Identification of Proteins Secreted by Malaria Parasite into Erythrocyte using SVM and PSSM profiles

Background  

Malaria parasite secretes various proteins in infected RBC for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine/drug against malaria. The existing motif-based methods have got limited success due to lack of universal motif in all secretory proteins of malaria parasite.     

MALDI–MS Profiling to Address Honey Bee Health Status under Bacterial Challenge through Computational Modeling

Honey bees play a critical role in the maintenance of plant biodiversity and sustainability of food webs. In the past few decades, bees have been subjected to biotic and abiotic threats causing various colony disorders. Therefore, monitoring solutions to help beekeepers to improve bee health are necessary. Matrix‐assisted laser desorption ionization–mass spectrometry (MALDI–MS) profiling has emerged within this decade as a powerful tool to identify in routine micro‐organisms and is currently used in real‐time clinical diagnosis. MALDI BeeTyping is developed to monitor significant hemolymph molecular changes in honey bees upon infection with a series of entomopathogenic Gram‐positive and ‐negative bacteria. A Serratia marcescens strain isolated from one naturally infected honey bee collected from the field is also considered. A series of hemolymph molecular mass fingerprints is individually recorded and to the authors' knowledge, the first computational model harboring a predictive score of 97.92% and made of nine molecular signatures that discriminate and classify the honey bees’ systemic response to the bacteria is built. Hence, the model is challenged by classifying a training set of hemolymphs and an overall recognition of 91.93% is obtained. Through this work, a novel, time and cost saving high‐throughput strategy that addresses honey bee health on an individual scale is introduced.     

A novel <Emphasis Type="SmallCaps">l</Emphasis>-aspartate dehydrogenase from the mesophilic bacterium <Emphasis Type="Italic">Pseudomonas aeruginosa</Emphasis> PAO1: molecular characterization and application for <Emphasis Type="SmallCaps">l</Emphasis>-aspartate production

l-aspartate dehydrogenase (EC 1.4.1.21; l-AspDH) is a rare member of amino acid dehydrogenase superfamily and so far, two thermophilic enzymes have been reported. In our study, an ORF PA3505 encoding for a putative l-AspDH in the mesophilic bacterium Pseudomonas aeruginosa PAO1 was identified, cloned, and overexpressed in Escherichia coli. The homogeneously purified enzyme (PaeAspDH) was a dimeric protein with a molecular mass of about 28 kDa exhibiting a very high specific activity for l-aspartate (l-Asp) and oxaloacetate (OAA) of 127 and 147 U mg⁻¹, respectively. The enzyme was capable of utilizing both nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP) as coenzyme. PaeAspDH showed a T _m value of 48°C for 20 min that was improved to approximately 60°C by the addition of 0.4 M NaCl or 30% glycerol. The apparent K _m values for OAA, NADH, and ammonia were 2.12, 0.045, and 10.1 mM, respectively; comparable results were observed with NADPH. The l-Asp production system B consisting of PaeAspDH, Bacillus subtilis malate dehydrogenase and E. coli fumarase, achieved a high level of l-Asp production (625 mM) from fumarate in fed-batch process with a molar conversion yield of 89.4%. Furthermore, the fermentative production system C released 33 mM of l-Asp after 50 h by using succinate as carbon source. This study represented an extensive characterization of the mesophilic AspDH and its potential applicability for efficient and attractive production of l-Asp. Our novel production systems are also hopeful for developing the new processes for other compounds production.     

Mechanical Signaling on the Single Protein Level Studied Using Steered Molecular Dynamics

Efficient communication between the cell and its external environment is of the utmost importance to the function of multicellular organisms. While signaling events can be generally characterized as information exchange by means of controlled energy conversion, research efforts have hitherto mainly been concerned with mechanisms involving chemical and electrical energy transfer. Here, we review recent computational efforts addressing the function of mechanical force in signal transduction. Specifically, we focus on the role of steered molecular dynamics (SMD) simulations in providing details at the atomic level on a group of protein domains, which play a fundamental role in signal exchange by responding properly to mechanical strain. We start by giving a brief introduction to the SMD technique and general properties of mechanically stable protein folds, followed by specific examples illustrating three general regimes of signal transfer utilizing mechanical energy: purely mechanical, mechanical to chemical, and chemical to mechanical. Whenever possible the physiological importance of the example at hand is stressed to highlight the diversity of the processes in which mechanical signaling plays a key role. We also provide an overview of future challenges and perspectives for this rapidly developing field.     

A Simple Allele-Specific PCR Assay for Detecting <Emphasis Type="Italic">FAD2</Emphasis> Alleles in Both A and B Genomes of the Cultivated Peanut for High-Oleate Trait Selection

In cultivated tetraploid peanut (2n = 4x = 40, AABB), the conversion of oleic acid to linoleic acid is mainly catalyzed by the Δ¹² fatty acid desaturase (FAD). Two homoeologous genes (FAD2A and FAD2B) encoding for the desaturase are located on the A and B genomes, respectively. Abolishing or reducing the desaturase activity by gene mutation can significantly increase the oleic acid/linoleic acid ratio. F435-derived high-oleate peanut cultivars contain two key mutations within the Δ¹² fatty acid desaturase gene which include a 1-bp substitution of G:C→A:T in the A genome and a 1-bp insertion of A:T in the B genome. Both of these mutations contribute to abolishing or reducing the desaturase activity, leading to accumulation of oleate versus linoleate. Currently, detection of FAD2 alleles can be achieved by a cleaved amplified polymorphic sequence marker for the A genome and a real-time polymerase chain reaction (PCR) marker for the B genome; however, detection of these key mutations has to use different assay platforms. Therefore, a simple PCR assay for detection of FAD2 alleles on both genomes was developed by designing allele-specific primers and altering PCR annealing temperatures. This assay was successfully used for detecting FAD2 alleles in peanut. Gas chromatography (GC) was used to determine fatty acid composition of PCR-assayed genotypes. The results from the PCR assay and GC analysis were consistent. This PCR assay is quick, reliable, economical, and easy to use. Implementation of this PCR assay will greatly enhance the efficiency of germplasm characterization and marker-assisted selection of high oleate in peanut.     

