Comparative proteomics analysis of the mouse mini-gut organoid: insights into markers of gluten challenge from celiac disease intestinal biopsies

Introduction

Organoid models enable three-dimensional representation of cellular systems, providing flexible and accessible research tools, and can highlight key biomolecules. Such models of the intestinal epithelium can provide significant knowledge for the study of celiac disease and provide an additional context for the nature of markers observed from patient biopsy data.

Methods

Using LC-MS/MS, the proteomes of the crypt and enterocyte-like states of a mouse mini-gut organoid model were measured. The data were further compared with published biopsy data by comparing the changes induced by gluten challenge after a gluten-free diet.

Results and discussion

These analyses identified 4,850 protein groups and revealed how 400 putative biomarkers of dietary challenge were differentially expressed in the organoid model. In addition to the extensive changes within the differentiated cells, the data reiterated the disruption of the crypt-villus axis after gluten challenge. The mass spectrometry data are available via ProteomeXchange with the identifier PXD025690.

Regulators of proteostasis are translationally repressed in fibroblasts from patients with sporadic and LRRK2-G2019S Parkinson's disease

Deficits in protein synthesis are associated with Parkinson's disease (PD). However, it is not known which proteins are affected or if there are synthesis differences between patients with sporadic and Leucine-Rich Repeat Kinase 2 (LRRK2) G2019S PD, the most common monogenic form. Here we used bio-orthogonal non-canonical amino acid tagging for global analysis of newly translated proteins in fibroblasts from sporadic and LRKK2-G2019S patients. Quantitative proteomic analysis revealed that several nascent proteins were reduced in PD samples compared to healthy without any significant change in mRNA levels. Using targeted proteomics, we validated which of these proteins remained dysregulated at the static proteome level and found that regulators of endo-lysosomal sorting, mRNA processing and components of the translation machinery remained low. These proteins included autophagy-related protein 9A (ATG9A) and translational stability regulator YTH N6-ethyladenosine RNA binding protein 3 (YTHDF3). Notably, 77% of the affected proteins in sporadic patients were also repressed in LRRK2-G2019S patients (False discovery rate (FDR) < 0.05) in both sporadic and LRRK2-G2019S samples. This analysis of nascent proteomes from PD patient skin cells reveals that regulators of proteostasis are repressed in both sporadic and LRRK2-G2019S PD.

Benchmarking differential expression, imputation and quantification methods for proteomics data

Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools for protein quantification, imputation and differential expression (DE) analysis were generated in the past decade and the search for optimal tools is still going on. Moreover, due to the rapid development of RNA sequencing (RNA-seq) technology, a vast number of DE analysis methods were created for that purpose. The applicability of these newly developed RNA-seq-oriented tools to proteomics data remains in doubt. In order to benchmark these analysis methods, a proteomics dataset consisting of proteins derived from humans, yeast and drosophila, in defined ratios, was generated in this study. Based on this dataset, DE analysis tools, including microarray- and RNA-seq-based ones, imputation algorithms and protein quantification methods were compared and benchmarked. Furthermore, applying these approaches to two public datasets showed that RNA-seq-based DE tools achieved higher accuracy (ACC) in identifying DEPs. This study provides useful guidelines for analyzing quantitative proteomics datasets. All the methods used in this study were integrated into the Perseus software, version 2.0.3.0, which is available at https://www.maxquant.org/perseus.

Importance of Mitochondrial-Related Genes in Dilated Cardiomyopathy Based on Bioinformatics Analysis

