Biology subjects

Hellberg, S.

Publications and source records attributed to Hellberg, S..

16 recordsLinked to original sources

bioRxiv2026

DNA Demethylation is Dispensable for Venetoclax-HMA Synergy in Acute Myeloid Leukaemia

The BCL-2 inhibitor venetoclax (VEN) in combination with hypomethylating agents (HMAs) has improved treatment responses in acute myeloid leukaemia (AML), but the mechanisms underlying their synergy remain unclear. We investigated the role of DNA demethylation in the enhanced cytotoxicity of VEN-HMA combinations. Using AML cell lines, we compared the effects of azacitidine (AZA), decitabine (DAC), cytarabine (ARA-C) and the DNMT1-selective inhibitor GSK-3685032 (GSK5032) with VEN. As expected, VEN showed strong synergy with AZA, DAC, and the DNA-damaging agent ARA-C, but not with GSK5032, despite the latter inducing extensive DNA demethylation. Genome-wide methylation profiling confirmed that loss of DNA methylation did not correlate with increased cytotoxicity or synergy with VEN. Moreover, combining GSK5032 with ARA-C did not enhance cytotoxicity, indicating that DNA demethylation and DNA damage do not act additively. Instead, synergy was consistently associated with the DNA damage-inducing properties of AZA, DAC, and ARA-C. Extensive DNA demethylation tended to antagonize VEN activity, suggesting that the epigenetic effects of HMAs may limit their synergistic potential. Overall, our findings demonstrate that DNA damage-related cytotoxicity, rather than DNA demethylation, is the dominant mechanism driving VEN-HMA synergy and provide evidence that VEN-mediated cytotoxicity arises primarily from genotoxic stress, supporting refinement of treatment strategies.

cancer biology↗

bioRxiv2025

Human ADME/PK is lost in translation and prediction from in silico to in vitro to in vivo

BackgroundMeasurements and predictions of aqueous solubility (S), apparent cell permeability (Papp), unbound microsomal intrinsic clearance (CLint,u), unbound fraction in plasma (fu), log D, Lipinskis Rule of 5 (Ro5) and BBB+/BBB- (blood-brain-barrier) are commonly used in early drug discovery to evaluate whether compounds are likely to have adequate ADME/PK in humans. The main objective was to evaluate the validity and proposed thresholds for commonly used in silico to in vitro (with particular interest in the ADMET Predictor software) and in vitro to in vivo human ADME/PK prediction methods, and physicochemical estimates and rules of thumb. A secondary aim was to compare validity and thresholds to that for the prediction software ANDROMEDA. MethodsData were collected from literature and own studies. Main measures of validity were Q2 (true predictive accuracy; in silico models), R2 (correlation coefficient; in vitro models), Q2 * R2 (for translation from in silico to in vivo via in vitro), skewness, range, % correct predicted class and clinical relevance. Results and DiscussionPoor accuracies (Q2 * R2=0.05, 0.05, 0.36 and 0.45; [~]0 at low to moderate, decision-critical levels) and class predictions, limited ranges (not covering low to moderate estimates), systematic errors (often considerable overprediction at low values), poor clinical relevance and inadequate thresholds were found. Predictive accuracy was mainly lost in the translation from in vitro to in vivo. Log D and Ro5 are poor predictors of oral bioavailability and half-life. Ro5 produced 63 % false negatives for prediction of poor/good oral absorption. The overall mean Q2 for ANDROMEDA was 3 times higher (0.57 vs 0.2). Advantages with ANDROMEDA compared to in vitro data-based in silico models include wider application domain (high resolution at low values), extrahepatic elimination models, minimal skewness, clinical relevance, balanced thresholds, and compound- and parameter-unique confidence intervals. ANDROMEDA successfully predicted the human clinical ADME/PK for all small drugs marketed in 2021, while the in silico to in vitro to in vivo approach was out of reach for all. ConclusionThe validity of investigated methodologies (including ADMET Predictor) and thresholds were overall very low. Unless both predicted S, permeability and fu are high and CLint is moderate (an overall criterium not met for investigated modern small drugs) one is more or less lost in the translation, and will jeopardize compound selection and optimization. This clearly shows the need for better and thoroughly validated prediction models and software. Marked improvements in accuracy, range, balance and clinical relevance were achieved with ANDROMEDA, which predicts human clinical ADME/PK directly from chemical structures and has undergone extensive validation.

