SARS-CoV-2 has spread rapidly across the world and is negatively impacting the global human population. COVID-19 patients display a wide variety of symptoms and clinical outcomes, including those attributed to genetic ancestry. Alu retrotransposons have played an important role in human evolution, and their variants influence host response to viral infection. Intronic Alus regulate gene expression through several mechanisms, including both genetic and epigenetic pathways. With respect to SARS-CoV-2, an intronic Alu within the ACE gene is hypothesized to be associated with COVID-19 susceptibility and morbidity. Here, we review specific Alu polymorphisms that are of particular interest when considering host response to SARS-CoV-2 infection, especially polymorphic Alu insertions in genes associated with immune response and coagulation/fibrinolysis cascade. We posit that additional research focused on Alu-related pathways could yield novel biomarkers capable of predicting clinical outcomes as well as patient-specific treatment strategies for COVID-19 and related infectious diseases.

Genome-wide association studies (GWAS) have identified several susceptibility loci of Alzheimer's disease (AD), which were mainly located in noncoding regions of the genome. Meanwhile, the putative biological mechanisms underlying AD susceptibility loci were still unclear. At present, identifying the functional variants of AD pathogenesis remains a major challenge. Herein, we first used summary data-based Mendelian randomization (SMR) with AD GWAS summary and expression quantitative trait loci (eQTL) data to identify variants who affects expression levels of nearby genes and contributed to the risk of AD. Using the SMR integrative analysis, we totally identified 14 SNPs significantly affected the expression level of 16 nearby genes in blood or brain tissues and contributed to the AD risk. Then, to confirm the results, we replicated the GWAS and eQTL results across multiple samples. Totally, four risk SNP (rs11682128, rs601945, rs3935067, and rs679515) were validated to be associated with AD and affected the expression level of nearby genes (BIN1, HLA-DRA, EPHA1-AS1, and CR1). Besides, our differential expression analysis showed that the BIN1 gene was significantly downregulated in the hippocampus (P = 2.0 × 10-3) and survived after multiple comparisons. These convergent lines of evidence suggest that the BIN1 gene identified by SMR has potential roles in the pathogenesis of AD. Further investigation of the roles of the BIN1 gene in the pathogenesis of AD is warranted.

With the emergence of the novel coronavirus SARS-CoV-2 since December 2019, more than 65 million cases have been reported worldwide. This virus has shown high infectivity and severe symptoms in some cases, leading to over 1.5 million deaths globally. Despite the collaborative and concerted research efforts that have been made, no effective medication for COVID-19 (coronavirus disease-2019) is currently available. SARS-CoV-2 uses the angiotensin-converting enzyme 2 (ACE2) as an initial mediator for viral attachment and host cell invasion. ACE2 is widely distributed in the human tissues including the cell surface of lung cells which represent the primary site of the infection. Inhibiting or reducing cell surface availability of ACE2 represents a promising therapy for tackling COVID-19. In this context, most ACE2-based therapeutic strategies have aimed to tackle the virus through the use of angiotensin-converting enzyme (ACE) inhibitors or neutralizing the virus by exogenous administration of ACE2, which does not directly aim to reduce its membrane availability. However, through this review, we present a different perspective focusing on the subcellular localization and trafficking of ACE2. Membrane targeting of ACE2, and shedding and cellular trafficking pathways including the internalization are not well elucidated in literature. Therefore, we hereby present an overview of the fate of newly synthesized ACE2, its post translational modifications, and what is known of its trafficking pathways. In addition, we highlight the possibility that some of the identified ACE2 missense variants might affect its trafficking efficiency and localization and hence may explain some of the observed variable severity of SARS-CoV-2 infections. Moreover, an extensive understanding of these processes is necessarily required to evaluate the potential use of ACE2 as a credible therapeutic target.

