BACKGROUND: Long-read sequencing technologies have the potential to overcome the limitations of short reads and provide a comprehensive picture of the human genome. However, the characterization of repetitive sequences by reconstructing genomic structures at high resolution solely from long reads remains difficult. Here, we developed a localized assembly method (LoMA) that constructs highly accurate consensus sequences (CSs) from long reads. METHODS: We developed LoMA by combining minimap2, MAFFT, and our algorithm, which classifies diploid haplotypes based on structural variants and CSs. Using this tool, we analyzed two human samples (NA18943 and NA19240) sequenced with the Oxford Nanopore sequencer. We defined target regions in each genome based on mapping patterns and then constructed a high-quality catalog of the human insertion solely from the long-read data. RESULTS: The assessment of LoMA showed a high accuracy of CSs (error rate < 0.3%) compared with raw data (error rate > 8%) and superiority to a previous study. The genome-wide analysis of NA18943 and NA19240 identified 5516 and 6542 insertions (≥ 100 bp), respectively. Most insertions (~ 80%) were derived from tandem repeats and transposable elements. We also detected processed pseudogenes, insertions in transposable elements, and long insertions (> 10 kbp). Finally, our analysis suggested that short tandem duplications are associated with gene expression and transposons. CONCLUSIONS: Our analysis showed that LoMA constructs high-quality sequences from long reads with substantial errors. This study revealed the true structures of the insertions with high accuracy and inferred the mechanisms for the insertions, thus contributing to future human genome studies. LoMA is available at our GitHub page: https://github.com/kolikem/loma .

BACKGROUND: Polymorphisms in the interleukin-10 (IL10) gene have been linked to the severity of the patients infected with the viral infections. This study aimed to assess if the IL10 gene polymorphisms rs1800871, rs1800872, and rs1800896 were linked to coronavirus disease 19 (COVID-19) mortality in different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in the Iranian population. METHODS: For genotyping IL10 rs1800871, rs1800872, and rs1800896, this study used the polymerase chain reaction-restriction fragment length polymorphism method in 1,734 recovered and 1,450 deceased patients. RESULTS: The obtained finding indicated IL10 rs1800871 CC genotype in the Alpha variant and CT genotype in the Delta variant had a relationship with COVID-19 mortality; however, there was no association between rs1800871 polymorphism and the Omicron BA.5 variant. The COVID-19 mortality rate was associated with IL10 rs1800872 TT genotype in the Alpha and Omicron BA.5 variants and GT in the Alpha and Delta variants. The COVID-19 mortality rate was associated with IL10 rs1800896 GG and AG genotypes in the Delta and Omicron BA.5; nevertheless, there was no association between rs1800896 polymorphism with the Alpha variant. According to the obtained data, the GTA haplotype was the most common of haplotype in different SARS-CoV-2 variants. The TCG haplotype was related to COVID-19 mortality in the Alpha, Delta and Omicron BA.5 variants. CONCLUSION: The IL10 polymorphisms had an impact on COVID-19 infection, and these polymorphisms had different effects in various SARS-CoV-2 variants. To verify the obtained results, further studies should be conducted on various ethnic groups.

BACKGROUND: The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS: In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS: The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS: We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

BACKGROUND: Phenylketonuria (PKU) is caused by mutations in the phenylalanine hydroxylase (PAH) gene. Our study aimed to predict the phenotype using the allelic genotype. METHODS: A total of 1291 PKU patients with 623 various variants were used as the training dataset for predicting allelic phenotypes. We designed a common machine learning framework to predict allelic genotypes associated with the phenotype. RESULTS: We identified 235 different mutations and 623 various allelic genotypes. The features extracted from the structure of mutations and graph properties of the PKU network to predict the phenotype of PKU were named PPML (PKU phenotype predicted by machine learning). The phenotype of PKU was classified into three different categories: classical PKU (cPKU), mild PKU (mPKU) and mild hyperphenylalaninemia (MHP). Three hub nodes (c.728G>A for cPKU, c.721 for mPKU and c.158G>A for HPA) were used as each classification center, and 5 node attributes were extracted from the network graph for machine learning training features. The area under the ROC curve was AUC = 0.832 for cPKU, AUC = 0.678 for mPKU and AUC = 0.874 for MHP. This suggests that PPML is a powerful method to predict allelic phenotypes in PKU and can be used for genetic counseling of PKU families. CONCLUSIONS: The web version of PPML predicts PKU allele classification supported by applicable real cases and prediction results. It is an online database that can be used for PKU phenotype prediction http://www.bioinfogenetics.info/PPML/ .

