Fungal lytic polysaccharide monooxygenases bind starch and β-cyclodextrin similarly to amylolytic hydrolases

Starch-binding modules of family 20 (CBM20) are present in 60% of lytic polysaccharide monooxygenases (LPMOs) catalyzing the oxidative breakdown of starch, which highlights functional importance in LPMO activity. The substrate-binding properties of starch-active LMPOs, however, are currently unexplored. Affinities and binding-thermodynamics of two recombinant fungal LPMOs toward starch and β-cyclodextrin were shown to be similar to fungal CBM20s. Amplex Red assays showed ascorbate and Cu-dependent activity, which was inhibited in the presence of β-cylodextrin and amylose. Phylogenetically, the clustering of CBM20s from starch-targeting LPMOs and hydrolases was in accord with taxonomy and did not correlate to appended catalytic activity. Altogether, these results demonstrate that the CBM20-binding scaffold is retained in the evolution of hydrolytic and oxidative starch-degrading activities.

Lytic polysaccharide monooxygenases and other oxidative enzymes are abundantly secreted by Aspergillus nidulans grown on different starches

BACKGROUND

Starch is the second most abundant plant-derived biomass and a major feedstock in non-food industrial applications and first generation biofuel production. In contrast to lignocellulose, detailed insight into fungal degradation of starch is currently lacking. This study explores the secretomes of Aspergillus nidulans grown on cereal starches from wheat and high-amylose (HA) maize, as well as legume starch from pea for 5 days.

RESULTS

Aspergillus nidulans grew efficiently on cereal starches, whereas growth on pea starch was poor. The secretomes at days 3-5 were starch-type dependent as also reflected by amylolytic activity measurements. Nearly half of the 312 proteins in the secretomes were carbohydrate-active enzymes (CAZymes), mostly glycoside hydrolases (GHs) and oxidative auxiliary activities (AAs). The abundance of the GH13 α-amylase (AmyB) decreased with time, as opposed to other starch-degrading enzymes, e.g., the GH13 AmyF, GH15 glucoamylases (GlaA and GlaB), and the GH31 α-glucosidase (AgdE). Two AA13 LPMOs displayed similar secretion patterns as amylolytic hydrolases and were among the most abundant CAZymes. The starch-active AnLPMO13A that possesses a CBM20 carbohydrate-binding module dominated the starch-binding secretome fraction. A striking observation is the co-secretion of several redox-active enzymes with the starch-active AA13 LPMOs and GHs, some at high abundance. Notably nine AA9 LPMOs, six AA3 sub-family 2 (AA_2) oxidoreductases, and ten AA7 glyco-oligosaccharide oxidases were identified in the secretomes in addition to other non-CAZyme oxidoreductases.

CONCLUSIONS

The co-secretion and high abundance of AA13 LPMOs are indicative of a key role in starch granule deconstruction. The increase in AA13 LPMO abundance with culture time may reflect accumulation of a more resistant starch fraction towards the later stages of the culture. The identification of AmyR sites upstream AA13 LPMOs unveils co-regulation of LPMOs featuring in starch utilization. Differential deployment of amylolytic hydrolases and LPMOs over time suggests additional regulatory mechanisms. The abundant co-secretion of distinct AA3 and AA7 oxidoreductases merits further studies into their roles and possible interplay with LPMOs and other enzymes in the deconstruction of starchy substrates. The study reports for the first time the biological significance of LPMOs in starch degradation and the temporal interplay between these and amylolytic hydrolases.

In vitro validation of an ultra-sensitive scanning fluorescence microscope for analysis of circulating tumor cells

Analysis of circulating tumor cells (CTC) holds promise of providing liquid biopsies from patients with cancer. However, current methods include enrichment procedures. We present a method (CytoTrack®), where CTC from 7.5 mL of blood is stained, analyzed and counted by a scanning fluorescence microscope. The method was validated by breast cancer cells (MCF-7) spiked in blood from healthy donors. The number of cells spiked in each blood sample was exactly determined by cell sorter and performed in three series of three samples spiked with 10, 33 or 100 cells in addition with three control samples for each series. The recovery rate of 10, 33 and 100 tumor cells in a blood sample was 55%, 70% and 78%, percent coefficient of variation (CV%) for samples was 59%, 32% and 18%, respectively. None of the control samples contained CTC. In conclusion, the method has been validated to highly sensitively detect breast cancer cells in spiking experiments and should be tested on blood samples from breast cancer patients. The method could benefit from automation that could reduce the CV%, and further optimization of the procedure to increase the recovery.