Jing Qin Wu

Diet is a key factor for many diseases, yet the underlying metabolic pathways involved remain poorly understood. We analyze data from 13,335 participants across two large Australian cohorts examining comprehensive lipidomic profiles in relation to diet. We also assess the link between metabolic signatures of dietary quality with cardiometabolic health and all-cause mortality. Here, using linear models and lipid set enrichment analysis, we report characteristic lipidomic profiles associated with dairy [sphingomyelins and lipids esterified with 14:0, 15:0, 17:0 or 17:1 fatty acid], red meat and poultry [alkyl- and alkenyl-phosphatidylcholine and phosphatidylethanolamine notably, with arachidonic acid], and fish intake [higher 22:6, and lower 22:4 fatty acids]. In a Cox proportional hazard regression, metabolic signatures of diet quality showed inverse associations with all–cause mortality with hazard ratios (95% confidence intervals) of 0.89 (0.84–0.93), 0.87 (0.83–0.92), and 0.88 (0.84–0.93) for the Australian Dietary Guideline Index (DGI), the Global Diet Quality Score (DGQS), and the Mediterranean-DASH (Dietary Approaches to Stop Hypertension) Intervention for Neurodegenerative Delay (MIND) score, respectively. Additionally, the intake of nuts (β = −0.07, p = 7.92 × 10⁻¹⁸) and the MIND score (β = −0.07, p = 1.51 × 10⁻¹⁸) were inversely associated with CVD risk. Our data provide new insights into how dietary exposure relates to lipid metabolism and metabolic health, opening avenues for future mechanistic studies and dietary interventions that can inform targeted strategies for the prevention of cardiometabolic diseases.

Traditional lipid biomarkers, such as cholesterol, HDL, LDL, and triglycerides, are frequently used to assess cardiometabolic health in clinical practice. However, these measures provide a limited view of the human lipidome, leaving clinically relevant information untapped. Lipidomic profiling using LC-MS/MS enables the measurement of hundreds of individual lipid species, yet low throughput and complex data processing have limited clinical integration. To overcome these limitations, we developed a Clinical Lipidomics Platform (CLP), a high-throughput (6 min runtime) LC-MS/MS assay measuring 270 lipid species (248 analytes + 22 internal standards) from 37 lipid subclasses in human plasma. The CLP incorporates automated data processing and normalization to an external reference material (NIST SRM 1950) to ensure reproducible and accurate data. We validated the CLP using the BioHEART-CT Discovery Cohort (n = 994) and compared lipidomic data to those from our Research Lipidomics Platform (RLP), which used a 16 min LC gradient and manual data processing to measure >800 lipids. CLP and RLP lipid measurements were highly correlated. A Lipidomic Risk Score (LRS), previously developed to model 10-year cardiovascular event risk using lipidomic, clinical, and demographic data, was calculated for each individual. CLP-derived LRS showed a strong correlation with RLP-derived LRS (R² = 0.97). The LRS outperformed traditional risk scores, such as the Framingham Risk Score (FRS), in predicting the coronary artery calcium score (CACS), particularly in intermediate-risk individuals. These findings demonstrate the clinical utility of the CLP for cardiovascular risk assessment and its potential for broader clinical application.

Diet is a key factor for many diseases, yet the underlying metabolic pathways involved remain poorly understood. We analyzed data from 13,335 participants across two large Australian cohorts examining comprehensive lipidomic profiles in relation to diet. We also assessed the link between metabolic signatures of dietary quality with cardiometabolic health and all-cause mortality. Here, we report characteristic lipidomic profiles linked to dairy [sphingomyelins and lipids esterified with 14:0, 15:0, 17:0 or 17:1 fatty acid], red meat and poultry [alkyl- and alkenyl-phosphatidylcholine and phosphatidylethanolamine notably, with arachidonic acid], and fish intake [higher 22:6, and lower 22:4 fatty acids]. The metabolic signatures of diet quality showed inverse associations with all–cause mortality with hazard ratios (95% confidence intervals) of 0.89 (0.84–0.93), 0.87 (0.83–0.92), and 0.88 (0.84–0.93) for the Australian Dietary Guideline Index (DGI), the Global Diet Quality Score (DGQS), and the Mediterranean-DASH (Dietary Approaches to Stop Hypertension) Intervention for Neurodegenerative Delay (MIND) score, respectively. Additionally, the intake of nuts (β = -0.07, p = 7.92 × 10⁻¹⁸) and the MIND score (β = -0.07, p = 1.51 × 10⁻¹⁸) were inversely associated with CVD risk. Our data highlight the critical role of diet in influencing lipid metabolism, thereby impacting metabolic health and mortality risk and provide a framework for the development of a precision health approach to dietary intervention.

