bluuebunny/crossref_metadata_2025_split huggingface dataset:
LOMDA: Linear optimization for miRNA-disease association prediction
Authors: Lee Yan-Li , Pech Ratha , Po Maryna , Hao Dong , Zhou Tao 
Published: August 2019

AbstractMicroRNAs (miRNAs) have been playing a crucial role in many important biological processes e.g., pathogenesis of diseases. Currently, the validated associations between miRNAs and diseases are insufficient comparing to the hidden associations. Testing all these hidden associations by biological experiments is expensive, laborious, and time consuming. Therefore, computationally inferring hidden associations from biological datasets for further laboratory experiments has attracted increasing interests from different communities ranging from biological to computational science. In this work, we propose an effective and efficient method to predict associations between miRNAs and diseases, namely linear optimization (LOMDA). The proposed method uses the heterogenous matrix incorporating of miRNA functional similarity information, disease similarity information and known miRNA-disease associations. Compared with the other methods, LOMDA performs best in terms of AUC (0.970), precision (0.566), and accuracy (0.971) in average over 15 diseases in local 5-fold cross-validation. Moreover, LOMDA has also been applied to two types of case studies. In the first case study, 30 predictions from breast neoplasms, 24 from colon neoplasms, and 26 from kidney neoplasms among top 30 predicted miRNAs are confirmed. In the second case study, for new diseases without any known associations, top 30 predictions from hepatocellular carcinoma and 29 from lung neoplasms among top 30 predicted miRNAs are confirmed.Author summaryIdentifying associations between miRNAs and diseases is significant in investigation of pathogenesis, diagnosis, treatment and preventions of related diseases. Employing computational methods to predict the hidden associations based on known associations and focus on those predicted associations can sharply reduce the experimental costs. We developed a computational method LOMDA based on the linear optimization technique to predict the hidden associations. In addition to the observed associations, LOMDA also can employ the auxiliary information (diseases and miRNAs similarity information) flexibly and effectively. Numerical experiments on global 5-fold cross validation show that the use of the auxiliary information can greatly improve the prediction performance. Meanwhile, the result on local 5-fold cross validation shows that LOMDA performs best among the seven related methods. We further test the prediction performance of LOMDA for two types of diseases based on HDMMv2.0 (2014), including (i) diseases with all the known associations, and (ii) new diseases without known associations. Three independent or updated databases (dbDEMC, 2010; miR2Disease, 2009; HDMMv3.2, 2019) are introduced to evaluate the prediction results. As a result, most miRNAs for target diseases are confirmed by at least one of the three databases. So, we believe that LOMDA can guide experiments to identify the hidden miRNA-disease associations.
Large scale active-learning-guided exploration to maximize cell-free production
Authors: Borkowski Olivier , Koch Mathilde , Zettor Agnès , Pandi Amir , Batista Angelo Cardoso , Soudier Paul , Faulon Jean-Loup 
Published: August 2019

AbstractLysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.
Primary cilia deficiency in neural crest cells causes Anterior Segment Dysgenesis
Authors: Portal Céline , Lwigale Peter , Rompolas Panteleimon , Iomini Carlo 
Published: August 2019

ABSTRACTDuring eye embryogenesis, neural crest cells (NCC) of the periocular mesenchyme (POM) migrate to the anterior segment (AS) of the eye and then differentiate into the corneal stroma and endothelium, ciliary body, iris stroma, and the trabecular meshwork. Defective development of these structures leads to anterior segment dysgenesis (ASD) that in 50% of the cases leads to glaucoma, a leading cause of blindness. Here, we show that the primary cilium is indispensable for normal AS development and that its ablation in NCC induces ASD phenotypes including; small and thin cornea, impaired stromal keratocyte organization, abnormal iridocorneal angle with reduced anterior chamber and corneal neovascularization. These defects are similar to those described in patients with AS conditions such as Axenfeld-Rieger syndrome and Peter’s anomaly. Mechanistically, disruption of the primary cilium in the NCC resulted in reduced hedgehog (Hh) signaling in the POM, canonically activated by the Indian Hedgehog ligand expressed by endothelial cells of the choroid. This caused decreased cell proliferation in a subpopulation of POM cells surrounding the retinal pigmented epithelium. Moreover, primary cilium ablation in NCC also led to a decreased expression ofFoxc1andPitx2, two transcription factors identified as major ASD causative genes. These findings suggest that primary cilia are indispensable for NCC to form normal AS structures via Hh signaling. Defects in primary cilia could, therefore, contribute to the pathogenesis of ASD, and to their complications such as congenital glaucoma.
Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma
Authors: Venkat Swati , Tisdale Arwen A. , Schwarz Johann R. , Alahmari Abdulrahman A. , Maurer H. Carlo , Olive Kenneth P. , Eng Kevin H. , Feigin Michael E. 
Published: August 2019

