Comparison of droplet spread in standard and laminar flow operating theatres: SPRAY study group.

BACKGROUND: Reducing COVID-19 transmission relies on controlling droplet and aerosol spread. Fluorescein staining reveals microscopic droplets. AIM: To compare the droplet spread in non-laminar and laminar air flow operating theatres. METHODS: A 'cough-generator' was fixed to a theatre trolley at 45°. Fluorescein-stained 'secretions' were projected on to a series of calibrated targets. These were photographed under UV light and 'source detection' software measured droplet splatter size and distance. FINDINGS: The smallest droplet detected was ∼120 µm and the largest ∼24,000 µm. An average of 25,862 spots was detected in the non-laminar theatre, compared with 11,430 in the laminar theatre (56% reduction). The laminar air flow mainly affected the smaller droplets (<1000 µm). The surface area covered with droplets was: 6% at 50 cm, 1% at 2 m, and 0.5% at 3 m in the non-laminar air flow; and 3%, 0.5%, and 0.2% in the laminar air flow, respectively. CONCLUSION: Accurate mapping of droplet spread in clinical environments is possible using fluorescein staining and image analysis. The laminar air flow affected the smaller droplets but had limited effect on larger droplets in our 'aerosol-generating procedure' cough model. Our results indicate that the laminar air flow theatre requires similar post-surgery cleaning to the non-laminar, and staff should consider full personal protective equipment for medium- and high-risk patients.

Conversion of AFLP fragments tightly linked to SCMV resistance genes Scmv1 and Scmv2 into simple PCR-based markers.

In a previous study, bulked segregant analysis with amplified fragment length polymorphisms (AFLPs) identified several markers closely linked to the sugarcane mosaic virus resistance genes Scmv1 on chromosome 6 and Scmv2 on chromosome 3. Six AFLP markers (E33M61-2, E33M52, E38M51, E82M57, E84M59 and E93M53) were located on chromosome 3 and two markers (E33M61-1 and E35M62-1) on chromosome 6. Our objective in the present study was to sequence the respective AFLP bands in order to convert these dominant markers into more simple and reliable polymerase chain reaction (PCR)-based sequence-tagged site markers. Six AFLP markers resulted either in complete identical sequences between the six inbreds investigated in this study or revealed single nucleotide polymorphisms within the inbred lines and were, therefore, not converted. One dominant AFLP marker (E35M62-1) was converted into an insertion/deletion (indel) marker and a second AFLP marker (E33M61-2) into a cleaved amplified polymorphic sequence marker. Mapping of both converted PCR-based markers confirmed their localization to the same chromosome region (E33M61-2 on chromosome 3; E35M62-1 on chromosome 6) as the original AFLP markers. Thus, these markers will be useful for marker-assisted selection and facilitate map-based cloning of SCMV resistance genes.

Development of RGA-CAPS markers and genetic mapping of candidate genes for sugarcane mosaic virus resistance in maize.

Three previously published resistance gene analogues (RGAs), pic13, pic21 and pic19, were mapped in relation to sugarcane mosaic virus (SCMV) resistance genes ( Scmv1, Scmv2) in maize. We cloned these RGAs from six inbreds including three SCMV-resistant lines (D21, D32, FAP1360A) and three SCMV-susceptible lines (D145, D408, F7). Pairwise sequence alignments among the six inbreds revealed a frequency of one single nucleotide polymorphism (SNP) per 33 bp for the three RGAs, indicating a high degree of polymorphism and a high probability of success in converting RGAs into codominant cleaved amplified polymorphic sequence (CAPS) markers compared to other sequences. SNPs were used to develop CAPS markers for mapping of the three RGAs in relation to Scmv1 (chromosome 6) and Scmv2 (chromosome 3), and for pedigree analyses of resistant inbred lines. By genetic mapping pic21 was shown to be different from Scmv2, whereas pic19 and pic13 are still candidates for Scmv1 and Scmv2, respectively, due to genetic mapping and consistent restriction patterns of ancestral lines.

Gene expression profiling in skeletal muscle of Zucker diabetic fatty rats: implications for a role of stearoyl-CoA desaturase 1 in insulin resistance.

AIMS/HYPOTHESIS: Insulin resistance in skeletal muscle is a hallmark of type 2 diabetes. Therefore, we sought to identify and validate genes involved in the development of insulin resistance in skeletal muscle. MATERIALS: Differentially regulated genes in skeletal muscle of male obese insulin-resistant, and lean insulin-sensitive Zucker diabetic fatty (ZDF) rats were determined using Affymetrix microarrays. Based on these data, various aspects of glucose disposal, insulin signalling and fatty acid composition were analysed in a muscle cell line overexpressing stearoyl-CoA desaturase 1 (SCD1). RESULTS: Gene expression profiling in insulin-resistant skeletal muscle revealed the most pronounced changes in gene expression for genes involved in lipid metabolism. Among these, Scd1 showed increased expression in insulin-resistant animals, correlating with increased amounts of palmitoleoyl-CoA. This was further investigated in a muscle cell line that overexpressed SCD1 and accumulated lipids, revealing impairments of glucose uptake and of different steps of the insulin signalling cascade. We also observed differential effects of high-glucose and fatty acid treatment on glucose uptake and long-chain fatty acyl-CoA profiles, and in particular an accumulation of palmitoleoyl-CoA in cells overexpressing SCD1. CONCLUSIONS/INTERPRETATION: Insulin-resistant skeletal muscle of ZDF rats is characterised by a specific gene expression profile with increased levels of Scd1. An insulin-resistant phenotype similar to that obtained by treatment with palmitate and high glucose can be induced in vitro by overexpression of SCD1 in muscle cells. This supports the hypothesis that elevated SCD1 expression is a possible cause of insulin resistance and type 2 diabetes.

