Synthèses ďacides nucléiques et de protéines au cours de ľinitiation de bourgeons adventifs sur des explantats de Cichorium intybus cultivés in vitro

Synthesis of nucleic acids and proteins during initiation of vegetative buds from explants of Cichorium intybus cultivated in vitro .
Explants of 6 mm diameter and 2 mm thickness from roots of Cichorium intybus L. (var. WitIoof cv. "Zoom"), grown in vitro in the presence of kinetin (l0_–6 M ), produced only buds. By comparing the histological events and the biochemical modifications which occurred during the neoformation of buds, it is possible to distinguish two distinct phases. The first phase starts immediately after the excision of the explants and continues for 30 h, when the mitotic index is at its highest. This corresponds to a phase of cellular activation, which is characterized by early synthesis of RNA, permitting the synthesis of proteins necessary for duplication of DNA and cell divisions. The second period starts after 30 h and ends after approximately 72 h of culture, at which time the first bud meristematic nodules were detected. This is a preparatory phase for organogenesis and above all related to synthesis of RNA and proteins.     

Essai ďApplication de ľAnalyse Phénétique à la Classification du Phylum des Ciliophora

ABSTRACT. Relationships among 59 genera and species are determined by analyzing 122 ultrastructural, morphological, stomatogenic, nuclear, and asexual reproductive features on the basis of a numerical phenetic analysis. At first, the study concerns 12 genera and species of the colpodid group whose numerous features have now been investigated. Next, the same procedure is used for the analysis of 47 kinetophragminophoran, oligohymenophoran, polyhymenophoran genera. On the basis of these results, a new macrosystem of the phylum Ciliophora arises. Three subphyla are distinguished: 1) Karyorelictophora with one class, Karyorelictea; 2) Kinetophrag-mophora with four classes, a) Colpodea, b) Hypostomea including Peniculida, Nassulida, Parapeniculida, and Peritrichida, c) Spirotrichea including Hypotrichida, Clevelandellida, Heterotrichida, and Oligotrichida, and d) Hymenostomea including Hymenostomatida and Apostomatida; 3) Hymenophora with two classes, a) Phyllopharyngea and b) Gymnostomea including two subclasses, Gymnostomia and Astomia.     

Influence de la benzylaminopurine sur ľabsorption du phosphate par des disques de tubercules de pomme de terre

The Influence of Benzylaminopurine on the Absorption of Phosphate by Potato Tubers Aging of disks of potato tuber in an aerated liquid medium induces a large increase of the rate of phosphate absorption. Experiments were performed with benzylaminopurine added to the aging solution at various times after slicing, and the rate of absorption was determined 24 h after slicing. The opposite experiment was also performed, with benzylaminopurine added at the beginning and withdrawn after different times. The addition of benzylaminopurine to the aging medium partially prevents the development of the phosphate uptake. The uptake mechanism is a metabolic phenomenon as shown by its sensitivity to temperature. The degree of inhibition depends on the benzylaminopurine concentration as well as on the time of aging. The results suggest that the cytokinin acts on a process which occurs all along the aging and underline the importance of the presence of benzylaminopurine during the first hours of aging.     

Formaldehyde kills spores of Bacillus subtilis by DNA damage and small, acid-soluble spore proteins of the ααααααα/βββββββ-type protect spores against this DNA damage

Killing of wild-type spores of Bacillus subtilis with formaldehyde also caused significant mutagenesis; spores (termed α⁻β⁻) lacking the two major α/β-type small, acid-soluble spore proteins (SASP) were more sensitive to both formaldehyde killing and mutagenesis. A recA mutation sensitized both wild-type and α⁻β⁻ spores to formaldehyde treatment, which caused significant expression of a recA - lacZ fusion when the treated spores germinated. Formaldehyde also caused protein–DNA cross-linking in both wild-type and α⁻β⁻ spores. These results indicate that: (i) formaldehyde kills B. subtilis spores at least in part by DNA damage and (b) α/β-type SASP protect against spore killing by formaldehyde, presumably by protecting spore DNA.     

Stimulation par la lumière rouge lointain de la synthèse de la δ-aminolévulinate déshydratase des cotylédons de radis

Stimulation of de novo synthesis of δ-aminolevulinate dehydralasc of radishes grown under far-red light .
Density labelling studies of δ-aminolevulinate dehydratase (ALAD) in cotyledons of radish ( Raphanus sativus L. cv. Longue Rave Saumonée) seedlings demonstrate that far-red light stimulates de novo synthesis of ALAD and that the turn-over of this enzyme is very poor. Cycloheximide reduces considerably both the increase of ALAD activity and the incorporation of deuterium in ALAD, which indicates that ALAD synthesis depends upon cytoplasmic ribosomes.     

