Patent Application
20200096524
The invention relates to (i) seven plasma triglyceride (TAG) species and/or (ii) seven liver triglyceride (TAG) and eight liver cardiolipin/monolysocardiolipin species measured by targeted LC-MS and use thereof as diagnostic and prognostic biomarkers of fatty liver, as well as to monitor the efficacy of preventive and therapeutic measures to lower liver fat content.
Fatty liver is characterized by excess of fat/triglyceride accumulation in the liver. It is the common characteristic feature of most liver diseases including non-alcoholic fatty liver disease (NAFLD) and alcoholic fatty liver disease (AFLD). Both NAFLD and AFLD encompasses a spectrum of liver abnormality which includes fatty liver/steatosis, steatohepatitis, fibrosis and cirrhosis. The major causes of NAFLD are obesity, diabetes, hyperlipidemia, rapid weight loss, genetic inheritance and side effect of certain medications, including aspirin, steroids, tamoxifen, and tetracycline. Whereas AFLD is induced by excess intake of alcohol. While the disease aetiology is different for NAFLD and AFLD, the underlying abnormality of excess fat accumulation in the liver or fatty liver is common. The current diagnosis of these diseases relies on invasive liver biopsies, which are discretionary in nature due to the histological scoring involved and may result in “under-” or “mis-diagnosis” of these diseases and often a late start of treatment. Thus there is a need to develop further methods that are easy to carry out, safe, accurate and reliable for diagnosing liver diseases and for monitoring the efficacy of preventive and therapeutic measures to lower liver fat content.
In the present invention, MS-based lipidomics in plasma and liver have been used, combined with phenomics from 50 BxD genetic reference population strains, to identify biomarkers of fatty liver. The MS approach was standardized to capture triglyceride (TAG) species and cardiolipin (CL) species abundant in plasma and liver of all the genetically diverse 50 BXD strains, which enhanced the scope of these species as biomarkers to different degrees of fatty liver. These five beneficial (CL(LLLL), MLCL(LLL), TAG(52:5), TAG(52:4) and TAG(54:6)) and ten harmful (MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP), TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2)) CL and TAG biomarkers reflect carefully their levels in liver and plasma and correlate with other biological indicators of liver fat accumulation. Importantly, the beneficial lipid species correlate negatively and the harmful species correlate positively with obesity and NAFLD traits. They are not restricted to a particular diet and therefore can be suitable for the diagnosis and prognosis of individuals with fatty liver resulting from either genetic or different dietary or environmental (e.g. medication, toxins) insults. The above proposed plasma TAG biomarkers are hence easy to apply and non-invasive, less labor intensive and standardized. They also pose risk for adverse effects, associated with liver biopsies. Finally, these plasma biomarkers also offer a chance to monitor disease progression and the efficacy of preventive or therapeutic interventions. The liver TAG, CL and MLCL biomarkers also offer a chance to monitor disease progression and the efficacy of preventive or therapeutic interventions in liver.
In one aspect the invention provides a method for diagnosing a fatty liver disease or a predisposition therefor in a subject, said method comprising
In another aspect the invention provides a method for monitoring the progression of a fatty liver disease in a subject diagnosed to suffer from a fatty liver disease, said method comprising
In a further aspect the invention provides a method for monitoring and/or adapting the efficacy of a therapy for treatment of a fatty liver disease in a subject diagnosed to suffer from a fatty liver disease, said method comprising
In a further aspect the invention provides use of at least one plasma or liver triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof, and/or at least one liver cardiolipin (CL) biomarker and/or monolysocardiolipin (MLCL) biomarker selected from the group comprising CL(LLLL), MLCL(LLL), MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP) or a combination thereof, for in-vitro diagnosing fatty liver disease or the predisposition therefor.
In a further aspect the invention provides a plasma or liver triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof and/or at least one liver cardiolipin (CL) biomarker and/or monolysocardiolipin (MLCL) biomarker selected from the group comprising CL(LLLL), MLCL(LLL), MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP) or a combination thereof for use in diagnosing fatty liver disease or the predisposition therefor in a subject.
Table 1. Lipid species link to GWAS genes for blood lipid levels and associated metabolic traits. Lipid species having a QTL at the position of the genes identified by GWAS for blood lipids and associated metabolic traits (indicated under GWAS phenotypes).
For each GWAS selected gene, a chromosomal position of ±5 Mb on either side of the gene position was used to match the peak QTL of lipid species. LOD score above 3.5 and local p-value <0.05 was used for the match. Each box with more than one gene represents a syntenic region on the mouse chromosome. The genes in red font (7 out of 10 syntenic regions) are syntenic in humans also. Exclusive literature reference for the GWAS phenotype of the given genes is provided as PMID. Abbreviation: TG: TAG; TC: total cholesterol; HDL-C: HDL cholesterol; LDL-C: LDL cholesterol; CAD: coronary artery disease; BMI: body mass index. An extended version of the table with the metabolic function of the genes and p-value of the QTLs is obtained (data not shown).
Table 2A. List of plasma lipid species measured in each class and their predominant side chain fatty acid (FA) composition. Log2fold change of HFD vs. CD and p-value for each lipid is provided. Nominal p value (Student's t-test) and adjusted P.value (Benjamini-hochberg corrected significance) are indicated for all lipids. The abbreviations of the lipid species are also indicated.
Table 2B. Lipids and their abundance in each class Table 2C. Lipids in each module Table 2D. Heritability of all lipid species Table 2E Spearman correlation between serum and liver lipids.
All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The publications and applications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. In addition, the materials, methods, and examples are illustrative only and are not intended to be limiting.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in art to which the subject matter herein belongs. As used herein, the following definitions are supplied in order to facilitate the understanding of the present invention.
The term “comprise” is generally used in the sense of include, that is to say permitting the presence of one or more features or components. Also as used in the specification and claims, the language “comprising” can include analogous embodiments described in terms of “consisting of” and/or “consisting essentially of”.
As used in the specification and claims, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise.
The term “and/or” used in a phrase such as “A and/or B” herein is intended to include “A and B”, “A or B”, “A”, and “B”.
The term “at least one” as used herein can refer to one triglyceride (TAG) biomarker, or two TAG biomarkers, or three TAG biomarkers, or four TAG biomarkers, or five TAG biomarkers, or six TAG biomarkers or seven TAG biomarkets selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6). It can also refer to one cardiolipin (CL) biomarker, or two cardiolipin (CL) biomarkers, or three cardiolipin (CL) biomarkers, or four cardiolipin (CL) biomarkers or five cardiolipin (CL) biomarkers selected from the group comprising CL(LLLL), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP). It can also refer to one monolysocardiolipin (MLCL) biomarker, or two monolysocardiolipin (MLCL) biomarkers, or three monolysocardiolipin (MLCL) biomarkers selected from the group comprising MLCL(LLL), MLCL(LOO), MLCL(LLO).
The term “diagnosing” as used herein refers to assessing whether a subject suffers from the fatty liver disease, or not. The term includes individual diagnosis of fatty liver disease, or its symptoms as well as continuous monitoring of a patient. Monitoring, i.e. diagnosing the presence or absence of fatty liver disease or the symptoms accompanying it at various time points, includes monitoring of subjects known to suffer from fatty liver disease as well as monitoring of subjects known to be at risk of developing fatty liver disease. Furthermore, monitoring can also be used to determine whether a subject is treated successfully or whether at least symptoms of liver disease can be ameliorated over time by a certain therapy. Diagnosing as used herein also refers to diagnosing a predisposition of a fatty liver disease and, thus, predicting whether a subject is at increased risk of developing a fatty liver disease within a predictive window starting from the time when the sample to be analyzed has been taken. Preferably, the predictive window is at least three months, six months, one year, two years, five years, ten years or up to the entire life span of the subject. A subject is at increased risk if the probability by which it will develop the disease is statistically significantly increased with respect to the average or mean probability, i.e. the prevalence for the disease in the respective population from which analyzed subject originates.
