A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19.

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.

SCovid: single-cell atlases for exposing molecular characteristics of COVID-19 across 10 human tissues.

SCovid (http://bio-annotation.cn/scovid) aims at providing a comprehensive resource of single-cell data for exposing molecular characteristics of coronavirus disease 2019 (COVID-19) across 10 human tissues. COVID-19, an epidemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been found to be accompanied with multiple-organ failure since its first report in Dec 2019. To reveal tissue-specific molecular characteristics, researches regarding to COVID-19 have been carried out widely, especially at single-cell resolution. However, these researches are still relatively independent and scattered, limiting the comprehensive understanding of the impact of virus on diverse tissues. To this end, we developed a single-cell atlas of COVID-19. Firstly we collected 21 single-cell datasets of COVID-19 across 10 human tissues paired with control datasets. Then we constructed a pipeline for the analysis of these datasets to reveal molecular characteristics of COVID-19 based on manually annotated cell types. The current version of SCovid documents 1 042 227 single cells of 21 single-cell datasets across 10 human tissues, 11 713 stably expressed genes and 3778 significant differentially expressed genes (DEGs). SCovid provides a user-friendly interface for browsing, searching, visualizing and downloading all detailed information.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective.

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

Functional alterations caused by mutations reflect evolutionary trends of SARS-CoV-2.

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, the COVID-19 pandemic has spread rapidly worldwide. Due to the limited virus strains, few key mutations that would be very important with the evolutionary trends of virus genome were observed in early studies. Here, we downloaded 1809 sequence data of SARS-CoV-2 strains from GISAID before April 2020 to identify mutations and functional alterations caused by these mutations. Totally, we identified 1017 nonsynonymous and 512 synonymous mutations with alignment to reference genome NC_045512, none of which were observed in the receptor-binding domain (RBD) of the spike protein. On average, each of the strains could have about 1.75 new mutations each month. The current mutations may have few impacts on antibodies. Although it shows the purifying selection in whole-genome, ORF3a, ORF8 and ORF10 were under positive selection. Only 36 mutations occurred in 1% and more virus strains were further analyzed to reveal linkage disequilibrium (LD) variants and dominant mutations. As a result, we observed five dominant mutations involving three nonsynonymous mutations C28144T, C14408T and A23403G and two synonymous mutations T8782C, and C3037T. These five mutations occurred in almost all strains in April 2020. Besides, we also observed two potential dominant nonsynonymous mutations C1059T and G25563T, which occurred in most of the strains in April 2020. Further functional analysis shows that these mutations decreased protein stability largely, which could lead to a significant reduction of virus virulence. In addition, the A23403G mutation increases the spike-ACE2 interaction and finally leads to the enhancement of its infectivity. All of these proved that the evolution of SARS-CoV-2 is toward the enhancement of infectivity and reduction of virulence.

An integrated strategy to identify COVID-19 causal genes and characteristics represented by LRRC37A2.

Genome-wide association study (GWAS) could identify host genetic factors associated with coronavirus disease 2019 (COVID-19). The genes or functional DNA elements through which genetic factors affect COVID-19 remain uncharted. The expression quantitative trait locus (eQTL) provides a path to assess the correlation between genetic variations and gene expression. Here, we firstly annotated GWAS data to describe genetic effects, obtaining genome-wide mapped genes. Subsequently, the genetic mechanisms and characteristics of COVID-19 were investigated by an integrated strategy that included three GWAS-eQTL analysis approaches. It was found that 20 genes were significantly associated with immunity and neurological disorders, including prior and novel genes such as OAS3 and LRRC37A2. The findings were then replicated in single-cell datasets to explore the cell-specific expression of causal genes. Furthermore, associations between COVID-19 and neurological disorders were assessed as a causal relationship. Finally, the effects of causal protein-coding genes of COVID-19 were discussed using cell experiments. The results revealed some novel COVID-19-related genes to emphasize disease characteristics, offering a broader insight into the genetic architecture underlying the pathophysiology of COVID-19.

Meta-analysis of quality of life in patients with cancer treated with antibody-drug conjugates in randomized controlled trials.

