1
|
Carter CW. Base Pairing Promoted the Self-Organization of Genetic Coding, Catalysis, and Free-Energy Transduction. Life (Basel) 2024; 14:199. [PMID: 38398709 PMCID: PMC10890426 DOI: 10.3390/life14020199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
How Nature discovered genetic coding is a largely ignored question, yet the answer is key to explaining the transition from biochemical building blocks to life. Other, related puzzles also fall inside the aegis enclosing the codes themselves. The peptide bond is unstable with respect to hydrolysis. So, it requires some form of chemical free energy to drive it. Amino acid activation and acyl transfer are also slow and must be catalyzed. All living things must thus also convert free energy and synchronize cellular chemistry. Most importantly, functional proteins occupy only small, isolated regions of sequence space. Nature evolved heritable symbolic data processing to seek out and use those sequences. That system has three parts: a memory of how amino acids behave in solution and inside proteins, a set of code keys to access that memory, and a scoring function. The code keys themselves are the genes for cognate pairs of tRNA and aminoacyl-tRNA synthetases, AARSs. The scoring function is the enzymatic specificity constant, kcat/kM, which measures both catalysis and specificity. The work described here deepens the evidence for and understanding of an unexpected consequence of ancestral bidirectional coding. Secondary structures occur in approximately the same places within antiparallel alignments of their gene products. However, the polar amino acids that define the molecular surface of one are reflected into core-defining non-polar side chains on the other. Proteins translated from base-paired coding strands fold up inside out. Bidirectional genes thus project an inverted structural duality into the proteome. I review how experimental data root the scoring functions responsible for the origins of coding and catalyzed activation of unfavorable chemical reactions in that duality.
Collapse
Affiliation(s)
- Charles W Carter
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7260, USA
| |
Collapse
|
2
|
Ardern Z. Alternative Reading Frames are an Underappreciated Source of Protein Sequence Novelty. J Mol Evol 2023; 91:570-580. [PMID: 37326679 DOI: 10.1007/s00239-023-10122-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023]
Abstract
Protein-coding DNA sequences can be translated into completely different amino acid sequences if the nucleotide triplets used are shifted by a non-triplet amount on the same DNA strand or by translating codons from the opposite strand. Such "alternative reading frames" of protein-coding genes are a major contributor to the evolution of novel protein products. Recent studies demonstrating this include examples across the three domains of cellular life and in viruses. These sequences increase the number of trials potentially available for the evolutionary invention of new genes and also have unusual properties which may facilitate gene origin. There is evidence that the structure of the standard genetic code contributes to the features and gene-likeness of some alternative frame sequences. These findings have important implications across diverse areas of molecular biology, including for genome annotation, structural biology, and evolutionary genomics.
Collapse
|
3
|
Singer-Berk M, Gudmundsson S, Baxter S, Seaby EG, England E, Wood JC, Son RG, Watts NA, Karczewski KJ, Harrison SM, MacArthur DG, Rehm HL, O'Donnell-Luria A. Advanced variant classification framework reduces the false positive rate of predicted loss-of-function variants in population sequencing data. Am J Hum Genet 2023; 110:1496-1508. [PMID: 37633279 PMCID: PMC10502856 DOI: 10.1016/j.ajhg.2023.08.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 08/28/2023] Open
Abstract
Predicted loss of function (pLoF) variants are often highly deleterious and play an important role in disease biology, but many pLoF variants may not result in loss of function (LoF). Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines' PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 genes associated with autosomal-recessive disease from the Genome Aggregation Database (gnomAD v.2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in a low proportion expressed across transcripts (pext) scored region, or the presence of cryptic in-frame splice rescues. Variants predicted to evade LoF or to be potential artifacts were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of pLoF variants predicted as likely not LoF/not LoF, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.
