A comparison between protein profiles of B cell subpopulations and mantle cell lymphoma cells

<p>Abstract</p> <p>Background</p> <p>B-cell lymphomas are thought to reflect different stages of B-cell maturation. Based on cytogenetics and molecular markers, mantle cell lymphoma (MCL) is presumed to derive predominantly from naïve, pre-germinal centre (pre-GC) B lymphocytes. The aim of this study was to develop a method to investigate the similarity between MCL cells and different B-cell compartments on a protein expression level.</p> <p>Methods</p> <p>Subpopulations of B cells representing the germinal centre (GC), the pre-GC mantle zone and the post-GC marginal zone were isolated from tonsils using automated magnetic cell sorting (AutoMACS) of cells based on their expression of CD27 and IgD. Protein profiling of the B cell subsets, of cell lines representing different lymphomas and of primary MCL samples was performed using top-down proteomics profiling by surface-enhanced laser detection/ionization time-of-flight mass spectrometry (SELDI-TOF-MS).</p> <p>Results</p> <p>Quantitative MS data of significant protein peaks (p-value < 0.05) separating the three B-cell subpopulations were generated. Together, hierarchical clustering and principal component analysis (PCA) showed that the primary MCL samples clustered together with the pre- and post-GC subpopulations. Both primary MCL cells and MCL cell lines were clearly separated from the B cells representing the GC compartment.</p> <p>Conclusion</p> <p>AutoMACS sorting generates sufficient purity to enable a comparison between protein profiles of B cell subpopulations and malignant B lymphocytes applying SELDI-TOF-MS. Further validation with an increased number of patient samples and identification of differentially expressed proteins would enable a search for possible treatment targets that are expressed during the early development of MCL.</p

Multivariate meta-analysis of proteomics data from human prostate and colon tumours

<p>Abstract</p> <p>Background</p> <p>There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work.</p> <p>Results</p> <p>We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS) model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples.</p> <p>Conclusions</p> <p>The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.</p

Correlating gene and protein expression data using Correlated Factor Analysis

<p>Abstract</p> <p>Background</p> <p>Joint analysis of transcriptomic and proteomic data taken from the same samples has the potential to elucidate complex biological mechanisms. Most current methods that integrate these datasets allow for the computation of the correlation between a gene and protein but only after a one-to-one matching of genes and proteins is done. However, genes and proteins are connected via biological pathways and their relationship is not necessarily one-to-one. In this paper, we investigate the use of Correlated Factor Analysis (CFA) for modeling the correlation of genome-scale gene and protein data. Unlike existing approaches, CFA considers all possible gene-protein pairs and utilizes all gene and protein information in its modeling framework. The Generalized Singular Value Decomposition (gSVD) is another method which takes into account all available transcriptomic and proteomic data. Comparison is made between CFA and gSVD.</p> <p>Results</p> <p>Our simulation study indicates that the CFA estimates can consistently capture the dominant patterns of correlation between two sets of measurements; in contrast, the gSVD estimates cannot do that. Applied to real cancer data, the list of co-regulated genes and proteins identified by CFA has biologically meaningful interpretation, where both the gene and protein expressions are pointing to the same processes. Among the GO terms for which the genes and proteins are most correlated, we observed blood vessel morphogenesis and development.</p> <p>Conclusion</p> <p>We demonstrate that CFA is a useful tool for gene-protein data integration and modeling, where the main question is in finding which patterns of gene expression are most correlated with protein expression.</p

A novel method for sample preparation of fresh lung cancer tissue for proteomics analysis by tumor cell enrichment and removal of blood contaminants

