bluuebunny/crossref_metadata_2025 huggingface dataset:
Pro-autoimmune allele of tyrosine phosphatase, PTPN22, enhances tumor immunity
Authors: Orozco Robin C. , Marquardt Kristi , Mowen Kerri , Sherman Linda A.
Published: March 2021

The 1858C>T allele of the tyrosine phosphatasePTPN22 (causing amino acid substitution R620W in encoded protein Lyp) is present in 5-10% of the North American population and is strongly associated with numerous autoimmune diseases. Although much research has been done to define how this allele potentiates autoimmunity, the influencePTPN22and its pro-autoimmune allele has in tumor immunity is poorly defined. To interrogate the role this allele may have in the anti-tumor immune response, we used CRISPR/Cas9 to generate mice in which the ortholog of Lyp, PEP, is mutated at position 619 to produce the relevant pro-autoimmune mutation (R619W). Results of this study show that mice homozygous for this alteration (PEP-619WW) resist tumor growth as compared with wildtype mice. Consistent with these results, tumors from PEP-619WW mice have more CD45 infiltrates containing more activated CD8 T cell and CD4 T cells. Additionally, there are more cDC1 cells and less MDSCs in tumors from PEP-619WW mice. Interestingly, the tumor infiltrating PEP-619WW cDC1s have decreased PD-L1 expression compared to cDC1s from PEP-WT mice. Taken together, our data show that the pro-autoimmune allele ofPtpn22drives a strong anti-tumor response in innate and adaptive immune cells resulting in superior control of tumors.
Bimodal fibrosis in a novel mouse model of bleomycin-induced usual interstitial pneumonia
Authors: Miura Yoko , Lam Maggie , Bourke Jane E. , Kanazawa Satoshi
Published: March 2021

Idiopathic pulmonary fibrosis (IPF) is pathologically classified by usual interstitial pneumonia (UIP). Conventional bleomycin models used to study pathogenic mechanisms of pulmonary fibrosis display transient inflammation and fibrosis so their relevance to UIP is limited. We developed a novel chronicinduced-UIP (iUIP) model, inducing fibrosis in D1CC×D1BC transgenic mice by intra-tracheal instillation ofbleomycin mixed withmicrobubbles followed bysonoporation (BMS). A bimodal fibrotic lung disease was observed over 14 weeks, with an acute phase similar to nonspecific interstitial pneumonia (NSIP), followed by partial remission and a chronic fibrotic phase with honeycombing similar to UIP. In this secondary phase, we observed poor vascularization despite elevated PDGFRβ expression. γ2PF- and MMP7-positive epithelial cells, consistent with an invasive phenotype, were predominantly adjacent to fibrotic areas. Most invasive cells were Scgb1a1 and/or keratin 5 positive. This iUIP mouse model displays key features of IPF and has identified potential mechanisms contributing to the onset of NSIP and progression to UIP. The model will provide a useful tool for the assessment of therapeutic interventions to oppose acute and chronic fibrosis.

Summary blurb

A novel mouse model of induced-usual interstitial pneumonia shows bimodal fibrosis with honeycombing, similar to chronic lung disease seen with idiopathic pulmonary fibrosis.
Assigning confidence to structural annotations from mass spectra with COSMIC
Authors: Hoffmann Martin A. , Nothias Louis-Félix , Ludwig Marcus , Fleischauer Markus , Gentry Emily C. , Witting Michael , Dorrestein Pieter C. , Dührkop Kai , Böcker Sebastian
Published: March 2021

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but these libraries are vastly incomplete;in silicomethods search in structure databases but cannot distinguish between correct and incorrect annotations. As biological interpretation relies on accurate structure annotations, the ability to assign confidence to such annotations is a key outstanding problem. We introduce the COSMIC workflow that combines structure database generation,in silicoannotation, and a confidence score consisting of kernel density p-value estimation and a Support Vector Machine with enforced directionality of features. In evaluation, COSMIC annotates a substantial number of hits at small false discovery rates, and outperforms spectral library search for this purpose. To demonstrate that COSMIC can annotate structures never reported before, we annotated twelve novel bile acid conjugates; nine structures were confirmed by manual evaluation and two structures using synthetic standards. Second, we annotated and manually evaluated 315 molecular structures in human samples currently absent from the Human Metabolome Database. Third, we applied COSMIC to 17,400 experimental runs and annotated 1,715 structures with high confidence that were absent from spectral libraries.
Distinct Hypoxia-induced Translational Profiles of Embryonic and Adult-derived Macrophages
Authors: Wilcox Nicholas S. , Yarovinsky Timur O. , Pandya Prakruti , Ramgolam Vinod S. , Moro Albertomaria , Wu Yinyu , Nicoli Stefania , Hirschi Karen K. , Bender Jeffrey R.
Published: March 2021

