Continuous Attractor Network Model for Conjunctive Position-by-Velocity Tuning of Grid Cells

The spatial responses of many of the cells recorded in layer II of rodent medial entorhinal cortex (MEC) show a triangular grid pattern, which appears to provide an accurate population code for animal spatial position. In layer III, V and VI of the rat MEC, grid cells are also selective to head-direction and are modulated by the speed of the animal. Several putative mechanisms of grid-like maps were proposed, including attractor network dynamics, interactions with theta oscillations or single-unit mechanisms such as firing rate adaptation. In this paper, we present a new attractor network model that accounts for the conjunctive position-by-velocity selectivity of grid cells. Our network model is able to perform robust path integration even when the recurrent connections are subject to random perturbations.     

Metabolic engineering of Escherichia coli for biosynthesis of d-galactonate

d-galactose is an attractive substrate for bioconversion. Herein, Escherichia coli was metabolically engineered to convert d-galactose into d-galactonate, a valuable compound in the polymer and cosmetic industries. d-galactonate productions by engineered E. coli strains were observed in shake flask cultivations containing 2 g L^?1 d-galactose. Engineered E. coli expressing gld coding for galactose dehydrogenase from Pseudomonas syringae was able to produce 0.17 g L^?1 d-galactonate. Inherent metabolic pathways for assimilating both d-galactose and d-galactonate were blocked to enhance the production of d-galactonate. This approach finally led to a 7.3-fold increase with d-galactonate concentration of 1.24 g L^?1 and yield of 62.0 %. Batch fermentation in 20 g L^?1 d-galactose of E. coli ?galK?dgoK mutant expressing the gld resulted in 17.6 g L^?1 of d-galactonate accumulation and highest yield of 88.1 %. Metabolic engineering strategy developed in this study could be useful for industrial production of d-galactonate.     

FastMG: a simple,fast, and accurate maximum likelihood procedure to estimate amino acid replacement rate matrices from large data sets

Background

Amino acid replacement rate matrices are a crucial component of many protein analysis systems such as sequence similarity search, sequence alignment, and phylogenetic inference. Ideally, the rate matrix reflects the mutational behavior of the actual data under study; however, estimating amino acid replacement rate matrices requires large protein alignments and is computationally expensive and complex. As a compromise, sub-optimal pre-calculated generic matrices are typically used for protein-based phylogeny. Sequence availability has now grown to a point where problem-specific rate matrices can often be calculated if the computational cost can be controlled.

Results

The most time consuming step in estimating rate matrices by maximum likelihood is building maximum likelihood phylogenetic trees from protein alignments. We propose a new procedure, called FastMG, to overcome this obstacle. The key innovation is the alignment-splitting algorithm that splits alignments with many sequences into non-overlapping sub-alignments prior to estimating amino acid replacement rates. Experiments with different large data sets showed that the FastMG procedure was an order of magnitude faster than without splitting. Importantly, there was no apparent loss in matrix quality if an appropriate splitting procedure is used.

Conclusions

FastMG is a simple, fast and accurate procedure to estimate amino acid replacement rate matrices from large data sets. It enables researchers to study the evolutionary relationships for specific groups of proteins or taxa with optimized, data-specific amino acid replacement rate matrices. The programs, data sets, and the new mammalian mitochondrial protein rate matrix are available at http://fastmg.codeplex.com.     

Heavy metal-regulated new microRNAs from rice

MicroRNAs (miRNAs) are a novel class of short, endogenous non-coding small RNAs that have the ability to base pair with their target mRNAs to repress their translation or induce their degradation in both plants and animals. To identify heavy metal stress-regulated novel miRNAs, we constructed a library of small RNAs from rice seedlings that were exposed to toxic levels of cadmium (Cd²⁺). Sequencing of the library and subsequent analysis revealed 19 new miRNAs representing six families. These cloned new rice miRNAs have sequence conservation neither in Arabidopsis nor in any other species. Most of the new rice miRNAs were up- or down-regulated in response to the metal exposure. On the base of sequence complementarity, a total of 34 miRNA targets were predicted, of which 23 targets are functionally annotated and the other 11 records belong to unknown proteins. Some predicted targets of miRNAs are associated with the regulation of the response to heavy metal-induced stresses. In addition to the new miRNAs, we detected nine previously reported miRNAs and 56 other novel endogenous small RNAs in rice. These findings suggest that the number of new miRNAs in rice is unsaturated and some of them may play critical roles in plant responses to environmental stresses.     