<p>Abstract</p> <p>Background</p> <p>In-depth proteomics analyses of tumors are frequently biased by the presence of blood components and stromal contamination, which leads to large experimental variation and decreases the proteome coverage. We have established a reproducible method to prepare freshly collected lung tumors for proteomics analysis, aiming at tumor cell enrichment and reduction of plasma protein contamination. We obtained enriched tumor-cell suspensions (ETS) from six lung cancer cases (two adenocarcinomas, two squamous-cell carcinomas, two large-cell carcinomas) and from two normal lung samples. The cell content of resulting ETS was evaluated with immunocytological stainings and compared with the histologic pattern of the original specimens. By means of a quantitative mass spectrometry-based method we evaluated the reproducibility of the sample preparation protocol and we assessed the proteome coverage by comparing lysates from ETS samples with the direct lysate of corresponding fresh-frozen samples.</p> <p>Results</p> <p>Cytological analyses on cytospin specimens showed that the percentage of tumoral cells in the ETS samples ranged from 20% to 70%. In the normal lung samples the percentage of epithelial cells was less then 10%. The reproducibility of the sample preparation protocol was very good, with coefficient of variation at the peptide level and at the protein level of 13% and 7%, respectively. Proteomics analysis led to the identification of a significantly higher number of proteins in the ETS samples than in the FF samples (244 vs 109, respectively). Albumin and hemoglobin were among the top 5 most abundant proteins identified in the FF samples, showing a high contamination with blood and plasma proteins, whereas ubiquitin and the mitochondrial ATP synthase 5A1 where among the top 5 most abundant proteins in the ETS samples.</p> <p>Conclusion</p> <p>The method is feasible and reproducible. We could obtain a fair enrichment of cells but the major benefit of the method was an effective removal of contaminants from red blood cells and plasma proteins resulting in larger proteome coverage compared to the direct lysis of frozen samples. This sample preparation method may be successfully implemented for the discovery of lung cancer biomarkers on tissue samples using mass spectrometry-based proteomics.</p

Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

<p>Abstract</p> <p>Background</p> <p>Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.</p> <p>Results</p> <p>We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.</p> <p>Conclusions</p> <p>The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.</p

DEqMS : A Method for Accurate Variance Estimation in Differential Protein Expression Analysis

Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.Peer reviewe

Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides

We have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling including optimized target/decoy generation by the algorithm DecoyPyrat. Finally, we have reanalyzed six public datasets in PRIDE by generating cell-type specific databases for 65 cell lines using the pypgatk and pgdb workflow, revealing a wealth of non-canonical or cryptic peptides amounting to >5% of the total number of peptides identified

Käytösoireisen muistisairaan lääkkeettömät hoitotyön keinot : Opas sairaanhoitajille

