Lectins.

M Vijayan; N Chandra

doi:10.1016/s0959-440x(99)00034-2

Home/Archive/PubMed

PubMed · 10607664

Lectins.

M Vijayan·N Chandra

View original source ↗Export metadata ↓

Abstract

Lectins - carbohydrate-binding proteins involved in a variety of recognition processes - exhibit considerable structural diversity. Three new lectin folds and further elaborations of known folds have been described recently. Large variability in quaternary association resulting from small alterations in essentially the same tertiary structure is a property exhibited specially by legume lectins. The strategies used by lectins to generate carbohydrate specificity include the extensive use of water bridges, post-translational modification and oligomerization. Recent results pertaining to influenza and foot-and-mouth viruses further elaborate the role of lectins in infection.

Explore related subjects

Carbohydrate Metabolism Foot-and-Mouth Disease Virus Lectins Models, Molecular Orthomyxoviridae Protein Folding Protein Structure, Quaternary Journal Article Research Support, Non-U.S. Gov't Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

M Vijayan, N Chandra. 1999. Lectins.. https://doi.org/10.1016/s0959-440x(99)00034-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Hybrid genome assembly and phenotypic assays reveal carbohydrate metabolism diversity in Lacticaseibacillus strains.

Investigation of carbohydrate metabolism in lactic acid bacteria is essential for the rational selection of strains for fermentation processes, particularly in emerging applications involving non-conventional substrates or building of synthetic microbial consortia. However, establishing robust genotype-phenotype relationships remains challenging, as gene presence alone often fails to explain observed metabolic traits without considering the genomic context and regulatory architecture. In the present study, we combined hybrid genome assembly (Illumina and Oxford Nanopore) with high-throughput phenotype profiling (Biolog GENIII and PM2A) to investigate carbohydrate utilization in five Lacticaseibacillus strains. Phenotypic assays revealed clear intra- and inter-specific variability in substrate utilization. We therefore investigated whether such differences could be attributed to the organization and regulatory context of carbohydrate-associated loci, rather than to gene presence alone. Functional annotation based on COG and CAZyme databases revealed candidate genomic regions potentially involved in carbohydrate metabolism. Comparative analysis between predicted and experimentally observed substrate usage highlighted specific loci associated with carbohydrate utilization profile. The trehalose (tre) operon was conserved across all strains, while at least two distinct cellobiose-associated loci were detected in each genome. Despite the presence of these loci, L. paracasei strains were unable to metabolize cellobiose, a phenotype likely linked to the presence of a downstream TetR-type transcriptional repressor within the cellobiose (cel) operon. Additionally, a genomic region uniquely found in L. rhamnosus strains was associated with gentiobiose utilization, consistent with phenotypic observations. Overall, these findings highlight the importance of integrating phenotypic validation with complete genome context to support the identification of candidate structural and regulatory determinants of carbohydrate utilization in lactic acid bacteria. KEY POINTS: • Phenotype microarrays reveal metabolic traits of interest in isolated strains. • Regulatory context is key to understanding carbohydrate metabolism differences. • Basis of subspecies-dependent cellobiose metabolism in L. paracasei is provided.

Carbohydrate Metabolism↗

PubMed2025

The genome of the polyextremophilic yeast, Naganishia friedmannii, reveals adaptations involved in stress response pathways, carbohydrate metabolism expansion, and a limited DNA repair repertoire.

Here we report the draft genome sequence of Naganishia friedmannii (formerly Cryptococcus friedmannii) isolate, a Basidiomycota yeast commonly found in some of the most extreme environments of the Earth's cryosphere. We isolated N. friedmannii strain Llullensis from soils at 6000 m above sea level on Volcán Llullaillaco, Argentina. The genome was 22.2 Mb with 6251 identified protein coding genes. Proteins known to be associated with thermal, osmotic, and radiation stress were identified in the genome. Comparative analysis with seven other Naganishia genomes revealed unique features underlying its polyextremophilic lifestyle. Naganishia friedmannii showed an expansion of genes involved in breaking down plant-derived carbohydrates, supporting the hypothesis that it survives at high elevations by metabolizing wind-deposited organic matter. Surprisingly, many genes involved in cell-cycle checkpoints and DNA repair were missing, as in several other Naganishia species. This extensive loss may be adaptive in extreme environments prone to abiotic stress, where a high mutation rate could generate advantageous traits, and reduced cell-cycle control may allow for faster reproduction that would be advantageous for rapid growth during brief periods of soil wetting following rare snow events.

Carbohydrate Metabolism↗

PubMed2001

Using historical data for bioprocess optimization: modeling wine characteristics using artificial neural networks and archived process information.

Optimization of fermentation processes is a difficult task that relies on an understanding of the complex effects of processing inputs on productivity and quality outputs. Because of the complexity of these biological systems, traditional optimization methods utilizing mathematical models and statistically designed experiments are less effective, especially on a production scale. At the same time, information is being collected on a regular basis during the course of normal manufacturing and process development that is rarely fully utilized. We are developing an optimization method in which historical process data is used to train an artificial neural network for correlation of processing inputs and outputs. Subsequently, an optimization routine is used in conjunction with the trained neural network to find optimal processing conditions given the desired product characteristics and any constraints on inputs. Wine processing is being used as a case study for this work. Using data from wine produced in our pilot winery over the past 3 years, we have demonstrated that trained neural networks can be used successfully to predict the yeast-fermentation kinetics, as well as chemical and sensory properties of the finished wine, based solely on the properties of the grapes and the intended processing. To accomplish this, a hybrid neural network training method, Stop Training with Validation (STV), has been developed to find the most desirable neural network architecture and training level. As industrial historical data will not be evenly spaced over the entire possible search space, we have also investigated the ability of the trained neural networks to interpolate and extrapolate with data not used during training. Because a company will utilize its own existing process data for this method, the result of this work will be a general fermentation optimization method that can be applied to fermentation processes to improve quality and productivity.

Carbohydrate Metabolism↗