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Annali Dell'Istituto Superiore Di Sanita 2024The completion of human DNA sequencing in the early 2000s initially generated widespread excitement and hope that it would revolutionize medicine. Over time, however, it...
The completion of human DNA sequencing in the early 2000s initially generated widespread excitement and hope that it would revolutionize medicine. Over time, however, it revealed major limitations due to a lack of understanding of the highly complex genotype-phenotype pathway. Precision medicine has emerged as a response to these biotechnological innovations, tailoring treatments based on an array of new molecular and clinical "omics" data. However, the large volume and heterogeneity of data available today requires the use of dedicated and highly efficient computational analyses. Widely used today are artificial intelligence techniques (such as machine learning) based on artificial neural networks, i.e., a mathematical model of how biological neurons work. Here, we show that artificial neural networks have nothing to do with biology, although their popularity is largely due to their alleged ability to simulate the human brain. Furthermore, we argue that the analysis of large molecular datasets cannot be left to the computational side alone, i.e., to be exclusively data-driven, but on the contrary must meet the challenge of integrating data and expertise, of getting clinicians and data analysts to work together to take into account the absolute and ineradicable uniqueness of each patient's characteristics.
Topics: Humans; Artificial Intelligence; Neural Networks, Computer; Precision Medicine
PubMed: 38920254
DOI: 10.4415/ANN_24_01_03 -
Frontiers in Genetics 2024Fracture healing is a complex process that involves multiple molecular events, and the regulation mechanism is not fully understood. We acquired miRNA and mRNA...
Fracture healing is a complex process that involves multiple molecular events, and the regulation mechanism is not fully understood. We acquired miRNA and mRNA transcriptomes of mouse fractures from the Gene Expression Omnibus database (GSE76197 and GSE192542) and integrated the miRNAs and genes that were differentially expressed in the control and fracture groups to construct regulatory networks. There were 130 differentially expressed miRNAs and 4,819 differentially expressed genes, including 72 upregulated and 58 downregulated miRNAs, along with 2,855 upregulated and 1964 downregulated genes during early fracture healing. Gene ontology analysis revealed that most of the differentially expressed genes were enriched in the extracellular matrix (ECM) structure and the ECM organization. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment suggested cell cycle, DNA replication, and mismatch repair were involved in the progression of fracture healing. Furthermore, we constructed a molecular network of miRNAs and mRNAs with inverse expression patterns to elucidate the molecular basis of miRNA-mRNA regulation in fractures. The regulatory network highlighted the potential targets, which may help to provide a mechanistic basis for therapies to improve fracture patient outcomes.
PubMed: 38919952
DOI: 10.3389/fgene.2024.1408404 -
Biodiversity Data Journal 2024Species of Latreille 1802 are rarely collected endoparasitoids of Chrysopidae larvae (Neuroptera). Previous work on the limits between the European species of this...
BACKGROUND
Species of Latreille 1802 are rarely collected endoparasitoids of Chrysopidae larvae (Neuroptera). Previous work on the limits between the European species of this species-poor genus, based on morphology only, has left some uncertainties. Here, we approach these cases and revisit previous taxonomic decisions using freshly collected and museum material.
NEW INFORMATION
We generated the first large-scale Heloridae DNA barcode dataset, combined these with morphological data in an integrative taxonomic approach, and added information from studying all relevant type material. We found five species, (Panzer, 1798), Haliday, 1857 stat. rev., Förster, 1856, Förster, 1856, and Cameron, 1906, for which we provide an updated identification key. DNA barcode data are added to publicly available DNA barcode reference databases, for all species, except .
PubMed: 38919770
DOI: 10.3897/BDJ.12.e122523 -
BMC Veterinary Research Jun 2024Transgene silencing provides a significant challenge in animal model production via gene engineering using viral vectors or transposons. Selecting an appropriate...
Transgene silencing provides a significant challenge in animal model production via gene engineering using viral vectors or transposons. Selecting an appropriate strategy, contingent upon the species is crucial to circumvent transgene silencing, necessitating long-term observation of in vivo gene expression. This study employed the PiggyBac transposon to create a GFP rat model to address transgene silencing in rats. Surprisingly, transgene silencing occurred while using the CAG promoter, contrary to conventional understanding, whereas the Ef1α promoter prevented silencing. GFP expression remained stable through over five generations, confirming efficacy of the Ef1α promoter for long-term protein expression in rats. Additionally, GFP expression was consistently maintained at the cellular level in various cellular sources produced from the GFP rats, thereby validating the in vitro GFP expression of GFP rats. Whole-genome sequencing identified a stable integration site in Akap1 between exons 1 and 2, mitigating sequence-independent mechanism-mediated transgene silencing. This study established an efficient method for producing transgenic rat models using PiggyBac transposon. Our GFP rats represent the first model to exhibit prolonged expression of foreign genes over five generations, with implications for future research in gene-engineered rat models.