Collapse
Affiliation(s)
- Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Sanna Gudmundsson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eleanor G Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Genomic Informatics Group, University Hospital Southampton, Southampton, UK
| | - Eleina England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan C Wood
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Rachel G Son
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas A Watts
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Ambry Genetics, Aliso Viejo, CA, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
4
|
Singer-Berk M, Gudmundsson S, Baxter S, Seaby EG, England E, Wood JC, Son RG, Watts NA, Karczewski KJ, Harrison SM, MacArthur DG, Rehm HL, O'Donnell-Luria A. Advanced variant classification framework reduces the false positive rate of predicted loss of function (pLoF) variants in population sequencing data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.08.23286955. [PMID: 36945502 PMCID: PMC10029069 DOI: 10.1101/2023.03.08.23286955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Predicted loss of function (pLoF) variants are highly deleterious and play an important role in disease biology, but many of these variants may not actually result in loss-of-function. Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines's PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 autosomal recessive disease-genes from the Genome Aggregation Database (gnomAD, v2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in low per-base expression (pext) score regions, or the presence of cryptic splice rescues. Variants predicted to be potential artifacts or to evade LoF were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of LoF evading variants assessed, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines, and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.
Collapse
Affiliation(s)
- Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Sanna Gudmundsson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eleanor G Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Genomic Informatics Group, University Hospital Southampton, Southampton, United Kingdom
| | - Eleina England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan C Wood
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Rachel G Son
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas A Watts
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ambry Genetics, Aliso Viejo, CA, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
5
|
Jayaraman V, Toledo‐Patiño S, Noda‐García L, Laurino P. Mechanisms of protein evolution. Protein Sci 2022; 31:e4362. [PMID: 35762715 PMCID: PMC9214755 DOI: 10.1002/pro.4362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/11/2022] [Accepted: 05/14/2022] [Indexed: 11/06/2022]
Abstract
How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.
Collapse
Affiliation(s)
- Vijay Jayaraman
- Department of Molecular Cell BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Saacnicteh Toledo‐Patiño
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | - Lianet Noda‐García
- Department of Plant Pathology and Microbiology, Institute of Environmental Sciences, Robert H. Smith Faculty of Agriculture, Food and EnvironmentHebrew University of JerusalemRehovotIsrael
| | - Paola Laurino
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| |
Collapse
|
6
|
Pavesi A, Romerio F. Extending the Coding Potential of Viral Genomes with Overlapping Antisense ORFs: A Case for the De Novo Creation of the Gene Encoding the Antisense Protein ASP of HIV-1. Viruses 2022; 14:v14010146. [PMID: 35062351 PMCID: PMC8781085 DOI: 10.3390/v14010146] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 02/04/2023] Open
Abstract
Gene overprinting occurs when point mutations within a genomic region with an existing coding sequence create a new one in another reading frame. This process is quite frequent in viral genomes either to maximize the amount of information that they encode or in response to strong selective pressure. The most frequent scenario involves two different reading frames in the same DNA strand (sense overlap). Much less frequent are cases of overlapping genes that are encoded on opposite DNA strands (antisense overlap). One such example is the antisense ORF, asp in the minus strand of the HIV-1 genome overlapping the env gene. The asp gene is highly conserved in pandemic HIV-1 strains of group M, and it is absent in non-pandemic HIV-1 groups, HIV-2, and lentiviruses infecting non-human primates, suggesting that the ~190-amino acid protein that is expressed from this gene (ASP) may play a role in virus spread. While the function of ASP in the virus life cycle remains to be elucidated, mounting evidence from several research groups indicates that ASP is expressed in vivo. There are two alternative hypotheses that could be envisioned to explain the origin of the asp ORF. On one hand, asp may have originally been present in the ancestor of contemporary lentiviruses, and subsequently lost in all descendants except for most HIV-1 strains of group M due to selective advantage. Alternatively, the asp ORF may have originated very recently with the emergence of group M HIV-1 strains from SIVcpz. Here, we used a combination of computational and statistical approaches to study the genomic region of env in primate lentiviruses to shed light on the origin, structure, and sequence evolution of the asp ORF. The results emerging from our studies support the hypothesis of a recent de novo addition of the antisense ORF to the HIV-1 genome through a process that entailed progressive removal of existing internal stop codons from SIV strains to HIV-1 strains of group M, and fine tuning of the codon sequence in env that reduced the chances of new stop codons occurring in asp. Altogether, the study supports the notion that the HIV-1 asp gene encodes an accessory protein, providing a selective advantage to the virus.
