Growing plants with modified cell wall compositions is a promising strategy to improve resistance to pathogens, increase biomass digestibility, and tune other important properties. In order to alter biomass architecture, a detailed knowledge of cell wall structure and biosynthesis is a prerequisite. We report here a glycan array‐based assay for the high‐throughput identification and characterization of plant cell wall biosynthetic glycosyltransferases (GTs). We demonstrate that different heterologously expressed galactosyl‐, fucosyl‐, and xylosyltransferases can transfer azido‐functionalized sugar nucleotide donors to selected synthetic plant cell wall oligosaccharides on the array and that the transferred monosaccharides can be visualized “on chip” by a 1,3‐dipolar cycloaddition reaction with an alkynyl‐modified dye. The opportunity to simultaneously screen thousands of combinations of putative GTs, nucleotide sugar donors, and oligosaccharide acceptors will dramatically accelerate plant cell wall biosynthesis research.

Summary There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER’s applicability to both simulated and experimental data and provide performance comparison with other existing approaches.

The mechanistic basis of the target-site preference of lentivirus DNA integration is not well understood. In the present in silico study, we describe the integrational profile of simultaneous HIV-1 and HIV-2 infection. A total of 352 genomic DNA sequences from human peripheral blood mononuclear cells (PBMCs) obtained from GenBank and possessing the 5' LTR of HIV were used to characterize the structure and composition of local chromatin associated with high frequency integration sites. These sequences were aligned with the draft human genome (hg18) using BLAST (NCBI) and BLAT (UCSC) in order to derive information about chromosome localization, functional aspects of coding protein genes, CpG island number, and repetitive elements flanking integration sites. No significant differences in the integrational profile between HIV-1 and HIV-2 were found. However, we observed a tendency in both lentiviruses to integrate in the vicinity of protein coding genes. Multiple regression analysis showed a strong correlation between the number of genes and the number of CpG islands in regions with high integration frequency, mainly in chromosome 17 (R = 0.95, p < 0.05). Our results provide strong evidence that HIV-1 and HIV-2 have common genomic environments in the local chromatin regions with high gene density and CpG islands. The understanding of local genomic environments with a high frequency of integration would be the starting point to develop novel antiviral strategies for lentiviral infection.