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Ardern, Z.

Publications and source records attributed to Ardern, Z..

10 recordsLinked to original sources

Deciphering the Proteome of Escherichia coli K-12: Integrating Transcriptomics and Machine Learning to Annotate Hypothetical Proteins

Omics technologies have led to the discovery of a vast number of proteins that are expressed but have no functional annotation - so called hypothetical proteins (HPs). Even in the best-studied model organism Escherichia coli K-12, over 2% of the proteome remains uncharacterized. This knowledge gap becomes even worse when looking at microbial dark matter. However, knowing the functions of proteins is crucial for elucidating cellular and metabolic processes and harnessing biotechnological potentials. Here, we employed machine learning to decipher the transcriptional regulatory network of E. coli K-12, as well as other in silico tools to assign functions to uncharacterized HPs. We further provide experimental validation of in silico predicted functions for three HP-encoding genes (yhdN, yeaC and ydgH) as proof of concept, by analyzing growth patterns of deletion mutants compared to the wild type, as well as their transcriptional responses to specific conditions. This study demonstrates that the use of Big Omics Data in combination with Artificial Intelligence and experimental controls is a powerful approach to illuminate functional dark matter. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/643886v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@9f5035org.highwire.dtl.DTLVardef@14ad641org.highwire.dtl.DTLVardef@385573org.highwire.dtl.DTLVardef@722780_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Why is the average collateral effect of synonymous mutations so similar across alternative reading frames?

The standard genetic code has been shown to have multiple interesting properties which impact on molecular biology and the evolutionary process. One facet of molecular biology where code structure is particularly important is the origin and evolution of overlapping genes. We have previously reported that the structure of the standard genetic code ensures that synonymous mutations in a protein coding gene will lead to a remarkably similar average "collateral" mutation effect size in at least four out of the five alternative reading frames. Here we show that only 0.26% of alternative codes with the block structure of the standard genetic code perform at least as well as the standard code in this property. Considering this finding within a code optimality framework suggests that this consistent effect size across the different frames may be adaptive. Here we give context for this finding and present a simple model where a trade-off between evolvability and robustness leads to an average mutation effect size which maximises population fitness. This supports the intuition that similar mutation effects across the different alternative reading frames may be an adaptive property of the standard genetic code which facilitates evolvability through the use of alternative reading frames.

evolutionary biology↗

Shadow ORFs illuminated: long overlapping genes in Pseudomonas aeruginosa are translated and under purifying selection

The existence of overlapping genes (OLGs) with significant coding overlaps revolutionises our understanding of genomic complexity. We report two exceptionally long (957 nt and 1536 nt), evolutionarily novel, translated antisense open reading frames (ORFs) embedded within annotated genes in the medically important Gram-negative bacterium Pseudomonas aeruginosa. Both OLG pairs show sequence features consistent with being genes and transcriptional signals in RNA sequencing data. Translation of both OLGs was confirmed by ribosome profiling and mass spectrometry. Quantitative proteomics of samples taken during different phases of growth revealed regulation of protein abundances, implying biological functionality. Both OLGs are taxonomically highly restricted, and likely arose by overprinting within the genus. Evidence for purifying selection further supports functionality. The OLGs reported here are the longest yet proposed in prokaryotes and are among the best attested in terms of translation and evolutionary constraint. These results highlight a potentially large unexplored dimension of prokaryotic genomes.

evolutionary biology↗

Evidence for Numerous Embedded Antisense Overlapping Genes in Diverse E. coli Strains