pharmacology and toxicology↗

bioRxiv2024

Comparing Lipinskis Rule of 5 and Machine Learning Based Prediction of Fraction Absorbed for Assessing Oral Absorption in Humans

BackgroundThe influential Lipinskis Rule of 5 (Ro5) describes molecular properties important for oral absorption in humans. According to Ro5, poor absorption is more likely when 2 or more of its criteria (molecular weight (MW) >500 g/mol, calculated octanol-water partition coefficient (clog P) >5, >5 hydrogen bond donors (HBD) and >10 hydrogen bond acceptors (HBA)) are violated. Earlier evaluations have shown that many drugs are sufficiently well absorbed into the systemic circulation despite many Ro5-violations. No evaluation of Ro5 vs fraction absorbed (fa) has, however, been done. MethodsDatasets of orally administered drugs violating and not violating Ro5 and with available human clinical fa-values were assembled, and contrasted to machine learning based predictions using the ANDROMEDA prediction software having a major MW-domain of 150-750 g/mol. Results129 Ro5-violent compounds (29 with MW>1000 g/mol) were found, 59 of which had fa-values (42 % mean fa). 34 % and 66 % of compounds were predicted as having fa[≤]10% and >10-30 % respectively, which was in good agreement with measured fa of 37 % and 63 %. The fa for all compounds with fa[≤]5 and [≤]10 % were correctly predicted. For compounds with fa>30 %, 81 % were predicted to have a fa>30 %, but none were predicted to have a fa<10 %. The Q2 for predicted vs observed fa was 0.64. For a set of 77 compounds without Ro5 violation (80 % mean fa), all compounds were correctly predicted to have a fa below or above 30 % (Q2=0.56). Among these are compounds with poor uptake (<1 % to 7 %). ConclusionWe show that machine learning based predictions of fa are superior to Ro5 for assessing oral absorption obstacles in humans. Too strict reliance on Ro5 may hence constitute a risk. ANDROMEDA predicts fa well, easily and quickly, and also differentiates well between poor and adequate oral uptake for compounds violating and not-violating Ro5. This makes it a valid and useful tool capable of predicting oral absorption in humans with good accuracy and replacing Ro5 for oral absorption assessments.

pharmacology and toxicology↗

bioRxiv2023

Prediction of the Human Pharmacokinetics of 30 Modern Antibiotics Using the ANDROMEDA Software

The ANDROMEDA software, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, was used to predict and characterize the human clinical pharmacokinetics of 30 selected modern small antibiotic compounds (investigational and marketed drugs). A majority of clinical pharmacokinetic data was missing. ANDROMEDA successfully filled this gap. Most antibiotics were predicted and measured to have limited permeability, good metabolic stability and multiple elimination pathways. According to predictions, most of the antibiotics are mainly eliminated renally and biliary and every other antibiotic is mainly eliminated via the renal route. Mean prediction errors for steady state volume of distribution, unbound fraction in plasma, renal and total clearance, oral clearance, fraction absorbed, fraction excreted renally, oral bioavailability and half-life were 1.3- to 2.3-fold. The overall median and maximum prediction errors were 1.5- and 4.8-fold, respectively, and 92 % of predictions had <3-fold error. Results are consistent with those obtained in previous validation studies and are better than with the best laboratory-based prediction methods, which validates ANDROMEDA for predictions of human clinical pharmacokinetics of modern antibiotic drugs, which to a great extent demonstrate pharmacokinetic characteristics challenging for laboratory methods (metabolic stability, limited permeability, efflux and multiple elimination pathways). Advantages with ANDROMEDA include that results are produced without the use of animals and cells and that predictions and decision-making can be done already at the design stage.

pharmacology and toxicology↗

bioRxiv2023

Application of the ANDROMEDA Software for Prediction of the Human Pharmacokinetics of Modern Anticancer Drugs

