The macrophage and its role in inflammation and tissue repair: mathematical and systems biology approaches.

Wiley Interdiscip Rev Syst Biol Med. 2016 Jan-Feb;8(1):87-99

Authors: Dunster JL

Abstract
Macrophages are central to the inflammatory response and its ability to resolve effectively. They are complex cells that adopt a range of subtypes depending on the tissue type and stimulus that they find themselves under. This flexibility allows them to play multiple, sometimes opposing, roles in inflammation and tissue repair. Their central role in the inflammatory process is reflected in macrophage dysfunction being implicated in chronic inflammation and poorly healing wounds. In this study, we discuss recent attempts to model mathematically and computationally the macrophage and how it partakes in the complex processes of inflammation and tissue repair. There are increasing data describing the variety of macrophage phenotypes and their underlying transcriptional programs. Dynamic mathematical and computational models are an ideal way to test biological hypotheses against experimental data and could aid in understanding this multi-functional cell and its potential role as an attractive therapeutic target for inflammatory conditions and tissue repair.

PMID: 26459225 [PubMed - indexed for MEDLINE]

Dysregulation of the endocannabinoid signaling system in the cerebellum and brainstem in a transgenic mouse model of spinocerebellar ataxia type-3.

Neuroscience. 2016 Oct 4;:

Authors: Rodríguez-Cueto C, Hernández-Gálvez M, Hillard CJ, Maciel P, García-García L, Valdeolivas S, Pozo MA, Ramos JA, Gómez-Ruiz M, Fernández-Ruiz J

Abstract
Spinocerebellar ataxia type-3 (SCA-3) is a rare disease but it is the most frequent type within the autosomal dominant inherited ataxias. The disease lacks an effective treatment to alleviate major symptoms and to modify disease progression. Our recent findings that endocannabinoid receptors and enzymes are significantly altered in the post-mortem cerebellum of patients affected by autosomal-dominant hereditary ataxias suggest that targeting the endocannabinoid signaling system may be a promising therapeutic option. Our goal was to investigate the status of the endocannabinoid signaling system in a transgenic mouse model of SCA-3, in the two CNS structures most affected in this disease -cerebellum and brainstem-. These animals exhibited progressive motor incoordination, imbalance, abnormal gait, muscle weakness, and dystonia, in parallel to reduced in vivo brain glucose metabolism, deterioration of specific neuron subsets located in the dentate nucleus and pontine nuclei, small changes in microglial morphology, and reduction in glial glutamate transporters. Concerning the endocannabinoid signaling, our data indicated no changes in CB2 receptors. By contrast, CB1 receptors increased in the Purkinje cell layer, in particular in terminals of basket cells, but they were reduced in the dentate nucleus. We also measured the levels of endocannabinoid lipids and found reductions in anandamide and oleoylethanolamide in the brainstem. These changes correlated with an increase in the FAAH enzyme in the brainstem, which also occurred in some cerebellar areas, whereas other endocannabinoid-related enzymes were not altered. Collectively, our results in SCA-3 mutant mice confirm a possible dysregulation in the endocannabinoid system in the most important brain structures affected in this type of ataxia, suggesting that a pharmacological manipulation addressed to correct these changes could be a promising option in SCA-3.

PMID: 27717809 [PubMed - as supplied by publisher]

Tandem Mass Spectrometry of Sphingolipids: Applications for Diagnosis of Sphingolipidoses.

Adv Clin Chem. 2016;77:177-219

Authors: Kuchař L, Asfaw B, Rybová J, Ledvinová J

Abstract
In recent years, mass spectrometry (MS) has become the dominant technology in lipidomic analysis. It is widely used in diagnosis and research of lipid metabolism disorders including those characterized by impairment of lysosomal functions and storage of nondegraded-degraded substrates. These rare diseases, which include sphingolipidoses, have severe and often fatal clinical consequences. Modern MS methods have contributed significantly to achieve a definitive diagnosis, which is essential in clinical practice to begin properly targeted patient care. Here we summarize MS and tandem MS methods used for qualitative and quantitative analysis of sphingolipids (SL) relative to the diagnostic process for sphingolipidoses and studies focusing on alterations in cell functions due to these disorders. This review covers the following topics: Tandem MS is sensitive and robust in determining the composition of sphingolipid classes in various biological materials. Its ability to establish SL metabolomic profiles using MS bench-top analyzers, significantly benefits the first stages of a diagnosis as well as metabolic studies of these disorders. It can thus contribute to a better understanding of the biological significance of SL.

