bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards improved resistance ofCorynebacterium glutamicumagainst nisin
Authors: Weixler Dominik , Goldbeck Oliver , Seibold Gerd. M. , Eikmanns Bernhard J. , Riedel Christian U. 
Published: August 2021

AbstractThe bacteriocin nisin is one of the best studied antimicrobial peptides. It is widely used as a food preservative due to its antimicrobial activity against various Gram-positive bacteria including human pathogens such asListeriamonocytogenesand others. The receptor of nisin is the universal cell wall precursor lipid II, which is present in all bacteria. Thus, nisin has a broad spectrum of target organisms. Consequently, heterologous production of nisin with biotechnological relevant organisms includingCorynebacterium glutamicumis difficult. Nevertheless, bacteria have evolved several mechanisms of resistance against nisin and other cationic antimicrobial peptides (CAMPs). Here, we transferred resistance mechanisms described in other organisms toC. glutamicumwith the aim to improve nisin resistance. The presented approaches included: expression of (i) nisin immunity genesnisIand/ornisFEGor (ii) nisin ABC-transporter genes ofStaphylococcus aureusand its homologues ofC. glutamicum, (iii) genes coding for enzymes for alanylation or lysinylation of the cell envelope to introduce positive charges, and/or (iv) deletion of genes for porins of the outer membrane. None of the attempts alone increased resistance ofC. glutamicummore than two-fold. To increase resistance ofC. glutamicumto levels that will allow heterologous production of active nisin at relevant titers, further studies are needed.
Integrated annotation and analysis of genomic features reveal new types of functional elements and large-scale epigenetic phenomena in the developing zebrafish
Authors: Baranasic Damir , Hörtenhuber Matthias , Balwierz Piotr , Zehnder Tobias , Mukarram Abdul Kadir , Nepal Chirag , Varnai Csilla , Hadzhiev Yavor , Jimenez-Gonzalez Ada , Li Nan , Wragg Joseph , D’Orazio Fabio , Díaz Noelia , Hernández-Rodríguez Benjamín , Chen Zelin , Stoiber Marcus , Dong Michaël , Stevens Irene , Ross Samuel E. , Eagle Anne , Martin Ryan , Obasaju Pelumi , Rastegar Sepand , McGarvey Alison C. , Kopp Wolfgang , Chambers Emily , Wang Dennis , Kim Hyejeong R. , Acemel Rafael D. , Naranjo Silvia , Lapinski Maciej , Chong Vanessa , Mathavan Sinnakaruppan , Peers Bernard , Sauka-Spengler Tatjana , Vingron Martin , Carninci Piero , Ohler Uwe , Lacadie Scott Allen , Burgess Shawn , Winata Cecilia , van Eeden Freek , Vaquerizas Juan M. , Gómez-Skarmeta José Luis , Onichtchouk Daria , Brown Ben James , Bogdanovic Ozren , Westerfield Monte , Wardle Fiona C. , Daub Carsten O. , Lenhard Boris , Müller Ferenc 
Published: August 2021

AbstractZebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals.
Discovery of aryl aminothiazole γ-secretase modulators with novel effects on amyloid β-peptide production
Authors: Bhattarai Sanjay , Liu Lei , Wolfe Michael S. 
Published: August 2021

AbstractA series of analogs based on a prototype aryl aminothiazole γ-secretase modulator (GSM) were synthesized and tested for their effects on the profile of 37-to-42-residue amyloid β-peptides (Aβ), generated through processive proteolysis of precursor protein substrate by γ-secretase. Certain substitutions on the terminal aryl D ring resulted in an altered profile of Aβ production compared to that seen with the parent molecule. Small structural changes led to concentration-dependent increases in Aβ37 and Aβ38 production without parallel decreases in their precursors Aβ40 and Aβ42, respectively. The new compounds therefore apparently also stimulate carboxypeptidase trimming of Aβ peptides ≥43 residues, providing novel chemical tools for mechanistic studies of processive proteolysis by γ-secretase.Graphical Abstract
NFIC regulates ribosomal biology and ER stress in pancreatic acinar cells and suppresses PDAC initiation
Authors: Cobo Isidoro , Paliwal Sumit , Melià-Alomà Júlia , Torres Ariadna , Martínez-Villarreal Jaime , García Fernando , Millán Irene , del Pozo Natalia , Park Joo-Cheol , MacDonald Ray J. , Muñoz Javier , Real Francisco X. 
Published: August 2021