We designed this study to identify potential key protein interaction networks, genes, and correlated pathways in dilated cardiomyopathy (DCM) via bioinformatics methods. We selected the GSE3586 microarray dataset, consisting of 15 dilated cardiomyopathic heart biopsy samples and 13 nonfailing heart biopsy samples. Initially, the GSE3586 dataset was downloaded and was analyzed with the limma package to identify differentially expressed genes (DEGs). A total of 172 DEGs consisting of 162 upregulated genes and ten downregulated genes in DCM were selected by the criterion of adjusted Pvalues less than 0.01 and the log2-fold change of 0.6 or greater. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to view the biological processes, cellular components, molecular function, and KEGG pathways of the DEGs. Next, protein-protein interactions were constructed, and the hub protein modules were identified. Then we selected the key genes DLD, UQCRC2, DLAT, SUCLA2, ATP5A1, PRDX3, FH, SDHD, and NDUFV1, which are involved in a wide range of biological activities, such as the citrate cycle, oxidation-reduction processes and cellular respiration, and energy derivation by oxidation of organic compounds in mitochondria. Finally, we found that currently there are no related gene-targeting drugs after exploring the predicted interactions between key genes and drugs, and transcription factors. In conclusion, our study provides greater understanding of the pathogenesis and underlying molecular mechanisms in DCM. This contributes to the exploration of potential gene therapy targets.

Characterization of the interleukin-17 effect on articular cartilage in a translational model: an explorative study

Background

Osteoarthritis (OA) is a progressive, chronic disease characterized by articular cartilage destruction. The pro-inflammatory cytokine IL-17 levels have been reported elevated in serum and synovial fluid of OA patients and correlated with increased cartilage defects and bone remodeling. The aim of this study was to characterize an IL-17-mediated articular cartilage degradation ex-vivo model and to investigate IL-17 effect on cartilage extracellular matrix protein turnover.

Methods

Full-depth bovine femoral condyle articular cartilage explants were cultured in serum-free medium for three weeks in the absence, or presence of cytokines: IL-17A (100 ng/ml or 25 ng/ml), or 10 ng OSM combined with 20 ng/ml TNFα (O + T). RNA isolation and PCR analysis were performed on tissue lysates to confirm IL-17 receptor expression. GAG and ECM-turnover biomarker release into conditioned media was assessed with dimethyl methylene blue and ELISA assays, respectively. Gelatin zymography was used for matrix metalloproteinase (MMP) 2 and MMP9 activity assessment in conditioned media, and shotgun LC-MS/MS for identification and label-free quantification of proteins and protein fragments in conditioned media. Western blotting was used to validate MS results.

Results

IL-17RA mRNA was expressed in bovine full-depth articular cartilage and the treatment with IL-17A did not interfere with metabolic activity of the model. IL-17A induced cartilage breakdown; conditioned media GAG levels were 3.6-fold-elevated compared to untreated. IL-17A [100 ng/ml] induced ADAMTS-mediated aggrecan degradation fragment release (14-fold increase compared to untreated) and MMP-mediated type II collagen fragment release (6-fold-change compared to untreated). MS data analysis revealed 16 differentially expressed proteins in IL-17A conditioned media compared to untreated, and CHI3L1 upregulation in conditioned media in response to IL-17 was confirmed by Western blotting.

Conclusions

We showed that IL-17A has cartilage modulating potential. It induces collagen and aggrecan degradation indicating an upregulation of MMPs. This was confirmed by zymography and mass spectrometry data. We also showed that the expression of other cytokines is induced by IL-17A, which provide further insight to the pathways that are active in response to IL-17A. This exploratory study confirms that IL-17A may play a role in cartilage pathology and that the applied model may be a good tool to further investigate it.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

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.

A comparative proteomic study of plasma in Colombian childhood acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Owing to the incorporation of risk-adapted therapy and the arrival of new directed agents, the cure rate and survival of patients with ALL have improved dramatically, get near to 90%. In Latin American countries, the mortality rates of ALL are high, for example in Colombia, during the last decade, ALL has been the most prevalent cancer among children between 0–14 years of age. In the face of this public health problem and coupled with the fact that the knowledge of the proteome of the child population is little, our investigation proposes the study of the plasma proteome of Colombian children diagnosed with B-cell ALL (B-ALL) to determine potential disease markers that could reflect processes altered by the presence of the disease or in response to it. A proteomic study by LC-MS/MS and quantification by label-free methods were performed in search of proteins differentially expressed between healthy children and those diagnosed with B-ALL. We quantified a total of 472 proteins in depleted blood plasma, and 25 of these proteins were differentially expressed (fold change >2, Bonferroni-adjusted P-values <0.05). Plasma Aggrecan core protein, alpha-2-HS-glycoprotein, coagulation factor XIII A chain and gelsolin protein were examined by ELISA assay and compared to shotgun proteomics results. Our data provide new information on the plasma proteome of Colombian children. Additionally, these proteins may also have certain potential as illness markers or as therapeutic targets in subsequent investigations.

Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase

Many marine animals, ranging from corals to fishes, synchronise reproduction to lunar cycles. In the annelid Platynereis dumerilii, this timing is orchestrated by an endogenous monthly (circalunar) clock entrained by moonlight. Whereas daily (circadian) clocks cause extensive transcriptomic and proteomic changes, the quality and quantity of regulations by circalunar clocks have remained largely elusive. By establishing a combined transcriptomic and proteomic profiling approach, we provide first systematic insight into the molecular changes in Platynereis heads between circalunar phases, and across sexual differentiation and maturation. Whereas maturation elicits large transcriptomic and proteomic changes, the circalunar clock exhibits only minor transcriptomic, but strong proteomic regulation. Our study provides a versatile extraction technique and comprehensive resources. It corroborates that circadian and circalunar clock effects are likely distinct and identifies key molecular brain signatures for reproduction, sex and circalunar clock phase. Examples include prepro-whitnin/proctolin and ependymin-related proteins as circalunar clock targets.

Pulmonary sarcoidosis is associated with exosomal vitamin D-binding protein and inflammatory molecules

BACKGROUND

Sarcoidosis is an inflammatory granulomatous disorder characterized by accumulation of T_H1-type CD4⁺ T cells and immune effector cells within affected organs, most frequently the lungs. Exosomes are extracellular vesicles conveying intercellular communication with possible diagnostic and therapeutic applications.

OBJECTIVES

We aimed to provide an understanding of the proinflammatory role of bronchoalveolar lavage fluid (BALF) exosomes in patients with sarcoidosis and to find candidates for disease biomarkers.

METHODS

We performed a mass spectrometric proteomics characterization of BALF exosomes from 15 patients with sarcoidosis and 5 healthy control subjects and verified the most interesting results with flow cytometry, ELISA, and Western blot analyses in an additional 39 patients and 22 control subjects.

RESULTS

More than 690 proteins were identified in the BALF exosomes, several of which displayed significant upregulation in patients, including inflammation-associated proteins, such as leukotriene A₄ hydrolase. Most of the complement-activating factors were upregulated, whereas the complement regulator CD55 was seen less in patients compared with healthy control subjects. In addition, for the first time, we detected vitamin D-binding protein in BALF exosomes, which was more abundant in patients. To evaluate exosome-associated vitamin D-binding protein as a biomarker for sarcoidosis, we investigated plasma exosomes from 23 patients and 11 healthy control subjects and found significantly higher expression in patients.

CONCLUSION

Together, these data contribute to understanding the role of exosomes in lung disease and provide suggestions for highly warranted sarcoidosis biomarkers. Furthermore, the validation of an exosome-associated biomarker in the blood of patients provides novel, and less invasive, opportunities for disease diagnosis.

Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse

Estrogens are suggested to lower the risk of developing metabolic syndrome in both sexes. In this study, we investigated how the increased circulating estrogen-to-androgen ratio (E/A) alters liver lipid metabolism in males. The cytochrome P450 aromatase (P450arom) is an enzyme converting androgens to estrogens. Male mice overexpressing human aromatase enzyme (AROM+ mice), and thus have high circulating E/A, were used as a model in this study. Proteomics and gene expression analyses indicated an increase in the peroxisomal β-oxidation in the liver of AROM+ mice as compared with their wild type littermates. Correspondingly, metabolomic analysis revealed a decrease in the amount of phosphatidylcholines with long-chain fatty acids in the plasma. With interest we noted that the expression of Cyp4a12a enzyme, which specifically metabolizes arachidonic acid (AA) to 20-hydroxy AA, was dramatically decreased in the AROM+ liver. As a consequence, increased amounts of phospholipids having AA as a fatty acid tail were detected in the plasma of the AROM+ mice. Overall, these observations demonstrate that high circulating E/A in males is linked to indicators of higher peroxisomal β-oxidation and lower AA metabolism in the liver. Furthermore, the plasma phospholipid profile reflects the changes in the liver lipid metabolism.

ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer

Recent comprehensive assessments of RNA-seq technology support its utility in quantifying gene expression in various samples. The next step of rigorously quantifying differences between sample groups, however, still lacks well-defined best practices. Although a number of advanced statistical methods have been developed, several studies demonstrate that their performance depends strongly on the data under analysis, which compromises practical utility in real biomedical studies. As a solution, we propose to use a data-adaptive procedure that selects an optimal statistic capable of maximizing reproducibility of detections. After demonstrating its improved sensitivity and specificity in a controlled spike-in study, the utility of the procedure is confirmed in a real biomedical study by identifying prognostic markers for clear cell renal cell carcinoma (ccRCC). In addition to identifying several genes previously associated with ccRCC prognosis, several potential new biomarkers among genes regulating cell growth, metabolism and solute transport were detected.

Empirical comparison of structure-based pathway methods

Multiple methods have been proposed to estimate pathway activities from expression profiles, and yet, there is not enough information available about the performance of those methods. This makes selection of a suitable tool for pathway analysis difficult. Although methods based on simple gene lists have remained the most common approach, various methods that also consider pathway structure have emerged. To provide practical insight about the performance of both list-based and structure-based methods, we tested six different approaches to estimate pathway activities in two different case study settings of different characteristics. The first case study setting involved six renal cell cancer data sets, and the differences between expression profiles of case and control samples were relatively big. The second case study setting involved four type 1 diabetes data sets, and the profiles of case and control samples were more similar to each other. In general, there were marked differences in the outcomes of the different pathway tools even with the same input data. In the cancer studies, the results of a tested method were typically consistent across the different data sets, yet different between the methods. In the more challenging diabetes studies, almost all the tested methods detected as significant only few pathways if any.

Statistical detection of quantitative protein biomarkers provides insights into signaling networks deregulated in acute myeloid leukemia

The increasing coverage and sensitivity of LC-MS/MS-based proteomics have expanded its applications in systems medicine. In particular, label-free quantitation approaches are enabling biomarker discovery in terms of statistical comparison of proteomic profiles across large numbers of clinical samples. However, it still remains poorly understood how much protein markers can add novel insights compared to markers derived from mRNA transcriptomic profiling. Using paired label-free LC-MS/MS and gene expression microarray measurements from primary samples of patients with acute myeloid leukemia (AML), we demonstrate here that while the quantitative proteomic and transcriptomic profiles were highly correlated, in general, the marker panels showing statistically significant expression changes across the disease and healthy groups were profoundly different between protein and mRNA levels. In particular, the proteomic assay enabled unique links to known leukemic processes, which were missed when using the transcriptomic profiling alone, as well as identified additional links to metabolic regulators and chromatin remodelers, such as GPX1, fumarate hydratase, and SET oncogene, which have subsequently been evaluated in independent AML samples. Overall, these results highlighted the complementary and informative view obtained from the quantitative LC-MS/MS approach into the AML deregulated signaling networks.

Comparison of software packages for detecting differential expression in RNA-seq studies

RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice.

Optimizing detection of transcription factor-binding sites in ChIP-seq experiments

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) offers a powerful means to study transcription factor binding on a genome-wide scale. While a number of advanced software packages have already become available for identifying ChIP-seq-binding sites, it has become evident that the choice of the package together with its adjustable parameters can considerably affect the biological conclusions made from the data. Therefore, to aid these choices, we have recently introduced a reproducibility-optimization procedure, which computationally adjusts the parameters of the popular peak detection algorithms for each ChIP-seq data separately. Here, we provide a detailed description of the procedure together with practical guidelines on how to apply its implementation, the peakROTS R-package, in a given ChIP-seq experiment.

