Lipidomics, a rapidly evolving discipline at the interface of biology and analytical chemistry, seeks to comprehensively characterize the lipid composition of biological systems. Driven by advances in mass spectrometry, chromatography and computational analysis, lipidomics has enabled the high-resolution mapping of lipid networks and their functional dynamics across molecular, cellular and organismal scales. In biomedical research, lipidomics is emerging as a powerful platform for biomarker discovery, enabling early diagnosis, prognosis, and therapeutic monitoring of cancer, metabolic, and neurodegenerative diseases. The field is also reshaping drug discovery by uncovering lipid-mediated pathways, identifying novel therapeutic targets, and refining assessments of drug efficacy and safety. Beyond medicine, lipidomic analyses are redefining food and nutrition science by elucidating how dietary lipids influence metabolic health and disease risk. In parallel, environmental and ecological lipidomics are emerging as powerful frameworks for assessing ecosystem health, tracking the impact of pollutants and exploring the biological consequences of climate change. Such approaches are also informing the discovery of sustainable lipid resources and the development of novel biotechnological and agricultural innovations. With its rapidly expanding analytical repertoire and cross-disciplinary relevance, lipidomics is poised to make substantial contributions to both fundamental biology and applied science. This Perspective aims to synthesise the current state of the field, delineate major analytical and conceptual challenges, and outline future directions for translating lipidomic knowledge into tangible societal and environmental benefits.

BACKGROUND:Genome-wide association studies of lipid species have identified several loci shared with various diseases, however, the relationship between lipid species and disease risk remains poorly understood. Here we investigated whether the plasma levels of lipid species are causally linked to disease risk. METHODS:We built genetic predictors of 179 lipid species, measured in 7174 Finnish individuals, by utilising either 11 high-impact genomic loci or genome-wide polygenic scores (PGS). We assessed the impact of the lipid species on seven diseases by performing disease association across FinnGen (n = 500,348), UK Biobank (n = 420,531), and Generation Scotland (n = 20,032). We performed univariable Mendelian randomisation (MR) and multivariable MR (MVMR) analyses to examine whether lipid species impact disease risk independently of standard lipids. FINDINGS:PGS explained >4% of the variance for 34 lipid species but variants outside the high-impact loci had only a marginal contribution. Variants within the high-impact loci showed association with all seven diseases. MVMR supported a causal role of ApoB in ischaemic heart disease after accounting for lipid species. Phosphatidylethanolamine-increasing LIPC variants seemed to lower age-related macular degeneration risk independently of HDL-cholesterol. MVMR suggested a protective effect of four lipid species containing arachidonic acid on cholelithiasis risk independently of Total Cholesterol. INTERPRETATION:Our study demonstrates how genetic predictors of lipid species can be utilised to gain insights into disease risk. We report potential links between lipid species and age-related macular degeneration and cholelithiasis risk, which can be explored for their utility in disease risk prediction and therapy. FUNDING:The funders had no role in the study design, data analyses, interpretation, or writing of this article.

Aims: In the past, only few studies have examined the effects of time-restricted eating (TRE) on plasma lipidome, and no study has investigated possible differences in this regard between early (eTRE) and late TRE (lTRE). Our study aimed to fill this gap to better understand and compare the impact of both eTRE and lTRE on lipid metabolism.

Understanding perturbations in circulating lipid levels that often occur years or decades before clinical symptoms may enhance our understanding of disease mechanisms and provide novel intervention opportunities. Here, we assessed if polygenic scores (PGSs) for complex traits could detect lipid dysfunctions related to the traits and provide new biological insights. We constructed genome-wide PGSs (approximately 1 million genetic variants) for 50 complex traits in 7,169 Finnish individuals with routine clinical lipid profiles and lipidomics measurements (179 lipid species). We identified 678 associations (P < 9.0 × 10-5) involving 26 traits and 142 lipids. Most of these associations were also validated with the actual phenotype measurements where available (89.5% of 181 associations where the trait was available), suggesting that these associations represent early signs of physiological changes of the traits. We detected many known relationships (e.g., PGS for body mass index (BMI) and lysophospholipids, PGS for type 2 diabetes and triacyglycerols) and those that suggested potential target for prevention strategies (e.g., PGS for venous thromboembolism and arachidonic acid). We also found association of PGS for favorable adiposity with increased sphingomyelins levels, suggesting a probable role of sphingomyelins in increased risk for certain disease, e.g., venous thromboembolism as reported previously, in favorable adiposity despite its favorable metabolic effect. Altogether, our study provides a comprehensive characterization of lipidomic alterations in genetic predisposition for a wide range of complex traits. The study also demonstrates potential of PGSs for complex traits to capture early, presymptomatic lipid alterations, highlighting its utility in understanding disease mechanisms and early disease detection.