Selective Anchoring of DNA Methyltransferases 3A and 3B to Nucleosomes Containing Methylated DNA

Proper DNA methylation patterns are essential for mammalian development and differentiation. DNA methyltransferases (DNMTs) primarily establish and maintain global DNA methylation patterns; however, the molecular mechanisms for the generation and inheritance of methylation patterns are still poorly understood. We used sucrose density gradients of nucleosomes prepared by partial and maximum micrococcal nuclease digestion, coupled with Western blot analysis to probe for the interactions between DNMTs and native nucleosomes. This method allows for analysis of the in vivo interactions between the chromatin modification enzymes and their actual nucleosomal substrates in the native state. We show that little free DNA methyltransferase 3A and 3B (DNMT3A/3B) exist in the nucleus and that almost all of the cellular contents of DNMT3A/3B, but not DNMT1, are strongly anchored to a subset of nucleosomes. This binding of DNMT3A/3B does not require the presence of other well-known chromatin-modifying enzymes or proteins, such as proliferating cell nuclear antigen, heterochromatin protein 1, methyl-CpG binding protein 2, Enhancer of Zeste homolog 2, histone deacetylase 1, and UHRF1, but it does require an intact nucleosomal structure. We also show that nucleosomes containing methylated SINE and LINE elements and CpG islands are the main sites of DNMT3A/3B binding. These data suggest that inheritance of DNA methylation requires cues from the chromatin component in addition to hemimethylation.Proper DNA methylation patterns are essential for mammalian development and differentiation. More than three decades ago, de novo cytosine DNA methylation and its maintenance were proposed to exist in eukaryotic cells (29, 54); however, the molecular mechanisms for the generation and inheritance of methylation patterns are still poorly understood. DNA methyltransferases (DNMTs) DNMT1, DNMT3A, and DNMT3B primarily establish and maintain global DNA methylation patterns (39, 48). DNMT1 preferentially methylates hemimethylated DNA in vitro (7) and is tethered to replication foci during S phase (38). In contrast, DNMT3A and DNMT3B (DNMT3A/3B) have no preference for hemimethylated DNA (49) and are required for de novo methylation of genomic DNA (48). It has been thought that DNMT1 acts mainly as a “maintenance methyltransferase” during DNA synthesis and that DNMT3A and DNMT3B act as “de novo” enzymes. However, more recent studies indicate that DNMT1 may also be required for de novo methylation of genomic DNA (17, 30) and that DNMT3A/3B are also required for maintenance functions (11, 40, 55). Furthermore, the different DNMTs cooperate in maintaining the methylation of some regions of the genome, particularly repetitive elements (40, 53).Recruitment of individual DNMT enzymes to different regions of chromatin in vivo, particularly to gene regulatory regions, may require interaction with auxiliary factors (28, 36). DNMT1, which is diffusely localized throughout nuclei in non-S-phase cells (38), is targeted to replication foci by interacting with proliferating cell nuclear antigen (PCNA) (15) and also physically interacts with UHRF1 (ubiquitinlike, containing PHD and RING finger domains 1) that binds to hemimethylated DNA (3, 4, 8, 27, 62). DNMT3 enzymes are usually found localized to heterochromatin regions in most transient-expression assays (5, 12). As genomic DNA in chromatin is packaged into nucleosomes which might limit the accessibility of target sites to the enzymes, the interaction of DNMTs with nucleosomes in a chromatin context is important for the regulation of genomic methylation.Genetic and biochemical studies have provided many insights into the distinct and cooperative functions of the DNMT enzymes; however, few of these studies have addressed how they interact with chromatin in vivo. Recombinant DNMT1 and DNMT3 enzymes can methylate the CpG sites on nucleosomes assembled in vitro (26, 50, 56, 65). Recently DNMT3L has been found to connect DNMT3A2 to nucleosomes in embryonic stem cells (52). However, DNMT3L is expressed only during gametogenesis and embryonic stages (1, 9), suggesting that other mechanisms might be necessary for directing the enzyme to specific chromatin regions in somatic cells.In the present study, we investigated how different DNMT enzymes interact with chromatin at the nucleosomal level in somatic cell lines. Micrococcal nuclease (MNase) treatment of nuclei in a low-ionic-strength buffer digests nucleosomal linker DNA regions, thereby minimizing the disruption of protein complexes on the nucleosomes. We prepared nucleosomes from partial or maximum MNase-digested nuclei and resolved them on sucrose density gradients to analyze their interactions with chromatin proteins. The results indicate that while DNMT1 interacts primarily with linker DNA, DNMT3A/3B enzymes interact strongly with nucleosomes containing methylated repetitive elements and also containing methylated CpG islands (CGIs) and may not require additional proteins for this strong binding. These data are particularly intriguing in that they provide insights into the mechanisms of the interaction of DNMTs with chromatin and maintenance of DNA methylation in somatic cells.