Secondary, but not primary CPP, induce VSMC calcification. Secondary CPP induce the expression and release of TNF-α, which enhances calcification via its receptor TNFR1.

Calciprotein particles, nanoscale aggregates of insoluble mineral and binding proteins, have emerged as potential mediators of phosphate toxicity in patients with Chronic Kidney Disease. Although existing immunochemical methods for their detection have provided compelling data, these approaches are indirect, lack specificity and are subject to a number of other technical and theoretical shortcomings. Here we have developed a rapid homogeneous fluorescent probe-based flow cytometric method for the detection and quantitation of individual mineral-containing nanoparticles in human and animal serum. This method allows the discrimination of membrane-bound from membrane-free particles and different mineral phases (amorphous vs. crystalline). Critically, the method has been optimised for use on a conventional instrument, without the need for manual hardware adjustments. Using this method, we demonstrate a consistency in findings across studies of Chronic Kidney Disease patients and commonly used uraemic animal models. These studies demonstrate that renal dysfunction is associated with the ripening of calciprotein particles to the crystalline state and reveal bone metabolism and dietary mineral as important modulators of circulating levels. Flow cytometric analysis of calciprotein particles may enhance our understanding of mineral handling in kidney disease and provide a novel indicator of therapeutic efficacy for interventions targeting Chronic Kidney Disease-Mineral Bone Disorder.

Sulfide attenuates CPP-induced VSMC calcification in vitro via the KEAP1-NRF2 redox sensing/stress response system by enhancing NQO1 expression.

The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a basis for engineering thermostable enzymes in the laboratory.

Colorectal cancer (CRC) is the third leading cause of cancer death worldwide, which is consequence of multistep tumorigenesis of several genetic and epigenetic events. Since CRC is mostly asymptomatic until it progresses to advanced stages, the early detection using effective screening approaches, selection of appropriate therapeutic strategies and efficient follow-up programs are essential to reduce CRC mortalities. Biomarker discovery for CRC based on the personalized genotype and clinical information could facilitate the classification of patients with certain types and stages of cancer to tailor preventive and therapeutic approaches. These cancer-related biomarkers should be highly sensitive and specific in a wide range of specimen(s) (including tumor tissues, patients' fluids or stool). Reliable biomarkers which enable the early detection of CRC, can improve early diagnosis, prognosis, treatment response prediction, and recurrence risk. Advances in our understanding of the natural history of CRC have led to the development of different CRC associated molecular and cellular biomarkers. This review highlights the new trends and approaches in CRC biomarker discovery, which could be potentially used for early diagnosis, development of new therapeutic approaches and follow-up of patients.