Fluxomics reveals cellular and molecular basis of increased renal ammoniagenesis.

The kidney plays a critical role in excreting ammonia during metabolic acidosis and liver failure. The mechanisms behind this process have been poorly explored. The present study combines results of in vivo experiments of increased total ammoniagenesis with systems biology modeling, in which eight rats were fed an amino acid-rich diet (HD group) and eight a normal chow diet (AL group). We developed a method based on elementary mode analysis to study changes in amino acid flux occurring across the kidney in increased ammoniagenesis. Elementary modes represent minimal feasible metabolic paths in steady state. The model was used to predict amino acid fluxes in healthy and pre-hyperammonemic conditions, which were compared to experimental fluxes in rats. First, we found that total renal ammoniagenesis increased from 264 ± 68 to 612 ± 87 nmol (100 g body weight)-1 min-1 in the HD group (P = 0.021) and a concomitated upregulation of NKCC2 ammonia and other transporters in the kidney. In the kidney metabolic model, the best predictions were obtained with ammonia transport as an objective. Other objectives resulting in a fair correlation with the measured fluxes (correlation coefficient >0.5) were growth, protein uptake, urea excretion, and lysine and phenylalanine transport. These predictions were improved when specific gene expression data were considered in HD conditions, suggesting a role for the mitochondrial glycine pathway. Further studies are needed to determine if regulation through the mitochondrial glycine pathway and ammonia transporters can be modulated and how to use the kidney as a therapeutic target in hyperammonemia.

Gut microbiota and short-chain fatty acid alterations in cachectic cancer patients.

BACKGROUND: Cancer cachexia is characterized by a negative energy balance, muscle and adipose tissue wasting, insulin resistance, and systemic inflammation. Because of its strong negative impact on prognosis and its multifactorial nature that is still not fully understood, cachexia remains an important challenge in the field of cancer treatment. Recent animal studies indicate that the gut microbiota is involved in the pathogenesis and manifestation of cancer cachexia, but human data are lacking. The present study investigates gut microbiota composition, short-chain fatty acids (SCFA), and inflammatory parameters in human cancer cachexia. METHODS: Faecal samples were prospectively collected in patients (N = 107) with pancreatic cancer, lung cancer, breast cancer, or ovarian cancer. Household partners (N = 76) of the patients were included as healthy controls with similar diet and environmental conditions. Patients were classified as cachectic if they lost >5% body weight in the last 6 months. Gut microbiota composition was analysed by sequencing of the 16S rRNA V4 gene region. Faecal SCFA levels were quantified by gas chromatography. Faecal calprotectin was assessed with enzyme-linked immunosorbent assay. Serum C-reactive protein and leucocyte counts were retrieved from medical records. RESULTS: Cachexia prevalence was highest in pancreatic cancer (66.7%), followed by ovarian cancer (25%), lung cancer (20.8%), and breast cancer (17.3%). Microbial α-diversity was not significantly different between cachectic cancer patients (N = 33), non-cachectic cancer patients (N = 74), or healthy controls (N = 76) (species richness P = 0.31; Shannon effective index P = 0.46). Community structure (ß-diversity) tended to differ between these groups (P = 0.053), although overall differences were subtle and no clear clustering of samples was observed. Proteobacteria (P < 0.001), an unknown genus from the Enterobacteriaceae family (P < 0.01), and Veillonella (P < 0.001) were more abundant among cachectic cancer patients. Megamonas (P < 0.05) and Peptococcus (P < 0.001) also showed differential abundance. Faecal levels of all SCFA tended to be lower in cachectic cancer patients, but only acetate concentrations were significantly reduced (P < 0.05). Faecal calprotectin levels were positively correlated with the abundance of Peptococcus, unknown Enterobacteriaceae, and Veillonella. We also identified several correlations and interactions between clinical and microbial parameters. CONCLUSIONS: This clinical study provided the first insights into the alterations of gut microbiota composition and SCFA levels that occur in cachectic cancer patients and how they are related to inflammatory parameters. These results pave the way for further research examining the role of the gut microbiota in cancer cachexia and its potential use as therapeutic target.

Mass spectrometry imaging of L-[ring-13C6]-labeled phenylalanine and tyrosine kinetics in non-small cell lung carcinoma.

