Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation

BACKGROUND

Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary.

RESULTS

Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet.

CONCLUSIONS

InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign . A free web service is also provided: http://www.injection.design .

Automated splitting into batches for observational biomedical studies with sequential processing

Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings.

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

HopE and HopD Porin-Mediated Drug Influx Contributes to Intrinsic Antimicrobial Susceptibility and Inhibits Streptomycin Resistance Acquisition by Natural Transformation in Helicobacter pylori

Helicobacter pylori is a human pathogen competent for natural transformation. Intrinsic and acquired antibiotic resistance contribute to the survival and multiplication of H. pylori under antibiotics. While drug-resistance dissemination by natural transformation (NT)-mediated horizontal gene transfer remains poorly understood in H. pylori. The purpose of the study was to investigate the role of H. pylori porins (HopA, HopB, HopC, HopD, and HopE) in the intrinsic antibiotic resistance and to preliminarily reveal the potential effect of HopE and HopD porins in streptomycin resistance acquisition after NT in the presence of antibiotics. Using traditional antibiotic susceptibility tests and growth curve analysis, we found the MIC values of metronidazole, clarithromycin, levofloxacin, tetracycline, rifampin, and streptomycin in mutants lacking HopE and/or HopD were significantly elevated compare to those in wild-type strain. The quantitative analysis of the tetramethyl rhodamine isothiocyanate (TRITC)-labeled streptomycin accumulation at the single-cell level showed reduced streptomycin intracellular fluorescence in ΔhopE and ΔhopD mutant cells. Furthermore, in the presence of translation-inhibiting antibiotic streptomycin, the resistance acquisition frequency was decreased in the wild-type strain, which could be reversed by mutants lacking HopE and HopD that restored relatively high resistance acquisition frequencies. By transforming a pUC19-rpsLmut-sfgfp linear plasmid carrying a streptomycin conferring mutation, we observed that the impaired ability of rpsLmut synthesis in the wild-type strain was restored in the ΔhopE and ΔhopD mutant transformants. Our study revealed that in the presence of streptomycin, resistance acquisition at least partially relied on the deletion of the hopE and hopD genes, because their loss reduced streptomycin concentration in the cell and thus restored the expression of the resistance-conferring gene, which was inhibited by streptomycin in wild-type strain. The loss of HopE and HopD influx activity may also preserve resistance acquisition by transformation in the presence of antibiotics with other modes of action. IMPORTANCE Helicobacter pylori is constitutively competent for natural transformation (NT) and possesses an efficient system for homologous recombination, which could be utilized to study the NT-mediated horizontal gene transfer induced antibiotic resistance acquisition. Bacterial porins have drawn renewed attention because of their crucial role in antibiotic susceptibility. From the perspective of porin-mediated influx in H. pylori, our study preliminarily revealed the important role of HopE and HopD porins not only in preserving the intrinsic susceptibility to specific antibiotic but also in evading acquired antibiotic resistance by NT in the presence of translation-inhibiting antimicrobial. Therefore, the loss of HopE or HopD porin in H. pylori genomes, combined with the large number of secreted or cell-free genetic elements carrying mutations conferring antibiotic resistance, may raise the possibility that this mechanism plays a potential role in the propagation of antibiotic resistance within H. pylori communities.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

The unique molecular targets associated antioxidant and antifibrotic activity of curcumin in in vitro model of acute lung injury: A proteomic approach

Bleomycin (BLM) injury is associated with the severity of acute lung injury (ALI) leading to fibrosis, a high-morbidity, and high-mortality respiratory disease of unknown etiology. BLM-induced ALI is marked by the activation of a potent fibrogenic cytokine transcription growth factor beta-1 (TGFβ-1), which is considered a critical cytokine in the progression of alveolar injury. Previously, our work demonstrated that a diet-derived compound curcumin (diferuloylmethane), represents its antioxidative and antifibrotic application in TGF-β1-mediated BLM-induced alveolar basal epithelial cells. However, curcumin-specific protein targets, as well as its mechanism using mass spectrometry-based proteomic approach, remain elusive. To elucidate the underlying mechanism, a quantitative proteomics approach and bioinformatics analysis were employed to identify the protein targets of curcumin in BLM or TGF-β1-treated cells. With subsequent in vitro experiments, curcumin-related pathways and cellular processes were predicted and validated. The current study discusses two separate proteomics experiments using BLM and TGF-β1-treated cells with the proteomics approach, various unique target proteins were identified, and proteomic analysis revealed that curcumin reversed the expressions of unique proteins like DNA topoisomerase 2-alpha (TOP2A), kinesin-like protein (KIF11), centromere protein F (CENPF), and so on BLM or TGF-β1 injury. For the first time, the current study reveals that curcumin restores TGF-β1 induced peroxisomes like PEX-13, PEX-14, PEX-19, and ACOX1. This was verified by subsequent in vitro assays. This study generated molecular evidence to deepen our understanding of the therapeutic role of curcumin at the proteomic level and may be useful to identify molecular targets for future drug discovery.

Proteotyping of knockout mouse strains reveals sex- and strain-specific signatures in blood plasma

We proteotyped blood plasma from 30 mouse knockout strains and corresponding wild-type mice from the International Mouse Phenotyping Consortium. We used targeted proteomics with internal standards to quantify 375 proteins in 218 samples. Our results provide insights into the manifested effects of each gene knockout at the plasma proteome level. We first investigated possible contamination by erythrocytes during sample preparation and labeled, in one case, up to 11 differential proteins as erythrocyte originated. Second, we showed that differences in baseline protein abundance between female and male mice were evident in all mice, emphasizing the necessity to include both sexes in basic research, target discovery, and preclinical effect and safety studies. Next, we identified the protein signature of each gene knockout and performed functional analyses for all knockout strains. Further, to demonstrate how proteome analysis identifies the effect of gene deficiency beyond traditional phenotyping tests, we provide in-depth analysis of two strains, C8a-/- and Npc2+/-. The proteins encoded by these genes are well-characterized providing good validation of our method in homozygous and heterozygous knockout mice. Ig alpha chain C region, a poorly characterized protein, was among the differentiating proteins in C8a-/-. In Npc2+/- mice, where histopathology and traditional tests failed to differentiate heterozygous from wild-type mice, our data showed significant difference in various lysosomal storage disease-related proteins. Our results demonstrate how to combine absolute quantitative proteomics with mouse gene knockout strategies to systematically study the effect of protein absence. The approach used here for blood plasma is applicable to all tissue protein extracts.

Importance of Block Randomization When Designing Proteomics Experiments

Randomization is used in experimental design to reduce the prevalence of unanticipated confounders. Complete randomization can however create imbalanced designs, for example, grouping all samples of the same condition in the same batch. Block randomization is an approach that can prevent severe imbalances in sample allocation with respect to both known and unknown confounders. This feature provides the reader with an introduction to blocking and randomization, and insights into how to effectively organize samples during experimental design, with special considerations with respect to proteomics.