Identification of Soybean Genes Involved in Circadian Clock Mechanism and Photoperiodic Control of Flowering Time by In Silico Analyses

Glycine max is a photoperiodic short-day plant and the practical consequence of the response is latitude and sowing period limitations to commercial crops. Genetic and physiological studies using the model plants Arabidopsis thaliana and rice （Oryza sativa） have uncovered several genes and genetic pathways controlling the process, however information about the corresponding pathways in legumes is scarce. Data mining prediction methodologies, including multiple sequence alignment, phylogeneUc analysis, bioinformaUcs expression and sequence motif pattern identification, were used to identify soybean genes involved in day length perception and photoperiodic flowering induction. We have investigated approximately 330 000 sequences from open-access databases and have identified all bona fide central oscillator genes and circadian photoreceptors from A. thaliana in soybean sequence databases. We propose a working model for the photoperiodic control of flowering time in G. max, based on the identified key components. These results demonstrate the power of comparative genomics between model systems and crop species to elucidate the several aspects of plant physiology and metabolism.     

In Silico and In Vitro Analyses Reveal Promising Antimicrobial Peptides from Myxobacteria

Probiotics and Antimicrobial Proteins - Antimicrobial resistance (AMR) is a global concern, and as soon as new antibiotics are introduced, resistance to those agents emerges. Therefore, there is an...     

Regulation of Flowering Time by MicroRNAs

The shoot apical meristem (SAM) continuously produces lateral organs in plants.Based on the identity of the lateral organs,the life cycle of a plant can be divided into two phases:vegetative and reproductive.The SAM produces leaves during the vegetative phase,whereas it gives rise to flowers in the reproductive phase (reviewed in Poethig,2003).The floral transition,namely the switch from vegetative to reproductive growth,is controlled by diverse endogenous and exogenous cues such as age,hormones,photoperiod,and temperature (reviewed in B(a)urle and Dean,2006;Srikanth and Schmid,2011;Andres and Coupland,2012). The model annual Arabidopsis thaliana has been extensively used for the dissection of the molecular mechanism underlying the floral transition during the last two decades.The molecular and genetic analyses have revealed five flowering time pathways,including age,autonomous,gibberellins (GAs),photoperiod and vernalization (reviewed in Amasino and Michaels,2010).Growing lines of evidence indicate that there are extensive crosstalks,feedback or feed-forward loops between the components within these pathways,and that these multiple floral inductive cues are integrated into a set of floral promoting MADS-box genes including APETALA 1 (AP1),SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1),FRUITFULL (FUL) and LEAFY (LFY) (Amasino and Michaels,2010;Lee and Lee,2010;Srikanth and Schmid,2011).     

In Silico Analyses of Staphylococcal Enterotoxin B as a DNA Vaccine for Cancer Therapy

Immunotherapy has been suggested as a compelling alternative approach for conventional breast cancer treatment methods. Despite the paramount rolesof T cells in this approach, insufficient numbers of them in the combat against progressive tumor growth still remain to be dealt with. Super antigens are a class of antigens, capable of eliciting T cell proliferation response against desired antigens. Staphylococcal enterotoxin B (SEB) is categorized as a super antigen, its anti-tumor properties has been previously reported. However, to the best our knowledge, SEB has not been ever administered as a DNA construct. In the present study, we exploited bioinformatics tools to assess the immunoreactivity of a SEB-coding DNA construct that serves as a DNA vaccine for breast cancer therapy. Potential B and T (MHC class I and II binders) cell epitopes of the hypothetically expressed protein, along with its sub cellular localization were predicted. Moreover, probable glycosylation and phosphorylation sites within the protein sequence were determined. The gene sequence was optimized according to murine model codon bias and its mRNA stability was analyzed. Employing an integrative in silico approach, we revealed that apparently the construct could be efficiently expressed in mouse model. Moreover, the hypothetically expressed protein could act as an amenable adjuvant in cancer immunotherapy.     

Cellular Morphogenesis In Silico

We describe a model that simulates spherical cells of different types that can migrate and interact either attractively or repulsively. We find that both expected morphologies and previously unreported patterns spontaneously self-assemble. Among the newly discovered patterns are a segmented state of alternating discs, and a “shish-kebab” state, in which one cell type forms a ring around a second type. We show that these unique states result from cellular attraction that increases with distance (e.g., as membranes stretch viscoelastically), and would not be seen in traditional, e.g., molecular, potentials that diminish with distance. Most of the states found computationally have been observed in vitro, and it remains to be established what role these self-assembled states may play in in vivo morphogenesis.     

In Silico and Quantitative Analyses of the Putative FLC-like Homologue in Coffee (Coffea arabica L.)