BACKGROUND: Super-enhancers (SEs), driving high-level expression of genes with tumor-promoting functions, have been investigated recently. However, the roles of super-enhancer-associated lncRNAs (SE-lncRNAs) in tumors remain undetermined, especially in gliomas. We here established a SE-lncRNAs expression-based prognostic signature to choose the effective treatment of glioma and identify a novel therapeutic target. METHODS: Combined analysis of RNA sequencing (RNA-seq) data and ChIP sequencing (ChIP-seq) data of glioma patient-derived glioma stem cells (GSCs) screened SE-lncRNAs. Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets served to construct and validate SE-lncRNA prognostic signature. The immune profiles and potential immuno- and chemotherapies response prediction value of the signature were also explored. Moreover, we verified the epigenetic activation mechanism of LINC00945 via the ChIP assay, and its effect on glioma was determined by performing the functional assay and a mouse xenograft model. RESULTS: 6 SE-lncRNAs were obtained and identified three subgroups of glioma patients with different prognostic and clinical features. A risk signature was further constructed and demonstrated to be an independent prognostic factor. The high-risk group exhibited an immunosuppressive microenvironment and was higher enrichment of M2 macrophage, regulatory T cells (Tregs), and Cancer-associated fibroblasts (CAFs). Patients in the high-risk group were better candidates for immunotherapy and chemotherapeutics. The SE of LINC00945 was further verified via ChIP assay. Mechanistically, BRD4 may mediate epigenetic activation of LINC00945. Additionally, overexpression of LINC00945 promoted glioma cell proliferation, EMT, migration, and invasion in vitro and xenograft tumor formation in vivo. CONCLUSION: Our study constructed the first prognostic SE-lncRNA signature with the ability to optimize the choice of patients receiving immuno- and chemotherapies and provided a potential therapeutic target for glioma.

BACKGROUND: Genome-wide association studies (GWAS) have highlighted over 200 autosomal variants associated with multiple sclerosis (MS). However, variants in non-coding regions such as those encoding microRNAs have not been explored thoroughly, despite strong evidence of microRNA dysregulation in MS patients and model organisms. This study explores the effect of microRNA-associated variants in MS, through the largest publicly available GWAS, which involved 47,429 MS cases and 68,374 controls. METHODS: We identified SNPs within the coordinates of microRNAs, ± 5-kb microRNA flanking regions and predicted 3'UTR target-binding sites using miRBase v22, TargetScan 7.0 RNA22 v2.0 and dbSNP v151. We established the subset of microRNA-associated SNPs which were tested in the summary statistics of the largest MS GWAS by intersecting these datasets. Next, we prioritised those microRNA-associated SNPs which are among known MS susceptibility SNPs, are in strong linkage disequilibrium with the former or meet a microRNA-specific Bonferroni-corrected threshold. Finally, we predicted the effects of those prioritised SNPs on their microRNAs and 3'UTR target-binding sites using TargetScan v7.0, miRVaS and ADmiRE. RESULTS: We have identified 30 candidate microRNA-associated variants which meet at least one of our prioritisation criteria. Among these, we highlighted one microRNA variant rs1414273 (MIR548AC) and four 3'UTR microRNA-binding site variants within SLC2A4RG (rs6742), CD27 (rs1059501), MMEL1 (rs881640) and BCL2L13 (rs2587100). We determined changes to the predicted microRNA stability and binding site recognition of these microRNA and target sites. CONCLUSIONS: We have systematically examined the functional, structural and regulatory effects of candidate MS variants among microRNAs and 3'UTR targets. This analysis allowed us to identify candidate microRNA-associated MS SNPs and highlights the value of prioritising non-coding RNA variation in GWAS. These candidate SNPs could influence microRNA regulation in MS patients. Our study is the first thorough investigation of both microRNA and 3'UTR target-binding site variation in multiple sclerosis using GWAS summary statistics.

