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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

In metabolomics, the detection of new biomarkers from NMR spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses.\n\nWe present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independant datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketting. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the conditions.\n\nThis workflow is available through the R package ASICS, available on the Bioconductor platform.

systems biology↗

bioRxiv2018

Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible

Null models for the effect of combination therapies are widely used to evaluate synergy and antagonism of drugs. Due to the relevance of null models, their suitability is continuously discussed. Here, we contribute to the discussion by investigating the properties of five null models. Our study includes the model proposed by David J. Hand, which we refer to as Hand model. The Hand model has been introduced almost 20 years ago but hardly was used and studied. We show that the Hand model generalizes the principle of dose equivalence compared to the Loewe model and resolves the ambiguity of the Tallarida model. This provides a solution to the persisting conflict about the compatibility of two essential model properties: the sham combination principle and the principle of dose equivalence. By embedding several null models into a common framework, we shed light in their biochemical validity and provide indications that the Hand model is biochemically most plausible. We illustrate the practical implications and differences between null models by examining differences of null models on published data.

systems biology↗

bioRxiv2018

Integrative analysis of Paneth cell proteomic data from intestinal organoids reveals functional processes affected in Crohn’s disease due to autophagy impairment

Summary statementUsing an integrative approach encompassing intestinal organoid culture, proteomics, transcriptomics and protein-protein interaction networks, we list Paneth cell functions dependent on autophagy.\n\nAbstractPaneth cells are key epithelial cells providing an antimicrobial barrier and maintaining integrity of the small intestinal stem cell niche. Paneth cell abnormalities are unfortunately detrimental to gut health and often associated with digestive pathologies such as Crohns disease or infections. Similar alterations are observed in individuals with impaired autophagy, a process which recycles cellular components. The direct effect of autophagy-impairment on Paneth cells has not been analysed. To investigate this, we generated a mouse model lacking Atg16l1 specifically in intestinal epithelial cells making these cells impaired in autophagy. Using 3D intestinal organoids enriched for Paneth cells, we compared the proteomic profiles of wild-type (WT) and autophagy-impaired organoids. We used an integrated computational approach combining protein-protein interaction networks, autophagy targeted proteins and functional information to identify the mechanistic link between autophagy-impairment and disrupted pathways. Of the 284 altered proteins, 198 (70%) were more abundant in autophagy-impaired organoids, suggesting reduced protein degradation. Interestingly, these differentially abundant proteins comprised 116 proteins (41%), predicted targets of the selective autophagy proteins p62, LC3 and ATG16L1. Our integrative analysis revealed autophagy-mediated mechanisms degrading proteins key to Paneth cell functions, such as exocytosis, apoptosis and DNA damage repair. Transcriptomic profiling of additional organoids confirmed that 90% of the observed changes upon autophagy alteration affect protein level and not gene expression. We performed further validation experiments showing differential lysozyme secretion, confirming our computationally inferred down-regulation of exocytosis. Our observations could explain how protein level alterations affect Paneth cell homeostatic functions upon autophagy impairment.

systems biology↗

bioRxiv2018

Stage specific classification of DEGs via statistical profiling and network analysis reveals potential biomarker associated with various stages of TB

BackgroundTuberculosis (TB) is a deadly transmissible disease that can infect almost any body-part of the host but is mostly infect the lungs. It is one of the top 10 causes of death worldwide. In the 30 high TB burden countries, 87% of new TB cases occurred in 2016. Seven countries: India, Indonesia, China, Philippines, Pakistan, Nigeria, and South Africa accounted for 64% of the new TB cases. To stop the infection and progression of the disease, early detection of TB is important. In our study, we used microarray data set and compared the gene expression profiles obtained from blood samples of patients with different datasets of Healthy control, Latent infection, Active TB and performed network-based analysis of DEGs to identify potential biomarker.\n\nObjectivesWe want to observe the transition of genes from normal condition to different stages of the TB and identify, annotate those genes/pathways/processes that play key role in the progression of TB disease during its cyclic interventions in human body.\n\nResultsWe identified 319 genes that are differentially expressed in various stages of TB (Normal to LTTB, Normal to Active TB and LTTB to active TB) and allocated to pathways from multiple databases which comprised of curated class of associated genes. These pathways importance was then evaluated according to the no. of DEGs present in the pathway and these genes show the broad spectrum of processes that take part in every state. In addition, we studied the regulatory networks of these classified genes, network analysis does consider the interactions between genes (specific for TB) or proteins provide us new facts about TB disease, which in turn can be used for potential biomarkers identification. We identified total 29 biomarkers from various comparison groups of TB stages in which 14 genes are over expressed as host responses against pathogen, but 15 genes are down regulated that means these genes has allowed the process of host defense to cease and give time to pathogen for its progression.\n\nConclusionsThis study revealed that gene-expression profiles can be used to identify and classified the genes on stage specific pattern among normal, LTTB and active TB and network modules associated with various stages of TB were elucidated, which in turn provided a basis for the identification of potential pathways and key regulatory genes that may be involved in progression of TB disease.

