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Abstract Background The mouse mammary tumor virus (MMTV) is unique from other retroviruses in having multiple viral promoters, which can be regulated by hormones in a tissue specific manner. This unique property has lead to increased interest in studying MMTV replication with the hope of developing MMTV based vectors for human gene therapy. However, it has recently been reported that related as well as unrelated retroviruses can cross-package each other's genome raising safety concerns towards the use of candidate retroviral vectors for human gene therapy. Therefore, using a trans complementation assay, we looked at the ability of MMTV RNA to be cross-packaged and propagated by an unrelated primate Mason-Pfizer monkey virus (MPMV) that has intracellular assembly process similar to that of MMTV. Results Our results revealed that MMTV and MPMV RNAs could be cross-packaged by the heterologous virus particles reciprocally suggesting that pseudotyping between two genetically distinct retroviruses can take place at the RNA level. However, the cross-packaged RNAs could not be propagated further indicating a block at post-packaging events in the retroviral life cycle. To further confirm that the specificity of cross-packaging was conferred by the packaging sequences (ψ), we cloned the packaging sequences of these viruses on expression plasmids that generated non-viral RNAs. Test of these non-viral RNAs confirmed that the reciprocal cross-packaging was primarily due to the recognition of ψ by the heterologous virus proteins. Conclusion The results presented in this study strongly argue that MPMV and MMTV are promiscuous in their ability to cross-package each other's genome suggesting potential RNA-protein interactions among divergent retroviral RNAs proposing that these interactions are more complicated than originally thought. Furthermore, these observations raise the possibility that MMTV and MPMV genomes could also co-package providing substrates for exchanging genetic information.

Introduction: This study addresses the rising cardiovascular disease (CVD) rates in the United Arab Emirates (UAE) by investigating the occurrence and impact of genetic variants in CVD-related genes., Methods: We collected all genes linked to heritable CVD from public and diagnostic databases and mapped them to their corresponding biological processes and molecular pathways. We then evaluated the types and burden of genetic variants within these genes in 343 individuals from the Emirati Mendelian Study Cohort and 3,007 national electronic health records., Results: We identified a total of 735 genes associated with heritable CVD, covering a range of cardiovascular conditions. Enrichment analysis revealed key biological processes and pathways, including Apelin, FoxO, and Ras signaling, that are implicated across all forms of heritable CVD. Analysis of a UAE cohort of 3,350 individuals showed a predominance of rare and unique CVD variants specific to the population. The study found a significant burden of pathogenic variants in families with CVD within the Emirati Mendelian cohort and re-assessed the pathogenicity of 693 variants from national health records, leading to the discovery of new CVD-causing variants., Discussion: This study underscores the importance of continuously updating our understanding of genes and pathways related to CVD. It also highlights the significant underrepresentation of the UAE population in public databases and clinical literature on CVD genetics, offering valuable insights that can inform future research and intervention strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Akawi, Al Mansoori, Al Zaabi, Badics, Al Dhaheri, Al Shamsi, Al Tenaiji, Alzohily, Almesmari, Al Hammadi, Al Dhahouri, Irshaid, Kizhakkedath, Al Shibli, Tabouni, Allam, Baydoun, Alblooshi, Ali, Foo and Al Jasmi.)

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions., (© 2024. The Author(s).)

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Orofacial clefts (OFCs) are congenital malformations of the face and palate, with an incidence of 1 per 700 live births. Clubfoot or congenital talipes equinovarus (CTEV) is a three-dimensional abnormality of the leg, ankle, and feet that leads to the anomalous positioning of foot and ankle joints and has an incidence of 1 per 1000 live births. OFCs and CTEV may occur together or separately in certain genetic syndromes in addition to other congenital abnormalities. Here, we sought to decipher the genetic etiology of OFC and CTEV that occurred together in six probands., Methods: At the time of recruitment, the most clinically obvious congenital anomalies in these individuals were the OFC and CTEV. We carried out whole-exome sequencing (WES) on DNA samples from probands and available parents employing the Agilent SureSelect XT kit and Illumina HiSeq2500 platform, followed by bioinformatics analyses. WES variants were validated by clinical Sanger Sequencing., Results: Of the six probands, we observed probable pathogenic genetic variants in four. In three probands with probable pathogenic genetic variants, each individual had variants in three different genes, whereas one proband had probable pathogenic variant in just one gene. In one proband, we observed variants in DIS3L2, a gene associated with Perlman syndrome. A second proband had variants in EPG5 (associated with Vici Syndrome), BARX1 and MKI67, while another proband had potentially etiologic variants in FRAS1 (associated with Fraser Syndrome 1), TCOF1 (associated with Treacher Collins Syndrome 1) and MKI67. The last proband had variants in FRAS1, PRDM16 (associated with Cardiomyopathy, dilated, 1LL/Left ventricular noncompaction 8) and CHD7 (associated with CHARGE syndrome/Hypogonadotropic hypogonadism 5 with or without anosmia)., Conclusion: Our results suggest that clubfoot and OFCs are two congenital abnormalities that can co-occur in certain individuals with varying genetic causes and expressivity, warranting the need for deep phenotyping., (© 2021 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC.)

Vertebral malformations (VMs) are caused by alterations in somitogenesis and may occur in association with other congenital anomalies. The genetic etiology of most VMs remains unknown and their identification may facilitate the development of novel therapeutic and prevention strategies. Exome sequencing was performed on both the discovery cohort of nine unrelated probands from the USA with VMs and the replication cohort from China (Deciphering Disorders Involving Scoliosis & COmorbidities study). The discovery cohort was analyzed using the PhenoDB analysis tool. Heterozygous and homozygous, rare and functional variants were selected and evaluated for their ClinVar, HGMD, OMIM, GWAS, mouse model phenotypes, and other annotations to identify the best candidates. Genes with candidate variants in three or more probands were selected. The replication cohort was analyzed by another in-house developed pipeline. We identified rare heterozygous variants in KIAA1217 in four out of nine probands in the discovery cohort and in five out of 35 probands in the replication cohort. Collectively, we identified 11 KIAA1217 rare variants in 10 probands, three of which have not been described in gnomAD and one of which is a nonsense variant. We propose that genetic variations of KIAA1217 may contribute to the etiology of VMs., (© 2020 Wiley Periodicals, Inc.)