Subunit asymmetry and roles of conformational switching in the hexameric AAA+ ring of ClpX

The hexameric AAA+ ring of Escherichia coli ClpX, an ATP-dependent machine for protein unfolding and translocation, functions with the ClpP peptidase to degrade target substrates. For efficient function, ClpX subunits must switch between nucleotide-loadable (L) and nucleotide-unloadable (U) conformations, but the roles of switching are uncertain. Moreover, it is controversial whether working AAA+-ring enzymes assume symmetric or asymmetric conformations. Here, we show that a covalent ClpX ring with one subunit locked in the U conformation catalyzes robust ATP hydrolysis, with each unlocked subunit able to bind and hydrolyze ATP, albeit with highly asymmetric position-specific affinities. Preventing U↔L interconversion in one subunit alters the cooperativity of ATP hydrolysis and reduces the efficiency of substrate binding, unfolding and degradation, showing that conformational switching enhances multiple aspects of wild-type ClpX function. These results support an asymmetric and probabilistic model of AAA+-ring activity.National Institutes of Health (U.S.) (Grant GM-101988)Massachusetts Institute of Technology (Poitras Predoctoral Fellowship

Circumstellar disks and planets. Science cases for next-generation optical/infrared long-baseline interferometers

We present a review of the interplay between the evolution of circumstellar disks and the formation of planets, both from the perspective of theoretical models and dedicated observations. Based on this, we identify and discuss fundamental questions concerning the formation and evolution of circumstellar disks and planets which can be addressed in the near future with optical and infrared long-baseline interferometers. Furthermore, the importance of complementary observations with long-baseline (sub)millimeter interferometers and high-sensitivity infrared observatories is outlined.Comment: 83 pages; Accepted for publication in "Astronomy and Astrophysics Review"; The final publication is available at http://www.springerlink.co

Natural disasters in the history of the eastern Turk empire

This article analyzes the effect of climate extremes on the historical processes that took place (AD 536, 581, 601, 626 and 679) in the Eastern Turk Empire (AD 534–745) in Inner Asia. Climate extremes are sharp, strong and sometimes protracted periods of cooling and drought caused by volcanic eruptions that in this case resulted in a negative effect on the economy of a nomadic society and were often accompanied by famine and illness. In fact, many of these natural catastrophes coincided with the Black Death pandemics among the Eastern Turks and the Chinese living in the north of China. The Turk Empire can be split into several chronological periods during which significant events that led to changes in the course of history of the nomadic state took place: AD 534–545—the rise of the Turk Empire; AD 581–583—the division of the Turk Empire into theWestern and the Eastern Empires; AD 601–603—the rise of Qimin Qaghan; AD 627–630—the Eastern Turks are conquered by China; AD 679–687—the second rise of the Eastern Turk Empire. The research shows that there is clearly-discernable interplay between important historical events and climate extremes in the history of the Turk Empire. This interplay has led us to the conclusion that the climatic factor did have an impact on the historical processes that took place in the eastern part of Inner Asia, especially on the territories with a nomadic economy. © The Author(s) 2019

A perspective on SIDS pathogenesis. The hypotheses: plausibility and evidence

Several theories of the underlying mechanisms of Sudden Infant Death Syndrome (SIDS) have been proposed. These theories have born relatively narrow beach-head research programs attracting generous research funding sustained for many years at expense to the public purse. This perspective endeavors to critically examine the evidence and bases of these theories and determine their plausibility; and questions whether or not a safe and reasoned hypothesis lies at their foundation. The Opinion sets specific criteria by asking the following questions: 1. Does the hypothesis take into account the key pathological findings in SIDS? 2. Is the hypothesis congruent with the key epidemiological risk factors? 3. Does it link 1 and 2? Falling short of any one of these answers, by inference, would imply insufficient grounds for a sustainable hypothesis. Some of the hypotheses overlap, for instance, notional respiratory failure may encompass apnea, prone sleep position, and asphyxia which may be seen to be linked to co-sleeping. For the purposes of this paper, each element will be assessed on the above criteria

Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020-December 2021

This paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020-December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population

