Diagnostic markers based on a computational model of lipoprotein metabolism

Abstract Background: Dyslipidemia is an important risk factor for cardiovascular disease and type II diabetes. Lipoprotein diagnostics, such as LDL cholesterol and HDL cholesterol, help to diagnose these diseases. Lipoprotein profile measurements could improve lipoprotein diagnostics, but interpretational complexity has limited their clinical application to date. We have previously developed a computational model called Particle Profiler to interpret lipoprotein profiles. In the current study we further developed and calibrated Particle Profiler using subjects with specific genetic conditions. We subsequently performed technical validation and worked at an initial indication of clinical usefulness starting from available data on lipoprotein concentrations and metabolic fluxes. Since the model outcomes cannot be measured directly, the only available technical validation was corroboration. For an initial indication of clinical usefulness, pooled lipoprotein metabolic flux data was available from subjects with various types of dyslipidemia. Therefore we investigated how well lipoprotein metabolic ratios derived from Particle Profiler distinguished reported dyslipidemic from normolipidemic subjects. Results: We found that the model could fit a range of normolipidemic and dyslipidemic subjects from fifteen out of sixteen studies equally well, with an average 8.8% ± 5.0% fit error; only one study showed a larger fit error. As initial indication of clinical usefulness, we showed that one diagnostic marker based on VLDL metabolic ratios better distinguished dyslipidemic from normolipidemic subjects than triglycerides, HDL cholesterol, or LDL cholesterol. The VLDL metabolic ratios outperformed each of the classical diagnostics separately; they also added power of distinction when included in a multivariate logistic regression model on top of the classical diagnostics. Conclusions: In this study we further developed, calibrated, and corroborated the Particle Profiler computational model using pooled lipoprotein metabolic flux data. From pooled lipoprotein metabolic flux data on dyslipidemic patients, we derived VLDL metabolic ratios that better distinguished normolipidemic from dyslipidemic subjects than standard diagnostics, including HDL cholesterol, triglycerides and LDL cholesterol. Since dyslipidemias are closely linked to cardiovascular disease and diabetes type II development, lipoprotein metabolic ratios are candidate risk markers for these diseases. These ratios can in principle be obtained by applying Particle Profiler to a single lipoprotein profile measurement, which makes clinical application feasible

