Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods

BackgroundAlzheimer’s disease (AD) is characterised by a high degree of heterogeneity in the patient population. GWAS have identified several disease‐associated genes, but these findings have not translated into progress in the clinic. This likely reflects the limitations of GWAS in only identifying single variants, while the key to understanding and treating complex diseases that are influenced by multiple genetic loci is to find combinations of variants that distinguish patient subgroups.MethodThe PrecisionLife platform utilises a hypothesis‐free method for the detection of combinations of features that together are strongly associated with variations in disease risk, symptoms, progression rates and therapy response.To explore the diversity within the AD population we analysed a genomic dataset from the UK Biobank, including 882 patients diagnosed with AD, compared against healthy controls. Subsequently, pathway enrichment analysis was performed to identify biological mechanisms underlying the AD patient subgroups that are shared with other CNS indications.ResultOur analysis identified combinations of genetic variants which mapped to 113 genes that are significantly associated with AD pathology. These include known and novel genes. Clustering of these combinations, based upon the degree of patient overlap, revealed six distinct subgroups. Each patient subgroup reflected a specific biological function ‐ lipid metabolism, neuroinflammation, autophagy, serotonin signalling, metal ion homeostasis, and metabolic dysfunction.PrecisionLife’s patient stratification approach enables understanding of shared genetic aetiologies and underlying dysregulated biological processes across multiple diseases where there is evidence of overlapping symptomology. We performed a pathway enrichment analysis, using the patient stratification results from the AD study, to find commonalities identified in other patient datasets, including FTD, schizophrenia and long COVID. We were able to identify multiple AD‐associated pathways that are significantly enriched in at least one additional disease. These included processes associated with neurotransmission, cell death signalling and lipoprotein metabolism.ConclusionThe results demonstrate that combinatorial analysis can stratify heterogenous patient populations with complex pathologies to identify effective therapeutic strategies with accompanying biomarkers, improving the probability of success in AD drug development and opening an opportunity for identifying biological mechanisms and pathways relevant to treating multiple, related CNS diseases.

Bipolar disorder (BD) is associated with alterations in plasma lipid profiles, which may contribute to its pathogenesis. However, the detailed plasma lipidomic composition of bipolar II disorder during depressive episodes (BPD-II) remains underexplored. This study investigates the temporal changes in plasma lipidomics in BPD-II patients at emerging adulthood undergoing treatment with quetiapine combined with magnesium valproate. We conducted a follow-up study involving individuals with BPD-II at emerging adulthood at baseline (BD-BL, n = 30), 3 weeks (BD-3w, n = 30), and 8 weeks (BD-8w, n = 30) after treatment initiation, as well as gender- and age-matched healthy controls (HC, n = 30). Plasma lipidomics were analyzed using a comprehensive lipidomic profiling approach to identify potential biomarkers. Significant differences in plasma lipid composition were observed between BPD-II patients and healthy controls. Treatment with quetiapine combined with magnesium valproate alleviated symptoms in a time-dependent manner, although residual symptoms persisted after 8 weeks. We identified 64 lipid species with diagnostic potential to distinguish BPD-II from HC and 42 lipid species that could predict treatment outcomes with high reliability. Our findings provide valuable insights into the therapeutic effects of quetiapine combined with magnesium valproate and highlight the potential role of plasma lipidomics in understanding the pathophysiology of BPD-II at emerging adulthood.

The endoplasmic reticulum (ER) is the site of protein folding and secretion, Ca2+ storage and lipid synthesis in eukaryotic cells. Disruption to protein folding or Ca2+ homeostasis in the ER leads to the accumulation of unfolded proteins, a condition known as ER stress. This leads to activation of the unfolded protein response (UPR) pathway in order to restore protein homeostasis. Three ER membrane proteins, namely inositol‐requiring enzyme 1 (IRE1), protein kinase RNA‐like ER kinase (PERK) and activating transcription factor 6 (ATF6), sense the accumulation of unfolded/misfolded proteins and are activated, initiating an integrated transcriptional programme. Recent literature demonstrates that activation of these sensors can alter lipid enzymes, thus implicating the UPR in the regulation of lipid metabolism. Given the presence of ER stress and UPR activation in several diseases including cancer and neurodegenerative diseases, as well as the growing recognition of altered lipid metabolism in disease, it is timely to consider the role of the UPR in the regulation of lipid metabolism. This review provides an overview of the current knowledge on the impact of the three arms of the UPR on the synthesis, function and regulation of fatty acids, triglycerides, phospholipids and cholesterol.