ABSTRACTTissue-specific differentiation is driven by specialized transcriptional networks. Pancreatic acinar cells crucially rely on the PTF1 complex, and on additional transcription factors, to deploy their transcriptional program. Here, we identify NFIC as a novel regulator of acinar differentiation using a variety of methodological strategies. NFIC binding sites are found at very short distances from NR5A2-bound genomic regions and both proteins co-occur in the same complex.Nficknockout mice show reduced expression of acinar genes and, in ChIP-seq experiments, NFIC binds the promoters of acinar genes. In addition, NFIC binds to the promoter of, and regulates, genes involved in RNA and protein metabolism; inNficknockout mice, p-RS6K1 and p-IEF4E are down-regulated indicating reduced activity of the mTOR pathway. In 266-6 acinar cells, NFIC dampens the ER stress program through its binding to ER stress gene promoters and is required for complete resolution of Tunicamycin-mediated ER stress. Normal human pancreata from subjects with low NFIC mRNA levels display reduced epxression of genes down-regulated inNficknockout mice. Consistently, NFIC displays reduced expression upon induced acute pancreatitis and is required for proper recovery after damage. Finally, expression of NFIC is lower in samples of mouse and human pancreatic ductal adenocarcinoma andNficknockout mice develop an increased number of mutantKras-driven pre-neoplastic lesions.
Activity budget and gut microbiota stability and flexibility across reproductive states in wild capuchin monkeys in a seasonal biome
Authors: Webb Shasta E. , Orkin Joseph D. , Williamson Rachel E. , Melin Amanda D. 
Published: August 2021

ABSTRACTEnergy demands associated with pregnancy and lactation are significant forces in mammalian evolution. To mitigate increased energy costs associated with reproduction, female mammals have evolved behavioural and physiological responses. Some species alter activity to conserve energy during pregnancy and lactation, while others experience changes in metabolism and fat deposition. Restructuring of gut microbiota with shifting reproductive states may also help females increase energy harvest from foods, especially during pregnancy. Here, we combine life history data with >13,000 behavioural scans and >300 fecal samples collected longitudinally across multiple years from 33 white-faced capuchin monkey females to examine the relationships among behaviour, gut microbiota composition, and reproductive state. We used 16S-based amplicon sequencing and the DADA2 pipeline to analyze microbial diversity and putative functions. Reproductive state explained some variation in activity, but overall resting and foraging behaviours were relatively stable across the reproductive cycle. We found evidence for increases in biotin synthesis pathways among microbes in lactating females, and that relatoe abundance of major phyla among the states was small but significant. Otherwise, gut microbiota structure and estimated functions were not substantially different among reproductive states. These data contribute to a broader understanding of plasticity in response to physiological shifts associated with mammalian reproduction.
Combined single cell proteomics and transcriptomics reveals discrete human tendon cells populations persist in vitro and on fibrous scaffolds
Authors: Kendal Adrian , Lach Antonina , Mouthuy Pierre-Alexis , Brown Rick , Loizou Constantinos , Rogers Mark , Sharp Robert , Carr Andrew 
Published: August 2021

