Blood multiomics reveal insights into population clusters with low prevalence of diabetes, dyslipidemia and hypertension

Integrating multi-omics data of childhood asthma using a deep association model

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases.

LIMA1 links the E3 ubiquitin ligase RNF40 to lipid metabolism

LIMA1 is a LIM domain and Actin binding 1 protein that acts as a skeleton protein to promote cholesterol absorption, which makes it an ideal target for interfering with lipid metabolism. However, the detailed regulation of LIMA1 remains unclear. Here, we identified that ring finger protein 40 (RNF40), an E3 ubiquitin ligase previously known as an epigenetic modifier to increase H2B ubiquitination, mediated the ubiquitination of LIMA1 and thereby promoted its degradation in a proteasome-dependent manner. Fraction studies revealed that the 1-166aa fragment of LIMA1 was indispensable for the interaction with RNF40, and at least two domains of RNF40 might mediate the association of RNF40 with LIMA1. Notably, treatment with simvastatin dramatically decreased the levels of CHO and TG in control cells rather than cells with overexpressed LIMA1. Moreover, RNF40 significantly decreased lipid content, which could be reversed by LIMA1 overexpression. These findings suggest that E3 ubiquitin ligase RNF40 could directly target LIMA1 and promote its protein degradation in cytoplasm, leading to the suppression of lipid accumulation mediated by LIMA1. Collectively, this study unveils that RNF40 is a novel E3 ubiquitin ligase of LIMA1, which underpins its high therapeutic value to combat dysregulation of lipid metabolism.

Exploring the Enigma: The Role of the Epithelial Protein Lost in Neoplasm in Normal Physiology and Cancer Pathogenesis

The cytoskeleton plays a pivotal role in maintaining the epithelial phenotype and is vital to several hallmark processes of cancer. Over the past decades, researchers have identified the epithelial protein lost in neoplasm (EPLIN, also known as LIMA1) as a key regulator of cytoskeletal dynamics, cytoskeletal organization, motility, as well as cell growth and metabolism. Dysregulation of EPLIN is implicated in various aspects of cancer progression, such as tumor growth, invasion, metastasis, and therapeutic resistance. Its altered expression levels or activity can disrupt cytoskeletal dynamics, leading to aberrant cell motility and invasiveness characteristic of malignant cells. Moreover, the involvement of EPLIN in cell growth and metabolism underscores its significance in orchestrating key processes essential for cancer cell survival and proliferation. This review provides a comprehensive exploration of the intricate roles of EPLIN across diverse cellular processes in both normal physiology and cancer pathogenesis. Additionally, this review discusses the possibility of EPLIN as a potential target for anticancer therapy in future studies.

Characterization of LIMA1 and its emerging roles and potential therapeutic prospects in cancers

Actin is the most abundant and highly conserved cytoskeletal protein present in all eukaryotic cells. Remodeling of the actin cytoskeleton is controlled by a variety of actin-binding proteins that are extensively involved in biological processes such as cell motility and maintenance of cell shape. LIM domain and actin-binding protein 1 (LIMA1), as an important actin cytoskeletal regulator, was initially thought to be a tumor suppressor frequently downregulated in epithelial tumors. Importantly, the deficiency of LIMA1 may be responsible for dysregulated cytoskeletal dynamics, altered cell motility and disrupted cell-cell adhesion, which promote tumor proliferation, invasion and migration. As research progresses, the roles of LIMA1 extend from cytoskeletal dynamics and cell motility to cell division, gene regulation, apical extrusion, angiogenesis, cellular metabolism and lipid metabolism. However, the expression of LIMA1 in malignant tumors and its mechanism of action have not yet been elucidated, and many problems and challenges remain to be addressed. Therefore, this review systematically describes the structure and biological functions of LIMA1 and explores its expression and regulatory mechanism in malignant tumors, and further discusses its clinical value and therapeutic prospects.