The genetic code allows six reading frames at a double-stranded DNA locus, and many open reading frames (ORFs) overlap extensively with ORFs of annotated genes (e.g., at least 30 bp or having an embedded ORF). Currently, bacterial genome annotation systematically discards embedded overlapping ORFs of genes (OLGs) due to an assumed information-content constraint, and, consequently, very few OLGs are known. Here we use strand-specific RNAseq and ribosome profiling, detecting about 200 embedded or partially overlapping ORFs of gene candidates in the pathogen E. coli O157:H7 EDL933. These are typically short, many of them show clear promoter motifs as determined by Cappable-seq, indistinguishable from those of annotated genes, and are expressed at a low level. We could express most of them as stable proteins, and 49 displayed a potential phenotype. Ribosome profiling analyses in three other E. coli strains predicted between 84 and 190 embedded antisense OLGs per strain except in E. coli K-12, which is an atypical lab strain. We also found evidence of homology to annotated genes for 100 to 300 OLGs per E. coli strain investigated. Based on this evidence we suggest that bacterial OLGs deserve attention with respect to genome annotation and coding complexity of bacterial genomes. Such sequences may constitute an important coding reserve, opening up new research in genetics and evolutionary biology.

microbiology↗

Computational design of genes encoding completely overlapping protein domains: Influence of genetic code and taxonomic rank

Overlapping genes (OLGs) with long protein-coding overlapping sequences are often excluded by genome annotation programs, with the exception of virus genomes. A recent study used a novel algorithm to construct OLGs from arbitrary protein domain pairs and concluded that virus genes are best suited for creating OLGs, a result which fitted with common assumptions. However, improving sequence evaluation using Hidden Markov Models shows that the previous result is an artifact originating from dataset-database biases. When parameters for OLG design and evaluation are optimized we find that 94.5% of the constructed OLG pairs score at least as highly as naturally occurring sequences, while 9.6% of the artificial OLGs cannot be distinguished from typical sequences in their protein family. Constructed OLG sequences are also indistinguishable from natural sequences in terms of amino acid identity and secondary structure, while the minimum nucleotide change required for overprinting an overlapping sequence can be as low as 1.8% of the sequence. Separate analysis of datasets containing only sequences from either archaea, bacteria, eukaryotes or viruses showed that, surprisingly, virus genes are much less suitable for designing OLGs than bacterial or eukaryotic genes. An important factor influencing OLG design is the structure of the standard genetic code. Success rates in different reading frames strongly correlate with their code-determined respective amino acid constraints. There is a tendency indicating that the structure of the standard genetic code could be optimized in its ability to create OLGs while conserving mutational robustness. The findings reported here add to the growing evidence that OLGs should no longer be excluded in prokaryotic genome annotations. Determining the factors facilitating the computational design of artificial overlapping genes may improve our understanding of the origin of these remarkable genetic constructs and may also open up exciting possibilities for synthetic biology.

bioinformatics↗

A previously uncharacterized gene in SARS-CoV-2 illuminates the functional dynamics and evolutionary origins of the COVID-19 pandemic

Understanding the emergence of novel viruses requires an accurate and comprehensive annotation of their genomes. Overlapping genes (OLGs) are common in viruses and have been associated with pandemics, but are still widely overlooked. We identify and characterize ORF3d, a novel OLG in SARS-CoV-2 that is also present in Guangxi pangolin-CoVs but not other closely related pangolin-CoVs or bat-CoVs. We then document evidence of ORF3d translation, characterize its protein sequence, and conduct an evolutionary analysis at three levels: between taxa (21 members of Severe acute respiratory syndrome-related coronavirus), between human hosts (3978 SARS-CoV-2 consensus sequences), and within human hosts (401 deeply sequenced SARS-CoV-2 samples). ORF3d has been independently identified and shown to elicit a strong antibody response in COVID-19 patients. However, it has been misclassified as the unrelated gene ORF3b, leading to confusion. Our results liken ORF3d to other accessory genes in emerging viruses and highlight the importance of OLGs.

evolutionary biology↗

Are Antisense Proteins in Prokaryotes Functional?

