio-sequence data pools are increasing in size exponentially over time, hence raising PSI-BLAST runtime as well. A prominent solution claims to speed up the current process by 100 folds. The MMseqs2 method, given enough memory, will load the whole database in memory and apply certain heuristics to retrieve the relevant set of aligned sequences. However, this solution cannot be used directly to generate the final output in the desired PSI-BLAST alignment and PSSM profile data format. In this research project, we analyzed the runtime performance of each tool separately. Furthermore, we built a pipeline that combines both MMseqs2 and PSI-BLAST to obtain a robust, optimized and very fast hybrid alignment tool, faster than PSI-BLAST by two orders of magnitude. It is implemented in C++ and is freely available under the MIT license at https://github.com/issararab/IsarPipeline. The output of our pipeline was evaluated on two previously built predictive models.