The term “fatty liver disease” is well known in the art. Preferably, the term refers to an impairment of the liver. Preferably, said impairment is the result of a surplus of triglycerides, sich as triacylglyceride, that accumulate in the liver and form large and small vacuoles. The symptoms accompanying fatty liver disease are well known from standard text books of medicine such as Stedman's or Pschyrembel. Fatty liver disease may result from alcohol abuse, diabetes mei!itus, nutritional defects and wrong diets, toxicity of drugs or genetic predisposition (see Carulli et al. 2009, Dig Liver Dis. 41(11):823-8. Epub 2009 Apr. 28 “Genetic polymorphisms in nonalcoholic fatty liver disease: interleukin-6-174G/C polymorphism is associated with nonalcoholic steatohepatitis”; or Yoneda et al. 2009, Liver Int. 29(7): 1078-85. Epub 2009 Mar. 3 “Association between angiotensin II type 1 receptor polymorphisms and the occurrence of nonalcoholic fatty liver disease”). Fatty liver disease as used in accordance with the present invention also include the more severe forms thereof and, in particular, steatosis, NASH or NAFDL. Symptoms accompanying these diseases are also well known to the physicians and are described in detail in standard text books of medicine.
As used herein, “treatment” means any manner in which one or more of the symptoms of a disorder are ameliorated or otherwise beneficially altered. Thus, the terms “treating” or “treatment” of a disorder as used herein includes: reverting the disorder (e.g., causing regression of the disorder or its clinical symptoms wholly or partially); preventing the disorder (e.g., causing the clinical symptoms of the disorder not to develop in a subject that can be exposed to or predisposed to the disorder but does not yet experience or display symptoms of the disorder); inhibiting the disorder (e.g., arresting or reducing the development of the disorder or its clinical symptoms); attenuating the disorder (e.g., weakening or reducing the severity or duration of a disorder or its clinical symptoms); or relieving the disorder (e.g., causing regression of the disorder or its clinical symptoms). Further, amelioration of the symptoms of a particular disorder by administration of a particular composition can include any lessening, whether permanent or temporary, lasting or transient that can be attributed to or associated with administration of a composition of the presently disclosed subject matter and/or practice of the presently disclosed methods.
As used herein, the term “subject” relates to animals and, preferably, to mammals. More preferably, the subject is a primate and, most preferably, a human. The subject, preferably, is suffering from a fatty liver disease or is suspected to suffer from a fatty liver disease, i.e. it may already show some or all of the symptoms associated with the disease. Preferably, the subject, however, is besides the aforementioned diseases and disorders apparently healthy. The said subject may also be at increased risk of developing a fatty liver disease, i.e. having a predisposition for a fatty liver disease. Such a subject may be also apparently healthy with respect to a fatty liver disease. A subject being at increased risk may be a subject suffering from diabetes meilitus or an alcohol addict. Moreover, a subject being at increased risk and, thus, having a predisposition for a fatty liver disease, may be a subject which is exposed to toxic substances or harmful drugs or which is affected by a wrong nutritional diet or which has a genetic predisposition.
Lipids are central to all biological processes, from energy storage to forming the matrix of cell membranes to signaling (Han, 2016). They exist in a staggering array of sizes, biophysical properties, and relative abundance. Although structurally lipids are composed of not many ‘building blocks’, they have the potential to generate ˜100,000 different molecular species (http://www.lipidmaps.org/) (Fahy et al., 2005), whose precise function is not easy to decipher given their high complexity. Lipid profiles are determined by the combined influences of genes and environmental factors and their interactions (GxE). The profile changes based on dietary lipids, de novo lipogenesis (DNL), and the hundreds of enzymes that modulate the length and desaturation of fatty acid (FA) chains and their incorporation into more complex lipid molecules. A considerable part of human genome is required to synthesize, metabolize and regulate this lipid diversity. Although it is still a challenge to detect all types of lipid species in a sample, LC-MS-based methods combined with sophisticated software to aid in lipid identification can detect thousands of distinct lipids (Han, 2016; Hyotylainen and Oresic, 2015).
Storage lipids (triacylglycerol, TAG), circulating lipid-protein complexes (lipoprotein particles), and membrane lipids (phospholipids (PL), diacylglycerol (DAG)) as a “class” have previously been linked to the metabolic syndrome, whose features include, obesity, insulin resistance, cardiovascular diseases, non-alcoholic liver disease (NAFLD). (Farese et al., 2012; Han, 2016; Hyotylainen and Oresic, 2014; Puri et al., 2009). However, the precise contribution of most lipid species to the metabolic dysfunction is not well understood. In the past decade, numerous genome wide association studies (GWAS) have identified over 200 genetic variants associated with different lipid class and complex metabolic traits (Diabetes Genetics Initiative of Broad Institute of et al., 2007; Global Lipids Genetics et al., 2013; Surakka et al., 2015; Teslovich et al., 2010). However, with few exceptions (Gieger et al., 2008; IIlig et al., 2010; Shin et al., 2014), these studies focused on the total lipid class and not on individual lipid species, which may be one of the many factors contributing to low variance explained by the loci identified sofar (Johansen et al., 2011). Therefore it is imperative that the focus shifts towards analyzing individual lipid species rather than total class in a population based study. Additionally, human GWAS have rarely identified risk factors that explain more than 5-20% of variance in the target phenotype (Manolio et al., 2009). This problem is particularly compounded by traits, which are strongly modulated by GxE. In contrast, studies on mouse genetic reference populations (GRPs) in which environmental factors can be controlled have been able to provide a stable platform for identifying major genetic, environmental, and GxE factors influencing complex traits, including lipids (Hui et al., 2015; Sittig et al., 2016; Williams and Auwerx, 2015). Studies using only a few hundred such animals have identified loci that explain 30% or more of the phenotypic variance even for complex traits (Burke et al., 2012). While earlier mouse population studies have identified specific loci associated with different lipid class (Hui et al., 2015; Zhang et al., 2012), no study in a GRP has specifically profiled lipid species or identified loci associated with lipid species—nor has GxE been examined for these traits. In this disclosure, targeted lipidomics profiling of 129 lipid species across 271 individuals from 49 distinct inbred strains of the BXD GRP fed either chow diet (CD) or high fat diet (HFD) were performed and that were in parallel subjected to an extensive battery of metabolic tests (Williams et al., 2016). Fasting lipid species in plasma from different lipid class including free fatty acids (FFA), TAG, DAG, phosphotidylcholine (PC), phosphotidylethanolamine (PE), phosphatidylinositol (PI), phosphotidylglycerol (PG) and conenzymeQ (CoQ) were measured. Both genetic (quantitative trait locus (QTL) mapping) and multilayered omics (correlation, network analysis, module trait correlation) approaches were used to gain a comprehensive understanding of the genetic and dietary impact on lipid species and to uncover the potential of lipid species to predict metabolic health. Over 200 QTLs associated with 70% of the lipids measured were identified. 91 of these QTLs, in 28 different loci harbored 35 genes that were associated with lipid levels and metabolic syndrome associated traits in human GWAS. Lastly, the multi-scalar approach enabled to link a subset of TAG species as biomarkers of non-alcoholic fatty liver disease (NAFLD) that was validated is a mouse model of NAFLD and in human NAFLD subjects.
