Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

Effect of older age on treatment decisions and outcomes among patients with traumatic spinal cord injury

Background:

Older people are at increased risk of traumatic spinal cord injury from falls. We evaluated the impact of older age (≥ 70 yr) on treatment decisions and outcomes.

Methods:

We identified patients with traumatic spinal cord injury for whom consent and detailed data were available from among patients recruited (2004–2013) at any of the 31 acute care and rehabilitation hospitals participating in the Rick Hansen Spinal Cord Injury Registry. Patients were assessed by age group (< 70 v. ≥ 70 yr). The primary outcome was the rate of acute surgical treatment. We used bivariate and multivariate regression models to assess patient and injury-related factors associated with receiving surgical treatment and with the timing of surgery after arrival to a participating centre.

Results:

Of the 1440 patients included in our study cohort, 167 (11.6%) were 70 years or older at the time of injury. Older patients were more likely than younger patients to be injured by falling (83.1% v. 37.4%; p < 0.001), to have a cervical injury (78.0% v. 61.6%; p = 0.001), to have less severe injuries on admission (American Spinal Injury Association Impairment Scale grade C or D: 70.5% v. 46.9%; p < 0.001), to have a longer stay in an acute care hospital (median 35 v. 28 d; p < 0.005) and to have a higher in-hospital mortality (4.2% v. 0.6%; p < 0.001). Multivariate analysis did not show that age of 70 years or more at injury was associated with a decreased likelihood of surgical treatment (adjusted odds ratio [OR] 0.48, 95% confidence interval [CI] 0.22–1.07). An unplanned sensitivity analysis with different age thresholds showed that a threshold of 65 years was associated with a decreased chance of surgical treatment (OR 0.39, 95% CI 0.19–0.80). Older patients who underwent surgical treatment had a significantly longer wait time from admission to surgery than younger patients (37 v. 19 h; p < 0.001).

Interpretation:

We found chronological age to be a factor influencing treatment decisions but not at the 70-year age threshold that we had hypothesized. Older patients waited longer for surgery and had a substantially higher in-hospital mortality despite having less severe injuries than younger patients. Further research into the link between treatment delays and outcomes among older patients could inform surgical guideline development.Globally there has been an epidemiologic shift in the age of patients who sustain a traumatic spinal cord injury.^1–3 Although most people who have traumatic spinal cord injuries are 16–30 years old, there has been a progressive increase in the number who are over 70. The average age at injury has increased from 29 to 40 years.⁴ By 2032, patients over 70 are predicted to account for most patients with new traumatic spinal cord injuries.⁵ This change is attributed in part to aging baby boomers. It is unknown whether the management and outcomes of these older patients differ compared with younger patients.Older patients typically have more comorbid conditions, including cardiovascular disease, respiratory disorders, cerebrovascular disease and dementia, which are thought to increase their risk of perioperative adverse events.⁶ The use of anticoagulants for cardiac and cerebrovascular indications can delay timely surgical interventions. Older patients are also at increased risk of postoperative and medication-related adverse events, such as delirium.⁷ As a direct consequence of this perceived risk of perioperative adverse events and ambiguity about the optimal treatment for spinal cord injury in older patients, surgeons may deliberate for some time before making a clear therapeutic decision, they may choose nonoperative treatment,⁸ or they may delay the surgical treatment in an effort to optimize the patient’s condition medically.Given the increasing incidence of traumatic spinal cord injury in older adults, and the potential for differences in treatment among older and younger patients, we evaluated the impact of age on treatment decisions and outcomes among patients with traumatic spinal cord injury. We hypothesized that surgical management would differ at an age threshold of 70 years.     