Tissue homeostasis and repair are orchestrated by resident and newly recruited macrophages that alter their gene expression program in response to changes in tissue microenvironment. Embryonic macrophages, such as fetal liver derived macrophages (FLDM) seed the organs, including heart and lung during embryonic development and persist throughout the adult lifetime, while bone marrow-derived macrophages (BMDM) are recruited following an acute perturbation. Transcriptome analyses of FLDM and BMDM identified differences between them at the level of RNA expression, which correlates imperfectly with protein levels. Post-transcriptional regulation by microRNAs (miRNAs) and RNA-binding proteins determines mRNA stability and translation rate and may override transcriptional cues in response to environmental changes, such as hypoxia. To identify distinct features of FLDM and BMDM response to hypoxia at the level of translation, we employed translating ribosome affinity purification (TRAP) to isolate polysomal RNA. RNA-seq profiling of translated RNA identified distinct hypoxia-induced translational signature of BMDM (Ly6e, vimentin and glycolysis-associated enzymes Pgk1, Tpi1, Aldoa, Ldha) and FLDM (chemokines Ccl7 and Ccl2). By translational profiling of BMDM and FLDM with deletion of the RNA-binding protein HuR, we identified transcripts that were dependent on HuR. These findings highlight the importance of HuR and identify its distinct targets for post-transcriptional regulation of gene expression in embryonic vs. adult-derived macrophages.
Differential responses of amphibians and reptiles to land-use change in the biodiversity hotspot of north-eastern Madagascar
Authors: Fulgence Thio Rosin , Martin Dominic Andreas , Randriamanantena Romual , Botra Ronick , Befidimanana Erosiniot , Osen Kristina , Wurz Annemarie , Kreft Holger , Andrianarimisa Aristide , Ratsoavina Fanomezana Mihaja
Published: March 2021

Large expanses of tropical rainforest have been converted into agricultural landscapes cultivated by smallholder farmers. This is also the case in north-eastern Madagascar; a region that retains significant proportions of forest cover despite slash-and-burn hill rice cultivation and vanilla agroforestry expansion. The region is also a global hotspot for herpetofauna diversity, but how amphibians and reptiles are affected by land-use change remains largely unknown. Using a space-for-time study design, we compared species diversity and community composition across seven prevalent land uses: unburned (old-growth forest, forest fragment, and forest-derived vanilla agroforest) and burned (fallow-derived vanilla agroforest, woody fallow, and herbaceous fallow) land-use types, and rice paddy. We conducted six comprehensive, time-standardized searches across at least ten replicates of each land-use type and applied genetic barcoding to confirm species identification. We documented an exceptional diversity of herpetofauna (119 species; 91% endemic). Plot-level amphibian species richness was significantly higher in old-growth forest than in all other land-use types. Plot-level reptile species richness was significantly higher in unburned land-use types compared to burned land-use types. For both amphibians and reptiles, the less-disturbed land-use types showed more uneven communities and the species composition in old-growth forest differed significantly from all other land-use types. Amphibians had higher forest dependency (38% of species occurred exclusively in old-growth forest) than reptiles (26%). Our analyses thus revealed that the two groups respond differently to land-use change: we found less pronounced losses of reptile species richness especially in unburned agricultural habitats, suggesting that reptiles are less susceptible to land-use change than amphibians, possibly due to their ability to cope with hotter and drier microclimates. Overall, old-growth forest harboured a unique diversity, but some species also thrived in vanilla agroforestry systems, especially if these were forest-derived. This highlights the importance of conserving old-growth forests and non-burned land-use types within agricultural landscapes.
Copper dependent ERK1/2 phosphorylation is essential for the viability of neurons and not glia
Authors: Chakraborty Kaustav , Kar Sumanta , Rai Bhawana , Bhagat Reshma , Naskar Nabanita , Seth Pankaj , Gupta Arnab , Bhattacharjee Ashima
Published: March 2021

Intracellular Copper [Cu(I)] has been hypothesized to play role in the differentiation of the neurons. This necessitates understanding the role of Cu(I) not only in the neurons but also in the glia considering their anatomical proximity, contribution towards ion homeostasis, neuronal physiology, and neurodegeneration. In this study we did a systematic investigation of the changes in the cellular copper homeostasis during neuronal and glial differentiation and the pathways triggered by them. Our study demonstrates increased mRNA for the plasma membrane copper transporter CTR1 leading to an increased Cu(I) during neuronal (PC-12) differentiation. ATP7A is retained in the Trans Golgi Network (TGN) despite high Cu(I) demonstrating its utilization in triggering the pathways towards the neuronal differentiation. One of these pathways is ERK1/2 phosphorylation accompanying the differentiation of both PC-12 and human fetal brain derived neuronal progenitor cells. The study demonstrates that the ERK1/2 phosphorylation is essential for the viability of the neurons. In contrast, differentiated C-6 (glia) cells contain low intracellular copper and significant downregulation of the ERK1/2 phosphorylation. Interestingly ATP7A shows vesicular localization despite the low copper in the glia. In addition to the TGN in the perinuclear region, ATP7A localizes into RAB11 positive recycling endosomes in the glial neurites, not observed in the neurons. Our study demonstrates role of the copper dependent ERK1/2 phosphorylation in the neuronal differentiation. Whereas glial differentiation largely involves sequestration of Cu(I) into the endosomes potentially (i) for ready release to the neurons (ii) rendering cytosolic copper unavailable for pathways like the ERK1/2 activation.
Predicting intelligence from fMRI data of the human brain in a few minutes of scan time
Authors: Lohmann Gabriele , Lacosse Eric , Ethofer Thomas , Kumar Vinod J. , Scheffler Klaus , Jost Jürgen
Published: March 2021