BACKGROUND: The American College of Medical Genetics and Genomics (ACMG) recently published new tier-based carrier screening recommendations. While many pan-ethnic genetic disorders are well established, some genes carry pathogenic founder variants (PFVs) that are unique to specific ethnic groups. We aimed to demonstrate a community data-driven approach to creating a pan-ethnic carrier screening panel that meets the ACMG recommendations. METHODS: Exome sequencing data from 3061 Israeli individuals were analyzed. Machine learning determined ancestries. Frequencies of candidate pathogenic/likely pathogenic (P/LP) variants based on ClinVar and Franklin were calculated for each subpopulation based on the Franklin community platform and compared with existing screening panels. Candidate PFVs were manually curated through community members and the literature. RESULTS: The samples were automatically assigned to 13 ancestries. The largest number of samples was classified as Ashkenazi Jewish (n = 1011), followed by Muslim Arabs (n = 613). We detected one tier-2 and seven tier-3 variants that were not included in existing carrier screening panels for Ashkenazi Jewish or Muslim Arab ancestries. Five of these P/LP variants were supported by evidence from the Franklin community. Twenty additional variants were detected that are potentially pathogenic tier-2 or tier-3. CONCLUSIONS: The community data-driven and sharing approaches facilitate generating inclusive and equitable ethnically based carrier screening panels. This approach identified new PFVs missing from currently available panels and highlighted variants that may require reclassification.

BACKGROUND: Non-recombining regions of the Y-chromosome recorded the evolutionary traces of male human populations and are inherited haplotype-dependently and male-specifically. Recent whole Y-chromosome sequencing studies have identified previously unrecognized population divergence, expansion and admixture processes, which promotes a better understanding and application of the observed patterns of Y-chromosome genetic diversity. RESULTS: Here, we developed one highest-resolution Y-chromosome single nucleotide polymorphism (Y-SNP) panel targeted for uniparental genealogy reconstruction and paternal biogeographical ancestry inference, which included 639 phylogenetically informative SNPs. We genotyped these loci in 1033 Chinese male individuals from 33 ethnolinguistically diverse populations and identified 256 terminal Y-chromosomal lineages with frequency ranging from 0.0010 (singleton) to 0.0687. We identified six dominant common founding lineages associated with different ethnolinguistic backgrounds, which included O2a2b1a1a1a1a1a1a1-M6539, O2a1b1a1a1a1a1a1-F17, O2a2b1a1a1a1a1b1a1b-MF15397, O2a2b2a1b1-A16609, O1b1a1a1a1b2a1a1-F2517, and O2a2b1a1a1a1a1a1-F155. The AMOVA and nucleotide diversity estimates revealed considerable differences and high genetic diversity among ethnolinguistically different populations. We constructed one representative phylogenetic tree among 33 studied populations based on the haplogroup frequency spectrum and sequence variations. Clustering patterns in principal component analysis and multidimensional scaling results showed a genetic differentiation between Tai-Kadai-speaking Li, Mongolic-speaking Mongolian, and other Sinitic-speaking Han Chinese populations. Phylogenetic topology inferred from the BEAST and Network relationships reconstructed from the popART further showed the founding lineages from culturally/linguistically diverse populations, such as C2a/C2b was dominant in Mongolian people and O1a/O1b was dominant in island Li people. We also identified many lineages shared by more than two ethnolinguistically different populations with a high proportion, suggesting their extensive admixture and migration history. CONCLUSIONS: Our findings indicated that our developed high-resolution Y-SNP panel included major dominant Y-lineages of Chinese populations from different ethnic groups and geographical regions, which can be used as the primary and powerful tool for forensic practice. We should emphasize the necessity and importance of whole sequencing of more ethnolinguistically different populations, which can help identify more unrecognized population-specific variations for the promotion of Y-chromosome-based forensic applications.

BACKGROUND: Recently, several rare variants of SPTLC1 were identified as disease cause for juvenile amyotrophic lateral sclerosis (ALS) by disrupting the normal homeostatic regulation of serine palmitoyltransferase (SPT). However, further exploration of the rare variants in large cohorts was still necessary. Meanwhile, SPTLC2 plays a similar role as SPTLC1 in the SPT function. METHODS: To explore the genetic role of SPTLC1 and SPTLC2 in ALS, we analyzed the rare protein-coding variants in 2011 patients with ALS and 3298 controls from the Chinese population with whole exome sequencing. Fisher's exact test was performed between each variant and disease risk, while at gene level over-representation of rare variants in patients was examined with optimized sequence kernel association test (SKAT-O). RESULTS: Totally 33 rare variants with minor allele frequency < 0.01 were identified, including 17 in SPTLC1 and 16 in SPTLC2. One adult-onset patient carried the variant p.E406K (SPTLC1) which was reported in previous study. Additionally, three adult-onset patients carried variants in the same amino acids as the variants identified in previous studies (p.Y509C, p.S331T, and p.R239Q in SPTLC1). At gene level, rare variants of SPTLC1 and STPLC2 were not enriched in patients. CONCLUSION: These results broadened the variant spectrum of SPTLC1 and SPTLC2 in ALS, and paved the way for future research. Further replication was still needed to explore the genetic role of SPTLC1 in ALS.

