Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Integrating trans-omics, cellular experiments and clinical validation to identify ILF2 as a diagnostic serum biomarker and therapeutic target in gastric cancer.

BACKGROUND: Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets. METHODS: A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments. RESULTS: Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629-0.950 in the TCGA dataset and 0.736-0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells. CONCLUSIONS: Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer.

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.

A Large-Scale Exome-Wide Association Study Identifies Novel Germline Mutations in Lung Cancer.

Rationale: Genome-wide association studies have identified common variants of lung cancer. However, the contribution of rare exome-wide variants, especially protein-coding variants, to cancers remains largely unexplored. Objectives: To evaluate the role of human exomes in genetic predisposition to lung cancer. Methods: We performed exome-wide association studies to detect the association of exomes with lung cancer in 30,312 patients and 652,902 control subjects. A scalable and accurate implementation of a generalized mixed model was used to detect the association signals for loss-of-function, missense, and synonymous variants and gene-level sets. Furthermore, we performed association and Bayesian colocalization analyses to evaluate their relationships with intermediate exposures. Measurements and Main Results: We systematically analyzed 216,739 single-nucleotide variants in the human exome. The loss-of-function variants exhibited the most notable effects on lung cancer risk. We identified four novel variants, including two missense variants (rs202197044TET3 [Pmeta (P values of meta-analysis) = 3.60 × 10-8] and rs202187871POT1 [Pmeta = 2.21 × 10-8]) and two synonymous variants (rs7447927TMEM173 [Pmeta = 1.32 × 10-9] and rs140624366ATRN [Pmeta = 2.97 × 10-9]). rs202197044TET3 was significantly associated with emphysema (odds ratio, 3.55; Pfdr = 0.015), whereas rs7447927POT1 was strongly associated with telomere length (ß = 1.08; Pfdr (FDR corrected P value) = 3.76 × 10-53). Functional evidence of expression of quantitative trait loci, splicing quantitative trait loci, and isoform expression was found for the four novel genes. Gene-level association tests identified several novel genes, including POT1 (protection of telomeres 1), RTEL1, BSG, and ZNF232. Conclusions: Our findings provide insights into the genetic architecture of human exomes and their role in lung cancer predisposition.

Liquid Crystal Monomer: A Potential PPARγ Antagonist.

Liquid crystal monomers (LCMs) are a large family of artificial ingredients that have been widely used in global liquid crystal display (LCD) industries. As a major constituent in LCDs as well as the end products of e-waste dismantling, LCMs are of growing research interest with regard to their environmental occurrences and biochemical consequences. Many studies have analyzed LCMs in multiple environmental matrices, yet limited research has investigated the toxic effects upon exposure to them. In this study, we combined in silico simulation and in vitro assay validation along with omics integration analysis to achieve a comprehensive toxicity elucidation as well as a systematic mechanism interpretation of LCMs for the first time. Briefly, the high-throughput virtual screen and reporter gene assay revealed that peroxisome proliferator-activated receptor gamma (PPARγ) was significantly antagonized by certain LCMs. Besides, LCMs induced global metabolome and transcriptome dysregulation in HK2 cells. Notably, fatty acid ß-oxidation was conspicuously dysregulated, which might be mediated through multiple pathways (IL-17, TNF, and NF-kB), whereas the activation of AMPK and ligand-dependent PPARγ antagonism may play particularly important parts. This study illustrated LCMs as a potential PPARγ antagonist and explored their toxicological mode of action on the trans-omics level, which provided an insightful overview in future chemical risk assessment.

Bone Trans-omics: Integrating Omics to Unveil Mechanistic Molecular Networks Regulating Bone Biology and Disease.

