Modes of deformation of walled cells.

The bewildering morphological diversity found in cells is one of the starkest illustrations of life's ability to self-organize. Yet the morphogenetic mechanisms that produce the multifarious shapes of cells are still poorly understood. The shared similarities between the walled cells of prokaryotes, many protists, fungi, and plants make these groups particularly appealing to begin investigating how morphological diversity is generated at the cell level. In this review, I attempt a first classification of the different modes of surface deformation used by walled cells. Five modes of deformation were identified: inextensional bending, equi-area shear, elastic stretching, processive intussusception, and chemorheological growth. The two most restrictive modes-inextensional and equi-area deformations-are embodied in the exine of pollen grains and the wall-like pellicle of euglenoids, respectively. For these modes, it is possible to express the deformed geometry of the cell explicitly in terms of the undeformed geometry and other easily observable geometrical parameters. The greatest morphogenetic power is reached with the processive intussusception and chemorheological growth mechanisms that underlie the expansive growth of walled cells. A comparison of these two growth mechanisms suggests a possible way to tackle the complexity behind wall growth.

Imaging living cells surface and quantifying its properties at high resolution using AFM in QI™ mode.

Since the last 10 years, AFM has become a powerful tool to study biological samples. However, the classical modes offered (imaging or tapping mode) often damage sample that are too soft or loosely immobilized. If imaging and mechanical properties are required, it requests long recording time as two different experiments must be conducted independently. In this study we compare the new QI™ mode against contact imaging mode and force volume mode, and we point out its benefit in the new challenges in biology on six different models: Escherichia coli, Candida albicans, Aspergillus fumigatus, Chinese hamster ovary cells and their isolated nuclei, and human colorectal tumor cells.

Relative importance of nutrients and mortality factors on prokaryotic community composition in two lakes of different trophic status: microcosm experiments.

The effect of nutrient resources (N and P enrichment) and of different grazing communities on the prokaryotic community composition (PCC) was investigated in two freshwater ecosystems: Sep reservoir (oligomesotrophic) and lake Aydat (eutrophic). An experimental approach using microcosms was chosen, that allowed control of both predation levels, by size fractionation of predators, and resources, by nutrient amendments. Changes in PCC were monitored by fluorescent in situ hybridization (FISH) and terminal-restriction fragment length polymorphism (T-RFLP). The main mortality agents were (i) heterotrophic nanoflagellates and virus-like particles in Aydat and (ii) cladocerans in Sep. All the nutritional elements assayed (N-NO3, P-PO4 and N-NH4) together with prokaryotic production (PP) always accounted for a significant part of the variations in PCC. Overall, prokaryotic diversity was mainly explained by resources in Sep, by a comparable contribution of resources and mortality factors in lake Aydat and, to a lesser extent, by the combined action of both.

Prokaryotic K(+) channels: from crystal structures to diversity.

The deep roots and wide branches of the K(+)-channel family are evident from genome surveys and laboratory experimentation. K(+)-channel genes are widespread and found in nearly all the free-living bacteria, archaea and eukarya. The conservation of basic structures and mechanisms such as the K(+) filter, the gate, and some of the gate's regulatory domains have allowed general insights on animal K(+) channels to be gained from crystal structures of prokaryotic channels. Since microbes are the great majority of life's diversity, it is not surprising that microbial genomes reveal structural motifs beyond those found in animals. There are open-reading frames that encode K(+)-channel subunits with unconventional filter sequences, or regulatory domains of different sizes and numbers not previously known. Parasitic or symbiotic bacteria tend not to have K(+) channels, while those showing lifestyle versatility often have more than one K(+)-channel gene. It is speculated that prokaryotic K(+) channels function to allow adaptation to environmental and metabolic changes, although the actual roles of these channels in prokaryotes are not yet known. Unlike enzymes in basic metabolism, K(+) channel, though evolved early, appear to play more diverse roles than revealed by animal research. Finding and sorting out these roles will be the goal and challenge of the near future.

