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[This corrects the article DOI: 10.3389/fendo.2023.1266985.].

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The detection and quantification of hormones are important to assess the reproductive and stress status of experimental models and for the diagnosis of diseases in human and veterinary clinics. Traditionally, steroid, peptide, and protein hormones are analyzed in individual experiments using different extraction methodologies. With the new advancement on HPLC sorbents, the simultaneous measurement of hormones from different categories becomes possible. In this study, we present a novel sample processing strategy for the simultaneous extraction and detection of peptides, steroids, and proteins using high-resolution liquid chromatography tandem mass spectrometry. We demonstrate the sensitivity of our method for small tissues by acquiring data from brain, pituitary gland, and gonads of single zebrafish samples. This approach promises to shed light on the hormonal pathways and their interrelationships, providing knowledge on the integration of hormone systems.

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Human skeletal muscle is a heterogeneous tissue composed of multiple fiber types that express unique contractile and metabolic properties. While analysis of mixed fiber samples predominates and holds value, increasing attention has been directed toward studying proteins segregated by fiber type, a methodological distinction termed "fiber type-specific." Fiber type-specific protein studies have the advantage of uncovering key molecular effects that are often missed in mixed fiber homogenate studies but also require greater time and resource-intensive methods, particularly when applied to human muscle. This review summarizes and compares current methods used for fiber type-specific protein analysis, highlighting their advantages and disadvantages for human muscle studies, in addition to recent advances in these techniques. These methods can be grouped into three categories based on the initial processing of the tissue: 1) muscle-specific fiber homogenates, 2) cross sections of fiber bundles, and 3) isolated single fibers, with various subtechniques for performing fiber type identification and protein quantification. The relative implementation for each unique methodological approach is analyzed from 83 fiber type-specific studies of proteins in live human muscle found in the literature to date. These studies have investigated several proteins involved in a wide range of cellular functions that are important to muscle tissue. The second half of this review summarizes key findings from this ensemble of fiber type-specific human protein studies. We highlight examples of where this analytical approach has helped to improve understanding of important physiological topics such as insulin sensitivity, muscle hypertrophy, muscle fatigue, and adaptation to different exercise programs.

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The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

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Following the outcome from a previously performed yeast two-hybrid experiment, the binding strength between T. gondii SAG1 and SAG2 and their respective prey proteins were further confirmed in this study. The sag1, sag2 and their prey genes were amplified and cloned into a pGEMT vector. To express the recombinant proteins, the fragments were then subcloned into a pRSETA vector and transformed into E. coli BL21 (DE3) cells. The recombinant proteins were expressed optimally at 37°C and 1mM of IPTG. The 6X His-tag fusion proteins were purified, dialyzed and concentrated. To confirm the expressed proteins, the recombinant proteins were analysed by SDS-PAGE and Western blot. As expected, the size of SAG1, SAG2, HLY and HZF protein were 32, 23, 28 and 37 kDa, respectively. The purified proteins were loaded onto a MicroCal Auto-iTC200 calorimeter from MicroCal™ to quantify binding strength. ITC results indicated there was a typical binding curve for interactions between SAG1 and HLY protein. However, there was an atypical binding curve obtained for interactions between SAG2 and HZF protein. By observing the data obtained from the ITC assay, both of the human proteins (HLY and HZF) were demonstrated to bind to their respective SAG1 and SAG2 proteins.

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Determining total protein content is a routine operation in many laboratories. Despite substantial work on assay optimization interferences, the widely used bicinchoninic acid (BCA) assay remains widely recognized for its robustness. Especially in the field of bioprocess engineering the inaccuracy caused by interfering substances remains hardly predictable and not well understood. Since the introduction of the assay, sample pre-treatment by trichloroacetic acid (TCA) precipitation has been indicated as necessary and sufficient to minimize interferences. However, the sample matrix in cultivation media is not only highly complex but also dynamically changing over process time in terms of qualitative and quantitative composition. A significant misestimation of the total protein concentration of bioprocess samples is often observed when following standard work-up schemes such as TCA precipitation, indicating that this step alone is not an adequate means to avoid measurement bias. Here, we propose a modification of the BCA assay, which is less influenced by sample complexity. The dynamically changing sample matrix composition of bioprocessing samples impairs the conventional approach of compensating for interfering substances via a static offset. Hence, we evaluated the use of a correction factor based on an internal spike measurement for the respective samples. Using protein spikes, the accuracy of the BCA protein quantification could be improved fivefold, taking the BCA protein quantification to a level of accuracy comparable to other, more expensive methods. This will allow reducing expensive iterations in bioprocess development to due inaccurate total protein analytics.

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Two major issues need to be addressed in applying semiconductor biosensors to detecting proteins in immunoassays. First, the length of the antibody on the sensor surface surpasses the Debye lengths (approximately 1 nm, in normal ionic strength solution), preventing certain specifically bound proteins from being tightly attached to the sensor surface. Therefore, these proteins do not contribute to the sensor's surface potential change. Second, these proteins carry a small charge and can be easily affected by the pH of the surrounding solution. This study proposes a magnetic bead-based immunoassay using a secondary antibody to label negatively charged DNA fragments for signal amplification. An externally imposed magnetic force attaches the analyte tightly to the sensor surface, thereby effectively solving the problem of the analyte protein's distance to the sensor surface surpassing the Debye lengths. In addition, a normal ion intensity buffer can be used without dilution for the proposed method. Experiments revealed that the sensitivity can be improved by using a longer DNA fragment for labeling and smaller magnetic beads as solid support for the antibody. By using a 90 base pair DNA label, the signal was 15 times greater than that without labeling. In addition, by using a 120 nm magnetic bead, a minimum detection limit of 12.5 ng mL(-1) apolipoprotein A1 can be measured. Furthermore, this study integrates a semiconductor sensor with a microfluidic chip. With the help of microvalves and micromixers in the chip, the length of the mixing step for each immunoassay has been reduced from 1h to 20 min, and the sample volume has been reduced from 80 µL to 10 µL. In practice, a protein biomarker in a urinary bladder cancer patient's urine was successfully measured using this technique. This study provides a convenient and effective method to measure protein using a semiconductor sensor.

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Although Bicinchoninic acid (BCA) has been widely used to determine protein concentration, the mechanism of interaction between protein, copper ion and BCA in this assay is still not well known. Using the Micro BCA protein assay kit (Pierce Company), we measured the absorbance at 562 nm of BSA solutions with different concentrations of protein, and also varied the BCA concentration. When the concentration of protein was increased, the absorbance exhibited the known linear and nonlinear increase, and then reached an unexpected plateau followed by a gradual decrease. We introduced a model in which peptide chains competed with BCA for binding to cuprous ions. Formation of the well-known chromogenic complex of BCA-Cu(1+)-BCA was competed with the binding of two peptide bonds (NTPB) to cuprous ion, and there is the possibility of the existence of two new complexes. A simple equilibrium equation was established to describe the correlations between the substances in solution at equilibrium, and an empirical exponential function was introduced to describe the reduction reaction. Theoretical predictions of absorbance from the model were in good agreement with the measurements, which not only validated the competitive binding model, but also predicted a new complex of BCA-Cu(1+)-NTPB that might exist in the final solution. This work provides a new insight into understanding the chemical bases of the BCA protein assay and might extend the assay to higher protein concentration.