systems biology↗

bioRxiv2018

Immune differentiation regulator p100 tunes NF-κB responses to TNF

Stringent regulation of TNF signaling prevents aberrant inflammation. TNF engages the canonical NF-{kappa}B pathway for activating the RelA:p50 heterodimer, which mediates specific expressions of pro-inflammatory and immune response genes. Importantly, the NF-{kappa}B system discriminates between time-varied TNF inputs. Negative feedback regulators of the canonical pathway, including I{kappa}B, are thought to ensure transient RelA:p50 responses to brief TNF stimulations. The noncanonical NF-{kappa}B pathway controls a separate RelB activity associated with immune differentiation. In a systems modeling approach, we uncovered an unexpected role of p100, a constituent of the noncanonical pathway, in TNF signaling. Brief TNF stimulation of p100-deficient cells produced an additional late NF-{kappa}B activity consisted of the RelB:p50 heterodimer, which distorted the TNF-induced gene-expression program. Periodic TNF pulses augmented this RelB:p50 activity, which was reinforced by NF-{kappa}B-dependent RelB synthesis. In sum, the NF-{kappa}B system seems to engage distantly related molecules for enforcing dynamical and gene controls of immune-activating TNF signaling.

systems biology↗

bioRxiv2018

Reconciling high-throughput gene essentiality data with metabolic network reconstructions

The identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.\n\nAuthor SummaryWith the rise of antibiotic resistance, there is a growing need to discover new therapeutic targets to treat bacterial infections. One attractive strategy is to target genes that are essential for growth and survival. Essential genes can be identified with transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification and analysis of essential genes. We performed a large-scale comparison of multiple gene essentiality screens of the microbial pathogen Pseudomonas aeruginosa. We implemented a computational model-driven approach to provide functional explanations for essentiality and reconcile differences between screens. The integration of computational modeling with high-throughput experimental screens may enable the identification of drug targets with high-confidence and provide greater understanding for the development of novel therapeutic strategies.

systems biology↗

bioRxiv2018

Multiple sources of signal amplification within the B cell Ras/MAPK pathway

The Ras-Map kinase (MAPK) cascade underlies functional decisions in a wide range of cell types and organisms. In B cells, positive feedback-driven Ras activation is the proposed source of the digital (all-or-none) MAPK responses following antigen stimulation. However, an inability to measure endogenous Ras activity in living cells has hampered our ability to test this model directly. Here we leverage biosensors of endogenous Ras and ERK activity to revisit this question. We find that BCR ligation drives switch-like Ras activation and that lower BCR signaling output is required for the maintenance versus the initiation of Ras activation. Surprisingly, digital ERK responses persist in the absence of positive feedback-mediated Ras activation, and digital ERK is observed at a threshold level of Ras activation. These data suggest an independent analog-to-digital switch downstream of Ras activation, and reveals that multiple sources of signal amplification exist within the Ras-ERK module of the BCR pathway.

systems biology↗

bioRxiv2018

Persistent alterations in plasma lipid profiles prior to introduction of gluten in the diet associate with progression to celiac disease