PMID: 27717417 [PubMed - in process]

Molecular modelling and molecular dynamics of CFTR.

Cell Mol Life Sci. 2016 Oct 7;:

Authors: Callebaut I, Hoffmann B, Lehn P, Mornon JP

Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) protein is a member of the ATP-binding cassette (ABC) transporter superfamily that functions as an ATP-gated channel. Considerable progress has been made over the last years in the understanding of the molecular basis of the CFTR functions, as well as dysfunctions causing the common genetic disease cystic fibrosis (CF). This review provides a global overview of the theoretical studies that have been performed so far, especially molecular modelling and molecular dynamics (MD) simulations. A special emphasis is placed on the CFTR-specific evolution of an ABC transporter framework towards a channel function, as well as on the understanding of the effects of disease-causing mutations and their specific modulation. This in silico work should help structure-based drug discovery and design, with a view to develop CFTR-specific pharmacotherapeutic approaches for the treatment of CF in the context of precision medicine.

PMID: 27717958 [PubMed - as supplied by publisher]

Analysis of nasal potential in murine cystic fibrosis models.

Int J Biochem Cell Biol. 2016 Oct 4;:

Authors: Cunha MF, Simonin J, Sassi A, Freund R, Hatton A, Cottart CH, Elganfoud N, Zoubairi R, Dragu C, Jais JP, Hinzpeter A, Edelman A, Sermet-Gaudelus I

Abstract
The nasal epithelium of the mouse closely mimics the bioelectrical phenotype of the human airways. Ion transport across the nasal epithelium induces a nasal transepithelial potential difference. Its measurement by a relatively non-invasive method adapted from humans allows in vivo longitudinal measurements of CFTR-dependent ionic transport in the murine nasal mucosa. This test offers a useful tool to assess CFTR function in preclinical studies for novel therapeutics modulating CFTR activity. Here we extensively review work done to assess transepithelial transport in the murine respiratory epithelium in the basal state and after administration of CFTR modulators. Factors of variability and discriminative thresholds between the CF and the WT mice for different readouts are discussed.

PMID: 27717840 [PubMed - as supplied by publisher]

Inquilinus limosus in pulmonary disease: case report and review of the literature.

Diagn Microbiol Infect Dis. 2016 Sep 16;:

Authors: McHugh KE, Rhoads DD, Wilson DA, Highland KB, Richter SS, Procop GW

Abstract
Inquilinus limosus is a slow growing, gram-negative, oxidase-positive, non-fermentative bacillus that is rarely isolated from clinical samples. When clinically identified, I. limosus is almost exclusively isolated from the respiratory tracts of patients with cystic fibrosis (CF). We report the first case of I. limosus isolation from a pulmonary specimen in an individual without a diagnosis of CF. A review of the English-language literature has been made and shows 33 cases (excluding the present report) in which I. limosus was isolated from the respiratory tracts of patients. Our patient, at 60years of age, is more than two decades older than the any previously reported patient. Similar to previous reports, the I. limosus isolated from her lungs demonstrated intrinsic multidrug resistance. The pathogenicity, clinical relevance, and optimal therapeutic management of I. limosus remains largely unknown due to its infrequent recovery from clinical samples.

PMID: 27717651 [PubMed - as supplied by publisher]

Optimization of techniques for multiple platform testing in small, precious samples such as Human Chorionic Villus Sampling.