BACKGROUND: Pharmacists' contribution to pharmacogenomics (PGx) implementation in clinical practice is vital, but a great proportion of them are not aware of PGx and its applications. This highlights the university education's crucial role to prepare pharmacists to face future challenges in such a constantly evolving and demanding environment. OBJECTIVES: Our study aims to examine pharmacy students' training satisfaction, knowledge, self-confidence and attitudes towards PGx on their intentions for postgraduate training in PGx and personalised medicine (PM). METHODS: An initial model on students' intention to pursue postgraduate training in PGx and PM and its predicting factors, based on the Theory of Planned Behaviour (TPB), was proposed. Based on it, a questionnaire was developed and distributed to 346 pharmacy students of all study years, capturing the selected factors influencing students' intentions to postgraduate training in PGx and PM, as well as their demographics. Structural equation modelling (SEM) analysis was employed to determine the effects of both the examined factors and demographics on students' intentions. RESULTS: Students did not consider themselves adequately prepared for using PGx in clinical practice. Their attitudes towards PGx implementation were the most important factor influencing their intentions to pursue postgraduate training in PGx and PM. Other factors such as self-confidence and training satisfaction also affected students' intentions, but to a lower extent. Students of the last two study years (40% of the whole sample) and male (36%) students stated to be less willing to pursue PGx-related studies in the future. Only 10% of the participants claimed to have undergone a recent PGx or genetic test, but this did not affect their intentions. CONCLUSION: There is an important gap in pharmacy school curriculum regarding PGx and PM training which coupled with the slow rate of PGx and PM implementation into clinical practice seems to restrain students' aspiration to further expand their knowledge and horizons in terms of PGx and PM.

BACKGROUND: G6PD deficiency is a common inherited disorder worldwide and has a higher incidence rate in southern China. Many variants of G6PD result from point mutations in the G6PD gene, leading to decreased enzyme activity. This study aimed to analyse the genotypic and phenotypic characteristics of G6PD deficiency in Guangzhou, China. METHODS: In this study, a total of 20,208 unrelated participants were screened from 2020 to 2022. G6PD deficiency was further analysed by quantitative enzymatic assay and G6PD mutation analysis. The unidentified genotype of the participants was further ascertained by direct DNA sequencing. RESULTS: A total of 12 G6PD mutations were identified. Canton (c.1376G>T) and Kaiping (c.1388G>A) were the most common variants, and different mutations led to varying levels of G6PD enzyme activity. Comparing the enzyme activities of the 6 missense mutations between the sexes, we found significant differences (P < 0.05) in the enzyme activities of both male hemizygotes and female heterozygotes. Two previously unreported mutations (c.1438A>T and c.946G>A) were identified. CONCLUSIONS: This study provided detailed genotypes of G6PD deficiency in Guangzhou, which could be valuable for diagnosing and researching G6PD deficiency in this area.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) and DNA methylation are crucial regulators of essential hypertension (EH). Amyloid precursor protein (APP) mutations are implicated in hypertension development. Nonetheless, studies on the association of APP gene polymorphism and promoter methylation with hypertension are limited. Therefore, this case-control aims to evaluate the genetic association of APP gene polymorphism and promoter methylation with EH in Guizhou populations. OBJECTIVE AND METHODS: We conducted a case-control study on 343 EH patients and 335 healthy controls (including Miao, Buyi, and Han populations) in the Guizhou province of China to analyze 11 single-nucleotide polymorphisms (rs2040273, rs63750921, rs2211772, rs2830077, rs467021, rs368196, rs466433, rs364048, rs364051, rs438031, rs463946) in the APP gene via MassARRAY SNP. The MassARRAY EpiTYPER was employed to detect the methylation levels of the promoters. RESULTS: In the Han population, the rs2211772 genotype distribution was significantly different between disease and control groups (χ2 = 6.343, P = 0.039). The CC genotype reduced the risk of hypertension compared to the TT or TC genotype (OR 0.105, 95%CI 0.012-0.914, P = 0.041). For rs2040273 in the Miao population, AG or GG genotype reduced the hypertension risk compared with the AA genotype (OR 0.533, 95%CI 0.294-0.965, P = 0.038). Haplotype TCC (rs364051-rs438031-rs463946) increased the risk of EH in Guizhou (OR 1.427, 95%CI 1.020-1.996, P = 0.037). Each 1% increase in CpG_19 (- 613 bp) methylation level was associated with a 4.1% increase in hypertension risk (OR 1.041, 95%CI 1.002-1.081, P = 0.039). Each 1% increase in CpG_1 (- 296 bp) methylation level was associated with an 8% decrease in hypertension risk in women (OR 0.920, 95%CI 0.860-0.984, P = 0.015). CpG_19 significantly correlated with systolic blood pressure (r = 0.2, P = 0.03). The methylation levels of CpG_19 in hypertensive patients with rs466433, rs364048, and rs364051 minor alleles were lower than that with wild-type alleles (P < 0.05). Moreover, rs467021 and rs364051 showed strong synergistic interaction with EH (χ2 = 7.633, P = 0.006). CpG_11, CpG_19, and rs364051 showed weak synergistic interaction with EH (χ2 = 19.874, P < 0.001). CONCLUSION: In summary, rs2211772 polymorphism and promoter methylation level of APP gene may be linked to EH in Guizhou populations. Our findings will provide novel insights for genetic research of hypertension and Alzheimer's disease.