The ANDROMEDA toolkit for prediction of human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, was used to predict and characterize the human clinical pharmacokinetics of 12 small anticancer drugs marketed in 2021 and 2022 (molecular weight 355 to 1326 g/mol). The study is part of a series of software validations. A majority of clinical pharmacokinetic data was missing. ANDROMEDA successfully filled this gap. Most drugs were predicted/measured to have relatively complex pharmacokinetics, with limited passive permeability+efflux, high degree of plasma protein binding, significant gut-wall elimination and food interaction, biliary excretion and/or limited dissolution potential. Median, mean and maximum prediction errors for steady state volume of distribution, unbound fraction in plasma, blood-to-plasma concentration ratio, hepatic, renal and total clearance, fraction absorbed, oral bioavailability, half-life and degree of food interaction were 1.6-, 2.4- and 17-fold, respectively. Less than 3-fold errors were found for 78 % of predictions. Results are consistent with those obtained in previous validation studies and are better than with the best laboratory-based prediction methods, which validates ANDROMEDA for predictions of human clinical pharmacokinetics of modern small anticancer drugs with multi-mechanistical and challenging pharmacokinetics.

pharmacology and toxicology↗

bioRxiv2022

Predicting the Influence of Fat Food Intake on the Absorption and Systemic Exposure of Modern Small Drugs using ANDROMEDA by Prosilico Software

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSThe ANDROMEDA software by Prosilico has previously been successfully applied and validated for predictions of absorption characteristics of small drugs in man. The influence of fat food on the gastrointestinal uptake and systemic exposure of drugs have, however, not yet been evaluated with the software. Objective and MethodologyThe main objective was to use ANDROMEDA to predict area under the plasma concentration-time curve ratios in the fed (fat food) and fasted states (AUCfed/AUCfast) for small drugs (including those marketed in 2021) and compare results with corresponding measured clinical estimates. Actual dose sizes were considered. Another objective was to compare the performance of ANDROMEDA vs physiologically based pharmacokinetic (PBPK) modelling and simulations by The Food Effect PBPK IQ Working Group. PBPK results generated using Simcyp and GastroPlus software were based on various physicochemical, in vitro and in vivo data and a decision tree for model verification and optimization. Results and Discussion63 drugs, including 17 new drugs, with observed AUCfed/AUCfast between 0.2 and 5.5 were found and used for this evaluation. Predicted AUCfed/AUCfast had mean and maximum errors of 1.5- and 4.1-fold, respectively, and the predictive accuracy (correlation between predicted and observed AUCfed/AUCfast; Q2) was 0.3. 14 % of predictions had >2-fold error. For 72 % of drugs, food interaction class was correctly predicted. The level of predictive accuracy was overall similar to results obtained with PBPK modelling and simulations, however, with lower maximum error and higher compound coverage. With PBPK models, maximum simulation error was 7.7-fold and 3 highly lipophilic compounds were not possible to simulate. ConclusionThe results validate ANDROMEDA for prediction of fat food-drug interaction size for small drugs in man. Major advantages with the methodology include that prediction results are produced directly from molecular structure and oral dose and are similar to PBPK-simulation results obtained using in vitro and clinical data. Furthermore, ANDROMEDA showed lower maximum errors and wider compound range.

pharmacology and toxicology↗

bioRxiv2022

Predicting Gastrointestinal Absorption of Prodrugs and their Drugs with the ANDROMEDA by Prosilico Software

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSSome prodrugs are developed in order to improve gastrointestinal absorption properties such as permeability and solubility/dissolution. Prediction of the uptake of prodrugs and their drugs is challening for reasons including gastrointestinal hydrolysis and active transport. Objective and MethodologyThe objective was to use the ANDROMEDA software by Prosilico to predict absorption characteristics - passive fraction absorbed (fa,passive), dose-adjusted dissolution potential (fdiss) and total fa (fa) - of prodrugs and their drugs (including drugs and their active metabolites), and to evaluate how they differ between prodrugs and drugs and the predictive accuracy of the software. Results70 prodrug-drug pairs were found and selected for the study. The mean predicted fa,passive and fdiss for the prodrugs were 0.74 and 0.94, respectively. Corresponding estimates for the drugs were 0.72 and 0.98, respectively. For non-hydrolyzed prodrugs, the median relative and absolute prediction errors for fa were 1.17-fold and 0.08, respectively. Corresponding values for drugs were 1.11-fold and 0.07, respectively. The correlation between predicted and observed fa for non-hydrolyzed ester prodrugs and drugs combined (predictive accuracy) was 0.6. ConclusionProdrugs and drugs had similar average predicted fa,passive and fdiss, and most had or were predicted to have at least 50 % fa. The fa for about 1/3 of non-hydrolyzed prodrugs was higher than for corresponding drugs, showing successful prodrug design. Adequate prediction accuracy validates ANDROMEDA for prediction of prodrug and drug absorption in man.