Background and AimsCeliac disease (CD) is a chronic enteropathy characterized by an autoimmune reaction in the small intestine in genetically-susceptible individuals. Gluten is the required environmental trigger of clinical CD, but the underlying causes of the autoimmune reaction remain unknown. Herein, we apply lipidomics to elucidate the early events preceding clinical CD in a prospective study of children observed from birth until diagnosis of CD and subsequent introduction of a gluten-free diet.\n\nMethodsMass spectrometry-based lipidomics profiling was applied to a longitudinal series of 233 plasma samples from the Type 1 Diabetes Prediction and Prevention (DIPP) study, spanning the period between birth and the introduction of a gluten-free diet following CD diagnosis (n=23 CD progressors, n=23 controls matched for gender, HLA risk, period of birth, and age).\n\nResults23 children progressed to CD at a mean age of 4.8 years. They showed increased amounts of triacylglycerols (TGs) of low carbon number and double bond count and a decreased level of phosphatidylcholines by 3 months of age as compared to controls. These differences were exacerbated with age but were not observed at birth. No significant differences were observed in essential (dietary) TGs such as those containing polyunsaturated fatty acids.\n\nConclusionOur findings suggest that abnormal lipid metabolism associated with development of clinical CD may occur prior to the introduction of gluten to the diet. Moreover, our data suggest that the specific TGs found elevated in CD progressors may be due to a host response to compromised intake of essential lipids in the small intestine, requiring de novo lipogenesis.

systems biology↗

bioRxiv2018

Metabolic modeling of Streptococcus mutans reveals complex nutrient requirements of an oral pathogen

Streptococcus mutans is a Gram positive bacterium that thrives under acidic conditions and is a primary cause of tooth decay (dental caries). To better understand the metabolism of S. mutans on a systematic level, we manually constructed a genome-scale metabolic model of the S. mutans type strain UA159. The model, called iSMU, contains 656 reactions involving 514 metabolites and the products of 488 genes.\n\nWe interrogated S. mutans nutrient requirements using model simulations and nutrient removal experiments in defined media. The iSMU model matched experimental results in greater than 90% of the conditions tested. We also simulated effects of single gene deletions. The models predictions agreed with 78.1% and 84.4% of the gene essentiality predictions from two experimental datasets. Our manually curated model is more accurate than S. mutans models generated from automated reconstruction pipelines. We believe the iSMU model is an important resource for understanding how metabolism enables the cariogenicity of S. mutans.

systems biology↗

bioRxiv2018

Rewiring of RSK-PDZ interactions by linear motif phosphorylation

Protein phosphorylation is a key regulator of protein-protein interactions. How does the interactome of a protein change during extracellular stimulations? While many individual examples of phosphorylation-regulated interactions were described previously, studies addressing the interactome changes induced by a particular phosphorylation event remain scarce. Here, we try to answer this question, by focusing on interactions between a phosphorylable PDZ-binding linear motif and the entire complement of human PDZ domains. Using a combination of in vitro quantitative techniques and cell-based approaches, we demonstrate that the activation of the mitotic effector kinase RSK1 causes dramatic changes in its connectivity with PDZ domain containing proteins. These changes consist of modulations of the binding affinity of numerous interactions, rather than on/off switching of a few interactions. Our results highlight the previously unappreciated role of phosphorylation in the complex and subtle rewiring of large numbers of protein-protein interactions.

systems biology↗

bioRxiv2018

Computational framework for exploring the interplay of diet and gut microbiota in autism

Autism spectrum disorder (ASD)1 refers to the set of complex neurological disorders characterized by repetitive behaviour. The reported occurrence of abnormal gut bacteria, along with prevalence of gastrointestinal disorders in ASD indicate its strong correlation with the gut microflora. Our study aims to understand the role of diet and gut bacteria in ASD via an integrated constraint-based and PBPK model. Genome scale models of five major gut bacteria, which were reported to be associated with ASD, were integrated with the human host, i.e., the combined small intestinal enterocyte and neuronal brain model. Simultaneously, a permeability-limited two sub compartment PBPK model was developed to determine the distribution of bacteria-derived toxins in the body. The important results include, (i) inclusion of probiotics into the diet of autistic case restores gut balance, majorly seen as a result of reduced oxidative stress in the brain and the gut, (ii) microbiome and diet together mediate host metabolism in autism, majorly via the nucleotide, central carbon, amino acid, and reactive oxygen species metabolisms, and (iii) gut bacterial-specific secretions contribute to autistic metabotype. Thus, the presented integrated model is the first ever quantitative model, providing a mechanistic basis for autism pathogenesis, capturing known biomarkers, as well as, highlighting the potential of novel dietary modifications in alleviating the symptoms of autism.

systems biology↗

bioRxiv2018

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available, that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

systems biology↗

bioRxiv2018

Inhibition of nucleotide synthesis mediates replicative senescence of human mammary epithelial cells