IntroductionType 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma.MethodsCirculating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.ResultsFrom a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.ConclusionsUsing an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.

Lipids are the defining features of cellular membranes. They act collectively to form a variety of different structures, and understanding their complex behavior represents an early example of systems biology. A multidisciplinary approach is needed to analyse the functions of lipids in biological systems, and new work is providing fascinating insights into their roles in membrane biology, metabolism, signaling, subcellular dynamics and various disease processes.

To identify the pathways that are coordinately regulated in pancreatic β cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2, and Balb/c mice a regular chow or a high fat diet for 5, 13, and 33 days. Physiological, transcriptomic and lipidomic data were used in a data fusion approach to identify organ-specific pathways linked to fasting glycemia across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels was associated with reduced expression of hormone and neurotransmitter receptors, OXPHOS, cadherins, integrins, and gap junction mRNAs. Higher glycemia and insulin resistance were associated, in muscle, with decreased insulin signaling, glycolytic, Krebs' cycle, OXPHOS, and endo/exocytosis mRNAs; in hepatocytes, with reduced insulin signaling, branched chain amino acid catabolism and OXPHOS mRNAs; in adipose tissue, with increased innate immunity and lipid catabolism mRNAs. These data provide a resource for further studies of interorgan communication in glucose homeostasis.

Oxidation of PUFAs in LDLs trapped in the arterial intima plays a critical role in atherosclerosis. Though there have been many studies on the atherogenicity of oxidized derivatives of PUFA-esters of cholesterol, the effects of cholesteryl hemiesters (ChEs), the oxidation end products of these esters, have not been studied. Through lipidomics analyses, we identified and quantified two ChE types in the plasma of CVD patients and identified four ChE types in human endarterectomy specimens. Cholesteryl hemiazelate (ChA), the ChE of azelaic acid (n-nonane-1,9-dioic acid), was the most prevalent ChE identified in both cases. Importantly, human monocytes, monocyte-derived macrophages, and neutrophils exhibit inflammatory features when exposed to subtoxic concentrations of ChA in vitro. ChA increases the secretion of proinflammatory cytokines such as interleukin-1β and interleukin-6 and modulates the surface-marker profile of monocytes and monocyte-derived macrophage. In vivo, when zebrafish larvae were fed with a ChA-enriched diet, they exhibited neutrophil and macrophage accumulation in the vasculature in a caspase 1- and cathepsin B-dependent manner. ChA also triggered lipid accumulation at the bifurcation sites of the vasculature of the zebrafish larvae and negatively impacted their life expectancy. We conclude that ChA behaves as an endogenous damage-associated molecular pattern with inflammatory and proatherogenic properties.

Considerable heterogeneity exists in Type 2 diabetes disease onset and subsequent outcomes. Multi-omics analysis may provide a means to better assess inter-patient variations and design personalised approaches. Previous T2D biomarker discovery mainly focused on associating biomarkers with clinical pre-assigned patients. Here, we attempt to group T2D patients with similar multi-omics profiles using a multi-omics integrating clustering approach, Similarity Network Fusion (SNF). Each T2D subgroup’s unique omics profiles were then associated with T2D progression. In both cohorts, 180 and 1195 circulating lipids and proteins from a total of 1,134 subjects in DCS and GoDARTS cohorts group patients into two subgroups. Two subgroups are likely to represent the different stages of T2D insulin insensitivity with differences at HOMA2 (p=0.0008;4.2e-11;1.1e-09, -B, -IR and -S, DCS), C-peptide (p=3.7e-11;2.5e-06) and overall disease progression (HR=0.6;0.7). This is the first study solely relying on integrated lipidomics and proteomics to capture the stages of T2D insulin insensitivity, allowing potential novel biomarker discovery which may be masked by traditional approaches. In both cohorts, a number of discriminative omics features can be observed. Immune proteins, such as IL-18R, CLP, IL-1R and COD antigen showed strong but differing associations with insulin insensitivity, which may be related to the inflammation associated with T2D. Moreover, growth factors such as GHR and IGFs also exhibit strong associations with the present study. For lipids, patients with less severe insulin insensitivity exhibit elevated levels of sphingomyelins. In conclusion, we systematically analysed the multi-omics profiles associated with T2D insulin insensitivity. The molecular signatures may be used to investigate molecular mechanisms underpinning progression and provide insights for precision medicine. Disclosure S.Li: None. I.J.Dragan: None. K.Simons: Other Relationship; Lipotype, I have no there compoanz. L.A.Donnelly: None. V.T.Tran: None. F.Mehl: None. L.M.'t hart: Research Support; European Union. M.Ibberson: Consultant; Novo Nordisk Foundation. G.A.Rutter: Consultant; Sun Pharmaceutical Industries Ltd. E.Pearson: Speaker's Bureau; Novo Nordisk, Lilly, Illumina. R.Slieker: None. J.Beulens: None. M.K.Hansen: None. D.Kuznetsov: None. M.J.Gerl: Employee; Lipotype GmbH.