Duplicate marker loci can result in incorrect locus orders on linkage maps.

Genetic linkage maps, constructed from multi-locus recombination data, are the basis for many applications of molecular markers. For the successful employment of a linkage map, it is essential that the linear order of loci on a chromosome is correct. The objectives of this theoretical study were to (1) investigate the occurrence of incorrect locus orders caused by duplicate marker loci, (2) develop a statistical test for the detection of duplicate markers, and (3) discuss the implications for practical applications of linkage maps. We derived conditions, under which incorrect locus orders do or do not occur with duplicate marker loci for the general case of n markers on a chromosome in a BC(1) mapping population. We further illustrated these conditions numerically for the special case of four markers. On the basis of the extent of segregation distortion, an exact test for the presence of duplicate marker loci was suggested and its power was investigated numerically. Incorrect locus orders caused by duplicate marker loci can (1) negatively affect the assignment of target genes to chromosome regions in a map-based cloning experiment, (2) hinder indirect selection for a favorable allele at a quantitative trait locus, and (3) decrease the efficiency of reducing the length of the chromosome segment attached to a target gene in marker-assisted backcrossing.

Saturation of two chromosome regions conferring resistance to SCMV with SSR and AFLP markers by targeted BSA.

Quantitative trait loci (QTLs) and bulked segregant analyses (BSA) identified the major genes Scmv1 on chromosome 6 and Scmv2 on chromosome 3, conferring resistance against sugarcane mosaic virus (SCMV) in maize. Both chromosome regions were further enriched for SSR and AFLP markers by targeted bulked segregant analysis (tBSA) in order to identify and map only markers closely linked to either Scmv1 or Scmv2. For identification of markers closely linked to the target genes, symptomless individuals of advanced backcross generations BC5 to BC9 were employed. All AFLP markers, identified by tBSA using 400 EcoRI/ MseI primer combinations, mapped within both targeted marker intervals. Fourteen SSR and six AFLP markers mapped to the Scmv1 region. Eleven SSR and 18 AFLP markers were located in the Scmv2 region. Whereas the linear order of SSR markers and the window size for the Scmv2 region fitted well with publicly available genetic maps, map distances and window size differed substantially for the Scmv1 region on chromosome 6. A possible explanation for the observed discrepancies is the presence of two closely linked resistance genes in the Scmv1 region.

Identification of genetically linked RGAs by BAC screening in maize and implications for gene cloning, mapping and MAS.

The resistance gene analogue (RGA) pic19 in maize, a candidate for sugarcane mosaic virus (SCMV) resistance gene (R gene) Scmv1, was used to screen a maize BAC library to identify homologous sequences in the maize genome and to investigate their genomic organisation. Fifteen positive BAC clones were identified and could be classified into five physically independent contigs consisting of overlapping clones. Genetic mapping clustered three contigs into the same genomic region as Scmv1 on chromosome 6S. The two remaining contigs mapped to the same region as a QTL for SCMV resistance on chromosome 1. Thus, RGAs mapping to a target region can be successfully used to identify further-linked candidate sequences. The pic19 homologous sequences of these clones revealed a sequence similarity of 94-98% on the nucleotide level. The high sequence similarity reveals potential problems for the use of RGAs as molecular markers. Their application in marker-assisted selection (MAS) and the construction of high-density genetic maps is complicated by the existence of closely linked homologues resulting in 'ghost' marker loci analogous to 'ghost' QTLs. Therefore, implementation of genomic library screening, including genetic mapping of potential homologues, seems necessary for the safe application of RGA markers in MAS and gene isolation.

Permissive and suppressive effects of dexamethasone on enzyme induction in hepatocyte co-cultures.

1. Steroids are known to act as permissive factors in hepatocytes. This study shows that dexamethasone (DEX) is a permissive factor for induction of CYP2B1/2, CYP3A1, CYP2A1 and probably also CYP2C11 in cultures with primary rat hepatocytes. 2. The induction factor of phenobarbital (PB)-induced formation of 16beta-hydroxytestosterone (OHT), a testosterone biotransformation product predominantly formed by CYP2B1, is increased 18-fold by the addition of 32 nM DEX to the culture medium. Interestingly, higher concentrations of DEX up to 1000 nM led to a concentration-dependent maximally 5-fold decrease (p = 0.002) of phenobarbital-induced 16beta-OHT formation compared with the effect observed with 32 nM DEX. Thus, DEX shows permissive and suppressive effects on enzyme induction depending on the concentration of the glucocorticoid. 3. Qualitatively similar but smaller permissive and suppressive effects of DEX were observed for PB-induced CYP3A1 activity as evidenced by formation of 2beta-, 6beta- and 15beta-OHT. 4. DEX is a permissive factor for induction of CYP2A1 activity by 3-methylcholanthrene (3MC), as evidenced by the formation of 7alpha-OHT. Without addition of DEX, 3MC did not induce formation of 7alpha-OHT, whereas an almost 3-fold induction occurred in the presence of DEX. In contrast to CYP2B and CYP3A, concentrations up to 1000 nM DEX were not suppressive for the induction of CYP2A1. 5. We described recently a technique that allows preparation of cultures from cryopreserved hepatocytes. An almost identical influence of dexamethasone on enzyme induction was observed here in cultures from cryopreserved compared with freshly isolated hepatocytes. 6. Cultures with primary hepatocyte cultures represent a well-established technique for the study of drug-drug interactions. However, a large interlaboratory variation is known. Our study provides evidence that differences in glucocorticoid concentration in the culture medium contribute to this variation.