Nestedness across biological scales

This is the final published version. Available from Public Library of Science via the DOI in this record.All data sets are available for download in the repository https:// bitbucket.org/maucantor/unodf_analyses/src.Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks.Conselho Nacional de Desenvolvimento Científico e TecnológicoSão Paulo Research FoundationKillam TrustsThe Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Ministry of Education of BrazilFundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarin

The Human Nucleolar Protein FTSJ3 Associates with NIP7 and Functions in Pre-rRNA Processing

NIP7 is one of the many trans-acting factors required for eukaryotic ribosome biogenesis, which interacts with nascent pre-ribosomal particles and dissociates as they complete maturation and are exported to the cytoplasm. By using conditional knockdown, we have shown previously that yeast Nip7p is required primarily for 60S subunit synthesis while human NIP7 is involved in the biogenesis of 40S subunit. This raised the possibility that human NIP7 interacts with a different set of proteins as compared to the yeast protein. By using the yeast two-hybrid system we identified FTSJ3, a putative ortholog of yeast Spb1p, as a human NIP7-interacting protein. A functional association between NIP7 and FTSJ3 is further supported by colocalization and coimmunoprecipitation analyses. Conditional knockdown revealed that depletion of FTSJ3 affects cell proliferation and causes pre-rRNA processing defects. The major pre-rRNA processing defect involves accumulation of the 34S pre-rRNA encompassing from site A′ to site 2b. Accumulation of this pre-rRNA indicates that processing of sites A0, 1 and 2 are slower in cells depleted of FTSJ3 and implicates FTSJ3 in the pathway leading to 18S rRNA maturation as observed previously for NIP7. The results presented in this work indicate a close functional interaction between NIP7 and FTSJ3 during pre-rRNA processing and show that FTSJ3 participates in ribosome synthesis in human cells

The Essential Nucleolar Yeast Protein Nop8p Controls the Exosome Function during 60S Ribosomal Subunit Maturation

The yeast nucleolar protein Nop8p has previously been shown to interact with Nip7p and to be required for 60S ribosomal subunit formation. Although depletion of Nop8p in yeast cells leads to premature degradation of rRNAs, the biochemical mechanism responsible for this phenotype is still not known. In this work, we show that the Nop8p amino-terminal region mediates interaction with the 5.8S rRNA, while its carboxyl-terminal portion interacts with Nip7p and can partially complement the growth defect of the conditional mutant strain Δnop8/GAL::NOP8. Interestingly, Nop8p mediates association of Nip7p to pre-ribosomal particles. Nop8p also interacts with the exosome subunit Rrp6p and inhibits the complex activity in vitro, suggesting that the decrease in 60S ribosomal subunit levels detected upon depletion of Nop8p may result from degradation of pre-rRNAs by the exosome. These results strongly indicate that Nop8p may control the exosome function during pre-rRNA processing

Regulation of the htpX gene of Xylella fastidiosa and its expression in E-coli

Xylella fastidiosa was the first phytopathogen to be completely sequenced, and its genome revealed several interesting features to be used in functional studies. In the present work, the htpX gene, which encodes a protein involved in the heat shock response in other bacteria, was analyzed by RT-PCR by using cells derived from different cultural conditions. This gene was induced after a temperature upshift to 37degreesC after growth in minimal medium, XDM, but showed constitutive expression in rich medium or in XDM plus plant extracts. Sequences upstream to the htpX gene, containing a putative regulatory region, were also transferred to E. coli, and the thermoregulation was maintained in the new host, since it was constitutively transcribed at 37degreesC or 45degreesC in all culture media tested, but not at 28degreesC in minimal culture medium. The gene was also cloned into the expression vector pET32Xa/LIC, and the expression of the corresponding protein was confirmed by Western blotting.48639139

Transcription analysis of pilS and xpsEL genes from Xylella fastidiosa

Xylella fastidiosa is a xylem-limited phytopathogen responsible for diseases in several plants such as citrus and coffee. Analysis of the bacterial genome revealed some putative pathogenicity-related genes that could help to elucidate the molecular mechanisms of plant-pathogen interactions. In the present work, the transcription of three genes of the bacterium, grown in defined and rich media and also in media containing host plant extracts (sweet orange, 'ponkan' and coffee) was analyzed by RT-PCR. The pilS gene, which encodes a sensor histidine kinase responsible for the biosynthesis of fimbriae, was transcribed when the bacterium was grown in more complex media such as PW and in medium containing plant extracts. The xps genes (xpsL and xpsE) which are related to the type II secretion system were also detected when the bacterium was grown in rich media and media with 'ponkan' and coffee extracts. It was thus observed that pilS and xpsEL genes of X. fastidiosa can be modulated by environmental factors and their expression is dependent on the nutritional status of the growth medium.87325325

Coffee Crop's Biomass and Carbon Stock Estimation With Usage of High Resolution Satellites Images

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coffee is one of the most important crops in Brazil. Monitoring the crop is necessary to understand future production and a sound understanding of coffee's biophysical properties improves such monitoring. Biophysical properties such as dry biomass can be estimated using remote sensing, including the new generation of high-resolution images (GeoEye-1, for instance). In this study we aim to investigate the relationship between vegetation indices (VI) of high-resolution images (GeoEye-1) and coffee biophysical properties, including dry biomass and carbon. The study also aims at establishing an empirical relationship between remote sensing data (vegetation indices), simple field measurements and dry biomass, allowing calculation of coffee biomass and carbon without resorting to destructive methods. Individual GeoEye-1 satellite's bands (NIR, RED and GREEN) showed significant correlation with biomass, but the best correlation occurred with vegetation index. There is a strong correlation between NDVI, RVI, GNDVI and dry biomass, allowing the estimation of coffee crops' carbon stock. RVI had correlation with plant area index (PAI). The empirical correlation was established and the forecast equation of coffee biomass was created.63SI17861795EPAMIGConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)EMBRAPA CafeAgrodataming ProjectAlcsens ProjectConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user's attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) - given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing - in the same setting, find clusters, the top-N-O outlier images, and the N-R images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method's practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images. (C) 2013 Elsevier Inc. All rights reserved.264211229Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)National Science Foundation [DBI-0640543, IIS-0970179]Microsoft ResearchGoogle Focused Research AwardFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)National Science Foundation [DBI-0640543, IIS-0970179