BACKGROUND: Spinocerebellar ataxia type 1 (SCA1) is a neurodegenerative disease caused by a polyglutamine expansion in the ataxin-1 protein. The pathogenic mechanism resulting in SCA1 is still unclear. Protein-protein interactions affect the function and stability of ataxin-1. METHODS: Wild-type and mutant ataxin-1 were expressed in HEK-293T cells. The levels of expression were assessed using real-time polymerase chain reaction (PCR) and Western blots. Co-immunoprecipitation was done in HEK-293T cells expressing exogenous wild-type and mutant ataxin-1 using anti-Flag antibody following by tandem affinity purification in order to study protein-protein interactions. The candidate interacting proteins were validated by immunoprecipitation. Chromatin immunoprecipitation and high-throughput sequencing and RNA immunoprecipitation and high-throughput sequencing were performed using HEK-293T cells expressing wild-type or mutant ataxin-1. RESULTS: In this study using HEK-293T cells, we found that wild-type ataxin-1 interacted with MCM2, GNAS, and TMEM206, while mutant ataxin-1 lost its interaction with MCM2, GNAS, and TMEM206. Two ataxin-1 binding targets containing the core GGAG or AAAT were identified in HEK-293T cells using ChIP-seq. Gene Ontology analysis of the top ataxin-1 binding genes identified SLC6A15, NTF3, KCNC3, and DNAJC6 as functional genes in neurons in vitro. Ataxin-1 also was identified as an RNA-binding protein in HEK-293T cells using RIP-seq, but the polyglutamine expansion in the ataxin-1 had no direct effects on the RNA-binding activity of ataxin-1. CONCLUSIONS: An expanded polyglutamine tract in ataxin-1 might interfere with protein-protein or protein-DNA interactions but had little effect on protein-RNA interactions. This study suggested that the dysfunction of protein-protein or protein-DNA interactions is involved in the pathogenesis of SCA1.

RT-PCR is the foremost clinical test for diagnosis of COVID-19. Unfortunately, PCR-based testing has limitations and may not result in a positive test early in the course of infection before symptoms develop. Enveloped RNA viruses, such as coronaviruses, alter peripheral blood methylation and DNA methylation signatures may characterize asymptomatic versus symptomatic infection. We used Illumina's Infinium MethylationEPIC BeadChip array to profile peripheral blood samples from 164 patients who tested positive for SARS-CoV-2 by RT-PCR, of whom 8 had no symptoms. Epigenome-wide association analysis identified 10 methylation sites associated with infection and a quantile-quantile plot showed little inflation. These preliminary results suggest that differences in methylation patterns may distinguish asymptomatic from symptomatic infection.

BACKGROUND: High-cost, time-consuming and complex processes of several current approaches limit the use of noninvasive prenatal diagnosis (NIPD) for monogenic disorders in clinical application. Thus, a more cost-effective and easily implementable approach is required. METHODS: We established a low-cost and convenient test to noninvasively deduce fetal genotypes of the mutation and single nucleotide polymorphisms (SNPs) loci by means of targeted amplification combined with deep sequencing of maternal genomic and plasma DNA. The sequential probability ratio test was performed to detect the allelic imbalance in maternal plasma. This method can be employed to directly examine familial pathogenic mutations in the fetal genome, as well as infer the inheritance of parental haplotypes through a group of selected SNPs linked to the pathogenic mutation. RESULTS: The fetal mutations in 17 families with different types of monogenic disorders including hemophilia A, von Willebrand disease type 3, Duchenne muscular dystrophy, hyper-IgM type 1, glutaric acidemia type I, Nagashima-type palmoplantar keratosis, and familial exudative vitreoretinopathy were identified in the study. The mutations included various forms: point mutations, gene inversion, deletions/insertions and duplication. The results of 12 families were verified by sequencing of amniotic fluid samples, the accuracy of the approach in fetal genotyping at the mutation and SNPs loci was 98.85% (172/174 loci), and the no-call rate was 28.98% (71/245 loci). The overall accuracy was 12/12 (100%). Moreover, the approach was successfully applied in plasma samples with a fetal fraction as low as 2.3%. CONCLUSIONS: We have shown in this study that the approach is a cost-effective, less time consuming and accurate method for NIPD of monogenic disorders.

