GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort.

J Affect Disord. 2018 Sep 07;243:16-22

Authors: Heinzman JT, Hoth KF, Cho MH, Sakornsakolpat P, Regan EA, Make BJ, Kinney GL, Wamboldt FS, Holm KE, Bormann N, Robles J, Kim V, Iyer AS, Silverman EK, Crapo JD, Han S, Potash JB, Shinozaki G, COPDGene Investigators

Abstract
BACKGROUND: Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression.
METHODS: Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression.
RESULTS: The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10-6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10-6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10-4).
LIMITATIONS: Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives.
CONCLUSIONS: Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.

PMID: 30219690 [PubMed - as supplied by publisher]

Engineering NADH/NAD+ Ratio in Halomonas bluephagenesis for Enhanced Production of Polyhydroxyalkanoates (PHA).

Metab Eng. 2018 Sep 13;:

Authors: Ling C, Qiao GQ, Shuai BW, Olavarria K, Yin J, Xiang RJ, Song KN, Shen YH, Guo Y, Chen GQ

Abstract
Halomonas bluephagenesis has been developed as a platform strain for the next generation industrial biotechnology (NGIB) with advantages of resistances to microbial contamination and high cell density growth (HCD), especially for production of polyhydroxyalkanoates (PHA) including poly(3-hydroxybutyrate) (PHB), poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P34HB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). However, little is known about the mechanism behind PHA accumulation under oxygen limitation. This study for the first time found that H. bluephagenesis utilizes NADH instead of NADPH as a cofactor for PHB production, thus revealing the rare situation of enhanced PHA accumulation under oxygen limitation. To increase NADH/NAD+ ratio for enhanced PHA accumulation under oxygen limitation, an electron transport pathway containing electron transfer flavoprotein subunits α and β encoded by etf operon was blocked to increase NADH supply, leading to 90% PHB accumulation in the cell dry weight (CDW) of H. bluephagenesis compared with 84% by the wild type. Acetic acid, a cost-effective carbon source, was used together with glucose to balance the redox state and reduce inhibition on pyruvate metabolism, resulting in 22% more CDW and 94% PHB accumulation. The cellular redox state changes induced by the addition of acetic acid increased 3HV ratio in its copolymer PHBV from 4% to 8%, 4HB in its copolymer P34HB from 8% to 12%, respectively, by engineered H. bluephagenesis. The strategy of systematically modulation on the redox potential of H. bluephagenesis led to enhanced PHA accumulation and controllable monomer ratios in PHA copolymers under oxygen limitation, reducing energy consumption and scale-up complexity.

PMID: 30219528 [PubMed - as supplied by publisher]

Genome-scale analysis of evolutionary rate and selection in a fast-expanding Spanish cluster of HIV-1 subtype F1.

Infect Genet Evol. 2018 Sep 13;:

Authors: Patiño-Galindo JÁ, Domínguez F, Cuevas MT, Delgado E, Sánchez M, Pérez-Álvarez L, Thomson MM, Sanjuán R, González-Candelas F, Cuevas JM

Abstract
This work is aimed at assessing the presence of positive selection and/or shifts of the evolutionary rate in a fast-expanding HIV-1 subtype F1 transmission cluster affecting men who have sex with men in Spain. We applied Bayesian coalescent phylogenetics and selection analyses to 23 full-coding region sequences from patients belonging to that cluster, along with other 19 F1 epidemiologically-unrelated sequences. A shift in the overall evolutionary rate of the virus, explained by positively selected sites in the cluster, was detected. We also found one substitution in Nef (H89F) that was specific to the cluster and experienced positive selection. These results suggest that fast transmission could have been facilitated by some inherent genetic properties of this HIV-1 variant.

PMID: 30219320 [PubMed - as supplied by publisher]

Characterization of CTLA4 Trafficking and Implications for Its Function.

Biophys J. 2018 Aug 23;:

Authors: Khailaie S, Rowshanravan B, Robert PA, Waters E, Halliday N, Badillo Herrera JD, Walker LSK, Sansom DM, Meyer-Hermann M

Abstract
CTLA4 is an essential negative regulator of T-cell immune responses and a key checkpoint regulating autoimmunity and antitumor responses. Genetic mutations resulting in quantitative defects in the CTLA4 pathway are also associated with the development of immune dysregulation syndromes in humans. It has been proposed that CTLA4 functions to remove its ligands CD80 and CD86 from opposing cells by a process known as transendocytosis. A quantitative characterization of CTLA4 synthesis, endocytosis, degradation, and recycling and how these affect its function is currently lacking. In a combined in vitro and in silico study, we developed a mathematical model and identified these trafficking parameters. Our model predicts optimal ligand removal in an intermediate affinity range. The intracellular CTLA4 pool as well as fast internalization, recovery of free CTLA4 from internalized complexes, and recycling is critical for sustained functionality. CD80-CTLA4 interactions are predicted to dominate over CD86-CTLA4. Implications of these findings in the context of control of antigen-presenting cells by regulatory T cells and of pathologic genetic deficiencies are discussed. The presented mathematical model can be reused in the community beyond these questions to better understand other trafficking receptors and study the impact of CTLA4 targeting drugs.