Cellular senescence is a mechanism by which cells permanently withdraw from the cell cycle in response to stresses including telomere shortening, DNA damage, or oncogenic signaling. Senescent cells contribute to both age-related degeneration and hyperplastic pathologies, including cancer. In culture, normal human epithelial cells enter senescence after a limited number of cell divisions, known as replicative senescence. Here, to investigate how metabolic pathways regulate replicative senescence, we used LC-MS-based metabolomics to analyze senescent primary human mammary epithelial cells (HMECs). We did not observe significant changes in glucose uptake or lactate secretion in senescent HMECs. However, analysis of intracellular metabolite pool sizes indicated that senescent cells exhibit depletion of metabolites from nucleotide synthesis pathways. Furthermore, stable isotope tracing with 13C-labeled glucose or glutamine revealed a dramatic blockage of flux of these two metabolites into nucleotide synthesis pathways in senescent HMECs. To test whether cellular immortalization would reverse these observations, we expressed telomerase in HMECs. In addition to preventing senescence, telomerase expression maintained metabolic flux from glucose into nucleotide synthesis pathways. Finally, we investigated whether inhibition of nucleotide synthesis in proliferating HMECs is sufficient to induce senescence. In proliferating HMECs, both pharmacological and genetic inhibition of ribonucleotide reductase regulatory subunit M2 (RRM2), a rate-limiting enzyme in dNTP synthesis, induced premature senescence with concomitantly decreased metabolic flux from glucose into nucleotide synthesis. Taken together, our results suggest that nucleotide synthesis inhibition plays a causative role in the establishment of replicative senescence in HMECs.

systems biology↗

bioRxiv2018

Carbonic anhydrase network of genes trigger cytosolic pH enabling differentiation from quiescence

BackgroundCarbonic anhydrase regulates various cellular processes. Intracellular pH flux impacted by carbonic anhydrase alters the enzymes allosteric active site which effects several downstream cellular processes. Earlier, we reported that, the catalytic activity of carbonic anhydrase is independent, but direction of catalysis is affected by cellular pH level. On the other hand carbonic anhydrase alters the cytosolic pH level to facilitate allosteric phosphorylation of proteins which further leads to cellular differentiation through a process being regulated by ncRNAs.\n\nResultsThis study illustrates various ways of cell differentiation/ organ development regulation via carbonic anhydrase interacting network of proteins involved in various cellular processes. It is involved in protein degradation process of other proteins like RPT, 26S proteasome, AT3G15120 and its variant producing ncRNA, etc. Carbonic anhydrase indirectly involved in signaling process along with MAPK in providing innate resistance against biotic and abiotic stresses. It is also indirectly linked to cell membrane transporters like H+-ATPase and V-ATPase B.\n\nConclusionsThough carbonic anhydrase is not directly linked with EMS1 as revealed by network analyses and protein-protein interaction there could be a suitable condition generated by the carbonic anhydrase for EMS1 to be active. Hence, we report that carbonic anhydrase, along with other pH regulating gene complexes plays a major role for making EMS1 functional.

systems biology↗

bioRxiv2018

Metatranscriptomic and thermodynamic insights into medium-chain fatty acid production using an anaerobic microbiome

Biomanufacturing from renewable feedstocks represents a sustainable strategy to offset fossil fuel-based chemical production. One potential biomanufacturing strategy is production of medium chain fatty acids (MCFA) from organic feedstocks using either pure cultures or microbiomes. While the set of microbes in a microbiome can often metabolize more diverse organic materials than a single species, and the role of specific species may be known, knowledge of the carbon and energy flow within and between organisms in MCFA producing microbiomes is only starting to emerge. Here, we integrate metagenomic, metatranscriptomic, and thermodynamic analyses to predict and characterize the metabolic network of an anaerobic microbiome producing MCFA from organic matter derived from lignocellulosic ethanol fermentation conversion residue. A total of 37 high quality (>80% complete, < 10% contamination) metagenome-assembled genomes (MAGs) were recovered from the microbiome and metabolic reconstruction of the 10 most abundant MAGs was performed. Metabolic reconstruction combined with metatranscriptomic analysis predicted that organisms affiliated with Lactobacillus and Coriobacteriaceae degraded carbohydrates and fermented sugars to lactate and acetate. Lachnospiraceae and Eubacteriaceae affiliated organisms were predicted to transform these fermentation products to MCFA. Thermodynamic analyses identified conditions in which H2 is expected to be either produced or consumed, suggesting a potential role of H2 partial pressure on MCFA production. From an integrated systems analysis perspective, we propose that MCFA production could be improved if microbiomes are engineered to use homofermentative instead of heterofermentative Lactobacillus, and if MCFA-producing organisms are engineered to preferentially use a thioesterase instead of a CoA transferase as the terminal enzyme in reverse {beta}-oxidation.\n\nImportanceMixed communities of microbes play important roles in health, the environment, agriculture, and biotechnology. While tapping the combined activity of organisms within microbiomes may allow the utilization of a wider range of substrate over the use of pure cultures for biomanufacturing, harnessing the metabolism of these mixed cultures remains a major challenge. Here, we predict metabolic functions of bacteria in a microbiome that produces medium-chain fatty acids from a renewable feedstock. Our findings lay the foundation to begin addressing how to engineer and control microbiomes for improved biomanufacturing; to build synthetic mixtures of microbes that produce valuable chemicals from renewable resources; and to better understand microbial communities that contribute to health, agriculture, and the environment.