Plasma Lipidomics Profile Across the BXD GRP Shows a Coherent Dietary Effect
In an attempt to characterize the plasma lipidome signatures, a targeted lipidomics approach was used to measure 129 lipid species from different lipid classes in 78 BXD cohorts, 44 cohorts fed CD and 34 fed HFD. While on their respective diet, mice underwent extensive metabolic phenotyping (Williams et al., 2016; Wu et al., 2014). At the end of the phenotyping program, over-night fasted mice were sacrificed and plasma samples were used for lipidomics analyses. The detection of lipid species was based on their abundance, stability, polarity and ease of ionization. Lipid species characterized by their number of carbon atoms and double bonds in constituent acyl residues were measured, identifying 8 different lipid class including FFAs (16 species), TAG (53 species), DAG (6 species), PC (28 species), PE (15 species), PI (7), PG (2) and CoQ (2) (Table 2A). The quality of the mass spectrometry (MS) measurement was assessed by its coefficient of variation (CV) for technical and extraction replicates, which was ˜5% and ˜10% of the overall variation respectively, suggesting a very good reproducibility (
To identify lipid species and strains that show similar characteristics, hierarchical cluster analysis was used, which demonstrated that the BXD cohorts were clearly—but notably, not 100% segregated, indicating that genetic background can overpower even the massive effects of CD vs. HFD on lipid profiles (
It was next evaluated the impact of diet on DNL, monounsaturated fatty acid (MUFA) (
It was next verified the potential of plasma FFAs as representative of DNL and MUFA synthesis by analyzing the enrichment (HFD vs. CD) of the esterified forms of these FFAs in other lipid species. For the majority of the lipid species, their increase or decrease in either diet (Table 2A) could be easily predicted by their FA side chain composition. For instance, if the side chain was enriched in a FA that was increased in CD then that lipid species was increased in CD as illustrated for two lipid species increased in either CD or HFD (
In order to evaluate the effect of diet on relative enrichment of side chains in lipid species, it was next calculated the side chain composition of all lipid species measured. The majority of side chains comprised of palmitic (16:0), stearic (18:0), oleic (18:1n9), linoleic (18:2n6), arachidonic (20:4n6) and docosahexaenoic acid (DHA; 22:6n3). Lipid species with at least one palmitic, stearic and oleic acids in their side chain were increased in HFD cohorts, while lipid species with at least one linoleic acid in their side chain was increased in CD cohorts. Lipid species with an arachidonic acid side chain were either increased in HFD or unchanged and those with a DHA side chain were unchanged (
Unweighted Correlation Networks of Lipid Species and Identification of Predictors of Metabolic Health
Next, unweighted correlation network analysis of all the lipid species was performed, to obtain an overview of the interaction between the different lipid species and lipid class (
Based on this notion, it was considered that it may be possible to predict a metabolic phenotype of mice based on their lipid species profile in plasma and vice-versa. From the extensive phenotypic profiling performed in these mice (Williams et al., 2016), 31 unique traits as predictors of metabolic health/fitness were shortlisted. These traits covered a wide spectrum of tests including, fat and lean mass, physical fitness (treadmill exercise, activity wheel, VO2 max), oral glucose tolerance test, heart rate and plasma biochemical markers (ALT, AST, cholesterol etc), among others. Spearman correlation was performed for all lipid species and the shortlisted traits (data not shown). Correlation rho value was used to perform a heatmap analysis with unsupervised hierarchical clustering of all lipid species (
Note that the trait—FFA, clusters with healthy metabolic phenotypes, including heart rate (indicated in grey on the right side of
Weighted Correlation Network Analysis (WGCNA) Reflects Strong Dietary Impact and Identifies Lipid Modules as Predictors of Metabolic Health
In order to reduce the complexity of the plasma lipid profile, WGCNA was performed (Langfelder and Horvath, 2008) in both CD and HFD cohorts. WGCNA helped identify clusters of highly correlated lipids, which are potentially co-regulated within 9 and 10 different modules in CD and HFD, respectively (Table 2C). Each module is named and represented by different colors according to the major lipid class contained in them (
It was next compared the impact of diet on the lipid composition of each module. Correspondence analysis between CD and HFD evidenced a general moderate-low conservation in the lipid modules, indicating a strong diet effect on module formation (
It was then analyzed the FA side-chain composition of the lipids in each module, in order to have a global overview of the degree of unsaturation in each module. The side chain FA profile varied between modules in each diet, however, in general the proportion of FAs in each module showed a high conservation between CD and HFD (
To assess the functional relevance of the lipid modules identified, it was performed a correlation analysis between the modules and a set of metabolic traits used in the previous figure (
Plasma Lipid Species are Highly Heritable in Both Diets and Influenced by Many Genomic Loci
Next, it was assessed the degree of genetic, dietary and GxE regulation of lipid species. Heritability (h2; percentage of trait variation attributed to additive genetic factors) was calculated for all lipid species within dietary groups (CD and HFD) and across all cohorts combined (CD+HFD/Mixed) (
It was then examined the h2 of the most and least abundant lipid species in each lipid class (
For most lipids, the difference in their levels between the diets reflected their variance explained by genetics (CD+HFD/Mixed), diet and GxE (
Next, quantitative trait loci (QTL) were mapped for all lipid species and lipid modules (data not shown). The most significant QTLs—those with the highest LOD score—correlated positively with lipids' h2 and their significance (
Lipid Species Link to GWAS Genes for Blood Lipid Levels and Associated Metabolic Traits in Humans
Human GWAS studies have identified many genetic variants associated with the total concentration of different lipid class (Diabetes Genetics Initiative of Broad Institute of et al., 2007; Global Lipids Genetics et al., 2013; Kathiresan et al., 2009). Taking advantage of the novel lipid species QTLs herein disclosed (data not shown) and the known human GWAS loci for the total lipid class and their associated metabolic traits, it was attempted to extrapolate between the two. Of the many lipid QTLs herein disclosed, several of them (51 QTLs in CD and 40 in HFD) linked to 35 out of ˜300 genes pre-selected from GWAS for metabolic traits including plasma TAG, total cholesterol (TC), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), coronary artery disease (CAD), glucose and insulin levels, obesity and NAFLD traits (
The Gckr locus, encoding the glucokinase (GCK) regulatory protein (Gckr), is one of the most robust loci implicated in TAG metabolism (Diabetes Genetics Initiative of Broad Institute of et al., 2007). It has been replicated in GWAS of plasma TAG concentration, NAFLD and hypertriglyceridemia by an excess of rare variants in patients(Johansen et al., 2011; Kathiresan et al., 2009; Speliotes et al., 2011). Of all the TAGs measured, TAG(50:3) having side chain FA composition of (16:1/16:1/18:1) had a QTL at the Gckr locus. Lrpap1 (LDL Receptor Related Protein Associated Protein 1), at the same locus as Gckr has been associated with TAG, TC and LDL-C(Table 1,
Identification of Lipid Species as Biomarkers of Non-Alcoholic Fatty Liver Disease (NAFLD)
Plasma TAG concentration is a complex polygenic trait that follows a rightward-skewed distribution in the population, due to the wide range hypertriglyceridemias encountered (Johansen et al., 2011). Even the hepatic TAG content varies widely among individuals: from <1% to >50% of liver weight (Browning et al., 2004). Recent GWAS have identified both known and novel loci associated with plasma and liver TAG concentration. However, genetic variation at these loci explains only 10% of the overall TAG variation within the population corresponding to 25-30% of the total genetic contribution to TAG variability (Johansen et al., 2011; Speliotes et al., 2011). This may be partly due to the fact that total TAG concentration is always considered as a proxy for all TAG species. However, it has been shown that all TAGs do not follow similar trend in NALFD (Gorden et al., 2015; Hyysalo et al., 2014; Oresic et al., 2013; Puri et al., 2009). Measurement of TAG species is hence imperative since different species exhibit contradictory effects and have vastly different functions and biological impact (
Plasma and liver triglyceride levels are highly variable even across different mice strains and are not always increased under HFD indicating an underlying genetic regulation of triglyceride levels (Champy et al., 2004; Kirk et al., 1995; Lin et al., 2005). In line, plasma or liver TAG levels were highly variable and invariant between CD and HFD in the genetically diverse BXD cohorts (
Correlation of these lipid species, alongside total TAG concentration in liver and plasma, was performed with a number of phenotypes linked with NAFLD including, fasting Insulin, glucose, cholesterol, alanine transaminase (ALT), aspartate transaminase (AST), fat mass, liver mass and body weight (
Validation of Lipid Species as Biomarkers of NAFLD and Metabolic Fitness in Mice and Humans.
