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      Genetic interaction network has a very limited impact on the evolutionary trajectories in continuous culture-grown populations of yeast

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          Abstract

          Background

          The impact of genetic interaction networks on evolution is a fundamental issue. Previous studies have demonstrated that the topology of the network is determined by the properties of the cellular machinery. Functionally related genes frequently interact with one another, and they establish modules, e.g., modules of protein complexes and biochemical pathways. In this study, we experimentally tested the hypothesis that compensatory evolutionary modifications, such as mutations and transcriptional changes, occur frequently in genes from perturbed modules of interacting genes.

          Results

          Using Saccharomyces cerevisiae haploid deletion mutants as a model, we investigated two modules lacking COG7 or NUP133, which are evolutionarily conserved genes with many interactions. We performed laboratory evolution experiments with these strains in two genetic backgrounds (with or without additional deletion of MSH2), subjecting them to continuous culture in a non-limiting minimal medium. Next, the evolved yeast populations were characterized through whole-genome sequencing and transcriptome analyses. No obvious compensatory changes resulting from inactivation of genes already included in modules were identified. The supposedly compensatory inactivation of genes in the evolved strains was only rarely observed to be in accordance with the established fitness effect of the genetic interaction network. In fact, a substantial majority of the gene inactivations were predicted to be neutral in the experimental conditions used to determine the interaction network. Similarly, transcriptome changes during continuous culture mostly signified adaptation to growth conditions rather than compensation of the absence of the COG7, NUP133 or MSH2 genes . However, we noticed that for genes whose inactivation was deleterious an upregulation of transcription was more common than downregulation.

          Conclusions

          Our findings demonstrate that the genetic interactions and the modular structure of the network described by others have very limited effects on the evolutionary trajectory following gene deletion of module elements in our experimental conditions and has no significant impact on short-term compensatory evolution. However, we observed likely compensatory evolution in functionally related (albeit non-interacting) genes.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12862-021-01830-9.

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          Most cited references86

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            fastp: an ultra-fast all-in-one FASTQ preprocessor

            Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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              A new mathematical model for relative quantification in real-time RT-PCR.

              M. Pfaffl (2001)
              Use of the real-time polymerase chain reaction (PCR) to amplify cDNA products reverse transcribed from mRNA is on the way to becoming a routine tool in molecular biology to study low abundance gene expression. Real-time PCR is easy to perform, provides the necessary accuracy and produces reliable as well as rapid quantification results. But accurate quantification of nucleic acids requires a reproducible methodology and an adequate mathematical model for data analysis. This study enters into the particular topics of the relative quantification in real-time RT-PCR of a target gene transcript in comparison to a reference gene transcript. Therefore, a new mathematical model is presented. The relative expression ratio is calculated only from the real-time PCR efficiencies and the crossing point deviation of an unknown sample versus a control. This model needs no calibration curve. Control levels were included in the model to standardise each reaction run with respect to RNA integrity, sample loading and inter-PCR variations. High accuracy and reproducibility (<2.5% variation) were reached in LightCycler PCR using the established mathematical model.
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                Author and article information

                Contributors
                szymon@ibb.waw.pl
                Journal
                BMC Ecol Evol
                BMC Ecol Evol
                BMC Ecology and Evolution
                BioMed Central (London )
                2730-7182
                26 May 2021
                26 May 2021
                2021
                : 21
                : 99
                Affiliations
                [1 ]GRID grid.418825.2, ISNI 0000 0001 2216 0871, Department of Microbial Biochemistry, , Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ; Pawińskiego 5a, 02-106, Warsaw, Poland
                [2 ]GRID grid.418825.2, ISNI 0000 0001 2216 0871, Department of Genetics, , Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ; Pawińskiego 5a, 02-106, Warsaw, Poland
                [3 ]GRID grid.418825.2, ISNI 0000 0001 2216 0871, Laboratory of Mutagenesis and DNA Repair, , Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ; Pawińskiego 5a, 02-106, Warsaw, Poland
                [4 ]GRID grid.418825.2, ISNI 0000 0001 2216 0871, Department of Bioinformatics, , Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ; Pawińskiego 5a, 02-106, Warsaw, Poland
                Author information
                http://orcid.org/0000-0002-2006-0073
                http://orcid.org/0000-0003-0615-7546
                http://orcid.org/0000-0001-9922-2276
                http://orcid.org/0000-0003-4059-2146
                http://orcid.org/0000-0002-6395-0746
                http://orcid.org/0000-0002-8086-3337
                Article
                1830
                10.1186/s12862-021-01830-9
                8157726
                34039270
                eac0c8fb-1b8e-4cfd-a70c-bebb52b70ea4
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 4 November 2020
                : 19 May 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004281, Narodowe Centrum Nauki;
                Award ID: 2018/29/N/NZ2/00902
                Award ID: 2014/13/B/NZ8/04719
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                compensatory evolution,experimental evolution,genetic interactions,yeast,genomics,transcriptomics

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