Recurrent SARS-CoV-2 mutations in immunodeficient patients

Long-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in immunodeficient patients are an important source of variation for the virus but are understudied. Many case studies have been published which describe one or a small number of long-term infected individuals but no study has combined these sequences into a cohesive dataset. This work aims to rectify this and study the genomics of this patient group through a combination of literature searches as well as identifying new case series directly from the COVID-19 Genomics UK (COG-UK) dataset. The spike gene receptor-binding domain and N-terminal domain (NTD) were identified as mutation hotspots. Numerous mutations associated with variants of concern were observed to emerge recurrently. Additionally a mutation in the envelope gene, T30I was determined to be the second most frequent recurrently occurring mutation arising in persistent infections. A high proportion of recurrent mutations in immunodeficient individuals are associated with ACE2 affinity, immune escape, or viral packaging optimisation.There is an apparent selective pressure for mutations that aid cell–cell transmission within the host or persistence which are often different from mutations that aid inter-host transmission, although the fact that multiple recurrent de novo mutations are considered defining for variants of concern strongly indicates that this potential source of novel variants should not be discounted

The SARS-CoV-2 Alpha variant was associated with increased clinical severity of COVID-19 in Scotland: A genomics-based retrospective cohort analysis

Objectives The SARS-CoV-2 Alpha variant was associated with increased transmission relative to other variants present at the time of its emergence and several studies have shown an association between Alpha variant infection and increased hospitalisation and 28-day mortality. However, none have addressed the impact on maximum severity of illness in the general population classified by the level of respiratory support required, or death. We aimed to do this. Methods In this retrospective multi-centre clinical cohort sub-study of the COG-UK consortium, 1475 samples from Scottish hospitalised and community cases collected between 1st November 2020 and 30th January 2021 were sequenced. We matched sequence data to clinical outcomes as the Alpha variant became dominant in Scotland and modelled the association between Alpha variant infection and severe disease using a 4-point scale of maximum severity by 28 days: 1. no respiratory support, 2. supplemental oxygen, 3. ventilation and 4. death. Results Our cumulative generalised linear mixed model analyses found evidence (cumulative odds ratio: 1.40, 95% CI: 1.02, 1.93) of a positive association between increased clinical severity and lineage (Alpha variant versus pre-Alpha variants). Conclusions The Alpha variant was associated with more severe clinical disease in the Scottish population than co-circulating lineages.

Tracking SARS-CoV-2 mutations and variants through the COG-UK-Mutation Explorer

COG-UK Mutation Explorer (COG-UK-ME, http://sars2.cvr.gla.ac.uk/cog-uk/-last accessed date 16 March 2022) is a web resource that displays knowledge and analyses on SARS-CoV-2 virus genome mutations and variants circulating in the UK, with a focus on the observed amino acid replacements that have an antigenic role in the context of the human humoral and cellular immune response. This analysis is based on more than 2 million genome sequences (as of March 2022) for UK SARS-CoV-2 data held in the CLIMB-COVID centralised data environment. COG-UK-ME curates these data and displays analyses that are cross-referenced to experimental data collated from the primary literature. The aim is to track mutations of immunological importance that are accumulating in current variants of concern and variants of interest that could alter the neutralising activity of monoclonal antibodies (mAbs), convalescent sera, and vaccines. Changes in epitopes recognised by T cells, including those where reduced T cell binding has been demonstrated, are reported. Mutations that have been shown to confer SARS-CoV-2 resistance to antiviral drugs are also included. Using visualisation tools, COG-UK-ME also allows users to identify the emergence of variants carrying mutations that could decrease the neutralising activity of both mAbs present in therapeutic cocktails, e.g. Ronapreve. COG-UK-ME tracks changes in the frequency of combinations of mutations and brings together the curated literature on the impact of those mutations on various functional aspects of the virus and therapeutics. Given the unpredictable nature of SARS-CoV-2 as exemplified by yet another variant of concern, Omicron, continued surveillance of SARS-CoV-2 remains imperative to monitor virus evolution linked to the efficacy of therapeutics

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC

Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020-December 2021

This paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020–December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population