The sequential pattern of coffee flowering is a major constraint that directly affects productivity, increases harvest costs, and generates a final product of lower quality for mixing dry fruits with ripe and unripe ones. The objective of this work was to identify and analyze one of the main genes involved in flowering regulation, FLOWERING LOCUS C (FLC) in coffee (Coffea arabica L.). The identification of this gene was conducted in silico using a coffee EST database (CAFEST) and bioinformatics tools. Quantitative PCR results suggest that the identified CaFLC-like homologue is directly involved in flowering regulation in coffee. This expands our knowledge on evolutionary conservation of flowering pathways in dicot species. The functional studies of CaFLC-like with mutants of a more tractable species will lead to a better understanding of the molecular regulation as well as the specific functions of each gene flowering during floral induction in coffee.     

In Silico and In Vitro Analyses Identified Three Amino Acid Residues Critical to the Catalysis of Two Aphid Farnesyl Diphosphate Synthase

Farnesyl diphosphate synthase (FPPS) plays an essential role in the isoprenoid biosynthetic pathway of microbes, plants and animals. In the present study, we first cloned two FPPSs from the bird cherry-oat aphid (RpFPPS1 and RpFPPS2), and activity assay by gas chromatography-mass spectrometry showed that both RpFPPS1 and RpFPPS2 were active in vitro. They were then subjected to homology modeling and molecular docking. Molecular interaction analysis indicated that three amino acid residues (R120, R121 and K266) might play key roles in the catalysis of the two aphid FPPSs by forming hydrogen bonds with the diphosphate moiety of the allylic substrate. These in silico results were subsequently confirmed by site-directed mutagenesis and in vitro activity assay of the mutant enzymes, in which each of the single mutations R120G, R121G and K266I abolished the activities of the two FPPSs. This study contributes to our understanding of the catalytic mechanism of farnesyl diphosphate synthases.     

几个板栗株系的开花结果习性比较分析

采用在美国育成的几个株系,并以常见的本地栽培品种‘桂花香’作对照,对刚进入结果幼树的枝梢开花习性,枝梢生长情况与开花坐果关系,以及树冠不同部位的枝梢开花与坐果进行调查分析。结果表明：对全部参试材料而言,末级梢的长、粗度区间分别以20．0～59．9cm和0．5～1．49cm的雌花量和坐果数较高;株系95—1—26和95—1-9雌花着生部位较其它3个株系或品种的节位高出一倍以上,其平均着生节位与末级梢平均节位基本上相等;从树冠立体分布上看,其外围和树冠上部水平方向中膛结果数较多,但对于每个株系来说,又存在一定的差异。以上研究结果对幼年结果树的枝梢培养、树体的整形修剪,以及达到早期丰产具有实践指导意义。     

Understanding the Adaptive Growth Strategy of Lactobacillus plantarum by In Silico Optimisation

In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy.     

Novel In Silico Analyses of Repetitive Spider Silk Sequences to Understand the Evolution and Mechanical Properties of Fibrous Protein Materials

The mechanical properties of spider silks have diverged as spiders have diversely speciated. Because the main components of silks are proteins, it is valuable to investigate their sequences. However, silk sequences have been regarded as difficult information to analyze due to their imbalance and imperfect tandem repeats (ITR). Here, an in silico approach is applied to systemically analyze a group of silk sequences. It is found that every time new spider groups emerge, unique trimer motifs appear. These trimer motifs are used to find additional clues of evolution and to determine relationships with mechanical properties. For the first time, crucial evidence is provided that shows silk sequences coevolved with spider species and the mechanical properties of their fibers to adapt to new living environments. This novel approach can be used as a platform for analyzing other groups of ITR‐harboring proteins and to obtain information for the design of tailor‐made fibrous protein materials.     

In Silico Approaches for Prediction of Anti-CRISPR Proteins

Numerous viruses infecting bacteria and archaea encode CRISPR-Cas system inhibitors, known as anti-CRISPR proteins (Acr). The Acrs typically are highly specific for particular CRISPR variants, resulting in remarkable sequence and structural diversity and complicating accurate prediction and identification of Acrs. In addition to their intrinsic interest for understanding the coevolution of defense and counter-defense systems in prokaryotes, Acrs could be natural, potent on–off switches for CRISPR-based biotechnological tools, so their discovery, characterization and application are of major importance. Here we discuss the computational approaches for Acr prediction. Due to the enormous diversity and likely multiple origins of the Acrs, sequence similarity searches are of limited use. However, multiple features of protein and gene organization have been successfully harnessed to this end including small protein size and distinct amino acid compositions of the Acrs, association of acr genes in virus genomes with genes encoding helix-turn-helix proteins that regulate Acr expression (Acr-associated proteins, Aca), and presence of self-targeting CRISPR spacers in bacterial and archaeal genomes containing Acr-encoding proviruses. Productive approaches for Acr prediction also involve genome comparison of closely related viruses, of which one is resistant and the other one is sensitive to a particular CRISPR variant, and “guilt by association” whereby genes adjacent to a homolog of a known Aca are identified as candidate Acrs. The distinctive features of Acrs are employed for Acr prediction both by developing dedicated search algorithms and through machine learning. New approaches will be needed to identify novel types of Acrs that are likely to exist.     