Prenat Diagn. 2016 Oct 8;:

Authors: Pisarska MD, Akhlaghpour M, Lee B, Barlow GM, Xu N, Wang ET, Mackey AJ, Farber CR, Rich SS, Rotter JI, Chen YI, Goodarzi MO, Guller S, Williams J

Abstract
BACKGROUND: Multiple testing to understand global changes in gene expression based on genetic and epigenetic modifications is evolving. Chorionic villi, obtained for prenatal testing, is limited, but can be used to understand ongoing human pregnancies. However, optimal storage, processing and utilization of CVS for multiple platform testing has not been established.
RESULTS: Leftover CVS samples were flash-frozen or preserved in RNAlater. Modifications to standard isolation kits were performed to isolate quality DNA and RNA from samples as small as 2-5 mg. RNAlater samples had significantly higher RNA yields and quality and were successfully used in microarray and RNA-sequencing (RNA-seq). RNA-seq libraries generated using 200 vs. 800 ng RNA showed similar biological coefficients of variation. RNAlater samples had lower DNA yields and quality, which improved by heating the elution buffer to 70 °C. Purification of DNA was not necessary for bisulfite-conversion and genome-wide methylation profiling. CVS cells were propagated and continue to express genes found in freshly-isolated chorionic villi.
CONCLUSIONS: CVS samples preserved in RNAlater are superior. Our optimized techniques provide specimens for genetic, epigenetic and gene expression studies from a single small sample which can be used to develop diagnostics and treatments using a systems biology approach in the prenatal period.

PMID: 27718505 [PubMed - as supplied by publisher]

An efficient algorithm for identifying primary phenotype attractors of a large-scale Boolean network.

BMC Syst Biol. 2016 Oct 7;10(1):95

Authors: Choo SM, Cho KH

Abstract
BACKGROUND: Boolean network modeling has been widely used to model large-scale biomolecular regulatory networks as it can describe the essential dynamical characteristics of complicated networks in a relatively simple way. When we analyze such Boolean network models, we often need to find out attractor states to investigate the converging state features that represent particular cell phenotypes. This is, however, very difficult (often impossible) for a large network due to computational complexity.
RESULTS: There have been some attempts to resolve this problem by partitioning the original network into smaller subnetworks and reconstructing the attractor states by integrating the local attractors obtained from each subnetwork. But, in many cases, the partitioned subnetworks are still too large and such an approach is no longer useful. So, we have investigated the fundamental reason underlying this problem and proposed a novel efficient way of hierarchically partitioning a given large network into smaller subnetworks by focusing on some attractors corresponding to a particular phenotype of interest instead of considering all attractors at the same time. Using the definition of attractors, we can have a simplified update rule with fixed state values for some nodes. The resulting subnetworks were small enough to find out the corresponding local attractors which can be integrated for reconstruction of the global attractor states of the original large network.
CONCLUSIONS: The proposed approach can substantially extend the current limit of Boolean network modeling for converging state analysis of biological networks.

PMID: 27717349 [PubMed - in process]

12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/10/08

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

10 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/10/08

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

PLoS Comput Biol. 2016 Oct;12(10):e1005135

Authors: Lim H, Poleksic A, Yao Y, Tong H, He D, Zhuang L, Meng P, Xie L

Abstract
Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

PMID: 27716836 [PubMed - in process]

Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles.

J Chem Inf Model. 2015 Dec 28;55(12):2705-16

Authors: Iwata H, Sawada R, Mizutani S, Kotera M, Yamanishi Y

Abstract
The identification of beneficial drug combinations is a challenging issue in pharmaceutical and clinical research toward combinatorial drug therapy. In the present study, we developed a novel computational method for large-scale prediction of beneficial drug combinations using drug efficacy and target profiles. We designed an informative descriptor for each drug-drug pair based on multiple drug profiles representing drug-targeted proteins and Anatomical Therapeutic Chemical Classification System codes. Then, we constructed a predictive model by learning a sparsity-induced classifier based on known drug combinations from the Orange Book and KEGG DRUG databases. Our results show that the proposed method outperforms the previous methods in terms of the accuracy of high-confidence predictions, and the extracted features are biologically meaningful. Finally, we performed a comprehensive prediction of novel drug combinations for 2,639 approved drugs, which predicted 142,988 new potentially beneficial drug-drug pairs. We showed several examples of successfully predicted drug combinations for a variety of diseases.

PMID: 26624799 [PubMed - indexed for MEDLINE]

Structural Basis of Metallo-β-Lactamase Inhibition by Captopril Stereoisomers.

