Biol Rev Camb Philos Soc , IF:10.701 , 2020 Aug , V95 (4) : P1020-1035 doi: 10.1111/brv.12599
A multilevel analytical framework for studying cultural evolution in prehistoric hunter-gatherer societies.
Instituto Universitario de Investigacion en Arqueologia y Patrimonio Historico (INAPH), Universidad de Alicante, Edificio Institutos Universitarios, 03690, San Vicente del Raspeig, Alicante, Spain.; Institut Catala de Paleoecologia Humana i Evolucio Social (IPHES), Edificio W3, Campus Sescelades URV, Zona Educacional 4, 43007, Tarragona, Spain.; Departament d'Historia Economica, Institucions, Politica i Economia Mundial, Universitat de Barcelona, Av. Diagonal 690, 08034, Barcelona, Spain.; Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Marti Franques 1, 08028, Barcelona, Spain.
Over the past decade, a major debate has taken place on the underpinnings of cultural changes in human societies. A growing array of evidence in behavioural and evolutionary biology has revealed that social connectivity among populations and within them affects, and is affected by, culture. Yet the interplay between prehistoric hunter-gatherer social structure and cultural transmission has typically been overlooked. Interestingly, the archaeological record contains large data sets, allowing us to track cultural changes over thousands of years: they thus offer a unique opportunity to shed light on long-term cultural transmission processes. In this review, we demonstrate how well-developed methods for social structure analysis can increase our understanding of the selective pressures underlying cumulative culture. We propose a multilevel analytical framework that considers finer aspects of the complex social structure in which regional groups of prehistoric hunter-gatherers were embedded. We put forward predictions of cultural transmission based on local- and global-level network metrics of small-scale societies and their potential effects on cumulative culture. By bridging the gaps between network science, palaeodemography and cultural evolution, we draw attention to the use of the archaeological record to depict patterns of social interactions and transmission variability. We argue that this new framework will contribute to improving our understanding of social interaction patterns, as well as the contexts in which cultural changes occur. Ultimately, this may provide insights into the evolution of human behaviour.
PMID: 32237025
Proc Natl Acad Sci U S A , IF:9.412 , 2020 Jul , V117 (30) : P18099-18109 doi: 10.1073/pnas.2000078117
Robustness of plant quantitative disease resistance is provided by a decentralized immune network.
Laboratoire des Interactions Plantes-Microorganismes, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, CNRS, Universite de Toulouse, 31326 Castanet-Tolosan, France.; KWS SAAT SE & Co, 37574 Einbeck, Germany.; iMean, 31520 Toulouse, France.; Laboratoire des Interactions Plantes-Microorganismes, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, CNRS, Universite de Toulouse, 31326 Castanet-Tolosan, France; dominique.roby@inrae.fr.
Quantitative disease resistance (QDR) represents the predominant form of resistance in natural populations and crops. Surprisingly, very limited information exists on the biomolecular network of the signaling machineries underlying this form of plant immunity. This lack of information may result from its complex and quantitative nature. Here, we used an integrative approach including genomics, network reconstruction, and mutational analysis to identify and validate molecular networks that control QDR in Arabidopsis thaliana in response to the bacterial pathogen Xanthomonas campestris To tackle this challenge, we first performed a transcriptomic analysis focused on the early stages of infection and using transgenic lines deregulated for the expression of RKS1, a gene underlying a QTL conferring quantitative and broad-spectrum resistance to X campestris RKS1-dependent gene expression was shown to involve multiple cellular activities (signaling, transport, and metabolism processes), mainly distinct from effector-triggered immunity (ETI) and pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) responses already characterized in A thaliana Protein-protein interaction network reconstitution then revealed a highly interconnected and distributed RKS1-dependent network, organized in five gene modules. Finally, knockout mutants for 41 genes belonging to the different functional modules of the network revealed that 76% of the genes and all gene modules participate partially in RKS1-mediated resistance. However, these functional modules exhibit differential robustness to genetic mutations, indicating that, within the decentralized structure of the QDR network, some modules are more resilient than others. In conclusion, our work sheds light on the complexity of QDR and provides comprehensive understanding of a QDR immune network.
PMID: 32669441
Genomics Proteomics Bioinformatics , IF:7.051 , 2020 Jul doi: 10.1016/j.gpb.2019.11.007
Identification of Key Genes for the Ultrahigh Yield of Rice Using Dynamic Cross-tissue Network Analysis.