AbstractChronic tendinopathy represents a growing burden to healthcare services in an active and ageing global population. The ability to identify, isolate and interrogate, in vitro, key pathogenic and reparative tendon cell populations is essential to developing precision therapies and implantable materials.Human hamstring tendon cells were cultured for 8 days on either tissue culture plastic or aligned electrospun fibres made of polydioxanone (absorbable polymer). Combined single cell surface proteomics and unbiased single cell transcriptomics (CITE-Seq) revealed six discrete cell clusters, four of which shared key gene expression determinants with ex vivo human cell clusters. These were PTX3_PAPPA, POST_SCX, DCN_LUM and ITGA7_NES cell clusters. Surface proteomics found that PTX3_PAPPA cells were CD10+CD26+CD54+. ITGA7_NES cells were CD146+, and POSTN_SCX cells were CD90+CD95+CD10+.Three clusters preferentially survived and proliferated on the aligned electrospun fibres; DCN_LUM, POSTN_SCX, and PTX3_PAPPA. They maintained high expression of tendon matrix associated genes, includingCOL1A1, COL1A2, COL3A1, ELN, FBLN1, and up-regulated genesets enriched for TNF-Ɣsignalling via NFκB, IFN-Ɣ signalling and IL-6/ STAT3 signalling. When cells were pre-selected based on surface protein markers, a similar up-regulation of pro-inflammatory signalling pathways was observed, particularly inPTX3gene expressing CD10+CD26+CD54+ cells, with increased expression of genes associated with TNF-αsignalling and IFN-γ signalling.Discrete human tendon cell sub populations persist in vitro culture and can be recognised by specific gene and surface protein signatures. Aligned PDO fibres promote the survival of three clusters, including pro-inflammatoryPTX3expressing CD10+CD26+CD54+ cells found in chronic tendon disease.
Benchmarking artificial intelligence methods for end-to-end computational pathology
Authors: Laleh Narmin Ghaffari , Muti Hannah Sophie , Lavinia Loeffler Chiara Maria , Echle Amelie , Saldanha Oliver Lester , Mahmood Faisal , Lu Ming Y. , Trautwein Christian , Langer Rupert , Dislich Bastian , Buelow Roman D. , Grabsch Heike Irmgard , Brenner Hermann , Chang-Claude Jenny , Alwers Elizabeth , Brinker Titus J. , Khader Firas , Truhn Daniel , Gaisa Nadine T. , Boor Peter , Hoffmeister Michael , Schulz Volkmar , Kather Jakob Nikolas 
Published: August 2021

AbstractArtificial intelligence (AI) can extract subtle visual information from digitized histopathology slides and yield scientific insight on genotype-phenotype interactions as well as clinically actionable recommendations. Classical weakly supervised pipelines use an end-to-end approach with residual neural networks (ResNets), modern convolutional neural networks such as EfficientNet, or non-convolutional architectures such as vision transformers (ViT). In addition, multiple-instance learning (MIL) and clustering-constrained attention MIL (CLAM) are being used for pathology image analysis. However, it is unclear how these different approaches perform relative to each other. Here, we implement and systematically compare all five methods in six clinically relevant end-to-end prediction tasks using data from N=4848 patients with rigorous external validation. We show that histological tumor subtyping of renal cell carcinoma is an easy task which approaches successfully solved with an area under the receiver operating curve (AUROC) of above 0.9 without any significant differences between approaches. In contrast, we report significant performance differences for mutation prediction in colorectal, gastric and bladder cancer. Weakly supervised ResNet-and ViT-based workflows significantly outperformed other methods, in particular MIL and CLAM for mutation prediction. As a reason for this higher performance we identify the ability of ResNet and ViT to assign high prediction scores to highly informative image regions with plausible histopathological image features. We make all source codes publicly available athttps://github.com/KatherLab/HIA, allowing easy application of all methods on any end-to-end problem in computational pathology.
Construction of competing endogenous RNA interaction networks as prognostic markers in metastatic melanoma
Authors: He Zan , Xin Zijuan , Peng Yongfei , Zhao Hua , Fang Xiangdong 
Published: August 2021