Antimicrob Agents Chemother. 2015 Oct 19;60(1):142-50

Authors: Brem J, van Berkel SS, Zollman D, Lee SY, Gileadi O, McHugh PJ, Walsh TR, McDonough MA, Schofield CJ

Abstract
β-Lactams are the most successful antibacterials, but their effectiveness is threatened by resistance, most importantly by production of serine- and metallo-β-lactamases (MBLs). MBLs are of increasing concern because they catalyze the hydrolysis of almost all β-lactam antibiotics, including recent-generation carbapenems. Clinically useful serine-β-lactamase inhibitors have been developed, but such inhibitors are not available for MBLs. l-Captopril, which is used to treat hypertension via angiotensin-converting enzyme inhibition, has been reported to inhibit MBLs by chelating the active site zinc ions via its thiol(ate). We report systematic studies on B1 MBL inhibition by all four captopril stereoisomers. High-resolution crystal structures of three MBLs (IMP-1, BcII, and VIM-2) in complex with either the l- or d-captopril stereoisomer reveal correlations between the binding mode and inhibition potency. The results will be useful in the design of MBL inhibitors with the breadth of selectivity required for clinical application against carbapenem-resistant Enterobacteriaceae and other organisms causing MBL-mediated resistant infections.

PMID: 26482303 [PubMed - indexed for MEDLINE]

IRAK1 is a novel DEK transcriptional target and is essential for head and neck cancer cell survival.

Oncotarget. 2015 Dec 22;6(41):43395-407

Authors: Adams AK, Bolanos LC, Dexheimer PJ, Karns RA, Aronow BJ, Komurov K, Jegga AG, Casper KA, Patil YJ, Wilson KM, Starczynowski DT, Wells SI

Abstract
The chromatin-binding DEK protein was recently reported to promote the growth of HPV+ and HPV- head and neck squamous cell carcinomas (HNSCCs). Relevant cellular and molecular mechanism(s) controlled by DEK in HNSCC remain poorly understood. While DEK is known to regulate specific transcriptional targets, global DEK-dependent gene networks in HNSCC are unknown. To identify DEK transcriptional signatures we performed RNA-Sequencing (RNA-Seq) in HNSCC cell lines that were either proficient or deficient for DEK. Bioinformatic analyses and subsequent validation revealed that IRAK1, a regulator of inflammatory signaling, and IRAK1-dependent regulatory networks were significantly repressed upon DEK knockdown in HNSCC. According to TCGA data, 14% of HNSCC specimens overexpressed IRAK1, thus supporting possible oncogenic functions. Furthermore, genetic or pharmacologic inhibition of IRAK1 in HNSCC cell lines was sufficient to attenuate downstream signaling such as ERK1/2 and to induce HNSCC cell death by apoptosis. Finally, targeting DEK and IRAK1 simultaneously enhanced cell death as compared to targeting either alone. Our findings reveal that IRAK1 promotes cell survival and is an attractive therapeutic target in HNSCC cells. Thus, we propose a model wherein IRAK1 stimulates tumor signaling and phenotypes both independently and in conjunction with DEK.

PMID: 26527316 [PubMed - indexed for MEDLINE]

Cross-Species Analysis of Gene Expression and Function in Prefrontal Cortex, Hippocampus and Striatum.