Collapse
Affiliation(s)
- Angelo Pavesi
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy;
| | - Fabio Romerio
- Department of Molecular and Comparative Pathobiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205-2196, USA
- Correspondence:
| |
Collapse
|
7
|
Heinen T, Xie C, Keshavarz M, Stappert D, Künzel S, Tautz D. Evolution of a New Testis-Specific Functional Promoter Within the Highly Conserved Map2k7 Gene of the Mouse. Front Genet 2022; 12:812139. [PMID: 35069705 PMCID: PMC8766832 DOI: 10.3389/fgene.2021.812139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/08/2021] [Indexed: 12/03/2022] Open
Abstract
Map2k7 (synonym Mkk7) is a conserved regulatory kinase gene and a central component of the JNK signaling cascade with key functions during cellular differentiation. It shows complex transcription patterns, and different transcript isoforms are known in the mouse (Mus musculus). We have previously identified a newly evolved testis-specific transcript for the Map2k7 gene in the subspecies M. m. domesticus. Here, we identify the new promoter that drives this transcript and find that it codes for an open reading frame (ORF) of 50 amino acids. The new promoter was gained in the stem lineage of closely related mouse species but was secondarily lost in the subspecies M. m. musculus and M. m. castaneus. A single mutation can be correlated with its transcriptional activity in M. m. domesticus, and cell culture assays demonstrate the capability of this mutation to drive expression. A mouse knockout line in which the promoter region of the new transcript is deleted reveals a functional contribution of the newly evolved promoter to sperm motility and the spermatid transcriptome. Our data show that a new functional transcript (and possibly protein) can evolve within an otherwise highly conserved gene, supporting the notion of regulatory changes contributing to the emergence of evolutionary novelties.
Collapse
Affiliation(s)
| | - Chen Xie
- Max-Plank Institute for Evolutionary Biology, Plön, Germany
| | - Maryam Keshavarz
- Max-Plank Institute for Evolutionary Biology, Plön, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen e. V. (DZNE), Bonn, Germany
| | - Dominik Stappert
- Deutsches Zentrum für Neurodegenerative Erkrankungen e. V. (DZNE), Bonn, Germany
| | - Sven Künzel
- Max-Plank Institute for Evolutionary Biology, Plön, Germany
| | - Diethard Tautz
- Max-Plank Institute for Evolutionary Biology, Plön, Germany
| |
Collapse
|
8
|
Wichmann S, Scherer S, Ardern Z. Biological factors in the synthetic construction of overlapping genes. BMC Genomics 2021; 22:888. [PMID: 34895142 PMCID: PMC8665328 DOI: 10.1186/s12864-021-08181-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 11/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Overlapping genes (OLGs) with long protein-coding overlapping sequences are disallowed by standard genome annotation programs, outside of viruses. Recently however they have been discovered in Archaea, diverse Bacteria, and Mammals. The biological factors underlying life's ability to create overlapping genes require more study, and may have important applications in understanding evolution and in biotechnology. A previous study claimed that protein domains from viruses were much better suited to forming overlaps than those from other cellular organisms - in this study we assessed this claim, in order to discover what might underlie taxonomic differences in the creation of gene overlaps. RESULTS After overlapping arbitrary Pfam domain pairs and evaluating them with Hidden Markov Models we find OLG construction to be much less constrained than expected. For instance, close to 10% of the constructed sequences cannot be distinguished from typical sequences in their protein family. Most are also indistinguishable from natural protein sequences regarding identity and secondary structure. Surprisingly, contrary to a previous study, virus domains were much less suitable for designing OLGs than bacterial or eukaryotic domains were. In general, the amount of amino acid change required to force a domain to overlap is approximately equal to the variation observed within a typical domain family. The resulting high similarity between natural sequences and those altered so as to overlap is mostly due to the combination of high redundancy in the genetic code and the evolutionary exchangeability of many amino acids. CONCLUSIONS Synthetic overlapping genes which closely resemble natural gene sequences, as measured by HMM profiles, are remarkably easy to construct, and most arbitrary domain pairs can be altered so as to overlap while retaining high similarity to the original sequences. Future work however will need to assess important factors not considered such as intragenic interactions which affect protein folding. While the analysis here is not sufficient to guarantee functional folding proteins, further analysis of constructed OLGs will improve our understanding of the origin of these remarkable genetic elements across life and opens up exciting possibilities for synthetic biology.