PURPOSE OF REVIEW: Recent advancements in "omics" technologies and bioinformatics have afforded researchers new tools to study bone biology in an unbiased and holistic way. The purpose of this review is to highlight recent studies integrating multi-omics data gathered from multiple molecular layers (i.e.; trans-omics) to reveal new molecular mechanisms that regulate bone biology and underpin skeletal diseases. RECENT FINDINGS: Bone biologists have traditionally relied on single-omics technologies (genomics, transcriptomics, proteomics, and metabolomics) to profile measureable differences (both qualitative and quantitative) of individual molecular layers for biological discovery and to investigate mechanisms of disease. Recently, literature has grown on the implementation of integrative multi-omics to study bone biology, which combines computational and informatics support to connect multiple layers of data derived from individual "omic" platforms. This emerging discipline termed "trans-omics" has enabled bone biologists to identify and construct detailed molecular networks, unveiling new pathways and unexpected interactions that have advanced our mechanistic understanding of bone biology and disease. While the era of trans-omics is poised to revolutionize our capacity to answer more complex and diverse questions pertinent to bone pathobiology, it also brings new challenges that are inherent when trying to connect "Big Data" sets. A concerted effort between bone biologists and interdisciplinary scientists will undoubtedly be needed to extract physiologically and clinically meaningful data from bone trans-omics in order to advance its implementation in the field.

Quantitative metabolic fluxes regulated by trans-omic networks.

Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.

Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program.

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12% in African-Americans, MAF = 2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

Sequencing Analysis at 8p23 Identifies Multiple Rare Variants in DLC1 Associated with Sleep-Related Oxyhemoglobin Saturation Level.

Average arterial oxyhemoglobin saturation during sleep (AvSpO2S) is a clinically relevant measure of physiological stress associated with sleep-disordered breathing, and this measure predicts incident cardiovascular disease and mortality. Using high-depth whole-genome sequencing data from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) project and focusing on genes with linkage evidence on chromosome 8p23,1,2 we observed that six coding and 51 noncoding variants in a gene that encodes the GTPase-activating protein (DLC1) are significantly associated with AvSpO2S and replicated in independent subjects. The combined DLC1 association evidence of discovery and replication cohorts reaches genome-wide significance in European Americans (p = 7.9 × 10-7). A risk score for these variants, built on an independent dataset, explains 0.97% of the AvSpO2S variation and contributes to the linkage evidence. The 51 noncoding variants are enriched in regulatory features in a human lung fibroblast cell line and contribute to DLC1 expression variation. Mendelian randomization analysis using these variants indicates a significant causal effect of DLC1 expression in fibroblasts on AvSpO2S. Multiple sources of information, including genetic variants, gene expression, and methylation, consistently suggest that DLC1 is a gene associated with AvSpO2S.

A general framework for integrative analysis of incomplete multiomics data.

There is a tremendous current interest in measuring multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation profiles, metabolic profiles, protein expressions) on a large number of subjects. Although genotypes are typically available for all study subjects, other data types may be measured only on a subset of subjects due to cost or other constraints. In addition, quantitative omics measurements, such as metabolite levels and protein expressions, are subject to detection limits in that the measurements below (or above) certain thresholds are not detectable. In this article, we propose a rigorous and powerful approach to handle missing values and detection limits in integrative analysis of multiomics data. We relate quantitative omics variables to genetic variants and other variables through linear regression models and relate phenotypes to quantitative omics variables and other variables through generalized linear models. We derive the joint-likelihood for the two sets of models by allowing arbitrary patterns of missing values and detection limits for quantitative omics variables. We carry out maximum-likelihood estimation through computationally fast and stable algorithms. The resulting estimators are approximately unbiased and statistically efficient. An application to a major study on chronic obstructive lung disease yielded new biological insights.

Reconstruction of global regulatory network from signaling to cellular functions using phosphoproteomic data.

Cellular signaling regulates various cellular functions via protein phosphorylation. Phosphoproteomic data potentially include information for a global regulatory network from signaling to cellular functions, but a procedure to reconstruct this network using such data has yet to be established. In this paper, we provide a procedure to reconstruct a global regulatory network from signaling to cellular functions from phosphoproteomic data by integrating prior knowledge of cellular functions and inference of the kinase-substrate relationships (KSRs). We used phosphoproteomic data from insulin-stimulated Fao hepatoma cells and identified protein phosphorylation regulated by insulin specifically over-represented in cellular functions in the KEGG database. We inferred kinases for protein phosphorylation by KSRs, and connected the kinases in the insulin signaling layer to the phosphorylated proteins in the cellular functions, revealing that the insulin signal is selectively transmitted via the Pi3k-Akt and Erk signaling pathways to cellular adhesions and RNA maturation, respectively. Thus, we provide a method to reconstruct global regulatory network from signaling to cellular functions based on phosphoproteomic data.

