bluuebunny/crossref_metadata_2025_split huggingface dataset:
Microbiota control of maternal behavior regulates early postnatal growth of offspring
Authors: Lee Yujung Michelle , Wallace Martina , Gengatharan Jivani M. , Furst Annalee J. , Bode Lars , Metallo Christian M. , Ayres Janelle S. 
Published: June 2020

AbstractMaternal-offspring bonding and maturation of maternal behavior is necessary during the early postnatal period of an infant to promote optimal development and growth. The regulation of maternal behavior is multifactorial relying both on sensory cues including auditory and olfactory signals emitted by the infant and received by the mother, as well as proper neuroendocrine responses including that of the oxytocin system. The intestinal microbiota has emerged as a critical regulator of growth and development in the early postnatal period of an individual’s life. While, this is best appreciated in the context of direct interactions between the microbiota and the infant, the microbiota can indirectly influence postnatal growth and development by regulating maternal factors. Here, we describe the identification of an intestinalE. colistrain that is pathogenic to the maternal-offspring system during the early postnatal stage of life and results in growth stunting of the offspring. However, rather than having a direct pathogenic effect on the infant, we found that this particularE. colistrain was pathogenic to the dams by interfering with the maturation of maternal behavior. The poor maternal behavior resulted in malnourishment of the pups and impaired IGF-1 signaling leading to the consequential stunted growth. Our work provides a new understanding of how the microbiota regulates postnatal growth and an additional variable that must be considered when studying the regulation of maternal behavior.
Simulating the Impact of Glenohumeral Capsulorrhaphy on Movement Kinematics and Muscle Function in Activities of Daily Living
Authors: Fox Aaron S. , Gill Stephen D. , Bonacci Jason , Page Richard S. 
Published: June 2020

AbstractThis study aimed to use a predictive simulation framework to examine shoulder kinematics, muscular effort and task performance during functional upper limb movements under simulated selective glenohumeral capsulorrhaphy. A musculoskeletal model of the torso and upper limb was adapted to include passive restraints that simulated the changes in shoulder range of motion stemming from selective glenohumeral capsulorrhaphy procedures (anteroinferior, anterosuperior, posteroinferior, posterosuperior, and total anterior, inferior, posterior and superior). Predictive muscle-driven simulations of three functional movements (upward reach, forward reach and head touch) were generated with each model. Shoulder kinematics (elevation, elevation plane and axial rotation), muscle cost (i.e. muscular effort) and task performance time were compared to a baseline model to assess the impact of the capsulorrhaphy procedures. Minimal differences in shoulder kinematics and task performance times were observed, suggesting that task performance could be maintained across the capsulorrhaphy conditions. Increased muscle cost was observed under the selective capsulorrhaphy conditions, however this was dependent on the task and capsulorrhaphy condition. Larger increases in muscle cost were observed under the capsulorrhaphy conditions that incurred the greatest reductions in shoulder range of motion (i.e. total inferior, total anterior, anteroinferior and total posterior conditions) and during tasks that required shoulder kinematics closer to end range of motion (i.e. upward reach and head touch). The elevated muscle loading observed could present a risk to joint capsule repair. Appropriate rehabilitation following glenohumeral capsulorrhaphy is required to account for the elevated demands placed on muscles, particularly when significant range of motion loss presents.
Estimating Microbial Interaction Network:Zero-inflated Latent Ising Model Based Approach
Authors: Zhou Jie , Viles Weston D. , Lu Boran , Li Zhigang , Madan Juliette C. , Karagas Margaret R. , Gui Jiang , Hoen Anne G. 
Published: June 2020

AbstractMotivationThroughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the relationships among the microbes of a microbiota. In this high-dimensional setting, the zero-inflated and compositional data structure (subject to unit-sum constraint) pose challenges to the accurate estimation of microbial interaction networks.MethodWe propose thezero-inflated latent Ising(ZILI) model for microbial interaction network which assumes that the distribution of relative abundance of microbiota is determined by finite latent states. This assumption is partly supported by the existing findings in literature [20]. The ZILI model can circumvents the unit-sum constraint and alleviates the zero-inflation problem under given assumptions. As for the model selection of ZILI, a two-step algorithm is proposed. ZILI and two-step algorithm are evaluated through simulated data and subsequently applied in our investigation of an infant gut microbiome dataset from New Hampshire Birth Cohort Study. The results are compared with results from traditional Gaussian graphical model (GGM) and dichotomous Ising model (DIS).ResultsThrough the simulation studies, provided that the ZILI model is the true generative model for the data, it is shown that the two-step algorithm can estimate the graphical structure effectively and is robust to a range of alternative settings of the related factors. Both GGM and DIS can not achieve a satisfying performance in these settings. For the infant gut microbiome dataset, we use both ZILI and GGM to estimate microbial interaction network. The final estimated networks turn out to share a statistically significant overlap in which the ZILI and two-step algorithm tend to select the sparser network than those modeled by GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature.AvailabilityThe data and programs involved in Section 4 and 5 are available on request from the correspondence author.ContactAnne.G.Hoen@dartmouth.eduSupplementary informationSupplementary materials are available atBioinformatics
Molecular Basis of Far-red Sensing in Cyanobacteriochrome
Authors: Bandara Sepalika , Rockwell Nathan , Zeng Xiaoli , Ren Zhong , Wang Cong , Shin Heewhan , Martin Shelley S. , Moreno Marcus V. , Lagarias J. Clark , Yang Xiaojing 
Published: June 2020

AbstractCyanobacteriochromes are small, panchromatic photoreceptors in the phytochrome superfamily that regulate diverse light-mediated adaptive processes in cyanobacteria. The molecular basis of far-red (FR) light perception by cyanobacteriochromes is currently unknown. Here we report the crystal structure of a far-red-sensing cyanobacteriochrome fromAnabaena cylindricaPCC 7122, which exhibits a reversible far-red/orange photocycle. The 2.7 Å structure of its FR-absorbing dark state, determined by room temperature serial crystallography and cryo-crystallography, reveals anall-Z,synconfiguration of its bound linear tetrapyrrole (bilin) chromophore that is less extended than the bilin chromophores of all known phytochromes. Based on structural comparisons with other bilin-binding proteins and extensive spectral analyses on mutants, we identify key protein-chromophore interactions that enable far-red sensing in bilin-binding proteins. We propose that FR-CBCRs employ two distinct tuning mechanisms, which work together to produce a large batho-chromatic shift. Findings of this work have important implications for development and improvement of photoproteins with far-red absorption and fluorescence.Significance StatementPhytochromes are well known far-red-light sensors found in plants that trigger adaptive responses to facilitate competition for light capture with neighboring plants. Red- and far-red-sensing are critical to cyanobacteria living in the far-red-enriched shade of plants. Here we report the crystal structure of a far-red-sensing cyanobacteriochrome, a distant cyanobacterial relative of phytochrome. These studies shed insight into the poorly understood molecular basis of far-red-sensing by phytobilin-based photoreceptors. Owing to the deep tissue penetration of far-red light, far-red-sensing photoreceptors offer promising protein scaffolds for developing gene-based photoswitches, optoacoustic contrast agents and fluorescent probes forin situimaging and optogenetic applications.
Identification of repurposable cytoprotective drugs for Vanishing White Matter Disease
Authors: Ng Neville , Cabral-da-Silva Mauricio Castro , Maksour Simon , Berg Tracey , Engel Martin , Silva Dina M. , Do-Ha Dzung , Lum Jeremy S. , Muñoz Sonia Sanz , Suarez-Bosche Nadia , Stevens Claire H. , Ooi Lezanne 
Published: June 2020

