Uninfected agarwood branch is readily available as raw material in agarwood plantation as new practices of agarwood plantation scheme were opted as substitute to the endangered wild type agarwood. The uninfected branch can be easily obtained during pruning process (one of plantation’s common maintenance procedure), throughout the years before inoculation stage. This current study aimed to investigate the optimal extraction process conditions of agarwood branch using ethanol as solvent system for maximal yield, and assess its cytotoxic effects towards MCF-7 breast cancer cells. Uninfected branch of Aquilaria subintegra was subjected to One Factor at a Time (OFAT) and Response Surface Methodology (RSM)-guided ethanolic extraction to achieve maximal yield. The extract was then subjected to cytotoxicity, cell attachment and cell viability assays, respectively. Optimization Run 2 (temperature 45 °C, solid-liquid ratio of 1:30, 16 hours maceration) gave the highest agarwood branch ethanolic extract (ABEE) yield of 44.70 ± 18.9 mg/g dried material (DM). Meanwhile Run 7 (temperature 45 °C, solid-liquid ratio of 1:10, 16 hours of maceration) gave the lowest yield (19.34 ± 14.1 mg/g DM). However, while maintaining the 16 hour-maceration, the model predicted a slightly lower yield of 30.232 ± 0.266 mg/g DM of ABEE with process conditions of 45 °C and solid-liquid ratio of 1:19 when the desirable parameters were factored in namely using (ⅰ) the most suitable temperature (that does not risk the bioactivities of the extract), and (ⅱ) an economical volume of solvent. Crude ABEE obtained from the optimal process conditions resulted in cytotoxicity effects on MCF-7 breast cancer cells with IC50 estimate of 3.645 ± 0.099 μg/mL. The extract also affected MCF-7 cell attachment and viability with altered morphology. More work to elucidate the mechanism of actions of the extract are warranted; which could further lead to development of breast cancer natural product-based therapeutics.
Biocatalyst should have sufficient and efficient activity for the intended
biotechnological application. In the quest for novel biocatalyst, there is a need to have a
genetic diversity either by finding it within the astronomically large number of possible
candidates or to obtain it by bioengineering an existing gene supported by various
bioinformatic and molecular engineering tools. Nowadays, it is well-known that a huge
number of microorganisms is unculturable and poses great challenges to access biocatalysts
from these microbes. Metagenomics is one of the methods widely applied to reach out
maximum possible variants to “bioprospect” biocatalysts. On the other hand, other approaches
are available to bioengineer enzymes by modifying the DNA sequence precisely based on the
structure and the function information of the protein in the case of rational design, or by a
brave creation of anarchic mutations of the DNA sequence with directed evolution method. In
this regard, both approaches, whether to bioprospect or to bioengineer biocatalysts have
advantages and disadvantages which will be discussed in this paper.KEY WORDS: Sugar
industry wastewater; aluminium sulphate; primary treatment, ferric chloride; polyaluminium
chloride
Farah Fadwa Benbelgacem, Oualid Abdelkader Bellag, Adibah Parman, Ibrahim Ali Noorbatcha, Mohd Noor Mat Isa, Muhammad Alfatih Muddathir Abdelrahim, et al.
Metagenomic DNA library from palm oil mill effluent (POME) was constructed and subjected to high-throughput screening
to find genes encoding cellulose- and xylan-degrading enzymes. DNA of 30 positive fosmid clones were sequenced with next
generation sequencing technology and the raw data (short insert-paired) was analyzed with bioinformatic tools. First,
the quality of 64,821,599 reverse and forward sequences of 101 bp length raw data was tested using Fastqc and SOLEXA.
Then, raw data filtering was carried out by trimming low quality values and short reads and the vector sequences were
removed and again the output was checked and the trimming was repeated until a high quality read sets was obtained.
The second step was the de novo assembly of sequences to reconstruct 2900 contigs following de Bruijn graph algorithm.
Pre-assembled contigs were arranged in order, the distances between contigs were identified and oriented with SSPACE,
where 2139 scaffolds have been reconstructed. 16,386 genes have been identified after gene prediction using Prodigal
and putative ID assignment with Blastp vs NR protein. The acceptable strategy to handle metagenomic NGS-data in order
to detect known and potentially unknown genes is presented and we showed the computational efficiency of de Bruijn
graph algorithm of de novo assembly to 21 bioprospect genes encoding cellulose-degrading enzymes and 6 genes
encoding xylan-degrading enzymes of 30.3% to 100% identity percentage.