Enhancing Accuracy in the Diagnosis and Treatment of Genetic Disorders
Notice bibliographique
Résumé
Discovery and Engineering of Retrons for Precise Genome Editing The challenges of currently available genome editing techniques are mainly lack of precision of double-stranded breaks, lack of targeted delivery and efficient repair, and genotoxicity. The study by Buffington et al. on identification and engineering of retrons to improve their efficiency in mammalian cell lines and vertebrates, helps to address these challenges.[1] Retrons produce single-stranded DNA which are found in bacteria as a part of their defense system. The investigators used metagenomic analysis and screening by confocal imaging, flow cytometry, and next-generation sequencing in human embryonic kidney cells to select active retrons, among which Efe1-RT was found to be highly precise and efficient across different loci. With the same approach, the plasmid was optimized by nuclear localization signal (NLS) and linker engineering. The investigators found high activity with N- and C-terminal (BP-SV40) NLSs, (SGGS) 2 linker, nonhomologous end joining inhibitors and homology-directed repair (HDR)-promting fusions. They proved cross-compatibility with Cas12a, Cas12a Ultra, and Cas9 nickase, which demonstrated the ability to bypass the need for a DNA double-strand break for gene editing. The strategy of using all RNA delivery to enable DNA-free gene editing in cells and vertebrates like the zebrafish model, was also established. Through bridging bacterial retrons and eukaryotic genome engineering, retron editors have been proven to be versatile and precise tools. An Enhanced Eco1 Retron Editor Enables Precision Genome Engineering in Human Cells without Double-strand Breaks Bacterial retroelements called retrons have become new tools for precise DNA editing. However, in mammalian cells, the efficiency is uncertain. Cattle et al. have identified and addressed the major limitations in retron-based editing.[2] The team fused Eco1 retron to Cas9 and used reporter mCherry to check the transfection; catalytically dead RT was used as a control. Noncoding RNA (ncRNA) plasmids were designed with a 100-nt insert between the multicopy single-stranded DNA and RNA (msd and msr) regions. Retron ncRNAs or sgRNAs encoded by separate plasmids were expressed from the U6 RNAPIII promoter and terminated by a poly (T) transcription termination signal. The ncRNA stability is achieved using an exoribonuclease-resistant RNA (xrRNA) pseudoknot at the 5' end and a poly (A) tail. The team demonstrated that Csy4 endoribonuclease cleaves the transcript giving the sgRNA a natural 5′ start. Efficient editing in human cells with a nickase Cas9 version (Cas9 H840A) and genome editing with single-stranded nick was successfully demonstrated by the team. This study demonstrates the improved editing outcomes achieved by stabilizing ncRNA and correcting sgRNA processing. ThinkRare: A Search Algorithm to Identify Patients with Undiagnosed Rare Genetic Disease in an Electronic Medical Record The diagnosis of rare genetic disorders is often missed in routine clinical practice. Addressing this issue, the team of Ediae et al. developed a search-based algorithm called “ThinkRare” designed to flag undiagnosed and suspected rare diseases.[3] Retrospective electronic medical records (EMR) from the Children’s Hospital of Eastern Ontario over the past 20 years were analyzed, and clinical details and comprehensive information about laboratory tests was obtained. First, potential EMR data fields that can be used for the algorithm were identified, and a gold-standard dataset was created of patients with certain criteria. An artificial intelligence-based algorithm version was developed; multiple versions of the algorithm were optimized using a test dataset, which is a prospective application of real-world data. Random sampling and different sampling inputs to different versions were used. The team manually validated the output data into true positives and false positives. The specificity and negative predictive value were calculated from the final version of the search tool. Finally, the data generated by the algorithm were statistically validated. The result of using this “ThinkRare” among 262,296 patients was that the algorithm achieved 60% sensitivity and 15% precision, indicating a need for further refinement to improve precision when maintaining sensitivity. Efforts toward applying this kind of search tool to national-level available data are required for a better understanding of rare genetic disorders and to provide proper diagnostic testing options to patients. Toward Same-day Genome Sequencing in the Critical Care Setting In critical and crucial conditions like neonatal intensive care units (NICUs), rapid genetic diagnosis is essential. The challenge lies in compressing preanalytical, analytical, and interpretive steps into a single-day workflow. However, very fast genome sequencing methods demonstrated in other studies prove to be expensive, low-throughput, and impractical for routine use. Wojcik et al. evaluated new technology using the expansion method for sequencing.[4] In this method, nanopore sequencing alongside real-time data analysis is done where VCF can be generated in 30 min after sequencing. The team piloted this method in 15 infants, with the parents’ consent. Out of 15 genomes, 3 were reference genomes (HG220), 5 were from previously diagnosed patients, and 7 were test subjects. In parallel, the samples were rapidly sequenced by a Clinical Laboratory Improvement Amendments laboratory to validate the results of sequencing by expansion. The average time for the report was 4 h and 4 min, significantly faster than traditional genome sequencing methods, which can take several days. Of the seven test subjects, two had diagnostic findings and five had negative reports. Among the positive cases, one had multiple anomalies, and an unbalanced chromosomal translocation was reported. This method is scalable, fast, and suitable for critical care decisions, enabling NICU diagnostics to achieve true same-day precision medicine. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».