It was next validated the lipid biomarkers of NAFLD and metabolic fitness identified in
In this disclosure, it was used a systems genetics approach—consisting of a multilayered omics (genetics, lipidomics and phenomics) analysis—to examine the plasma lipidome in the mouse BXD GRP. For the 129 distinct lipid species measured, it was demonstrated the dietary footprint on lipid species, their interaction and their impact on metabolic traits. It was then examined how genetic, environmental and GxE factors influence lipid species and identified genetic loci that may drive changes for the 112 of 129 lipid species which mapped to QTLs. Subsequently 65 of these lipid species QTLs were linked to GWAS genes associated with blood lipids. Finally, 7 lipid species were identified as highly predictive plasma biomarkers reflective of NAFLD and their relevance was validated in a mouse model of fatty liver disease and in human subjects with various severity of NAFLD. While it was earlier (Chick et al., 2016; Hui et al., 2015; Williams et al., 2016; Wu et al., 2014) used the mouse GRP to study different omics layers, including transcriptomics, proteomics, and metabolomics, this is the first disclosure to quantify individual lipid species across classes in any mouse population (129 lipids in 271 mice from 78 cohorts) and provide the physiological impact of these lipids. Additionally, though human studies have profiled plasma (Gorden et al., 2015; Oresic et al., 2013; Puri et al., 2009) and liver lipids (Puri et al., 2007) separately from NAFLD patients, it is important to note that an integrated study elucidating the representative lipids of liver in plasma as a biomarker has not been performed.
Using different bioinformatics approaches, it was shown that majority of the lipids species are highly correlated and co-regulated, both within and across different 10 lipid classes measured. This resonates the underlying fact that any change in a lipid metabolic pathway induced by an experimental or physiological intervention will almost assuredly result in compensatory changes in other pathways affecting discrete lipid pools (Farese et al., 2012). In order to reduce the complexity of the lipids measured, it was used an unbiased network method—WGCNA—to group lipids in modules/clusters. Though the modules were primarily enriched in lipids from the same class, they also contained lipids from other classes, reiterating the co-regulation and interconnectivity of lipid species from different classes. The analysis herein disclosed shows that within the same lipid class, some lipid species are associated with healthy metabolic traits and other with unhealthy metabolic traits, providing testimony to the fact that all lipid species in a class do not have the same physiological impact (Farese et al., 2012). Therefore, entire classes of lipids should not be categorized as good or bad lipids, yet until recently more granular measurements of specific lipid species have been prohibitively difficult (Han, 2016; Hyotylainen and Oresic, 2014, 2015). In both research and clinical diagnosis, lipids have been predominantly measured only as the sum of total lipid classes (e.g. total TAG, total cholesterol, total phospholipids), which results in measurements that are skewed by the most abundant lipids in each particular class. These conglomerate lipid measurements can be misleading in terms of their physiological impact, as the data herein disclosed show that many of the less abundant species are good predictors of metabolic health than the most abundant ones.
The change in diet from chow diet (6% calories from fat) to high fat diet (60% calories from fat) had a significant impact on nearly all plasma lipid species measured—73%. Likewise, even within a dietary set, all lipids had significantly different regulation between the strains with the lowest and highest levels. Across all conditions, roughly 50% of variation could be attributed to diet-independent genetic factors, with an additional ˜10% of variation being attributable to uniform diet-induced changes, or a strain-dependent response to dietary differences. Together, the present disclosure can explain more than half of the observed variation in plasma lipid levels for 85% of the 129 measured lipid species. For the variation attributable to genetic or GxE factors, it was identified over 200 novel lipid QTLs containing known and novel putative regulators of lipid metabolism. Taking advantage of the known human GWAS loci/genes associated with blood lipid levels and associated traits; it was gone one step ahead and uncovered via a cross-species extrapolation, the association between these human genes and the lipid species identified in BXD study of the present disclosure.
This demonstrated the association between 65 lipid species in the BXDs and 32 human loci for blood lipids, including 7 syntenic regions in both mice and humans. This link underscores the power of mouse GRP studies to not only complement human genetic studies in a more refined manner, but also to find novel loci/genes controlling lipid species levels and to provide a proper setting for mechanistic studies.
Triglycerides are often considered as a signature of the metabolic syndrome, including NAFLD, despite a very wide interindividual difference in its levels (Cohen et al., 2011; Johansen et al., 2011; Listenberger et al., 2003). In line, it was not seen any significant difference in plasma or liver triglyceride levels between the diets, suggesting a strong underlying genetic control in modulating triglyceride levels. This goes in line with numerous human GWAS, which have identified at least 32 loci associated with only triglyceride levels (Teslovich et al., 2010). Furthermore, liver and plasma triglycerides did not correlate indicating that plasma triglyceride levels do not reflect liver triglyceride levels always (Romeo et al., 2008). By comparing the lipidome of serum (this disclosure) and liver (Jha et al. accompanying paper), 55 common lipids were identified, of which only 9 in plasma (8 TAGs and 1 DAGs) were a reflection of their levels in liver. Importantly, 4 of these lipid species were identified as pro-NAFLD biomarkers and 3 as anti-NAFLD biomarkers. Some of these biomarkers could separately be verified in both mice and human NAFLD subjects. The identification of these biomarkers using mouse GRP and validation in humans underscores the potential of mouse population genetics for translational research. While currently the diagnosis of NAFLD relies on invasive liver biopsies, which are also discretionary in nature and relying on histological scoring (Chalasani et al., 2012); MS-based lipidomics analysis coupled with plasma biochemistry markers may prove to be a more forward looking approach, which is non-invasive, less labor intensive and standardized. These plasma biomarkers also offer a chance to monitor disease progression and efficacies of preventive or therapeutic interventions.
This disclosure uncovers the potential of lipidomics combined with systems genetics to identify powerful biomarkers for heath and disease. The wealth of information on novel lipid QTLs and the phenotypic footprint of these lipid species provide a robust resource to the scientific community for in-silico data analysis. Further, the identification of lipid species as plasma biomarkers reflective of NAFLD from mice to humans strengthen the fact that lipid species can be used as powerful biomarkers of metabolic health and disease. Improved lipid analysis will benefit both molecular medicine and nutritional research, as human health is clearly dependent on human diet and genetics.
Validation of Lipid Species as Biomarkers of NAFLD and Metabolic Fitness in Mice and Humans.