PMID: 30219287 [PubMed - as supplied by publisher]

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"systems biology"

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Analytical Platforms and Techniques to Study Stem Cell Metabolism.

Methods Mol Biol. 2018;1842:265-281

Authors: Tang C, Chen K, Bajic A, Choi WT, Baluya DL, Maletic-Savatic M

Abstract
Over the past decade, advances in systems biology or 'omics techniques have enabled unprecedented insights into the biological processes that occur in cells, tissues, and on the organism level. One of these technologies is the metabolomics, which examines the whole content of the metabolites in a given sample. In a biological system, a stem cell for instance, there are thousands of single components, such as genes, RNA, proteins, and metabolites. These multiple molecular species interact with each other and these interactions may change over the life-time of a cell or in response to specific stimuli, adding to the complexity of the system. Using metabolomics, we can obtain an instantaneous snapshot of the biological status of a cell, tissue, or organism and gain insights on the pattern(s) of numerous analytes, both known and unknown, that result because of a given biological condition. Here, we outline the main methods to study the metabolism of stem cells, including a relatively recent technology of mass spectrometry imaging. Given the abundant and increasing interest in stem cell metabolism in both physiological and pathological conditions, we hope that this chapter will provide incentives for more research in these areas to ultimately reach wide network of applications in biomedical, pharmaceutical, and nutritional research and clinical medicine.

PMID: 30196417 [PubMed - in process]

Differential Co-expression and Regulatory Network Analysis Uncover the Relapse Factor and Mechanism of T Cell Acute Leukemia.

Mol Ther Nucleic Acids. 2018 Sep 07;12:184-194

Authors: Luo M, Zhang Q, Xia M, Hu F, Ma Z, Chen Z, Guo AY

Abstract
The pediatric T cell acute lymphoblastic leukemia (T-ALL) still remains a cancer with worst prognosis for high recurrence. Massive studies were conducted for the leukemia relapse based on diagnosis and relapse paired samples. However, the initially diagnostic samples may contain the relapse information and mechanism, which were rarely studied. In this study, we collected mRNA and microRNA (miRNA) data from initially diagnosed pediatric T-ALL samples with their relapse or remission status after treatment. Integrated differential co-expression and miRNA-transcription factor (TF)-gene regulatory network analyses were used to reveal the possible relapse mechanisms for pediatric T-ALL. We detected miR-1246/1248 and NOTCH2 served as key nodes in the relapse network, and they combined with TF WT1/SOX4/REL to form regulatory modules that influence the progress of T-ALL. A regulatory loop miR-429-MYCN-MFHAS1 was found potentially associated with the remission of T-ALL. Furthermore, we proved miR-1246/1248 combined with NOTCH2 could promote cell proliferation in the T-ALL cell line by experiments. Meanwhile, analysis based on the miRNA-drug relationships demonstrated that drugs 5-fluorouracil, ascorbate, and trastuzumab targeting miR-1246 could serve as potential supplements for the standard therapy. In conclusion, our findings revealed the potential molecular mechanisms of T-ALL relapse by the combination of co-expression and regulatory network, and they provide preliminary clues for precise treatment of T-ALL patients.

PMID: 30195757 [PubMed]

Parallel Genome-wide Profiling of Coding and Non-coding RNAs to Identify Novel Regulatory Elements in Embryonic and Maturated Heart.

