New "deCODE bill" restarts controversy.
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Biotechnology↗
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cDNA analyses in the human genome project.
The ultimate goal of the human genome project is to decode all the genetic information carried in the genome. Towards this goal, the physical structure of the genome, as well as the functional aspects of the genome, must be understood. We initiated a cDNA project to collect the 'expression profiles' of all human genes, a database with which to describe which genes are expressed, and to what extent, in any given human cell at a particular time. Single-cycle sequencing of randomly selected members from a 3'-directed cDNA library is most appropriate for this purpose: the sequence data serve as a 'gene signature' to identify the expressing gene, and the frequency of appearance of the gene signature reflects the activity of the gene. The compiled data, which usually cover some 1000 sequencing results per sample, are referred to as an 'expression profile.' We applied this analysis to HepG2 (a cell line derived from a hepatocellular carcinoma), liver cells and lung cells. The expression profiles shed some light upon the unique features of gene expression in the cell or tissue tested. A comparison of the expression profiles among different cells has allowed active genes to be classified as housekeepers or those with cell-specific functions. A significant fraction of the abundantly expressed genes include those that are unique to the cell. In addition, the resulting collection of thousands of gene signatures is a useful source of probes for mapping and for isolating full-size cDNAs.
Animals↗
Identification of new genes by systematic analysis of cDNAs and database construction.
The large-scale collection of partial cDNA sequences is becoming a powerful tool in biology. Similarity or motif searches in DNA databases using these partial cDNA sequences have facilitated the discovery of new genes of interest. By collecting and registering large numbers of partial sequences with a well designed non-biased cDNA library, an expression profile of active genes in a particular tissue can be obtained. Tissue-specific or stage-specific genes can be discovered by comparing the profiles from different tissues or from a tissue at different stages of development, respectively. The compilation of such expression profiles enables genes to be mapped to the tissue(s) where they are actively transcribed. The large-scale collation of gene sequences actively expressed in the body into databases complements efforts directed towards the structural analysis of the genome, with the ultimate aim of decoding all the genetic information carried in the human genome. This cDNA strategy is also being widely applied to organisms other than man.
Animals↗
Inhibition of adenovirus early region IV transcription in vitro by a purified viral DNA binding protein.
Adenoviruses depend on cellular mechanisms for the decoding of their genetic information, and so provide a useful and simple model system for the investigation of mammalian gene expression. The five regions transcribed early in adenovirus infection are termed EIa, EIb, EII, EIII and EIV. We report here that the primary product of the EII region, a 72,000 molecular weight DNA-binding protein (DBP), specifically represses transcription from the EIV promoter in an in vitro transcription system. Single-stranded DNA binds to the DBP with high affinity, and as a result inhibits its repressor activity. Our data extend previous genetic evidence that the DBP represses EIV transcription in vivo, and suggest that it acts directly by suppressing transcription from the EIV promoter.
Adenoviridae↗
Integrative proteomic analysis provides novel therapeutic insights for etiological subtypes of diabetes.
AIMS: Type 2 diabetes (T2D) is a highly heterogeneous disease characterised by subtypes with variations in aetiology, disease progression, and risk of complications. However, potential drug targets for these subtypes have not been explored. This study aims to investigate potential drug targets by integrating proteomics. MATERIALS AND METHODS: Summary-level data of circulating proteins were extracted from the UK Biobank and the deCODE Health Study. Genetic associations with five diabetes subtypes were obtained from Swedish All New Diabetics in Scania and Malmö Diet and Cancer cohort, including severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). The associations between circulating proteins and diabetes subtypes were assessed through Mendelian randomisation, followed by multiple sensitivity and colocalization analyses. Additionally, tissue-specific, pathway and functional enrichment analysis, assessment of protein druggability, and the protein-protein interaction (PPI) networks were used to further explore biological mechanisms and therapeutic potential. RESULTS: Genetically predicted levels of 2, 2, 9, 3, and 5 circulating proteins were associated with SIRD, SIDD, MARD, MOD, and SAID, respectively. Colocalization analyses further revealed links between GRN with MARD/SIRD, LILRB5 with SIDD/MARD, CR1 with MARD, TNFSF12 with MOD, and DAPK2 with SAID. Enrichment analysis suggested that these proteins were mainly enriched in blood and adipose tissues and involved in immune and inflammatory related pathways. PPI analysis revealed GRN, TNFSF12, and DAPK2 are associated with known T2D targets. CONCLUSIONS: Our study identified several potential drug targets for different subtypes of diabetes using an integrated genetic approach, yielding new insights for precision medicine of diabetes.