Topics: Animals; DNA Transposable Elements; Green Fluorescent Proteins; Rats, Transgenic; Rats; Gene Transfer Techniques; Transgenes; Male; Gene Silencing; Female; Promoter Regions, Genetic
PubMed: 38918814
DOI: 10.1186/s12917-024-04123-7 -
Scientific Reports Jun 2024The benefits of breastfeeding for the health and wellbeing of both infants and mothers are well documented, yet global breastfeeding rates are low. One factor associated...
The benefits of breastfeeding for the health and wellbeing of both infants and mothers are well documented, yet global breastfeeding rates are low. One factor associated with low breast feeding is maternal body mass index (BMI), which is used as a measure of obesity. The negative relationship between maternal obesity and breastfeeding is likely caused by a variety of social, psychological, and physiological factors. Maternal obesity may also have a direct biological association with breastfeeding through changes in maternal DNA methylation. Here, we investigate this potential biological association using data from a UK-based cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). We find that pre-pregnancy body mass index (BMI) is associated with lower initiation to breastfeed and shorter breastfeeding duration. We conduct epigenome-wide association studies (EWAS) of pre-pregnancy BMI and breastfeeding outcomes, and run candidate-gene analysis of methylation sites associated with BMI identified via previous meta-EWAS. We find that DNA methylation at cg11453712, annotated to PHTP1, is associated with pre-pregnancy BMI. From our results, neither this association nor those at candidate-gene sites are likely to mediate the link between pre-pregnancy BMI and breastfeeding.
Topics: Humans; DNA Methylation; Breast Feeding; Body Mass Index; Female; Pregnancy; Adult; Longitudinal Studies; Genome-Wide Association Study; United Kingdom; Obesity; Epigenesis, Genetic
PubMed: 38918574
DOI: 10.1038/s41598-024-65605-0 -
Scientific Reports Jun 2024Engineered mammalian cells are key for biotechnology by enabling broad applications ranging from in vitro model systems to therapeutic biofactories. Engineered cell...
Engineered mammalian cells are key for biotechnology by enabling broad applications ranging from in vitro model systems to therapeutic biofactories. Engineered cell lines exist as a population containing sub-lineages of cell clones that exhibit substantial genetic and phenotypic heterogeneity. There is still a limited understanding of the source of this inter-clonal heterogeneity as well as its implications for biotechnological applications. Here, we developed a genomic barcoding strategy for a targeted integration (TI)-based CHO antibody producer cell line development process. This technology provided novel insights about clone diversity during stable cell line selection on pool level, enabled an imaging-independent monoclonality assessment after single cell cloning, and eventually improved hit-picking of antibody producer clones by monitoring of cellular lineages during the cell line development (CLD) process. Specifically, we observed that CHO producer pools generated by TI of two plasmids at a single genomic site displayed a low diversity (< 0.1% RMCE efficiency), which further depends on the expressed molecules, and underwent rapid population skewing towards dominant clones during routine cultivation. Clonal cell lines from one individual TI event demonstrated a significantly lower variance regarding production-relevant and phenotypic parameters as compared to cell lines from distinct TI events. This implies that the observed cellular diversity lies within pre-existing cell-intrinsic factors and that the majority of clonal variation did not develop during the CLD process, especially during single cell cloning. Using cellular barcodes as a proxy for cellular diversity, we improved our CLD screening workflow and enriched diversity of production-relevant parameters substantially. This work, by enabling clonal diversity monitoring and control, paves the way for an economically valuable and data-driven CLD process.
Topics: CHO Cells; Cricetulus; Animals; DNA Barcoding, Taxonomic; Clone Cells; Genomics; Antibodies, Monoclonal
PubMed: 38918509
DOI: 10.1038/s41598-024-65323-7 -
Scientific Reports Jun 2024A previous study suggested that fetal inheritance of chromosomally integrated human herpesvirus 6 (ici-HHV6) is associated with the hypertensive pregnancy disorder... (Observational Study)
Observational Study
A previous study suggested that fetal inheritance of chromosomally integrated human herpesvirus 6 (ici-HHV6) is associated with the hypertensive pregnancy disorder preeclampsia (PE). We aimed to study this question utilizing cord plasma samples (n = 1276) of the Finnish Genetics of Preeclampsia Consortium (FINNPEC) cohort: 539 from a pregnancy with PE and 737 without. We studied these samples and 30 placentas from PE pregnancies by a multiplex qPCR for the DNAs of all nine human herpesviruses. To assess the population prevalence of iciHHV-6, we studied whole-genome sequencing data from blood-derived DNA of 3421 biobank subjects. Any herpes viral DNA was detected in only two (0.37%) PE and one (0.14%) control sample (OR 2.74, 95% CI 0.25-30.4). One PE sample contained iciHHV-6B and another HHV-7 DNA. The control's DNA was of iciHHV-6B; the fetus having growth restriction and preterm birth without PE diagnosis. Placentas showed no herpesviruses. In the biobank data, 3 of 3421 subjects (0.08%) had low level HHV-6B but no iciHHV-6. While iciHHV-6 proved extremely rare, both fetuses with iciHHV-6B were growth-restricted, preterm, and from a pregnancy with maternal hypertension. Our findings suggest that human herpesviruses are not a significant cause of PE, whereas iciHHV-6 may pose some fetal risk.
