Calculating and comparing codon usage values in rare disease genes highlights codon clustering with disease-and tissue- specific hierarchy.

We designed a novel strategy to define codon usage bias (CUB) in 6 specific small cohorts of human genes. We calculated codon usage (CU) values in 29 non-disease-causing (NDC) and 31 disease-causing (DC) human genes which are highly expressed in 3 distinct tissues, kidney, muscle, and skin. We applied our strategy to the same selected genes annotated in 15 mammalian species. We obtained CUB hierarchical clusters for each gene cohort which showed tissue-specific and disease-specific CUB fingerprints. We showed that DC genes (especially those expressed in muscle) display a low CUB, well recognizable in codon hierarchical clustering. We defined the extremely biased codons as "zero codons" and found that their number is significantly higher in all DC genes, all tissues, and that this trend is conserved across mammals. Based on this calculation in different gene cohorts, we identified 5 codons which are more differentially used across genes and mammals, underlining that some genes have favorite synonymous codons in use. Since of the muscle genes clear clusters, and, among these, dystrophin gene surprisingly does not show any "zero codon" we adopted a novel approach to study CUB, we called "mapping-on-codons". We positioned 2828 dystrophin missense and nonsense pathogenic variations on their respective codon, highlighting that its frequency and occurrence is not dependent on the CU values. We conclude our strategy consents to identify a hierarchical clustering of CU values in a gene cohort-specific fingerprints, with recognizable trend across mammals. In DC muscle genes also a disease-related fingerprint can be observed, allowing discrimination between DC and NDC genes. We propose that using our strategy which studies CU in specific gene cohorts, as rare disease genes, and tissue specific genes, may provide novel information about the CUB role in human and medical genetics, with implications on synonymous variations interpretation and codon optimization algorithms.

Nanodiagnostics and Nanodelivery Applications in Genetic Alterations.

BACKGROUND: Genetic alterations cause Hereditary Diseases (HDs) with a wide range of incidences. Some, like cystic fibrosis, occur frequently (1/1,000 newborns), whilst others, such as Pompe disease and other metabolic disorders are very rare (1/100,000 newborns). They are well under the threshold of 1/3,000, denoted by the European Community as Rare Diseases (RDs). Genetic alterations are also associated with multifactorial disorders like diabetes, and underline both somatic and germline mutations in cancer. Nowadays, thanks to the interventions of the European Union and the American National Health Institute as well as others, Hds are under an international lense, which has stimulated discussions and research targeting gene identification, prenatal diagnosis and care optimization leading to the development of new treatment options. Nanomedicine is paving the way toward some highly appealing clinical and research avenues in HDs. Nanotechnologies lend themselves to many aspects in human healthcare, such as in vitro diagnostics (nanobiosensors and nanoplatforms), drug delivery (nanovectors), drug monitoring (nanosensors) and artificial organs to study the genome variant meaning (nanostructures). METHODS AND RESULTS: With a significant reduction in costs and simplified healthcare delivery, nanodiagnostics can potentially provide the tools to diagnose diseases at an early stage with precision. In vitro nanodiagnostics are already diagnosing RDs, with many nanodevices having been successfully introduced over the last few decades. Nanovectors represent an emerging approach in drug delivery and treatment for several diseases such as cancers, infectious diseases, cardiovascular disorders and neurological pathologies. Artificial tissues have valuable implications in replacing compromised organs, thus offering unique opportunities to explore pathogenic mechanisms as well as new drug targets in a personalized context. CONCLUSION: This article outlines and discusses the recent progress in nanotechnology and its potential applications in HDs. It is a pivotal field for research and innovation in healthcare, with emphasis on diagnostics, disease monitoring, biomarker assaying and drug delivery. We underlined the nanomethod's capacity to identify genetic alterations and the follow up of important aspects of the disease course, including therapies. We extensively described the new field of nanodelivery for experimental drugs, focusing on new genetic therapies and their implications in hereditary disorders. We also detailed innovative tools as artificial tissues based on nanomatrices and their use to identify or study genetic alterations.