AbstractVanishing white matter disease (VWMD) is a rare leukodystrophy involving loss of function mutations of the guanine exchange factor eIF2B and typically presenting with juvenile onset. We aimed to identify repurposable FDA approved drugs in anin vitrodrug screen using patient-derived fibroblasts and induced pluripotent stem cell (iPSC)-derived astrocytes. Dysregulated GADD34 and CHOP were identified in patient fibroblasts and iPSC-derived astrocytes under proteasomal stress conditions. A drug screen from a 2400 FDA approved drug library withEIF2B5disease patient fibroblasts identified 113 anti-inflammatory drugs as a major class of hits with cytoprotective effects. A panel of potential candidate drugs including berberine, deflazacort, ursodiol, zileuton, guanabenz and Anavex 2-73, and preclinical ISRIB, increased cell survival of MG132-stressedEIF2B2andEIF2B5disease VWMD astrocytes, and were further investigated for their effect on the integrated stress response and mitochondrial stress. ISRIB but not other drugs significantly affected eIF2α phosphorylation and GADD34 expression. Ursodiol demonstrated capacity to reduce complex I subunit upregulation, ameliorate oxidative stress, loss of mitochondrial membrane potential and upregulation of eIF2B subunits in VWMD astrocytes, highlighting its potential as a cytoprotective compound for VWMD.
Learning Peptide Recognition Rules for a Low-Specificity Protein
Authors: Wheeler Lucas C. , Perkins Arden , Wong Caitlyn E. , Harms Michael J. 
Published: June 2020

AbstractMany proteins interact with short linear regions of target proteins. For some proteins, however, it is difficult to identify a well-defined sequence motif that defines its target peptides. To overcome this difficulty, we used supervised machine learning to train a model that treats each peptide as a collection of easily-calculated biochemical features rather than as an amino acid sequence. As a test case, we dissected the peptide-recognition rules for human S100A5 (hA5), a low-specificity calcium binding protein. We trained a Random Forest model against a recently released, high-throughput phage display dataset collected for hA5. The model identifies hydrophobicity and shape complementarity, rather than polar contacts, as the primary determinants of peptide binding specificity in hA5. We tested this hypothesis by solving a crystal structure of hA5 and through computational docking studies of diverse peptides onto hA5. These structural studies revealed that peptides exhibit multiple binding modes at the hA5 peptide interface—all of which have few polar contacts with hA5. Finally, we used our trained model to predict new, plausible binding targets in the human proteome. This revealed a fragment of the protein α-1-syntrophin binds to hA5. Our work helps better understand the biochemistry and biology of hA5, as well as demonstrating how high-throughput experiments coupled with machine learning of biochemical features can reveal the determinants of binding specificity in low-specificity proteins.
The Role of Swab Tests to Decrease the Stress by COVID-19 on the Health System using AI, MLR & Statistical Analysis
Authors: Pirouz Behzad , Nejad Hana Javadi , Violini Galileo , Pirouz Behrouz 
Published: June 2020

AbstractThe outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and confirmed cases of infection, with special attention to the sickness level. The study is carried out with reference to the Italian case, but the result is of more general importance, in particular for countries with limited availability of ICUs (intensive care units). The statistical analysis shows correlation between the number of swab tests and those of daily positive cases, mild cases admitted to hospital, intensive care cases, recovery, and death rate, and provides a basis to carry on an AI study. The results were validated using a multivariate linear regression (MLR) approach. Our main result is the identification of a significant statistical effect of reduction of the pressure on the Health system as result of the increase of the tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality disease, such as e.g. cardiological, and oncological. Our results show that swab testing may play a major role to decrease the stress on the Health system of a country. Therefore, this case study is relevant in particular for the planning of the control of the pandemic in countries with a limited capacity of admission to ICU’s units.
The Relationship Between COVID-19 Infection and Risk Perception, Knowledge, Attitude As Well As Four Non-pharmaceutical Interventions (NPIs) During the Late Period Of The COVID-19 Epidemic In China – An Online Cross-sectional Survey of 8158 Adults
Authors: Xu Hong , Gan Yong , Zheng Daikun , Wu Bo , Zhu Xian , Xu Chang , Liu Chenglu , Tao Zhou , Hu Yaoyue , Chen Min , Li Mingjing , Lu Zuxun , Chen Jack 
Published: June 2020

