Sci Total Environ , IF:6.551 , 2020 Feb , V705 : P135886 doi: 10.1016/j.scitotenv.2019.135886
Functional connectivity network between terrestrial and aquatic habitats by a generalist waterbird, and implications for biovectoring.
Department of Wetland Ecology, Estacion Biologica de Donana EBD-CSIC, Americo Vespucio 26, 41092 Sevilla, Spain. Electronic address: victormartin_velez@hotmail.com.; Department of Wetland Ecology, Estacion Biologica de Donana EBD-CSIC, Americo Vespucio 26, 41092 Sevilla, Spain.; Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, the Netherlands.; Theoretical and Computational Ecology, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, PO Box 94240, 1090 GE Amsterdam, the Netherlands.; British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP24 2PU, UK.; Terrestrial Ecology Unit (TEREC), Ghent University, K.L. Ledeganckstraat 35, 9000 Ghent, Belgium; Behavioural Ecology and Ecophysiology group, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium.; COS Department, Netherlands Institute for Sea Research, P.O. Box 59, 1790 AB Den Burg, Texel, the Netherlands; Utrecht University, the Netherlands.
Birds are vectors of dispersal of propagules of plants and other organisms including pathogens, as well as nutrients and contaminants. Thus, through their movements they create functional connectivity between habitat patches. Most studies on connectivity provided by animals to date have focused on movements within similar habitat types. However, some waterbirds regularly switch between terrestrial, coastal and freshwater habitats throughout their daily routines. Lesser black-backed gulls that overwinter in Andalusia use different habitat types for roosting and foraging. In order to reveal their potential role in biovectoring among habitats, we created an inter-habitat connectivity network based on GPS tracking data. We applied connectivity measures by considering frequently visited sites as nodes, and flights as links, to determine the strength of connections in the network between habitats, and identify functional units where connections are more likely to happen. We acquired data for 42 tagged individuals (from five breeding colonies), and identified 5676 direct flights that connected 37 nodes. These 37 sites were classified into seven habitat types: reservoirs, natural lakes, ports, coastal marshes, fish ponds, rubbish dumps and ricefields. The Donana ricefields acted as the central node in the network based on centrality measures. Furthermore, during the first half of winter when rice was harvested, ricefields were the most important habitat type in terms of total time spent. Overall, 90% of all direct flights between nodes were between rubbish dumps (for foraging) and roosts in other habitats, thereby connecting terrestrial and various wetland habitats. The strength of connections decreased between nodes as the distance between them increased, and was concentrated within ten independent spatial and functional units, especially between December and February. The pivotal role for ricefields and rubbish dumps in the network, and their high connectivity with aquatic habitats in general, have important implications for biovectoring into their surroundings.
PMID: 31838416
Cell Mol Life Sci , IF:6.496 , 2020 Feb , V77 (3) : P433-440 doi: 10.1007/s00018-019-03379-9
Computational systems biology of cellular processes in Arabidopsis thaliana: an overview.
INF 267 (Bioquant), Heidelberg University, 69120, Heidelberg, Germany.; INF 267 (Bioquant), Heidelberg University, 69120, Heidelberg, Germany. ursula.kummer@bioquant.uni-heidelberg.de.
Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation and computational modeling. While there is already a large quantity of systems biology studies describing human, animal and especially microbial cell biological systems, plant biology has been lagging behind for many years. However, in the case of the model plant Arabidopsis thaliana, the steadily increasing amount of information on the levels of its genome, proteome and on a variety of its metabolic and signalling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated, showing also for plant sciences how fruitful systems biology research can be. In this review, we provide an overview over some of these recent studies which use different systems biological approaches to get a better understanding of the cell biology of A. thaliana. The approaches used in these are genome-scale metabolic modeling, as well as kinetic modeling of metabolic and signalling pathways. Furthermore, we selected several cases to exemplify how the modeling approaches have led to significant advances or new perspectives in the field.
PMID: 31768604
Plant J , IF:6.141 , 2020 Feb , V101 (3) : P716-730 doi: 10.1111/tpj.14558
tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks.
Electrical and Computer Engineering Department, North Carolina State University, Raleigh, NC, 27695, USA.; Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, 27695, USA.; Biomathematics Graduate Program, North Carolina State University, Raleigh, NC, 27695, USA.; Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, 50010, USA.; Elo Life Systems, Durham, NC, 27709, USA.
Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user-friendly platform that can process raw RNA-sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat 2 + cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star. We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp-star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild-type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell-type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.
PMID: 31571287
Biol Psychiatry Cogn Neurosci Neuroimaging , IF:5.335 , 2020 Feb , V5 (2) : P152-162 doi: 10.1016/j.bpsc.2019.09.004
Neuroanatomical Dysconnectivity Underlying Cognitive Deficits in Bipolar Disorder.
Centre for Neuroimaging & Cognitive Genomics, Clinical Neuroimaging Lab, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Republic of Ireland. Electronic address: g.mcphilemy1@nuigalway.ie.; Centre for Neuroimaging & Cognitive Genomics, Clinical Neuroimaging Lab, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Republic of Ireland.; College of Science and Engineering, National University of Ireland Galway, Galway, Republic of Ireland.; School of Psychology, National University of Ireland Galway, Galway, Republic of Ireland.
BACKGROUND: Graph theory applied to brain networks is an emerging approach to understanding the brain's topological associations with human cognitive ability. Despite well-documented cognitive impairments in bipolar disorder (BD) and recent reports of altered anatomical network organization, the association between connectivity and cognitive impairments in BD remains unclear. METHODS: We examined the role of anatomical network connectivity derived from T1- and diffusion-weighted magnetic resonance imaging in impaired cognitive performance in individuals with BD (n = 32) compared with healthy control individuals (n = 38). Fractional anisotropy- and number of streamlines-weighted anatomical brain networks were generated by mapping constrained spherical deconvolution-reconstructed white matter among 86 cortical/subcortical bilateral brain regions delineated in the individual's own coordinate space. Intelligence and executive function were investigated as distributed functions using measures of global, rich-club, and interhemispheric connectivity, while memory and social cognition were examined in relation to subnetwork connectivity. RESULTS: Lower executive functioning related to higher global clustering coefficient in participants with BD, and lower IQ performance may present with a differential relationship between global and interhemispheric efficiency in individuals with BD relative to control individuals. Spatial recognition memory accuracy and response times were similar between diagnostic groups and associated with basal ganglia and thalamus interconnectivity and connectivity within extended anatomical subnetworks in all participants. No anatomical subnetworks related to episodic memory, short-term memory, or social cognition generally or differently in BD. CONCLUSIONS: Results demonstrate selective influence of subnetwork patterns of connectivity in underlying cognitive performance generally and abnormal global topology underlying discrete cognitive impairments in BD.
PMID: 31806486
J Proteome Res , IF:4.074 , 2020 Feb , V19 (2) : P719-732 doi: 10.1021/acs.jproteome.9b00616
Longitudinal Transcriptomic, Proteomic, and Metabolomic Analyses of Citrus sinensis (L.) Osbeck Graft-Inoculated with "Candidatus Liberibacter asiaticus".
Department of Food Science and Technology , University of California, Davis , Davis , California 95616 , United States.; Emerging Pests and Pathogens Research Unit, Robert W. Holley Center for Agriculture and Health , USDA Agricultural Research Service , Ithaca , New York 14853 , United States.; Boyce Thompson Institute for Plant Research , Ithaca , New York 14853 , United States.; Contained Research Facility , University of California, Davis , Davis , California 95616 , United States.; Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States.; National Clonal Germplasm Repository for Citrus & Dates , Riverside , California 92507 , United States.; Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science , Cornell University , Ithaca , New York 14853 , United States.
"Candidatus Liberibacter asiaticus" (CLas) is the bacterium associated with the citrus disease Huanglongbing (HLB). Current CLas detection methods are unreliable during presymptomatic infection, and understanding CLas pathogenicity to help develop new detection techniques is challenging because CLas has yet to be isolated in pure culture. To understand how CLas affects citrus metabolism and whether infected plants produce systemic signals that can be used to develop improved detection techniques, leaves from Washington Navel orange (Citrus sinensis (L.) Osbeck) plants were graft-inoculated with CLas and longitudinally studied using transcriptomics (RNA sequencing), proteomics (liquid chromatography-tandem mass spectrometry), and metabolomics (proton nuclear magnetic resonance). Photosynthesis gene expression and protein levels were lower in infected plants compared to controls during late infection, and lower levels of photosynthesis proteins were identified as early as 8 weeks post-grafting. These changes coordinated with higher sugar concentrations, which have been shown to accumulate during HLB. Cell wall modification and degradation gene expression and proteins were higher in infected plants during late infection. Changes in gene expression and proteins related to plant defense were observed in infected plants as early as 8 weeks post-grafting. These results reveal coordinated changes in greenhouse navel leaves during CLas infection at the transcript, protein, and metabolite levels, which can inform of biomarkers of early infection.
