Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

in Metabolic Engineering (2022)

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of ... [more ▼]

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis. [less ▲]

PredictProtein - Predicting Protein Structure and Function for 29 Years

in Nucleic Acids Research (2021)

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and ... [more ▼]

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. [less ▲]

Cardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129

in Cardiovascular Research (2021)

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths ... [more ▼]

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances. [less ▲]

A rare loss-of function variant of ADAM17 is associated with late-onset familial Alzheimer disease

in Molecular Psychiatry (2020), 25(3), 629-639

Common variants of about 20 genes contributing to AD risk have so far been identified through genome-wide association studies (GWAS). However, there is still a large proportion of heritability that might ... [more ▼]

Common variants of about 20 genes contributing to AD risk have so far been identified through genome-wide association studies (GWAS). However, there is still a large proportion of heritability that might be explained by rare but functionally important variants. One of the so far identified genes with rare AD causing variants is ADAM10. Using whole-genome sequencing we now identified a single rare nonsynonymous variant (SNV) rs142946965 [p.R215I] in ADAM17 co-segregating with an autosomal-dominant pattern of late-onset AD in one family. Subsequent genotyping and analysis of available whole-exome sequencing data of additional case/control samples from Germany, the UK and the USA identified five variant carriers among AD patients only. The mutation inhibits pro-protein cleavage and the formation of the active enzyme, thus leading to loss-of-function of ADAM17 α-secretase. Further, we identified a strong negative correlation between ADAM17 and APP gene expression in human brain and present in vitro evidence that ADAM17 negatively controls the expression of APP. As a consequence, p.R215I mutation of ADAM17 leads to elevated Aß formation in vitro. Together our data supports a causative association of the identified ADAM17 variant in the pathogenesis of AD. [less ▲]

Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

in Nature Genetics (2019), 51(3), 414

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of ... [more ▼]

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and A$\beta$ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education. [less ▲]

Presenting and Sharing Clinical Data using the eTRIKS Standards Master Tree for tranSMART

in Bioinformatics (2018)

Motivation Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into ... [more ▼]

Motivation Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. Results We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. Availability The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS - Master Tree branch, where the discussed examples can be visualized. [less ▲]

IDENTIFICATION OF A RARE GENE VARIANT THAT IS ASSOCIATED WITH FAMILIAL ALZHEIMER DISEASE AND REGULATES APP EXPRESSION

in Alzheimer's and Dementia: the Journal of the Alzheimer's Association (2017), 13(7, Supplement), 648

Background Genetic mutations leading to familial forms of Alzheimer disease (AD) have so far been reported for a few genes including APP, PSEN1 and PSEN2, UNC5C, PLD3, ABCA7, TTC3, and possibly ADAM10 ... [more ▼]

Background Genetic mutations leading to familial forms of Alzheimer disease (AD) have so far been reported for a few genes including APP, PSEN1 and PSEN2, UNC5C, PLD3, ABCA7, TTC3, and possibly ADAM10. With the advent of whole exome and whole genome sequencing approaches new genes and mutations are likely to be identified. Methods We analyzed the genetic cause of AD in a large multiplex family with an autosomal-dominant pattern of inheritance with LOAD. The family lacked pathogenic mutations of known AD genes. We performed whole-genome sequencing (WGS) in six family members (two affected and four unaffected) and prioritized rare, potential damaging, variants that segregated with disease. Variants were further characterized by subsequent molecular analyzes in human brain and cell culture models. Results We identified a single rare nonsynonymous variant co-segregating with AD. The mutation inhibits pro-protein cleavage and the formation of the active enzyme, thus leading to a loss-of-function of the gene. We further found a strong negative correlation between the identified gene and APP gene expression in human brain and in cells over-expressing the gene. The negative regulation of APP expression was only observed for the wt gene, but not for mutated forms, thus causing beside the loss of enzyme function a decoupling of both APPexpression and subsequent beta-amyloid formation. The identity of the gene will be presented on the conference. Conclusions This novel pathway strongly supports a causative association of the identified gene with the pathogenesis of AD. [less ▲]

The miRNome of Alzheimer's disease: consistent downregulation of the miR-132/212 cluster

in Neurobiology of Aging (2016), 50

MicroRNAs (miRNAs) are small noncoding RNA molecules, with essential functions in RNA silencing and post-transcriptional regulation of gene expression. miRNAs appear to regulate the development and ... [more ▼]

MicroRNAs (miRNAs) are small noncoding RNA molecules, with essential functions in RNA silencing and post-transcriptional regulation of gene expression. miRNAs appear to regulate the development and function of the nervous system. Alterations of miRNA expression have been associated with Alzheimer's disease (AD). To characterize the AD miRNA signature, we examined genome-wide miRNA and mRNA expression patterns in the temporal cortex of AD and control samples. We validated our miRNA results by semiquantitative real-time polymerase chain reaction (PCR) in independent prefrontal cortex. Furthermore, we separated gray and white matter brain sections to identify the cellular origin of the altered miRNA expression. We observed genome-wide downregulation of hsa-miR-132-3p and hsa-miR-212-3p in AD with a stronger decrease in gray matter AD samples. We further identified 10 differently expressed transcripts achieving genome-wide levels of significance. Significantly deregulated miRNAs and mRNAs were correlated and examined for potential binding sites (in silico). This miRNome-wide study in AD provides supportive evidence and corroborates an important contribution of miR-132/212 and corresponding target mRNAs to the pathogenesis of AD. [less ▲]

Amyloid-β Protein Precursor Cleavage Products in Postmortem Ventricular Cerebrospinal Fluid of Alzheimer’s Disease Patients

in Journal of Alzheimer's Disease (2015), 47(2), 365-372

Gene-wide analysis detects two new susceptibility genes for Alzheimer's disease.

in PloS one (2014), 9(6), 94661

BACKGROUND: Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study ... [more ▼]

BACKGROUND: Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls. PRINCIPAL FINDINGS: In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4x10-6) and 14 (IGHV1-67 p = 7.9x10-8) which indexed novel susceptibility loci. SIGNIFICANCE: The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease. [less ▲]