Humans↗
The human genome efforts and the cDNA project.
Molecular biology has been moving swiftly toward clarifying the entire genome of organisms, including human. The human genome efforts that promote this transaction are characterized by the large-scale, high throughput production of data about the structure of the genome of the molecular level and its computer-assisted management. In addition, functional analyses of the genome have become important for decoding the entire genetic information carried in the human genome. In the functional analyses of the genome, the large-scale collection of partial cDNA sequences, the cDNA project, is becoming important, because it allows researchers to register genes active in any given tissue, on one hand, and, on the other hand, it allows for quantitative description of gene activities in tissues. Tissue-specific or stage-specific genes can be discovered by comparing expression profiles from different tissues or from a tissue at different stages of development, respectively.
DNA, Complementary↗
[Body mapping of human genes].
The ultimate goal of the human genome project to decode all the genetic information carried in the genome. Towards this goal, the physical structure of the genome, as well as the functional aspects of the genome, must be understood. We initiated a cDNA project to collect the "expression profiles" of all human genes, a database with which to describe which genes are expressed, and to what extent, in any given human cell at a particular time. Single-cycle sequencing of randomly selected members from a 3'-directed cDNA library is most appropriate for this purpose: the sequence data serve as a "gene signature" to identify the expressing gene, and the frequency of appearance of the gene signature reflects the activity of the gene. The compiled data, which usually covers some 1000 sequencing results per sample, is referred to as an "expression profile". We applied this analysis to HepG2 (a cell line derived from a hepatocellular carcinoma), liver cells and lung cells. The expression profiles shed some light upon the unique features of gene expression in the cell or tissue tested. A comparison of the expression profiles among different cells has allowed active genes to be classified as housekeepers or those with cell-specific functions. A significant fraction of the abundantly expressed genes include those that are unique to the cell. In addition, the resulting collection of thousands of gene signatures is a useful source of probes for mapping and for isolating full size cDNAs.
Cells, Cultured↗
Genetic interaction between yeast Saccharomyces cerevisiae release factors and the decoding region of 18 S rRNA.
Functional and structural similarities between tRNA and eukaryotic class 1 release factors (eRF1) described previously, provide evidence for the molecular mimicry concept. This concept is supported here by the demonstration of a genetic interaction between eRF1 and the decoding region of the ribosomal RNA, the site of tRNA-mRNA interaction. We show that the conditional lethality caused by a mutation in domain 1 of yeast eRF1 (P86A), that mimics the tRNA anticodon stem-loop, is rescued by compensatory mutations A1491G (rdn15) and U1495C (hyg1) in helix 44 of the decoding region and by U912C (rdn4) and G886A (rdn8) mutations in helix 27 of the 18 S rRNA. The rdn15 mutation creates a C1409-G1491 base-pair in yeast rRNA that is analogous to that in prokaryotic rRNA known to be important for high-affinity paromomycin binding to the ribosome. Indeed, rdn15 makes yeast cells extremely sensitive to paromomycin, indicating that the natural high resistance of the yeast ribosome to paromomycin is, in large part, due to the absence of the 1409-1491 base-pair. The rdn15 and hyg1 mutations also partially compensate for inactivation of the eukaryotic release factor 3 (eRF3) resulting from the formation of the [PSI+] prion, a self-reproducible termination-deficient conformation of eRF3. However, rdn15, but not hyg1, rescues the conditional cell lethality caused by a GTPase domain mutation (R419G) in eRF3. Other antisuppressor rRNA mutations, rdn2(G517A), rdn1T(C1054T) and rdn12A(C526A), strongly inhibit [PSI+]-mediated stop codon read-through but do not cure cells of the [PSI+] prion. Interestingly, cells bearing hyg1 seem to enable [PSI+] strains to accumulate larger Sup35p aggregates upon Sup35p overproduction, suggesting a lower toxicity of overproduced Sup35p when the termination defect, caused by [PSI+], is partly relieved.
Anti-Bacterial Agents↗
Analysis of codon:anticodon interactions within the ribosome provides new insights into codon reading and the genetic code structure.
