Allosteric regulation of CAD modulates de novo pyrimidine synthesis during the cell cycle.

Metabolism is a fundamental cellular process that is coordinated with cell cycle progression. Despite this association, a mechanistic understanding of cell cycle phase-dependent metabolic pathway regulation remains elusive. Here we report the mechanism by which human de novo pyrimidine biosynthesis is allosterically regulated during the cell cycle. Combining traditional synchronization methods and metabolomics, we characterize metabolites by their accumulation pattern during cell cycle phases and identify cell cycle phase-dependent regulation of carbamoyl-phosphate synthetase 2, aspartate transcarbamylase and dihydroorotase (CAD), the first, rate-limiting enzyme in de novo pyrimidine biosynthesis. Through systematic mutational scanning and structural modelling, we find allostery as a major regulatory mechanism that controls the activity change of CAD during the cell cycle. Specifically, we report evidence of two Animalia-specific loops in the CAD allosteric domain that involve sensing and binding of uridine 5'-triphosphate, a CAD allosteric inhibitor. Based on homology with a mitochondrial carbamoyl-phosphate synthetase homologue, we identify a critical role for a signal transmission loop in regulating the formation of a substrate channel, thereby controlling CAD activity.

Evaluating the effectiveness of ensemble voting in improving the accuracy of consensus signals produced by various DTWA algorithms from step-current signals generated during nanopore sequencing.

Nanopore sequencing device analysis systems simultaneously generate multiple picoamperage current signals representing the passage of DNA or RNA nucleotides ratcheted through a biomolecule nanopore array by motor proteins. Squiggles are a noisy and time-distorted representation of an underlying nucleotide sequence, "gold standard model", due to experimental and algorithmic artefacts. Other research fields use dynamic time warped-space averaging (DTWA) algorithms to produce a consensus signal from multiple time-warped sources while preserving key features distorted by standard, linear-averaging approaches. We compared the ability of DTW Barycentre averaging (DBA), minimize mean (MM) and stochastic sub-gradient descent (SSG) DTWA algorithms to generate a consensus signal from squiggle-space ensembles of RNA molecules Enolase, Sequin R1-71-1 and Sequin R2-55-3 without knowledge of their associated gold standard model. We propose techniques to identify the leader and distorted squiggle features prior to DTWA consensus generation. New visualization and warping-path metrics are introduced to compare consensus signals and the best estimate of the "true" consensus, the study's gold standard model. The DBA consensus was the best match to the gold standard for both Sequin studies but was outperformed in the Enolase study. Given an underlying common characteristic across a squiggle ensemble, we objectively evaluate a novel "voting scheme" that improves the local similarity between the consensus signal and a given fraction of the squiggle ensemble. While the gold standard is not used during voting, the increase in the match of the final voted-on consensus to the underlying Enolase and Sequin gold standard sequences provides an indirect success measure for the proposed voting procedure in two ways: First is the decreased least squares warped distance between the final consensus and the gold model, and second, the voting generates a final consensus length closer to the known underlying RNA biomolecule length. The results suggest considerable potential in marrying squiggle analysis and voted-on DTWA consensus signals to provide low-noise, low-distortion signals. This will lead to improved accuracy in detecting nucleotides and their deviation model due to chemical modifications (a.k.a. epigenetic information). The proposed combination of ensemble voting and DTWA has application in other research fields involving time-distorted, high entropy signals.