PLoS One. 2016;11(10):e0164295

Authors: Chen W, Xia X, Song N, Wang Y, Zhu H, Deng W, Kong Q, Pan X, Qin C

Abstract
BACKGROUND: Mouse has been extensively used as a tool for investigating the onset and development of human neurological disorders. As a first step to construct a transgenic mouse model of human brain lesions, it is of fundamental importance to clarify the similarity and divergence of genetic background between non-diseased human and mouse brain tissues.
METHODS: We systematically compared, based on large scale integrated microarray data, the transcriptomes of three anatomically distinct brain regions; prefrontal cortex (PFC), hippocampus (HIP) and striatum (STR), across human and mouse. The widely used DAVID web server was used to decipher the biological functions of the highly expressed genes that were identified using a previously reported approach. Venn analysis was used to depict the overlapping ratios of the notably enriched biological process (BP) terms (one-tailed Fisher's exact test and Benjamini correction; adjusted p < 0.01) between two brain tissues. GOSemSim, an R package, was selected to perform GO semantic similarity analysis. Next, we adjusted signal intensities of orthologous genes by the total signals in all samples within species, and used one minus Pearson's correlation coefficient to assess the expression distance. Hierarchical clustering and principal component analysis (PCA) were selected for expression pattern analysis. Lineage specific expressed orthologous genes were identified by comparison of the most extreme sub-datasets across species and further verified using reverse transcription PCR (RT-PCR) and quantitative real-time PCR (qRT-PCR).
RESULTS: We found that the number of the significantly enriched BP terms of the highly expressed genes in human brain regions is larger than that in mouse corresponding brain regions. The mainly involved BP terms in human brain tissues associated with protein-membrane targeting and selenium metabolism are species-specific. The overlapping ratios of all the significantly enriched BP terms between any two brain tissues across species are lower than that within species, but the pairwise semantic similarities are very high between any two brain tissues from either human or mouse. Hierarchical clustering analysis shows the biological functions of the highly expressed genes in brain tissues are more consistent within species than interspecies; whereas it shows the expression patterns of orthologous genes are evidently conserved between human and mouse equivalent brain tissues. In addition, we identified four orthologous genes (COX5B, WIF1, SLC4A10 and PLA2G7) that are species-specific, which have been widely studied and confirmed to be closely linked with neuro- physiological and pathological functions.
CONCLUSION: Our study highlights the similarities and divergences in gene function and expression between human and mouse corresponding brain regions, including PFC, HIP and STR.

PMID: 27716781 [PubMed - in process]

PGMD: a comprehensive manually curated pharmacogenomic database.

Pharmacogenomics J. 2016 Apr;16(2):124-8

Authors: Kaplun A, Hogan JD, Schacherer F, Peter AP, Krishna S, Braun BR, Nambudiry R, Nitu MG, Mallelwar R, Albayrak A

Abstract
The PharmacoGenomic Mutation Database (PGMD) is a comprehensive manually curated pharmacogenomics database. Two major sources of PGMD data are peer-reviewed literature and Food and Drug Administration (FDA) and European Medicines Agency (EMA) drug labels. PGMD curators capture information on exact genomic location and sequence changes, on resulting phenotype, drugs administered, patient population, study design, disease context, statistical significance and other properties of reported pharmacogenomic variants. Variants are annotated into functional categories on the basis of their influence on pharmacokinetics, pharmacodynamics, efficacy or clinical outcome. The current release of PGMD includes over 117 000 unique pharmacogenomic observations, covering all 24 disease superclasses and nearly 1400 drugs. Over 2800 genes have associated pharmacogenomic variants, including genes in proximity to intergenic variants. PGMD is optimized for use in annotating next-generation sequencing data by providing genomic coordinates for all covered variants, including Single Nucleotide Polymorphisms (SNPs), insertions, deletions, haplotypes, diplotypes, Variable Number Tandem Repeats (VNTR), copy number variations and structural variations.

PMID: 25939485 [PubMed - indexed for MEDLINE]

Importance of rare gene copy number alterations for personalized tumor characterization and survival analysis.

Genome Biol. 2016 Oct 3;17(1):204

Authors: Seifert M, Friedrich B, Beyer A

Abstract
It has proven exceedingly difficult to ascertain rare copy number alterations (CNAs) that may have strong effects in individual tumors. We show that a regulatory network inferred from gene expression and gene copy number data of 768 human cancer cell lines can be used to quantify the impact of patient-specific CNAs on survival signature genes. A focused analysis of tumors from six tissues reveals that rare patient-specific gene CNAs often have stronger effects on signature genes than frequent gene CNAs. Further comparison to a related network-based approach shows that the integration of indirectly acting gene CNAs significantly improves the survival analysis.

PMID: 27716417 [PubMed - in process]

A systems biology approach reveals new endoplasmic reticulum-associated targets for the correction of the ATP7B mutant causing Wilson disease.