Purpose: To evaluate the patient-reported outcomes of patients treated with commercially approved antibody-drug conjugates (ADC) reported in randomized controlled trials (RCT) published up to September 2023. Methods: A meta-analysis of 6430 patients from 12 randomized controlled trials was conducted. Results: No significant change was observed between the groups from baseline to end of treatment and end of follow-up, with a standardized mean difference of -0.08 (95% CI: -0.27-0.12) and 0.01 (95% CI: -0.11-0.12), respectively. Treatment with ADCs delayed the deterioration of patients' clinical condition compared with treatment with non-ADCs, with a hazard ratio of 0.78 (95% CI: 0.67-0.92). Conclusion: ADCs have a good correlation with delay of clinical deterioration in patients with cancer.

Study on Rapid Detection of Pesticide Residues in Shanghaiqing Based on Analyzing Near-Infrared Microscopic Images.

Aiming at guiding agricultural producers to harvest crops at an appropriate time and ensuring the pesticide residue does not exceed the maximum limit, the present work proposed a method of detecting pesticide residue rapidly by analyzing near-infrared microscopic images of the leaves of Shanghaiqing (Brassica rapa), a type of Chinese cabbage with computer vision technology. After image pre-processing and feature extraction, the pattern recognition methods of K nearest neighbors (KNN), naïve Bayes, support vector machine (SVM), and back propagation artificial neural network (BP-ANN) were applied to assess whether Shanghaiqing is sprayed with pesticides. The SVM method with linear or RBF kernel provides the highest recognition accuracy of 96.96% for the samples sprayed with trichlorfon at a concentration of 1 g/L. The SVM method with RBF kernel has the highest recognition accuracy of 79.16~84.37% for the samples sprayed with cypermethrin at a concentration of 0.1 g/L. The investigation on the SVM classification models built on the samples sprayed with cypermethrin at different concentrations shows that the accuracy of the models increases with the pesticide concentrations. In addition, the relationship between the concentration of the cypermethrin sprayed and the image features was established by multiple regression to estimate the initial pesticide concentration on the Shanghaiqing leaves. A pesticide degradation equation was established on the basis of the first-order kinetic equation. The time for pesticides concentration to decrease to an acceptable level can be calculated on the basis of the degradation equation and the initial pesticide concentration. The present work provides a feasible way to rapidly detect pesticide residue on Shanghaiqing by means of NIR microscopic image technique. The methodology laid out in this research can be used as a reference for the pesticide detection of other types of vegetables.

Blood microbial analyses reveal long-term effects of SARS-CoV-2 infection on patients who recovered from COVID-19.

OBJECTIVE: Few symptoms persist for a long time after patients recover from COVID-19, called "long COVID". We explored the potential microbial risk factors for COVID-19 for a deeper understanding and assistance in the follow-up treatment of these sequelae. METHODS: Microbiome re-annotation was performed using whole blood RNA-Seq data collected from recovered COVID-19 patients and healthy controls at multiple time points. Subsequently, a series of downstream analyses were conducted to reveal the microbial characteristics of patients who recovered from SARS-CoV-2 infection. RESULTS: The blood microbiome at 12 weeks post-infection was most evidently disturbed, including an increasing ratio of Bacillota/Bacteroidota and a higher microbial alpha diversity. In addition, a group of pathogenic microbes at 12 weeks post-infection were identified, including Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa, which were positively associated with host genes involved in immune regulatory and olfactory transduction pathways. Several microbes, such as Streptococcus pneumoniae were associated with infiltrating immune cells, such as M2 macrophages. CONCLUSION: This study provides insights into the relationship between the blood microbiome and COVID-19 sequelae. Several pathogenic microbes were enriched in recovered COVID-19 patients and thus affected host genes participating in the immune and olfactory transduction pathways, which play critical roles in COVID-19 sequelae.

Epstein-Barr Virus Promotes Inflammatory Cytokine Production in Human Gingival Fibroblasts.

