WFS1 and non-syndromic low-frequency sensorineural hearing loss: A novel mutation in a Portuguese case

Low-frequency sensorineural hearing loss (LFSNHL) is an unusual type of HL in which frequencies at 2000 Hz and below are predominantly affected. Most of the families with LFSNHL carry missense mutations in WFS1 gene, coding for wolframin.     

Simple But Efficacious Enrichment of Integral Membrane Proteins and Their Interactions for In-Depth Membrane Proteomics

Membrane proteins play essential roles in various cellular processes, such as nutrient transport, bioenergetic processes, cell adhesion, and signal transduction. Proteomics is one of the key approaches to exploring membrane proteins comprehensively. Bottom–up proteomics using LC–MS/MS has been widely used in membrane proteomics. However, the low abundance and hydrophobic features of membrane proteins, especially integral membrane proteins, make it difficult to handle the proteins and are the bottleneck for identification by LC–MS/MS. Herein, to improve the identification and quantification of membrane proteins, we have stepwisely evaluated methods of membrane enrichment for the sample preparation. The enrichment methods of membranes consisted of precipitation by ultracentrifugation and treatment by urea or alkaline solutions. The best enrichment method in the study, washing with urea after isolation of the membranes, resulted in the identification of almost twice as many membrane proteins compared with samples without the enrichment. Notably, the method significantly enhances the identified numbers of multispanning transmembrane proteins, such as solute carrier transporters, ABC transporters, and G-protein–coupled receptors, by almost sixfold. Using this method, we revealed the profiles of amino acid transport systems with the validation by functional assays and found more protein–protein interactions, including membrane protein complexes and clusters. Our protocol uses standard procedures in biochemistry, but the method was efficient for the in-depth analysis of membrane proteome in a wide range of samples.     

Emergence,evolution, and vaccine production approaches of SARS-CoV-2 virus: Benefits of getting vaccinated and common questions

The emergence of coronavirus disease 2019 (COVID-19) pandemic in Wuhan city, China at the end of 2019 made it urgent to identify the origin of the causal pathogen and its molecular evolution, to appropriately design an effective vaccine. This study analyzes the evolutionary background of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or SARS-2) in accordance with its close relative SARS-CoV (SARS-1), which was emerged in 2002. A comparative genomic and proteomic study was conducted on SARS-2, SARS-1, and Middle East respiratory syndrome coronavirus (MERS), which was emerged in 2012. In silico analysis inferred the genetic variability among the tested viruses. The SARS-1 genome harbored 11 genes encoding 12 proteins, while SARS-2 genome contained only 10 genes encoding for 10 proteins. MERS genome contained 11 genes encoding 11 proteins. The analysis also revealed a slight variation in the whole genome size of SARS-2 comparing to its siblings resulting from sequential insertions and deletions (indels) throughout the viral genome particularly ORF1AB, spike, ORF10 and ORF8. The effective indels were observed in the gene encoding the spike protein that is responsible for viral attachment to the angiotensin-converting enzyme 2 (ACE2) cell receptor and initiating infection. These indels are responsible for the newly emerging COVID-19 variants αCoV, βCoV, γCoV and δCoV. Nowadays, few effective COVID-19 vaccines developed based on spike (S) glycoprotein were approved and become available worldwide. Currently available vaccines can relatively prevent the spread of COVID-19 and suppress the disease. The traditional (killed or attenuated virus vaccine and antibody-based vaccine) and innovated vaccine production technologies (RNA- and DNA-based vaccines and viral vectors) are summarized in this review. We finally highlight the most common questions related to COVID-19 disease and the benefits of getting vaccinated.     

Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction

Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a “beads-on-a-string” model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.     

Analysis of searching movements of a leafminer parasitoid in a structured environment

ABSTRACT. Pnigalio soemius (Hymenoptera, Eulophidae) is a polyphagous ectoparasitoid of the apple leafminer Phyllonorycter cydo-niella (Lepidoptera, Gracillaridae). The tracks of females searching on the unmined leaf lamina and on the mine were segmented into 2 mm steps and analysed for angle distributions and correlations between successive steps. The movements on the mine are characterized by a higher mean and higher variance of turn angles compared to the turn angles on the leaf (kinesis-reaction family). Both klinokinesis and schemakinesis, for which a more precise definition is proposed, are present. Varying the step length has a great influence on the result and can be used to identify different orientation reactions within one track. The insect turns sharply once the mine border has been reached, a behaviour shown to be a crude taxis. An ecologically related species, Sympiesis sericeicornis (Hymenoptera, Eulophidae), moves in the same way. For this species a taxis towards the host could also be observed. A complex mixture of these orientation behaviours allows the parasitoid to remain on the mine and to localise the host.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

PRASA: An integrated web server that analyzes protein interaction types

This work presents the Protein Association Analyzer (PRASA) (http://zoro.ee.ncku.edu.tw/prasa/) that predicts protein interactions as well as interaction types. Protein interactions are essential to most biological functions. The existence of diverse interaction types, such as physically contacted or functionally related interactions, makes protein interactions complex. Different interaction types are distinct and should not be confused. However, most existing tools focus on a specific interaction type or mix different interaction types. This work collected 7234058 associations with experimentally verified interaction types from five databases and compiled individual probabilistic models for different interaction types. The PRASA result page shows predicted associations and their related references by interaction type. Experimental results demonstrate the performance difference when distinguishing between different interaction types. The PRASA provides a centralized and organized platform for easy browsing, downloading and comparing of interaction types, which helps reveal insights into the complex roles that proteins play in organisms.