Genome-centric view of carbon processing in thawing permafrost.

As global temperatures rise, large amounts of carbon sequestered in permafrost are becoming available for microbial degradation. Accurate prediction of carbon gas emissions from thawing permafrost is limited by our understanding of these microbial communities. Here we use metagenomic sequencing of 214 samples from a permafrost thaw gradient to recover 1,529 metagenome-assembled genomes, including many from phyla with poor genomic representation. These genomes reflect the diversity of this complex ecosystem, with genus-level representatives for more than sixty per cent of the community. Meta-omic analysis revealed key populations involved in the degradation of organic matter, including bacteria whose genomes encode a previously undescribed fungal pathway for xylose degradation. Microbial and geochemical data highlight lineages that correlate with the production of greenhouse gases and indicate novel syntrophic relationships. Our findings link changing biogeochemistry to specific microbial lineages involved in carbon processing, and provide key information for predicting the effects of climate change on permafrost systems.

Branching morphogenesis in the developing kidney is governed by rules that pattern the ureteric tree.

Metanephric kidney development is orchestrated by the iterative branching morphogenesis of the ureteric bud. We describe an underlying patterning associated with the ramification of this structure and show that this pattern is conserved between developing kidneys, in different parts of the organ and across developmental time. This regularity is associated with a highly reproducible branching asymmetry that is consistent with locally operative growth mechanisms. We then develop a class of tip state models to represent elaboration of the ureteric tree and describe rules for 'half-delay' branching morphogenesis that describe almost perfectly the patterning of this structure. Spatial analysis suggests that the observed asymmetry may arise from mutual suppression of bifurcation, but not extension, between the growing ureteric tips, and demonstrates that disruption of patterning occurs in mouse mutants in which the distribution of tips on the surface of the kidney is altered. These findings demonstrate that kidney development occurs by way of a highly conserved reiterative pattern of asymmetric bifurcation that is governed by intrinsic and locally operative mechanisms.

Comparing and distinguishing the structure of biological branching.

Bifurcating developmental branching morphogenesis gives rise to complex organs such as the lung and the ureteric tree of the kidney. However, a few quantitative methods or tools exist to compare and distinguish, at a structural level, the critical features of these important biological systems. Here we develop novel graph alignment techniques to quantify the structural differences of rooted bifurcating trees and demonstrate their application in the analysis of developing kidneys from in normal and mutant mice. We have developed two graph based metrics: graph discordance, which measures how well the graphs representing the branching structures of distinct trees graphs can be aligned or overlayed; and graph inclusion, which measures the degree of containment of a tree graph within another. To demonstrate the application of these approaches we first benchmark the discordance metric on a data set of 32 normal and 28Tgfß(+/-) mutant mouse ureteric trees. We find that the discordance metric better distinguishes control and mutant mouse kidneys than alternative metrics based on graph size and fingerprints - the distribution of tip depths. Using this metric we then show that the structure of the mutant trees follows the same pattern as the normal kidneys, but undergo a major delay in elaboration at later stages. Analysis of both controls and mutants using the inclusion metric gives strong support to the hypothesis that ureteric tree growth is stereotypic. Additionally, we present a new generalised multi-tree alignment algorithm that minimises the sum of pairwise graph discordance and which can be used to generate maximum consensus trees that represent the archetype for fixed developmental stages. These tools represent an advance in the analysis and quantification of branching patterns and will be invaluable in gaining a deeper understanding of the mechanisms that drive development. All code is being made available with documentation and example data with this publication.

On linear models and parameter identifiability in experimental biological systems.

A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.