Concluding Remarks.

The regulation of immune checkpoint is a pivotal mechanism mediating both self-tolerance physiologically and tumor immune evasion pathologically. Along with an increasing number of identified checkpoint ligand-receptor pairs, the complexity of regulation at genetic, epigenetic, transcriptional, translational, and post-translational levels makes it highly challenging to assemble a comprehensive regulatory network. Advanced animal models are required for determining the exact regulatory effects, given the differences in human and mouse immune systems. Our further understanding on checkpoint regulation may energize translational studies aimed to improve cancer immunotherapy, and collaborations between researchers with different expertise would help to tackle existing challenges in this field.

Proteogenomic Analyses of Cellular Lysates Using a Phenol-Guanidinium Thiocyanate Reagent.

Protein and RNA profiles are highly informative when generating a comprehensive understanding of the dynamics of complex biological systems. Quantitative correlations between protein and RNA profiles are not always high, yet simultaneous acquisition of both profiles remains challenging, in part because of the limited availability of samples and the inconvenience of separately preparing protein and RNA fractions. In a previous study, protein, DNA, and RNA fractions were simultaneously prepared from the same sample using phenol-guanidinium isothiocyanate reagent (P/GTC), although the performance of P/GTC-extracts in proteogenomic analyses remains poorly understood. We therefore evaluated the performance of the P/GTC-extraction method in proteogenomic analyses using standard HEK293-F cells and human peripheral blood neutrophils. The latter cell type is renowned for its extreme vulnerability to protein/RNA degradation, reflecting high protease and RNase activities. Our data indicate that the P/GTC extraction method provides superior protein profiles from neutrophil and HEK293-F cell samples for simultaneous preparation of RNA and protein, as compared with those from conventional protein extraction methods. The P/GTC extraction method therefore provides a powerful and robust tool for a broad range of proteogenomic studies.

Comparative multi-omics analysis reveals diverse latex-based defense strategies against pests among latex-producing organs of the fig tree (Ficus carica).

MAIN CONCLUSION: Latexes in immature fruit, young petioles and lignified trunks of fig trees protect the plant using toxic proteins and metabolites in various organ-dependent ways. Latexes from plants contain high amounts of toxic proteins and metabolites, which attack microbes and herbivores after exudation at pest-induced wound sites. The protein and metabolite constituents of latexes are highly variable, depending on the plant species and organ. To determine the diversity of latex-based defense strategies in fig tree (Ficus carica) organs, we conducted comparative proteomic, transcriptomic and metabolomic analyses on latexes isolated from immature fruit, young petioles and lignified trunks of F. carica after constructing a unigene sequence library using RNA-seq data. Trypsin inhibitors were the most abundant proteins in petiole latex, while cysteine proteases ("ficins") were the most abundant in immature fruit and trunk latexes. Galloylglycerol, a possible defense-related metabolite, appeared to be highly accumulated in all three latexes. The expression levels of pathogenesis-related proteins were highest in the latex of trunk, suggesting that this latex had adapted a defensive role against microbe attacks. Although young petioles and immature fruit are both unlignified soft organs, and potential food for herbivorous insects, unigenes for the sesquiterpenoid pathway, which likely produces defense-associated volatiles, and the phenylpropanoid pathway, which produces toxic furanocoumarins, were expressed less in immature fruit latex. This difference may indicate that while petioles and fruit protect the plant from attack by herbivores, the fruit must also attract insect pollinators at younger stages and animals after ripening. We also suggest possible candidate transcription factors and signal transduction proteins that are involved in the differential expression of the unigenes.

Trans-omic profiling uncovers molecular controls of early human cerebral organoid formation.