A revision of the <Emphasis Type="Italic">Heterospathe elegans</Emphasis> (<Emphasis Type="Italic">Arecaceae</Emphasis>) complex in New Guinea

Summary  Three closely affiliated species of Heterospathe Scheff. (H. elegans (Becc.) Becc., H. humilis Becc. and H. versteegiana Becc.) from New Guinea are revised. They are reduced to a single species which is divided into two subspecies, and the new combination H. elegans subsp. humilis (Becc.) M. S. Trudgen & W. J. Baker is made. The subspecies can be readily distinguished by their growth habit. Epitypes are designated for the three previously published names, as informative material on the habit is not included in the existing type specimens. A new, potentially related species is described as H. pullenii M. S. Trudgen & W. J. Baker.     

Targeting cleavage and polyadenylation specific factor 1 via shRNA inhibits cell proliferation in human ovarian cancer

Cleavage and polyadenylation specificity factor 1 (CPSF1), a member of CPSF complex, has been reported to play a key role in pre-mRNA 3′-end formation, but its possible role in ovarian cancer remains unclear. In the present study, we found the mRNA level of CPSF1 was overexpressed in ovarian cancer tissues using Oncomine Cancer Microarray database. Then the loss-of-function assays, including CCK-8, colony formation and flow cytometry assays, were performed to determine the effects of CPSF1 on cell viability, proliferation, cell cycle and apoptosis of human ovarian cancer cell lines (SKOV-3 and OVCAR-3). The results indicated that depletion of CPSF1 suppressed cell viability, impaired colony formation ability, induced cell cycle arrest at G0/G1 phase and promoted cell apoptosis in ovarian cancer cells. Furthermore, knockdown of CPSF1 upregulated the expression of cleaved caspase-3 and PARP and downregulated CDK4/cyclin D1 expression. These data suggested that CPSF1 could promote ovarian cancer cell growth and proliferation in vitro and its depletion might serve as a potential therapeutic target for human ovarian cancer.     

CRlncRNA: a manually curated database of cancer-related long non-coding RNAs with experimental proof of functions on clinicopathological and molecular features

Background

Recent studies demonstrated that long non-coding RNAs (lncRNAs) could be intricately implicated in cancer-related molecular networks, and related to cancer occurrence, development and prognosis. However, clinicopathological and molecular features for these cancer-related lncRNAs, which are very important in bridging lncRNA basic research with clinical research, fail to well settle to integration.

Results

After manually reviewing more than 2500 published literature, we collected the cancer-related lncRNAs with the experimental proof of functions. By integrating from literature and public databases, we constructed CRlncRNA, a database of cancer-related lncRNAs. The current version of CRlncRNA embodied 355 entries of cancer-related lncRNAs, covering 1072 cancer-lncRNA associations regarding to 76 types of cancer, and 1238 interactions with different RNAs and proteins. We further annotated clinicopathological features of these lncRNAs, such as the clinical stages and the cancer hallmarks. We also provided tools for data browsing, searching and download, as well as online BLAST, genome browser and gene network visualization service.

Conclusions

CRlncRNA is a manually curated database for retrieving clinicopathological and molecular features of cancer-related lncRNAs supported by highly reliable evidences. CRlncRNA aims to provide a bridge from lncRNA basic research to clinical research. The lncRNA dataset collected by CRlncRNA can be used as a golden standard dataset for the prospective experimental and in-silico studies of cancer-related lncRNAs. CRlncRNA is freely available for all users at http://crlnc.xtbg.ac.cn.

     

A dynamic, architectural plant model simulating resource-dependent growth

BACKGROUND AND AIMS: Physiological and architectural plant models have originally been developed for different purposes and therefore have little in common, thus making combined applications difficult. There is, however, an increasing demand for crop models that simulate the genetic and resource-dependent variability of plant geometry and architecture, because man is increasingly able to transform plant production systems through combined genetic and environmental engineering. MODEL: GREENLAB is presented, a mathematical plant model that simulates interactions between plant structure and function. Dual-scale automaton is used to simulate plant organogenesis from germination to maturity on the basis of organogenetic growth cycles that have constant thermal time. Plant fresh biomass production is computed from transpiration, assuming transpiration efficiency to be constant and atmospheric demand to be the driving force, under non-limiting water supply. The fresh biomass is then distributed among expanding organs according to their relative demand. Demand for organ growth is estimated from allometric relationships (e.g. leaf surface to weight ratios) and kinetics of potential growth rate for each organ type. These are obtained through parameter optimization against empirical, morphological data sets by running the model in inverted mode. Potential growth rates are then used as estimates of relative sink strength in the model. These and other 'hidden' plant parameters are calibrated using the non-linear, least-square method. KEY RESULTS AND CONCLUSIONS: The model reproduced accurately the dynamics of plant growth, architecture and geometry of various annual and woody plants, enabling 3D visualization. It was also able to simulate the variability of leaf size on the plant and compensatory growth following pruning, as a result of internal competition for resources. The potential of the model's underlying concepts to predict the plant's phenotypic plasticity is discussed.