It was then tested the relevance of these lipids as signatures of fatty liver in a different mouse model of NAFLD (induced by high-fat high-sucrose (HFHS) diet) and tested whether a NAFLD lowering therapeutic intervention, i.e. nicotinamide adenine dinucleotide (NAD+) precursor, nicotinamide riboside (NR), impacts these lipid markers. As such, the liver lipids were compared of mice fed CD or HFHS diet for 18 weeks, or mice that were fed HFHS diet supplemented with NR 9 weeks after the start of HFHS diet when they had already developed NAFLD (a therapeutic intervention, HFHS+NR) (
To further explore the clinical relevance of these findings, the plasma lipid species of healthy patients, patients with steatosis, early stage NASH (mild to moderate fibrosis (F1+F2)) and advanced stage NASH (severe bridging fibrosis or cirrhosis (F3+F4)) were analyzed. The first dimension (PC1) of the PCA of 55 TAG species segregated nearly all of the healthy individuals from the steatosis and NASH group (
In order to obtain insight on the affinity of TAG-metabolizing enzymes for the NAFLD signatures, the association of adipose triglyceride lipase (ATGL)—the rate limiting TAG-metabolizing enzyme—with these NAFLD TAG signatures was tested. Atgl expression in white adipose tissue (WAT) negatively correlated with the pro-NAFLD signatures and positively correlated with the anti-NAFLD signatures in both CD and HFD cohorts (
Identification of Cardiolipin Species as Signatures of Healthy and Fatty Liver
From all lipids measured, two clusters of lipid species with strong diet-independent association with liver mass were identified (
It was next tested whether this change in the profile of nine CL species is a general phenomenon in other dietary-induced models of hepatic steatosis and mitochondrial dysfunction and if the profile can be reverted by ameliorating hepato-steatosis via enhancing mitochondrial function. It has been previously shown that nicotinamide riboside (NR) treatment ameliorates high-fat high-sucrose (HFHS) diet induced fatty liver disease by boosting nicotinamide adenine dinucleotide (NAD+) levels and thereby enhancing mitochondrial function. Therefore, lipidomic profiling from the livers of C57BL/6J mice—the most commonly used laboratory mouse strain—fed on (i) CD or (ii) HFHS diet for 18 weeks or (iii) HFHS+nicotinamide riboside (NR), added 9 weeks after the start of the HFHS diet (therapeutic approach) was performed. HFHS diet decreased the CL signatures of healthy/normal liver—CL(LLLL) and MLCL(LLL)—whereas it increased the six CL signatures of unhealthy/fatty liver—MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP)—enriched in MUFAs (
The invention relates to liver lipid species selected from the triacylglycerol (TAG) and cardiolipin (CL) lipid classes measured by LC-MS/MS and use thereof as diagnostic and prognostic biomarkers of fatty liver. The CL species, enriched in polyunsaturated linoleic (L) acid, including its precursor/remodeling intermediate monolysocardiolipins (MLCL): CL(LLLL) and MLCL(LLL) and the TAG species: TAG(52:5), TAG(52:4) and TAG(54:6) are biomarkers of a healthy liver and are decreased in the presence of fat accumulation. Whereas, the CL species: enriched in monounsaturated fatty acids—oleic (O) and palmitoleic (Po) acid—including MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP) and the TAG species: TAG(52:2), TAG(54:3), TAG(56:3) and TAG(50:2) are biomarkers of unhealthy liver and are increased in the presence of fat accumulation. These lipid species are hence reliable biomarkers to diagnose various diseases with fatty liver, including non-alcoholic fatty liver disease (NAFLD), alcoholic fatty liver disease (AFLD), non-alcoholic steatohepatitis (NASH), alcoholic steatohepatitis (ASH); but also other diseases that are typified by liver fat accumulation. Moreover, the diagnostic precision can be increased by the combined profile of these 5 positive (beneficial) and 10 negative (harmful) biomarkers. These quantitative biomarkers will facilitate the diagnosis of these diseases, which now relies on liver biopsies coupled with discretionary histological analysis, resulting in their “under-” or “mis-diagnosis” and often a late start of treatment. Furthermore, they can be used to monitor the efficacy of preventive and therapeutic measures to lower liver fat content.
CL and MLCL species are usually detectable only in liver and therefore their biomarker relevance is limited to liver, whereas the TAG species are present in both liver and plasma and therefore their biomarker utility is applicable to both plasma and liver. Thus specifically (i) in plasma, the TAG species: TAG(52:2), TAG(54:3), TAG(56:3) and TAG(50:2) are biomarkers of an unhealthy liver and are increased in the plasma in the presence of a fatty liver, while the TAG species: TAG(52:5), TAG(52:4) and TAG(54:6) are biomarkers of a healthy liver and are decreased in the presence of a fatty liver; (ii) in liver, the CL species, enriched in polyunsaturated linoleic (L) acid, including its precursor/remodeling intermediate monolysocardiolipins (MLCL): CL(LLLL) and MLCL(LLL) and the TAG species: TAG(52:5), TAG(52:4) and TAG(54:6) are biomarkers of a healthy liver and are decreased in the presence of fat accumulation, whereas the CL species: enriched in monounsaturated fatty acids—oleic (O) and palmitoleic (Po) acid—including MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP) and the TAG species: TAG(52:2), TAG(54:3), TAG(56:3) and TAG(50:2) are biomarkers of unhealthy liver and are increased in the presence of fat accumulation.
According to an aspect, the present invention provides a method for diagnosing a fatty liver disease or a predisposition therefor in a subject, said method comprising
In a preferred embodiment, the sample is selected from the group comprising liver biopsy (sample), plasma sample or blood cells sample. In most preferred embodiment, the sample is selected from the group comprising liver biopsy (sample) and plasma sample.
Plasma samples are easy to obtain, easy to manipulate, non-invasive, less labor intensive and standardized. They also avoid risk for adverse effects, associated with liver biopsies. In plasma samples only the plasma TAG species of the invention can be detected, whereas the liver CL and MLCL biomarkers cannot be detected; these plasma biomarkers also offer a possibility to monitor disease progression and the efficacy of preventive or therapeutic interventions. In case more specific and/or reliable and/or accurate and/or sensitive results are needed, liver biopsy samples may be needed, where the liver TAG, CL and MLCL biomarkers of the invention can be detected to offer a possibility to monitor disease progression and the efficacy of preventive or therapeutic interventions in liver.
Accoridng to another aspect, the present invention provides a method for diagnosing a fatty liver disease or a predisposition therefor in a subject, said method comprising
According to another aspect, the present invention provides a method for diagnosing a fatty liver disease or a predisposition therefor in a subject, said method comprising
According to a further aspect, the present invention provides a method for monitoring the progression of a fatty liver disease in a subject diagnosed to suffer from a fatty liver disease, said method comprising
In a preferred embodiment, the sample is selected from the group comprising liver biopsy (sample), plasma sample or blood cells sample. In most preferred embodiment, the sample is selected from the group comprising liver biopsy (sample) and plasma sample.
Plasma samples are easy to obtain, easy to manipulate, non-invasive, less labor intensive and standardized. They also avoid risk for adverse effects, associated with liver biopsies. In plasma samples only the plasma TAG species of the invention can be detected; these plasma biomarkers also offer a possibility to monitor disease progression and the efficacy of preventive or therapeutic interventions. In case more specific and/or reliable and/or accurate and/or sensitive results are needed, liver biopsy samples may be needed, where the liver TAG, CL and MLCL biomarkers of the invention can be detected to offer a possibility to monitor disease progression and the efficacy of preventive or therapeutic interventions in liver.
According to a further aspect, the present invention provides a method for monitoring the progression of a fatty liver disease in a subject diagnosed to suffer from a fatty liver disease, said method comprising
According to a further aspect, the present invention provides a method for monitoring the progression of a fatty liver disease in a subject diagnosed to suffer from a fatty liver disease, said method comprising
In the context of the present invention, the reference is derived from a sample of a subject or group of subjects known not to suffer from the fatty liver disease or the predisposition therefor. In a preferred embodiment, the sample is selected from the group comprising liver biopsy (sample), plasma sample or blood cells sample. In most preferred embodiment, the sample is selected from the group comprising liver biopsy (sample) and plasma sample.