Analysis of system disorders in liver metabolism with the SteatoNet model

Sistemski pristopi so ključni pri razumevanju kompleksnih bioloških sistemov, zato smo jih uporabili na primeru jeter. S sistemskimi pristopi k analizi na osnovi modela SteatoNet smo pridobili vpogled v celovit metabolizem jeter ter komunikacijo med jetri in drugimi tkivi. Različne prilagoditve modela SteatoNet aktualnim področjem raziskav s področja hepatologije so odprle vrata razumevanju bioloških procesov, ki jih še ne razumemo v celoti. Z modelom SteatoNet smo in silico testirali izbitje gena Cyp51 iz biosinteze holesterola v jetrih, kar je pokazalo, da adipozno tkivo poveča sintezo ketonskih teles in zmanjša hidrolizo trigliceridov. Pri modeliranju sinteze holesterola smo uporabili tako naše lastne eksperimentalne podatke iz meritev v jetrih miši s pogojno izbitim genom Cyp51 v jetrih kot tudi podatke iz literature. Med procesom validacije modela sinteze holesterola smo kot izjemno pomemben dejavnik opredelili tudi hormonski vpliv, zato smo model SteatoNet prilagodili razlikam med spoloma. Razlike so glede na podatke iz literature v izražanju spolnih hormonov in dinamiki rastnega hormona. Prilagojeni model smo poimenovali LiverSex in predstavlja prvi matematični model presnove jeter, s katerim lahko preiskujemo razlike med spoloma. Model LiverSex smo validirali z eksperimentalnimi podatki, pridobljenimi iz miši, na katerih smo preverjali vpliv prehrane z različnimi laboratorijskimi krmami. Z občutljivostno analizo modela LiverSex smo opredelili kritične točke v razvoju nealkoholne zamaščenosti jeter pri obeh spolih. Rezultat občutljivostne analize modela LiverSex je izpostavil, da je prav komunikacija med jetri in maščevjem ključna na prvi stopnji razvoja nealkoholne zamaščenosti jeter ter da je to možen razlog za manjšo prevalenco bolezni pri ženskah pred menopavzo. VLDL, shranjevanje trigliceridov v maščobnih kapljicah in/ali razpad maščobnih kislin v ketonska telesa verjetno predstavljajo zaščitne mehanizme, ki ženske pred menopavzo ščitijo pred razvojem nealkoholne zamaščenosti jeter. Pri razgradnji alkohola imajo glavno vlogo prav jetra, zato smo model SteatoNet prilagodili tudi vplivu alkohola na jetra. V model smo vstavili presnovo alkohola, ki inhibira dva koraka glukoneogeneze, in sicer presnovo piruvata v laktat in oksaloacetata v malat. Tako je nastal model StAlco, ki opisuje biokemijske hepatične posledice prekomernega uživanja alkohola. Model StAlco smo validirali s podatki iz literature. Modela LiverSex in StAlco sta prva računska modela za raziskovanje spolnega dimorfizma jeter in vpliva alkohola na jetra. Predstavljata začetno točko za še boljše razumevanje povezav med jetri in drugimi organi v smislu nastanka z jetri povezanih bolezni. Tovrstne prilagoditve dajejo priložnost novim aplikacijam, kot je npr. personaliziran pristop k diagnostiki in zdravljenju kompleksnih bolezni jeter.Systems approaches are crucial for understanding of complex biological systems, such as liver. With systems approaches based on the SteatoNet model, we gained novel insights into the liver metabolism and liver interactions with other tissues. Various adaptations of the SteatoNet to currently actual areas of hepatology research, have opened the door to understanding the biological processes that are not yet fully understood. With SteatoNet, we tested the in silico liver knock-out of the Cyp51 gene from cholesterol biosynthesis. Simulation results showed that adipose tissue increases the synthesis of ketone bodies and reduces the triglyceride hydrolysis. For modeling the cholesterol synthesis we used our own experimental data as well as data from literature. During the validation, hormonal effects were also identified as extremely important factors. Therefore, we have adapted the SteatoNet to gender differences. Main differences are in expression of sex hormones and in the growth hormone release. The adapted model was named LiverSex and it represents the first mathematical model of hepatic metabolism related to gender differences. The LiverSex was validated with experimental data obtained from mice in which we examined the effect of diet with different laboratory chows. With the sensitivity analysis of the LiverSex we identified critical points in the development of non-alcoholic fatty liver in both sexes. The result of a sensitivity analysis of LiverSex emphasized that communication between the liver and adipose tissue is crucial at the first stage in the development of non- alcoholic fatty liver disease, and that this is a possible reason for a lower prevalence of disease in women before menopause. VLDL, the storage of triglycerides in fat droplets, and/or the decomposition of fatty acids into the ketone body are likely the mechanisms that protect women from the development of non-alcoholic fatty liver disease before menopause. The liver has the main role in the decomposition of alcohol. We have adapted the SteatoNet to the hepatic effect of alcohol. SteatoNet was extended to StAlco model with the introduction of alcohol metabolism as well as inhibition of two gluconeogenesis steps: the reaction of pyruvate to lactate in oxaloacetate to malate. StAlco can describe the biochemical hepatic effects of excessive alcohol consumption and was validated with literature data. The LiverSex and StAlco are the first computational models for exploring influences of sexual dimorphism and the alcohol on the liver. They represent an excellent starting point for an even better understanding of liver communication with other organs, in terms of liver-related disease. Such adaptations give an opportunity to new applications, such as a personal approach to diagnose and treat complex liver related diseases