BACKGROUND: Metabolic reprogramming is a common phenomenon in tumorigenesis and tumor progression. Amino acids are important mediators in cancer metabolism, and their kinetics in tumor tissue are far from being understood completely. Mass spectrometry imaging is capable to spatiotemporally trace important endogenous metabolites in biological tissue specimens. In this research, we studied L-[ring-13C6]-labeled phenylalanine and tyrosine kinetics in a human non-small cell lung carcinoma (NSCLC) xenografted mouse model using matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI). METHODS: We investigated the L-[ring-13C6]-Phenylalanine (13C6-Phe) and L-[ring-13C6]-Tyrosine (13C6-Tyr) kinetics at 10 min (n = 4), 30 min (n = 3), and 60 min (n = 4) after tracer injection and sham-treated group (n = 3) at 10 min in mouse-xenograft lung tumor tissues by MALDI-FTICR-MSI. RESULTS: The dynamic changes in the spatial distributions of 19 out of 20 standard amino acids are observed in the tumor tissue. The highest abundance of 13C6-Phe was detected in tumor tissue at 10 min after tracer injection and decreased progressively over time. The overall enrichment of 13C6-Tyr showed a delayed temporal trend compared to 13C6-Phe in tumor caused by the Phe-to-Tyr conversion process. Specifically, 13C6-Phe and 13C6-Tyr showed higher abundances in viable tumor regions compared to non-viable regions. CONCLUSIONS: We demonstrated the spatiotemporal intra-tumoral distribution of the essential aromatic amino acid 13C6-Phe and its de-novo synthesized metabolite 13C6-Tyr by MALDI-FTICR-MSI. Our results explore for the first time local phenylalanine metabolism in the context of cancer tissue morphology. This opens a new way to understand amino acid metabolism within the tumor and its microenvironment.

Proteomics analysis of human intestinal organoids during hypoxia and reoxygenation as a model to study ischemia-reperfusion injury.

Intestinal ischemia-reperfusion (IR) injury is associated with high mortality rates, which have not improved in the past decades despite advanced insight in its pathophysiology using in vivo animal and human models. The inability to translate previous findings to effective therapies emphasizes the need for a physiologically relevant in vitro model to thoroughly investigate mechanisms of IR-induced epithelial injury and test potential therapies. In this study, we demonstrate the use of human small intestinal organoids to model IR injury by exposing organoids to hypoxia and reoxygenation (HR). A mass-spectrometry-based proteomics approach was applied to characterize organoid differentiation and decipher protein dynamics and molecular mechanisms of IR injury in crypt-like and villus-like human intestinal organoids. We showed successful separation of organoids exhibiting a crypt-like proliferative phenotype, and organoids exhibiting a villus-like phenotype, enriched for enterocytes and goblet cells. Functional enrichment analysis of significantly changing proteins during HR revealed that processes related to mitochondrial metabolism and organization, other metabolic processes, and the immune response were altered in both organoid phenotypes. Changes in protein metabolism, as well as mitophagy pathway and protection against oxidative stress were more pronounced in crypt-like organoids, whereas cellular stress and cell death associated protein changes were more pronounced in villus-like organoids. Profile analysis highlighted several interesting proteins showing a consistent temporal profile during HR in organoids from different origin, such as NDRG1, SDF4 or DMBT1. This study demonstrates that the HR response in human intestinal organoids recapitulates properties of the in vivo IR response. Our findings provide a framework for further investigations to elucidate underlying mechanisms of IR injury in crypt and/or villus separately, and a model to test therapeutics to prevent IR injury.

The cutaneous microbiome in hospitalized patients with pressure ulcers.

This study investigated whether there are differences in the composition of the cutaneous microbiome of the unaffected skin between patients with pressure ulcers compared with those without pressure ulcers. The cutaneous microbiome of the unaffected skin of 15 patients with sacral pressure ulcers compared to 15 patients without pressure ulcers was analysed. It demonstrated that the inter-individual variation in skin microbiota of patients with pressure ulcers was significantly higher (P = 0.01). The abundance of 23 species was significantly different with Staphylococcus aureus and unclassified Enterococcus the most abundant species in patients with pressure ulcers. Random Forest models showed that eight species were associated with pressure ulcers occurrence in 81% of the patients. A subset of four species gave the strongest interaction. The presence of unclassified Enterococcus had the highest association with pressure ulcer occurrence. This study is the first to demonstrate that the cutaneous microbiome is altered in patients with pressure ulcers.

Glutamine deprivation counteracts hypoxia-induced chemoresistance.

The microenvironment of solid tumors is a key determinant of therapy efficacy. The co-occurrence of oxygen and nutrient deprivation is a common phenomenon of the tumor microenvironment and associated with treatment resistance. Cholangiocarcinoma (CCA) is characterized by a very poor prognosis and pronounced chemoresistance. A better understanding of the underlying molecular mechanisms is urgently needed to improve therapy strategies against CCA. We sought to investigate the importance of the conditionally essential amino acid glutamine, a centrally important nutrient for a variety of solid tumors, for CCA. Glutamine levels were strongly decreased in CCA samples and the growth of established human CCA cell lines was highly dependent on glutamine. Using gradual reduction of external glutamine, we generated derivatives of CCA cell lines which were able to grow without external glutamine (termed glutamine-depleted (GD)). To analyze the effects of coincident oxygen and glutamine deprivation, GD cells were treated with cisplatin or gemcitabine under normoxia and hypoxia. Strikingly, the well-established phenomenon of hypoxia-induced chemoresistance was completely reversed in GD cells. In order to better understand the underlying mechanisms, we focused on the oncogene c-Myc. The combination of cisplatin and hypoxia led to sustained c-Myc protein expression in wildtype cells. In contrast, c-Myc expression was reduced in response to the combinatorial treatment in GD cells, suggesting a functional importance of c-Myc in the process of hypoxia-induced chemoresistance. In summary, these findings indicate that the mechanisms driving adaption to tumor microenvironmental changes and their relevance for the response to therapy are more complex than expected.