Chronic urticaria (CU) comes as chronic spontaneous urticaria (CSU) and chronic inducible urticaria (CIndU).1 Across its types and subtypes, CU is a heterogeneous disease that has different phenotypes with distinct clinical characteristics and different endotypes with distinct underlying pathophysiological mechanisms.2, 3 It may be possible that subtypes of CU patients exhibit distinct phenotypic disease signatures that can point to differences in what drives their condition and in their response to treatments. Cluster analysis is a popular unsupervised machine learning (ML) method for discovering previously undetected data patterns.4 ML-based cluster analysis has been used in several diseases for the identification and characterization of patient subgroups.5, 6 As of now, no study has attempted to identify CU subtypes with this method. Here, we performed a proof-of-concept study to test whether cluster analysis using ML algorithms can identify subgroups of CU patients based on clinical and routine laboratory characteristics. We retrospectively analyzed the medical charts of a cohort of 431 CU patients. Institutional review board was obtained, and due to retrospective nature of the study, patient consent was not required. ML-based k-means clustering with principal component silhouette analyses (PCA) and use of the elbow method of dimensionally reduced data showed 4 clusters of CU patients, with a homogeneous balance between the clusters and the selected evaluation metrics (methods are provided in supplementary material) (Figure S3). Clustering analyses with PCA resulted in more meaningful clusters than without and supported the positive impact of reduced dimensions, and cluster number identified. Cluster characteristics and comparisons identified clinically distinct patient subgroups (Table 1). Cluster 1 (The “CIndU only” cluster) was the smallest cluster and consisted of all and only CIndU patients who did not have comorbid CSU. Of all clusters, cluster 1 patients had the highest age [median 42 (28–51) years], the shortest duration of disease [12 (5–96) months], and the lowest IgE levels [74.6 (35.1–188.5) IU/ml]. Cluster 2 (The “high IgE” cluster) was the largest cluster. All patients had CSU, and half of them had comorbid CIndU. Cluster 2 patients, on average, had the highest IgE levels [132 (56.4–271.5) IU/ml], the highest rate of comorbid atopic dermatitis (7.7%), and the lowest rate of ANA and IgG-anti-TPO positivity (1.4% and 4.2%, respectively). Cluster 3 (The “autoimmune” cluster) had the highest percentage of women (92%) in all clusters. All patients had CSU, and more than half also had CIndU (56.3%). Three of four patients (77.3%) had angioedema, the highest percentage of any cluster. Cluster 3 patients also had the second-lowest IgE levels (84.2 IU/ml) of any CSU cluster and the highest rates of IgG-anti-TPO and ANA positivity across all clusters (39.8% and 52.3%, respectively). Cluster 4 (The “high comorbidity” cluster) consisted only of CSU patients, and comorbid CIndU was rare (14.3%). The defining characteristics of patients in this cluster, the high comorbidity cluster, were their high rates of hypertension (74%), diabetes mellitus (62%), and hypothyroidism (38%), each at least twice as high as in any other cluster. The results of our study provide proof of concept that the use of unsupervised ML algorithms can identify meaningful and distinct groups of patients with CU and cluster CU into four different and distinct subtypes. Three of these four clusters are remarkably similar to how patients with CU are classified in real life, that is, as having CIndU or CSU as their primary form of CU and, in the latter, as having autoimmune or autoallergic CSU (Figure 1). This suggests that ML-based algorithms can be used to establish patient signatures, which may then be used to better characterize relevant and distinct pathomechanisms of CU subgroups. This, in turn, will allow us to better manage CU, by optimizing the use of available treatments and guiding the development of new and better ones. This project benefitted from the support (non-financial) of the GA2LEN network of urticaria centers of reference and excellence (UCARE, www.ga2len-ucare.com). MT has no relevant conflict of interest in relation to this work. Outside of it, MT is or recently was a speaker and/or advisor for Novartis. RE has no relevant conflict of interest in relation to this work. Outside of it, RE is or recently was a speaker and/or advisor for Novartis. EZ, YT, MA, and AG have no conflict of interest. MM has no relevant conflicts of interest in relation to this work. Outside of it, MM is or recently was a speaker and/or advisor for and/or has received research funding from Allakos, Amgen, Aralez, ArgenX, AstraZeneca, Celldex, Centogene, CSL Behring, FAES, Genentech, GIInnovation, Innate Pharma, Kyowa Kirin, Leo Pharma, Lilly, Menarini, Moxie, Novartis, Roche, Sanofi/Regeneron, Third HarmonicBio, UCB, and Uriach. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Our comprehension of the function of adipose tissue has progressed from perceiving it merely as an energy storage organ to recognizing it as an active endocrine organ (1). Although factors such as genetics, sexual characteristics, and sex hormones can affect the quantity and distribution of adipose tissue, it's important to note that white fat also exert a reciprocal effect on both male and female reproduction (2). The intricate interplay between adipose-derived hormones "adipokines" and human reproductive functions has garnered significant attention in recent years (3). This intricate relationship has been highlighted in our four resourceful manuscripts published in our research topic in Frontiers in Endocrinology, focusing on "Adipose tissue and adipokines: their roles in human reproduction". This editorial summarizes the research findings of these studies, highlight their contributions, and outline future avenues for research.Obesity plays a sophisticated and diversified role in the biology of female reproductive health (4). The first manuscript, "Obesity and its impact on female reproductive health: unraveling the connections" (10.3389/fendo.2023.1326546), explores the multifaceted impact of obesity on female reproductive health. The study comprehensively reviews how excess body fat influences various reproductive parameters, including menstrual cycles, fertility, and pregnancy outcomes. It underscores the need for targeted interventions to mitigate the adverse effects of obesity on women's reproductive health.The role of contrary adipokines, specifically leptin and adiponectin, in female reproductive health is a burgeoning research field. (2,5). In "The association between leptin, adiponectin levels and the ovarian reserve in women of reproductive age" (10.3389/fendo.2024.1369248), the authors investigate the roles of leptin and adiponectin-two key adipokines-in determining ovarian reserve. Their findings reveal a significant relationship between these adipokines and ovarian function, suggesting that hormonal and metabolic changes may influence female fertility by affecting ovarian reserve.Shifting the focus to male reproductive health (6), the third manuscript, "Male infertility risk and plasma lipidome: a Mendelian randomization study" (10.3389/fendo.2024.1412684) provides a novel perspective. Through Mendelian randomization, the study examines the association between plasma lipid profiles and the risk of male infertility. This research highlights how lipidomic alterations can impact male reproductive health and opens new avenues for potential therapeutic interventions.The final manuscript in our collection, titled "Genetic association of lipids and lipid-lowering drug target genes with Endometrial carcinoma: a drug target Mendelian randomization study" (10.3389/fendo.2024.1446457), delves into the genetic interplay between lipids, lipid-lowering drugs, and endometrial carcinoma. This research employs Mendelian randomization to identify genetic factors that may contribute to the risk of endometrial cancer, offering insights into the role of lipid metabolism in the development of this disease (7).Collectively, these studies enrich our understanding of how adipose tissue and adipokines impact reproductive health across genders. They offer valuable insights into the mechanisms underlying reproductive disorders and emphasize the importance of integrating metabolic and hormonal factors into reproductive health research.As we move forward, several research directions emerge. Future studies should explore the potential of targeting adipokines as therapeutic interventions for reproductive disorders. Additionally, the application of multiomics approaches could provide a comprehensive understanding of the complex interactions between adipose tissue, metabolic dysfunction, and reproductive outcomes (8). Further research is needed to elucidate the exact molecular pathways involved in these processes.In conclusion, this Research Topic has significantly advanced our knowledge of the complex interactions between adipose tissue, adipokines, and human reproduction. We extend our gratitude to all the contributors for their valuable research and look forward to future discoveries in this vital area of study.

