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Glossary of scientific and technical terms in bioengineering and biological engineering
Published in Megh R. Goyal, Scientific and Technical Terms in Bioengineering and Biological Engineering, 2018
Ploidy is the number of complete sets of chromosomes per cell, e.g., one set: haploid, two sets: diploid, etc. Normally a gamete (sperm or egg) carries a full set of chromosomes that includes a single copy of each chromosome, as aneuploidy generally leads to severe genetic disease in the offspring. The haploid number (n) is the number of chromosomes in a gamete.
Effects of pyruvate decarboxylase (pdc1, pdc5) gene knockout on the production of metabolites in two haploid Saccharomyces cerevisiae strains
Published in Preparative Biochemistry & Biotechnology, 2022
Wen Zhang, Jie Kang, Changli Wang, Wenxiang Ping, Jingping Ge
Compared with diploid S. cerevisiae, haploid S. cerevisiae has many advantages. Haploids are more prone to single-nucleotide mutations and mitochondrial mutations, while diploids are more prone to structural mutations, which may be harmful.[5] Haploid cells have higher genetic stability, which is mainly determined by cell ploidy.[6] Haploids produce less ethanol than diploids;[7] haploids are 12% tolerant to ethanol, and diploids are 7% tolerant.[8] The tolerance of haploids is thought to be related to low levels of glycolysis intermediates and amino acids,[9,10] so haploids can adapt to the environment faster than diploids.[11] Especially in nutrient-poor environments, higher surface area and volume ratios are more conducive to survival for haploids.[12] In addition, because of their short cell cycle, easy culture and rapid gene deletion, S. cerevisiae haploids have been used in classical genetics, molecular biology, biochemistry and comparative genomics in recent years.[13]
Cytotoxic profile study, DNA and protein binding activity of a new dinuclear nickel(II) thiocyanato complex
Published in Journal of Coordination Chemistry, 2022
Niladri Biswas, Sandeepta Saha, Barun Kumar Biswas, Manas Chowdhury, Ashikur Rahaman, Deba Prasad Mandal, Shamee Bhattacharjee, Ennio Zangrando, Ruma Roy Choudhury, Chirantan Roy Choudhury
The cytotoxicity induction by 1 was visually evaluated using an inverted phase contrast microscope, which showed that the normal architecture of the cells was compromised by the presence of 1 in both A375 and MDA-MB-231 cell lines (Figure S14), with shrinkage of cells and condensation of cytoplasm. The cells appear round, bright colored in contrast to the live cells, and membrane blebs were seen visually. These observations were also fortified by cell cycle analysis, whose treatment with 1 caused cell cycle phase distribution changes with a high percentage of cells in hypo ploidy region as compared to control (Figure 9). This feature implies that the compound is able to induce cytotoxicity in these cancer cell lines.
Assays and enumeration of bioaerosols-traditional approaches to modern practices
Published in Aerosol Science and Technology, 2020
Maria D. King, Ronald E. Lacey, Hyoungmook Pak, Andrew Fearing, Gabriela Ramos, Tatiana Baig, Brooke Smith, Alexandra Koustova
Another software called the Quantitative Insights Into Microbial Ecology (QIIME) and QIIME2 can be used for sequencing 16S rRNA gene fragments and identifying their taxonomy classification (Lawley and Tannock 2016; Hall and Beiko 2018). However, it has been reported that amplicons from Illumina sequencing sometimes contain errors in quality filtering and constructing OTUs for clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. This problem can be overcome by using another open-source R package called DADA2, which contains an algorithm that can correct these errors enabling amplicon sequence variants (ASVs) to be resolved to the level of single-nucleotide differences over the sequenced gene region (Callahan et al. 2016). Recent debates focus on phasing out the traditional OTUs of marker gene sequences and instead delineating microbial taxa using exact sequence variants (ESVs) (Callahan, McMurdie, and Holmes 2017; Glassman and Martiny 2018). This approach avoids clustering sequences and instead uses only unique, identical 16S rRNA sequences for downstream community analyses that could differ by only one base pair. Another error-prone process is the conversion of genome abundances to cell numbers that requires taxa-specific information about the number of genome copies in the cell (Bonk et al. 2018). However, this is a useful way to normalize NGS data (Dannemiller et al. 2014). A number of additional tools are available to ‘de-noise’ and identify ESVs including Deblur, oligotyping, and UNOISE2 (Amir et al. 2017). Recent studies have found that archaea and bacteria can have less or more than 10 genome copies per cell, making it hard to correct for ploidy (Soppa 2014).