Butyric acid production from red algae by a newly isolated <Emphasis Type="Italic">Clostridium</Emphasis> sp. S1

Objective

To produce butyric acid from red algae such as Gelidium amansii in which galactose is a main carbohydrate, microorganisms utilizing galactose and tolerating inhibitors in hydrolysis including levulinic acid and 5-hydroxymethylfurfural (HMF) are required.

Results

A newly isolated bacterium, Clostridium sp. S1 produced butyric acid not only from galactose as the sole carbon source but also from a mixture of galactose and glucose through simultaneous utilization. Notably, Clostridium sp. S1 produced butyric acid and a small amount of acetic acid with the butyrate:acetate ratio of 45.4:1 and it even converted acetate to butyric acid. Clostridium sp. S1 tolerated 0.5–2 g levulinic acid/l and recovered from HMF inhibition at 0.6–2.5 g/l, resulting in 85–92 % butyric acid concentration of the control culture. When acid-pretreated G. amansii hydrolysate was used, Clostridium sp. S1 produced 4.83 g butyric acid/l from 10 g galactose/l and 1 g glucose/l.

Conclusion

Clostridium sp. S1 produces butyric acid from red algae due to its characteristics in sugar utilization and tolerance to inhibitors, demonstrating its advantage as a red algae-utilizing microorganism.

     

Highly reproducible quantification of apoptotic cells using micropatterned culture of neurons

The quantification of apoptotic cells is an integral component of many cell-based assays in biological studies. However, current methods for quantifying apoptotic cells using conventional random cultures have shown great limitations, especially for the quantification of primary neurons. Randomly distributed neurons under primary culture conditions can lead to biased estimates, and vastly different estimates of cell numbers can be produced within the same experiment. In this study, we developed a simple, accurate, and reliable technique for quantifying apoptotic neurons by means of micropatterned cell cultures. A polydimethylsiloxane (PDMS) microstencil was used as a physical mask for micropatterning cell cultures, and primary granular neurons (GNs) were successfully cultured within the micropattern-confined regions and homogeneously distributed over the entire field of each pattern. As compared with the conventional method based on random cultures, the micropatterned culture method allowed for highly reproducible quantification of apoptotic cells. These results were also confirmed by using GNs derived from mice with neurodegeneration. We hope that this micropatterning method based on the use of a PDMS microstencil can overcome the technical obstacles existing in current biological studies and will serve as a powerful tool for facilitating the study of apoptosis-involved diseases.     

Minimalistic Predictor of Protein Binding Energy: Contribution of Solvation Factor to Protein Binding

It has long been known that solvation plays an important role in protein-protein interactions. Here, we use a minimalistic solvation-based model for predicting protein binding energy to estimate quantitatively the contribution of the solvation factor in protein binding. The factor is described by a simple linear combination of buried surface areas according to amino-acid types. Even without structural optimization, our minimalistic model demonstrates a predictive power comparable to more complex methods, making the proposed approach the basis for high throughput applications. Application of the model to a proteomic database shows that receptor-substrate complexes involved in signaling have lower affinities than enzyme-inhibitor and antibody-antigen complexes, and they differ by chemical compositions on interfaces. Also, we found that protein complexes with components that come from the same genes generally have lower affinities than complexes formed by proteins from different genes, but in this case the difference originates from different interface areas. The model was implemented in the software PYTHON, and the source code can be found on the Shakhnovich group webpage: http://faculty.chemistry.harvard.edu/shakhnovich/software.