Topics: Humans; Female; Pregnancy; Pre-Eclampsia; Adult; Herpesvirus 6, Human; Cohort Studies; Fetal Blood; Finland; DNA, Viral; Placenta; Herpesviridae
PubMed: 38918446
DOI: 10.1038/s41598-024-65386-6 -
Scientific Reports Jun 2024Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the...
Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.
Topics: Humans; Diabetes Mellitus, Type 2; Machine Learning; DNA Methylation; Islets of Langerhans; Polymorphism, Single Nucleotide; Male; Female; Middle Aged; Biomarkers; Adult; Aged
PubMed: 38918439
DOI: 10.1038/s41598-024-64846-3 -
Cell Genomics Jun 2024Alterations in the structure and location of telomeres are pivotal in cancer genome evolution. Here, we applied both long-read and short-read genome sequencing to assess...
Alterations in the structure and location of telomeres are pivotal in cancer genome evolution. Here, we applied both long-read and short-read genome sequencing to assess telomere repeat-containing structures in cancers and cancer cell lines. Using long-read genome sequences that span telomeric repeats, we defined four types of telomere repeat variations in cancer cells: neotelomeres where telomere addition heals chromosome breaks, chromosomal arm fusions spanning telomere repeats, fusions of neotelomeres, and peri-centromeric fusions with adjoined telomere and centromere repeats. These results provide a framework for the systematic study of telomeric repeats in cancer genomes, which could serve as a model for understanding the somatic evolution of other repetitive genomic elements.
PubMed: 38917803
DOI: 10.1016/j.xgen.2024.100588 -
Poultry Science Apr 2024Magang geese are typical short-day breeders whose reproductive behaviors are significantly influenced by photoperiod. Exposure to a long-day photoperiod results in...
Magang geese are typical short-day breeders whose reproductive behaviors are significantly influenced by photoperiod. Exposure to a long-day photoperiod results in testicular regression and spermatogenesis arrest in Magang geese. To investigate the epigenetic influence of DNA methylation on the seasonal testicular regression in Magang geese, we conducted whole-genome bisulfite sequencing and transcriptome sequencing of testes across 3 reproductive phases during a long-day photoperiod. A total of 250,326 differentially methylated regions (DMR) were identified among the 3 comparison groups, with a significant number showing hypermethylation, especially in intronic regions of the genome. Integrating bisulfite sequencing with transcriptome sequencing data revealed that DMR-associated genes tend to be differentially expressed in the testes, highlighting a potential regulatory role for DNA methylation in gene expression. Furthermore, there was a significant negative correlation between changes in the methylation of CG DMRs and changes in the expression of their associated genes in the testes. A total of 3,359 DMR-associated differentially expressed genes (DEG) were identified; functional enrichment analyses revealed that motor proteins, MAPK signaling pathway, ECM-receptor interaction, phagosome, TGF-beta signaling pathway, and calcium signaling might contribute to the testicular regression process. GSEA revealed that the significantly enriched activated hallmark gene set was associated with apoptosis and estrogen response during testicular regression, while the repressed hallmark gene set was involved in spermatogenesis. Our study also revealed that methylation changes significantly impacted the expression level of vitamin A metabolism-related genes during testicular degeneration, with hypermethylation of STRA6 and increased calmodulin levels indicating vitamin A efflux during the testicular regression. These findings were corroborated by pyrosequencing and real-time qPCR, which revealed that the vitamin A metabolic pathway plays a pivotal role in testicular degeneration under long-day conditions. Additionally, metabolomics analysis revealed an insufficiency of vitamin A and an abnormally high level of oxysterols accumulated in the testes during testicular regression. In conclusion, our study demonstrated that testicular degeneration in Magang geese induced by a long-day photoperiod is linked to vitamin A homeostasis disruption, which manifests as the hypermethylation status of STRA6, vitamin A efflux, and a high level of oxysterol accumulation. These findings offer new insights into the effects of DNA methylation on the seasonal testicular regression that occurs during long-day photoperiods in Magang geese.
PubMed: 38917605
DOI: 10.1016/j.psj.2024.103769