In recent years, the prediction of individual behaviour from the fMRI-based functional connectome has become a major focus of research. The motivation behind this research is to find generalizable neuromarkers of cognitive functions. However, insufficient prediction accuracies and long scan time requirements are still unsolved issues. Here we propose a new machine learning algorithm for predicting intelligence scores of healthy human subjects from resting state (rsfMRI) or task-based fMRI (tfMRI). In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50 percent in tfMRI, and of around 59 percent when results from two tasks are combined. Surprisingly, we found that the tfMRI data were significantly more predictive of intelligence than rsfMRI even though they were acquired at much shorter scan times (approximately 10 minutes versus 1 hour). Existing methods that we investigated in a benchmark comparison underperformed on tfMRI data and produced prediction accuracies well below our results. Our proposed algorithm differs from existing methods in that it achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight.
Dynamic changes of genomic and transcriptomic tumor diversity during melanoma progression
Authors: Moore Ariane L. , Dimitrakopoulos Christos , Flückiger-Mangual Stefanie , Frischknecht Lukas , Beisel Christian , Krek Wilhelm , Beerenwinkel Niko
Published: March 2021

Metastatic melanoma is an aggressive disease that is notoriously difficult to treat. Here, we used a BRAFV600E-driven mouse model to study melanoma progression and metastasis in the absence of extrinsic mutagenic factors such as UV radiation. The tumors were grown for different time periods in seven different mice such that disease progression was tracked over time. We sequenced the whole exome and whole transcriptome of 16 melanoma samples from these mice. The timed samples revealed that many genes are increasingly upregulated during progression, such as genes involved in extracellular matrix organization, andVegfc, a factor that induces lymphatic vessel growth and metastases to lymph nodes, which was identified as an important signaling hub in our analysis. TheVegfcgene interaction network highlighted multiple genes from the MAPK pathway that are increasingly deregulated during tumor evolution. The genomic diversity of primary and metastatic tumors showed an increase over time. Additionally, our integrative multi-omics analysis adds further evidence thatRock1is a crucial player driving metastasis formation, and may be upregulated due to mutations in the PI3K/AKT pathway. In summary, our study contributes towards an improved understanding of melanoma which may eventually lead to improved treatment strategies.
Kawasaki Disease patient stratification and pathway analysis based on host transcriptomic and proteomic profiles
Authors: Jackson Heather , Menikou Stephanie , Hamilton Shea , McArdle Andrew , Shimizu Chisato , Galassini Rachel , Huang Honglei , Kim Jihoon , Tremoulet Adriana , de Jonge Marien , Kuijpers Taco , Wright Victoria , Burns Jane , Casals-Pascual Climent , Herberg Jethro , Levin Mike , Kaforou Myrsini
Published: March 2021

The aetiology of Kawasaki Disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host ‘omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple ‘omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infection at the transcriptomic and proteomic levels through comparison of ‘omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both ‘omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both ‘omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition.
Antigen-specific stimulation and expansion of CAR-T cells using membrane vesicles as target cell surrogates
Authors: Ukrainskaya V.M. , Rubtsov Y.P. , Pershin D.S. , Podoplelova N.A. , Terekhov S.S. , Kalinin R.S. , Yaroshevich I.A. , Sokolova A.I. , Bagrov D.V. , Kulakovskaya E.A. , Shipunova V.O. , Deyev S.M. , Maksimov E.G. , Markov O.V. , Oshchepkova A.L. , Zenkova M.A. , Xie J. , Gabibov A.G. , Maschan M.A. , Stepanov A.V. , Lerner. R.A.
Published: March 2021

