Biology subjects

Kolaczkowski, B.

Publications and source records attributed to Kolaczkowski, B..

2 recordsLinked to original sources

bioRxiv2019

Reconstructed ancient nitrogenases suggest Mo-specific ancestry

The nitrogenase metalloenzyme family, essential for supplying fixed nitrogen to the biosphere, is one of lifes key biogeochemical innovations. The three isozymes of nitrogenase differ in their metal dependence, each binding either a FeMo-, FeV-, or FeFe-cofactor where the reduction of dinitrogen takes place. The history of nitrogenase metal dependence has been of particular interest due to the possible implication that ancient marine metal availabilities have significantly constrained nitrogenase evolution over geologic time. Here, we reconstructed the evolutionary history of nitrogenases, and combined phylogenetic reconstruction, ancestral sequence inference, and structural homology modeling to evaluate the potential metal dependence of ancient nitrogenases. We find that active-site sequence features can reliably distinguish extant Mo-nitrogenases from V- and Fe-nitrogenases, and that inferred ancestral sequences at the deepest nodes of the phylogeny suggest these ancient proteins most resemble modern Mo-nitrogenases. Taxa representing early-branching nitrogenase lineages lack one or more biosynthetic nifE and nifN genes that both contribute to the assembly of the FeMo-cofactor in studied organisms, suggesting that early Mo-nitrogenases may have utilized an alternate and/or simplified pathway for cofactor biosynthesis. Our results underscore the profound impacts that protein-level innovations likely had on shaping global biogeochemical cycles throughout the Precambrian, in contrast to organism-level innovations that characterize the Phanerozoic Eon.

evolutionary biology↗

bioRxiv2019

PIME: a package for discovery of novel differences among microbial communities

Massive sequencing of genetic markers, such as the 16S rRNA gene for prokaryotes, allows the comparative analysis of diversity and abundance of whole microbial communities. However, the data used for profiling microbial communities is usually low in signal and high in noise preventing the identification of real differences among treatments. PIME (Prevalence Interval for Microbiome Evaluation) fills this gap by removing those taxa that may be high in relative abundance in just a few samples but have a low prevalence overall. The reliability and robustness of PIME were compare against the existing methods and verified by a number of approaches using 16S rRNA independent datasets. To remove the noise, PIME filters microbial taxa not shared in a per treatment prevalence interval starting at 5% with increments of 5% at each filtering step. For each prevalence interval, hundreds of decision trees are calculated to predict the likelihood of detecting differences in treatments. The best prevalence-filtered dataset is user-selected by choosing the prevalence interval that keeps the majority of the 16S rRNA reads in the dataset and shows the lowest error rate. To obtain the likelihood of introducing bias while building prevalence-filtered datasets, an error detection step based in random permutations is also included. A reanalysis of previews published datasets with PIME uncovered previously missed microbial associations improving the ability to detect important organisms, which may be masked when only relative abundance is considered.

bioinformatics↗