pharmacology and toxicology↗

bioRxiv2022

Validation of predicted conformal intervals for prediction of human clinical pharmacokinetics

IntroductionConformal prediction (CP) methodology sits on top of machine learning methods and produces prediction confidence intervals that depend on how "strange" (non-conforming) test compounds are compared to training set compounds. CP has previously been successfully applied for prediction of steady-state volume of distribution (Vss) in humans, with 69 % of observations within the prediction interval at a 70 % confidence level. We have developed CP models for a variety of human pharmacokinetic (PK) parameters and validated their predictive accuracy (predicted vs observed estimates), but not validated prediction confidence intervals for them. The main objective of this study was to predict 70 % confidence intervals for Vss, unbound fraction in plasma (fu), intrinsic metabolic clearance (CLint), fraction absorbed passively (fa,passive) and maximum fraction dissolved (fdiss) for a variety of compounds in man and investigate the consistency between prediction intervals and observed/measured values. MethodologyCP models featured in the ANDROMEDA software by Prosilico were used for prediction of 70 % confidence intervals of Vss, fu, CLint, fa,passive and fdiss for compounds from different chemical classes and with broad physicochemical variety and for small drugs marketed in 2021. Results70 % prediction confidence intervals for 217, 117, 117, 89 and 89 compounds were produced for Vss, fu, CLint, fa,passive and fdiss, respectively. 78 % (expected 70 %) of observed data were within 70 % confidence intervals for the parameters. 70 % of predictions of Vss, fu, CLint fa,passive and fdiss are expected to have errors of maximally 2-, 4- and 6-fold and 7 and 12 %, respectively, which is in line with prediction errors. These findings validate the CP methodology. ConclusionIn conclusion, the results further validate CP models and confidence intervals of ANDROMEDA for prediction of human PK.

pharmacology and toxicology↗

bioRxiv2022

Kupffer cells dictate hepatic responses to the atherogenic dyslipidemic insult

Apolipoprotein-B (APOB) containing lipoproteins are causative for atherosclerotic cardiovascular disease. Whether the vasculature is the initial responding site or if atherogenic-dyslipidemia effects other organs simultaneously is unknown. We set out to discover how the liver responds to a dyslipidemic insult through the creation of inducible mouse models based on human familial hypercholesterolemia mutations and in vivo tracing of APOB. An acute transition to atherogenic APOB-lipoprotein plasma levels resulted in rapid accumulation of triglycerides and cholesterol in the liver. Single cell RNA-seq and flow cytometry disclosed that multiple immune cells have the ability to engulf APOB-lipoproteins. However bulk RNA-seq of the liver revealed an inflammatory Kupffer cell-specific transcriptional program that could not be activated by a western diet alone. Depletion of Kupffer cells through clodronate liposomes or CD8 T cell targeting rapidly raised plasma lipoprotein levels, indicating that these liver macrophages help restrain and buffer atherogenic lipoproteins, whilst simultaneously secreting pro-atherosclerotic factors into plasma. Our results place Kupffer cells as a key gateway in organizing systemic responses at the initiation of atherosclerosis.

immunology↗

bioRxiv2022

ANDROMEDA by Prosilico Software Successfully Predicts Human Clinical Pharmacokinetics of 70 Drugs Out of Reach for In Vitro Methods

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSIn vitro measurements and predictions of human clinical pharmacokinetics (PK) are sometimes hindered and made impossible due to factors such as extensive binding to materials, low methodological sensitivity and large variability. MethodsThe objective was to find compounds out of reach for in vitro PK-methods and (if possible) predict corresponding human clinical estimates using the ANDROMEDA by Prosilico software. In vitro methods selected for the investigation were human microsomes and hepatocytes for measuring and predicting intrinsic hepatic metabolic clearance (CLint), Caco-2 and Ralph Russ canine kidney cells (RRCK) cells for measuring apparent intestinal permeability (Papp) for prediction of fraction absorbed (fa), plasma for measurement and estimation of unbound fraction (fu), and water and buffers for measuring solubility (S) for prediction of in vivo dissolution potential (fdiss). Results and ConclusionAs many as 329 non-quantifiable in vitro PK-measurements for 300 compounds were found in the literature: 191 for CLint, 101 for Papp, 11 for fu and 26 for S. ANDROMEDA was successful in predicting all corresponding clinical PK-estimates for the selection of compounds with non-quantifiable in vitro PK, and predicted estimates (1.6-fold median prediction error; n=159) were generally in line with observed in vivo data and results/problems at in vitro laboratories. Thus, ANDROMEDA is applicable for predicting human clinical PK for compounds out of reach for laboratory methods.