Development of CAR-T therapy led to immediate success in the treatment of B cell leukemia and lymphoma. It also raised an opportunity to design new protocols to target solid tumors. Manufacturing of therapy-competent functional CAR-T cells needs robust protocols forex vivo/in vitroexpansion of modified T-cells. This step is challenging, especially if non-viral low efficiency delivery protocols are used to generate CAR-T cells. Modern protocols for CAR-T cell expansion are based on incubation with high doses of recombinant cytokines to support proliferation, non-specific stimulation with surface-bound antibodies to induce TCR cross-linking, or co-cultivation with antigen-expressing feeder cell lines. These approaches are imperfect since non-specific stimulation results in rapid outgrowth of CAR-negative T cells, and removal of feeder cells from mixed cultures necessitates additional purification steps. In an effort to develop a specific and improved protocol for CAR-T cell expansion, we took advantage of cell-derived membrane vesicles, and the simple structural demands of the CAR-antigen interaction. Our approach was to make antigenic microcytospheres from common cell lines stably expressing surface-bound CAR antigens (antigenic vesicles, AVs), and then use them for stimulation and expansion of CAR-T cells. We developed a rapid, simple, efficient, and inexpensive protocol to generate, stabilize and purify AVs. As proof-of-concept we tested the efficacy of our AV constructs on several CAR-antigen pairs. The data presented in this article clearly demonstrate that our protocol produced AVs with the capacity to induce stronger stimulation, proliferation and functional activity of CAR-T cells than is possible with existing protocols. We predict that this new methodology will significantly improve the ability to obtain improved populations of functional CAR-T cells for therapy.

Graphical abstract
Exploration-based learning of a stabilizing controller predicts locomotor adaptation
Authors: Seethapathi Nidhi , Clark Barrett , Srinivasan Manoj
Published: March 2021

Humans adapt their locomotion seamlessly in response to changes in the body or the environment. We do not understand how such adaptation improves performance measures like energy consumption or symmetry while avoiding falling. Here, we model locomotor adaptation as interactions between a stabilizing controller that reacts quickly to perturbations and a reinforcement learner that gradually improves the controller’s performance through local exploration and memory. This model predicts time-varying adaptation in many settings: walking on a split-belt treadmill (i.e. with both feet at different speeds), with asymmetric leg weights, or using exoskeletons — capturing learning and generalization phenomena in ten prior experiments and two model-guided experiments conducted here. The performance measure of energy minimization with a minor cost for asymmetry captures a broad range of phenomena and can act alongside other mechanisms such as reducing sensory prediction error. Such a model-based understanding of adaptation can guide rehabilitation and wearable robot control.
Deep Learning Shows Cellular Senescence Is a Barrier to Cancer Development
Authors: Heckenbach Indra , Ezra Michael Ben , Mkrtchyan Garik V , Madsen Jakob Sture , Nielsen Malte Hasle , Oró Denise , Mortensen Laust , Verdin Eric , Westendorp Rudi , Scheibye-Knudsen Morten
Published: March 2021

Cellular senescence is a critical component of aging and many age-related diseases, but understanding its role in human health is challenging in part due to the lack of exclusive or universal markers. Using neural networks, we achieve high accuracy in predicting senescence state and type from the nuclear morphology of DAPI-stained human fibroblasts, murine astrocytes and fibroblasts derived from premature aging diseasesin vitro. After generalizing this approach, the predictor recognizes an increasing rate of senescent cells with age in H&E-stained murine liver tissue and human dermal biopsies. Evaluating corresponding medical records reveals that individuals with increased senescent cells have a significantly decreased rate of malignant neoplasms, lending support for the protective role of senescence in limiting cancer development. In sum, we introduce a novel predictor of cellular senescence and apply it to diagnostic medical images, indicating cancer occurs more frequently for those with a lower rate of senescence.
Ocular pigmentation in humans, great apes, and gibbons is not suggestive of communicative functions - having an eye on the ‘cooperative eye hypothesis’
Authors: Caspar Kai R. , Biggemann Marco , Geissmann Thomas , Begall Sabine
Published: March 2021

Pigmentation patterns of the visible part of the eyeball, encompassing the iris and portions of the sclera, have been discussed to be linked to social cognition in primates. In the context of thecooperative eye hypothesis, the white sclera of humans has been viewed as a derived adaptive trait, enhancing communication via glance cueing. Here, we provide a comparative analysis of ocular pigmentation patterns in 15 species of hominoids (humans, great apes & gibbons) representing all extant ape genera, based on photographs and literature data. Additionally, we quantify hominoid scleral exposure on the genus level during different glancing situations. Our data reveals a continuum of eye pigmentation traits among the studied taxa. Gibbons display darker, more uniformly colored eyes than great apes and expose less sclera, particularly during averted glancing. Iridoscleral contrasts in orangutans and gorillas approach the human condition but differ between congeneric species. Contrary to recent discussions, we found chimpanzee eyes to exhibit a cryptic coloration scheme that resembles gibbons more than other great apes and that does not enhance glance cueing or gaze following. We critically evaluate the evidence for links between social cognition and eye pigmentation in primates, concluding that thecooperative eye hypothesiscannot convincingly explain the patterns observed. Although the human eye exhibits unique traits that are likely linked to social communication, high iridoscleral contrast is not one of them. Differences in scleral pigmentation between great apes and humans are gradual and might have arisen via genetic drift and sexual selection.
Personalized Closed-Loop Brain Stimulation for Effective Neurointervention Across Participants
Authors: van Bueren Nienke E.R. , Reed Thomas L. , Nguyen Vu , Sheffield James G. , van der Ven Sanne H.G. , Osborne Michael A. , Kroesbergen Evelyn H. , Cohen Kadosh Roi
Published: March 2021

