Probability landscapes for integrative genomics

<p>Abstract</p> <p>Background</p> <p>The comprehension of the gene regulatory code in eukaryotes is one of the major challenges of systems biology, and is a requirement for the development of novel therapeutic strategies for multifactorial diseases. Its bi-fold degeneration precludes brute force and statistical approaches based on the genomic sequence alone. Rather, recursive integration of systematic, whole-genome experimental data with advanced statistical regulatory sequence predictions needs to be developed. Such experimental approaches as well as the prediction tools are only starting to become available and increasing numbers of genome sequences and empirical sequence annotations are under continual discovery-driven change. Furthermore, given the complexity of the question, a decade(s) long multi-laboratory effort needs to be envisioned. These constraints need to be considered in the creation of a framework that can pave a road to successful comprehension of the gene regulatory code.</p> <p>Results</p> <p>We introduce here a concept for such a framework, based entirely on systematic annotation in terms of probability profiles of genomic sequence using any type of relevant experimental and theoretical information and subsequent cross-correlation analysis in hypothesis-driven model building and testing.</p> <p>Conclusion</p> <p>Probability landscapes, which include as reference set the probabilistic representation of the genomic sequence, can be used efficiently to discover and analyze correlations amongst initially heterogeneous and un-relatable descriptions and genome-wide measurements. Furthermore, this structure is usable as a support for automatically generating and testing hypotheses for alternative gene regulatory grammars and the evaluation of those through statistical analysis of the high-dimensional correlations between genomic sequence, sequence annotations, and experimental data. Finally, this structure provides a concrete and tangible basis for attempting to formulate a mathematical description of gene regulation in eukaryotes on a genome-wide scale.</p

Climatic Signal In Earlywood And Latewood In Conifer Forests In The Monarch Butterfly Biosphere Reserve, Mexico

Earlywood (EW) and latewood (LW) chronologies can be used to analyze seasonal climatic variation. We constructed and analyzed total ring (RW), EW, and LW ring growth in Abies religiosa and Pinus pseudostrobus trees from the Monarch Butterfly Biosphere Reserve and evaluated their climatic signal (monthly precipitation and mean average, minimum and maximum temperatures) in the growth of tree rings by correlation and response function analyses. Precipitation during October and December of the previous year and during January, February, April, and May of the year of growth had a positive influence in the growth of both P. pseudostrobus and A. religiosa. Mean maximum temperatures had a negative effect on tree growth in both species. Additionally, growth of A. religiosa was more sensitive to variations of mean, minimum, and maximum temperatures in comparison with P. pseudostrobus, and monthly mean minimum temperature was positively correlated with EW and LW series in A. religiosa. We conclude that EW and LW growth of A. religiosa and P. pseudostrobus might be reduced by lower precipitation during the winter-spring season. Consequently, in the eventuality of warmer and drier climate during the latter season as projected by climate change scenarios, growth rates of A. religiosa could become severely affected, negatively impacting the overwintering habitat of the monarch butterfly (Danaus plexippus L.).This item is part of the Tree-Ring Research (formerly Tree-Ring Bulletin) archive. For more information about this peer-reviewed scholarly journal, please email the Editor of Tree-Ring Research at [email protected]

Baseflow control on sediment flux connectivity : insights from a nested catchment study in Central Mexico

In order to assess the extent of sediment connectivity between uplands and lowlands and to quantify the processes of in-channel deposition and remobilization, measurements of suspended sediment fluxes were conducted in a nested rural catchment of the Mexican Volcanic Belt. Data were collected over one year at three upland sites (3 to 12 km(2)) and two downstream stations (390-630 km(2)). Our results show that a structural discontinuity in the catchment (i.e. abrupt slope decrease at the junction between piedmonts and the alluvial plain from 2 to 10% to <0.1%) could be compensated by functional continuity during floods. Direct conveyance of fine sediment to the outlet occurred when a high stream transport capacity was reached. Erosion of the streambed was observed on various occasions and accounted for up to 50% of the flux leaving the catchment during one event. Conversely, temporary in-channel storage was apparent on other occasions, amounting to up to 52% of the flux recorded upstream during one storm. These two distinct behaviours were approximately equally distributed along the rainy season and strongly driven by the extent of coupling between surface and subsurface water. This work indeed highlights the role of baseflow spatial variations in determining the extent of lowland sediment conveyance. Riverbed erosional processes occurred when large differences in pre-event baseflow values (i.e. at least a twofold longitudinal increase) were observed between the 5-km distant lowland stations. Our findings outline the importance of systematically taking into consideration the baseflow parameter in research focusing on fine sediment transport across scales

Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global health. Understanding the emergence, evolution, and transmission of individual antibiotic resistance genes (ARGs) is essential to develop sustainable strategies combatting this threat. Here, we use metagenomic sequencing to analyse ARGs in 757 sewage samples from 243 cities in 101 countries, collected from 2016 to 2019. We find regional patterns in resistomes, and these differ between subsets corresponding to drug classes and are partly driven by taxonomic variation. The genetic environments of 49 common ARGs are highly diverse, with most common ARGs carried by multiple distinct genomic contexts globally and sometimes on plasmids. Analysis of flanking sequence revealed ARG-specific patterns of dispersal limitation and global transmission. Our data furthermore suggest certain geographies are more prone to transmission events and should receive additional attention