Autoimmune hepatitis (AIH) is a rare chronic progressive liver disease with autoimmune features. It mainly affects middle-aged women. AIH is occasionally complicated with liver cirrhosis that worsens the prognosis. Genetic and environmental factors are involved in the pathogenesis of AIH. Genetic studies of other diseases have been revealing of pathogenesis and drug efficacy. In this review, we summarize the genetic risk factors for AIH, including human leukocyte antigen (HLA) and non-HLA genes. A genome-wide association study (GWAS) on European AIH revealed the strongest associations to be with single nucleotide variants (SNVs) in HLA. Predisposing alleles for AIH were DRB1*03:01 and DRB1*04:01 in Europeans; DRB1*04:04, DRB1*04:05, and DRB1*13:01 in Latin Americans; and DRB1*04:01 and DRB1*04:05 in Japanese. Other risk SNVs in non-HLA genes for AIH were found by a candidate gene approach, but several SNVs were confirmed in replication studies. Some genetic factors of AIH overlapped with those of other autoimmune diseases. Larger-scale GWASs of other ethnic groups are required. The results of genetic studies might provide an explanation for the phenotypic heterogeneity of AIH and biomarkers for drug responses.

BACKGROUND: RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. RESULTS: Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. CONCLUSIONS: High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

BACKGROUND: Based on the molecular expression level, this paper compares lncRNA and mRNA expressions respectively in peripheral blood samples of the patients after SCI with NP and without NP, and screens disease-related biomarkers related to NP after SCI in peripheral blood samples of patients. METHOD: The expression spectrum of 25 human peripheral blood samples (12 samples of refractory NP patients after SCI) was downloaded and data were normalized. Screening of GO annotations significantly associated with significant differentially expressed mRNAs and significant involvement of the KEGG pathway. The WGCNA algorithm was used to screen for modules and RNAs that were significantly associated with disease characterization. A co-expression network was constructed to extract the genes involved in the disease pathway from the co-expression network, construct a network of SCI pain-related pathways, and screen important disease-related biomarkers. Quantitative real-time PCR was used to detect the mRNA expression of hub genes. RESULTS: Data were normalized and re-annotated by detection of platform information, resulting in a total of 289 lncRNA and 18197 mRNAs. Screening resulted in 338 significant differentially expressed RNAs that met the threshold requirements. Differentially expressed RNAs were significantly enriched with the brown and magenta modules. Six KEGG signaling pathways were screened in the co-expression network, and three KEGG pathways with direct neuropathic pain were identified. The expression levels of E2F1, MAX, MITF, CTNNA1, and ADORA2B in the disease group were all significantly upregulated (p < 0.01). Compared with the normal group, the expression of OXTR was upregulated. CONCLUSION: We speculate that there are 7 genes and 2 lncRNAs directly involved in the pain pathway: E2F1, MAX, MITF, CTNNA1, ADORA2B, GRIK3, OXTR, LINC01119, and LINC02447. These molecules may be important for NP after SCI.

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

BACKGROUND: Functional disruptions by large germline genomic structural variants in susceptible genes are known risks for cancer. We used deletion structural variants (DSVs) generated from germline whole-genome sequencing (WGS) and DSV immune-related association tumor microenvironment (TME) to predict cancer risk and prognosis. METHODS: We investigated the contribution of germline DSVs to cancer susceptibility and prognosis by silicon and causal inference models. DSVs in germline WGS data were generated from the blood samples of 192 cancer and 499 non-cancer subjects. Clinical information, including family cancer history (FCH), was obtained from the National Cheng Kung University Hospital and Taiwan Biobank. Ninety-nine colorectal cancer (CRC) patients had immune response gene expression data. We used joint calling tools and an attention-weighted model to build the cancer risk predictive model and identify DSVs in familial cancer. The survival support vector machine (survival-SVM) was used to select prognostic DSVs. RESULTS: We identified 671 DSVs that could predict cancer risk. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of the attention-weighted model was 0.71. The 3 most frequent DSV genes observed in cancer patients were identified as ADCY9, AURKAPS1, and RAB3GAP2 (p < 0.05). The DSVs in SGSM2 and LHFPL3 were relevant to colorectal cancer. We found a higher incidence of FCH in cancer patients than in non-cancer subjects (p < 0.05). SMYD3 and NKD2DSV genes were associated with cancer patients with FCH (p < 0.05). We identified 65 immune-associated DSV markers for assessing cancer prognosis (p < 0.05). The functional protein of MUC4 DSV gene interacted with MAGE1 expression, according to the STRING database. The causal inference model showed that deleting the CEP72 DSV gene affect the recurrence-free survival (RFS) of IFIT1 expression. CONCLUSIONS: We established an explainable attention-weighted model for cancer risk prediction and used the survival-SVM for prognostic stratification by using germline DSVs and immune gene expression datasets. Comprehensive assessments of germline DSVs can predict the cancer risk and clinical outcome of colon cancer patients.