Spatial Systems Lipidomics Reveals Nonalcoholic Fatty Liver Disease Heterogeneity.

Hepatocellular lipid accumulation characterizes nonalcoholic fatty liver disease (NAFLD). However, the types of lipids associated with disease progression are debated, as is the impact of their localization. Traditional lipidomics analysis using liver homogenates or plasma dilutes and averages lipid concentrations, and does not provide spatial information about lipid distribution. We aimed to characterize the distribution of specific lipid species related to NAFLD severity by performing label-free molecular analysis by mass spectrometry imaging (MSI). Fresh frozen liver biopsies from obese subjects undergoing bariatric surgery ( n = 23) with various degrees of NAFLD were cryosectioned and analyzed by matrix-assisted laser desorption/ionization (MALDI)-MSI. Molecular identification was verified by tandem MS. Tissue sections were histopathologically stained, annotated according to the Kleiner classification, and coregistered with the MSI data set. Lipid pathway analysis was performed and linked to local proteome networks. Spatially resolved lipid profiles showed pronounced differences between nonsteatotic and steatotic tissues. Lipid identification and network analyses revealed phosphatidylinositols and arachidonic acid metabolism in nonsteatotic regions, whereas low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) metabolism was associated with steatotic tissue. Supervised and unsupervised discriminant analysis using lipid based classifiers outperformed simulated analysis of liver tissue homogenates in predicting steatosis severity. We conclude that lipid composition of steatotic and nonsteatotic tissue is highly distinct, implying that spatial context is important for understanding the mechanisms of lipid accumulation in NAFLD. MSI combined with principal component-linear discriminant analysis linking lipid and protein pathways represents a novel tool enabling detailed, comprehensive studies of the heterogeneity of NAFLD.

Detection of Localized Hepatocellular Amino Acid Kinetics by using Mass Spectrometry Imaging of Stable Isotopes.

Mass spectrometry imaging (MSI) simultaneously detects and identifies the spatial distribution of numerous molecules throughout tissues. Currently, MSI is limited to providing a static and ex vivo snapshot of highly dynamic systems in which molecules are constantly synthesized and consumed. Herein, we demonstrate an innovative MSI methodology to study dynamic molecular changes of amino acids within biological tissues by measuring the dilution and conversion of stable isotopes in a mouse model. We evaluate the method specifically on hepatocellular metabolism of the essential amino acid l-phenylalanine, associated with liver diseases. Crucially, the method reveals the localized dynamics of l-phenylalanine metabolism, including its in vivo hydroxylation to l-tyrosine and co-localization with other liver metabolites in a time course of samples from different animals. This method thus enables the dynamics of localized biochemical synthesis to be studied directly from biological tissues.

Metabolic flux prediction in cancer cells with altered substrate uptake.

Proliferating cells, such as cancer cells, are known to have an unusual metabolism, characterized by an increased rate of glycolysis and amino acid metabolism. Our understanding of this phenomenon is limited but could potentially be used in order to develop new therapies. Computational modelling techniques, such as flux balance analysis (FBA), have been used to predict fluxes in various cell types, but remain of limited use to explain the unusual metabolic shifts and altered substrate uptake in human cancer cells. We implemented a new flux prediction method based on elementary modes (EMs) and structural flux (StruF) analysis and tested them against experimentally measured flux data obtained from (13)C-labelling in a cancer cell line. We assessed the quality of predictions using different objective functions along with different techniques in normalizing a metabolic network with more than one substrate input. Results show a good correlation between predicted and experimental values and indicate that the choice of cellular objective critically affects the quality of predictions. In particular, lactate gives an excellent correlation and correctly predicts the high flux through glycolysis, matching the observed characteristics of cancer cells. In contrast with FBA, which requires a priori definition of all uptake rates, often hard to measure, atomic StruFs (aStruFs) are able to predict uptake rates of multiple substrates.

Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.

Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.

MicroRNA-519a is a novel oncomir conferring tamoxifen resistance by targeting a network of tumour-suppressor genes in ER+ breast cancer.