Membrane-associated DegP in Bordetella chaperones a repeat-rich secretory protein

The chaperone/protease DegP belongs to the HtrA superfamily and is involved in protein quality control in the periplasm of Gram-negative bacteria. In Escherichia coli, typical substrates are unfolded or misfolded globular proteins that trigger the rearrangement of inactive DegP hexamers into substrate-sequestering 12- or 24-mers 'cages' for refolding or degradation. In Bordetella pertussis, DegP(Bp) facilitates, in addition, the secretion of FHA, a long β-helical adhesin that passes through the periplasm in an extended conformation. We show that DegP(Bp) exists as soluble trimers and as a membrane-associated form. Different substrates interact differently with the distinct forms of DegP(Bp), and membrane-associated DegP(Bp) has high affinity for non-native FHA. Unlike more globular substrates, FHA does not efficiently mediate rearrangement of trimers into proteolytically active, short-lived dodecamers. In contrast to these dodecamers, membrane-associated DegP(Bp) is not committed to substrate degradation, although it is proteolytically competent. In B. pertussis, membrane-associated DegP(Bp) thus represents a specific functional form serving as a holding chaperone for client proteins including FHA. If FHA secretion is impaired, membrane-associated DegP(Bp) participates in its degradation. This form of DegP(Bp) is appropriate to handle substrates unsuitable to be sequestered in cages or non-folded, secretory proteins that must not be degraded.     

Meta-analysis for genome-wide association study identifies multiple variants at the BIN1 locus associated with late-onset Alzheimer's disease

Recent GWAS studies focused on uncovering novel genetic loci related to AD have revealed associations with variants near CLU, CR1, PICALM and BIN1. In this study, we conducted a genome-wide association study in an independent set of 1034 cases and 1186 controls using the Illumina genotyping platforms. By coupling our data with available GWAS datasets from the ADNI and GenADA, we replicated the original associations in both PICALM (rs3851179) and CR1 (rs3818361). The PICALM variant seems to be non-significant after we adjusted for APOE e4 status. We further tested our top markers in 751 independent cases and 751 matched controls. Besides the markers close to the APOE locus, a marker (rs12989701) upstream of BIN1 locus was replicated and the combined analysis reached genome-wide significance level (p = 5E-08). We combined our data with the published Harold et al. study and meta-analysis with all available 6521 cases and 10360 controls at the BIN1 locus revealed two significant variants (rs12989701, p = 1.32E-10 and rs744373, p = 3.16E-10) in limited linkage disequilibrium (r² = 0.05) with each other. The independent contribution of both SNPs was supported by haplotype conditional analysis. We also conducted multivariate analysis in canonical pathways and identified a consistent signal in the downstream pathways targeted by Gleevec (P = 0.004 in Pfizer; P = 0.028 in ADNI and P = 0.04 in GenADA). We further tested variants in CLU, PICALM, BIN1 and CR1 for association with disease progression in 597 AD patients where longitudinal cognitive measures are sufficient. Both the PICALM and CLU variants showed nominal significant association with cognitive decline as measured by change in Clinical Dementia Rating-sum of boxes (CDR-SB) score from the baseline but did not pass multiple-test correction. Future experiments will help us better understand potential roles of these genetic loci in AD pathology.     

ACTH Enhancement of T-Lymphocyte Cytotoxic Responses

1. Corticotropin (ACTH) was one of the first neuropeptides shown to bind to receptors on leukocytes and modulate immune responses. Generally ACTH inhibits immune responses, but certain functions can be enhanced. The present study was performed to determine the effects of ACTH on cytotoxic T-lymphocyte responses, the components, and the major phenotypes of the participating cells.2. The action of ACTH on cytotoxicity was measured in vitro, in assays utilizing T-lymphocytes that had been previously sensitized in vivo. The cells were then cultured with ACTH and target cells bearing the appropriate stimulatory major histocompatiblity antigens.3. ACTH did not significantly affect a primary mixed lymphocyte reaction whereas it enhanced a secondary (memory) cytotoxic response up to 100% following 2 days of ACTH treatment. The effect was a shift in the kinetics of effector cell generation so that ACTH-treated cultures demonstrated an augmented cytotoxic activity on day 2, that was not as pronounced on day 3 as cytotoxic activity in control cultures became maximal. ACTH also inhibited Concanavalin A-stimulated T-lymphocyte mitogenesis. Immature thymocyte mitogenesis was inhibited more than that of mature thymocytes.4. The finding that IFN-γ was elevated in the cultures suggested that ACTH may enhance memory cytotoxic responses through a combination of mechanisms such as direct cell alterations or synergy with regulatory cytokines. While corticosteroids are probably the most recognized neuroendocrine, stress hormone to affect immune functions, our study illustrates that other neuroendocrine factors such as ACTH, also directly affect immune functions.     