[Role of hypothalamic nonapeptides in interaction of prokaryotic and eukaryotic cells]. / Rol' gipotalamicheskikh nonapeptidov vo vzaimodeistvii pro- i éukarioticheskikh kletok.

The role of hypothalamic nonapeptides in the interaction of prokaryotic and eukaryotic cells was studied in the experimental setting. Nonapeptides were found to stimulate the adaptive and regenerative properties of eukaryotic cells and they are likely to have an antimicrobic effect on prokaryotic ones. The paper discusses the modulating role of nonapeptides in the establishment of symbiotic relations in the bacterial agent-host system.

A complementary circular code in the protein coding genes.

Recently, shifted periodicities 1 modulo 3 and 2 modulo 3 have been identified in protein (coding) genes of both prokaryotes and eukaryotes with autocorrelation functions analysing eight of 64 trinucleotides (Arquès et al., 1995). This observation suggests that the trinucleotides are associated with frames in protein genes. In order to verify this hypothesis, a distribution of the 64 trinucleotides AAA,..., TTT is studied in both gene populations by using a simple method based on the trinucleotide frequencies per frame. In protein genes, the trinucleotides can be read in three frames: the reading frame 0 established by the ATG start trinucleotide and frame 1 (resp. 2) which is the frame 0 shifted by 1 (resp. 2) nucleotide in the 5'-3' direction. Then, the occurrence frequencies of the 64 trinucleotides are computed in the three frames. By classifying each of the 64 trinucleotides in its preferential occurrence frame, i.e. the frame associated with its highest frequency, three subsets of trinucleotides can be identified in the three frames. This approach is applied in the two gene populations. Unexpectedly, the same three subsets of trinucleotides are identified in these two gene populations: Tzero = Xzero [symbol: see text] {AAA,TTT} with Xzero = {AAC,AAT,ACC,ATC,ATT, CAG,CTC,CTG,GAA,GAC,GAG, GAT,GCC,GGC,GGT,GTA,GTC,GTT,TAC,TTC} in frame 0, T1 = X1 [symbol: see text] {CCC} in frame 1 and T2 = X2 [symbol: see text] {GGG} in frame 2, each subset Xzero, X1 and X2 having 20 trinucleotides. Surprisingly, these three subsets have five important properties: (i) the property of maximal circular code for Xzero (resp. X1, X2) allowing the automatical retrieval of frame 0 (resp. 1, 2) in any region of a protein gene model (formed by a series of trinucleotides of Xzero) without using a start codon; (ii) the DNA complementarity property C (e.g. C(AAC) = GTT): C(T0) = T0, C(T1) = T2 and C(T2) = T1 allowing the two paired reading frames of a DNA double helix simultaneously to code for amino acids; (iii) the circular permutation property P (e.g. P(AAC) = ACA): P(Xzero) = X1 and P(X1) = X2 implying that the two subsets X1 and X2 can be deduced from Xzero; (iv) the rarity property with an occurrence probability of Xzero equal to 6 x 10(-8); and (v) the concatenation property with: a high frequency (27.5%) of misplaced trinucleotides in the shifted frames, a maximum (13 nucleotides) length of the minimal window to automatically retrieve the frame and an occurrence of the four types of nucleotides in the three trinucleotides sites, in favour of an evolutionary code. In the Discussion, the identified subsets Tzero, T1 and T2 replaced in the three two-letter genetic alphabets purine/pyrimidine, amino/ceto and strong/weak interaction, allow us to deduce that the RNY model (R = purine = A or G, Y = pyrimidine = C or T, N = R or Y) (Eigen & Schuster, 1978) is the closest two-letter codon model to the trinucleotides of Tzero. Then, these three subsets are related to the genetic code. The trinucleotides of Tzero code for 13 amino acids: Ala, Asn, Asp, Gln, Glu, Gly, Ile, Leu, Lys, Phe, Thr, Tyr and Val. Finally, a strong correlation between the usage of the trinucleotides of Tzero in protein genes and the amino acid frequencies in proteins is observed as six among seven amino acids not coded by Tzero, have as expected the lowest frequencies in proteins of both prokaryotes and eukaryotes.