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

BackgroundSystemic sclerosis (SSc) is an autoimmune disease that is characterized by systemic vasculopathy and fibrosis. Pulmonary hypertension (PH), defined as elevated pulmonary arterial pressure (PAP), is one of the leading...

BackgroundThe course of IBD varies substantially between individuals, but there are a lack of reliable prognostic markers to guide clinical practice. Previously, we have described a transcriptional signature detectable within...

Lipids are a broad range of small molecules that are associated with nearly all biological processes. These important molecules have rapidly gained interest as the field has advanced in the past decade due to improvements to mass spectrometry and chromatographic technologies. In this thesis, we developed and improved our lipidomics platform to better examine these lipids and applied them to two Australian cohorts to explore their relationship with type 2 diabetes and Alzheimer's disease. This thesis details the relationship between the plasma lipidome and these diseases.

It's Diagnostics, Stupid

The role of the IFI6 gene has been described in several cancers, but its involvement in esophageal cancer (ESCA) remains unclear. This study aimed to identify novel prognostic indicators for ESCA-targeted therapy by investigating IFI6’s expression, epigenetic mechanisms, and signaling activities. We utilized public data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) to analyze IFI6’s expression, clinical characteristics, gene function, pathways, and correlation with different immune cells in ESCA. The TIMER2.0 database was employed to assess the pan-cancer expression of IFI6, while UALCAN was used to examine its expression across tumor stages and histology subtypes. Additionally, the KEGG database helped identify related pathways. Our findings revealed 95 genes positively correlated and 15 genes negatively correlated with IFI6 in ESCA. IFI6 was over-expressed in ESCA and other cancers, impacting patient survival and showing higher expression in tumor tissues than normal tissues. IFI6 was also correlated with CD4+ T cells and B cell receptors (BCRs), both essential in immune response. GO Biological Process (GO BP) enrichment analysis indicated that IFI6 was primarily associated with the Type I interferon signaling pathway and the defense response to viruses. Intriguingly, KEGG pathway analysis demonstrated that IFI6 and its positively correlated genes in ESCA were mostly linked to the Cytosolic DNA-sensing pathway, which plays a crucial role in innate immunity and viral defense, and the RIG-I-like receptor (RLR) signaling pathway, which detects viral infections and activates immune responses. Pathways related to various viral infections were also identified. It is important to note that our study relied on online databases. Given that ESCA consists of two distinct subgroups (ESCC and EAC), most databases combine them into a single category. Future research should focus on evaluating IFI6 expression and its impact on each subgroup to gain more specific insights. In conclusion, inhibiting IFI6 using targeted therapy could be an effective strategy for treating ESCA considering its potential as a biomarker and correlation with immune cell factors.