Polygenic scores (PGSs) have transformed human genetic research and have numerous potential clinical applications. Here we present a series of recent enhancements to the PGS Catalog and highlight the PGS Catalog Calculator, an open-source, scalable and portable pipeline for reproducibly calculating PGSs that democratizes equitable PGS applications.

Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.

The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD− (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI < 25 kg/m²), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals.

The leaf rust pathogen, Puccinia triticina (Pt), threatens global wheat production. The deployment of leaf rust (Lr) resistance (R) genes in wheat varieties is often followed by the development of matching virulence in Pt due to presumed changes in avirulence (Avr) genes in Pt. Identifying such Avr genes is a crucial step to understand the mechanisms of wheat-rust interactions. This study is the first to develop and apply an integrated framework of gene expression, single nucleotide polymorphism (SNP), insertion/deletion (InDel), and copy number variation (CNV) analysis in a rust fungus and identify candidate avirulence genes. Using a long-read based de novo genome assembly of an isolate of Pt (‘Pt104’) as the reference, whole-genome resequencing data of 12 Pt pathotypes derived from three lineages Pt104, Pt53, and Pt76 were analyzed. Candidate avirulence genes were identified by correlating virulence profiles with small variants (SNP and InDel) and CNV, and RNA-seq data of an additional three Pt isolates to validate expression of genes encoding secreted proteins (SPs). Out of the annotated 29,043 genes, 2392 genes were selected as SP genes with detectable expression levels. Small variant comparisons between the isolates identified 27–40 candidates and CNV analysis identified 14–31 candidates for each Avr gene, which when combined, yielded the final 40, 64, and 69 candidates for AvrLr1, AvrLr15, and AvrLr24, respectively. Taken together, our results will facilitate future work on experimental validation and cloning of Avr genes. In addition, the integrated framework of data analysis that we have developed and reported provides a more comprehensive approach for Avr gene mining than is currently available.

BACKGROUND: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. METHODS: We conducted the largest to date genome-wide genotype–by–sex (GxS) interaction of risk for these disorders, using 85,735 cases (33,403 SCZ, 19,924 BIP, 32,408 MDD) and 109,946 controls from the Psychiatric Genomics Consortium (PGC) and iPSYCH. RESULTS: Across disorders, genome-wide significant SNP-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815; p =3.2×10 ⁻⁸ ), that interacts with sodium/potassium-transporting ATPase enzymes implicating neuronal excitability. Three additional loci showed evidence ( p −6 ) for cross-disorder GxS interaction (rs7302529, p =1.6×10 ⁻⁷ ; rs73033497, p =8.8×10 ⁻⁷ ; rs7914279, p =6.4×10 ⁻⁷ ) implicating various functions. Gene-based analyses identified GxS interaction across disorders ( p =8.97×10 ⁻⁷ ) with transcriptional inhibitor SLTM . Most significant in SCZ was a MOCOS gene locus (rs11665282; p =1.5×10 ⁻⁷ ), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509; p =1.1×10 ⁻⁷ ) in a locus containing IDO2 , a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant GxS of genes regulating vascular endothelial growth factor (VEGF) receptor signaling in MDD ( p _FDR <0.05). CONCLUSIONS: In the largest genome-wide GxS analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development, immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway enrichment levels.