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, stimulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.
Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions
Authors: Seo Sangmin , Choi Jonghwan , Park Sanghyun , Ahn Jaegyoon
Published: March 2021

Accurate prediction of protein-ligand binding affinity is important in that it can lower the overall cost of drug discovery in structure-based drug design. For more accurate prediction, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient interactions energy terms to describe complex interactions between proteins and ligands. Recent deep-learning techniques show strong potential to solve this problem, but the search for more efficient and appropriate deep-learning architectures and methods to represent protein-ligand complexes continues. In this study, we proposed a deep-neural network for more accurate prediction of protein-ligand complex binding affinity. The proposed model has two important features, descriptor embeddings that contains embedded information about the local structures of a protein-ligand complex and an attention mechanism for highlighting important descriptors to binding affinity prediction. The proposed model showed better performance on most benchmark datasets than existing binding affinity prediction models. Moreover, we confirmed that an attention mechanism was able to capture binding sites in a protein-ligand complex and that it contributed to improvement in predictive performance. Our code is available athttps://github.com/Blue1993/BAPA.

Author summary

The initial step in drug discovery is to identify drug candidates for a target protein using a scoring function. Existing scoring functions, however, lack the ability to accurately predict the binding affinity of protein-ligand complexes. In this study, we proposed a deep learning-based approach to extract patterns from the local structures of protein-ligand complexes and to highlight the important local structures via an attention mechanism. The proposed model showed good performance for various benchmark datasets compared to existing models.
CONSULT: Accurate contamination removal using locality-sensitive hashing
Authors: Rachtman Eleonora , Bafna Vineet , Mirarab Siavash
Published: March 2021

A fundamental question appears in many bioinformatics applications: Does a sequencing read belong to a large dataset of genomes from some broad taxonomic group, even when the closest match in the set is evolutionarily divergent from the query? For example, low-coverage genome sequencing (skimming) projects either assemble the organelle genome or compute genomic distances directly from unassembled reads. Using unassembled reads needs contamination detection because samples often include reads from unintended groups of species. Similarly, assembling the organelle genome needs distinguishing organelle and nuclear reads. While k-mer-based methods have shown promise in read-matching, prior studies have shown that existing methods are insufficiently sensitive for contamination detection. Here, we introduce a new read-matching tool called CONSULT that tests whether k-mers from a query fall within a user-specified distance of the reference dataset using locality-sensitive hashing. Taking advantage of large memory machines available nowadays, CONSULT libraries accommodate tens of thousands of microbial species. Our results show that CONSULT has higher true-positive and lower false-positive rates of contamination detection than leading methods such as Kraken-II and improves distance calculation from genome skims. We also demonstrate that CONSULT can distinguish organelle reads from nuclear reads, leading to dramatic improvements in skims-based mitochondrial assemblies.
Phosphorylation of Ribosomal Protein S6 differentially affects mRNA translation based on ORF length
Authors: Bohlen Jonathan , Teleman Aurelio A.
Published: March 2021

Phosphorylation of Ribosomal Protein S6 (RPS6) was the first post-translational modification of the ribosome to be identified and is a commonly-used readout for mTORC1 activity. Although the cellular and organismal functions of RPS6 phosphorylation are known, its molecular consequences on translation are less well understood. Here we use selective ribosome footprinting to analyze the location of ribosomes containing phosphorylated RPS6 on endogenous mRNAs in cells. We find that RPS6 becomes progressively dephosphorylated on ribosomes as they translate an mRNA. As a consequence, average RPS6 phosphorylation is higher on mRNAs with short coding sequences (CDSs) compared to mRNAs with long CDSs. In particular, ribosomes translating on the endoplasmic reticulum are more rapidly dephosphorylated than cytosolic ribosomes. Loss of RPS6 phosphorylation causes a correspondingly larger drop in translation efficiency of mRNAs with short CDSs than long CDSs. Interestingly, mRNAs with 5’ TOP motifs are translated well also in the absence of RPS6 phosphorylation despite short CDS lengths, suggesting they are translated via a different mode. In sum this provides a dynamic view of RPS6 phosphorylation on ribosomes as they translate mRNAs in different subcellular localizations and the functional consequence on translation.
A new drug candidates for glycogen storage disorders enhances glycogen catabolism: Lessons from Adult Polyglucosan Body Disease models
Authors: Vaknin Hilla , Mishra Kumudesh , D’Souza Jeevitha , Marisat Monzer , Sprecher Uri , Wald-Altman Shane , Dukhovny Anna , Raviv Yuval , Da’adoosh Benny , Engel Hamutal , Benhamron Sandrine , Nitzan Keren , Permyakova Anna , Rosenmann Hanna , Lossos Alexander , Tam Joseph , Minassian Berge A. , Kakhlon Or , Weil Miguel
Published: March 2021