B-Cell Receptor Driven MALT1 Activity Regulates MYC Signaling in Mantle Cell Lymphoma

Objective To explore the network pharmacological mechanism of Wugedan Pill in improving insulin resistance (IR). Methods The main active ingredients and corresponding target genes of Wugedan Pill were retrieved from the Traditional Chinese Medicine System Pharmacology Technology Platform (TCMSP), and the related targets of insulin resistance were obtained through the Human Gene Database (GeneCards) and the Human Online Mendelian Inheritance Database (OMIM). Gene, the active ingredient target of the drug is mapped with the insulin resistance target, and the intersection target is obtained, which is the predicted target of Wugedan Pill acting on insulin resistance. The "pharmaceutical active ingredient-target" network model was constructed by Cytoscape 3.7.0 software, and the key active ingredients were selected. The STRING database was used to construct an intersecting target-protein interaction network (PPI), and key proteins were selected. Intersection targets were subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the Metascape platform. Results There are 94 active ingredients and 163 related targets of Wugedan Pill acting on insulin resistance. The key active ingredients are quercetin, β-sitosterol and kaempferol, and the key proteins are estrogen receptor 1 (ESR1), aldose reductase (AR), nuclear receptor coactivator protein 1 (NCOA1), Fos protein et al.; GO enrichment analysis showed that Wugedan Pill could affect the activities of oxidoreductase, tyrosine kinase-binding protein, steroid-binding protein and other proteins in improving insulin resistance; KEGG pathway enrichment analysis showed that the effect was the most Notably lipid and atherosclerotic pathways, also significantly affected diabetes complications AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, neuroactive ligand-receptor interactions, alcoholic liver disease, prostate cancer, cancer miRNAs expression, pancreatic cancer, thyroid hormone signaling and other pathways. Conclusion Wugedan Pill may play a role in improving insulin resistance, regulating lipid metabolism, reducing blood glucose and preventing complications by regulating the above targets and pathways with the effective ingredients quercetin, β-sitosterol and kaempferol.

Background and Objectives: A number of sources suggest changes in anxiety across the transition to parenthood may be experienced by parents in different ways, yet no studies have examined whether new parents experience changes in anxiety in distinct subgroups. Design: We conducted a longitudinal study of 208 first-time parents (104 couples) from a low-risk population. Parents were interviewed from the third trimester of pregnancy to nine-months postpartum. Methods: The current study utilized latent class growth analysis to explore subgroups of change in symptoms of anxiety. Based on stress and coping theory, we also examined a number of personal and social prenatal predictors of subgroup membership. Results: We identified two distinct change trajectories: (1) moderate and stable and (2) low and declining. We also found prenatal depression, expected parenting efficacy, and relationship satisfaction were significantly associated with subgroup membership. Conclusions: Our results suggest a majority of new parents adjust well to parenthood in terms of anxiety, while a smaller subgroup of parents experience continually higher levels of anxiety months after the baby is born.

Assessing students’ participation in science practices presents several challenges, especially when aiming to differentiate meaningful (vs. rote) forms of participation. In this study, we sought to use machine learning (ML) for a novel purpose in science assessment: developing a construct map for students’ consideration of generality, a key epistemic understanding that undergirds meaningful participation in knowledge-building practices. We report on our efforts to assess the nature of 845 students’ ideas about the generality of their model-based explanations through the combination of an embedded written assessment and a novel data analytic approach that combines unsupervised and supervised machine learning methods and human-driven, interpretive coding. We demonstrate how unsupervised machine learning methods, when coupled with qualitative, interpretive coding, were used to revise our construct map for generality in a way that allowed for a more nuanced evaluation that was closely tied to empirical patterns in the data. We also explored the application of the construct map as a framework for coding used as a part of supervised machine learning methods, finding that it demonstrates some viability for use in future analyses. We discuss implications for the assessment of students’ meaningful participation in science practices in terms of their considerations of generality, the role of unsupervised methods in science assessment, and combining machine learning and human-driven approach for understanding students’ complex involvement in science practices.