Inhibition de ľAccumulation des Chlorophylles dans les Feuilles Primordiales de Phaseolus vulgaris après Irradiation Neutronique

Etiolated bean leaves have been irradiated in an experimental cavity of a nuclear reactor. The greening is allowed either under continuous light or under intermittent light. A relationship between the decrease of the chlorophyll accumulation and the exposure dose is observed; moreover, the accumulation of chlorophyll b is more strongly decreased than the accumulation of chlorophyll a, by irradiation. A recovery phenomenon of the chlorophyll accumulation has been observed. ?auteur remercie le Personnel du Service Exploitation du BR1 et du Département de la Physique des Réacteurs pour le concours précieux apporté, ainsi que Messieurs E. Fagniart, Y. Hauglustaine et Madame El. Bonnijns-Van Gelder pour leur collaboration technique.     

PepExplorer: A Similarity-driven Tool for Analyzing de Novo Sequencing Results

Peptide spectrum matching is the current gold standard for protein identification via mass-spectrometry-based proteomics. Peptide spectrum matching compares experimental mass spectra against theoretical spectra generated from a protein sequence database to perform identification, but protein sequences not present in a database cannot be identified unless their sequences are in part conserved. The alternative approach, de novo sequencing, can make it possible to infer a peptide sequence directly from a mass spectrum, but interpreting long lists of peptide sequences resulting from large-scale experiments is not trivial. With this as motivation, PepExplorer was developed to use rigorous pattern recognition to assemble a list of homologue proteins using de novo sequencing data coupled to sequence alignment to allow biological interpretation of the data. PepExplorer can read the output of various widely adopted de novo sequencing tools and converge to a list of proteins with a global false-discovery rate. To this end, it employs a radial basis function neural network that considers precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates, from a target-decoy database, usually obtained from phylogenetically related species. Alignments are performed using a modified Smith–Waterman algorithm tailored for the task at hand. We verified the effectiveness of our approach using a reference set of identifications generated by ProLuCID when searching for Pyrococcus furiosus mass spectra on the corresponding NCBI RefSeq database. We then modified the sequence database by swapping amino acids until ProLuCID was no longer capable of identifying any proteins. By searching the mass spectra using PepExplorer on the modified database, we were able to recover most of the identifications at a 1% false-discovery rate. Finally, we employed PepExplorer to disclose a comprehensive proteomic assessment of the Bothrops jararaca plasma, a known biological source of natural inhibitors of snake toxins. PepExplorer is integrated into the PatternLab for Proteomics environment, which makes available various tools for downstream data analysis, including resources for quantitative and differential proteomics.Very often, groundbreaking discoveries with a significant impact on the biotechnological and biomedical fields have emerged from studying “non-canonical” organisms. For example, the study of Thermus aquaticus allowed us to ultimately pave the way to modern molecular biology with the characterization of that organism''s thermostable DNA polymerase (1). The characterization of the green fluorescent protein in Aequoria victoria led to a revolution in cellular biology and to a Nobel Prize being awarded to Osamu Shimomura, Martin Chalfie, and Roger Tsien. In Brazil, Sergio Ferreira''s work on the venom of the Brazilian poisonous snake Bothrops jararaca enabled the development of the first angiotensin-converting enzyme inhibitor drug (Captopril) for the treatment of hypertension (2).In scenarios such as these, proteomics has the potential to allow a better understanding of the complexity of biological systems and the process of evolution than the study of the genetic code alone. It enables the characterization of molecular processes according to their protein content, facilitating new discoveries. In proteomics, the most frequently used strategy for protein identification is so-called peptide spectrum matching (PSM),¹ or the comparison of experimental mass spectra obtained by fragmenting peptides in a mass spectrometer to theoretical spectra generated from a sequence database. In general, the identification process follows from the sequence whose theoretical spectrum yields the highest matching score according to some empirical or probabilistic function. Examples of search engines adopting this strategy are SEQUEST (3), X!Tandem (4), and Mascot (5).Back in the 1990s, establishment of a cutoff score for confident identification relied mostly on user experience; for example, given a specific charge state, Washburn et al. established cross-correlation and deltaCn cutoff values for SEQUEST in order to allow the selection of a subset of confident identifications from LCQ data. This has since been termed “the Washburn criterion.” In what followed, target-decoy databases were implemented to allow for more sophisticated refinements in filtering the data (6). In 2007, Elias and Gygi published a seminal paper on the target-decoy approach to shotgun proteomics (7) that ultimately firmed this approach as a standard and motivated the development of several statistical filters capable of converging to a list of confident identifications satisfying a user-specified false-discovery rate (FDR) with significantly more sensitivity than the conservative Washburn criterion. Such statistical filters include mixtures of probabilities (8), quadratic discriminant analysis (9), semi-supervised learning with support vector machines (10), and Bayesian logic (11) using a semi-labeled decoy analysis to account for overfitting (12). With so many advances, the PSM workflow has become the gold standard, as it is very sensitive and the least error-prone method when a database is available with the corresponding proteins. The latter factor limits the application of PSM to those organisms for which accurate sequence databases have been established. If a peptide''s sequence is not contained within the sequence database, it cannot be identified via the PSM method. However, efforts in developing error-tolerant PSM approaches such as implemented in Mascot have made it possible to handle minor sequence modifications constrained by a simple set of rules. Nevertheless, increasing the search space in the PSM approach leads to decreased sensitivity (13).Even