AbstractMalignant melanoma (MM) is a highly aggressive, metastatic cancer originating from melanocytes. These tumors have an extremely poor prognosis. MM accounts for 4% of skin cancers with an 80% mortality rate. The median survival of patients with metastatic melanoma is approximately six months, with a five-year survival rate of less than 10%. In recent years, the incidence of melanoma has gradually increased and has become one of the deadliest cancers. Competitive endogenous RNA (ceRNA) models the mechanism by which long chain non-coding RNAs (lncRNAs) play a regulatory role in the disease. LncRNAs can act as a sponge, competitively attracting small RNAs (micoRNAs; miRNAs) and interfering with miRNA function. This can affect the expression of target gene messenger RNAs (mRNAs), ultimately promoting tumorigenesis and tumor progression. Bioinformatics analysis may identify potentially prognostic and therapeutically-relevant differentially expressed genes in MM, as well as lncRNAs, miRNAs and mRNAs that are interconnected through the ceRNA network. This may provide further insight into gene regulation and the prognosis of metastatic melanoma. Weighted co-expression networks were used to identify lncRNA and mRNA modules associated with the metastatic phenotype, as well as the co-expression genes contained in the modules. We used 17 lncRNAs, six miRNAs, and 11 mRNAs to construct a ceRNA interaction network with a regulatory role in patients with metastatic melanoma. The prognostic risk model was used as a sorter to classify the survival prognosis of melanoma patients. Four groups of ceRNA interaction triplets were obtained, and miR-3662 may be used in the treatment of metastatic melanoma patients. Experiments confirmed the regulating relationship and phenotype of this assumption. This study provides new targets to regulate the metastatic process, predict metastatic potential and determine that miR-3662 may be used in the treatment of melanoma.
DSResSol: A sequence-based solubility predictor created with dilated squeeze excitation residual networks
Authors: Madani Mohammad , Lin Kaixiang , Tarakanova Anna 
Published: August 2021

ABSTRACTProtein solubility is an important thermodynamic parameter critical for the characterization of a protein’s function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g. pharmaceutical) applications. Thus, a highly accurate in silico bioinformatics tool for predicting protein solubility from protein sequence is sought. In this study, we developed a deep learning sequence-based solubility predictor, DSResSol, that takes advantage of the integration of squeeze excitation residual networks with dilated convolutional neural networks. The model captures the frequently occurring amino acid k-mers and their local and global interactions, and highlights the importance of identifying long-range interaction information between amino acid k-mers to achieve higher performance in comparison to existing deep learning-based models. DSResSol uses protein sequence as input, outperforming all available sequence-based solubility predictors by at least 5% in accuracy when the performance is evaluated by two different independent test sets. Compared to existing predictors, DSResSol not only reduces prediction bias for insoluble proteins, but also predicts soluble proteins within the test sets with an accuracy that is at least 13% higher. We derive the key amino acids, dipeptides, and tripeptides contributing to protein solubility, identifying glutamic acid and serine as critical amino acids for protein solubility prediction. Overall, DSResSol can be used for fast, reliable, and inexpensive prediction of a protein’s solubility to guide experimental design.AvailabilityThe source code, datasets, and web server for this model are available at https://github.com/mahan-fcb/DSResSol
Relevance of Auditory Errors Decreases When Errors Are Introduced Suddenly
Authors: Chao Sara-Ching , Daliri Ayoub 
Published: August 2021

AbstractPurposeWhen the speech motor system encounters errors, it generates adaptive responses to compensate for the errors. We previously showed that adaptive responses to task-irrelevant errors are significantly smaller than responses to task-relevant errors when errors are introduced gradually. The current study aimed to examine responses to task-irrelevant and task-relevant errors when errors are introduced suddenly.MethodWe used an adaptation paradigm in which participants experienced task-relevant errors (induced by formant-shift perturbations) and task-irrelevant errors (induced by formant-clamp perturbations). For one group of participants (N = 30), we applied the perturbations gradually. The second group of participants (N = 30) received the perturbations suddenly. We designed the perturbations based on participant-specific vowel configurations such that a participant’s first and second formants of /ε/ were perturbed toward their /æ/. To estimate adaptive responses, we measured formant changes (within 0–100 ms of the vowel onset) in response to the formant perturbations.ResultsWe found that (1) the difference between adaptive responses to formant-shift and formant-clamp perturbations was the smallest for the suddenly introduced perturbations, and (2) responses to formant-shift perturbations positively correlated with responses to formant-clamp perturbations for the suddenly (but not gradually) introduced perturbations.ConclusionsThese results showed that the speech motor system responds to task-relevant errors and task-irrelevant errors more differently when errors are introduced gradually than suddenly. Overall, the speech motor system evaluates the relevance of errors and uses its evaluation to modulate its adaptive responses to errors.
Synergistic Phase Separation of Two Pathways Promotes Integrin Clustering and Integrin Adhesion Complex Formation
Authors: Case L.B. , Henry L. , Rosen M.K. 
Published: August 2021

