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Biomedical subjects

Roman Sásik

Publications and source records attributed to Roman Sásik.

2 recordsLinked to original sources

A computer-based, image-analysis method to quantify HIV-1 infection in a single-cycle infectious center assay.

Rapid and precise quantitation of the infectivity of HIV is important for molecular virologic studies as well as for measuring the activities of antiviral drugs and neutralizing antibodies. In the present study, an indicator cell line and image-analysis software were used to quantify HIV infectivity. Cells of the P4R5 line, which express the receptors for HIV infection as well as beta-galactosidase under the control of the HIV-1 long terminal repeat, were infected either with CXCR4- or CCR5-using viruses, including primary isolates, then stained 2 days later with X-gal to turn infected cells blue. Digital images of monolayers of the infected cells were captured using a high resolution CCD video camera and a macro video zoom lens. A software program was developed to process the images and to count the blue-stained foci of infection. The assay was applied to assess the infectivity of site-directed viral mutants, and to measure the activity of antiviral drugs and neutralizing antibody. The results indicate that the described method allows for the rapid quantitation of infected cells over a wide range of viral inocula with reproducibility, accuracy and relatively low cost.

Anti-HIV Agents↗

Comparative gene expression analysis of blood and brain provides concurrent validation of SELENBP1 up-regulation in schizophrenia.

Microarray techniques hold great promise for identifying risk factors for schizophrenia (SZ) but have not yet generated widely reproducible results due to methodological differences between studies and the high risk of type I inferential errors. Here we established a protocol for conservative analysis and interpretation of gene expression data from the dorsolateral prefrontal cortex of SZ patients using statistical and bioinformatic methods that limit false positives. We also compared brain gene expression profiles with those from peripheral blood cells of a separate sample of SZ patients to identify disease-associated genes that generalize across tissues and populations and further substantiate the use of gene expression profiling of blood for detecting valid SZ biomarkers. Implementing this systematic approach, we: (i) discovered 177 putative SZ risk genes in brain, 28 of which map to linked chromosomal loci; (ii) delineated six biological processes and 12 molecular functions that may be particularly disrupted in the illness; (iii) identified 123 putative SZ biomarkers in blood, 6 of which (BTG1, GSK3A, HLA-DRB1, HNRPA3, SELENBP1, and SFRS1) had corresponding differential expression in brain; (iv) verified the differential expression of the strongest candidate SZ biomarker (SELENBP1) in blood; and (v) demonstrated neuronal and glial expression of SELENBP1 protein in brain. The continued application of this approach in other brain regions and populations should facilitate the discovery of highly reliable and reproducible candidate risk genes and biomarkers for SZ. The identification of valid peripheral biomarkers for SZ may ultimately facilitate early identification, intervention, and prevention efforts as well.

Biomarkers↗