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

BACKGROUND: Genome-wide association studies (GWASs) have identified hundreds of loci for body mass index (BMI), but functional variants in these loci are less known. The purpose of this study was to identify RNA modification-related SNPs (RNAm-SNPs) for BMI in GWAS loci. BMI-associated RNAm-SNPs were identified in a GWAS of approximately 700,000 individuals. Gene expression and circulating protein levels affected by the RNAm-SNPs were identified by QTL analyses. Mendelian randomization (MR) methods were applied to test whether the gene expression and protein levels were associated with BMI. RESULTS: A total of 78 RNAm-SNPs associated with BMI (P < 5.0 × 10-8) were identified, including 65 m6A-, 10 m1A-, 3 m7G- and 1 A-to-I-related SNPs. Two functional loss, high confidence level m6A-SNPs, rs6713978 (P = 6.4 × 10-60) and rs13410999 (P = 8.2 × 10-59), in the intron of ADCY3 were the top significant SNPs. These two RNAm-SNPs were associated with ADCY3 gene expression in adipose tissues, whole blood cells, the tibial nerve, the tibial artery and lymphocytes, and the expression levels in these tissues were associated with BMI. Proteins enriched in specific KEGG pathways, such as natural killer cell-mediated cytotoxicity, the Rap1 signaling pathway and the Ras signaling pathway, were affected by the RNAm-SNPs, and circulating levels of some of these proteins (ADH1B, DOCK9, MICB, PRDM1, STOM, TMPRSS11D and TXNDC12) were associated with BMI in MR analyses. CONCLUSIONS: Our study identified RNAm-SNPs in BMI-related genomic loci and suggested that RNA modification may affect BMI by affecting the expression levels of corresponding genes and proteins.

BACKGROUND: Phenylketonuria (PKU) is a common, autosomal recessive inborn error of metabolism caused by PAH gene variants. After routine genetic analysis methods were applied, approximately 5% of PKU patients were still not diagnosed with a definite genotype. METHODS: In this study, for the first time, we identified PKU patients with unknown genotypes via single-gene full-length sequencing. RESULTS: The detection rate of PKU genotype increased from 94.6 to 99.4%, an increase of approximately 5%. The variants c.1199 + 502A > T and 1065 + 241C > A were found at a high frequency in Chinese PKU patients. CONCLUSION: Our study suggest that single-gene full-length sequencing is a rapid, efficient and cost-effective tool to improve the genotype detection rate of PKU patients. Moreover, we provides additional case data to support pathogenicity of deep intronic variants in PAH.

BACKGROUND: More than half of human protein-coding genes have an alternative transcription start site (TSS). We aimed to investigate the contribution of alternative TSSs to the acute-stress-induced transcriptome response in human tissue (skeletal muscle) using the cap analysis of gene expression approach. TSSs were examined at baseline and during recovery after acute stress (a cycling exercise). RESULTS: We identified 44,680 CAGE TSS clusters (including 3764 first defined) belonging to 12,268 genes and annotated for the first time 290 TSSs belonging to 163 genes. The transcriptome dynamically changes during the first hours after acute stress; the change in the expression of 10% of genes was associated with the activation of alternative TSSs, indicating differential TSSs usage. The majority of the alternative TSSs do not increase proteome complexity suggesting that the function of thousands of alternative TSSs is associated with the fine regulation of mRNA isoform expression from a gene due to the transcription factor-specific activation of various alternative TSSs. We identified individual muscle promoter regions for each TSS using muscle open chromatin data (ATAC-seq and DNase-seq). Then, using the positional weight matrix approach we predicted time course activation of "classic" transcription factors involved in response of skeletal muscle to contractile activity, as well as diversity of less/un-investigated factors. CONCLUSIONS: Transcriptome response induced by acute stress related to activation of the alternative TSSs indicates that differential TSSs usage is an essential mechanism of fine regulation of gene response to stress stimulus. A comprehensive resource of accurate TSSs and individual promoter regions for each TSS in muscle was created. This resource together with the positional weight matrix approach can be used to accurate prediction of TFs in any gene(s) of interest involved in the response to various stimuli, interventions or pathological conditions in human skeletal muscle.