Considerable heterogeneity exists in Type 2 diabetes disease onset and subsequent outcomes. Multi-omics analysis may provide a means to better assess inter-patient variations and design personalised approaches. Previous T2D biomarker discovery mainly focused on associating biomarkers with clinical pre-assigned patients. Here, we attempt to group T2D patients with similar multi-omics profiles using a multi-omics integrating clustering approach, Similarity Network Fusion (SNF). Each T2D subgroup’s unique omics profiles were then associated with T2D progression. In both cohorts, 180 and 1195 circulating lipids and proteins from a total of 1,134 subjects in DCS and GoDARTS cohorts group patients into two subgroups. Two subgroups are likely to represent the different stages of T2D insulin insensitivity with differences at HOMA2 (p=0.0008;4.2e-11;1.1e-09, -B, -IR and -S, DCS), C-peptide (p=3.7e-11;2.5e-06) and overall disease progression (HR=0.6;0.7). This is the first study solely relying on integrated lipidomics and proteomics to capture the stages of T2D insulin insensitivity, allowing potential novel biomarker discovery which may be masked by traditional approaches. In both cohorts, a number of discriminative omics features can be observed. Immune proteins, such as IL-18R, CLP, IL-1R and COD antigen showed strong but differing associations with insulin insensitivity, which may be related to the inflammation associated with T2D. Moreover, growth factors such as GHR and IGFs also exhibit strong associations with the present study. For lipids, patients with less severe insulin insensitivity exhibit elevated levels of sphingomyelins. In conclusion, we systematically analysed the multi-omics profiles associated with T2D insulin insensitivity. The molecular signatures may be used to investigate molecular mechanisms underpinning progression and provide insights for precision medicine. Disclosure S.Li: None. I.J.Dragan: None. K.Simons: Other Relationship; Lipotype, I have no there compoanz. L.A.Donnelly: None. V.T.Tran: None. F.Mehl: None. L.M.'t hart: Research Support; European Union. M.Ibberson: Consultant; Novo Nordisk Foundation. G.A.Rutter: Consultant; Sun Pharmaceutical Industries Ltd. E.Pearson: Speaker's Bureau; Novo Nordisk, Lilly, Illumina. R.Slieker: None. J.Beulens: None. M.K.Hansen: None. D.Kuznetsov: None. M.J.Gerl: Employee; Lipotype GmbH.

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Background and Aims: Changing lifestyle behavior is the first-line treatment for cardiometabolic disorders. Though the health benefits of lifestyle modifications through weight loss have been recognized, little is known about the effect of weight loss on plasma lipidome. Here we aimed to determine (1) lipidomic signature of obesity, (2) alterations in lipidomic profiles with changes in adiposity measures in longitudinal cohort, and (3) reversibility of altered lipidomic profile in obesity by weight loss. Methods: Lipidomic profiles (179 lipid species) for 4,488 individuals from Finnish GeneRISK cohort at baseline and ∼1.5-year follow-up were measured by mass spectrometry-based analysis and differences between the two time-points were calculated after log2 transformation. P values <7.0'10-4(0.05/179) were considered significant. Results: Obesity (BMI³30kg/m2) was associated with altered levels of 135 lipid species, with strong associations with cholesteryl esters, ceramides and triacylglycerides, and decrease in lysophospholipids and ether-linked phospholipids (Figure 1). Analyses of longitudinal data also showed that changes in BMI and waist circumference were associated with significant alterations in the lipidomic profiles (137 and 142 lipids, respectively). We further found that weight loss in obese individuals could change plasma levels of lipid species associated with obesity in favorable directions (Figure 1). Contrary to previous reports, we did not find major effect of baseline lipidome on the changes in adiposity measures. Conclusions: The longitudinal study of lipidomic profiles demonstrate profound effect of changes in adiposity measures on lipidome and suggest that weight loss could lead to favorable lipidome profile, and hence could reduce risk for associated cardiometabolic disorders.