AbstractBackgroundSo far, there has been no published population study on the relationship between COVID-19 infection and public’s risk perception, information source, knowledge, attitude and four non-pharmaceutical interventions(NPI: hand washing, proper coughing habits, social distancing and mask wearing) during the COVID-19 outbreak in China.MethodsAn online survey of 8158 Chinese adults between 22 February to 5 March 2020 was conducted. Bivariate associations between categorical variables were examined using Fisher exact test. We also explored the determinants of four NPIs as well as their association with COVID-19 infection using logistic regression.ResultsOf 8158 adults included, 57 (0.73%) were infected with COVID-19. The overwhelming majority of respondents showed a positive attitude (99.2%), positive risk perception (99.9%) and high knowledge levels that were among the strongest predictors of four highly adopted NPIs (hand washing:96.8%; proper coughing: 93.1%; social distancing:87.1%; mask wearing:97.9%). There was an increased risk of COVID-19 infection for those who not washing hands (2.28% vs 0.65%; RR=3.53: 95%CI: 1.53-8.15; P<0.009); not practicing proper coughing (1.79% vs 0.73%; RR=2.44: 95%CI: 1.15-5.15;P=0.026); not practicing social distancing (1.52% vs 0.58%; RR=2.63:95%CI:1.48 – 4.67; P=0.002); and not wearing a mask (7.41% vs 0.6%; RR=12.38:95%CI:5.81-26.36; P<0.001). For those who did practice all other three NPIs, wearing mask was associated with significantly reduced risk of infection compared to those who did not wear a mask (0.6% vs 16.7%; p=0.035). Similarly, for those who did not practice all or part of the other three NPIs, wearing mask was also associated with significantly reduced risk of infection. In a penalised logistic regression model including all four NPIs, wearing a mask was the only significant predictor of COVID-19 infection among four NPIs (OR=7.20; 95%CI:2.24-23.11; p<0.001).ConclusionsWe found high levels of risk perception, positive attitude, desirable knowledge as well as a high level of adopting four NPIs. The relevant knowledge, risk perception and attitude were strong predictors of adapting the four NPIs. Mask wearing, among four personal NPIs, was the most effective protective measure against COVID-19 infection with added preventive effect among those who practised all or part of the other three NPIs.
Purification of age-distinct insulin secretory granules through antigen restriction
Authors: Neukam Martin , Sala Pia , Brunner Andreas-David , Ganß Katharina , Palladini Alessandra , Grzybek Michal , Vasiljević Jovana , Broichhagen Johannes , Johnsson Kai , Kurth Thomas , Mann Matthias , Coskun Ünal , Solimena Michele 
Published: June 2020

SummaryEndocrine cells employ regulated exocytosis of secretory granules to secrete hormones and neurotransmitters. The competence of secretory granules for exocytosis depends on spatio-temporal variables such as proximity to the plasma membrane and age, with newly-generated granules being preferentially released. Despite recent advances, we still lack a comprehensive view about the molecular composition of insulin granules and how this may change over the lifetime of the organelles. Here we report a strategy for the purification of insulin secretory granules of distinct age from insulinoma INS-1 cells. Tagging the granule-resident protein phogrin with a cleavable CLIP tag, we obtained intact fractions of age-distinct granules for proteomic and lipidomic analyses. We find that the lipid composition changes over time along with the physical properties of the membrane.
Lipin-1 regulates lipid catabolism in pro-resolving macrophages
Authors: Schilke Robert M. , Blackburn Cassidy M.R. , Rao Shashanka , Krzywanski David , Finck Brian N. , Woolard Matthew D. 
Published: June 2020

AbstractMacrophages reprogram their metabolism to promote appropriate responses. Pro-resolving macrophages primarily utilize fatty acid oxidation as an energy source. Metabolites generated during the catabolism of fatty acids aid in the resolution of inflammation and tissue repair, but the regulatory mechanisms that control lipid metabolism in macrophages is not fully elucidated. In this current study we show that lipin-1, a phosphatidic acid phosphatase and regulator of lipid metabolism, is required for increased oxidative phosphorylation during IL-4 mediated responses. We also show that the transcriptional coregulatory function of lipin-1 is required for β-oxidation in response to palmitate (free fatty acid) and apoptotic cell derived lipids. BMDMs lacking lipin-1 have a reduction in critical TCA cycle metabolites following IL-4 stimulation, suggesting a break in the TCA cycle that is supportive of lipid synthesis rather than lipid catabolism. Together, our data demonstrate that lipin-1 regulates intermediary metabolism within pro-resolving macrophages and highlights the importance of aligning macrophage metabolism with proper responses to stimuli.
Modelling live fuel moisture content at leaf and canopy scale under extreme drought using a lumped plant hydraulic model
Authors: Martin-StPaul N , Ruffault J , Blackmann C , Cochard H , De Cáceres M , Delzon S , Dupuy JL , Fargeon H , Lamarque L , Moreno M , Parsell R , Pimont F , Ourcival JM , Torres-Ruiz J , Limousin JM 
Published: June 2020