Tamoxifen is an endocrine therapy which is administered to up to 70% of all breast cancer patients with oestrogen receptor alpha (ERα) expression. Despite the initial response, most patients eventually acquire resistance to the drug. MicroRNAs (miRNAs) are a class of small non-coding RNAs which have the ability to post-transcriptionally regulate genes. Although the role of a few miRNAs has been described in tamoxifen resistance at the single gene/target level, little is known about how concerted actions of miRNAs targeting biological networks contribute to resistance. Here we identified the miRNA cluster, C19MC, which harbours around 50 mature miRNAs, to be up-regulated in resistant cells, with miRNA-519a being the most highly up-regulated. We could demonstrate that miRNA-519a regulates tamoxifen resistance using gain- and loss-of-function testing. By combining functional enrichment analysis and prediction algorithms, we identified three central tumour-suppressor genes (TSGs) in PI3K signalling and the cell cycle network as direct target genes of miR-519a. Combined expression of these target genes correlated with disease-specific survival in a cohort of tamoxifen-treated patients. We identified miRNA-519a as a novel oncomir in ER+ breast cancer cells as it increased cell viability and cell cycle progression as well as resistance to tamoxifen-induced apoptosis. Finally, we could show that elevated miRNA-519a levels were inversely correlated with the target genes' expression and that higher expression of this miRNA correlated with poorer survival in ER+ breast cancer patients. Hence we have identified miRNA-519a as a novel oncomir, co-regulating a network of TSGs in breast cancer and conferring resistance to tamoxifen. Using inhibitors of such miRNAs may serve as a novel therapeutic approach to combat resistance to therapy as well as proliferation and evasion of apoptosis in breast cancer.

Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes.

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.

Time-lapse imaging of neuroblastoma cells to determine cell fate upon gene knockdown.

Neuroblastoma is the most common extra-cranial solid tumor of early childhood. Standard therapies are not effective in case of poor prognosis and chemotherapy resistance. To improve drug therapy, it is imperative to discover new targets that play a substantial role in tumorigenesis of neuroblastoma. The mitotic machinery is an attractive target for therapeutic interventions and inhibitors can be developed to target mitotic entry, spindle apparatus, spindle activation checkpoint, and mitotic exit. We present an elaborate analysis pipeline to determine cancer specific therapeutic targets by first performing a focused gene expression analysis to select genes followed by a gene knockdown screening assay of live cells. We interrogated gene expression studies of neuroblastoma tumors and selected 240 genes relevant for tumorigenesis and cell cycle. With these genes we performed time-lapse screening of gene knockdowns in neuroblastoma cells. We classified cellular phenotypes and used the temporal context of the perturbation effect to determine the sequence of events, particularly the mitotic entry preceding cell death. Based upon this phenotype kinetics from the gene knockdown screening, we inferred dynamic gene functions in mitosis and cell proliferation. We identified six genes (DLGAP5, DSCC1, SMO, SNRPD1, SSBP1, and UBE2C) with a vital role in mitosis and these are promising therapeutic targets for neuroblastoma. Images and movies of every time point of all screened genes are available at https://ichip.bioquant.uni-heidelberg.de.

Random sampling of elementary flux modes in large-scale metabolic networks.

MOTIVATION: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. RESULTS: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. AVAILABILITY: Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. CONTACT: dmachado@deb.uminho.pt SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Scaling-up vaccine production: implementation aspects of a biomass growth observer and controller.

This study considers two aspects of the implementation of a biomass growth observer and specific growth rate controller in scale-up from small- to pilot-scale bioreactors towards a feasible bulk production process for whole-cell vaccine against whooping cough. The first is the calculation of the oxygen uptake rate, the starting point for online monitoring and control of biomass growth, taking into account the dynamics in the gas-phase. Mixing effects and delays are caused by amongst others the headspace and tubing to the analyzer. These gas phase dynamics are modelled using knowledge of the system in order to reconstruct oxygen consumption. The second aspect is to evaluate performance of the monitoring and control system with the required modifications of the oxygen consumption calculation on pilot-scale. In pilot-scale fed-batch cultivation good monitoring and control performance is obtained enabling a doubled concentration of bulk vaccine compared to standard batch production.

Online automatic tuning and control for fed-batch cultivation.

Performance of controllers applied in biotechnological production is often below expectation. Online automatic tuning has the capability to improve control performance by adjusting control parameters. This work presents automatic tuning approaches for model reference specific growth rate control during fed-batch cultivation. The approaches are direct methods that use the error between observed specific growth rate and its set point; systematic perturbations of the cultivation are not necessary. Two automatic tuning methods proved to be efficient, in which the adaptation rate is based on a combination of the error, squared error and integral error. These methods are relatively simple and robust against disturbances, parameter uncertainties, and initialization errors. Application of the specific growth rate controller yields a stable system. The controller and automatic tuning methods are qualified by simulations and laboratory experiments with Bordetella pertussis.