BACKGROUND: Moyamoya angiopathy (MMA) is a rare cerebrovascular condition leading to stroke. Mutations in 15 genes have been identified in Mendelian forms of MMA, but they explain only a very small proportion of cases. Our aim was to investigate the genetic basis of MMA in consanguineous patients having unaffected parents in order to identify genes involved in autosomal recessive MMA. METHODS: Exome sequencing (ES) was performed in 6 consecutive consanguineous probands having MMA of unknown etiology. Functional consequences of variants were assessed using western blot and protein 3D structure analyses. RESULTS: Causative homozygous variants of NOS3, the gene encoding the endothelial nitric oxide synthase (eNOS), and GUCY1A3, the gene encoding the alpha1 subunit of the soluble guanylate cyclase (sGC) which is the major nitric oxide (NO) receptor in the vascular wall, were identified in 3 of the 6 probands. One NOS3 variant (c.1502 + 1G > C) involves a splice donor site causing a premature termination codon and leads to a total lack of eNOS in endothelial progenitor cells of the affected proband. The other NOS3 variant (c.1942 T > C) is a missense variant located into the flavodoxine reductase domain; it is predicted to be destabilizing and shown to be associated with a reduction of eNOS expression. The GUCY1A3 missense variant (c.1778G > A), located in the catalytic domain of the sGC, is predicted to disrupt the tridimensional structure of this domain and to lead to a loss of function of the enzyme. Both NOS3 mutated probands suffered from an infant-onset and severe MMA associated with posterior cerebral artery steno-occlusive lesions. The GUCY1A3 mutated proband presented an adult-onset MMA associated with an early-onset arterial hypertension and a stenosis of the superior mesenteric artery. None of the 3 probands had achalasia. CONCLUSIONS: We show for the first time that biallelic loss of function variants in NOS3 is responsible for MMA and that mutations in NOS3 and GUCY1A3 are causing fifty per cent of MMA in consanguineous patients. These data pinpoint the essential role of the NO pathway in MMA pathophysiology.