State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; State Key Laboratory of Hybrid rice, College of life sciences, Wuhan University, Wuhan 430072, China.; Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai 201210, China.; Institute of Food Crop of Yunnan Academy of Agricultural Sciences, Kunming 650205, China.; School of Agriculture, Yunnan University, Kunming 650500, China.; State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; BGI-Baoshan, Baoshan 678004, China.; Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.; State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.; State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Center for Ecological and Environmental Sciences, Northwestern Polytechnical University, Xi'an 710072, China.; State Key Laboratory of Hybrid rice, College of life sciences, Wuhan University, Wuhan 430072, China.; School of Agriculture, Yunnan University, Kunming 650500, China. Electronic address: hfengyi@ynu.edu.cn.; Institute of Food Crop of Yunnan Academy of Agricultural Sciences, Kunming 650205, China. Electronic address: yangcd2005@163.com.; Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210 China. Electronic address: lnchen@sibs.ac.cn.; State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Center for Ecological and Environmental Sciences, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: wwang@mail.kiz.ac.cn.
Significantly increasing crop yield is a major and worldwide challenge for food supply and security. It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide. Yet, the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery. Here, we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group. We identified the top 24 candidate high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method, i.e., dynamic cross-tissue (DCT) network analysis. We used one of the candidate genes, OsSPL4, whose function was previously unknown, for gene editing experimental validation of the high yield, and confirmed that OsSPL4 significantly affects panicle branching and increases the rice yield. This study, which included extensive field phenotyping, cross-tissue systems biology analyses, and functional validation, uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice. The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample. DCT can be downloaded from https://github.com/ztpub/DCT.
PMID: 32736037
J Exp Bot , IF:5.908 , 2020 Jul , V71 (15) : P4442-4451 doi: 10.1093/jxb/eraa054
A balancing act: how plants integrate nitrogen and water signals.
Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA.; Centro de Genomica y Bioinformatica, Facultad de Ciencias, Universidad Mayor, Santiago, Chile.; International Rice Research Institute, Metro Manila, Philippines.
Nitrogen (N) and water (W) are crucial inputs for plant survival as well as costly resources for agriculture. Given their importance, the molecular mechanisms that plants rely on to signal changes in either N or W status have been under intense scrutiny. However, how plants sense and respond to the combination of N and W signals at the molecular level has received scant attention. The purpose of this review is to shed light on what is currently known about how plant responses to N are impacted by W status. We review classic studies which detail how N and W combinations have both synergistic and antagonistic effects on key plant traits, such as root architecture and stomatal aperture. Recent molecular studies of N and W interactions show that mutations in genes involved in N metabolism affect drought responses, and vice versa. Specifically, perturbing key N signaling genes may lead to changes in drought-responsive gene expression programs, which is supported by a meta-analysis we conduct on available transcriptomic data. Additionally, we cite studies that show how combinatorial transcriptional responses to N and W status might drive crop phenotypes. Through these insights, we suggest research strategies that could help to develop crops adapted to marginal soils depleted in both N and W, an important task in the face of climate change.
PMID: 31990028
Brain Connect , IF:5.263 , 2020 Jul doi: 10.1089/brain.2019.0709
Resting-State Network Patterns Underlying Cognitive Function in Bipolar Disorder: A Graph Theoretical Analysis.
Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland.; College of Science and Engineering, National University of Ireland Galway, Galway, Republic of Ireland.; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom.
Background: Synchronous and antisynchronous activity between neural elements at rest reflects the physiological processes underlying complex cognitive ability. Regional and pairwise connectivity investigations suggest that perturbations in these activity patterns may relate to widespread cognitive impairments seen in bipolar disorder (BD). Here we take a network-based perspective to more meaningfully capture interactions among distributed brain regions compared to focal measurements and examine network-cognition relationships across a range of commonly affected cognitive domains in BD in relation to healthy controls. Methods: Resting-state networks were constructed as matrices of correlation coefficients between regionally averaged resting-state time series from 86 cortical/subcortical brain regions (FreeSurferv5.3.0). Cognitive performance measured using the Wechsler Adult Intelligence Scale, Cambridge Automated Neuropsychological Test Battery (CANTAB), and Reading the Mind in the Eyes tests was examined in relation to whole-brain connectivity measures and patterns of connectivity using a permutation-based statistical approach. Results: Faster response times in controls (n = 49) related to synchronous activity between frontal, parietal, cingulate, temporal, and occipital regions, while a similar response times in BD (n = 35) related to antisynchronous activity between regions of this subnetwork. Across all subjects, antisynchronous activity between the frontal, parietal, temporal, occipital, cingulate, insula, and amygdala regions related to improved memory performance. No resting-state subnetworks related to intelligence, executive function, short-term memory, or social cognition performance in the overall sample or in a manner that would explain deficits in these facets in BD. Conclusions: Our results demonstrate alterations in the intrinsic connectivity patterns underlying response timing in BD that are not specific to performance or errors on the same tasks. Across all individuals, no strong effects of resting-state global topology on cognition are found, while distinct functional networks supporting episodic and spatial memory highlight intrinsic inhibitory influences present in the resting state that facilitate memory processing. Impact Statement Regional and pairwise-connectivity investigations suggest altered interactions between brain areas may contribute to impairments in cognition that are observed in bipolar disorder. However, the distributed nature of these interactions across the brain remains poorly understood. Using recent advances in network neuroscience, we examine functional connectivity patterns associated with multiple cognitive domains in individuals with and without bipolar disorder. We discover distinct patterns of connectivity underlying response-timing performance uniquely in bipolar disorder and, independent of diagnosis, inhibitory interactions that relate to memory performance.