though the concept of computer-aided de novo sequencing predates that of PSM (14), advances in the quality of mass spectrometry data and the power of computer hardware have allowed it to reemerge at the heart of a highly active field. De novo sequencing is unbiased insofar as it is not constrained by a sequence database, and it is therefore complementary to PSM. However, it has remained the most error prone of the two methods (15). The challenges of de novo sequencing notwithstanding, a few recent and notable improvements in computer-aided de novo analysis are PepNovo (16), which combines graph theory with machine learning; pNovo+ (17), which is optimized for high-resolution HCD data; NovoHMM (18), relying on hidden Markov models for increased sensitivity; and PEAKS (19), which creates a spectrum graph model by performing dynamic programming on the mass values regardless of the presence of an observed fragment ion. By considering the complementarities of different fragmentation strategies (e.g. collision induced dissociation, electron transfer dissociation (20), and electron capture dissociation (21)), computational proteomics scientists have also demonstrated significant advances in de novo accuracy (22). In particular, the Bandeira group has continually pushed the limits and redefined the notion of what de novo sequencing can do by introducing the spectral networks paradigm (23–25). Briefly, this strategy can assemble mass spectra into spectral pairs by joining overlapping spectra obtained from sample aliquots digested by different enzymes. As a consequence, it reduces noise and significantly improves protein coverage. Its latest version also combines data from different fragmentation techniques.These algorithm developments have improved de novo sequencing, shifting the bottleneck to post-sequence processing of data. This is because the output of de novo software is a long list of highly similar full and partial peptide sequence and scores. An initial attempt to overcome these limitations consisted of a tag approach that was a hybrid of de novo sequencing and database searching: short sequence tags were derived from tandem mass spectra and used to search a sequence database (26). In what followed, a modified version based on the FASTA homology search tool was proposed for homology-driven proteomics (27). This strategy was implemented as part of the CIDentify tool, whose novelty was to account, in the alignment score, for limitations of mass spectrometry sequencing such as switching between leucine and isoleucine or other combinations of amino acids having the same mass. The next steps were taken mainly by the Shevchenko group through the introduction of the MS-Blast algorithm, which relies on a different set of scores and uses the PAM30MS substitution matrix, itself tailored for mass-spectrometry-based proteomics (28, 29). For a complete review of de novo sequencing and homology searching, we suggest Ref. 30.The current de novo post-processing paradigm presents several limitations that are similar to those of the early PSM workflow. Output files generally consist of a peptide list with corresponding scores, demanding an experienced user to assess trustworthy identifications. If the same peptide is analyzed by different mass spectrometers, different scores might be generated, which makes data comparison between different groups a challenging task. In a sense, problems are similar to those encountered when adopting the early Washburn criterion. Assembling protein information from a list of peptides is not a simple task, and usually it is not performed using state-of-the-art de novo tools. Although there are great tools for doing this at the PSM level, there is still a lack of similar tools for de novo sequencing.To tackle the aforementioned shortcomings, and in line with our strong interest in diversity-driven proteomics (29), we present a methodology for post-processing de novo sequencing data that allows inference of protein identification through statistical mapping of de novo sequencing results to a protein sequence database. Our approach begins with the use of Gotoh''s version of the Smith–Waterman algorithm, based on affine gap scoring (31) for increased scalability, to align de novo sequences against those in a target-decoy database. Then a radial basis function neural network (RBF-NN) is used to rank results according to alignment score, de novo score, precursor charge state, and peptide length. Finally, a heuristic method is used to present protein identification results in a user-friendly, interactive report. The resulting algorithm was implemented as the software PepExplorer. In essence, its goal is somewhat similar to that of post-processing tools such as DTASelect (9), Percolator (10), and SEPro (11), but with an extra layer of complexity inherent from de novo sequencing. PepExplorer can handle the output of several widely adopted de novo tools, such as PepNovo, pNovo+, and PEAKS, and accepts a generic format to enable result analysis from a broader range of tools once results are run through simple parsers. Similarly, the software accepts a series of database formats for input analysis. These features are not found in other tools. PepExplorer is freely available to the scientific community and is provided with the necessary documentation.The effectiveness of our methodology has been verified in two distinct scenarios, the first a real but controlled experiment and the other pertaining to comprehensive profiling of the plasma components of Bothrops jararaca, a venomous viper endemic to Brazil, southern Paraguay, and northern Argentina. The first scenario''s purpose was to validate the effectiveness of the tool in analyzing a published Pyrococcus furiosus dataset (11). We note that this organism is recognized by the proteomics community as well suited for benchmarking, because it allows for the rigorous testing of identification algorithms at the peptide and protein levels (32, 33). We modified the P. furiosus sequence database in such a way that no more peptides were identified via the PSM approach or another widely adopted error-tolerant search tool, Mod-A (34). We then found that we could recover protein identifications using our tool. The B. jararaca scenario has allowed us to explore uncharted territory, as this organism has an incomplete sequence database and we were therefore required to rely on those of orthologous organisms. In particular, B. jararaca plasma was chosen because it is a main research model studied at the Laboratory of Toxinology (FIOCRUZ, Brazil), and several natural inhibitors of snake toxins have already been identified/characterized from this biological matrix (35–37).     