Aiheena opinnäytetyölle oli Käytösoireisen muistisairaan lääkkeettömät hoitotyön keinot – opas sairaanhoitajille. Opinnäytetyön tavoitteena oli tuottaa tietoa muistisairaan käytöshäiriöiden syistä ja lisäksi lisätä ymmärrystä sairaanhoitajille muistisairaan haasteellisen käytöksen lisääntymisestä. Opinnäytetyön tarkoituksena oli tuottaa yhteistyökumppanille opas, joka sisältää yleisimmät muistisairaudet ja niihin liittyvien käytöshäiriöiden ilmenemismuodot aiheuttajineen ja vaikuttavine tekijöineen. Tämän lisäksi opas sisältää hoitotyön keinoja, joiden avulla sairaanhoitaja pystyy havainnoimaan ja lieventämään muistisairaan käytösoireita. Opinnäytetyö on tehty yhteistyössä Suupohjan peruspalveluliikelaitoskuntayhtymän hoidon ja hoivan alueen kanssa. Etenevien muistisairauksien diagnostiikkaan kuuluu käytösoireiden lisääntyminen. Käytösoireita on muistisairauksien kaikissa vaiheissa ja niiden ilmaantuminen voi johtaa liialliseen ja turhaan rauhoittavien lääkkeiden määräämiseen ja käyttöön, on kuitenkin hyvä huomioida kokonaisvaltainen hoito, joka on potilaan yksilöllisen tilanteen huomioon ottava. Muistisairaiden määrä kasvaa nopeasti, varhaisen diagnosoinnin avulla pystytään ylläpitämään sairastuneen toimintakykyä ja huomioimaan sairastuneen oman elämänlaadun pysyminen hyvänä, unohtamatta hänen läheisiään. Käytösoireisen potilaan hoitolinja tulisi valita arvioimalla oireita ja selvittämällä niiden syy. Lääkkeettömän hoidon tarkoitus on, että muistisairaasta huolehditaan kokonaisvaltaisesti ja mahdollisimman hyvin hänen tarpeensa huomioon ottaen. Sairastuneen toimintakyvyn tukeminen on tärkeää, silloin hän tuntee olonsa turvatuksi ja arvostetuksi. Hyvien elämäntapojen huomioiminen, riittävän unen ja aktiviteetin turvaaminen tukevat sairastuneen tasapainon tunnetta. Käytöshäiriöiden syntyyn vaikuttaa myös ympäristössä tapahtuvat muutokset. Sairaanhoitajan on tärkeä luoda sairastuneelle tässä tilanteessa rauhallinen ja turvattu ympäristö.The subject for the thesis is non-drug nursing methods of a patient with memory disease and behavioural disorder. The aim of the thesis was to provide information on the causes of behavioural disorders of a patient with memory disease, and in addition, to increase nurses’ understanding about the negative behaviour of memory patients. The purpose of the thesis was to produce a guide containing the most common memory disorders and related manifestations of behavioural disorders, with their causes and contributing factors. In addition, the guide includes nursing tools that help the nurse to observe and mitigate the behavioural disorders of a patient with a memory disease. The thesis has been carried out in cooperation with the treatment and care area of The Suupohja Area Health and Social Services Joint Municipal Board. The diagnostics for progressive memory diseases include an increase in behavioural disorders. There are behavioural disorders at all stages of memory disorders, and their appearance may lead to the prescription and use of excessive and unnecessary medication. However, it is good to take into account the holistic treatment that is appropriate to the patient's individual condition. The number of patients with memory disease is increasing rapidly. With early diagnosis, it is pos-sible to maintain patients’ functional ability and take account of their quality of life, not forgetting their close relatives. The treatment line for the patient with behavioural disorder should be selected by evaluating the symptoms and finding out their cause. The purpose of non-drug nursing is that the patient with memory disorder is taken care of comprehensively, and his or her needs are taken into account as well as possible. Supporting the functional ability of the patient is important, and he or she feels secure and appreciated. Paying attention to a good lifestyle, ensuring adequate sleep and activity support the balance feeling of the patient. Changes in the environment also affect the appearance of behavioural disorders. It is important for the nurse to create a calm and secure environment for the patient in this situation

Therapeutic Cancer Vaccination with Immunopeptidomics-Discovered Antigens Confers Protective Antitumor Efficacy

Simple Summary Immunotherapy has revolutionized cancer treatment, yet many tumors remain resistant to current immuno-oncology therapies. Here we explore a novel, customized oncolytic adenovirus vaccine platform as immunotherapy in a resistant tumor model. We present a workflow for customizing the oncolytic vaccine for improved tumor targeting. This targeting is based on experimentally discovered tumor antigens, which are incorporated as active components of the vaccine formulation. The pipeline may be further applied for designing personalized therapeutic cancer vaccines. Knowledge of clinically targetable tumor antigens is becoming vital for broader design and utility of therapeutic cancer vaccines. This information is obtained reliably by directly interrogating the MHC-I presented peptide ligands, the immunopeptidome, with state-of-the-art mass spectrometry. Our manuscript describes direct identification of novel tumor antigens for an aggressive triple-negative breast cancer model. Immunopeptidome profiling revealed 2481 unique antigens, among them a novel ERV antigen originating from an endogenous retrovirus element. The clinical benefit and tumor control potential of the identified tumor antigens and ERV antigen were studied in a preclinical model using two vaccine platforms and therapeutic settings. Prominent control of established tumors was achieved using an oncolytic adenovirus platform designed for flexible and specific tumor targeting, namely PeptiCRAd. Our study presents a pipeline integrating immunopeptidome analysis-driven antigen discovery with a therapeutic cancer vaccine platform for improved personalized oncolytic immunotherapy.Peer reviewe