The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.

ABSTRACT Oxidation of polyunsaturated fatty acids (PUFA) in low-density lipoproteins (LDL) trapped in the arterial intima plays a critical role in atherosclerosis. Though there have been many studies on the atherogenicity of oxidized derivatives of unsaturated fatty acid esters of cholesterol, the effects of the oxidation end-products of these esters has been ignored in the literature. Through lipidomics analyses of the plasma of cardiovascular disease patients and human endarterectomy specimens we identified and quantified cholesteryl hemiesters (ChE), end-products of oxidation of polyunsaturated-fatty acid esters of cholesterol. Cholesteryl hemiazelate (ChA) was the most prevalent ChE identified. Importantly human monocytes, monocyte-derived macrophages (MDM) and neutrophils exhibit inflammatory features when exposed to sub-toxic concentrations of ChA in vitro . ChA increases the secretion of proinflammatory cytokines such as IL-1β and IL-6 and modulates the surface markers profile of monocytes and MDM. In vivo , when zebrafish larvae were fed with a ChA-enriched diet they exhibited neutrophil and macrophage accumulation in the vasculature in a caspase 1- and cathepsin B-dependent manner. ChA also triggered lipid accumulation at the bifurcation sites of the vasculature of the zebrafish larvae and negatively impacted their life expectancy. We conclude that ChA has pro-atherogenic properties and can be considered part of a damage-associated molecular pattern (DAMP) in the development of atherosclerosis.

Background and Aims : The human plasma lipidome captures information beyond routinely clinically used lipids and has disease relevance in cardiometabolic diseases and beyond. Genome-wide association studies (GWAS) of individual lipid species (univariate analysis) have identified many lipid-associated loci, however the influence of genetic variants on lipid metabolism and cardiovascular disease risk is not fully understood. Multivariate analysis of multiple correlated lipid species improves statistical power and could help identify additional lipid loci.Methods: We performed univariate GWAS of 179 lipid species for 7177 participants from the Finnish GeneRisk cohort and multivariate analyses for 11 clusters of correlated lipid species by metaCCA software. The associations were fine-mapped with FINEMAP to identify causal variants and the causal variants were examined for associations with cardiometabolic traits in FinnGen R6, Gene Atlas and GWAS Atlas. We performed gene prioritization analysis with the tool FOCUS.Results: Multivariate analysis across 11 clusters identified 65 loci with P-value < 5e-8, of which 58 reached the Bonferroni-corrected significance threshold (BF). We identified ten new loci whose lead variants were in or near genes DTL, STK39, CDS1, AGPAT2, RIC1, SGPL1, KCNJ12, SPHK2, NINL and AGPAT3 at BF in multivariate analysis. Of these, only SGPL1 (Cer42:2;2) and AGPAT2 (PC16:0;0_22:5;0) reached BF in univariate analysis. Fine-mapping identified missense variants as potential causal variants for novel loci RIC1, SPHK2 and AGPAT3. FOCUS prioritized 28 genes for multivariate GWAS.Conclusions: Multivariate genetic analysis is a powerful tool for high-dimensional data such as lipidomics and helped us identify ten novel lipid loci, of which only two were identified by univariate analysis. Background and Aims : The human plasma lipidome captures information beyond routinely clinically used lipids and has disease relevance in cardiometabolic diseases and beyond. Genome-wide association studies (GWAS) of individual lipid species (univariate analysis) have identified many lipid-associated loci, however the influence of genetic variants on lipid metabolism and cardiovascular disease risk is not fully understood. Multivariate analysis of multiple correlated lipid species improves statistical power and could help identify additional lipid loci. Methods: We performed univariate GWAS of 179 lipid species for 7177 participants from the Finnish GeneRisk cohort and multivariate analyses for 11 clusters of correlated lipid species by metaCCA software. The associations were fine-mapped with FINEMAP to identify causal variants and the causal variants were examined for associations with cardiometabolic traits in FinnGen R6, Gene Atlas and GWAS Atlas. We performed gene prioritization analysis with the tool FOCUS. Results: Multivariate analysis across 11 clusters identified 65 loci with P-value < 5e-8, of which 58 reached the Bonferroni-corrected significance threshold (BF). We identified ten new loci whose lead variants were in or near genes DTL, STK39, CDS1, AGPAT2, RIC1, SGPL1, KCNJ12, SPHK2, NINL and AGPAT3 at BF in multivariate analysis. Of these, only SGPL1 (Cer42:2;2) and AGPAT2 (PC16:0;0_22:5;0) reached BF in univariate analysis. Fine-mapping identified missense variants as potential causal variants for novel loci RIC1, SPHK2 and AGPAT3. FOCUS prioritized 28 genes for multivariate GWAS. Conclusions: Multivariate genetic analysis is a powerful tool for high-dimensional data such as lipidomics and helped us identify ten novel lipid loci, of which only two were identified by univariate analysis.