BACKGROUND: Periodontitis is one of the most common chronic oral inflammatory diseases. Over the past decade, herpes viruses, particularly Epstein-Barr virus (EBV), have been considered promising pathogenic candidates for periodontitis. However, the specific mechanism by which EBV contributes to the development of periodontitis is still unknown. This study aimed to explore the mechanism of EBV underlying the inflammatory response in human gingival fibroblasts (HGFs). MATERIALS AND METHODS: HGFs were stimulated with different concentrations of EBV (104, 105, 106, 107, and 108 DNA copies/mL) for 0, 8, 24, or 48 hours. The mRNA levels of interleukin (IL)-1ß, tumour necrosis factor-α (TNF-α), IL-8, monocyte chemoattractant protein-1 (MCP-1), and Toll-like receptor 9 (TLR9) were measured using quantitative real-time polymerase chain reaction (PCR). Enzyme-linked immunosorbent assays (ELISAs) were performed for determining the mRNA and protein levels of IL-1ß, TNF-α, IL-8, and MCP-1. Real-time PCR and ELISA were performed to determine the protein levels of IL-1ß, TNF-α, IL-8, and MCP-1. Activation of the TLR9/myeloid differentiation factor 88 (MyD88)/nuclear factor kappa B (NF-κB) pathway was evaluated using western blotting. RESULTS: The expressions of IL-1ß, TNF-α, IL-8, and MCP-1 were significantly upregulated in HGFs under EBV stimulation in a concentration- and time-dependent manner. EBV promoted TLR9 and MyD88 expression and induced NF-κB transcription. On the contrary, the upregulation of these factors and the activation of NF-κB pathway were drastically inhibited by TLR9 antagonists. CONCLUSIONS: Our findings demonstrate that EBV promotes the production of inflammatory cytokines IL-1ß and TNF-α and chemokines IL-8 and MCP-1 in HGFs through the TLR9/MyD88/NF-κB pathway.

Design of Broadband High-Frequency Multi-Throw RF-MEMS Switches.

This paper introduces a broadband triple-pole triple-throw (3P3T) RF MEMS switch with a frequency range from DC to 380 GHz. The switch achieves precise signal control and efficient modulation through its six-port design. It achieves an insertion loss of -0.66 dB across its frequency range, with isolation and return loss metrics of -32 dB and -15 dB, respectively. With its low actuation voltage of 6.8 V and rapid response time of 2.28 µs, the switch exemplifies power-efficient and prompt switching performance. The compact design is ideal for integration into space-conscious systems. This switch is pivotal for 6G research and has potential applications in satellite communications, military radar systems, and next-generation radio applications that require multi-antenna access.

Exploring COVID-19 causal genes through disease-specific Cis-eQTLs.

Genome-wide association study (GWAS) analysis has exposed that genetic factors play important roles in COVID-19. Whereas a deeper understanding of the underlying mechanism of COVID-19 was hindered by the lack of expression of quantitative trait loci (eQTL) data specific for disease. To this end, we identified COVID-19-specific cis-eQTLs by integrating nucleotide sequence variations and RNA-Seq data from COVID-19 samples. These identified eQTLs have different regulatory effect on genes between patients and controls, indicating that SARS-CoV-2 infection may cause alterations in the human body's internal environment. Individuals with the TT genotype in the rs1128320 region seemed more susceptible to SARS-CoV-2 infection and developed into severe COVID-19 due to the abnormal expression of IFITM1. We subsequently discovered potential causal genes, of the result, a total of 48 genes from six tissues were identified. siRNA-mediated depletion assays in SARS-CoV-2 infection proved that 14 causal genes were directly associated with SARS-CoV-2 infection. These results enriched existing research on COVID-19 causal genes and provided a new sight in the mechanism exploration for COVID-19.

Protein O-fucosyltransferase 1 promotes PD-L1 stability to drive immune evasion and directs liver cancer to immunotherapy.

BACKGROUND AND AIMS: The immunosuppressive tumor microenvironment (TME) plays an essential role in cancer progression and immunotherapy response. Despite the considerable advancements in cancer immunotherapy, the limited response to immune checkpoint blockade (ICB) therapies in patients with hepatocellular carcinoma (HCC) remains a major challenge for its clinical implications. Here, we investigated the molecular basis of the protein O-fucosyltransferase 1 (POFUT1) that drives HCC immune evasion and explored a potential therapeutic strategy for enhancing ICB efficacy. METHODS: De novo MYC/Trp53-/- liver tumor and the xenograft tumor models were used to evaluate the function of POFUT1 in immune evasion. Biochemical assays were performed to elucidate the underlying mechanism of POFUT1-mediated immune evasion. RESULTS: We identified POFUT1 as a crucial promoter of immune evasion in liver cancer. Notably, POFUT1 promoted HCC progression and inhibited T-cell infiltration in the xenograft tumor and de novo MYC/Trp53-/- mouse liver tumor models. Mechanistically, we demonstrated that POFUT1 stabilized programmed death ligand 1 (PD-L1) protein by preventing tripartite motif containing 21-mediated PD-L1 ubiquitination and degradation independently of its protein-O-fucosyltransferase activity. In addition, we further demonstrated that PD-L1 was required for the tumor-promoting and immune evasion effects of POFUT1 in HCC. Importantly, inhibition of POFUT1 could synergize with anti-programmed death receptor 1 therapy by remodeling TME in the xenograft tumor mouse model. Clinically, POFUT1 high expression displayed a lower response rate and worse clinical outcome to ICB therapies. CONCLUSIONS: Our findings demonstrate that POFUT1 functions as a novel regulator of tumor immune evasion and inhibition of POFUT1 may be a potential therapeutic strategy to enhance the efficacy of immune therapy in HCC.