Mol Ther Nucleic Acids. 2018 Sep 07;12:158-173

Authors: Sabour D, Machado RSR, Pinto JP, Rohani S, Sahito RGA, Hescheler J, Futschik ME, Sachinidis A

Abstract
Heart development is a complex process, tightly regulated by numerous molecular mechanisms. Key components of the regulatory network underlying heart development are transcription factors (TFs) and microRNAs (miRNAs), yet limited investigation of the role of miRNAs in heart development has taken place. Here, we report the first parallel genome-wide profiling of polyadenylated RNAs and miRNAs in a developing murine heart. These data enable us to identify dynamic activation or repression of numerous biological processes and signaling pathways. More than 200 miRNAs and 25 long non-coding RNAs were differentially expressed during embryonic heart development compared to the mature heart; most of these had not been previously associated with cardiogenesis. Integrative analysis of expression data and potential regulatory interactions suggested 28 miRNAs as novel regulators of embryonic heart development, representing a considerable expansion of the current repertoire of known cardiac miRNAs. To facilitate follow-up investigations, we constructed HeartMiR (http://heartmir.sysbiolab.eu), an open access database and interactive visualization tool for the study of gene regulation by miRNAs during heart development.

PMID: 30195755 [PubMed]

Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery.

J Thorac Cardiovasc Surg. 2018 Jul 27;:

Authors: Choi N, Whitlock R, Klassen J, Zappitelli M, Arora RC, Rigatto C, Ho J

Abstract
OBJECTIVES: Iron regulation is an important modifier of renal ischemia-reperfusion injury, but the role of iron-binding proteins during cardiopulmonary bypass remains unclear. The goal was to characterize iron-binding proteins throughout ischemia-reperfusion injury to determine their association with acute kidney injury development.
METHODS: A prospective observational cohort of adult patients who underwent cardiac surgery (n = 301) was obtained, and acute kidney injury was defined by Kidney Disease Improving Global Outcomes. Serum ferritin, transferrin saturation, and urine hepcidin-25 were measured.
RESULTS: Intraoperative serum ferritin was lower at the start of cardiopulmonary bypass (P = .005) and 1-hour cardiopulmonary bypass (P = .001) in patients with acute kidney injury versus patients without acute kidney injury. Lower serum ferritin and higher transferrin saturation at 1-hour cardiopulmonary bypass were independent predictors of acute kidney injury (serum ferritin odds ratio, 0.66; 95% confidence interval [CI], 0.48-0.91; transferrin saturation odds ratio, 1.26; 95% CI, 1.02-1.55) and improved model discrimination (area under the curve [AUC], 0.76; 95% CI, 0.67-0.85) compared with clinical prediction alone (AUC, 0.72; 95% CI, 0.62-0.81; ΔAUC and net reclassification index, P = .01). Lower ferritin, higher transferrin saturation at 1-hour cardiopulmonary bypass, and lower urine hepcidin-25 at postoperative day 1 were also independent predictors for acute kidney injury development, and this model demonstrated an AUC of 0.80 (0.72-0.87), which was superior to clinical prediction (ΔAUC P = .002, integrated discrimination improvement and net reclassification index P = .003).
CONCLUSIONS: Our findings suggest that lower levels of intraoperative iron-binding proteins may reflect an impaired capacity to rapidly handle catalytic iron released during cardiopulmonary bypass, leading to kidney injury. These data highlight the importance of iron homeostasis in human ischemia-reperfusion injury and suggest it is a potentially modifiable risk during cardiac surgery. Intraoperative detection of incipient acute kidney injury may be feasible and could be used as an enrichment strategy for clinical trials.

PMID: 30195593 [PubMed - as supplied by publisher]

Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota.

Cell Syst. 2018 Aug 31;:

Authors: Medlock GL, Carey MA, McDuffie DG, Mundy MB, Giallourou N, Swann JR, Kolling GL, Papin JA

Abstract
The diversity and number of species present within microbial communities create the potential for a multitude of interspecies metabolic interactions. Here, we develop, apply, and experimentally test a framework for inferring metabolic mechanisms associated with interspecies interactions. We perform pairwise growth and metabolome profiling of co-cultures of strains from a model mouse microbiota. We then apply our framework to dissect emergent metabolic behaviors that occur in co-culture. Based on one of the inferences from this framework, we identify and interrogate an amino acid cross-feeding interaction and validate that the proposed interaction leads to a growth benefit in vitro. Our results reveal the type and extent of emergent metabolic behavior in microbial communities composed of gut microbes. We focus on growth-modulating interactions, but the framework can be applied to interspecies interactions that modulate any phenotype of interest within microbial communities.

PMID: 30195437 [PubMed - as supplied by publisher]

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts.

Cell Syst. 2018 Aug 20;:

Authors: Zelezniak A, Vowinckel J, Capuano F, Messner CB, Demichev V, Polowsky N, Mülleder M, Kamrad S, Klaus B, Keller MA, Ralser M

Abstract
A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

PMID: 30195436 [PubMed - as supplied by publisher]