Biomedical subjects

Mikko Arvas

Publications and source records attributed to Mikko Arvas.

4 recordsLinked to original sources

PubMed2006

A highly sensitive and multiplexed method for focused transcript analysis.

We describe a novel, multiplexed method for focused transcript analysis of tens to hundreds of genes. In this method TRAC (transcript analysis with aid of affinity capture) mRNA targets, a set of amplifiable detection probes of distinct sizes and biotinylated oligo(dT) capture probe are hybridized in solution. The formed sandwich hybrids are collected on magnetic streptavidin-coated microparticles and washed. The hybridized probes are eluted, optionally amplified by a PCR using a universal primer pair and detected with laser-induced fluorescence and capillary electrophoresis. The probes were designed by using a computer program developed for the purpose. The TRAC method was adapted to 96-well format by utilizing an automated magnetic particle processor. Here we demonstrate a simultaneous analysis of 18 Saccharomyces cerevisiae transcripts from two experimental conditions and show a comparison with a qPCR system. The sensitivity of the method is significantly increased by the PCR amplification of the hybridized and eluted probes. Our data demonstrate a bias-free use of at least 16 cycles of PCR amplification to increase probe signal, allowing transcript analysis from 2.5 ng of the total mRNA sample. The method is fast and simple and avoids cDNA conversion. These qualifications make it a potential, new means for routine analysis and a complementing method for microarrays and high density chips.

Amino Acid Sequence↗

PubMed2006

Common features and interesting differences in transcriptional responses to secretion stress in the fungi Trichoderma reesei and Saccharomyces cerevisiae.

BACKGROUND: Secretion stress is caused by compromised folding, modification or transport of proteins in the secretory pathway. In fungi, induction of genes in response to secretion stress is mediated mainly by the unfolded protein response (UPR) pathway. This study aims at uncovering transcriptional responses occurring in the filamentous fungi Trichoderma reesei exposed to secretion stress and comparing these to those found in the yeast Saccharomyces cerevisiae. RESULTS: Chemostat cultures of T. reesei expressing human tissue plasminogen activator (tPA) and batch bioreactor cultures treated with dithiothreitol (DTT) to prevent correct protein folding were analysed with cDNA subtraction and cDNA-amplified fragment length polymorphism (AFLP) experiments. ESTs corresponding to 457 unique genes putatively induced under secretion stress were isolated and the expression pattern of 60 genes was confirmed by Northern analysis. Expression of these genes was also studied in a strain over-expressing inositol-requiring enzyme 1 (IREI) protein, a sensor for the UPR pathway. To compare the data with that of S. cerevisiae, published transcriptome profiling data on various stress responses in S. cerevisiae was reanalysed. The genes up-regulated in response to secretion stress included a large number of secretion related genes in both organisms. In addition, analysis of T. reesei revealed up regulation of the cpc1 transcription factor gene and nucleosomal genes. The induction of the cpcA and histone gene H4 were shown to be induced also in cultures of Aspergillus nidulans treated with DTT. CONCLUSION: Analysis of the genes induced under secretion stress has revealed novel features in the stress response in T. reesei and in filamentous fungi. We have demonstrated that in addition to the previously rather well characterised induction of genes for many ER proteins or secretion related proteins also other types of responses exist.

Amino Acids↗

PubMed2005

Optimization of cDNA-AFLP experiments using genomic sequence data.

MOTIVATION: cDNA amplified fragment length polymorphism (cDNA-AFLP) is one of the few genome-wide level expression profiling methods capable of finding genes that have not yet been cloned or even predicted from sequence but have interesting expression patterns under the studied conditions. In cDNA-AFLP, a complex cDNA mixture is divided into small subsets using restriction enzymes and selective PCR. A large cDNA-AFLP experiment can require a substantial amount of resources, such as hundreds of PCR amplifications and gel electrophoresis runs, followed by manual cutting of a large number of bands from the gels. Our aim was to test whether this workload can be reduced by rational design of the experiment. RESULTS: We used the available genomic sequence information to optimize cDNA-AFLP experiments beforehand so that as many transcripts as possible could be profiled with a given amount of resources. Optimization of the selection of both restriction enzymes and selective primers for cDNA-AFLP experiments has not been performed previously. The in silico tests performed suggest that substantial amounts of resources can be saved by the optimization of cDNA-AFLP experiments.

Algorithms↗

PubMed2002

Assigning probes into a small number of pools separable by electrophoresis.

MOTIVATION: Measuring transcriptional expression levels (transcriptional profiling) has become one of the most important methods in functional genomics. Still, new measuring methods are needed to obtain more reliable, quantitative data about transcription on a genomic scale. In this paper we concentrate on certain computational optimization problems arising in the design of one such novel method. From a computational point of view the key feature of the new method is that the hybridized probes are distinguished from each other based on their different size. Therefore the probes have to be assigned into pools such that the probes in the same pool have unique sizes different enough from each other. Identification of expressed RNA is given by probe pool and probe size while quantification is given by the label of the probe, e.g. fluorescence intensity. RESULTS: We show how to computationally find the probes and assign them into pools for a whole genome such that (i) each gene has a specific probe suitable for amplification and hybridization, and (ii) the expression level measurement can be done in a minimal number of pools separable by electrophoresis in order to minimize the total experiment cost of the measurement. Our main result is a polynomial-time approximation algorithm for assigning the probes into pools. We demonstrate the feasibility of the procedure by selecting probes for the yeast genome and assigning them into less than 100 pools. The probe sequences and their assignment into pools are available for academic research on request from the authors.

Algorithms↗