This review discusses the epidemiology, pathophysiology, genetic etiology, and management of phenylketonuria (PKU). PKU, an autosomal recessive disease, is an inborn error of phenylalanine (Phe) metabolism caused by pathogenic variants in the phenylalanine hydroxylase (PAH) gene. The prevalence of PKU varies widely among ethnicities and geographic regions, affecting approximately 1 in 24,000 individuals worldwide. Deficiency in the PAH enzyme or, in rare cases, the cofactor tetrahydrobiopterin results in high blood Phe concentrations, causing brain dysfunction. Untreated PKU, also known as PAH deficiency, results in severe and irreversible intellectual disability, epilepsy, behavioral disorders, and clinical features such as acquired microcephaly, seizures, psychological signs, and generalized hypopigmentation of skin (including hair and eyes). Severe phenotypes are classic PKU, and less severe forms of PAH deficiency are moderate PKU, mild PKU, mild hyperphenylalaninaemia (HPA), or benign HPA. Early diagnosis and intervention must start shortly after birth to prevent major cognitive and neurological effects. Dietary treatment, including natural protein restriction and Phe-free supplements, must be used to maintain blood Phe concentrations of 120-360 μmol/L throughout the life span. Additional treatments include the casein glycomacropeptide (GMP), which contains very limited aromatic amino acids and may improve immunological function, and large neutral amino acid (LNAA) supplementation to prevent plasma Phe transport into the brain. The synthetic BH4 analog, sapropterin hydrochloride (i.e., Kuvan®, BioMarin), is another potential treatment that activates residual PAH, thus decreasing Phe concentrations in the blood of PKU patients. Moreover, daily subcutaneous injection of pegylated Phe ammonia-lyase (i.e., pegvaliase; PALYNZIQ®, BioMarin) has promised gene therapy in recent clinical trials, and mRNA approaches are also being studied.

Both genomics- and proteomics-based investigations have identified several essential genes, proteins, and pathways that may facilitate human adaptive genotype/phenotype in a population-specific manner. This comprehensive review provides an up-to-date list of genes and proteins identified for human adaptive responses to high altitudes. Genomics studies for indigenous high-altitude populations like Tibetans, Andeans, Ethiopians, and Sherpas have identified 169 genes under positive natural selection. Similarly, global proteomics studies have identified 258 proteins (± 1.2-fold or more) for Tibetan, Sherpa, and Ladakhi highlanders. The primary biological processes identified for genetic signatures include hypoxia-inducible factor (HIF)-mediated oxygen sensing, angiogenesis, and erythropoiesis. In contrast, major biological processes identified for proteomics signatures include 14-3-3 mediated sirtuin signaling, integrin-linked kinase (ILK), phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT), and integrin signaling. Comparing genetic and protein signatures, we identified 7 common genes/proteins (HBB/hemoglobin subunit beta, TF/serotransferrin, ANGPTL4/angiopoietin-related protein 4, CDC42/cell division control protein 42 homolog, GC/vitamin D-binding protein, IGFBP1/insulin-like growth factor-binding protein 1, and IGFBP2/insulin-like growth factor-binding protein 2) involved in crucial molecular functions like IGF-1 signaling, LXR/RXR activation, ferroptosis signaling, iron homeostasis signaling and regulation of cell cycle. Our combined multi-omics analysis identifies common molecular targets and pathways for human adaptation to high altitude. These observations further corroborate convergent positive selection of hypoxia-responsive molecular pathways in humans and advocate using multi-omics techniques for deciphering human adaptive responses to high altitude.