Optimized detection of transcription factor-binding sites in ChIP-seq experiments

We developed a computational procedure for optimizing the binding site detections in a given ChIP-seq experiment by maximizing their reproducibility under bootstrap sampling. We demonstrate how the procedure can improve the detection accuracies beyond those obtained with the default settings of popular peak calling software, or inform the user whether the peak detection results are compromised, circumventing the need for arbitrary re-iterative peak calling under varying parameter settings. The generic, open-source implementation is easily extendable to accommodate additional features and to promote its widespread application in future ChIP-seq studies. The peakROTS R-package and user guide are freely available at http://www.nic.funet.fi/pub/sci/molbio/peakROTS.

Chipster: user-friendly analysis software for microarray and other high-throughput data

BACKGROUND

The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software.

RESULTS

Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies.

CONCLUSIONS

Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.

Microarray assessment of the influence of the conceptus on gene expression in the mouse uterus during decidualization

During pregnancy in several species including humans and rodents, the endometrium undergoes decidualization. This process of differentiation from endometrial to decidual tissue occurs only after the onset of implantation in mice. It can also be artificially induced causing the formation of deciduomal tissue. The purpose of this study was to compare the gene expression profile of the developing decidua in pregnant mice with the deciduoma formed after artificial induction in an effort to identify conceptus-influenced changes in uterine gene expression during decidualization. We induced decidualization artificially by transferring blastocyst-sized ConA-coated agarose beads into the uterus on day 2.5 of pseudopregnancy. Recently published work has found this model to be more 'physiological' than other methods. Total RNA was isolated from blastocyst and bead-induced 'implantation' sites of the uteri of day 7.5 pregnant (decidua) and pseudopregnant (deciduoma) mice respectively. This RNA was then used for microarray analysis using Mouse Illumina BeadArray chips. This analysis revealed potential differential mRNA levels of only 45 genes between the decidua and bead-induced deciduoma tissues. We confirmed the differential mRNA levels of 31 of these genes using quantitative RT-PCR. Finally, the level and localization of some of the mRNAs for select genes (Aldh3a1, Bcmo1, Guca2b, and Inhbb) identified by our microarray analysis were examined in more detail. This study provides the identity of a small set of genes whose expression in the uterus during decidualization may be influenced by molecular signals from the conceptus.

MMP1 bimodal expression and differential response to inflammatory mediators is linked to promoter polymorphisms

Background

Identifying the functional importance of the millions of single nucleotide polymorphisms (SNPs) in the human genome is a difficult challenge. Therefore, a reverse strategy, which identifies functionally important SNPs by virtue of the bimodal abundance across the human population of the SNP-related mRNAs will be useful. Those mRNA transcripts that are expressed at two distinct abundances in proportion to SNP allele frequency may warrant further study. Matrix metalloproteinase 1 (MMP1) is important in both normal development and in numerous pathologies. Although much research has been conducted to investigate the expression of MMP1 in many different cell types and conditions, the regulation of its expression is still not fully understood.

Results

In this study, we used a novel but straightforward method based on agglomerative hierarchical clustering to identify bimodally expressed transcripts in human umbilical vein endothelial cell (HUVEC) microarray data from 15 individuals. We found that MMP1 mRNA abundance was bimodally distributed in un-treated HUVECs and showed a bimodal response to inflammatory mediator treatment. RT-PCR and MMP1 activity assays confirmed the bimodal regulation and DNA sequencing of 69 individuals identified an MMP1 gene promoter polymorphism that segregated precisely with the MMP1 bimodal expression. Chromatin immunoprecipation (ChIP) experiments indicated that the transcription factors (TFs) ETS1, ETS2 and GATA3, bind to the MMP1 promoter in the region of this polymorphism and may contribute to the bimodal expression.

Conclusions

We describe a simple method to identify putative bimodally expressed RNAs from transcriptome data that is effective yet easy for non-statisticans to understand and use. This method identified bimodal endothelial cell expression of MMP1, which appears to be biologically significant with implications for inflammatory disease. (271 Words)