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Cellulose has been developed as an alternative to petrochemical materials. By comparison with refined nanofiber (RCNF), lignocellulose nanofiber (LCNF) shows particular promise because it is produced from biomass using only mild pretreatment. The mechanical properties of LCNF depends on the contained lignin. However, the microscopic location of the lignin contained in LCNF has not been determined. Thus, we developed two methods to detect and visualize lignin. One uses a scanning transmission electron microscope (STEM) equipped with an energy dispersive X-ray spectroscopy (EDS) detector. The other method uses an atomic force microscope (AFM) equipped with a cantilever coated with an aromatic molecule. Both methods revealed that the lignin in LCNF covers a thin cellulose fiber and is precipitated in a grained structure. In particular, the AFM system was able to determine the nanoscopic location of lignin-rich areas. The present study establishes a strong tool for analyzing the characteristics of lignin-containing materials.

Engineering the microbial production of secondary metabolites is limited by the known reactions of correctly annotated enzymes in sequence databases. To expand the range of biosynthesis pathways, machine learning is herein demonstrated for the discovery of missing link enzymes, using benzylisoquinoline alkaloid production as a model application with potential to revolutionize the paradigm of sustainable biomanufacturing. Bacterial studies utilize a tetrahydropapaveroline pathway, whereas plants are reported to contain a more stable norcoclaurine pathway, which is exploited in yeast. However, committed aromatic precursors are currently produced by microbial enzymes that remain elusive in plants, and additional downstream missing links are hidden within highly-duplicated plant gene families. Accordingly, the machine learning enzyme selection algorithm is first applied to predict the missing links from diverse candidate sequences. Metabolomics-based characterization of selected sequences reveals distinct oxidases and carboxy-lyases in reconstructed plant gene-only benzylisoquinoline alkaloid pathways from tyrosine. Synergistic application of aryl acetaldehyde producing enzymes results in enhanced production through hybrid norcoclaurine and tetrahydropapaveroline pathways. Transplantation of features into homologous enzyme templates leads to the highest levels of bacterial norcoclaurine and N-methylcoclaurine. Mechanism-directed isotope tracing patterns confirm alternative flux branches from aromatic precursors to alkaloids. This machine learning-driven workflow can be adapted to numerous pathways.

A sensing system was constructed to monitor the target peptide via two aptamer-based sensors pinching. First, aptamers against somatostatin (SST) were selected via the systematic evolution of ligands by exponential enrichment, and four aptamers were selected from a single-stranded DNA library. Their specificities to SST were evaluated via surface plasmon resonance and atomic force microscopy (AFM). Next, two aptamers with higher specificities to SST were used as aptamer-based sensors; one aptamer was modified with a chip, and the other was modified with a probe. Based on AFM system, the probe was surveyed on the chip in SST solutions, simultaneously measuring an interactive force. The label-free SST could be detected, and then the change in its concentration could be monitored at levels that ranged from 2 to 2000 nM. The interactive force of a single pair was approximately 45 pN, and the molecule number was associated with the interactive force. Therefore, we could firstly select the aptamers against somatostatin, and the sandwich-like monitoring system can be used to promote peptide sensor or monitoring system using an aptamer-based sensor.

A split aptamers-based aptasensor is applied to peptide sensing using atomic force microscopy (AFM). Somatostatin (SST) is targeted as a model peptide. The aptamer against SST is split, and each part is chemically modified with a probe or a chip. Hybridized aptamers can detect label-free SSTs and monitor changes in their concentration from 2 nM to 2000 nM as an index of interactive force. The change in an interactive force could be firstly evaluated via Hill model. Additionally, the analysis based on the fitting enables clarification of the differences in hybridization and in interaction, as well as an estimation of the number of SSTs. The proposed sensing system can promote peptide sensing based on the use of an aptasensor.

Data-driven engineering of microbes has been demonstrated for the sustainable production of high-performance chemicals. Metabolic profiling analysis is essential to increase the productivity of target compounds. However, improvement of comprehensive analysis methodologies is required for the high demands of metabolic engineering. Therefore, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodology was designed and applied to cover a wide target range with high precision. Ion-pair free separation of metabolites on a pentafluorophenyl propyl column enabled high-precision quantification of 113 metabolites. The method was further evaluated for high reproducibility and robustness. Target analytes consisted of primary metabolites and intermediate metabolites for microbial production of high-performance chemicals. 95 metabolites could be detected with high reproducibility of peak area (intraday data: CV

Actinobacteria plays a key role in the cycling of organic matter in soils. They secret biomass-degrading enzymes that allow it to produce the unique metabolites that originate in plant biomass. Although past studies have focused on these unique metabolites, a large-scale screening of Actinobacteria is yet to be reported to focus on their biomass-degrading ability. In the present study, a rapid and simple method is constructed for a large-scale screening, and the novel resources that form the plant biomass-degrading enzyme cocktail are identified from 850 isolates of Actinobacteria. As a result, Nonomuraea fastidiosa secretes a biomass degrading enzyme cocktail with the highest enzyme titer, although cellulase activities are lower than a commercially available enzyme. So the rich accessory enzymes are suggested to contribute to the high enzyme titer for a pretreated bagasse with a synergistic effect. Additionally, an optimized cultivation method of biomass induction caused to produce the improved enzyme cocktail indicated strong enzyme titers and a strong synergistic effect. Therefore, the novel enzyme cocktails are selected via the optimized method for large-scale screening, and then the enzyme cocktail can be improved via the optimized production with biomass-induction.

DNA-duplex interactions in thymines and adenins are used as a linker for the novel methodology of Atomic Force Microscope-Systematic Evolution of Ligands by EXpotential enrichment (AFM-SELEX). This study used the hydrogen bonds in 10 mer of both thymines (T10) and adenines (A10). Initially, the interactive force in T10-A10 was measured by AFM, which returned an average interactive force of approximately 350pN. Based on this result, DNA aptamers against human serum albumin could be selected in the 4th round, and 15 different clones could be sequenced. The lowest dissociation constant of the selected aptamer was identified via surface plasmon resonance, and it proved to be identical to that of the commercial aptamer. Therefore, specific hydrogen bonds in DNA can be useful linkers for AFM-SELEX.

The surface of yeast cells has been an attractive interface for the effective use of cellulose. Surface enzymes, however, are difficult to visualize and evaluate. In this study, two kinds of unique anchoring regions were used to display the cellulase, endoglucanase (EG), on a yeast cell surface. Differences in the display level and the localization of EG were observed by atomic force microscopy. By surveying the yeast cell surface with a chemically modified cantilever, the interactive force between the cellulose and EG was measured. Force curve mapping revealed differences in the display levels and the localization of EG according to anchoring regions. The proposed methodology enables visualization of displayed enzymes such as EG on the yeast cell surface.

An atomic force microscope (AFM) can measure the adhesion force between a sample and a cantilever while simultaneously applying a rupture force during the imaging of a sample. An AFM should be useful in targeting specific proteins on a cell surface. The present study proposes the use of an AFM to measure the adhesion force between targeting receptors and their ligands, and to map the targeting receptors. In this study, Ste2p, one of the G protein-coupled receptors (GPCRs), was chosen as the target receptor. The specific force between Ste2p on a yeast cell surface and a cantilever modified with its ligand, α-factor, was measured and found to be approximately 250 pN. In addition, through continuous measur- ing of the cell surface, a mapping of the receptors on the cell surface could be performed, which indi- cated the differences in the Ste2p expression levels. Therefore, the proposed AFM system is accurate for cell diagnosis.