This work employs Adult Polyglucosan Body Disease (APBD) models to explore the efficacy and mechanism of action of 144DG11, a new polyglucosan-reducing lead compound discovered by a high-throughput screen (HTS). APBD is an adult onset glycogen storage disorder (GSD) manifesting as a debilitating progressive axonopathic leukodystrophy. APBD is caused by glycogen branching enzyme (GBE) deficiency leading to poorly branched and insoluble glycogen inclusions, which precipitate as neuropathogenic polyglucosans (PG). 144DG11 led to prolonged survival and improved motor parameters in a GBE knockin (Gbeys/ys) APBD mouse model. Histopathologically, 144DG11 reduced PG and glycogen levels in brain, liver, heart, and peripheral nerve. Indirect calorimetry experiments revealed that 144DG11 increases carbohydrate burn at the expense of fat burn, suggesting metabolic mobilization of pathogenic PG. These results were also reflected at the cellular level by increased glycolytic, mitochondrial and total ATP production. Mechanistically, we show that the molecular target of 144DG11 is the lysosomal membrane protein LAMP1, whose interaction with the compound, similar to LAMP1 knockdown, enhanced autolysosomal degradation of glycogen and lysosomal acidification. Enhanced mitochondrial activity and lysosomal modifications were also the most pronounced effects of 144DG11 in APBD patient fibroblasts as discovered by image-based multiparametric phenotyping analysis and corroborated by proteomics. In summary, this work presents a broad mechanistic and target-based characterization of 144DG11 in in vivo and cell models of the prototypical GSD APBD. This investigation warrants development of 144DG11 into a safe and efficacious GSD therapy.

One Sentence Summary

A new compound, demonstrated to ameliorate APBD in vivo and ex vivo by autophagic catabolism of glycogen, may potentially become a universal drug for glycogen storage disorders.
Nonsense Mediated RNA Decay Is a Unique Vulnerability of Cancer Cells with SF3B1 and U2AF1 Mutations
Authors: Cheruiyot Abigael , Li Shan , Srivatsan Sridhar Nonavinkere , Ahmed Tanzir , Chen Yuhao , Lemacon Delphine Sangotokun , Li Ying , Yang Zheng , Wadugu Brian A. , Warner Wayne , Pruett-Miller Shondra M. , Obeng Esther A. , Link Daniel C. , He Dalin , Xiao Fei , Wang Xiaowei , Bailis Julie M. , Walter Matthew J. , You Zhongsheng
Published: March 2021

Nonsense-mediated RNA decay (NMD) is well recognized as an RNA surveillance pathway that targets aberrant mRNAs with premature translation termination codons (PTCs) for degradation; however, its molecular mechanisms and roles in health and disease remain incompletely understood. In this study, we developed a novel reporter system that can accurately measure NMD activity in individual cells. By carrying out a genome-wide CRISPR/Cas9 knockout screen using this reporter system, we identified novel NMD-promoting factors, including multiple components of the SF3B complex and other U2 spliceosome factors. Interestingly, we also found that cells with mutations in the U2 spliceosome genesSF3B1andU2AF1—which are commonly found in myelodysplastic syndrome (MDS) and cancers—have overall attenuated NMD activity. Furthermore, we found that compared to wild type cells,SF3B1andU2AF1mutant cells are more sensitive to NMD inhibition, a phenotype that is accompanied by elevated DNA replication obstruction, DNA damage and chromosomal instability. Remarkably, the sensitivity of spliceosome mutant cells to NMD inhibition could be rescued by overexpression of RNase H1, which removes R-loops in the genome. Together, our findings shed new light on the functional interplay between NMD and RNA splicing and suggest a novel strategy for the treatment of MDS and cancers with spliceosome mutations.
Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching
Authors: Chen Mengjie , Zhan Qi , Mu Zepeng , Wang Lili , Zheng Zhaohui , Miao Jinlin , Zhu Ping , Li Yang I.
Published: March 2021

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for many biological and medical applications as it allows users to measure gene expression levels in a cell type–specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq data sets with cell types that may overlap only partially and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering and in terms of avoiding overcorrection. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell type–specific differential gene expression comparisons across biopsy sites and disease conditions and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online.
Fast decoding cell type–specific transcription factor binding landscape at single-nucleotide resolution
Authors: Li Hongyang , Guan Yuanfang
Published: March 2021

Decoding the cell type–specific transcription factor (TF) binding landscape at single-nucleotide resolution is crucial for understanding the regulatory mechanisms underlying many fundamental biological processes and human diseases. However, limits on time and resources restrict the high-resolution experimental measurements of TF binding profiles of all possible TF–cell type combinations. Previous computational approaches either cannot distinguish the cell context–dependent TF binding profiles across diverse cell types or can only provide a relatively low-resolution prediction. Here we present a novel deep learning approach, Leopard, for predicting TF binding sites at single-nucleotide resolution, achieving the average area under receiver operating characteristic curve (AUROC) of 0.982 and the average area under precision recall curve (AUPRC) of 0.208. Our method substantially outperformed the state-of-the-art methods Anchor and FactorNet, improving the predictive AUPRC by 19% and 27%, respectively, when evaluated at 200-bp resolution. Meanwhile, by leveraging a many-to-many neural network architecture, Leopard features a hundredfold to thousandfold speedup compared with current many-to-one machine learning methods.
First crystal structure of a Pigment Red 52 compound: DMSO solvate hydrate of the monosodium salt
Authors: Tapmeyer Lukas , Eisenbeil Daniel , Bolte Michael , Schmidt Martin U.
Published: March 2021