Polypyrrole-Modified Nanocellulose Exhibits Superior Performance for Hg(II) Adsorption.

Cellulose, a kind of polymer containing abundant functional groups, has widespread use in the adsorptive removal of environmental pollutants. An efficient and environmental friendly polypyrrole (PPy) coating approach is employed to modify the agricultural by-product straw derived cellulose nanocrystal (CNC) into excellent property adsorbents for removing the heavy metal ion of Hg(II). The FT-IR and SEM-EDS results demonstrated that PPy is formed on the surface of CNC. Consequently, the adsorption measurements proved that the obtained PPy-modified CNC (CNC@PPy) possesses a remarkably enhanced Hg(II) adsorption capacity of 1095 mg g-1, owing to a plentiful functional group of doped Cl element on the surface of CNC@PPy by forming Hg2Cl2 precipitate. The results of the study suggest that the Freundlich model is more effective than the Langmuir model at describing the isotherms, while the pseudo-second order kinetic model is better suited to correlating with the experimental data compared to the pseudo-first order model. Further, the CNC@PPy exhibits an outstanding reusability, capable of maintaining 82.3% of its original Hg(II) adsorption capacity after five successive adsorption cycles. The findings of this work reveal a method to convert the agricultural by-product into high performance environmental remediation materials.

KDM6A promotes hepatocellular carcinoma progression and dictates lenvatinib efficacy by upregulating FGFR4 expression.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the major causes of death from cancer and has a very poor prognosis with few effective therapeutic options. Despite the approval of lenvatinib for the treatment of patients suffering from advanced HCC, only a small number of patients can benefit from this targeted therapy. METHODS: Diethylnitrosamine (DEN)-CCL4 mouse liver tumour and the xenograft tumour models were used to evaluate the function of KDM6A in HCC progression. The xenograft tumour model and HCC cell lines were used to evaluate the role of KDM6A in HCC drug sensitivity to lenvatinib. RNA-seq and ChIP assays were conducted for mechanical investigation. RESULTS: We revealed that KDM6A exhibited a significant upregulation in HCC tissues and was associated with an unfavourable prognosis. We further demonstrated that KDM6A knockdown remarkably suppressed HCC cell proliferation and migration in vitro. Moreover, hepatic Kdm6a loss also inhibited liver tumourigenesis in a mouse liver tumour model. Mechanistically, KDM6A loss downregulated the FGFR4 expression to suppress the PI3K-AKT-mTOR signalling pathway, leading to a glucose and lipid metabolism re-programming in HCC. KDM6A and FGFR4 levels were positively correlated in HCC specimens and mouse liver tumour tissues. Notably, KDM6A knockdown significantly inhibited the efficacy of lenvatinib therapy in HCC cells in vitro and in vivo. CONCLUSIONS: Our findings revealed that KDM6A promoted HCC progression by activating FGFR4 expression and may be an essential molecule for influencing the efficacy of lenvatinib in HCC therapy.

Incidence of antibody-drug conjugates-related pneumonitis in patients with solid tumors: A systematic review and meta-analysis.