Collapse
Affiliation(s)
- Stefan Wichmann
- Chair of Microbial Ecology, Department of Molecular Life Sciences, Technical University of Munich, Freising, Germany
| | - Siegfried Scherer
- Chair of Microbial Ecology, Department of Molecular Life Sciences, Technical University of Munich, Freising, Germany
| | - Zachary Ardern
- Chair of Microbial Ecology, Department of Molecular Life Sciences, Technical University of Munich, Freising, Germany.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
| |
Collapse
|
9
|
Gholizadeh Z, Iqbal MS, Li R, Romerio F. The HIV-1 Antisense Gene ASP: The New Kid on the Block. Vaccines (Basel) 2021; 9:vaccines9050513. [PMID: 34067514 PMCID: PMC8156140 DOI: 10.3390/vaccines9050513] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/04/2021] [Accepted: 05/13/2021] [Indexed: 01/14/2023] Open
Abstract
Viruses have developed incredibly creative ways of making a virtue out of necessity, including taking full advantage of their small genomes. Indeed, viruses often encode multiple proteins within the same genomic region by using two or more reading frames in both orientations through a process called overprinting. Complex retroviruses provide compelling examples of that. The human immunodeficiency virus type 1 (HIV-1) genome expresses sixteen proteins from nine genes that are encoded in the three positive-sense reading frames. In addition, the genome of some HIV-1 strains contains a tenth gene in one of the negative-sense reading frames. The so-called Antisense Protein (ASP) gene overlaps the HIV-1 Rev Response Element (RRE) and the envelope glycoprotein gene, and encodes a highly hydrophobic protein of ~190 amino acids. Despite being identified over thirty years ago, relatively few studies have investigated the role that ASP may play in the virus lifecycle, and its expression in vivo is still questioned. Here we review the current knowledge about ASP, and we discuss some of the many unanswered questions.
Collapse
|
10
|
Carter CW, Wills PR. Reciprocally-Coupled Gating: Strange Loops in Bioenergetics, Genetics, and Catalysis. Biomolecules 2021; 11:265. [PMID: 33670192 PMCID: PMC7916928 DOI: 10.3390/biom11020265] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/04/2021] [Accepted: 02/06/2021] [Indexed: 12/12/2022] Open
Abstract
Bioenergetics, genetic coding, and catalysis are all difficult to imagine emerging without pre-existing historical context. That context is often posed as a "Chicken and Egg" problem; its resolution is concisely described by de Grasse Tyson: "The egg was laid by a bird that was not a chicken". The concision and generality of that answer furnish no details-only an appropriate framework from which to examine detailed paradigms that might illuminate paradoxes underlying these three life-defining biomolecular processes. We examine experimental aspects here of five examples that all conform to the same paradigm. In each example, a paradox is resolved by coupling "if, and only if" conditions for reciprocal transitions between levels, such that the consequent of the first test is the antecedent for the second. Each condition thus restricts fluxes through, or "gates" the other. Reciprocally-coupled gating, in which two gated processes constrain one another, is self-referential, hence maps onto the formal structure of "strange loops". That mapping uncovers two different kinds of forces that may help unite the axioms underlying three phenomena that distinguish biology from chemistry. As a physical analog for Gödel's logic, biomolecular strange-loops provide a natural metaphor around which to organize a large body of experimental data, linking biology to information, free energy, and the second law of thermodynamics.
Collapse
Affiliation(s)
- Charles W. Carter
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7260, USA
| | - Peter R. Wills
- Department of Physics and Te Ao Marama Centre for Fundamental Inquiry, University of Auckland, PB 92019, Auckland 1142, New Zealand;
| |
Collapse
|