PMID: 32458698
Ann Bot , IF:4.005 , 2020 Jul doi: 10.1093/aob/mcaa143
Two decades of functional-structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology.
INRAE UR4 URP3F, BP6, Lusignan, France.; Anhui Agricultural University, School of Agronomy, Hefei, Anhui Province, PR China.
BACKGROUND: Functional-structural plant models (FSPMs) explore and integrate relationships between a plant's structure and processes that underlie its growth and development. In the last twenty years, scientists interested in functional-structural plant modelling have expanded greatly the range of topics covered and now handle dynamical models of growth and development occurring from the microscopic scale, and involving cell division in plant meristems, to the macroscopic scales of whole plants and plant communities. SCOPE: The FSPM approach occupies a central position in plant science; it is at the crossroads of fundamental questions in system biology and predictive ecology. This special issue of Annals of Botany features selected papers on critical areas covered by FSPMs and examples of comprehensive models that are used to solve theoretical and applied questions, ranging from developmental biology to plant phenotyping and management of plants for agronomic purposes. Altogether, they offer an opportunity to assess the progress, gaps and bottlenecks along the research path originally foreseen for FSPMs two decades ago. This review also allows discussion of current challenges of FSP models regarding i) integration of multi-disciplinary knowledge, ii) methods for handling complex models, iii) standards to achieve interoperability and greater genericity and iv) understanding of plant functioning across scales. CONCLUSIONS: This approach has demonstrated considerable progress, but has yet to reach its full potential in terms of integration and heuristic knowledge production. The research agenda of FSP modelers in the coming years should place a greater emphasis on explaining robust emergent patterns, and on the causes of possible deviation from it. Modelling such patterns could indeed fuel both generic integration across scales and transdisciplinary transfer. In particular, it could be beneficil to emergent fields of research such as model-assisted phenotyping and predictive ecology in managed ecosystems.
PMID: 32725187
BMC Genomics , IF:3.594 , 2020 Jul , V21 (1) : P494 doi: 10.1186/s12864-020-06913-3
Integrated small RNA and Degradome sequencing provide insights into salt tolerance in sesame (Sesamum indicum L.).
Cotton Research Center, Shandong Academy of Agricultural Sciences, Jinan, 250100, China. 723949246@qq.com.; Cotton Research Center, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.; Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.; Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, China. junyou@caas.cn.
BACKGROUND: MicroRNAs (miRNAs) exhibit important regulatory roles in the response to abiotic stresses by post-transcriptionally regulating the target gene expression in plants. However, their functions in sesame response to salt stress are poorly known. To dissect the complex mechanisms underlying salt stress response in sesame, miRNAs and their targets were identified from two contrasting sesame genotypes by a combined analysis of small RNAs and degradome sequencing. RESULTS: A total of 351 previously known and 91 novel miRNAs were identified from 18 sesame libraries. Comparison of miRNA expressions between salt-treated and control groups revealed that 116 miRNAs were involved in salt stress response. Using degradome sequencing, potential target genes for some miRNAs were also identified. The combined analysis of all the differentially expressed miRNAs and their targets identified miRNA-mRNA regulatory networks and 21 miRNA-mRNA interaction pairs that exhibited contrasting expressions in sesame under salt stress. CONCLUSIONS: This comprehensive integrated analysis may provide new insights into the genetic regulation mechanism of miRNAs underlying the adaptation of sesame to salt stress.
PMID: 32682396