Action de quelques phytohormones sur la respiration et ľabsorption du phosphate par des disques de tubercules de pomme de terre en survie

Action of some phytohormones on the respiration and on the absorption of phosphate by aging potato tuber discs. Discs of potato tuber incubated in aerated medium show an increase of the rates of respiration and phosphate absorption with aging time; the rates increase by two and nine respectively during the time period between 5 and 24 h of aging. Adenine or some N-6 substituted adenines [benzylaminopurine (BAP), furfurylaminopurine (FAP), methylaminopurine (MAP)], which present variable degrees of cytokinin activity, partially inhibit the increase of the rate of phosphate absorption and, to a lesser extent, the increase of the rate of respiration. Also abscisic acid (ABA), indole 3-acetic acid (IAA), and gibberellic acid (GA₃) produce inhibition of the increase of the rate of phosphate absorption with varied effects on the respiration. With regard to phosphate uptake, the effects of ABA, 1AA and GA₃ were additive to those of BAP. The effects on respiration were different from the effects on phosphate uptake, so that there is no direct relationship between inhibition of respiration and inhibition of phosphate uptake.     

Purification and partial amino acid sequence of plantaricin 1.25ααα and 1.25βββ, two bacteriocins produced by Lactobacillus plantarum TMW1.25

Two bacteriocins produced by Lactobacillus plantarum TMW1.25 have been purified by a four-step purification procedure, including ammonium sulphate precipitation and cation-exchange chromatography followed by hydrophobic-interaction chromatography on octyl sepharose. The final purification was performed by repeated reversed-phase chromatography steps which yielded two bacteriocin fractions designated plantaricin 1.25 alpha and plantaricin 1.25 beta. The molecular masses of the peptides in these fractions were 5979 and 5203 Da, respectively. Combination of the fractions did not have any synergistic effects on bacteriocin activity, indicating that they each contain a one-peptide bacteriocin. The major peptide in the alpha fraction was blocked at its N-terminus, and a partial sequence (25 residues) could only be obtained after cleavage with CNBr. This sequence did not show clear homologies with known bacteriocins. The beta peptide has been sequenced almost completely and consists, presumably, of 53 residues. This peptide displayed strong homology to the known N-terminal part of brevicin 27 produced by Lactobacillus brevis SB27. The results showed that the beta peptide contains as many as six consecutive lysine residues at the N-terminus.     

Immunohistochemical Localization of G Protein β1, β2, β3, β4, β5, and γ3 Subunits in the Adult Rat Brain

Abstract: The regional distributions of the G protein β subunits (Gβ1–β5) and of the Gγ3 subunit were examined by immunohistochemical methods in the adult rat brain. In general, the Gβ and Gγ3 subunits were widely distributed throughout the brain, with most regions containing several Gβ subunits within their neuronal networks. The olfactory bulb, neocortex, hippocampus, striatum, thalamus, cerebellum, and brainstem exhibited light to intense Gβ immunostaining. Negative immunostaining was observed in cortical layer I for Gβ1 and layer IV for Gβ4. The hippocampal dentate granular and CA1–CA3 pyramidal cells displayed little or no positive immunostaining for Gβ2 or Gβ4. No anti-Gβ4 immunostaining was observed in the pars compacta of the substantia nigra or in the cerebellar granule cell layer and Purkinje cells. Immunoreactivity for Gβ1 was absent from the cerebellar molecular layer, and Gβ2 was not detected in the Purkinje cells. No positive Gγ3 immunoreactivity was observed in the lateral habenula, lateral septal nucleus, or Purkinje cells. Double-fluorescence immunostaining with anti-Gγ3 antibody and individual anti-Gβ1–β5 antibodies displayed regional selectivity with Gβ1 (cortical layers V–VI) and Gβ2 (cortical layer I). In conclusion, despite the widespread overlapping distributions of Gβ1–β5 with Gγ3, specific dimeric associations in situ were observed within discrete brain regions.