BACKGROUND: Antibody-drug conjugates (ADCs) have demonstrated significant efficacy in treating solid tumors. However, the occurrence of ADC drug-associated pneumonitis can limit the use of ADCs or have severe consequences, and we know comparatively little about this. METHODS: PubMed, EMBASE, and the Cochrane library were exhaustively searched for articles and conference abstracts published before September 30, 2022. Two authors independently extracted data from the included studies. A random-effects model was used to conduct a meta-analysis of the relevant outcomes. Forest plots reflected the incidence rates from each study, and binomial methods were used to calculate the 95 % confidence interval. RESULTS: This meta-analysis included 7732 patients from 39 studies and evaluated the incidence of ADCs drug-associated pneumonitis which have received market approval for the treatment of solid tumors. The total incidence of solid tumors for all-grade pneumonitis was 5.86 % (95 % CI, 3.54-8.66 %) and for grade ≥3 was 0.68 % (95 % CI, 0.18-1.38 %). The incidence of all-grade pneumonitis was 5.08 % (95 % CI, 2.76-7.96 %) and for grade ≥3 was 0.57 % (95 % CI, 0.10-1.29 %) with ADC monotherapy. The incidence of all-grade and grade ≥3 pneumonitis in trastuzumab deruxtecan (T-DXd) was 13.58 % (95 % CI, 9.43-18.29 %) and 2.19 % (95 % CI, 0.94-3.81 %), respectively, the highest in ADC therapy. Total incidence of all-grade pneumonitis was 10.58 % (95 % CI, 4.34-18.81 %) and for grade ≥3 pneumonitis was 1.29 % (95 % CI, 0.22-2.92 %) with ADC combination therapy. The incidence of pneumonitis was higher with combination therapy than with monotherapy in both all-grade and grade ≥3 groups, but there was no statistical significance (P = .138 and P = .281, respectively). The incidence of ADC-associated pneumonitis in non-small cell lung cancer (NSCLC) was 22.18 % (95 % CI, 2.14-52.61 %), the highest among solid tumors. The 11 included studies reported 21 pneumonitis-related deaths. CONCLUSIONS: Our findings will assist clinicians in choosing the optimal therapeutic options for patients with solid tumors treated with ADCs.

Identification of Gene Signature Associated with Type 2 Diabetes Mellitus by Integrating Mutation and Expression Data.

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) is a chronic disease. The molecular diagnosis should be helpful for the treatment of T2DM patients. With the development of sequencing technology, a large number of differentially expressed genes were identified from expression data. However, the method of machine learning can only identify the local optimal solution as the signature. OBJECTIVE: The mutation information obtained by inheritance can better reflect the relationship between genes and diseases. Therefore, we need to integrate mutation information to more accurately identify the signature. METHODS: To this end, we integrated Genome-Wide Association Study (GWAS) data and expression data, combined with expression Quantitative Trait Loci (eQTL) technology to get T2DM predictive signature (T2DMSig-10). Firstly, we used GWAS data to obtain a list of T2DM susceptible loci. Then, we used eQTL technology to obtain risk Single Nucleotide Polymorphisms (SNPs), and combined with the pancreatic ß-cells gene expression data to obtain 10 protein-coding genes. Next, we combined these genes with equal weights. RESULTS: After Receiver Operating Characteristic (ROC), single-gene removal and increase method, gene ontology function enrichment and protein-protein interaction network were used to verify the results showed that T2DMSig-10 had an excellent predictive effect on T2DM (AUC=0.99), and was highly robust. CONCLUSION: In short, we obtained the predictive signature of T2DM, and further verified it.

Genome-wide Mendelian randomization and single-cell RNA sequencing analyses identify the causal effects of COVID-19 on 41 cytokines.

The elevated levels of inflammatory cytokines have attracted much attention during the treatment of COVID-19 patients. The conclusions of current observational studies are often controversial in terms of the causal effects of COVID-19 on various cytokines because of the confounding factors involving underlying diseases. To resolve this problem, we conducted a Mendelian randomization analysis by integrating the GWAS data of COVID-19 and 41 cytokines. As a result, the levels of 2 cytokines were identified to be promoted by COVID-19 and had unsignificant pleiotropy. In comparison, the levels of 10 cytokines were found to be inhibited and had unsignificant pleiotropy. Among down-regulated cytokines, CCL2, CCL3 and CCL7 were members of CC chemokine family. We then explored the potential molecular mechanism for a significant causal association at a single cell resolution based on single-cell RNA data, and discovered the suppression of CCL3 and the inhibition of CCL3-CCR1 interaction in classical monocytes (CMs) of COVID-19 patients. Our findings may indicate that the capability of COVID-19 in decreasing the chemotaxis of lymphocytes by inhibiting the CCL3-CCR1 interaction in CMs.