BACKGROUND: Congenital hydrocephalus is characterized by ventriculomegaly, defined as a dilatation of cerebral ventricles, and thought to be due to impaired cerebrospinal fluid (CSF) homeostasis. Primary congenital hydrocephalus is a subset of cases with prenatal onset and absence of another primary cause, e.g., brain hemorrhage. Published series report a Mendelian cause in only a minority of cases. In this study, we analyzed exome data of PCH patients in search of novel causal genes and addressed the possibility of an underlying oligogenic mode of inheritance for PCH. MATERIALS AND METHODS: We sequenced the exome in 28 unrelated probands with PCH, 12 of whom from families with at least two affected siblings and 9 of whom consanguineous, thereby increasing the contribution of genetic causes. Patient exome data were first analyzed for rare (MAF < 0.005) transmitted or de novo variants. Population stratification of unrelated PCH patients and controls was determined by principle component analysis, and outliers identified using Mahalanobis distance 5% as cutoff. Patient and control exome data for genes biologically related to cilia (SYScilia database) were analyzed by mutation burden test. RESULTS: In 18% of probands, we identify a causal (pathogenic or likely pathogenic) variant of a known hydrocephalus gene, including genes for postnatal, syndromic hydrocephalus, not previously reported in isolated PCH. In a further 11%, we identify mutations in novel candidate genes. Through mutation burden tests, we demonstrate a significant burden of genetic variants in genes coding for proteins of the primary cilium in PCH patients compared to controls. CONCLUSION: Our study confirms the low contribution of Mendelian mutations in PCH and reports PCH as a phenotypic presentation of some known genes known for syndromic, postnatal hydrocephalus. Furthermore, this study identifies novel Mendelian candidate genes, and provides evidence for oligogenic inheritance implicating primary cilia in PCH.

BACKGROUND: Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. RESULTS: We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. CONCLUSIONS: This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

BACKGROUND: Blood metabolites are important to various aspects of our health. However, currently, there is little evidence about the role of circulating metabolites in the process of skin aging. OBJECTIVES: To examine the potential effects of circulating metabolites on the process of skin aging. METHOD: In the primary analyses, we applied several MR methods to study the associations between 249 metabolites and facial skin aging risk. In the secondary analyses, we replicated the analyses with another array of datasets including 123 metabolites. MR Bayesian model averaging (MR-BMA) method was further used to prioritize the metabolites for the identification of predominant metabolites that are associated with skin aging. RESULTS: In the primary analyses, only the unsaturation degree of fatty acids was found significantly associated with skin aging with the IVW method after multiple testing (odds ratio = 1.084, 95% confidence interval = 1.049-1.120, p = 1.737 × 10-06). Additionally, 11 out of 17 unsaturation-related biomarkers showed a significant or suggestively significant causal effect [p < 0.05 and > 2 × 10-4 (0.05/249 metabolites)]. In the secondary analyses, seven metabolic biomarkers were found significantly associated with skin aging [p < 4 × 10-4 (0.05/123)], while six of them were related to the unsaturation degree. MR-BMA method validated that the unsaturation degree of fatty acids plays a dominant role in facial skin aging. CONCLUSIONS: Our study used systemic MR analyses and provided a comprehensive atlas for the associations between circulating metabolites and the risk of facial skin aging. Genetically proxied unsaturation degree of fatty acids was highlighted as a dominant factor correlated with the risk of facial skin aging.

BACKGROUND: Cuproptosis, as a copper-induced mitochondrial cell death, has attracted extensive attention recently, especially in cancer. Although some key regulatory genes have been identified in cuproptosis, the related lncRNAs have not been further studied. Exploring the prognostic and diagnostic value of cuproptosis-related lncRNAs (CRLs) in colon adenocarcinoma and providing guidance for individualized immunotherapy for patients are of great significance. RESULTS: A total of 2003 lncRNAs were correlated with cuproptosis genes and considered as CRLs. We screened 33 survival-associated CRLs and established a prognostic signature base on 7 CRLs in the training group. The patients in the low-risk group had better outcomes in both training group (P < 0.001) and test group (P = 0.016). More exciting, our model showed good prognosis prediction in both stage I-II (P = 0.020) and stage III-IV (P = 0.001). The nomogram model could further improve the accuracy of prognosis prediction. Interestingly, glucose-related metabolic pathways, which were closely related to cuproptosis, were enriched in the low-risk group. Meanwhile, the immune infiltration scores were lower in the high-risk group. The high-risk group was more sensitive to OSI.906 and ABT.888, while low-risk group was more sensitive to Sorafenib. Three lncRNAs, FALEC, AC083967.1 and AC010997.4, were highly expressed in serum of COAD patients, and the AUC was 0.772, 0.726 and 0.714, respectively, indicating their valuable diagnostic value. CONCLUSIONS: Our research constructed a prognostic signature based on 7 CRLs and found three promising diagnostic markers for COAD patients. Our results provided a reference to the personalized immunotherapy strategies.