Many prokaryotic RNAs are transcribed from loci outside of annotated protein coding genes. Across bacterial species hundreds of short open reading frames antisense to annotated genes show evidence of both transcription and translation, for instance in ribosome profiling data. Determining the functional fraction of these protein products awaits further research, including insights from studies of molecular interactions and detailed evolutionary analysis. There are multiple lines of evidence however that many of these newly discovered proteins are of use to the organism. Condition-specific phenotypes have been characterised for a few. These proteins should be added to genome annotations, and the methods for predicting them standardised. Evolutionary analysis of these typically young sequences also may provide important insights into gene evolution. This research should be prioritised for its exciting potential to uncover large numbers of novel proteins with extremely diverse potential practical uses, including applications in synthetic biology and responding to pathogens.

microbiology↗

OLGenie: Estimating Natural Selection to Predict Functional Overlapping Genes

Purifying (negative) natural selection is a hallmark of functional biological sequences, and can be detected in protein-coding genes using the ratio of nonsynonymous to synonymous substitutions per site (dN/dS). However, when two genes overlap the same nucleotide sites in different frames, synonymous changes in one gene may be nonsynonymous in the other, perturbing dN/dS. Thus, scalable methods are needed to estimate functional constraint specifically for overlapping genes (OLGs). We propose OLGenie, which implements a modification of the Wei-Zhang method. Assessment with simulations and controls from viral genomes (58 OLGs and 176 non-OLGs) demonstrates low false positive rates and good discriminatory ability in differentiating true OLGs from non-OLGs. We also apply OLGenie to the unresolved case of HIV-1s putative antisense protein gene, showing significant purifying selection. OLGenie can be used to study known OLGs and to predict new OLGs in genome annotation. Software and example data are freely available at https://github.com/chasewnelson/OLGenie.

evolutionary biology↗

Improving Bacterial Ribosome Profiling Data Quality

Ribosome profiling (RIBO-seq) in prokaryotes has the potential to facilitate accurate detection of translation initiation sites, to increase understanding of translational dynamics, and has already allowed detection of many unannotated genes. However, protocols for ribosome profiling and corresponding data analysis are not yet standardized. To better understand the influencing factors, we analysed 48 ribosome profiling samples from 9 studies on E. coli K12 grown in LB medium. We particularly investigated the size selection step in each experiment since the selection for ribosome-protected footprints (RPFs) has been performed at various read lengths. We suggest choosing a size range between 22-30 nucleotides in order to obtain protein-coding fragments. In order to use RIBO-seq data for improving gene annotation of weakly expressed genes, the total amount of reads mapping to protein-coding sequences and not rRNA or tRNA is important, but no consensus about the appropriate sequencing depth has been reached. Again, this causes significant variation between studies. Our analysis suggests that 20 million non rRNA/tRNA mapping reads are required for global detection of translated annotated genes. Further, we highlight the influence of drug induced ribosome stalling, causing bias at translation start sites. Drug induced stalling may be especially useful for detecting weakly expressed genes. These suggestions should improve both gene detection and the comparability of resulting ribosome profiling datasets.

molecular biology↗

A novel pH-regulated, unusual 603 bp overlapping protein coding gene pop is encoded antisense to ompA in Escherichia coli O157:H7 (EHEC)

Antisense transcription is well known in bacteria. However, translation of antisense RNAs is typically not considered, as the implied overlapping coding at a DNA locus is assumed to be highly improbable. Therefore, such overlapping genes are systematically excluded in prokaryotic genome annotation. Here we report an exceptional 603 bp long open reading frame completely embedded in antisense to the gene of the outer membrane protein ompA. Ribosomal profiling revealed translation of the mRNA and the protein was detected in Western blots. A {sigma}70 promoter, transcription start site, Shine-Dalgarno motif and rho-independent terminator were experimentally validated. A pH-dependent phenotype conferred by the protein was shown in competitive overexpression growth experiments of a translationally arrested mutant versus wild type. We designate this novel gene pop (pH-regulated overlapping protein-coding gene). Increasing evidence based on ribosome-profiling indicates translation of antisense RNA, suggesting that more overlapping genes of unknown function may exist in bacteria.

microbiology↗