AbstractWater content in living vegetation (or live fuel moisture content, LFMC), is increasingly recognized as a key factor linked to vegetation mortality and wildfire ignition and spread. Most often, empirical indices are used as surrogates for direct LFMC measurements.In this paper, we explore the functional and ecophysiological drivers of LFMC during drought at the leaf and canopy scale using the SurEau-Ecos model, and a three years dataset of leaf and canopy scale measurements on a matureQuercus ilexforest, including an extreme drought. The model is based on forest hydrology and plant hydraulics and allows to simulate temporal variations of water potential and content at a daily time step. At leaf level, it simulates the relationship between water potential and water content by separating the apoplasm and the symplasm. Symplasm water content is modeled using the pressure volume curve theory, and apoplasm water content is modelled using the xylem vulnerability to cavitation. Fuel moisture content was upscaled to the canopy level by accounting for foliage mortality estimated from drought induced cavitation.The model was parameterized either with site-measured traits or using a calibration procedure, and compared with water potential and LFMC measured at leaf level, and NDVI variation measured at canopy level and taken as a surrogate for foliage mortality.At leaf level, LFMC prediction using measured hydraulic traits could be improved by considering year-to-year osmotic adjustments. At canopy level, foliage mortality due to drought induced cavitation was a key driver of LFMC decline during the most extreme drought.A sensitivity analysis showed that parameters driving soil water balance (leaf area index, soil water capacity, and regulation of transpiration) and parameters determining pressure volume curves are key traits driving LFMC dynamics at leaf level. At the canopy level, parameters that drives hydraulic failure were the most sensitive and included, both soil water balance parameters and hydraulic traits (the leaf vulnerability to cavitation) were the main drivers of LFMC decline during extreme drought.We also showed that under normal historic weather conditions, most variation of LFMC are linked to reversible symplasm dehydration, however under future, hotter and dryer conditions, most variations are due to the decline canopy of LFMC driven by foliage mortality.
Chromatin Mechanics Dictates Subdiffusion and Coarsening Dynamics of Embedded Condensates
Authors: Lee Daniel S.W. , Wingreen Ned S. , Brangwynne Clifford P. 
Published: June 2020

AbstractDNA is organized into chromatin, a complex polymeric material which stores information and controls gene expression. An emerging mechanism for biological organization, particularly within the crowded nucleus, is biomolecular phase separation into condensed droplets of protein and nucleic acids. However, the way in which chromatin impacts the dynamics of phase separation and condensate formation is poorly understood. Here, we utilize a powerful optogenetic strategy to examine the interplay of droplet coarsening with the surrounding viscoelastic chromatin network. We demonstrate that droplet growth dynamics are directly inhibited by the chromatin-dense environment, which gives rise to an anomalously slow coarsening exponent,β∼0.12, contrasting with the classical prediction ofβ∼ 1/3. Using scaling arguments and simulations, we show how this arrested growth can arise due to subdiffusion of individual condensates, predictingβ∼α/3, where α is the diffusion exponent. Tracking the fluctuating motion of condensates within chromatin reveals a subdiffusive exponent, α∼0.5, which explains the anomalous coarsening behavior and is also consistent with Rouse-like dynamics arising from the entangled chromatin. Our findings have implications for the biophysical regulation of the size and shape of biomolecular condensates, and suggest that condensate emulsions can be used to probe the viscoelastic mechanical environment within living cells.