Biology Methods and Protocols最新文献

Human asparagine synthetase (ASNS) catalyzes the conversion of aspartate to asparagine in an ATP-dependent reaction that utilizes glutamine as a nitrogen source while generating glutamate, AMP, and pyrophosphate as additional products. Asparagine Synthetase Deficiency (ASNSD) is an inborn error of metabolism in which children present with homozygous or compound heterozygous mutations in the ASNS gene. These mutations result in ASNS variant protein expression. It is believed that these variant ASNS proteins have reduced enzymatic activity or stability resulting in a lack of sufficient asparagine production for cell function. Reduced asparagine production by ASNS appears to severely hinder fetal brain development. Although a variety of approaches for assaying ASNS activity have been reported, we present here a straightforward method for the in vitro enzymatic analysis by detection of AMP production. Our method overcomes limitations in technical feasibility, signal detection, and reproducibility experienced by prior methods like high-performance liquid chromatography, ninhydrin staining, and radioactive tracing. After purification of FLAG-tagged R49Q, G289A, and T337I ASNS variants from stably expressing HEK 293T cells, this method revealed a reduction in activity of 90, 36, and 96%, respectively. Thus, ASNS protein expression and purification, followed by enzymatic activity analysis, has provided a relatively simple protocol to evaluate structure-function relationships for ASNS variants reported for ASNSD patients.

Pandemics are, by definition, temporary intervals of substantially increased mortality rates experienced across a wide geographic area. One way of assessing the magnitude of the COVID-19 pandemic in the USA has been to compute the differences in life expectancy at birth during a pandemic year and the year before the pandemic. Such comparisons are misleading because they do not account for the duration of the pandemic. The computation of life expectancy in 2019 assumes that people spend their entire lives experiencing prepandemic mortality rates. The computation of life expectancy in 2021 assumes that people live their entire lives in a permanent pandemic. However, people do not live their entire lives experiencing the elevated mortality rates of 2021. This article introduces a method for calculating life expectancy that reflects the experience of people enduring pandemic-level mortality rates for fixed durations. We call the new quantity hybrid life expectancy because it integrates both pandemic and prepandemic mortality rates. The difference in life expectancy at birth in the USA in 2019 with and without a 3-year-long pandemic is 0.01 years. This is because mortality rates at ages 0, 1, and 2 in the pandemic were essentially unchanged from their prepandemic levels. Life expectancy at age 65 incorporating a 3-year pandemic is 0.18 years lower than life expectancy would have been without it. Reductions in life expectancy due to the COVID-19 pandemic using hybrid life expectancy are dramatically lower than differences in life expectancy that do not take the duration of the pandemic into account.

Genetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between cis-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.

It is a central goal of human microbiome studies to see the roles of the microbiome as a mediator that transmits environmental, behavioral, or medical exposures to health or disease outcomes. Yet, mediation analysis is not used as much as it should be. One reason is because of the lack of carefully planned routines, compilers, and automated computing systems for microbiome mediation analysis (MiMed) to perform a series of data processing, diversity calculation, data normalization, downstream data analysis, and visualizations. Many researchers in various disciplines (e.g. clinicians, public health practitioners, and biologists) are not also familiar with related statistical methods and programming languages on command-line interfaces. Thus, in this article, we introduce a web cloud computing platform, named as MiMed, that enables comprehensive MiMed on user-friendly web interfaces. The main features of MiMed are as follows. First, MiMed can survey the microbiome in various spheres (i) as a whole microbial ecosystem using different ecological measures (e.g. alpha- and beta-diversity indices) or (ii) as individual microbial taxa (e.g. phyla, classes, orders, families, genera, and species) using different data normalization methods. Second, MiMed enables covariate-adjusted analysis to control for potential confounding factors (e.g. age and gender), which is essential to enhance the causality of the results, especially for observational studies. Third, MiMed enables a breadth of statistical inferences in both mediation effect estimation and significance testing. Fourth, MiMed provides flexible and easy-to-use data processing and analytic modules and creates nice graphical representations. Finally, MiMed employs ChatGPT to search for what has been known about the microbial taxa that are found significantly as mediators using artificial intelligence technologies. For demonstration purposes, we applied MiMed to the study on the mediating roles of oral microbiome in subgingival niches between e-cigarette smoking and gingival inflammation. MiMed is freely available on our web server (http://mimed.micloud.kr).