Pigment Red 52, Na2[C18H11ClN2O6S], is an industrially produced hydrazone-laked pigment. It serves as an intermediate in the synthesis of the corresponding Ca2+ and Mn2+ salts, which are used commercially for printing inks and lacquers. Hitherto, no crystal structure of any salt of Pigment Red 52 is known. Now, single crystals have been obtained of a dimethyl sulfoxide solvate hydrate of the monosodium salt of Pigment Red 52, namely, monosodium 2-[2-(3-carboxy-2-oxo-1,2-dihydronaphthalen-1-ylidene)hydrazin-1-yl]-5-chloro-4-methylbenzenesulfonate dimethyl sulfoxide monosolvate monohydrate, Na+·C18H12ClN2O6S−·H2O·C2H6OS, obtained from in-house synthesized Pigment Red 52. The crystal structure was determined by single-crystal X-ray diffraction at 173 K. In this monosodium salt, the SO3
− group is deprotonated, whereas the COOH group is protonated. The residues form chains via ionic interactions and hydrogen bonds. The chains are arranged in polar/non-polar double layers.
Structural and theoretical studies of 4-chloro-2-methyl-6-oxo-3,6-dideuteropyrimidin-1-ium chloride (d
6)
Authors: Butcher Ray J. , Purdy Andrew P. , Fischer Sean A. , Gunlycke Daniel
Published: March 2021

The title compound, C5D6ClN2O+·Cl−, crystallizes in the orthorhombic space group, Pbcm, and consists of a 4-chloro-2-methyl-6-oxo-3,6-dihydropyrimidin-1-ium cation and a chloride anion where both moieties lie on a crystallographic mirror. The cation is disordered and was refined as two equivalent forms with occupancies of 0.750 (4)/0.250 (4), while the chloride anion is triply disordered with occupancies of 0.774 (12), 0.12 (2), and 0.11 (2). Unusually, the bond angles around the C=O unit range from 127.2 (6) to 115.2 (3)° and similar angles have been found in other structures containing a 6-oxo-3,6-dihydropyrimidin-1-ium cation, including the monclinic polymorph of the title compound, which crystallizes in the monoclinic space group P21/c [Kawai et al. (1973). Cryst. Struct. Comm.
2, 663–666]. The cations and anions pack into sheets in the ab plane linked by N—H...Cl hydrogen bonds as well as C—H...O and Cl...O interactions. In graph-set notation, these form R
3
3(11) and R
3
2(9) rings. Theoretical calculations seem to indicate that the reason for the unusual angles at the sp
2 C is the electrostatic interaction between the oxygen atom and the adjacent N—H hydrogen.
Crystal structure, Hirshfeld surface analysis and density functional theory study of 6-methyl-2-[(5-methylisoxazol-3-yl)methyl]-1H-benzimidazole
Authors: Idrissi Ahlam , Chkirate Karim , Abad Nadeem , Djerrari Bahia , Achour Redouane , Essassi El Mokhtar , Van Meervelt Luc
Published: March 2021

In the title molecule, C13H13N3O, the isoxazole ring is inclined to the benzimidazole ring at a dihedral angle of 69.28 (14)°. In the crystal, N—H...N hydrogen bonds between neighboring benzimidazole rings form chains along the a-axis direction. Hirshfeld surface analysis indicates that the most important contributions to the crystal packing are from H...H (48.8%), H...C/C...H (20.9%) and H...N/N...H (19.3%) interactions. The optimized structure calculated using density functional theory at the B3LYP/6–311 G(d,p) level is compared with the experimentally determined structure in the solid state. The calculated highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy gap is 4.9266 eV.
Crystal structure and Hirshfeld surface analysis of a copper(II) complex containing 2-nitrobenzoate and tetramethylethylenediamine ligands
Authors: Kansiz Sevgi , Qadir Adnan M. , Dege Necmi , Yongxin Li , Saif Eiad
Published: March 2021