PMID: 31885275
Microb Ecol , IF:3.356 , 2020 Feb , V79 (2) : P357-366 doi: 10.1007/s00248-019-01407-6
Phosphorus Input Alters the Assembly of Rice (Oryza sativa L.) Root-Associated Communities.
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, No. 1799 Jimei Road, Xiamen, 361021, China.; School of Geographic Sciences, Nantong University, No. 999 Tongjing Road, Nantong, 226007, China.; Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, No. 1799 Jimei Road, Xiamen, 361021, China. hyyao@iue.ac.cn.; Research Center for Environmental Ecology and Engineering, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, 206 Guanggu 1st Road, Wuhan, 430205, China. hyyao@iue.ac.cn.
Rice root-associated microbial community play an important role in plant nutrient acquisition, biomass production, and stress tolerance. Herein, root-associated community assembly was investigated under different phosphate input levels in phosphorus (P)-deficient paddy soil. Rice was grown in a long-term P-depleted paddy soil with 0 (P0), 50 (PL), or 200 (PH) mg P2O5 kg(-1) application. DNA from root endophytes was isolated after 46 days, and PCR amplicons from archaea, bacteria, and fungi were sequenced by an Illumina Miseq PE300 platform, respectively. P application had no significant effect on rice root endophytic archaea, which were dominated by ammonia-oxidizing Candidatus Nitrososphaera. By contrast, rice root endophytic community structure of the bacteria and fungi was affected by soil P. Low P input increased endophytic bacterial diversity, whereas high P input increased rhizosphere fungi diversity. Bacillus and Pleosporales, associated with phosphate solubilization and P uptake, dominated in P0 and PH treatments, and Pseudomonas were more abundant in the PL treatment than in the P0 and PH treatments. Co-occurrence network analysis revealed a close interaction between endophytic bacteria and fungi. Soil P application affected both the rice root endosphere and soil rhizosphere microbial community and interaction between rice root endophytic bacteria, and fungi, especially species related to P cycling.
PMID: 31342100
Curr Pharm Des , IF:2.208 , 2020 Feb doi: 10.2174/1381612826666200218104921
Precision Medicine Approach in Prostate Cancer.
The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy (MIRT), Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr. Iran.; Laboratory of Computational Biotechnology and Bioinformatics (CBB), Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol. Iran.; The Persian Gulf Marine Biotechnology Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr. Iran.; Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033. United States.; Department of Nuclear Medicine, Klinikum Westfalen, Knappschaft Hospital, Dortmund. Germany.
Prostate cancer is the most prevalent form of cancer and the second cause of death in men worldwide. Various diagnostic and treatment procedures are available for this type of malignancy, but High-risk or locally advanced prostate cancers showed the potential to develop to lethal phase that can be causing dead. Therefore, new approaches are needed to prolong patient survival and provide a better quality of life. Precision medicine is a novel emerging field that has an essential role in identifying new sub-classifications of disease and guiding treatment based on individual multi-omics data. Multi-omics approaches include the use of genomics, transcriptomics, proteomics, metabolomics, epigenomics and phenomics data to unravel the complexity of a disease-associated biological network, to predict prognostic biomarkers, and to identify new targeted drugs for individual cancer patients. We review the impact of multi-omics data in the framework of systems biology in the era of precision medicine, emphasising the combination of molecular imaging modalities with high-throughput techniques and the new treatments that target metabolic pathways involved in prostate cancer.
PMID: 32067601
J Intell , 2020 Feb , V8 (1) doi: 10.3390/jintelligence8010007
The Effects of Using Partial or Uncorrected Correlation Matrices When Comparing Network and Latent Variable Models.
National Center for Adaptive Neurotechnologies, Albany, NY 12208, USA.
Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models using both partial and uncorrected correlation matrices with both types of models. The results show that a network model provided better fit to matrices of partial correlations but latent variable models provided better fit to matrices of full correlations. This result is due to the fact that the use of partial correlations removes most of the covariance common to WAIS-IV tests. Modeling should be based on uncorrected correlations since these represent the majority of shared variance between WAIS-IV test scores.
PMID: 32075306