Comparison of peripheral blood automated hematopoietic progenitor cell count and flow cytometric CD34+ cell count

BACKGROUND

Stem cell apheresis in the context of autologous stem cell transplantation requires an accurate cluster of differentiantion 34 (CD34+) count determined by flow cytometry as the current gold standard. Since flow cytometry is a personnel and time-intensive diagnostic tool, automated stem cell enumeration may provide a promising alternative. Hence, this study aimed to compare automated hematopoietic progenitor enumeration carried out on a Sysmex XN-20 module compared with conventional flow cytometric measurements.

METHODS

One hundred forty-three blood samples from 41 patients were included in this study. Correlation between the two methods was calculated over all samples, depending on leukocyte count and diagnosis.

RESULTS

Overall, we found a high degree of correlation (r = 0.884). Furthermore, correlation was not impaired by elevated leukocyte counts (>10 000/μL, r = 0.860 vs <10 000/μL, r = 0.849; >20 000/μL, r = 0.843 vs <20 000/μL, r = 0.875). However, correlation was significantly impaired in patients with multiple myeloma (multiple myeloma r = 0.840 vs nonmyeloma r = 0.934).

SUMMARY

Stem cell measurement carried out on the Sysmex XN-20 module provides a significant correlation with flow cytometry and might be implemented in clinical practice. In clinical decision-making, there was discrepancy of under 15% of cases. In multiple myeloma patients, XN-20 should be used with caution.

Exploring the relationship between HCMV serostatus and outcomes in COVID-19 sepsis

Background

Sepsis, a life-threatening condition caused by the dysregulated host response to infection, is a major global health concern. Understanding the impact of viral or bacterial pathogens in sepsis is crucial for improving patient outcomes. This study aimed to investigate the human cytomegalovirus (HCMV) seropositivity as a risk factor for development of sepsis in patients with COVID-19.

Methods

A multicenter observational study enrolled 95 intensive care patients with COVID-19-induced sepsis and 80 post-surgery individuals as controls. HCMV serostatus was determined using an ELISA test. Comprehensive clinical data, including demographics, comorbidities, and 30-day mortality, were collected. Statistical analyses evaluated the association between HCMV seropositivity and COVID-19 induced sepsis.

Results

The prevalence of HCMV seropositivity did not significantly differ between COVID-19-induced sepsis patients (78%) and controls (71%, p = 0.382) in the entire cohort. However, among patients aged ≤60 years, HCMV seropositivity was significantly higher in COVID-19 sepsis patients compared to controls (86% vs 61%, respectively; p = 0.030). Nevertheless, HCMV serostatus did not affect 30-day survival.

Discussion

These findings confirm the association between HCMV seropositivity and COVID-19 sepsis in non-geriatric patients. However, the lack of an independent effect on 30-day survival can be explained by the cross-reactivity of HCMV specific CD8+ T-cells towards SARS-CoV-2 peptides, which might confer some protection to HCMV seropositive patients. The inclusion of a post-surgery control group strengthens the generalizability of the findings. Further research is needed to elucidate the underlying mechanisms of this association, explore different patient populations, and identify interventions for optimizing patient management.

Conclusion

This study validates the association between HCMV seropositivity and severe COVID-19-induced sepsis in non-geriatric patients, contributing to the growing body of evidence on viral pathogens in sepsis. Although HCMV serostatus did not independently influence 30-day survival, future investigations should focus on unraveling the intricate interplay between HCMV, immune responses, and COVID-19. These insights will aid in risk stratification and the development of targeted interventions for viral sepsis.

Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction

INTRODUCTION

An increasing amount of longitudinal health data is available on critically ill septic patients in the age of digital medicine, including daily sequential organ failure assessment (SOFA) score measurements. Thus, the assessment in sepsis focuses increasingly on the evaluation of the individual disease's trajectory. Machine learning (ML) algorithms may provide a promising approach here to improve the evaluation of daily SOFA score dynamics. We tested whether ML algorithms can outperform the conventional ΔSOFA score regarding the accuracy of 30-day mortality prediction.

METHODS

We used the multicentric SepsisDataNet.NRW study cohort that prospectively enrolled 252 sepsis patients between 03/2018 and 09/2019 for training ML algorithms, i.e. support vector machine (SVM) with polynomial kernel and artificial neural network (aNN). We used the Amsterdam UMC database covering 1,790 sepsis patients for external and independent validation.

RESULTS

Both SVM (AUC 0.84; 95% CI: 0.71-0.96) and aNN (AUC 0.82; 95% CI: 0.69-0.95) assessing the SOFA scores of the first seven days led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score between day 1 and 7 (AUC 0.73; 95% CI: 0.65-0.80; p = 0.02 and p = 0.05, respectively). These differences were even more prominent the shorter the time interval considered. Using the SOFA scores of day 1 to 3 SVM (AUC 0.82; 95% CI: 0.68 0.95) and aNN (AUC 0.80; 95% CI: 0.660.93) led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score (AUC 0.66; 95% CI: 0.58-0.74; p < 0.01 and p < 0.01, respectively). Strikingly, all these findings could be confirmed in the independent external validation cohort.