Studies on genetic diversity require biological material containing a reliable source of DNA that can be extracted and analyzed. Recently, non-invasive sampling has become a preferred sampling method of biological material. The suitability of a less invasive approach that involves obtaining samples by swabbing the fish skin (including live, non-anesthetized fish) should be considered. In this study, we compared the efficiency of DNA extraction, amplification, and sequencing of mtDNA fragments of two fish species Perca fluviatilis and Rutilus rutilus based on DNA collected from the scales and mucus using the modified Aljanabi and Martinez method. The results revealed a higher quality of DNA extracted from the mucus; however, the mean DNA concentration obtained from the scales of both fish species was higher. We verified the method suitable for amplification and sequencing of mtDNA fragments of both fish species using newly designed markers (D-loop, ATP6) and examined the potential risk of intraspecific cross-contamination. The DNA sequence alignment analysis revealed identical sequences attributed to the same individual when DNA, extracted from two different sources (scales and mucus), was used. We demonstrated that the quantity and quality of DNA extracted from the scales and mucus using the proposed method were high enough to carry out genetic diversity studies based on sampling of live fish with the possibility to release it after collecting samples.

Recent studies highlight the effectiveness of hybrid Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) vaccines combining wild-type nucleocapsid and Spike proteins. We have further enhanced this strategy by incorporating delta and omicron variants' spike protein mutations. Both delta and omicron mark the shifts in viral transmissibility and severity in unvaccinated and vaccinated patients. So their mutations are highly crucial for future viral variants also. Omicron is particularly adept at immune evasion by mutating spike epitopes. The rapid adaptations of Omicron and sub-variants to spike-based vaccines and simultaneous transmissibility underline the urgency for new vaccines in the continuous battle against SARS-CoV-2. Therefore, we have added three persistent T-cell-stimulating nucleocapsid peptides similar to homologous sequences from seasonal Human Coronaviruses (HuCoV) and an envelope peptide that elicits a strong T-cell immune response. These peptides are clustered in the hybrid spike's cytoplasmic region with non-immunogenic linkers, enabling systematic arrangement. AlphaFold (Artificial intelligence-based model building) analysis suggests omitting the transmembrane domain enhances these cytoplasmic epitopes' folding efficiency which can ensure persistent immunity for CD4⁺ structural epitopes. Further molecular dynamics simulations validate the compact conformation of the modeled structures and a flexible C-terminus region. Overall, the structures show stability and less conformational fluctuation throughout the simulation. Also, the AlphaFold predicted structural epitopes maintained their folds during simulation to ensure the specificity of CD4⁺ T-cell response after vaccination. Our proposed approach may provide options for incorporating diverse anti-viral T-cell peptides, similar to HuCoV, into linker regions. This versatility can be promising to address outbreaks and challenges posed by various viruses for effective management in this era of innovative vaccines.

Dried blood spots (DBS) are biological samples commonly collected from newborns and in geographic areas distanced from laboratory settings for the purposes of disease testing and identification. MicroRNAs (miRNAs)-small non-coding RNAs that regulate gene activity at the post-transcriptional level-are emerging as critical markers and mediators of disease, including cancer, infectious diseases, and mental disorders. This protocol describes optimized procedural steps for utilizing DBS as a reliable source of biological material for obtaining peripheral miRNA expression profiles. We outline key practices, such as the method of DBS rehydration that maximizes RNA extraction yield, and the use of degenerate oligonucleotide adapters to mitigate ligase-dependent biases that are associated with small RNA sequencing. The standardization of miRNA readout from DBS offers numerous benefits: cost-effectiveness in sample collection and processing, enhanced reliability and consistency of miRNA profiling, and minimal invasiveness that facilitates repeated testing and retention of participants. The use of DBS-based miRNA sequencing is a promising method to investigate disease mechanisms and to advance personalized medicine.