Selected Policy Measures Against the Debt Distress in Mongolia

The objective of this report is to examine the public external debt sustainability of Mongolia, and to propose appropriate regulatory actions for ongoing debates about economic reform. Following sharp external shocks that include a drop in foreign direct investment and a depreciation of the national currency, the country is at a critical moment of determining whether to default on its external debts or correct structural policy failures. Therefore, it is important that Mongolia identify its level of debt distress and determine which structural reforms should take place

In Vivo Quantitative Monitoring of Subunit Stoichiometry for Metabolic Complexes

Metabolic pathways often employ assemblies of individual enzymes to facilitate substrate channeling to improve thermodynamic efficiency and confer pathway directionality. It is often assumed that subunits to multienzyme complexes are coregulated and accumulate at fixed levels in vivo, reflecting complex stoichiometry. Such assumptions can be experimentally tested using modern tandem mass spectrometry, and herein we describe such an approach applied toward an important metabolic complex. The committed step of de novo fatty acid synthesis in the plastids of most plants is catalyzed by the multienzyme, heteromeric acetyl-CoA carboxylase (hetACCase). This complex is composed of four catalytic subunits and a recently discovered regulatory subunit resembling the biotin carboxyl carrier protein but lacking the biotinylation motif necessary for activity. To better understand this novel form of regulation, a targeted tandem mass-spectrometry-based assay was developed to absolutely quantify all subunits to the <i>Arabidopsis thaliana</i> hetACCase. After validation against pure, recombinant protein, this multiplexed assay was used to quantify hetACCase subunits in siliques in various stages of development. Quantitation provided a developmental profile of hetACCase and BADC protein expression that supports a recently proposed regulatory mechanism for hetACCase and demonstrates a promising application of targeted mass spectrometry for in vivo analysis of protein complexes

In Vivo Quantitative Monitoring of Subunit Stoichiometry for Metabolic Complexes

Metabolic pathways often employ assemblies of individual enzymes to facilitate substrate channeling to improve thermodynamic efficiency and confer pathway directionality. It is often assumed that subunits to multienzyme complexes are coregulated and accumulate at fixed levels in vivo, reflecting complex stoichiometry. Such assumptions can be experimentally tested using modern tandem mass spectrometry, and herein we describe such an approach applied toward an important metabolic complex. The committed step of de novo fatty acid synthesis in the plastids of most plants is catalyzed by the multienzyme, heteromeric acetyl-CoA carboxylase (hetACCase). This complex is composed of four catalytic subunits and a recently discovered regulatory subunit resembling the biotin carboxyl carrier protein but lacking the biotinylation motif necessary for activity. To better understand this novel form of regulation, a targeted tandem mass-spectrometry-based assay was developed to absolutely quantify all subunits to the <i>Arabidopsis thaliana</i> hetACCase. After validation against pure, recombinant protein, this multiplexed assay was used to quantify hetACCase subunits in siliques in various stages of development. Quantitation provided a developmental profile of hetACCase and BADC protein expression that supports a recently proposed regulatory mechanism for hetACCase and demonstrates a promising application of targeted mass spectrometry for in vivo analysis of protein complexes

Development of an Isoform-Specific Tandem Mass Spectrometry Assay for Absolute Quantitation of Maize Lipid Transfer Proteins

Precise and accurate quantitation of maize grain allergens is important for seed and food industries. The major allergen in maize grain is Zea m 14, a lipid transfer protein (LTP). The B73 maize genome encodes for at least six LTPs sharing 15%–87% sequence identity to Zea m 14. Phylogenetic analysis of the maize LTP family revealed one gene that corresponds to Zea m 14 (denoted as LTPa) and two other genes sharing 43% (LTPc) and 74% (LTPb) identity with Zea m 14 that are putative homologues. Using stable isotope peptide mimics as internal standards for LTPs, we present a multiple reaction monitoring mass spectrometry approach for multiplexed, absolute quantitation of all three LTP proteins and alternative transcript models therein. To validate quantitative accuracy, a redundant peptide, simultaneously representing the two most abundant LTPs, was included. Analysis of 21 maize varieties revealed LTPa was most prominently expressed in maize grain, ranging from 9 to 32 μg LTP/mg protein. Proteins belonging to the LTPb and LTPc gene models were also expressed but at approximately 10- and 100-fold lower levels than LTPa, respectively. The quantitative results provided by the redundant peptide show around 95% agreement with the sum of the two unique peptides, thus providing support for the LTP gene models and validating the accuracy of this method. Though not all Zea m 14-related LTPs are abundant in grain, their high sequence homology and detectable expression in maize grain signify that LTPb and LTPc are putative allergens and should be accounted for in any quantitation strategy for maize LTP allergens

Embryogenic Competence Acquisition in Sugar Cane Callus Is Associated with Differential H⁺‑Pump Abundance and Activity