In Silico Analysis of Peptide Potential Biological Functions

Over the past decade, tools of omics technologies have generated a large amount of data in various repositories, which are of interest for meta-analysis today. Now, researchers in the field of proteomics and peptidomics focus not on sequencing, but on functions performed by molecules and metabolic interactions, in which the proteins or peptides participate. As a result of a single LC-MS/MS analysis, several thousand unique peptides can be identified, each of which may be bioactive. A classic technique for determining the peptide function is a direct experiment. Bioinformatics approaches as a preliminary analysis of potential biological functions are an important step and are able to significantly reduce time and cost of experimental verification. This article provides an overview of computational methods for predicting biological functions of peptides. Approaches based on machine learning, which are the most popular today, algorithms using structural, evolutionary, or statistical patterns, as well as methods based on molecular docking, are considered. Databases of bioactive peptides are reported, providing information necessary to construct new algorithms for predicting biological functions. Attention is paid to the characteristics of peptides, on the basis of which it is possible to draw conclusions about their bioactivity. In addition, the report provides a list of online services that may be used by researchers to analyze potential activities of peptides with which they work.     

In Silico Kinetic Study of the Glucose Transporter

Glucose transport in plasma membranes is the prototypic example of facilitated diffusion through biological membranes, and transport in erythrocytes is the most widely studied. One of the oldest and simplest models describing the kinetics of the transport reaction is that of alternating conformers, schematized in a cycle of four partial reactions where glucose binds and dissociates at two opposite steps, and the transporter undergoes transconformations at the other two opposite steps. The transport kinetics is entirely defined by the forward and backward rate constants of the partial reactions and the glucose and transporter concentrations at each side of the membrane, related by the law of mass action. We studied, in silico, the effect of modifications of the variables on the transient kinetics of the transport reaction. The simulations took into account thermodynamic constraints and provided results regarding initial velocities of transport, maximal velocities in different conditions, apparent influx and efflux affinities, and the turnover number of the transporter. The results are in the range of those experimentally reported. Maximal initial velocities are obtained when the affinities of the ligand for the transporter are the same at the extra- and intracellular binding sites and when the equilibrium constants of the transconformation steps are equal among them and equal to 1, independently of the obvious effect of the increase of the rate constant values. The results are well adjusted to Michaelis–Menten kinetics. A larger initial velocity for efflux than for uptake described in human erythrocytes is demonstrated in a model with the same dissociation constants at the outer and inner sites of the membrane. The larger velocities observed for uptake and efflux when transport occurs towards a glucose-containing trans side can also be reproduced with the alternating conformer model, depending on how transport velocities are measured.     

Roles of Sugars in Controlling Flowering Time

Flowering time is influenced by environmental factors such as photosynthesis, temperature, nutrition, and water. The main products of photosynthesis are sugars that are mobilized to sink tissues to support plant growth and differentiation. They also function as signals to control various types of metabolism and developmental processes. One of the most important transitions in the plant life cycle is from the vegetative to reproductive phase. During that transition, sucrose levels rise rapidly but transiently in the phloem and shoot apexes. For several species, the addition of exogenous sucrose promotes flowering, possibly by acting as a main signal. Although other sugars, including glucose, also appear to be involved in this transition, evidence for their roles in flowering is limited. In Arabidopsis thaliana, trehalose-6-phosphate serves as a signal to induce flowering. However, its roles in other plants have not been reported. Sucrose seems to function primarily in the leaf phloem to enhance the generation of florigens such as Flowering Locus T (FT) while trehalose-6-phosphate functions in the shoot apical meristem to promote the flowering signal pathway downstream of those florigens.     

Transcriptional Analysis of Flowering Time in Switchgrass

Over the past two decades, switchgrass (Panicum virgatum) has emerged as a priority biofuel feedstock. The bulk of switchgrass biomass is in the vegetative portion of the plant; therefore, increasing the length of vegetative growth will lead to an increase in overall biomass yield. The goal of this study was to gain insight into the control of flowering time in switchgrass that would assist in development of cultivars with longer vegetative phases through delayed flowering. RNA sequencing was used to assess genome-wide expression profiles across a developmental series between switchgrass genotypes belonging to the two main ecotypes: upland, typically early flowering, and lowland, typically late flowering. Leaf blades and tissues enriched for the shoot apical meristem (SAM) were collected in a developmental series from emergence through anthesis for RNA extraction. RNA from samples that flanked the SAM transition stage was sequenced for expression analyses. The analyses revealed differential expression patterns between early- and late-flowering genotypes for known flowering time orthologs. Namely, genes shown to play roles in photoperiod response and the circadian clock in other species were identified as potential candidates for regulating flowering time in the switchgrass genotypes analyzed. Based on their expression patterns, many of the differentially expressed genes could also be classified as putative promoters or repressors of flowering. The candidate genes presented here may be used to guide switchgrass improvement through marker-assisted breeding and/or transgenic or gene editing approaches.