ABSTRACTAlternative polyadenylation (APA) is a gene regulatory process that dictates mRNA 3’-UTR length, resulting in changes in mRNA stability and localization. APA is frequently disrupted in cancer and promotes tumorigenesis through altered expression of oncogenes and tumor suppressors. Pan-cancer analyses have revealed common APA events across the tumor landscape; however, little is known about tumor type-specific alterations that may uncover novel events and vulnerabilities. Here we integrate RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) to comprehensively analyze APA events in 148 pancreatic ductal adenocarcinomas (PDAs). We report widespread, recurrent and functionally relevant 3’-UTR alterations associated with gene expression changes of known and newly identified PDA growth-promoting genes and experimentally validate the effects of these APA events on expression. We find enrichment for APA events in genes associated with known PDA pathways, loss of tumor-suppressive miRNA binding sites, and increased heterogeneity in 3’-UTR forms of metabolic genes. Survival analyses reveal a subset of 3’-UTR alterations that independently characterize a poor prognostic cohort among PDA patients. Finally, we identify and validate the casein kinase CK1α as an APA-regulated therapeutic target in PDA. Knockdown or pharmacological inhibition of CK1α attenuates PDA cell proliferation and clonogenic growth. Our single-cancer analysis reveals APA as an underappreciated driver of pro-tumorigenic gene expression in PDA via the loss of miRNA regulation.
Controlling Structural Bias in Intrinsically Disordered Proteins Using Solution Space Scanning
Authors: Holehouse Alex S , Sukenik Shahar 
Published: August 2019

AbstractIntrinsically disordered proteins or regions (IDRs) differ from their well-folded counterparts by lacking a stable tertiary state. Instead, IDRs exist in an ensemble of conformations and often possess localized, loosely held residual structure that can be a key determinant of their activity. With no extensive network of non-covalent bonds and a high propensity for exposed surface areas, the various features of an IDR’s ensemble – including local residual structure and global conformational biases – are an emergent property of both the amino acid sequence and the solution environment. Here, we attempt to understand how shifting solution conditions can alter an IDR’s ensemble. We present an efficient computational method to alter solution-protein interactions we term Solution Space (SolSpace) Scanning. SolSpace scanning uses all-atom Monte-Carlo simulations to construct ensembles under a wide range of distinct solution conditions. By tuning the interactions of specific protein moieties with the solution in a systematic manner we can both enhance and reduce local residual structure. This approach allows the ‘design’ of distinct residual structures in IDRs, offering an alternative approach to mutational studies for exploring sequence-to-ensemble relationships. Our results raise the possibility of solution-based regulation of protein functions both outside and within the dynamic solution environment of cells.
High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets
Authors: Shao XM , Bhattacharya R , Huang J , Sivakumar IKA , Tokheim C , Zheng L , Hirsch D , Kaminow B , Omdahl A , Bonsack M , Riemer AB , Velculescu VE , Anagnostou V , Pagel KA , Karchin R 
Published: August 2019

AbstractComputational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus onin silicoestimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p<2e−16) including CD8+ T cells. Notably, only 0.16% of predicted immunogenic missense mutations were observed in >2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.SynopsisWe developed a newin silicopredictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.
A Metagenomics – Based Diagnostic Approach for Central Nervous System Infections in Hospital Acute Care Setting
Authors: Hasan Mohammad Rubayet , Sundararaju Sathyavathi , Tang Patrick , Tsui Kin-Ming , Lopez Andres Perez , Janahi Mohammad , Tan Rusung , Tilley Peter 
Published: August 2019

