The stealth episome: suppression of gene expression on the excised genomic island PPHGI-1 from Pseudomonas syringae pv. phaseolicola

Pseudomonas syringae pv. phaseolicola is the causative agent of halo blight in the common bean, Phaseolus vulgaris. P. syringae pv. phaseolicola race 4 strain 1302A contains the avirulence gene avrPphB (syn. hopAR1), which resides on PPHGI-1, a 106 kb genomic island. Loss of PPHGI-1 from P. syringae pv. phaseolicola 1302A following exposure to the hypersensitive resistance response (HR) leads to the evolution of strains with altered virulence. Here we have used fluorescent protein reporter systems to gain insight into the mobility of PPHGI-1. Confocal imaging of dual-labelled P. syringae pv. phaseolicola 1302A strain, F532 (dsRFP in chromosome and eGFP in PPHGI-1), revealed loss of PPHGI-1::eGFP encoded fluorescence during plant infection and when grown in vitro on extracted leaf apoplastic fluids. Fluorescence-activated cell sorting (FACS) of fluorescent and non-fluorescent PPHGI-1::eGFP F532 populations showed that cells lost fluorescence not only when the GI was deleted, but also when it had excised and was present as a circular episome. In addition to reduced expression of eGFP, quantitative PCR on sub-populations separated by FACS showed that transcription of other genes on PPHGI-1 (avrPphB and xerC) was also greatly reduced in F532 cells harbouring the excised PPHGI-1::eGFP episome. Our results show how virulence determinants located on mobile pathogenicity islands may be hidden from detection by host surveillance systems through the suppression of gene expression in the episomal state

Factors influencing preoperative stress response in coronary artery bypass graft patients

BACKGROUND: In many studies investigating measures to attenuate the hemodynamic and humoral stress response during induction of anaesthesia, primary attention was paid to the period of endotracheal intubation since it has been shown that even short-lasting sympathetic cardiovascular stimulation may have detrimental effects on patients with coronary artery disease. The aim of this analysis was, however, to identify the influencing factors on high catecholamine levels before induction of anaesthesia. METHODS: Various potential risk factors that could impact the humoral stress response before induction of anaesthesia were recorded in 84 males undergoing coronary aortic bypass surgery, and were entered into a stepwise linear regression analysis. The plasma level of norepinephrine measured immediately after radial artery canulation was chosen as a surrogate marker for the humoral stress response, and it was used as the dependent variable in the regression model. Accordingly, the mean arterial blood pressure, heart rate and the calculated pressure-rate product were taken as parameters of the hemodynamic situation. RESULTS: Stepwise regression analysis revealed that the oral administration of low-dose clonidine (mean dose 1.75 μg·kg(-1)) on the morning of surgery was the only significant predictor (p = 0.004) of the high variation in preoperative norepinephrine plasma levels. This intervention decreased norepinephrine levels by more than 40% compared to no clonidine administration, from 1.26 to 0.75 nmol·l(-1). There was no evidence for dose-responsiveness of clonidine. All other potential predictors were removed from the model as insignificant (p > 0.05). The use of beta-blocker, ace-inhibitors, ejection fraction, and body mass index were significant determinants for the hemodynamic situation (heart rate, mean arterial pressure, pressure rate product) of the patient during the pre-induction period. CONCLUSION: The oral administration of clonidine is the only significant predictor for the observed variation of norepinephrine levels during the preoperative period. Lack of significant dose responsiveness suggests that even a low dose of the drug can attenuate the preoperative stress response and thus is recommended in cardiovascular high risk patients

Analysis of Microsatellite Polymorphism in Inbred Knockout Mice

Previously, we found that the genotype of 42 out of 198 mouse microsatellite loci, which are distributed among all chromosomes except the Y chromosome, changed from monomorphism to polymorphism (CMP) in a genetically modified inbred mouse strain. In this study, we further examined whether CMP also relates to the homologous recombination in gene knockout (KO) mouse strains. The same 42 microsatellite loci were analyzed by polymerase chain reaction (PCR) in 29 KO inbred mouse strains via short tandem sequence repeat (STR) scanning and direct sequence cloning to justify microsatellite polymorphisms. The C57BL/6J and 129 mouse strains, from which these 29 KO mice were derived, were chosen as the background controls. The results indicated that 10 out of 42 (23.8%) loci showed CMP in some of these mouse strains. Except for the trinucleotide repeat locus of D3Mit22, which had microsatellite CMP in strain number 9, the core sequences of the remaining 41 loci were dinucleotide repeats, and 9 out of 41 (21.95%) showed CMPs among detected mouse strains. However, 11 out of 29 (37.9%) KO mice strains were recognized as having CMPs. The popular dinucleotide motifs in CMP were (TG)n (50%, 2/4), followed by (GT)n (27.27%, 3/11) and (CA)n (23.08%, 3/13). The microsatellite CMP in (CT)n and (AG)n repeats were 20% (1/5). According to cloning sequencing results, 6 KO mouse strains showed insertions of nucleotides whereas 1 showed a deletion. Furthermore, 2 loci (D13Mit3 and D14Mit102) revealed CMP in 2 strains, and mouse strain number 9 showed CMPs in two loci (D3Mit22 and D13Mit3) simultaneously. Collectively, these results indicated that microsatellite polymorphisms were present in the examined inbred KO mice

Phenotypic Characterization of a Genetically Diverse Panel of Mice for Behavioral Despair and Anxiety

Animal models of human behavioral endophenotypes, such as the Tail Suspension Test (TST) and the Open Field assay (OF), have proven to be essential tools in revealing the genetics and mechanisms of psychiatric diseases. As in the human disorders they model, the measurements generated in these behavioral assays are significantly impacted by the genetic background of the animals tested. In order to better understand the strain-dependent phenotypic variability endemic to this type of work, and better inform future studies that rely on the data generated by these models, we phenotyped 33 inbred mouse strains for immobility in the TST, a mouse model of behavioral despair, and for activity in the OF, a model of general anxiety and locomotor activity.We identified significant strain-dependent differences in TST immobility, and in thigmotaxis and distance traveled in the OF. These results were replicable over multiple testing sessions and exhibited high heritability. We exploited the heritability of these behavioral traits by using in silico haplotype-based association mapping to identify candidate genes for regulating TST behavior. Two significant loci (-logp >7.0, gFWER adjusted p value <0.05) of approximately 300 kb each on MMU9 and MMU10 were identified. The MMU10 locus is syntenic to a major human depressive disorder QTL on human chromosome 12 and contains several genes that are expressed in brain regions associated with behavioral despair.We report the results of phenotyping a large panel of inbred mouse strains for depression and anxiety-associated behaviors. These results show significant, heritable strain-specific differences in behavior, and should prove to be a valuable resource for the behavioral and genetics communities. Additionally, we used haplotype mapping to identify several loci that may contain genes that regulate behavioral despair

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

Genomic reconstruction of the SARS-CoV-2 epidemic in England.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021

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