Type 2 diabetes is a complex, multifactorial disease with varying presentation and underlying pathophysiology. Recent studies using data-driven cluster analysis have led to a stratification of type 2 diabetes into novel subgroups based on six clinical measurements. Whether these subgroups truly correspond to the underlying phenotypic differences is nevertheless unclear. Here, we apply an unsupervised, data-driven clustering method (Similarity Network Fusion) to characterize type 2 diabetes in two independent cohorts involving 1,134 subjects in total based on integrated plasma lipidomics and peptidomics data without pre-selection. Logistic regression was then used to explore clustering based on ≥ 180 circulating lipids and 1,195 protein biomarkers, alongside clinical signatures. Two subgroups were identified, one of which associated with elevated C-peptide levels, diabetic complications and more severe insulin resistance compared to the other. GWAS analysis against 403 type 2 diabetes risk variants revealed associations of several SNPs with clusters and altered molecular profiles. We thus demonstrate that heterogeneity in type 2 diabetes can be captured by circulating omics alone using an unsupervised bottom-up approach. Such multiomics signatures could reflect pathological mechanisms underlying type 2 diabetes and thus may help inform on precision medicine approaches to disease management.

Background Despite well‐recognized differences in the atherosclerotic cardiovascular disease risk between men and women, sex differences in risk factors and sex‐specific mechanisms in the pathophysiology of atherosclerotic cardiovascular disease remain poorly understood. Lipid metabolism plays a central role in the development of atherosclerotic cardiovascular disease. Understanding sex differences in lipids and their genetic determinants could provide mechanistic insights into sex differences in atherosclerotic cardiovascular disease and aid in precise risk assessment. Herein, we examined sex differences in plasma lipidome and heterogeneity in genetic influences on lipidome in men and women through sex‐stratified genome‐wide association analyses. Methods and Results We used data consisting of 179 lipid species measured by shotgun lipidomics in 7266 individuals from the Finnish GeneRISK cohort and sought for replication using independent data from 2045 participants. Significant sex differences in the levels of 141 lipid species were observed ( P <7.0×10 −4 ). Interestingly, 121 lipid species showed significant age‐sex interactions, with opposite age‐related changes in 39 lipid species. In general, most of the cholesteryl esters, ceramides, lysophospholipids, and glycerides were higher in 45‐ to 50‐year‐old men compared with women of same age, but the sex differences narrowed down or reversed with age. We did not observe any major differences in genetic effect in the sex‐stratified genome‐wide association analyses, which suggests that common genetic variants do not have a major role in sex differences in lipidome. Conclusions Our study provides a comprehensive view of sex differences in circulatory lipids pointing to potential sex differences in lipid metabolism and highlights the need for sex‐ and age‐specific prevention strategies.

Enzyme specificity in lipid metabolic pathways often remains unresolved at the lipid species level, which is needed to link lipidomic molecular phenotypes with their protein counterparts to construct functional pathway maps. We created lipidomic profiles of 23 gene knockouts in a proof-of-concept study based on a CRISPR/Cas9 knockout screen in mammalian cells. This results in a lipidomic resource across 24 lipid classes. We highlight lipid species phenotypes of multiple knockout cell lines compared to a control, created by targeting the human safe-harbor locus AAVS1 using up to 1228 lipid species and subspecies, charting lipid metabolism at the molecular level. Lipid species changes are found in all knockout cell lines, however, some are most apparent on the lipid class level (e.g., SGMS1 and CEPT1), while others are most apparent on the fatty acid level (e.g., DECR2 and ACOT7). We find lipidomic phenotypes to be reproducible across different clones of the same knockout and we observed similar phenotypes when two enzymes that catalyze subsequent steps of the long-chain fatty acid elongation cycle were targeted.