According to an embodiment of the methods of the present invention, the reference could be from a subject or group of subjects known not to suffer from the fatty liver disease or condition, preferably, an apparently healthy subject. In such a case, an amount of the at least one triglyceride (TAG) biomarker, and/or at least one cardiolipin (CL) biomarker and/or at least one monolysocardiolipin (MLCL) biomarker found in the test sample being altered with respect to the reference is indicative for the presence of the fatty liver disease.
According to another embodiment of the methods of the present invention, the reference is derived from a sample of a subject or group of subjects known not to suffer from the fatty liver disease or the predisposition therefor or is a calculated reference. Preferably, said calculated reference is calculated from such a group of subjects. Preferably, the at least one triglyceride (TAG) biomarker is selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof, and wherein TAG(52:2), TAG(54:3), TAG(56:3) and TAG(50:2) are biomarkers of an unhealthy liver and are increased in the sample (liver, plasma and/or blood cells) in the presence of a fatty liver or the predisposition therefor, while TAG(52:5), TAG(52:4) and TAG(54:6) are biomarkers of a healthy liver and are decreased in the presence of a fatty liver or the predisposition therefor. Preferably, the at least one cardiolipin (CL) biomarker and/or monolysocardiolipin (MLCL) biomarker is selected from the group comprising CL(LLLL), MLCL(LLL), MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP) or a combination thereof, and wherein MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO) and CL(OOOP) are biomarkers of an unhealthy liver and are increased in the sample (liver, plasma, and/or blood cells) in the presence of a fatty liver or the predisposition therefor, while CL(LLLL) and MLCL(LLL) are biomarkers of a healthy liver and are decreased in the presence of a fatty liver or the predisposition therefor.
Typically, TAG(52:2) is triacylglycerol 52:2, a triglyceride in which the three acyl groups contain a total of 52 carbons and 2 double bonds; TAG(54:3) is triacylglycerol 54:3, a triglyceride in which the three acyl groups contain a total of 54 carbons and 3 double bonds; TAG(56:3) is triacylglycerol 56:3, a triglyceride in which the three acyl groups contain a total of 56 carbons and 3 double bonds; TAG(50:2) is triacylglycerol 50:2, a triglyceride in which the three acyl groups contain a total of 50 carbons and 2 double bonds; TAG(52:5) is triacylglycerol 52:5, a triglyceride in which the three acyl groups contain a total of 52 carbons and 5 double bonds; TAG(52:4) is triacylglycerol 52:4, a triglyceride in which the three acyl groups contain a total of 52 carbons and 4 double bonds; TAG(54:6) is triacylglycerol 54:6, a triglyceride in which the three acyl groups contain a total of 54 carbons and 6 double bonds.
The abbreviations are as follows: CL(LLLL) is cardiolipin-(18:2/18:2/18:2/18:2); MLCL(LLL) is monolysocardiolipin-(18:2/18:2/18:2); MLCL(LOO) is monolysocardiolipin-(18:2/18:1/18:1); MLCL(LLO) is monolysocardiolipin-(18:2/18:2/18:1); CL(LOOPo) is cardiolipin-(18:2/18:1/18:1/16:1); CL(LLPoP) is cardiolipin-(18:2/18:2/16:1/16:0); CL(LOOO) is cardiolipin-(18:2/18:1/18:1/18:1); CL(OOOP) is cardiolipin-(18:1/18:1/18:1/16:0). Cardiolipin has four alkyl chains, thus for example the feature 18:2 in CL(18:2/18:2/18:2/18:2) means 18-carbon fatty alkyl chains with 2 unsaturated bonds on each of them.
Determination of the amount of triglyceride (TAG) biomarker, cardiolipin (CL) biomarker and monolysocardiolipin (MLCL) biomarker in samples can be done by any suitable method known in the art.
According to a further aspect, the present invention provides a method for monitoring and/or adapting the efficacy of a therapy for treatment of a fatty liver disease in a subject diagnosed to suffer from a fatty liver disease, said method comprising
In a preferred embodiment, the sample is selected from the group comprising liver biopsy (sample), plasma sample or blood cells sample. In most preferred embodiment, the sample is selected from the group comprising liver biopsy (sample) and plasma sample.
According to another aspect, the present invention provides a method for monitoring and/or adapting the efficacy of a therapy for treatment of a fatty liver disease in a subject diagnosed to suffer from a fatty liver disease, said method comprising
a) providing plasma or liver samples derived from a subject before and after the subject undergoes the therapy,
b) determining in the plasma or liver samples of a subject the amount of at least one plasma or liver triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof,
c) comparing the amount of the at least one plasma or liver triglyceride (TAG) biomarker after therapy against the amount before therapy, wherein increase of TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2) or a combination thereof and/or decrease of TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof is indicative for inefficacy of the therapy,
d) adapting and/or changing the therapy for treatment of a fatty liver disease in a subject if the therapy is found to be ineffective according to the step c).
According to another aspect, the present invention provides a method for monitoring and/or adapting the efficacy of a therapy for treatment of a fatty liver disease in a subject diagnosed to suffer from a fatty liver disease, said method comprising
According to another aspect, the present invention provides a method for treating a subject suffering from a fatty liver disease by a therapy, wherein the subject exhibits increased amount of TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP) or a combination thereof and/or decreased amount of TAG(52:5), TAG(52:4), TAG(54:6), CL(LLLL), MLCL(LLL) or a combination thereof.
In a preferred embodiment, a therapy for treatment of a fatty liver disease is selected from the group comprising surgery, drug treatment or life style recommendations. Drug treatment comprises the administration of one or more drugs selected from Statins, Incretin analogues, Metformin, Rimonabant, Thiazolidinediones, or Orlistat. Life style recommendations typically include adapted diet plan (such as adapted food, adapted food quantites and adapted water intake) and exercise (sport) known in the art and able to have beneficial effects to treat a fatty liver disease.
In accordance with the methods of the present invention, it is possible to select one, two, three, four, five, six or seven triglyceride (TAG) biomarkers selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) and/or one, two, three, four or five cardiolipin (CL) biomarkers selected from the group comprising CL(LLLL), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP), and/or one, two or three monolysocardiolipin (MLCL) biomarkers selected from the group comprising MLCL(LLL), MLCL(LOO), MLCL(LLO) to be used in the methods of the present invention.
In an embodiment of the methods of the present invention, the fatty liver disease is selected from the group comprising non-alcoholic fatty liver disease (NAFLD), alcoholic fatty liver disease (AFLD), non-alcoholic steatohepatitis (NASH), alcoholic steatohepatitis (ASH), steatosis, other diseases that are typified by liver fat accumulation, diseases comprising genetic mutations of PNPLA3, FDFT1, GCKR, NCAN, PPP1R3B and LYPLAL1, a disease related to any genetic mutation, which confer susceptibility or predisposition to fatty liver, the metabolic syndrome including obesity, diabetes, hyperlipidemia, hypertension, and atherosclerosis, condition related to rapid weight loss and weight loss following bariatric surgery.
In a preferred embodiment of the methods of the present invention, the fatty liver disease is selected from the group comprising non-alcoholic fatty liver disease (NAFLD), alcoholic fatty liver disease (AFLD), non-alcoholic steatohepatitis (NASH), alcoholic steatohepatitis (ASH), steatosis, other diseases that are typified by liver fat accumulation.
In a more preferred embodiment of the methods of the present invention, the fatty liver disease is selected from the group comprising non-alcoholic fatty liver disease (NAFLD), alcoholic fatty liver disease (AFLD), non-alcoholic steatohepatitis (NASH), alcoholic steatohepatitis (ASH), steatosis.