Stomatal conductance and not stomatal density determines the long-term reduction in leaf transpiration of poplar in elevated CO<Subscript>2</Subscript>

Using a free-air CO₂ enrichment (FACE) experiment, poplar trees (Populus × euramericana clone I214) were exposed to either ambient or elevated [CO₂] from planting, for a 5-year period during canopy development, closure, coppice and re-growth. In each year, measurements were taken of stomatal density (SD, number mm⁻²) and stomatal index (SI, the proportion of epidermal cells forming stomata). In year 5, measurements were also taken of leaf stomatal conductance (g _s, μmol m⁻² s⁻¹), photosynthetic CO₂ fixation (A, mmol m⁻² s⁻¹), instantaneous water-use efficiency (A/E) and the ratio of intercellular to atmospheric CO₂ (C_i:C_a). Elevated [CO₂] caused reductions in SI in the first year, and in SD in the first 2 years, when the canopy was largely open. In following years, when the canopy had closed, elevated [CO₂] had no detectable effects on stomatal numbers or index. In contrast, even after 5 years of exposure to elevated [CO₂], g _s was reduced, A/E was stimulated, and C_i:C_a was reduced relative to ambient [CO₂]. These outcomes from the long-term realistic field conditions of this forest FACE experiment suggest that stomatal numbers (SD and SI) had no role in determining the improved instantaneous leaf-level efficiency of water use under elevated [CO₂]. We propose that altered cuticular development during canopy closure may partially explain the changing response of stomata to elevated [CO₂], although the mechanism for this remains obscure.     

Invertebrate central pattern generation moves along

Central pattern generators (CPGs) are circuits that generate organized and repetitive motor patterns, such as those underlying feeding, locomotion and respiration. We summarize recent work on invertebrate CPGs which has provided new insights into how rhythmic motor patterns are produced and how they are controlled by higher-order command and modulatory interneurons.     

Reactive oxygen species and protein kinases modulate the level of phospho-MEK-like proteins during human sperm capacitation

Capacitation is an essential process by which spermatozoa acquire fertilizing ability. Reactive oxygen species (ROS), protein kinase A (PKA), protein kinase C (PKC), protein tyrosine kinases (PTKs), and the extracellular signal-regulated protein kinase (ERK or mitogen-activated protein kinase [MAPK]) pathway regulate sperm capacitation. Our aim was to evaluate the phosphorylation of MEK (MAPK kinase or MAP2K) or MEK-like proteins in human sperm capacitation and its modulation by ROS and kinases. Immunoblotting using an anti-phospho-MEK antibody indicated that the phosphorylation of three protein bands (55, 94, and 115 kDa) increased in spermatozoa treated with fetal cord serum ultrafiltrate (FCSu), BSA, or isobutylmethylxanthine plus dibutyryl cAMP as capacitating agents. These phospho-MEK-like proteins are localized along the sperm flagellum. The MEK-inhibitors PD98059 and U126 prevented this phosphorylation, suggesting that these proteins are MEK-like proteins. The ROS scavengers prevented, and the addition of H(2)O(2) or spermine-NONOate (nitric oxide donor) triggered, the increase of phospho-MEK-like proteins. The capacitation-related increases in phospho-MEK-like proteins induced by FCSu, H(2)O(2), and spermine-NONOate were similarly modulated by PKA, PKC, and PTK, suggesting ROS as mediators in this phenomenon. These results indicate that phospho-MEK-like proteins are modulated by ROS and kinases and probably represent an intermediary step between the early events and the late tyrosine phosphorylation associated with capacitation.