systems biology↗

bioRxiv2018

Quantitative modelling explains distinct STAT1 and STAT3 activation dynamics in response to both IFNγ and IL-10 stimuli and predicts emergence of reciprocal signalling at the level of single cells

IFN{gamma} and IL10 pathways elicit functionally opposing cell fates, however, these two stimuli activate common transcription factors like STAT1 and STAT3 (S/1/3). How the two stimuli regulate the dynamics of S/1/3 activation remains less understood. Here, we experimentally measured the signaling dynamics of S/1/3 in response to IFN{gamma} and IL10 and found that STAT3, in particular, exhibits a bell-shaped response to both stimuli with maximal activation in an intermediate dose. We built a mathematical model, which quantitatively captured the S/1/3 dynamics and by model analysis, we identified the primary regulators controlling the bell-shaped STAT3 responses in both pathways. As the STATs are activated in response to both stimuli, in a scenario when cells are subjected to co-stimulation of IFN{gamma} and IL10, an interpathway competition to activate the common substrate is plausible. Additionally, a strong transcriptional induction of SOCS1 (a negative regulator of IFN{gamma} pathway) was observed upon IL10 stimulation, suggesting that IL10 pathway can potentially inhibit the IFN{gamma} pathway via SOCS1 induction. To quantitatively understand the S/1/3 responses to co-stimulation we next simulated the same which predicted STAT3 activation and SOCS1 induction dynamics would robustly remain IL10 driven. Subsequent experiments validated the model predictions. Further, the model analysis identified the primary regulators controlling the robustness of IL10-STAT3 axis in co-stimulation. Together, our data-driven model quantitatively captured the dynamics of the STATs in response to both pro (IFN{gamma}) and anti (IL10)-inflammatory stimuli and identifies distinct regulatory elements controlling STAT3 signaling in individual and co-stimulation conditions.

systems biology↗

bioRxiv2018

Visualizing metabolic network dynamics through time-series metabolomics data

New technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems--the human platelet and erythrocyte under cold storage for use in transfusion medicine.\n\nThe results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.\n\nIn conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.\n\nAUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

systems biology↗

bioRxiv2018

Redefining Nephrotic Syndrome in Molecular Terms: Outcome-associated molecular clusters and patient stratification with noninvasive surrogate biomarkers

A tissue transcriptome driven classification of nephrotic syndrome patients identified a high risk group of patients with TNF activation and established a non-invasive marker panel for pathway activity assessment paving the way towards precision medicine trials in NS.\n\nAbstractNephrotic syndrome from primary glomerular diseases can lead to chronic kidney disease (CKD) and/or end-stage renal disease (ESRD). Conventional diagnoses using a combination of clinical presentation and descriptive biopsy information do not accurately predict risk for progression in patients with nephrotic syndrome, which complicates disease management. To address this challenge, a transcriptome-driven approach was used to classify patients with minimal change disease and focal segmental glomerulosclerosis in the Nephrotic Syndrome Study Network (NEPTUNE). Transcriptome-based classification revealed a group of patients at risk for disease progression. High risk patients had a transcriptome profile consistent with TNF activation. Non-invasive urine biomarkers TIMP1 and CCL2 (MCP1), which are causally downstream of TNF, accurately predicted TNF activation in the NEPTUNE cohort setting the stage for patient stratification approaches and precision medicine in kidney disease.

systems biology↗