In another preferred embodiment of the methods of the present invention, the fatty liver disease is selected from the group of diseases comprising genetic mutations of PNPLA3, FDFT1, GCKR, NCAN, PPP1R3B and LYPLAL1.
In a further preferred embodiment of the methods of the present invention, the fatty liver disease is selected from the group comprising a disease related to any genetic mutation, which confer susceptibility or predisposition to fatty liver.
In a further preferred embodiment of the methods of the present invention, the fatty liver disease is selected from the group comprising the metabolic syndrome including obesity, diabetes, hyperlipidemia, hypertension, and atherosclerosis.
In a further preferred embodiment of the methods of the present invention, the fatty liver disease is selected from the group comprising condition related to rapid weight loss and weight loss following bariatric surgery.
In a preferred embodiment of the methods of the present invention, the fatty liver disease is selected from the group comprising toxic hepatitis induced by drugs, such as pain relievers (acetaminophen, aspirin, ibuprofen and naproxen), herbs and supplements (aloe vera, black cohosh, cascara, chaparral, comfrey, kava and ephedra); industrial chemicals (carbon tetrachloride, vinyl chloride, paraquat and polychlorinated biphenyls).
According to a further aspect, the present invention provides use of at least one plasma or liver triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof, and/or at least one liver cardiolipin (CL) biomarker and/or monolysocardiolipin (MLCL) biomarker selected from the group comprising CL(LLLL), MLCL(LLL), MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP) or a combination thereof, for in-vitro diagnosing fatty liver disease or the predisposition therefor.
According to a further aspect, the present invention provides use of at least one plasma or liver triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof for in-vitro diagnosing fatty liver disease or the predisposition therefor.
According to a further aspect, the present invention provides use of at least one plasma triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof for in-vitro diagnosing fatty liver disease or the predisposition therefor.
According to further aspect, the present invention provides a plasma or liver triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof and/or at least one liver cardiolipin (CL) biomarker and/or monolysocardiolipin (MLCL) biomarker selected from the group comprising CL(LLLL), MLCL(LLL), MLCL(LOO), MLCL(LLO), CL(LOOPo), CL(LLPoP), CL(LOOO), CL(OOOP) or a combination thereof for use in diagnosing fatty liver disease or the predisposition therefor in a subject.
According to another aspect, the present invention provides a plasma or liver triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof for use in diagnosing fatty liver disease or the predisposition therefor in a subject.
According to another aspect, the present invention provides a plasma triglyceride (TAG) biomarker selected from the group comprising TAG(52:2), TAG(54:3), TAG(56:3), TAG(50:2), TAG(52:5), TAG(52:4), TAG(54:6) or a combination thereof for use in diagnosing fatty liver disease or the predisposition therefor in a subject.
Advantageously, it has been found in the present invention that the amounts of the specific triglyceride biomarkers, cardiolipin (CL) biomarkers and monolysocardiolipin (MLCL) biomarkers referred to above are indicators for a fatty liver disease or a predisposition therefor. Thus fatty liver diseases can be safely, simply, not-invasively, efficiently and reliably diagnosed by diagnosis based on body fluids such as plasma samples. An analysis via a body fluid metabolite profile is significantly less cost intensive than the gold standard liver biopsy with subsequent histopathology. This is particularly helpful for an efficient diagnosis of the disease as well as for improving of the pre-clinical and clinical management of a fatty liver disease as well as an efficient monitoring of patients. Moreover, based on the methods according to the present invention, the development of therapeutic measures including drugs can be facilitated and guided. Further, therapies or life style recommendations which are applied can be easily monitored for success without taking a serious risk of adverse side effects caused by the monitoring method.
In light of the foregoing, the methods of the present invention can be used for i) monitoring a subject suffering from a fatty liver disease, i.e. disease progression or amelioration can be determined, ii) identifying a subject in need of a therapy of a fatty liver disease, iii) identifying whether a therapy against a fatty liver disease is successful in a subject.
The present invention, therefore, also relates to a method for identifying whether a subject is in need for a therapy of a fatty liver disease comprising the steps of the methods of the present invention and the further step of identifying a subject in need if the fatty liver disease is diagnosed.
Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications without departing from the spirit or essential characteristics thereof. The invention also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations or any two or more of said steps or features. The present disclosure is therefore to be considered as in all aspects illustrated, and not limiting, the scope of the invention being indicated by the appended Claims, and all changes which come within the meaning and range of equivalency are intended to be embraced therein. The foregoing description will be more fully understood with reference to the following examples. Such Examples, are, however, exemplary of methods of practising the present invention and are not intended to limit the scope of the invention.
Animals
50 strains of the BXD population were phenotyped. ˜10 male mice from each strain were separated at 8 weeks of age into two equal groups for each dietary cohort. Mice were fed a chow diet (Harlan Teklad Global 18% Protein Rodent Diet, 2018, 6% kcal/fat) after weaning at 3 weeks of age until 8 weeks of age. At 8 weeks of age, half the population switched to a HFD (Harlan Teklad, TD.06414, 60% kcal/fat) for 21 weeks. All mice were phenotyped as previously described (Williams et al., 2016). At week 29, animals were fasted overnight then anesthetised using isoflurane. After blood collection, perfusion was performed via vena cava and ˜23 organs including liver were collected. Due to insufficient plasma (used for plasma biochemistry), lipidomics analysis could be performed only on 44 CD and 34 HFD strains. All research was approved by the Swiss cantonal veterinary authorities of Vaud under licenses 2257 and 2257.1.
Lipids were normalized in three different ways—normalized to internal standards (of each class), to total lipids in each sample and to all lipids in each class. Basic quality check and QTL analysis was performed from all the datasets, however the dataset normalized to total lipids was used for all the analysis due to overall low relative standard deviation in this dataset (data not shown). Additionally, normalization to total lipids has two major advantages over the other normalization methods; 1) All lipids measured did not have a true internal standard, 2) for lipid classes that have few lipid species measured, normalizing to class will be largely driven by one or two highly abundant lipids.
Validation of lipid species as biomarkers of fatty liver and metabolic fitness in mouse and human subjects were analysed (data not shown). For mice: n=7-10 per group. For human subjects: n=12, healthy; n=7 simple steatosis (F0=no fibrosis); n=7 NASH, fibrosis grade 1; n=8 NASH, fibrosis grade 2; n=7 NASH, fibrosis grade 3; n=5 NASH, cirrhosis=fibrosis grade 4. Groups with fibrosis grade 1 and 2 were combined as one group (low grade fibrosis), whereas groups with fibrosis grade 3 and 4 were combined as one group (advance fibrosis).
Bioinformatic Analyses
Correlations are Pearson's r or Spearman's rho, depending on the distribution of data. Data normality were checked by the Shapiro-Wilk test in R, with a W≥0.90 considered “normal”. Student's t-test was used for two groups comparisons in normal data of equal varainces, and Welch's t-test otherwise. QTL calculations were performed using the R/qtl (v 1.39-5) package (Arends et al., 2010) on the log transformed data. Parametric QTL calculation was performed for normally distributed lipids and non-parametric for lipids, those were not normally distributed. QTLs with a LOD score >3.5 were considered locally significant (local p<0.05) while QTLs with a LOD score >3.5 were considered genome-wide significant (genome-wide p<0.05).
Biological enrichment analysis was performed using the WEB-based Enrichr (http://amp.pharm.mssm.edu/Enrichr/). Heatmaps were generated using the “heatmap.2” function in R. PCA analysis was performed using “prcomp” function in R. Unweighted correlation network graphs were performed using Spearman correlation, keeping all edges with P values less than 1e-05 in both D and HFD in R using the custom package imsblnfer, currently on Github (https://github.com/wolski/imsbInfer) but in the process of being added to Bioconductor. All graphs and analyses were either in R or GraphPad. For R, standard R plotting packages included in gplots or ggplot2—e.g., stripchart, plotCI, and barplot2. Final figures were all prepared with Adobe Illustrator.