Although the decoding rules have been largely elucidated, the physical-chemical reasons for the "correctness" of codon:anticodon duplexes have never been clear. In this work, on the basis of the available data, we propose that the correct codon:anticodon duplexes are those whose formation and interaction with the ribosomal decoding center are not accompanied by uncompensated losses of hydrogen and ionic bonds. Other factors such as proofreading, base-base stacking and aminoacyl-tRNA concentration contribute to the efficiency and accuracy of aminoacyl-tRNA selection, and certainly these factors are important; but we suggest that analyses of hydrogen and ionic bonding alone provides a robust first-order approximation of decoding accuracy. Thus our model can simplify predictions about decoding accuracy and error. The model can be refined with data, but is already powerful enough to explain all of the available data on decoding accuracy. Here we predict which duplexes should be considered correct, which duplexes are responsible for virtually all misreading, and we suggest an evolutionary scheme that gave rise to the mixed boxes of the genetic code.
Anticodon↗
[Perspectives of molecular genetics of hearing disorders].
In the last few years research efforts have succeeded in detectury the genetic background of several hereditary hearing disorders by molecular biological methods. The genetic code was been decoded for Neurofibromatosis types 1 and 2, as well as for such X-linked diseases as Alport's syndrome or Norrie's disease. Besides the classic genetic tools as chromosomal analysis, molecular biological techniques and methods have become important clinically for the ENT-specialist. In the present review we show the principles and applications of DNA-and RNA-analysis with hybridization techniques in Southern- and Northern-blot techniques, as well as in-situ hybridization and polymerase chain reaction (PCR). These molecular biological techniques will help improve the detection and analysis of hereditary inner ear disorders, but also be able to study in greater detail tumor carcinogenesis and mutagenesis. The various techniques are explained and the applications are demonstrated.
Chromosome Aberrations↗
Genes coding for the selenocysteine-inserting tRNA species from Desulfomicrobium baculatum and Clostridium thermoaceticum: structural and evolutionary implications.
The genes (selC) coding for the selenocysteine-inserting tRNA species (tRNA(Sec)) from Clostridium thermoaceticum and Desulfomicrobium baculatum were cloned and sequenced. Although they differ in numerous positions from the sequence of the Escherichia coli selC gene, they were able to complement the selC lesion of an E. coli mutant and to promote selenoprotein formation in the heterologous host. The tRNA(Sec) species from both organisms possess all of the unique primary, secondary, and tertiary structural features exhibited by E. coli tRNA(Sec) (C. Baron, E. Westhof, A. Böck, and R. Giegé, J. Mol. Biol. 231:274-292, 1993). The structural and functional properties of the tRNA(Sec) species from prokaryotes analyzed thus far support the notion that tRNA(Sec) may be an evolutionarily conserved structure whose function in the primordial genetic code was to decode UGA with selenocysteine.
Base Sequence↗
Codon recognition rules in yeast mitochondria.
The mitochondrial genome of Saccharomyces cerevisiae codes for 24 tRNAs. The nucleotide sequences of the tRNA genes suggest a unique set of rules that govern the decoding of the mitochondrial genetic code. The four codons of unmixed fmilies are recognized by single tRNAs that always have a U in the wobble position of the anticodon. The codons of the mixed families are read by two different tRNAs. Codons terminating in a C or U are recognized by tRNAs with a G and codons terminating in a G or A are recognized by tRNAs with a U in the corresponding positions of the anticodons. There are two exceptions to these rules. In the AUN family for isoleucine and methionine, the isoleucine tRNA has a G and the methionine tRNA has a C in the wobble position. The tRNA for the arginine CGN family also has an A in the wobble position of the anticodon. It is of interest that the CGN codons have not been found in the mitochondrial genes sequenced to date. The simplified decoding system of yeast mitochondria allows all the codons to be recognized by only 24 tRNAs.
Anticodon↗
[Antitubercular agents].
The personally experienced development of chemotherapy for tuberculosis during the last half century represents some highlights of new knowledges and practical successes: the discovery of antituberculosis drugs; the comprehension of their actions and side effects; the exploration of mechanisms of resistance against antituberculosis agents; the evaluation of therapeutic and epidemiologic consequences of resistant strains; the decoding of the mycobacterial genetic structure. For different economic, social and psychologic reasons, the worldwide results of the battle against tuberculosis are not nearly as good as possible. AIDS is only a partial factor of this failure.
AIDS-Related Opportunistic Infections↗
The impact of tokenizer selection in genomic language models.