pharmacology and toxicology↗

bioRxiv2022

Prediction and Classification of the Uptake and Disposition of Antidepressants and New CNS-Active Drugs in the Human Brain using the ANDROMEDA by Prosilico Software and Brainavailability-Matrix

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPassive blood-brain barrier permeability (BBB Pe), fraction bound to brain tissue (fb,brain) and efflux by transport proteins MDR-1 and BCRP are essential determinants for the brain uptake and disposition of drugs. MethodsThe main objective of the study was to use the software ANDROMEDA by Prosilico to predict passive BBB Pe- and fb,brain-classes and MDR-1- and BCRP-specificities for various classes of antidepressants and for CNS-active small drugs marketed during 2020 and 2021, and then to position them according to a new 2-dimensional Brainavailability-Matrix (8 passive BBB Pe x 4 fb,brain classes, where class 11 has highest and 84 lowest values/brainavailability). Predicted estimates were used, except for cases where measured values were available. Results and ConclusionResults for 53 drugs show that adequate CNS uptake and disposition are achieved for compounds placed in the zones for low, moderate and high brainavailability, despite efflux. They also show that high brainavailability and efflux are common for CNS-active drugs and that modern CNS-active drugs generally have lower brainavailability than older antidepressive drugs. Furthermore, they demonstrate that ANDROMEDA by Prosilico and the new Brainavailability-Matrix are applicable for prediction, optimization and positioning of CNS uptake and disposition of drugs and drug candidates in man.

pharmacology and toxicology↗

bioRxiv2022

Prediction of Biopharmaceutical Characteristics of PROTACs using the ANDROMEDA by Prosilico Software

BackgroundPROTACs are comparably large and flexible compounds with limited solubility (S) and permeability (Pe). It is crucial to better understand, predict and optimize their human clinical pharmacokinetics (PK). MethodsThe main objective was to use the ANDROMEDA by Prosilico software to predict the human clinical in vivo dissolution potential (fdiss) and fraction absorbed (fa) of 23 PROTACs at a dose level of 50 mg and to explore whether there is any relationship between in vitro S and in silico predicted in vivo fdiss. ResultsIn silico predictions showed that the PROTACs are effluxed by intestinal transporters and have limited fdiss (34 to 98 %), permeability and fa (13 to 58 %) in man. For some PROTACs this may be a major obstacle and jeopardize the clinical development programs, especially in cases of required high oral dose. A modest relationship between in vitro S and predicted in vivo fdiss was demonstrated (R2=0.26). Predicted human fa (27 %) and oral bioavailability (20 %) of ARV-110 (a PROTAC with some available in vivo PK data in rodents and man) were consistent with data obtained in rodents (estimated fa approximately 30-40 %; measured oral bioavailability 27-38 %). Laboratories were unable to quantify S for 7 (30 %) of the PROTACs. In contrast, ANDROMEDA could predict parameters for all. ConclusionANDROMEDA predicted fdiss and fa for all the chosen PROTACs and showed limited fdiss, Pe and fa and dose-dependent fdiss and fa. One available example shows promise for the applicability of ANDROMEDA for predicting biopharmaceutics of PROTACs in vivo in man. Weak to modest correlations between S and fdiss and a considerable portion of compounds with non-quantifiable S limit the use of S-data to predict the uptake of PROTACs.

pharmacology and toxicology↗

bioRxiv2022

Using the ANDROMEDA by Prosilico Software for Prediction of the Human Pharmacokinetics of 4 Compounds of Natural Origin - Colistin, Curucumin, UCN-01 and Voclosporin