Somatic embryogenesis is an important biological process in several plant species, including sugar cane. Proteomics approaches have shown that H⁺ pumps are differentially regulated during somatic embryogenesis; however, the relationship between H⁺ flux and embryogenic competence is still unclear. This work aimed to elucidate the association between extracellular H⁺ flux and somatic embryo maturation in sugar cane. We performed a microsomal proteomics analysis and analyzed changes in extracellular H⁺-flux and H⁺-pump (P-H⁺-ATPase, V-H⁺-ATPase, and H⁺-PPase) activity in embryogenic and non-embryogenic callus. A total of 657 proteins were identified, 16 of which were H⁺ pumps. We observed that P-H⁺-ATPase and H⁺-PPase were more abundant in embryogenic callus. Compared to non-embryogenic callus, embryogenic callus showed higher H⁺ influx, especially on maturation day 14, as well as higher H⁺-pump activity (mainly, P-H⁺-ATPase and H⁺-PPase activity). H⁺-PPase appears to be the major H⁺ pump in embryogenic callus during somatic embryo formation, functioning in both vacuole acidification and PPi homeostasis. These results provide evidence for an association between higher H⁺-pump protein abundance and, consequently, higher H⁺ flux and embryogenic competence acquisition in the callus of sugar cane, allowing for the optimization of the somatic embryo conversion process by modulating the activities of these H⁺ pumps

Embryogenic Competence Acquisition in Sugar Cane Callus Is Associated with Differential H⁺‑Pump Abundance and Activity

Somatic embryogenesis is an important biological process in several plant species, including sugar cane. Proteomics approaches have shown that H⁺ pumps are differentially regulated during somatic embryogenesis; however, the relationship between H⁺ flux and embryogenic competence is still unclear. This work aimed to elucidate the association between extracellular H⁺ flux and somatic embryo maturation in sugar cane. We performed a microsomal proteomics analysis and analyzed changes in extracellular H⁺-flux and H⁺-pump (P-H⁺-ATPase, V-H⁺-ATPase, and H⁺-PPase) activity in embryogenic and non-embryogenic callus. A total of 657 proteins were identified, 16 of which were H⁺ pumps. We observed that P-H⁺-ATPase and H⁺-PPase were more abundant in embryogenic callus. Compared to non-embryogenic callus, embryogenic callus showed higher H⁺ influx, especially on maturation day 14, as well as higher H⁺-pump activity (mainly, P-H⁺-ATPase and H⁺-PPase activity). H⁺-PPase appears to be the major H⁺ pump in embryogenic callus during somatic embryo formation, functioning in both vacuole acidification and PPi homeostasis. These results provide evidence for an association between higher H⁺-pump protein abundance and, consequently, higher H⁺ flux and embryogenic competence acquisition in the callus of sugar cane, allowing for the optimization of the somatic embryo conversion process by modulating the activities of these H⁺ pumps

Data_Sheet_1_MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants.docx

<p>Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino acid composition, protein position weight matrix, and gene co-expression information, and trained predictors using deep neural network and support vector machine. Benchmarked on two independent datasets, MU-LOC achieved substantial improvements over six state-of-the-art tools for plant mitochondrial targeting prediction. In addition, MU-LOC has the advantage of predicting plant mitochondrial proteins either possessing or lacking N-terminal pre-sequences. We applied MU-LOC to predict candidate mitochondrial proteins for the whole proteome of Arabidopsis and potato. MU-LOC is publicly available at http://mu-loc.org.</p

A Versatile Mass Spectrometry-Based Method to Both Identify Kinase Client-Relationships and Characterize Signaling Network Topology

While more than a thousand protein kinases (PK) have been identified in the <i>Arabidopsis thaliana</i> genome, relatively little progress has been made toward identifying their individual client proteins. Herein we describe the use of a mass spectrometry-based <i>in vitro</i> phosphorylation strategy, termed Kinase Client assay (KiC assay), to study a targeted-aspect of signaling. A synthetic peptide library comprising 377<i> in vivo</i> phosphorylation sequences from developing seed was screened using 71 recombinant <i>A. thaliana</i> PK. Among the initial results, we identified 23 proteins as putative clients of 17 PK. In one instance protein phosphatase inhibitor-2 (AtPPI-2) was phosphorylated at multiple-sites by three distinct PK, casein kinase1-like 10, AME3, and a Ser PK-like protein. To confirm this result, full-length recombinant AtPPI-2 was reconstituted with each of these PK. The results confirmed multiple distinct phosphorylation sites within this protein. Biochemical analyses indicate that AtPPI-2 inhibits type 1 protein phosphatase (TOPP) activity, and that the phosphorylated forms of AtPPI-2 are more potent inhibitors. Structural modeling revealed that phosphorylation of AtPPI-2 induces conformational changes that modulate TOPP binding

Table_9_MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants.XLSX

<p>Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino acid composition, protein position weight matrix, and gene co-expression information, and trained predictors using deep neural network and support vector machine. Benchmarked on two independent datasets, MU-LOC achieved substantial improvements over six state-of-the-art tools for plant mitochondrial targeting prediction. In addition, MU-LOC has the advantage of predicting plant mitochondrial proteins either possessing or lacking N-terminal pre-sequences. We applied MU-LOC to predict candidate mitochondrial proteins for the whole proteome of Arabidopsis and potato. MU-LOC is publicly available at http://mu-loc.org.</p

Table_8_MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants.xlsx

<p>Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino acid composition, protein position weight matrix, and gene co-expression information, and trained predictors using deep neural network and support vector machine. Benchmarked on two independent datasets, MU-LOC achieved substantial improvements over six state-of-the-art tools for plant mitochondrial targeting prediction. In addition, MU-LOC has the advantage of predicting plant mitochondrial proteins either possessing or lacking N-terminal pre-sequences. We applied MU-LOC to predict candidate mitochondrial proteins for the whole proteome of Arabidopsis and potato. MU-LOC is publicly available at http://mu-loc.org.</p