AbstractThe etiology of central nervous system (CNS) infections such as meningitis and encephalitis remains unknown in a large proportion of cases partly because the diversity of pathogens that may cause CNS infections greatly outnumber available test methods. Here we present a metagenomic next generation sequencing (mNGS) based approach for broad-range detection of pathogens associated with CNS infections, which is suitable for application in the acute care hospital setting. Using an Illumina MiSeq benchtop sequencer and the IDseq pipeline for identifying pathogens in metagenomic sequence data, we show that the analytical sensitivity of mNGS to detect pathogens is comparable to that of PCR in simulated cerebrospinal fluid (CSF) specimens. We then applied this method for pathogen detection in 74 CSF specimens from patients with suspected CNS infections that were previously tested by culture and/or PCR. Diagnostic accuracy, sensitivity and specificity of mNGS approach with reference to conventional methods were all 95%. Furthermore, confirmatory testing on specimens that gave discrepant results were mostly in favor of the mNGS assay. The clinical application of mNGS holds promise to benefit patients with CNS infections of unknown etiology.
EXP1 is required for organization of the intraerythrocytic malaria parasite vacuole
Authors: Nessel Timothy , Beck John M. , Rayatpisheh Shima , Jami-Alahmadi Yasaman , Wohlschlegel James A. , Goldberg Daniel E. , Beck Josh R. 
Published: August 2019

AbstractIntraerythrocytic malaria parasites reside within a parasitophorous vacuole membrane (PVM) that closely overlays the parasite plasma membrane (PPM) and constitutes the barrier between parasite and host compartments. The PVM is the site of several essential transport activities but the basis for organization of this membrane system is unknown. We utilized the second-generation promiscuous biotin ligase BioID2 fused to EXP2 or HSP101 to probe the content of the PVM, identifying known and novel candidate PVM proteins. Among the best represented hits were members of a group of single-pass integral membrane proteins that constitute a major component of the PVM proteome but whose function remains unclear. We investigated the function of EXP1, the longest known member of this group, by adapting a CRISPR/Cpf1 genome editing system to install the TetR-DOZI-aptamers system for conditional translational control. EXP1 knockdown was essential for intraerythrocytic development and accompanied by profound changes in vacuole ultrastructure, including increased separation of the PVM and PPM and formation of abnormal membrane structures in the enlarged vacuole lumen. While previous in vitro studies indicated EXP1 possesses glutathione S-transferase activity, a mutant version of EXP1 lacking a residue important for this activity in vitro still provides substantial rescue of endogenous exp1 knockdown in vivo. Intriguingly, while activity of the Plasmodium translocon of exported proteins was not impacted by depletion of EXP1, the distribution of the translocon pore-forming protein EXP2 was substantially altered. Collectively, our results reveal a novel PVM defect that indicates a critical role for EXP1 in maintaining proper PVM organization.ImportanceLike other obligate intracellular apicomplexans, blood-stage malaria parasites reside within a membrane-bound compartment inside the erythrocyte known as the parasitophorous vacuole. Although the vacuole is the site of several transport activities essential to parasite survival, little is known about its organization. To explore vacuole biology, we adopted recently developed proteomic (BioID2) and genetic (CRISPR/Cpf1) tools for use in Plasmodium falciparum, which allowed us to query the function of the prototypical vacuole membrane protein EXP1.Knockdown of EXP1 showed that a previously reported glutathione S-transferase activity cannot fully account for the essential function(s) of EXP1 and revealed a novel role for this protein in maintaining normal vacuole morphology and PVM protein arrangement. Our results provide new insight into vacuole organization and illustrate the power of BioID2 and Cpf1 (which utilizes a T-rich PAM uniquely suited to the P. falciparum genome) for proximity protein identification and genome editing in P. falciparum.
Genome Scans for Selection and Introgression based on k-nearest Neighbor Techniques
Authors: Pfeifer Bastian , Alachiotis Nikolaos , Pavlidis Pavlos , Schimek Michael G. 
Published: August 2019

AbstractIn recent years, genome-scan methods have been extensively used to detect local signatures of selection and introgression. Here, we introduce a series of versatile genome-scan methods that are based on non-parametric k-nearest neighbors (kNN) techniques, while incorporating pairwise Fixation Index (FST) estimates and pairwise nucleotide differences (dxy) as features. Simulations were performed for both positive directional selection and introgression, with varying parameters, such as recombination rates, population background histories, the proportion of introgression, and the time of gene flow. We find that kNN-based methods perform remarkably well while yielding stable results almost over the entire range of k. We provide a GitHub repository (pievos101/kNN-Genome-Scans) containing R source code to demonstrate how to apply the proposed methods to real-world genomic data using the population genomics R-package PopGenome.
A sample-to-report solution for taxonomic identification of cultured bacteria in the clinical setting based on nanopore sequencing
Authors: Neuenschwander Stefan Moritz , Miani Miguel Angel Terrazos , Amlang Heiko , Perroulaz Carmen , Bittel Pascal , Casanova Carlo , Droz Sara , Flandrois Jean-Pierre , Leib Stephen L. , Suter-Riniker Franziska , Ramette Alban 
Published: August 2019