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSIt is important that pharmacokinetic (PK) prediction methods are validated, and also for compounds with varying physicochemical properties, molecular weights and PK characteristics. MethodsThe objective was to investigate how well the ANDROMEDA by Prosilico software predicts the clinical PK of four compounds of natural origin and with PK obstacles, not yet fully characterized PK, and/or inaccurate lab method-based predictions - colistin (negligible absorption, good metabolic stability, significant excretion), curucumin (low solubility, apparently poor bioavailability), UCN-01 (extremely high degree of plasma protein binding, metabolic stability, long half-life and poor PK prediction) and voclosporin (poorly understood PK). ResultsAll categorial predictions except one were correct, and the median prediction error was 2.5-fold. Largest prediction errors were found for the unbound fraction in plasma (>24-fold), clearance (178-fold) and half-life (90-fold) of UCN-01. Corresponding errors for clearance and half-life obtained with allometry were greater, 5800- and 145-fold, respectively. Extremely high affinity for alpha1-acid glycoprotein could explain these large prediction errors for this compound. A substantial amount of data and knowledge was added with the predictions. ConclusionDespite challenging compounds and PK, predictions were comparably good. The results further validated ANDROMEDA by Prosilico for human clinical PK-predictions.

pharmacology and toxicology↗

bioRxiv2020

Progesterone specifically dampens disease-associated TH1- and TH17-related immune responses during T cell activation in vitro

The changes in progesterone (P4) levels during and after pregnancy coincide with the temporary improvement and worsening of several autoimmune diseases like multiple sclerosis (MS) and rheumatoid arthritis (RA). Most likely immune-endocrine interactions play a major role in these pregnancy-induce effects. In this study, we used next generation sequencing to investigate the direct effects of P4 on CD4+ T cell activation, of central importance in pregnancy and disease. We found that P4 had a profound dampening effect on T cell activation, altering the gene and protein expression profile and opposing many of the changes induced during the activation. The transcriptomic changes induced by P4 were significantly enriched for genes associated with diseases known to be modulated during pregnancy such as MS, RA and psoriasis. The TH1-and TH17-associated transcription factors STAT1 and STAT3 were significantly downregulated by P4 and their downstream targets were significantly enriched among the diseases-associated genes. Several of these genes included well-known and disease-relevant cytokines, such as IL-12{beta}, CXCL10 and OSM, that were further validated also at the protein level using proximity extension assay. Our results extend the previous knowledge of P4 as an immune regulatory hormone and supports its importance during pregnancy for regulating potentially detrimental immune responses towards the semi-allogenic fetus. Further, our results also point toward a potential role for P4 in the pregnancy-induced disease immunomodulation, suggestively through dampening of TH1 and TH17-associated immune responses and highlights the need for further studies evaluating P4 as a future treatment option.

immunology↗

bioRxiv2020

Alternate models of acute dyslipidemia reveal divergent pathways upon atherosclerosis initiation

Atherosclerosis is thought to be initiated by the sub-intimal retention of apolipoprotein-B containing lipoproteins within susceptible sites of the vasculature. Understanding this initiation is not possible with current legacy mouse models of atherosclerosis. We created two mouse strains of inducible hypercholesterolemia based on conditional loss of apolipoprotein E or through inducible expression of a gain-of-function proprotein convertase subtilisin/kexin type 9 D374Y mutation. Both strains rapidly broke plasma-lipid homeostasis and converted to a state of atherogenic dyslipidemia, resulting in overt aortic-accumulation of lipoproteins within 10 days. RNA-sequencing revealed that the vascular response is completely dependent on the route taken to dyslipidemia, which nevertheless implicates known pathogenic pathways in the aetiology of atherosclerosis, and further implicates APOE as an inhibitor of inflammation. As atherosclerosis develops, a convergence of common mechanistic processes emerge in both strains with significant involvement of the immune system, and targeting of CD8 T cells can regulate a conserved aortic response. Our results define atherosclerosis initiation as highly heterogeneous process and identify multiple potential therapeutic targets that may influence disease onset.

physiology↗

bioRxiv2019

On the prediction of protein abundance from RNA

BackgroundProfiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. MethodsWe characterised time-series of primary human naive CD4+ T cells during early T-helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We then performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time-delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-Seq datasets to evaluate biomarker discovery in five T-cell associated diseases, validating the findings for multiple sclerosis (MS) and asthma. ResultsThe new models demonstrated median correlations of mRNA-to-protein abundance of 0.79-0.94, significantly out-performing models not including the usage of multiple splice variants and time-delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was clinically validated in MS using two independent cohorts, for treatment response and prognosis. ConclusionOur splice variant and time-delay models substantially improved the prediction of protein abundance from mRNA data in three immune cell-types. The models provided valuable biomarker candidates, which were validated in clinical studies of MS and asthma. We propose that our strategy is generally applicable for biomarker discovery.

bioinformatics↗