Nanoparticle protein corona evolution: from biological impact to biomarker discovery

Nanoparticles exposed to biological fluids such as blood, quickly interact with their surrounding milieu resulting in a biological coating that results in large part as a function of the physicochemical...

Glycans for the greater good

Carbohydrates are ubiquitous in nature and present across all kingdoms of life – bacteria, fungi, viruses, yeast, plants, animals and humans. They are essential to many biological processes. However, due to their complexity and heterogeneous nature they are often neglected, sometimes referred to as the ‘dark matter’ of biology. Nevertheless, due to their extensive biological impact on health and disease, glycans and the field of glycobiology have become increasingly popular in recent years, giving rise to glycan-based drug development and therapeutics. Forecasting of communicable diseases predicts that we will see an increase in pandemics of humans and livestock due to global loss of biodiversity from changes to land use, intensification of agriculture, climate change and disruption of ecosystems. As such, the development of point-of-care devices to detect pathogens is vital to prevent the transmission of infectious disease, as we have seen with the COVID-19 pandemic. So, can glycans be exploited to detect COVID-19 and other infectious diseases? And is this technology sensitive and accurate? Here, I discuss the structure and function of glycans, the current glycan-based therapeutics and how glycan binding can be exploited for detection of infectious disease, like COVID-19.

A Geometric Complementarity-Based Tool for Protein–Protein Docking

The computationally hard protein–protein complex structure prediction problem is continuously fascinating to the scientific community due to its biological impact. The field has witnessed the application of geometric algorithms, randomized algorithms, and evolutionary algorithms to name a few. These techniques improve either the searching or scoring phase. An effective searching strategy does not generate a large conformation space that perhaps demands computational power. Another determining factor is the parameter chosen for score calculation. The proposed method is an attempt to curtail the conformations by limiting the search procedure to probable regions. In this method, partial derivatives are calculated on the coarse-grained representation of the surface residues to identify the optimal points on the protein surface. Contrary to the existing geometric-based algorithms that align the convex and concave regions of both proteins, this method aligns the concave regions of the receptor with convex regions of the ligand only and thus reduces the size of conformation space. The method’s performance is evaluated using the 55 newly added targets in Protein–Protein Docking Benchmark v 5 and is found to be successful for around 47% of the targets.

Measurement of Natural Radionuclides in Drinking Water and Risk Assessment in a Volcanic Region of Italy, Campania

The physical–chemical properties of water are closely linked to the geological nature of the site where they are located. This aspect becomes even more interesting when analyzing the natural radionuclides in the drinking water of a volcanic territory such as Campania in southern Italy. This study concerned the measurement of activity concentration of gross alpha and beta, radon, and tritium to evaluate their biological impact. The measurements were carried out using alpha spectrometry for alpha emitters, proportional counter for beta emitters, the electret system for radon in water, and finally liquid scintillation for the measurement of tritium concentration. The biological impact was assessed considering the indicative dose, if applicable, and the effective annual dose of radon. Although the results show that the values are below international and national references, the radiological characterization of drinking water is of fundamental importance to optimize the radiation protection of the population.

Dissecting the Biological Relevance and Clinical Impact of lncRNA MIAT in Multiple Myeloma

The biological impact of long non-coding RNAs (lncRNAs) in multiple myeloma (MM) is becoming an essential aspect of the investigation, which may contribute to understanding the disease’s complex pathobiology, providing novel potential therapeutic targets. Herein, we investigated the expression pattern and the clinical relevance of the lncRNA MIAT in MM, taking advantage of the publicly available CoMMpass database. MIAT expression in MM is highly heterogeneous and significantly associated with specific molecular lesions frequently occurring in MM. Transcriptome analyses of MM PCs from patients included in the CoMMpass database indicated a potential involvement of MIAT in different signaling pathways and ribosome biogenesis and assembly. These findings suggest that MIAT deregulation may play a pathogenetic role in MM by affecting both proliferation pathways and, indirectly, the translational process. Although MIAT expression levels seem not to be significantly associated with clinical outcome in multivariate analyses, high MIAT expression levels are associated with bortezomib resistance, this suggesting that MIAT targeting could overcome drug resistance in MM. These findings strongly prompt for further studies investigating the significance of MIAT in MM.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

New Short RNA Motifs Potentially Relevant in the Severe Acute Respiratory Syndrome Coronavirus 2 Genome

As time passes, identifying new pharmacological targets is becoming more difficult. Shortly, it will be necessary to devise new strategies to tackle the problem. The coronavirus disease outbreak caused by the severe acute respiratory syndrome coronavirus 2 , represents a threat to human health serving as example from what we just said. The present study was aimed to collect a set of short RNA motifs with potential biological impact, most of which have not been observed heretofore. Categorizing RNA triplets by their gross-composition, the study collected 88 short RNA motifs, shared by most coronavirus genera independent on the percent identity between genomes. Selected motifs contain all nearest-neighbours of the triplets A, T, G and A, C, G. The high percent identity between severe acute respiratory syndrome coronavirus genomes makes it difficult these peptides to be found by current methods. The results provide 50 motifs in the 1a polyprotein-encoding orf, 27 in the 1b polyprotein-encoding orf, 5 in the spike-encoding orf and 6 in the nucleocapsid-encoding orf. They also provide insights about the validity of the procedure, confirming some motifs interspersed or attached to known relevant functional fragments of the genome, although most of them have not yet been associated to any known function. The high level of preservation of these motifs in most coronavirus genera suggest they might have potential to be used for diagnostic, in vaccines, or as substrate for protease inhibitors.

The Evolving Faces of the SARS-CoV-2 Genome

Surveillance of the evolving SARS-COV-2 genome combined with epidemiological monitoring and emerging vaccination became paramount tasks to control the pandemic which is rapidly changing in time and space. Genomic surveillance must combine generation and sharing sequence data with appropriate bioinformatics monitoring and analysis methods. We applied molecular portrayal using self-organizing maps machine learning (SOM portrayal) to characterize the diversity of the virus genomes, their mutual relatedness and development since the beginning of the pandemic. The genetic landscape obtained visualizes the relevant mutations in a lineage-specific fashion and provides developmental paths in genetic state space from early lineages towards the variants of concern alpha, beta, gamma and delta. The different genes of the virus have specific footprints in the landscape reflecting their biological impact. SOM portrayal provides a novel option for ‘bioinformatics surveillance’ of the pandemic, with strong odds regarding visualization, intuitive perception and ‘personalization’ of the mutational patterns of the virus genomes.