Spent coffee ground (SCG) is the main residue generated during the production of instant coffee by thermal water extraction from roasted coffee beans. This waste is composed mainly of polysaccharides such as cellulose and galactomannans that are not solubilised during the extraction process, thus remaining as unextractable, insoluble solids. In this context, the application of an enzyme cocktail (mannanase, endoglucanase, exoglucanase, xylanase and pectinase) with more than one component that acts synergistically with each other is regarded as a promising strategy to solubilise/hydrolyse remaining solids, either to increase the soluble solids yield of instant coffee or for use as raw material in the production of bioethanol and food additives (mannitol). Wild fungi were isolated from both SCG and coffee beans and screened for enzyme production. The enzymes produced from the selected wild fungi and recombinant fungi were then evaluated for enzymatic hydrolysis of SCG, in comparison to commercial enzyme preparations. Out of the enzymes evaluated on SCG, the application of mannanase enzymes gave better yields than when only cellulase or xylanase was utilised for hydrolysis. The recombinant mannanase (Man1) provided the highest increments in soluble solids yield (17 %), even when compared with commercial preparations at the same protein concentration (0.5 mg/g SCG). The combination of Man1 with other enzyme activities revealed an additive effect on the hydrolysis yield, but not synergistic interaction, suggesting that the highest soluble solid yields was mainly due to the hydrolysis action of mannanase.
The advent and evolution of next generation sequencing has considerably impacted genomic research. Until recently, South African researchers were unable to access affordable platforms capable of human whole genome sequencing locally and DNA samples had to be exported. Here we report the whole genome sequences of the first six human DNA samples sequenced and analysed at the South African Medical Research Council’s Genomics Centre. We demonstrate that the data obtained is of high quality, with an average sequencing depth of 36.41, and that the output is comparable to data generated internationally on a similar platform. The Genomics Centre creates an environment where African researchers are able to access world class facilities, increasing local capacity to sequence whole genomes as well as store and analyse the data.
Type 2 diabetes (T2D) is characterized by metabolic derangements that cause a shift in substrate preference, inducing cardiac interstitial fibrosis. Interstitial fibrosis plays a key role in aggravating left ventricular diastolic dysfunction (LVDD), which has previously been associated with the asymptomatic onset of heart failure. The latter is responsible for 80% of deaths among diabetic patients and has been termed diabetic cardiomyopathy (DCM). Through in silico prediction and subsequent detection in a leptin receptor-deficient db/db mice model (db/db), we confirmed the presence of previously identified potential biomarkers to detect the early onset of DCM. Differential expression of Lysyl Oxidase Like 2 (LOXL2) and Electron Transfer Flavoprotein Beta Subunit (ETFβ), in both serum and heart tissue of 6-16-week-old db/db mice, correlated with a reduced left-ventricular diastolic dysfunction as assessed by high-resolution Doppler echocardiography. Principal component analysis of the combined biomarkers, LOXL2 and ETFβ, further displayed a significant difference between wild type and db/ db mice from as early as 9 weeks of age. Knockdown in H9c2 cells, utilising siRNA of either LOXL2 or ETFβ, revealed a decrease in the expression of Collagen Type I Alpha1 (COL1A1), a marker known to contribute to enhanced myocardial fibrosis. Additionally, receiver-operating curve (ROC) analysis of the proposed diagnostic profile showed that the combination of LOXL2 and ETFβ resulted in an area under the curve (AUC) of 0.813, with a cutoff point of 0.824, thus suggesting the favorable positive predictive power of the model and further supporting the use of LOXL2 and ETFβ as possible early predictive DCM biomarkers. Diabetes affects 463 million people worldwide and this number is said to increase to 700 million by 2045 1. Diabetes and its complications are inextricably linked to cardiovascular dysfunction, which is currently the leading cause of mortality worldwide, affecting 17.9 million individuals 2. This association was first reported in the Framingham Heart study and since then numerous reports have come to the forefront, confirming a 2-4 times increased susceptibility of diabetic individuals to heart failure (HF) 3,4. Coronary artery disease (CAD) is the major type of CVD responsible for HF in diabetic individuals, however diabetic cardiomyopathy (DCM) is an established complication of diabetes mellitus (DM) existing in the absence of CAD or hypertension 5. Furthermore, DCM is referred to as the silent killer, due to the manifestation of a long subclinical period in which the disease exists with no overt clinical symptoms 6-8 .
The conservation of plant biosecurity relies on the rapid identification of pathogenic organisms, including viruses. With next-generation sequencing (NGS), it is possible to identify multiple viruses within a metagenomic sample. In this study, we explored the use of electronic probes (e-probes) for the simultaneous detection of 11 recognized citrus viruses. E-probes were designed and screened against raw sequencing data to minimize the bioinformatic processing time required. The e-probes were able to accurately detect their cognate viruses in simulated datasets, without any false negatives or positives. The efficiency of the e-probe-based approach was validated with NGS datasets generated from different RNA preparations: double-stranded RNA (dsRNA) from 'Mexican' lime infected with different Citrus tristeza virus (CTV) genotypes, dsRNA from field samples, and small RNA and total RNA from grapefruit infected with the CTV T3 genotype. A set of probes was made available that is able to accurately detect CTV in sequence data regardless of the input dataset or the genotype that plants are infected with.
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