Defining the molecular networks orchestrating human brain formation is crucial for understanding neurodevelopment and neurological disorders. Challenges in acquiring early brain tissue have incentivized the use of three-dimensional human pluripotent stem cell (hPSC)-derived neural organoids to recapitulate neurodevelopment. To elucidate the molecular programs that drive this highly dynamic process, here, we generate a comprehensive trans-omic map of the phosphoproteome, proteome, and transcriptome of the exit of pluripotency and neural differentiation toward human cerebral organoids (hCOs). These data reveal key phospho-signaling events and their convergence on transcriptional factors to regulate hCO formation. Comparative analysis with developing human and mouse embryos demonstrates the fidelity of our hCOs in modeling embryonic brain development. Finally, we demonstrate that biochemical modulation of AKT signaling can control hCO differentiation. Together, our data provide a comprehensive resource to study molecular controls in human embryonic brain development and provide a guide for the future development of hCO differentiation protocols.

Novel lipidomes profile and clinical phenotype identified in pneumoconiosis patients.

BACKGROUND: Pneumoconiosis is a group of occupational lung diseases caused by the inhalation of mineral dust in the lungs, leading to lung dysfunction. Patients with pneumoconiosis are usually accompanied by weight loss, which suggests a lipid metabolism disorder. Recent progress in lipidomics uncovered detailed lipid profiles that play important roles in respiratory diseases, such as asthma, lung cancer and lung injury. The purpose of this study was to shed light on the different expression of lipidome between pneumoconiosis and healthy, hoping to bring new ideas for the diagnosis and treatment of pneumoconiosis. METHODOLOGY: This non-matching case-control study was performed among 96 subjects (48 outpatients with male pneumoconiosis and 48 healthy volunteers), data of clinical phenotypes were recorded, and plasma biochemistry (lipidomic profiles) was tested for both pneumoconiosis patients and healthy controls. A total of 426 species in 11 lipid classes were analyzed by high-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (HPLC-QqQ-MS) for the cases and controls. We also analyzed the correlation of lipid profiles with clinical phenomes from pneumoconiosis patients by expression quantitative trait locus (eQTL) model to evaluate trans-nodules between lipidomic profiles and clinical phenomes. All visually re-checked data were analyzed using appropriate statistical tools (t-test or one-way ANOVA test) on SPSS. RESULTS: Compared with healthy people, 26 significantly increased (> 1.5-fold) and 30 decreased lipid elements (< 2/threefold) in patients with pneumoconiosis were identified (P values all < 0.05). The majority of those elevated lipid elements were phosphatidylethanolamines (PEs), and the minority were free fatty acids (FFAs), while phosphatidylcholines (PCs) and lysophosphatidylcholines (lysoPCs) declined in pneumoconiosis. Clinical trans-omics analyses demonstrated that phenomes in pneumoconiosis connections with multiple lipids, which showed that pH, lung function, mediastinal lymph node calcification, and complication were highly correlated with lipid elements. Furthermore, up-regulated PE was corresponded to pH, smoking history and mediastinal lymph node calcification. PC was corresponded to dust exposure history, BMI and mediastinal lymph node calcification. CONCLUSION: We found altered lipid panels between male pneumoconiosis patients and healthy people by qualitatively and quantitatively measured plasma lipidomic profiles. The trans-omic analysis between clinical phenomes and lipidomes might have the potential to uncover the heterogeneity of lipid metabolism of pneumoconiosis patients and to screen out clinically significant phenome-based lipid panels.

Associations between prefrontal PI (16:0/20:4) lipid, TNC mRNA, and APOA1 protein in schizophrenia: A trans-omics analysis in post-mortem brain.

Background: Though various mechanisms have been proposed for the pathophysiology of schizophrenia, the full extent of these mechanisms remains unclear, and little is known about the relationships among them. We carried out trans-omics analyses by comparing the results of the previously reported lipidomics, transcriptomics, and proteomics analyses; all of these studies used common post-mortem brain samples. Methods: We collected the data from three aforementioned omics studies on 6 common post-mortem samples (3 schizophrenia patients and 3 controls), and analyzed them as a whole group sample. Three correlation analyses were performed for each of the two of three omics studies in these samples. In order to discuss the strength of the correlations in a limited sample size, the p-values of each correlation coefficient were confirmed using the Student's t-test. In addition, partial correlation analysis was also performed for some correlations, to verify the strength of the impact of each factor on the correlations. Results: The following three factors were strongly correlated with each other: the lipid level of phosphatidylinositol (PI) (16:0/20:4), the amount of TNC mRNA, and the quantitative signal intensity of APOA1 protein. PI (16:0/20:4) and TNC showed a positive correlation, while PI (16:0/20:4) and APOA1, and TNC and APOA1 showed negative correlations. All of these correlations reached at p < 0.01. PI (16:0/20:4) and TNC were decreased in the prefrontal cortex of schizophrenia samples, while APOA1 was increased. Partial correlation analyses among them suggested that PI (16:0/20:4) and TNC have no direct correlation, but their relationships are mediated by APOA1. Conclusion: The current results suggest that these three factors may provide new clues to elucidate the relationships among the candidate mechanisms of schizophrenia, and support the potential of trans-omics analyses as a new analytical method.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC.

