Stochastic De-repression of Rhodopsins in Single Photoreceptors of the Fly Retina

The photoreceptors of the Drosophila compound eye are a classical model for studying cell fate specification. Photoreceptors (PRs) are organized in bundles of eight cells with two major types – inner PRs involved in color vision and outer PRs involved in motion detection. In wild type flies, most PRs express a single type of Rhodopsin (Rh): inner PRs express either Rh3, Rh4, Rh5 or Rh6 and outer PRs express Rh1. In outer PRs, the K50 homeodomain protein Dve is a key repressor that acts to ensure exclusive Rh expression. Loss of Dve results in de-repression of Rhodopsins in outer PRs, and leads to a wide distribution of expression levels. To quantify these effects, we introduce an automated image analysis method to measure Rhodopsin levels at the single cell level in 3D confocal stacks. Our sensitive methodology reveals cell-specific differences in Rhodopsin distributions among the outer PRs, observed over a developmental time course. We show that Rhodopsin distributions are consistent with a two-state model of gene expression, in which cells can be in either high or basal states of Rhodopsin production. Our model identifies a significant role of post-transcriptional regulation in establishing the two distinct states. The timescale for interconversion between basal and high states is shown to be on the order of days. Our results indicate that even in the absence of Dve, the Rhodopsin regulatory network can maintain highly stable states. We propose that the role of Dve in outer PRs is to buffer against rare fluctuations in this network

Valuing Health Gain from Composite Response Endpoints for Multisystem Diseases

Objectives: This study aimed to demonstrate how to estimate the value of health gain after patients with a multisystem disease achieve a condition-specific composite response endpoint. Methods: Data from patients treated in routine practice with an exemplar multisystem disease (systemic lupus erythematosus) were extracted from a national register (British Isles Lupus Assessment Group Biologics Register). Two bespoke composite response endpoints (Major Clinical Response and Improvement) were developed in advance of this study. Difference-in-differences regression compared health utility values (3-level version of EQ-5D; UK tariff) over 6 months for responders and nonresponders. Bootstrapped regression estimated the incremental quality-adjusted life-years (QALYs), probability of QALY gain after achieving the response criteria, and population monetary benefit of response. Results: Within the sample (n = 171), 18.2% achieved Major Clinical Response and 49.1% achieved Improvement at 6 months. Incremental health utility values were 0.0923 for Major Clinical Response and 0.0454 for Improvement. Expected incremental QALY gain at 6 months was 0.020 for Major Clinical Response and 0.012 for Improvement. Probability of QALY gain after achieving the response criteria was 77.6% for Major Clinical Response and 72.7% for Improvement. Population monetary benefit of response was £1 106 458 for Major Clinical Response and £649 134 for Improvement. Conclusions: Bespoke composite response endpoints are becoming more common to measure treatment response for multisystem diseases in trials and observational studies. Health technology assessment agencies face a growing challenge to establish whether these endpoints correspond with improved health gain. Health utility values can generate this evidence to enhance the usefulness of composite response endpoints for health technology assessment, decision making, and economic evaluation

In silico characterization of mutations circulating in SARS-CoV-2 structural proteins

SARS-CoV-2 has recently emerged as a pandemic that has caused more than 2.4 million deaths worldwide. Since the onset of infections, several full-length sequences of viral genome have been made available which have been used to gain insights into viral dynamics. We utilised a meta-data driven comparative analysis tool for sequences (Meta-CATS) algorithm to identify mutations in 829 SARS-CoV-2 genomes from around the world. The algorithm predicted sixty-one mutations among SARS-CoV-2 genomes. We observed that most of the mutations were concentrated around three protein coding genes viz nsp3 (non-structural protein 3), RdRp (RNA-directed RNA polymerase) and Nucleocapsid (N) proteins of SARS-CoV-2. We used various computational tools including normal mode analysis (NMA), C-α discrete molecular dynamics (DMD) and all-atom molecular dynamic simulations (MD) to study the effect of mutations on functionality, stability and flexibility of SARS-CoV-2 structural proteins including envelope (E), N and spike (S) proteins. PredictSNP predictor suggested that four mutations (L37H in E, R203K and P344S in N and D614G in S) out of seven were predicted to be neutral whilst the remaining ones (P13L, S197L and G204R in N) were predicted to be deleterious in nature thereby impacting protein functionality. NMA, C-α DMD and all-atom MD suggested some mutations to have stabilizing roles (P13L, S197L and R203K in N protein) where remaining ones were predicted to destabilize mutant protein. In summary, we identified significant mutations in SARS-CoV-2 genomes as well as used computational approaches to further characterize the possible effect of highly significant mutations on SARS-CoV-2 structural proteins. Communicated by Ramaswamy H. Sarma.</p