The trans-activation domain of the sporulation response regulator Spo0A revealed by X-ray crystallography

Sporulation in Bacillus involves the induction of scores of genes in a temporally and spatially co-ordinated programme of cell development. Its initiation is under the control of an expanded two-component signal transduction system termed a phosphorelay. The master control element in the decision to sporulate is the response regulator, Spo0A, which comprises a receiver or phosphoacceptor domain and an effector or transcription activation domain. The receiver domain of Spo0A shares sequence similarity with numerous response regulators, and its structure has been determined in phosphorylated and unphosphorylated forms. However, the effector domain (C-Spo0A) has no detectable sequence similarity to any other protein, and this lack of structural information is an obstacle to understanding how DNA binding and transcription activation are controlled by phosphorylation in Spo0A. Here, we report the crystal structure of C-Spo0A from Bacillus stearothermophilus revealing a single alpha -helical domain comprising six alpha -helices in an unprecedented fold. The structure contains a helix-turn-helix as part of a three alpha -helical bundle reminiscent of the catabolite gene activator protein (CAP), suggesting a mechanism for DNA binding. The residues implicated in forming the sigma (A)-activating region clearly cluster in a flexible segment of the polypeptide on the opposite side of the structure from that predicted to interact with DNA. The structural results are discussed in the context of the rich array of existing mutational data

The SOD1-mediated ALS phenotype shows a decoupling between age of symptom onset and disease duration

Superoxide dismutase (SOD1) gene variants may cause amyotrophic lateral sclerosis, some of which are associated with a distinct phenotype. Most studies assess limited variants or sample sizes. In this international, retrospective observational study, we compare phenotypic and demographic characteristics between people with SOD1-ALS and people with ALS and no recorded SOD1 variant. We investigate which variants are associated with age at symptom onset and time from onset to death or censoring using Cox proportional-hazards regression. The SOD1-ALS dataset reports age of onset for 1122 and disease duration for 883 people; the comparator population includes 10,214 and 9010 people respectively. Eight variants are associated with younger age of onset and distinct survival trajectories; a further eight associated with younger onset only and one with distinct survival only. Here we show that onset and survival are decoupled in SOD1-ALS. Future research should characterise rarer variants and molecular mechanisms causing the observed variability

Computing linkage disequilibrium aware genome embeddings using autoencoders

Motivation The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction. Results We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block’s genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy—i.e. minimal information loss—while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data