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with PBonferroni ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (ßinteraction  = 0.018, P = 1.87 × 10-12 ), which mapped to RELA × HLA-G (ßinteraction  = 0.218, P = 8.82 × 10-11 ) and cg08872738 × cg27077312 (ßinteraction  = -0.010, P = 1.16 × 10-11 ), which mapped to TUBA1B × TOMM40 (ßinteraction =-0.250, P = 3.83 × 10-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

Causal genetic regulation of DNA replication on immune microenvironment in colorectal tumorigenesis: Evidenced by an integrated approach of trans-omics and GWAS.

The interplay between DNA replication stress and immune microenvironment alterations is known to play a crucial role in colorectal tumorigenesis, but a comprehensive understanding of their association with and relevant biomarkers involved in colorectal tumorigenesis is lacking. To address this gap, we conducted a study aiming to investigate this association and identify relevant biomarkers. We analyzed transcriptomic and proteomic profiles of 904 colorectal tumor tissues and 342 normal tissues to examine pathway enrichment, biological activity, and the immune microenvironment. Additionally, we evaluated genetic effects of single variants and genes on colorectal cancer susceptibility using data from genome-wide association studies (GWASs) involving both East Asian (7062 cases and 195745 controls) and European (24476 cases and 23073 controls) populations. We employed mediation analysis to infer the causal pathway, and applied multiplex immunofluorescence to visualize colocalized biomarkers in colorectal tumors and immune cells. Our findings revealed that both DNA replication activity and the flap structure-specific endonuclease 1 ( FEN1) gene were significantly enriched in colorectal tumor tissues, compared with normal tissues. Moreover, a genetic variant rs4246215 G>T in FEN1 was associated with a decreased risk of colorectal cancer (odds ratio = 0.94, 95% confidence interval: 0.90-0.97, P meta = 4.70 × 10 -9). Importantly, we identified basophils and eosinophils that both exhibited a significantly decreased infiltration in colorectal tumors, and were regulated by rs4246215 through causal pathways involving both FEN1 and DNA replication. In conclusion, this trans-omics incorporating GWAS data provides insights into a plausible pathway connecting DNA replication and immunity, expanding biological knowledge of colorectal tumorigenesis and therapeutic targets.

Ever-expanding NGLY1 biology.

The cytosolic peptide:N-glycanase (PNGase; NGLY1 in humans) is a deglycosylating enzyme that is widely conserved in eukaryotes. This enzyme is involved in the degradation of misfolded N-glycoproteins that are destined for proteasomal degradation in the cytosol, a process that is called endoplasmic reticulum-associated degradation. Although the physiological significance of NGLY1 remained unknown until recently, the discovery of NGLY1 deficiency, a human genetic disorder bearing mutations in the NGLY1 gene, has led to explosive research progress regarding the functional characterization of this enzyme. For example, it is now known that NGLY1 can also act as an 'editing enzyme' to convert N-glycosylated asparagine residues to aspartate residues, thus introducing negative charges into a core peptide and modulating the function of the target molecule. Diverse biological processes have also been found to be affected by compromised NGLY1 activity. In this special issue, recent research progress on the functional characterization of NGLY1 and its orthologues in worm/fly/rodents, assay methods/biomarkers useful for the development of therapeutics and the comprehensive transcriptome/proteome of NGLY1-KO cells as well as patient-derived cells are discussed.