Genomic and Metabolomic Landscape of Right-Sided and Left-Sided Colorectal Cancer: Potential Preventive Biomarkers

Colorectal cancer (CRC) is the third most common cancer worldwide. The incidence and mortality rates of CRC are significantly higher in Taiwan than in other developed countries. Genes involved in CRC tumorigenesis differ depending on whether the tumor occurs on the left or right side of the colon, and genomic analysis is a keystone in the study and treatment of CRC subtypes. However, few studies have focused on the genetic landscape of Taiwanese patients with CRC. This study comprehensively analyzed the genomes of 141 Taiwanese patients with CRC through whole-exome sequencing. Significant genomic differences related to the site of CRC development were observed. Blood metabolomic profiling and polygenic risk score analysis were performed to identify potential biomarkers for the early identification and prevention of CRC in the Taiwanese population. Our findings provide vital clues for establishing population-specific treatments and health policies for CRC prevention in Taiwan.

The geometry of clinical labs and wellness states from deeply phenotyped humans

Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.

Kernel machine SNP set analysis finds the association of BUD13, ZPR1, and APOA5 variants with metabolic syndrome in Tehran Cardio-metabolic Genetics Study

Metabolic syndrome (MetS) is one of the most important risk factors for cardiovascular disease. The 11p23.3 chromosomal region plays a potential role in the pathogenesis of MetS. The present study aimed to assess the association between 18 single nucleotide polymorphisms (SNPs) located at the BUD13, ZPR1, and APOA5 genes with MetS in the Tehran Cardio-metabolic Genetics Study (TCGS). In 5421 MetS affected and non-affected participants, we analyzed the data using two models. The first model (MetS model) examined SNPs' association with MetS. The second model (HTg-MetS Model) examined the association of SNPs with MetS affection participants who had a high plasma triglyceride (TG). The four-gamete rules were used to make SNP sets from correlated nearby SNPs. The kernel machine regression models and single SNP regression evaluated the association between SNP sets and MetS. The kernel machine results showed two sets over three sets of correlated SNPs have a significant joint effect on both models (p < 0.0001). Also, single SNP regression results showed that the odds ratios (ORs) for both models are almost similar; however, the p-values had slightly higher significance levels in the HTg-MetS model. The strongest ORs in the HTg-MetS model belonged to the G allele in rs2266788 (MetS: OR = 1.3, p = 3.6 × 10^–7; HTg-MetS: OR = 1.4, p = 2.3 × 10^–11) and the T allele in rs651821 (MetS: OR = 1.3, p = 2.8 × 10^–7; HTg-MetS: OR = 1.4, p = 3.6 × 10^–11). In the present study, the kernel machine regression models could help assess the association between the BUD13, ZPR1, and APOA5 gene variants (11p23.3 region) with lipid-related traits in MetS and MetS affected with high TG.

An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer's disease

BACKGROUND

Multiple pathophysiological processes have been described in Alzheimer's disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood. We hypothesize that specific molecular patterns indicating both known and yet unidentified pathway alterations are associated with distinct aspects of AD pathology.

METHODS

We performed multi-level cerebrospinal fluid (CSF) omics in a well-characterized cohort of older adults with normal cognition, mild cognitive impairment, and mild dementia. Proteomics, metabolomics, lipidomics, one-carbon metabolism, and neuroinflammation related molecules were analyzed at single-omic level with correlation and regression approaches. Multi-omics factor analysis was used to integrate all biological levels. Identified analytes were used to construct best predictive models of the presence of AD pathology and of cognitive decline with multifactorial regression analysis. Pathway enrichment analysis identified pathway alterations in AD.

RESULTS

Multi-omics integration identified five major dimensions of heterogeneity explaining the variance within the cohort and differentially associated with AD. Further analysis exposed multiple interactions between single 'omics modalities and distinct multi-omics molecular signatures differentially related to amyloid pathology, neuronal injury, and tau hyperphosphorylation. Enrichment pathway analysis revealed overrepresentation of the hemostasis, immune response, and extracellular matrix signaling pathways in association with AD. Finally, combinations of four molecules improved prediction of both AD (protein 14-3-3 zeta/delta, clusterin, interleukin-15, and transgelin-2) and cognitive decline (protein 14-3-3 zeta/delta, clusterin, cholesteryl ester 27:1 16:0 and monocyte chemoattractant protein-1).

CONCLUSIONS

Applying an integrative multi-omics approach we report novel molecular and pathways alterations associated with AD pathology. These findings are relevant for the development of personalized diagnosis and treatment approaches in AD.