AbstractAmplicon sequencing of 16S rRNA gene is commonly used for the identification of bacterial isolates in diagnostic laboratories, and mostly relies on the Sanger sequencing method. The latter, however, suffers from a number of limitations with the most significant being the inability to resolve mixed amplicons when closely related species are co-amplified from a mixed culture. This often leads to either increased turnover time or absence of usable sequence data. Short-read NGS technologies could address the mixed amplicon issue, but would lack both cost efficiency at low throughput and fast turnaround times. Nanopore sequencing developed by Oxford Nanopore Technologies (ONT) could solve those issues by enabling flexible number of samples per run and adjustable sequencing time. Here we report on the development of a standardized laboratory workflow combined with a fully automated analysis pipelineLORCAN(Long Read Consensus ANalysis), which together provide a sample-to-report solution for amplicon sequencing and taxonomic identification of the resulting consensus sequences. Validation of the approach was conducted on a panel of reference strains and on clinical samples consisting of single or mixed rRNA amplicons associated with various bacterial genera by direct comparison to the corresponding Sanger sequences. Additionally, artificial read mixtures of closely related species were used to assessLORCAN’s behaviour when dealing with samples with known cross-contamination level. We demonstrate that by combining ONT amplicon sequencing results withLORCAN, the accuracy of Sanger sequencing can be closely matched (>99.6% sequence identity) and that mixed samples can be resolved at the single base resolution level. The presented approach has the potential to significantly improve the flexibility, reliability and availability of amplicon sequencing in diagnostic settings.
Gene networks in cancer are biased by aneuploidies and sample impurities
Authors: Schubert Michael , Colomé-Tatché Maria , Foijer Floris 
Published: August 2019

AbstractGene regulatory network inference is a standard technique for obtaining structured regulatory information from, among other data sources, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. They are often applied to gene expression of human cancers. However, in contrast to the evaluations, these data sets often contain fewer samples, more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtaining reliable networks, and (2) even when taking these into account, we should expect between 20 and 80% of edges to be caused by copy number changes and cell mixtures rather than transcription factor regulation.
Molecular characterization of bacterial communities in sheep cheese through 16S rRNA gene sequencing
Authors: Endres Creciana Maria , de Castro Ícaro Maia Santos , Trevisol Laura Delpino , Mann Michele Bertoni , Varela Ana Paula Muterle , Frazzon Ana Paula Guedes , Mayer Fabiana Quoos , frazzon Jeverson 
Published: August 2019

AbstractThe production of sheep’s milk cheese has grown in recent years since it is a high value-added product with excellent properties. As such, it is necessary to provide data on the microbiota and organoleptic characteristics of this product, as well as the influence of these microorganisms on public health. Thus, the aim of the present study was to characterize the microbial community of different types of sheep cheeses using high-throughput sequencing of the 16S rRNA gene. The study was conducted with four groups of cheese: colonial, fresh, feta, and pecorino (n = 5 samples per group). The high-throughput 16S rRNA amplicon sequencing revealed 55 operational taxonomic units in the 20 samples, representing 9 genera of the two bacterial phyla Firmicutes and Proteobacteria. The predominant genera in the samples were Streptococcus and Lactobacillus. When evaluating alpha diversity by the indexes of Simpson, Chao1, Shannon, and Skew no significant differences were observed between the groups. Evaluating of the beta diversity using Bray-Curtis dissimilarity, the group of colonial cheeses presented a significant difference when compared to the feta (q = 0.030) and pecorino groups (q = 0.030). Additionally, the fresh group differed from the pecorino group (q = 0.030). The unweighted Unifrac distance suggests that the colonial cheese group differed from the others. Moreover, the feta cheese group differed from the fresh group. The distance-weighted Unifrac suggests that no significance exists between the groups. According to this information, the microbiota characterization of these cheese groups was useful in demonstrating the bacterial communities belonging to each group, its effects on processing, elaboration, maturation, and public health.