Weighted gene correlation network analysis Weighted gene correlation network analysis (WGCNA) was performed as previously described (Langfelder and Horvath, 2008) by using the WGCNA R software package (v1.51). To construct the weighed lipid coexpression network, a correlation matrix was calculated containing all pairwise Pearson's correlations between all pairs of lipids across all BXD strains for both CD and HFD. It was defined a “signed hybrid” network in which the adjacency takes values between 0 and 1 when the correlation is positive and 0 if the correlation is negative. A power of 26 was chosen for both CD and HFD datasets. It was selected the minimum power in which both datasets followed the Scale-Free Topology Criterion (model fitting index R2>0.8) and showed a similar connectivity. The selection of a high power (threshold) has the effect of suppressing low correlations that may be due to noise, penalize weaker connections and strengthen stronger connections. The result is a network adjacency that is zero for negatively correlated genes and is positive for positively correlated genes. Adjacency of weakly correlated genes is nearly zero due to the power transformation. Next, the lipids were hierarchically clustered using the distance measure and modules were determined by choosing a height cutoff for the resulting dendrogram by using a dynamic tree-cutting algorithm, selecting a minimum module size of 5. Modules with a correlation higher than 0.75 were merged. The resulting lipid modules were assigned color names and identified using the eigenvector of each module, named as module eigenlipid (ME). ME is defined as the first principal component of the standardized expression profiles and can be considered as the best summary of the standardized module expression data. Correspondence analysis between CD and HFD was performed by calculating the overlaps of each pair of CD-HFD modules and analyzed using the Fisher's exact test. Module-Trait relationships were calculated by Pearson's correlation between MEs and selected metabolic phenotypic variables obtained in previous BXD studies [ref] in order to identify modules related to metabolic traits. Module QTL (modQTL) mapping were calculated by selecting the MEs as phenotype traits using the R package R/qtl (v 1.39-5) (Broman et al., 2003).
Methods Corresponding to
Human Plasma Biochemistry
Fasting whole blood samples were obtained by venipuncture after an overnight fast of 8 hours or more and processed for plasma within 2 hours on the day of liver biopsy. Routine blood tests were performed and included measures of ALT, AST, fasting TAG, cholesterol, glucose and insulin. The clinical information of the participants is provided in the table 3 below.
Methods Corresponding to
Lipidomics Sample Preparation and Analysis
Internal Standards (IS) Used:
For BXD liver samples, Q6, PC(15:0/15:0), PS(17:0/17:0), PE (15:0/15:0), PA (17:0/17:0), PG (15:0/15:0), CL (56:0) and FA (15:0/15:0) were used as internal standards. For NAFLD signature validation experiments in mice (
Extractions:
Liver samples were weighed and homogenized using a Potter-Elvehjem tissue grinder in 1.5 mL homogenization buffer (8 M urea, 50 mM TEAB, 100 mM NaCl, 1 mM CaCI, protease and phosphatase inhibitors (Roche)). Protein concentration was determined by BCA and all samples were diluted to 8 mg/mL with homogenization buffer. Samples were aliquoted with each tube containing 1 mg of protein (125 μL) and flash frozen in liquid N2 and stored at −80° C. Frozen aliquots of liver extracts were thawed on ice then internal standards were added (20 μL) and samples were vortexed (30 s). Chloroform/methanol (1:1, v/v, 1000 μL) was added and samples were vortexed (60 s). Subsequently, HCl (1 M, 200 μL) was added to induce phase separation, followed by vortex (60 s) and centrifugation (3,000 g, 3 min, 4° C.) to complete phase separation. 550 μL of the organic phase was dried under Ar2(g). The organic residue was reconstituted in ACN/IPA/H2O (65:30:5, v/v/v, 100 μL) by vortexing (60 s) and transferred to a glass vial for LC-MS analysis. Samples were stored at ˜80° C. until further use. LC-MS analysis was performed on an Ascentis Express C18 column held at 50° C. (150 mm×2.1 mm×2.7 μm particle size; Supelco) using an Accela LC Pump (500 μL/min flow rate; Thermo). Mobile phase A consisted of 10 mM ammonium acetate in ACN/H2O (70:30, v/v) containing 250 μL/L acetic acid. Mobile phase B consisted of 10 mM ammonium acetate in IPA/ACN (90:10, v/v) with the same additives. 10 μL of sample were injected by an HTC PAL autosampler (Thermo). Initially, mobile phase B was held at 40% for 30 s and then increased to 50% over an additional 30 s. It was then increased to 55% over 4 min after which, it was increased to 99% over 6 min and held there for 3 min. Prior to the next injection, the column was reequilibrated for 2 min. The LC system was coupled to a Q Exactive mass spectrometer (Build 2.3 SP2) by a HESI II heated ESI source kept at 325° C. (Thermo). The inlet capillary was kept at 320° C., sheath gas was set to 35 units, auxiliary gas to 15 units, and the spray voltage was set to 3,000 V in negative mode and 4,000 V in positive mode.
Several scan functions, including targeted and untargeted, were used to ensure optimal data acquisition for each lipid class. For fatty acids, selected ion monitoring (SIM) scans were taken from 0-3 min. MS1 data was acquired in negative mode for 220-600 m/z at a resolving power of 17,500 and an AGC target of 1×105. For phospholipids, diacylglycerols, and CoQ, parallel reaction monitoring (PRM) was used. The instrument was run in negative mode with a resolving power of 17,500, an AGC target of 2×105, a maximum injection time of 75 ms, and isolation window of 1.2 Th. Scans targeting each species were scheduled between 0-10.3 min based on previously determined retention times. For triglycerides, a separate set of runs was done where in addition to the targeted method described above, the MS was operated in positive mode from 10.1-13 min with resolving power set at 17,500 and the AGC target set to 5×105. Ions from 750-1,100 m/z were isolated (Top 2) and fragmented.
Measurement, Normalization and Quality Control:
The selection of lipid species for measurement was based on their abundance, stability, polarity and ease of ionization. For the BXDs, peaks were automatically integrated using TraceFinder software (Thermo) and integrations were checked manually. NR mice were processed as described in Jha et al., 2018. Lipids were normalized in three different ways—to internal standards (of each class), to total lipids in each sample and to all lipids in each class. Basic quality check and QTL analysis was performed from all the datasets, however the dataset normalized to total lipids was used for all the analysis and figures shown in the manuscript due to the overall low relative standard deviation in this dataset (data not shown). Additionally, normalization to total lipids has two major advantages over the other normalization methods; 1) All lipids measured did not have a true internal standard, 2) for lipid classes that have few lipid species measured, normalizing to class will be largely driven by one or two highly abundant lipids. Quality assessment of the MS measurements was performed by comparing the reproducibility of the technical and extraction replicates (see
Farese, R. V., Jr., Zechner, R., Newgard, C. B., and Walther, T. C. (2012). The problem of establishing relationships between hepatic steatosis and hepatic insulin resistance. Cell Metab 15, 570-573.
Ma, D. W., Arendt, B. M., Hillyer, L. M., Fung, S. K., McGilvray, I., Guindi, M., and Allard, J. P. (2016). Plasma phospholipids and fatty acid composition differ between liver biopsy-proven nonalcoholic fatty liver disease and healthy subjects. Nutrition & diabetes 6, e220.
| Number | Date | Country | Kind |
|---|---|---|---|
| 17154690.6 | Feb 2017 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2018/052815 | 2/5/2018 | WO | 00 |