MOTIVATION: Genomic language models have recently emerged as a new method to decode, interpret, and generate genetic sequences. Existing genomic language models have utilized various tokenization methods, including character tokenization, overlapping and nonoverlapping k-mer tokenization, and byte-pair encoding, a method widely used in natural language models. Genomic sequences differ from natural language because of their low character variability, complex and overlapping features, and inconsistent directionality. These features make subword tokenization in genomic language models significantly different from both traditional language models and protein language models. RESULTS: This study explores the impact of tokenization in genomic language models by evaluating their downstream performance on 44 classification fine-tuning tasks. We also perform a direct comparison of byte pair encoding and character tokenization in Mamba, a state-space model. Our results indicate that character tokenization outperforms subword tokenization methods on tasks that rely on nucleotide-level resolution, such as splice site prediction and promoter detection. While byte-pair tokenization had stronger performance on the SARS-CoV-2 variant classification task, we observed limited statistically significant differences between tokenization methods on the remaining downstream tasks. AVAILABILITY AND IMPLEMENTATION: Detailed results of all benchmarking experiments are available in https://github.com/leannmlindsey/DNAtokenization. Training datasets and pretrained models are available at https://huggingface.co/datasets/leannmlindsey. Datasets and processing scripts are available at doi: 10.5281/zenodo.16287401 and doi: 10.5281/zenodo.16287130.
Natural Language Processing↗
Decoding Iceland: the next big medical breakthroughs may result from one scientist's battle to map the Viking gene pool.
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Blood Specimen Collection↗
The non-standard genetic code of Candida spp.: an evolving genetic code or a novel mechanism for adaptation?
A number of yeasts of the genus Candida translate the standard leucine-CUG codon as serine. This unique genetic code change is the only known alteration to the universal genetic code in cytoplasmic mRNAs, of either eukaryotes or prokaryotes, which involves reassignment of a sense codon. Translation of CUG as serine in these species is mediated by a novel serine-tRNA (ser-tRNACAG), which uniquely has a guanosine at position 33, 5' to the anticodon, a position that is almost invariably occupied by a pyrimidine (uridine in general) in all other tRNAs. We propose that G-33 has two important functions: lowering the decoding efficiency of the ser-tRNACAG and preventing binding of the leucyl-tRNA synthetase. This implicates this nucleotide as a key player in the evolutionary reassignment of the CUG codon. In addition, the novel ser-tRNACAG has 1-methylguanosine (m1G-37) at position 37, 3' to the anticodon, which is characteristic of leucine, but not serine tRNAs. Remarkably, m1G-37 causes leucylation of the ser-tRNACAG both in vitro and in vivo, making the CUG codon an ambiguous codon: the polysemous codon. This indicates that some Candida species tolerate ambiguous decoding and suggests either that (i) the genetic code change has not yet been fully established and is evolving at different rates in different Candida species; or (ii) CUG ambiguity is advantageous and represents the final stage of the reassignment. We propose that such dual specificity indicates that reassignment of the CUG codon evolved through a mechanism that required codon ambiguity and that ambiguous decoding evolved to generate genetic diversity and allow for rapid adaptation to environmental challenges.
Adaptation, Physiological↗
Quantitation of readthrough of termination codons in yeast using a novel gene fusion assay.
A simple quantitative in vivo assay has been developed for measuring the efficiency of translation of one or other of the three termination codons. UAA, UAG and UGA in Saccharomyces cerevisiae. The assay employs a 3-phosphoglycerate kinase-beta-galactosidase gene fusion, carried on a multicopy plasmid, in which the otherwise retained reading frame is disrupted by one or other of the three termination codons. Termination readthrough is thus quantitated by measuring beta-galactosidase in transformed strains. Using these plasmids to quantitate the endogenous levels of termination readthrough we show that readthrough of all three codons can be detected in a non-suppressor (sup+) strain of S. cerevisiae. The efficiency of this endogenous readthrough is much higher in a [psi+] strain than in a [psi-] strain with the UGA codon being the leakiest in the nucleotide context used. The utility of the assay plasmids for studying genetic modifiers of nonsense suppressors is also shown by their use to demonstrate that the cytoplasmic genetic determinant [psi+] broadens the decoding properties of a serine-inserting UAA suppressor tRNA (SUQ5) to allow it to translate the other two termination codons in the order of efficiency UAA greater than UAG greater than UGA.
Antisense Elements (Genetics)↗