Model-driven design of synthetic N-terminal coding sequences for fine-tuning gene expression in yeast and bacteria
Ontology highlight
ABSTRACT:
ORGANISM(S): Bacillus subtilis Saccharomyces cerevisiae
PROVIDER: GSE186378 | GEO | 2023/10/31
REPOSITORIES: GEO
Similar Datasets
Project description:In this paper, we present a new algorithm for alignment and haplogroup estimation of mitochondrial DNA (mtDNA) sequences. Based on 26,011 vetted full mitogenome sequences, we refined the 5435 original haplogroup motifs of Phylotree Build 17 without changing the haplogroup nomenclature. We adapted 430 motifs (about 8%) and added 966 motifs for yet undetermined subclades. In summary, this led to an 18% increase of haplogroup defining motifs for full mitogenomes and a 30% increase for the mtDNA control region that is of interest for a variety of scientific disciplines, such as medical, population and forensic genetics. The new algorithm is implemented in the EMPOP mtDNA database and is freely accessible.
| S-EPMC8198973 | biostudies-literature
Project description:Dendritic cells (DCs) are the sentinels of the mammalian immune system and they undergo a complex maturation process mediated by activation upon pathogen detection. Recent studies described the analysis of activated DCs by transcriptional profiling, but translation regulation was never taken in account. Therefore, the nature of the mRNAs being translated at various stages of DC activation was determined with the help of translational profiling, which is the sucrose gradient fractionation of polysomal-bound mRNAs combined to microarrays analysis. Total and polysomal-bound mRNA populations were compared in immature (0h) and LPS-stimulated (4h and 16h) human monocyte-derived DCs with the help of Affymetrix microarrays. Biostatistical analysis indicated that 296 mRNA molecules are translationally regulated during DC-activation. The most abundant biological process among the regulated mRNAs was protein biosynthesis, indicating the existence of a negative feedback loop regulating translation. Interestingly, a cluster of 17 ribosomal proteins were part of the regulated mRNAs, indicating that translation may be fine-tuned by particular components of the translational machinery. Our observations highlight the importance of translation regulation during the immune response, and may favour the identification of novel gene clusters or protein networks relevant for immunity. Our study also provides information on the possible absence of correlation between gene expression and real protein production in DCs. To identify translationally regulated mRNA molecules, gene expression derived from the polysome-bound mRNAs was compared by Affymetrix microarrays analysis to the gene expression derived from unfractionated total mRNAs derived from whole-cell lysates, as recently described on several reports (Johannes 1999, Rajasekhar 2003, Bushell 2006, Lü 2006, Parent 2008). Polysomal RNA (P) and total RNA (T) were isolated from MoDCs generated from four different blood donors. Since three timepoints (0h, 4h and 16h) were chosen for each blood donor and RNA type, twenty-four RNA samples were totally analyzed by microarrays.
2009-12-30 | E-GEOD-14000 | biostudies-arrayexpress
Project description:Dendritic cells (DCs) are the sentinels of the mammalian immune system and they undergo a complex maturation process mediated by activation upon pathogen detection. Recent studies described the analysis of activated DCs by transcriptional profiling, but translation regulation was never taken in account. Therefore, the nature of the mRNAs being translated at various stages of DC activation was determined with the help of translational profiling, which is the sucrose gradient fractionation of polysomal-bound mRNAs combined to microarrays analysis. Total and polysomal-bound mRNA populations were compared in immature (0h) and LPS-stimulated (4h and 16h) human monocyte-derived DCs with the help of Affymetrix microarrays. Biostatistical analysis indicated that 296 mRNA molecules are translationally regulated during DC-activation. The most abundant biological process among the regulated mRNAs was protein biosynthesis, indicating the existence of a negative feedback loop regulating translation. Interestingly, a cluster of 17 ribosomal proteins were part of the regulated mRNAs, indicating that translation may be fine-tuned by particular components of the translational machinery. Our observations highlight the importance of translation regulation during the immune response, and may favour the identification of novel gene clusters or protein networks relevant for immunity. Our study also provides information on the possible absence of correlation between gene expression and real protein production in DCs.
2009-12-31 | GSE14000 | GEO
Project description:Transfection is the process by which nucleic acids are introduced into eukaryotic cells. This is fundamental in basic research for studying gene function and modulation of gene expression as well as for many bioprocesses in the manufacturing of clinical-grade recombinant biologics from cells. Transfection efficiency is a critical parameter to increase biologics' productivity; the right protocol has to be identified to ensure high transfection efficiency and therefore high product yield. Design of experiments (DoE) is a mathematical method that has become a key tool in bioprocess development. Based on the DoE method, we developed an operational flow that we called "Design of Transfections" (DoT) for specific transfection modeling and identification of the optimal transfection conditions. As a proof of principle, we applied the DoT workflow to optimize a cell transfection chemical protocol for neural progenitors, using polyethyleneimine (PEI). We simultaneously varied key influencing factors, namely concentration and type of PEI, DNA concentration, and cell density. The transfection efficiency was measured by fluorescence imaging followed by automatic counting of the green fluorescent transfected cells. Taking advantage of the DoT workflow, we developed a new simple, efficient, and economically advantageous PEI transfection protocol through which we were able to obtain a transfection efficiency of 34%.
| S-EPMC9291525 | biostudies-literature
Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits.
[Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018.
[Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022
[Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023
2024-02-05 | MSV000094014 | MassIVE
Project description:In most eukaryotes, the wobble position of tRNA with a GUN anticodon is queuosine-modified (Q34). Q is synthesized exclusively by eubacteria and salvaged by eukaryotes as a nutrient to replace G34 in tRNAs. Q34 modification stimulates Dnmt2/Pmt1-dependent C38 methylation in the tRNAAsp anticodon loop in Schizosaccharomyces pombe. Due to the location of both modification in the anticodon loop, we anticipated an influence on translation. Our experimental setup allowed for the analysis of effects from each modification alone or a combination of both.
2019-02-13 | GSE102376 | GEO
Project description:Epithelial-mesenchymal transition (EMT) is driven by transcriptional and epigenetic reprogramming. However, it is now well established that the final step of gene expression, translation, is also key to the establishment of physiopathological phenotypes, and one of its regulators is the ribosomes themselves. Using genome-wide analyses, we report on how the translational landscape evolves and how the translational machinery rearranges itself during EMT. Ribosome profiling revealed that translation was as profoundly modulated as transcription during EMT. In addition, riboproteomics revealed an increased proportion of RPL36A-containing ribosomal particles in mesenchymal cells, indicating that ribosome composition is finely tuned during EMT. Finally, RPL36A, but not RPL19, is sufficient to induce the acquisition of mesenchymal features. Taken together, these data demonstrate that EMT is associated with both a profound translational reprogramming and changes in ribosome composition, and that a single change in ribosomal protein is sufficient to drive EMT.
2024-11-02 | GSE246352 | GEO
Project description:The synergism between c-MYC and miR-17-19b, a truncated version of the miR-17-92 cluster, is well documented during tumor initiation. However, little is known about miR-17-19b function in established cancers. Here we investigate the role of miR-17-19b in c-MYC-driven lymphomas by integrating SILAC-based quantitative proteomics, transcriptomics and 3’ UTR analysis upon miR-17-19b overexpression. We identify over one hundred novel miR-17-19b targets, of which 40% are co-regulated by c-MYC. Down-regulation of a new miR-17/20 target Chek2 increases the recruitment of HuR to c-MYC transcripts, resulting in the inhibition of c-MYC translation and thus interfering with in vivo tumor growth. Hence, in established lymphomas, miR-17-19b fine-tunes c-MYC activity through a tight control of its function and expression, ultimately ensuring cancer cell homeostasis. Our data highlight the plasticity of miRNA function, reflecting changes in the mRNA landscape and 3’ UTR shortening at different stages of tumorigenesis.
2015-11-06 | PXD002810 | Pride
Project description:BackgroundDecoding human genomic sequences requires comprehensive analysis of DNA sequence functionality. Through computational and experimental approaches, researchers have studied the genotype-phenotype relationship and generate important datasets that help unravel complicated genetic blueprints. Thus, the recently developed artificial intelligence methods can be used to interpret the functions of those DNA sequences.MethodsThis study explores the use of deep learning, particularly pre-trained genomic models like DNA_bert_6 and human_gpt2-v1, in interpreting and representing human genome sequences. Initially, we meticulously constructed multiple datasets linking genotypes and phenotypes to fine-tune those models for precise DNA sequence classification. Additionally, we evaluate the influence of sequence length on classification results and analyze the impact of feature extraction in the hidden layers of our model using the HERV dataset. To enhance our understanding of phenotype-specific patterns recognized by the model, we perform enrichment, pathogenicity and conservation analyzes of specific motifs in the human endogenous retrovirus (HERV) sequence with high average local representation weight (ALRW) scores.ResultsWe have constructed multiple genotype-phenotype datasets displaying commendable classification performance in comparison with random genomic sequences, particularly in the HERV dataset, which achieved binary and multi-classification accuracies and F1 values exceeding 0.935 and 0.888, respectively. Notably, the fine-tuning of the HERV dataset not only improved our ability to identify and distinguish diverse information types within DNA sequences but also successfully identified specific motifs associated with neurological disorders and cancers in regions with high ALRW scores. Subsequent analysis of these motifs shed light on the adaptive responses of species to environmental pressures and their co-evolution with pathogens.ConclusionsThese findings highlight the potential of pre-trained genomic models in learning DNA sequence representations, particularly when utilizing the HERV dataset, and provide valuable insights for future research endeavors. This study represents an innovative strategy that combines pre-trained genomic model representations with classical methods for analyzing the functionality of genome sequences, thereby promoting cross-fertilization between genomics and artificial intelligence.
| S-EPMC11318145 | biostudies-literature
Project description:Redox potentials are a major contributor in controlling the electron transfer (ET) rates and thus regulating the ET processes in the bioenergetics. To maximize the efficiency of the ET process, one needs to master the art of tuning the redox potential, especially in metalloproteins, as they represent major classes of ET proteins. In this review, we first describe the importance of tuning the redox potential of ET centers and its role in regulating the ET in bioenergetic processes including photosynthesis and respiration. The main focus of this review is to summarize recent work in designing the ET centers, namely cupredoxins, cytochromes, and iron-sulfur proteins, and examples in design of protein networks involved these ET centers. We then discuss the factors that affect redox potentials of these ET centers including metal ion, the ligands to metal center and interactions beyond the primary ligand, especially non-covalent secondary coordination sphere interactions. We provide examples of strategies to fine-tune the redox potential using both natural and unnatural amino acids and native and nonnative cofactors. Several case studies are used to illustrate recent successes in this area. Outlooks for future endeavors are also provided. This article is part of a Special Issue entitled Biodesign for Bioenergetics--the design and engineering of electronic transfer cofactors, proteins and protein networks, edited by Ronald L. Koder and J.L. Ross Anderson.
| S-EPMC4761536 | biostudies-literature