Metallomics. 2016 Sep 1;8(9):920-930

Authors: Concilli M, Iacobacci S, Chesi G, Carissimo A, Polishchuk R

Abstract
Copper (Cu) is an important trace element required for the activity of essential enzymes. However, excess Cu compromises the redox balance in cells and tissues causing serious toxicity. The process of disposal of excess Cu from organisms relies on the activity of Cu-transporting ATPase ATP7B. ATP7B is mainly expressed in liver hepatocytes where it sequesters the potentially toxic metal and mediates its excretion into the bile. Mutations in the ATP7B gene cause Wilson disease (WD), which is characterized by the accumulation of toxic Cu in the liver due to the scarce expression of ATP7B as well as the failure of ATP7B mutants to pump Cu and/or traffic to the Cu-excretion sites. The most frequent ATP7B mutant, H1069Q, still presents a significant Cu-transporting activity, but undergoes retention within the endoplasmic reticulum (ER) where the mutant is rapidly degraded. Expression of this ATP7B mutant has been recently reported to activate the p38 and JNK stress kinase pathways, which, in turn, trigger quality control mechanisms leading to the arrest of ATP7B-H1069Q in the ER and to the acceleration of its degradation. However, the main molecular players operating in these p38/JNK-dependent ER quality control pathways remain to be discovered. By using a combination of RNAseq, bioinformatics and RNAi approaches, we found a cluster of ER quality control genes whose expression is controlled by p38 and JNK and is required for the efficient retention of the ATP7B-H1069Q mutant in the ER. Silencing these genes reduced the accumulation of the ATP7B mutant in the ER and facilitated the mutant sorting and export to the Golgi and post-Golgi copper excretion sites. In sum, our findings reveal the ER-associated genes that could be utilized for the correction of ATP7B mutants and, hence, for the normalization of Cu homeostasis in Wilson disease.

PMID: 27714068 [PubMed - in process]

Evaluation of predictions of the stochastic model of organelle production based on exact distributions.

Elife. 2015 Dec 22;4:

Authors: Craven CJ

Abstract
We present a reanalysis of the stochastic model of organelle production and show that the equilibrium distributions for the organelle numbers predicted by this model can be readily calculated in three different scenarios. These three distributions can be identified as standard distributions, and the corresponding exact formulae for their mean and variance can therefore be used in further analysis. This removes the need to rely on stochastic simulations or approximate formulae (derived using the fluctuation dissipation theorem). These calculations allow for further analysis of the predictions of the model. On the basis of this we question the extent to which the model can be used to conclude that peroxisome biogenesis is dominated by de novo production when Saccharomyces cerevisiae cells are grown on glucose medium.

PMID: 26765560 [PubMed - indexed for MEDLINE]

Development of a Quantitative SRM-Based Proteomics Method to Study Iron Metabolism of Synechocystis sp. PCC 6803.

J Proteome Res. 2016 Jan 4;15(1):266-79

Authors: Vuorijoki L, Isojärvi J, Kallio P, Kouvonen P, Aro EM, Corthals GL, Jones PR, Muth-Pawlak D

Abstract
The cyanobacterium Synechocystis sp. PCC 6803 (S. 6803) is a well-established model species in oxygenic photosynthesis research and a potential host for biotechnological applications. Despite recent advances in genome sequencing and microarray techniques applied in systems biology, quantitative proteomics approaches with corresponding accuracy and depth are scarce for S. 6803. In this study, we developed a protocol to screen changes in the expression of 106 proteins representing central metabolic pathways in S. 6803 with a targeted mass spectrometry method, selected reaction monitoring (SRM). We evaluated the response to the exposure of both short- and long-term iron deprivation. The experimental setup enabled the relative quantification of 96 proteins, with 87 and 92 proteins showing adjusted p-values <0.01 under short- and long-term iron deficiency, respectively. The high sensitivity of the SRM method for S. 6803 was demonstrated by providing quantitative data for altogether 64 proteins that previously could not be detected with the classical data-dependent MS approach under similar conditions. This highlights the effectiveness of SRM for quantification and extends the analytical capability to low-abundance proteins in unfractionated samples of S. 6803. The SRM assays and other generated information are now publicly available via PASSEL and Panorama.

PMID: 26652789 [PubMed - indexed for MEDLINE]