Search for a Shared Genetic or Biochemical Basis for Biofilm Tolerance to Antibiotics across Bacterial Species.

Is there a universal genetically programmed defense providing tolerance to antibiotics when bacteria grow as biofilms? A comparison between biofilms of three different bacterial species by transcriptomic and metabolomic approaches uncovered no evidence of one. Single-species biofilms of three bacterial species (Pseudomonas aeruginosa, Staphylococcus aureus, and Acinetobacter baumannii) were grown in vitro for 3 days and then challenged with respective antibiotics (ciprofloxacin, daptomycin, and tigecycline) for an additional 24 h. All three microorganisms displayed reduced susceptibility in biofilms compared to planktonic cultures. Global transcriptomic profiling of gene expression comparing biofilm to planktonic and antibiotic-treated biofilm to untreated biofilm was performed. Extracellular metabolites were measured to characterize the utilization of carbon sources between biofilms, treated biofilms, and planktonic cells. While all three bacteria exhibited a species-specific signature of stationary phase, no conserved gene, gene set, or common functional pathway could be identified that changed consistently across the three microorganisms. Across the three species, glucose consumption was increased in biofilms compared to planktonic cells, and alanine and aspartic acid utilization were decreased in biofilms compared to planktonic cells. The reasons for these changes were not readily apparent in the transcriptomes. No common shift in the utilization pattern of carbon sources was discerned when comparing untreated to antibiotic-exposed biofilms. Overall, our measurements do not support the existence of a common genetic or biochemical basis for biofilm tolerance against antibiotics. Rather, there are likely myriad genes, proteins, and metabolic pathways that influence the physiological state of individual microorganisms in biofilms and contribute to antibiotic tolerance.

Physiological dynamic compression regulates central energy metabolism in primary human chondrocytes.

Chondrocytes use the pathways of central metabolism to synthesize molecular building blocks and energy for cartilage homeostasis. An interesting feature of the in vivo chondrocyte environment is the cyclical loading generated in various activities (e.g., walking). However, it is unknown whether central metabolism is altered by mechanical loading. We hypothesized that physiological dynamic compression alters central metabolism in chondrocytes to promote production of amino acid precursors for matrix synthesis. We measured the expression of central metabolites (e.g., glucose, its derivatives, and relevant co-factors) for primary human osteoarthritic chondrocytes in response to 0-30 minutes of compression. To analyze the data, we used principal components analysis and ANOVA-simultaneous components analysis, as well as metabolic flux analysis. Compression-induced metabolic responses consistent with our hypothesis. Additionally, these data show that chondrocyte samples from different patient donors exhibit different sensitivity to compression. Most importantly, we find that grade IV osteoarthritic chondrocytes are capable of synthesizing non-essential amino acids and precursors in response to mechanical loading. These results suggest that further advances in metabolic engineering of chondrocyte mechanotransduction may yield novel translational strategies for cartilage repair.

Combining Targeted Metabolomic Data with a Model of Glucose Metabolism: Toward Progress in Chondrocyte Mechanotransduction.

Osteoarthritis is a debilitating disease likely involving altered metabolism of the chondrocytes in articular cartilage. Chondrocytes can respond metabolically to mechanical loads via cellular mechanotransduction, and metabolic changes are significant because they produce the precursors to the tissue matrix necessary for cartilage health. However, a comprehensive understanding of how energy metabolism changes with loading remains elusive. To improve our understanding of chondrocyte mechanotransduction, we developed a computational model to calculate the rate of reactions (i.e. flux) across multiple components of central energy metabolism based on experimental data. We calculated average reaction flux profiles of central metabolism for SW1353 human chondrocytes subjected to dynamic compression for 30 minutes. The profiles were obtained solving a bounded variable linear least squares problem, representing the stoichiometry of human central energy metabolism. Compression synchronized chondrocyte energy metabolism. These data are consistent with dynamic compression inducing early time changes in central energy metabolism geared towards more active protein synthesis. Furthermore, this analysis demonstrates the utility of combining targeted metabolomic data with a computational model to enable rapid analysis of cellular energy utilization.

A new method for mapping discontinuous antibody epitopes to reveal structural features of proteins.

Antibodies that bind to protein surfaces of interest can be used to report the three-dimensional structure of the protein as follows: Proteins are composed of linear polypeptide chains that fold together in complex spatial patterns to create the native protein structure. These folded structures form binding sites for antibodies. Antibody binding sites are typically "assembled" on the protein surface from segments that are far apart in the primary amino acid sequence of the target proteins. Short amino acid probe sequences that bind to the active region of each antibody can be used as witnesses to the antibody epitope surface and these probes can be efficiently selected from random sequence peptide libraries. This paper presents a new method to align these antibody epitopes to discontinuous regions of the one-dimensional amino acid sequence of a target protein. Such alignments of the epitopes indicate how segments of the protein sequence must be folded together in space and thus provide long-range constraints for solving the 3-D protein structure. This new antibody-based approach is applicable to the large fraction of proteins that are refractory to current approaches for structure determination and has the additional advantage of requiring very small amounts of the target protein. The binding site of an antibody is a surface, not just a continuous linear sequence, so the epitope mapping alignment problem is outside the scope of classical string alignment algorithms, such as Smith-Waterman. We formalize the alignment problem that is at the heart of this new approach, prove that the epitope mapping alignment problem is NP-complete, and give some initial results using a branch-and-bound algorithm to map two real-life cases. Initial results for two validation cases are presented for a graph-based protein surface neighbor mapping procedure that promises to provide additional spatial proximity information for the amino acid residues on the protein surface.