Cell genomics最新文献

Genetic factors significantly influence the concentration of metabolites in adults. Nevertheless, the genetic influence on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified 19 previously reported associations and 11 novel associations between single-nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was estimated to be 76.2%, with a range of 69%-78.8%. These findings offer valuable insights into the genetic architecture of neonatal metabolism.

The molecular mechanisms underlying the paradoxical effects¹ of aneuploidy are still not completely understood. In this issue, Rojas et al.² systematically analyzed the associated costs of aneuploidy and the molecular drivers involved, which revealed that aneuploidy stress is primarily driven by the cumulative effects of genes per chromosome. Notably, gene length was predicted as the most significant indicator of aneuploidy toxicity by machine learning.

Human milk has long been recognized for its critical role in infant and maternal health. In this issue of Cell Genomics, Johnson et al.¹ apply a human genetics and genomics approach to shed light on the complex relationship between maternal genetics, milk variation, and the infant gut microbiome.

Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.

Deep mutational scanning enables high-throughput functional assessment of genetic variants. While phenotypic measurements from screening assays generally align with clinical outcomes, experimental noise may affect the accuracy of individual variant estimates. We developed the FUSE (functional substitution estimation) pipeline, which leverages measurements collectively within screening assays to improve the estimation of variant impacts. Drawing data from 115 published functional assays, FUSE assesses the mean functional effect per amino acid position and makes estimates for individual allelic variants. It enhances the correlation of variant functional effects from different assay platforms and increases the classification accuracy of missense variants in ClinVar across 29 genes (area under the receiver operating characteristic [ROC] curve [AUC] from 0.83 to 0.90). In UK Biobank patients with rare missense variants in BRCA1, LDLR, or TP53, FUSE improves the classification accuracy of associated phenotypes. FUSE can also impute variant effects for substitutions not experimentally screened. This approach improves accuracy and broadens the utility of data from functional screening.

Recent in vitro studies of human sex chromosome aneuploidy showed that the Xi ("inactive" X) and Y chromosomes broadly modulate autosomal and Xa ("active" X) gene expression. We tested these findings in vivo. Linear modeling of CD4⁺ T cells and monocytes from individuals with one to three X chromosomes and zero to two Y chromosomes revealed 82 sex-chromosomal and 344 autosomal genes whose expression changed significantly with Xi and/or Y dosage in vivo. Changes in sex-chromosomal expression were remarkably constant in vivo and in vitro; autosomal responses to Xi and/or Y dosage were largely cell-type specific (∼2.6-fold more variation than sex-chromosomal responses). Targets of the sex-chromosomal transcription factors ZFX and ZFY accounted for a significant fraction of these autosomal responses both in vivo and in vitro. We conclude that the human Xi and Y transcriptomes are surprisingly robust and stable, yet they modulate autosomal and Xa genes in a cell-type-specific fashion.

The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

In this issue of Cell Genomics, Rothschild et al.¹ reveal how ribosomal RNA diversity impacts ribosome structure and its implications for health and disease. Their innovative methodologies uncover distinct ribosome subtypes with significant structural variations and expression patterns. This work reveals connections to tissue-specific biology and cancer, positing new research avenues.

Genetic variants in ABCA7, an Alzheimer's disease (AD)-associated gene, elevate AD risk, yet its functional relevance to the etiology is unclear. We generated a CRISPR-Cas9-mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single-cell transcriptomics in heterozygous abca7^+/- knockout combined with Aβ42 toxicity revealed that ABCA7 is crucial for neuropeptide Y (NPY), brain-derived neurotrophic factor (BDNF), and nerve growth factor receptor (NGFR) expressions, which are crucial for synaptic integrity, astroglial proliferation, and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density, which are rescuable with ectopic NPY. In induced pluripotent stem cell-derived human neurons exposed to Aβ42, ABCA7^-/- suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages, genetic variants in NPY associated with AD, and epigenetic changes in NPY, NGFR, and BDNF promoters linked to ABCA7 variants. Therefore, ABCA7-dependent NPY signaling via BDNF-NGFR maintains synaptic integrity, implicating its impairment in increased AD risk through reduced brain resilience.

Neanderthal genomes have been recovered from sites across Eurasia, painting an increasingly complex picture of their populations' structure that mostly indicates that late European Neanderthals belonged to a single metapopulation with no significant evidence of population structure. Here, we report the discovery of a late Neanderthal individual, nicknamed "Thorin," from Grotte Mandrin in Mediterranean France, and his genome. These dentognathic fossils, including a rare example of distomolars, are associated with a rich archeological record of Neanderthal final technological traditions in this region ∼50-42 thousand years ago. Thorin's genome reveals a relatively early divergence of ∼105 ka with other late Neanderthals. Thorin belonged to a population with a small group size that showed no genetic introgression with other known late European Neanderthals, revealing some 50 ka of genetic isolation of his lineage despite them living in neighboring regions. These results have important implications for resolving competing hypotheses about causes of the disappearance of the Neanderthals.