The reaction of copper(II) sulfatepentahydrate with 2-nitrobenzoic acid and N,N,N′,N′-tetramethylethylenediamine (TMEDA) in basic solution produces the complex bis(2-nitrobenzoato-κO)(N,N,N′,N′-tetramethylethylenediamine-κ2
N,N′)copper(II), [Cu(C7H4NO4)2(C6H16N2)] or [Cu(2-nitrobenzoate)2(tmeda)]. Each carboxylate group of the 2-nitrobenzoate ligand is coordinated by CuII atom in a monodentate fashion and two TMEDA ligand nitrogen atoms are coordinate by the metal center, giving rise to a distorted square-planar coordination environment. In the crystal, metal complexes are linked by centrosymmetric C—H...O hydrogen bonds, forming ribbons via a R
2
2(10) ring motif. These ribbons are linked by further C—H...O hydrogen bonds, leading to two-dimensional hydrogen-bonded arrays parallel to the bc plane. Weak π–π stacking interactions provide additional stabilization of the crystal structure. Hirshfeld surface analysis, dnorm
and two-dimensional fingerprint plots were examined to verify the contributions of the different intermolecular contacts within the supramolecular structure. The major interactions of the complex are O...H/H...O (44.9%), H...H (34%) and C...H (14.5%).
Determining the effect of air transportation on air pollution in the most polluted city in Turkey
Authors: Gürçam Selçuk , Konuralp Emrah , Ekici Selcuk
Published: March 2021

Purpose

This study was carried out in Igdir, where Turkey’s urban air pollution is at the highest level, and the population is among the smallest. Thus, the study aims to determine the effect of air transportation on air pollution in the most polluted city in Turkey.

Design/methodology/approach

The approach includes six stages: choosing the airport, accessing the flight information for the airport, classifying the aircraft that operated at the airport, determining the aircraft engines, calculating the emission amounts, calculating the landing and takeoff-based emissions.

Findings

Rather than devoting the resources disproportionally to the aviation sector within the scope of economic globalization, as a policy recommendation, to realize its production potential, Igdir, which has a great agricultural production capacity, considering its microclimate, fertile soil and arable land, should be urgently integrated into neighboring markets and the national market via railways.

Practical implications

It is inferred from the research that Turkey has to consider implementing the emissions tax policy, while the Turkish aviation sector is to realize new regulations for aircraft-engine matching to take public health and the impacts of the airports on their surroundings into consideration more seriously.

Originality/value

This study is an original one, as it puts the increasing pollution caused by the aircraft into a historical and political-economic perspective. Also, it is an example of an interdisciplinary work that combines environmental science and political science.
The use of FEA and semi-empirical equations for weight estimation of a passenger aircraft
Authors: Kowalski Mariusz , Goraj Zdobyslaw Jan , Goliszek Bartłomiej
Published: March 2021

Purpose

The purpose of this paper is to present the result of calculations that were performed to estimate the structural weight of the passenger aircraft using novel technological solution. Mass penalty resulting from the installation of the fuselage boundary layer ingestion device was needed in the CENTRELINE project to be able to estimate the real benefits of the applied technology.

Design/methodology/approach

This paper focusses on the finite element analysis (FEA) of the fuselage and wing primary load-carrying structures. Masses obtained in these analyses were used as an input for the total structural mass calculation based on semi-empirical equations.

Findings

Combining FEA with semi-empirical equations makes it possible to estimate the mass of structures at an early technology readiness level and gives the possibility of obtaining more accurate results than those obtained using only empirical formulas. The applied methodology allows estimating the mass in case of using unusual structural solutions, which are not covered by formulas available in the literature.

Practical implications

Accurate structural mass estimation is possible at an earlier design stage of the project based on the presented methodology, which allows for easier and less costly changes in designed aircrafts.

Originality/value

The presented methodology is an original method of mass estimation based on a two-track approach. The analytical formulas available in the literature have worked well for aeroplanes of conventional design, but thanks to the connection with FEA presented in this paper, it is possible to estimate the structure mass of aeroplanes using unconventional technological solutions.
Group polarisation among location-based game players: an analysis of use and attitudes towards game slang
Authors: Laato Samuli , Inaba Nobufumi , Paloheimo Mauri , Laajala Teemu Daniel
Published: March 2021

Purpose

This study investigates how game design, which divides players into static teams, can reinforce group polarisation. The authors study this phenomenon from the perspective of social identity in the context of team-based location-based games, with a focus on game slang.

Design/methodology/approach

The authors performed an exploratory data analysis on an original dataset of n = 242,852 messages from five communication channels to find differences in game slang adoption between three teams in the location-based augmented reality game Pokémon GO. A divisive word “jym” (i.e. a Finnish slang derivative of the word “gym”) was discovered, and players' attitudes towards the word were further probed with a survey (n = 185). Finally, selected participants (n = 25) were interviewed in person to discover any underlying reasons for the observed polarised attitudes.

Findings

The players' teams were correlated with attitudes towards “jym”. Face-to-face interviews revealed association of the word to a particular player subgroup and it being used with improper grammar as reasons for the observed negative attitudes. Conflict over (virtual) territorial resources reinforced the polarisation.

Practical implications

Game design with static teams and inter-team conflict influences players' social and linguistic identity, which subsequently may result in divisive stratification among otherwise cooperative or friendly player-base.

Originality/value

The presented multi-method study connecting linguistic and social stratification is a novel approach to gaining insight on human social interactions, polarisation and group behaviour in the context of location-based games.