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Hybridoma technology has been valuable in the development of therapeutic antibodies. More recently, antigen-specific B-cell selection and display technologies are also gaining importance. A major limitation of these approaches used for antibody discovery is the extensive process of cloning and expression involved in transitioning from antibody identification to validating the function, which compromises the throughput of antibody discovery. In this study, we describe a process to identify and rapidly re-format and express antibodies for functional characterization. We used two different approaches to isolate antibodies to five different targets: 1) flow cytometry to identify antigen-specific single B cells from the spleen of immunized human immunoglobulin transgenic mice; and 2) panning of phage libraries. PCR amplification allowed recovery of paired V H and V L sequences from 79% to 96% of antigen-specific B cells. All cognate V H and V L transcripts were formatted into transcription and translation compatible linear DNA expression cassettes (LEC) encoding whole IgG or Fab. Between 92% and 100% of paired V H and V L transcripts could be converted to LECs, and nearly 100% of them expressed as antibodies when transfected into Expi293F cells. The concentration of IgG in the cell culture supernatants ranged from 0.05 µg/ml to 145.8 µg/ml (mean = 18.4 µg/ml). Antigen-specific binding was displayed by 78-100% of antibodies. High throughput functional screening allowed the rapid identification of several functional antibodies. In summary, we describe a plasmid-free system for cloning and expressing antibodies isolated by different approaches, in any format of choice for deep functional screening that can be applied in any research setting during antibody discovery.

This study was investigated to compare the natural variation of nutrients in rice variety by different environmental factors. Fifteen kinds of rices were used, which were cultivated in two locations for 2 years. All data were analyzed by the various statistical tools to identify the nutritional variations of nutrients. The results of variable importance in the prediction analysis were found to be consistent with the % variability. The nutrient compositions most affected by variety were fatty acids, and next were vitamins, proximate nutrients, minerals, and amino acids in order. The nutrient compositions most affected by location were proximate, followed by minerals, vitamins, fatty acids, and amino acids. For cultivation year, vitamins were most affected and then minerals, fatty acids, proximate nutrients, and amino acids in order. These findings could explain that each kind of nutrients can be naturally varied by different environmental factors.