ABSTRACTIntegrin adhesion complexes (IACs) are integrin-based plasma membrane-associated compartments where cells sense environmental cues. The physical mechanisms and molecular interactions that mediate nascent IAC formation are unclear. We found that both p130Cas (“Cas”) and Focal adhesion kinase (“FAK”) undergo liquid-liquid phase separation in vitro under physiologic conditions. Cas- and FAK- driven phase separation is sufficient to reconstitute kindlin-dependent integrin clustering in vitro. In vitro condensates and cellular IACs exhibit similar sensitivities to environmental perturbations including changes in temperature and pH. Furthermore, mutations that inhibit or enhance phase separation in vitro reduce or increase the number of IACs in cells, respectively. Finally, we find that the Cas and FAK pathways act synergistically to promote phase separation, integrin clustering and IAC formation in vitro and in cells. We propose that Cas- and FAK- driven phase separation provides an intracellular trigger for integrin clustering and nascent IAC formation.
Molecular studies into Copine-4 function in Retinal Ganglion Cells
Authors: Goel Manvi , Aponte Angel M. , Wistow Graeme , Badea Tudor C. 
Published: August 2021

ABSTRACTThe molecular mechanisms underlying morphological diversity in retinal cell types are poorly understood. We have previously reported that several members of the Copine family of Ca-dependent membrane adaptors are expressed in Retinal Ganglion Cells (RGCs) and transcriptionally regulated by Brn3 transcription factors. Several Copines are enriched in the retina and their over-expression leads to morphological changes reminiscent of neurite formation in HEK293 cells. However, the role of Copines in the retina is largely unknown. Here we focus on Cpne4, a Copine whose expression is restricted to RGCs. Over-expression of Cpne4 in RGCs in vivo led to formation of large varicosities on the dendrites but did not otherwise visibly affect dendrite or axon formation. Protein interactions studies using yeast two hybrid analysis from whole retina cDNA revealed two Cpne4 interacting proteins - HCFC1 and Morn2. Mass Spectrometry analysis of retina lysate pulled down using Cpne4 or its vonWillebrand A (vWA) domain identified a further 207 interacting proteins. Gene Ontology (GO) analysis of Cpne4 interactors suggests its involvement in several metabolic and signaling pathways, including processes related to vesicle trafficking, intracellular membrane bound organelles, and plasma membrane associated structures, including neurites. We conclude that, consistent with its domain structure, Cpne4 may be involved in assembly and trafficking of several membrane associated cell compartments.
Visualizing conformational space of functional biomolecular complexes by deep manifold learning
Authors: Wu Zhaolong , Chen Enbo , Zhang Shuwen , Ma Yinping , Liu Congcong , Yin Chang-Cheng , Mao Youdong 
Published: August 2021

AbstractThe cellular functions are executed by biological macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving conformational continuum of important biomolecular complexes at atomic level is essential to understand their functional mechanisms and to guide structure-based drug discovery. Here we introduce a deep learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy reconstructions of conformational continuum. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. By applications of this approach to analyze five experimental datasets, we demonstrate its generality in breaking resolution barrier of visualizing dynamic components of functional complexes, in choreographing continuous inter-subunit motions and in exploring their ‘hidden’ conformational space. Our approach expands the realm of structural ensemble determination to the nonequilibrium regime at atomic level, thus potentially transforming biomedical research and therapeutic development.