Anaerobic methanotroph 'Candidatus Methanoperedens nitroreducens' has a pleomorphic life cycle.

'Candidatus Methanoperedens' are anaerobic methanotrophic (ANME) archaea with global importance to methane cycling. Here meta-omics and fluorescence in situ hybridization (FISH) were applied to characterize a bioreactor dominated by 'Candidatus Methanoperedens nitroreducens' performing anaerobic methane oxidation coupled to nitrate reduction. Unexpectedly, FISH revealed the stable co-existence of two 'Ca. M. nitroreducens' morphotypes: the archetypal coccobacilli microcolonies and previously unreported planktonic rods. Metagenomic analysis showed that the 'Ca. M. nitroreducens' morphotypes were genomically identical but had distinct gene expression profiles for proteins associated with carbon metabolism, motility and cell division. In addition, a third distinct phenotype was observed, with some coccobacilli 'Ca. M. nitroreducens' storing carbon as polyhydroxyalkanoates. The phenotypic variation of 'Ca. M. nitroreducens' probably aids their survival and dispersal in the face of sub-optimal environmental conditions. These findings further demonstrate the remarkable ability of members of the 'Ca. Methanoperedens' to adapt to their environment.

Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP).

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

ChIP-R: Assembling reproducible sets of ChIP-seq and ATAC-seq peaks from multiple replicates.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the primary protocol for detecting genome-wide DNA-protein interactions, and therefore a key tool for understanding transcriptional regulation. A number of factors, including low specificity of antibody and cellular heterogeneity of sample, may cause "peak" callers to output noise and experimental artefacts. Statistically combining multiple experimental replicates from the same condition could significantly enhance our ability to distinguish actual transcription factor binding events, even when peak caller accuracy and consistency of detection are compromised. We adapted the rank-product test to statistically evaluate the reproducibility from any number of ChIP-seq experimental replicates. We demonstrate over a number of benchmarks that our adaptation "ChIP-R" (pronounced 'chipper') performs as well as or better than comparable approaches on recovering transcription factor binding sites in ChIP-seq peak data. We also show ChIP-R extends to evaluate ATAC-seq peaks, finding reproducible peak sets even at low sequencing depth. ChIP-R decomposes peaks across replicates into "fragments" which either form part of a peak in a replicate, or not. We show that by re-analysing existing data sets, ChIP-R reconstructs reproducible peaks from fragments with enhanced biological enrichment relative to current strategies.