Biomedical subjects

Jonas S Almeida

Publications and source records attributed to Jonas S Almeida.

5 recordsLinked to original sources

PubMed2003

Roles for sphingolipid biosynthesis in mediation of specific programs of the heat stress response determined through gene expression profiling.

Previous studies have demonstrated roles for de novo production of sphingolipids in Saccharomyces cerevisiae in the regulation of the transient cell cycle arrest and nutrient permease degradation associated with the heat stress response, suggesting multiple functions for yeast sphingolipids in this response. We, therefore, sought to determine the generalized involvement of sphingolipids in the heat stress response by using microarray hybridization of RNA isolated from heat-stressed cultures of the mutant strain lcb1-100, which is unable to produce sphingolipids in response to heat. Approximately 70 genes showed differential regulation during the first 15 min of heat stress in the lcb1-100 strain compared with the wild type strain, indicating a requirement for de novo sphingolipid biosynthesis for proper regulation of these genes during heat stress. Grouping these genes into functional categories revealed several pathways, including some in which sphingolipids were previously suspected to play a role, such as stress response pathways and cell cycle regulation. Hierarchical clustering analysis revealed sphingolipid involvement in regulation of tRNA synthesis and metabolic genes and transporters. Additionally, the microarray results demonstrated novel sphingolipid involvement in transcriptional regulation of pathways of translation and cell wall organization and biogenesis. Our results demonstrate a broad-reaching effect of sphingolipids in the yeast heat stress response and suggest that the mechanism of sphingolipid involvement in several cellular pathways occurs via sphingolipid-mediated regulation of message levels.

Amino Acids↗

PubMed2003

Autoinducer 2 production by Streptococcus gordonii DL1 and the biofilm phenotype of a luxS mutant are influenced by nutritional conditions.

The luxS gene, present in many bacterial genera, encodes the autoinducer 2 (AI-2) synthase. AI-2 has been implicated in bacterial signaling, and this study investigated its role in biofilm formation by Streptococcus gordonii, an organism that colonizes human tooth enamel within the first few hours after professional cleaning. Northern blotting and primer extension analyses revealed that S. gordonii luxS is monocistronic. AI-2 production was dependent on nutritional conditions, and maximum AI-2 induction was detected when S. gordonii was grown in the presence of serum and carbonate. In planktonic cultures, AI-2 production rose sharply during the transition from exponential to stationary phase, and the AI-2 concentration peaked approximately 4 h into stationary phase. An S. gordonii luxS mutant that did not produce AI-2 was constructed by homologous recombination. Complementation of the mutant by insertion of an intact luxS gene into the chromosome in tandem with the disrupted gene restored AI-2 production to a level similar to that of the wild-type strain. In planktonic culture, no growth differences were observed between the mutant and wild-type strains when five different media were used. However, when grown for 4 h as biofilms in 25% human saliva under flow, the luxS mutant formed tall microcolonies that differed from those formed by the wild-type and complemented mutant strains. Biofilms of the luxS mutant exhibited finger-like projections of cells that extended into the flow cell lumen. Thus, the inability to produce AI-2 is associated with altered microcolony architecture within S. gordonii biofilms formed in saliva during a time frame consistent with initial colonization of freshly cleaned enamel surfaces.

Bacterial Proteins↗

PubMed2002

Efficient Boolean implementation of universal sequence maps (bUSM).

BACKGROUND: Recently, Almeida and Vinga offered a new approach for the representation of arbitrary discrete sequences, referred to as Universal Sequence Maps (USM), and discussed its applicability to genomic sequence analysis. Their work generalizes and extends Chaos Game Representation (CGR) of DNA for arbitrary discrete sequences. RESULTS: We have considered issues associated with the practical implementation of USMs and offer a variation on the algorithm that: 1) eliminates the overestimation of similar segment lengths, 2) permits the identification of arbitrarily long similar segments in the context of finite word length coordinate representations, 3) uses more computationally efficient operations, and 4) provides a simple conversion for recovering the USM coordinates. Computational performance comparisons and examples are provided. CONCLUSIONS: We have shown that the desirable properties of the USM encoding of nucleotide sequences can be retained in a practical implementation of the algorithm. In addition, the proposed implementation enables determination of local sequence identity at increased speed.

Algorithms↗

PubMed2002

Universal sequence map (USM) of arbitrary discrete sequences.

BACKGROUND: For over a decade the idea of representing biological sequences in a continuous coordinate space has maintained its appeal but not been fully realized. The basic idea is that any sequence of symbols may define trajectories in the continuous space conserving all its statistical properties. Ideally, such a representation would allow scale independent sequence analysis--without the context of fixed memory length. A simple example would consist on being able to infer the homology between two sequences solely by comparing the coordinates of any two homologous units. RESULTS: We have successfully identified such an iterative function for bijective mapping psi of discrete sequences into objects of continuous state space that enable scale-independent sequence analysis. The technique, named Universal Sequence Mapping (USM), is applicable to sequences with an arbitrary length and arbitrary number of unique units and generates a representation where map distance estimates sequence similarity. The novel USM procedure is based on earlier work by these and other authors on the properties of Chaos Game Representation (CGR). The latter enables the representation of 4 unit type sequences (like DNA) as an order free Markov chain transition table. The properties of USM are illustrated with test data and can be verified for other data by using the accompanying web-based tool:http://bioinformatics.musc.edu/~jonas/usm/. CONCLUSIONS: USM is shown to enable a statistical mechanics approach to sequence analysis. The scale independent representation frees sequence analysis from the need to assume a memory length in the investigation of syntactic rules.

Algorithms↗

PubMed2002

Predictive non-linear modeling of complex data by artificial neural networks.

An artificial neural network (ANN) is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from experimental data. The use of ANNs has gained increasing popularity for applications where a mechanistic description of the dependency between dependent and independent variables is either unknown or very complex. This machine learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from experimental data. The application of ANNs to complex relationships makes them highly attractive for the study of biological systems. Recent applications include the analysis of expression profiles and genomic and proteomic sequences.

Biochemical Phenomena↗