CONCLUSIONS

The ML-based algorithms using daily SOFA scores markedly improved the accuracy of mortality compared to the conventional ΔSOFA score. Therefore, this approach could provide a promising and automated approach to assess the individual disease trajectory in sepsis. These findings reflect the potential of incorporating ML algorithms as robust and generalizable support tools on intensive care units.

Observations from the Proteomics Bench

Many challenges in proteomics result from the high-throughput nature of the experiments. This paper first presents pre-analytical problems, which still occur, although the call for standardization in omics has been ongoing for many years. This article also discusses aspects that affect bioinformatic analysis based on three sets of reference data measured with different orbitrap instruments. Despite continuous advances in mass spectrometer technology as well as analysis software, data-set-wise quality control is still necessary, and decoy-based estimation, although challenged by modern instruments, should be utilized. We draw attention to the fact that numerous young researchers perceive proteomics as a mature, readily applicable technology. However, it is important to emphasize that the maximum potential of the technology can only be realized by an educated handling of its limitations.

Use of Multiple Machine Learning Approaches for Selecting Urothelial Cancer-Specific DNA Methylation Biomarkers in Urine

Diagnosing urothelial cancer (UCa) via invasive cystoscopy is painful, specifically in men, and can cause infection and bleeding. Because the UCa risk is higher for male patients, urinary non-invasive UCa biomarkers are highly desired to stratify men for invasive cystoscopy. We previously identified multiple DNA methylation sites in urine samples that detect UCa with a high sensitivity and specificity in men. Here, we identified the most relevant markers by employing multiple statistical approaches and machine learning (random forest, boosted trees, LASSO) using a dataset of 251 male UCa patients and 111 controls. Three CpG sites located in ALOX5, TRPS1 and an intergenic region on chromosome 16 have been concordantly selected by all approaches, and their combination in a single decision matrix for clinical use was tested based on their respective thresholds of the individual CpGs. The combination of ALOX5 and TRPS1 yielded the best overall sensitivity (61%) at a pre-set specificity of 95%. This combination exceeded both the diagnostic performance of the most sensitive bioinformatic approach and that of the best single CpG. In summary, we showed that overlap analysis of multiple statistical approaches identifies the most reliable biomarkers for UCa in a male collective. The results may assist in stratifying men for cystoscopy.

Human cytomegalovirus seropositivity is associated with reduced patient survival during sepsis

BACKGROUND

Sepsis is one of the leading causes of death. Treatment attempts targeting the immune response regularly fail in clinical trials. As HCMV latency can modulate the immune response and changes the immune cell composition, we hypothesized that HCMV serostatus affects mortality in sepsis patients.

METHODS

We determined the HCMV serostatus (i.e., latency) of 410 prospectively enrolled patients of the multicenter SepsisDataNet.NRW study. Patients were recruited according to the SEPSIS-3 criteria and clinical data were recorded in an observational approach. We quantified 13 cytokines at Days 1, 4, and 8 after enrollment. Proteomics data were analyzed from the plasma samples of 171 patients.

RESULTS

The 30-day mortality was higher in HCMV-seropositive patients than in seronegative sepsis patients (38% vs. 25%, respectively; p = 0.008; HR, 1.656; 95% CI 1.135-2.417). This effect was observed independent of age (p = 0.010; HR, 1.673; 95% CI 1.131-2.477). The predictive value on the outcome of the increased concentrations of IL-6 was present only in the seropositive cohort (30-day mortality, 63% vs. 24%; HR 3.250; 95% CI 2.075-5.090; p < 0.001) with no significant differences in serum concentrations of IL-6 between the two groups. Procalcitonin and IL-10 exhibited the same behavior and were predictive of the outcome only in HCMV-seropositive patients.

CONCLUSION

We suggest that the predictive value of inflammation-associated biomarkers should be re-evaluated with regard to the HCMV serostatus. Targeting HCMV latency might open a new approach to selecting suitable patients for individualized treatment in sepsis.

Proteomic investigation of neural stem cell to oligodendrocyte precursor cell differentiation reveals phosphorylation-dependent Dclk1 processing

Oligodendrocytes are generated via a two-step mechanism from pluripotent neural stem cells (NSCs): after differentiation of NSCs to oligodendrocyte precursor/NG2 cells (OPCs), they further develop into mature oligodendrocytes. The first step of this differentiation process is only incompletely understood. In this study, we utilized the neurosphere assay to investigate NSC to OPC differentiation in a time course-dependent manner by mass spectrometry-based (phospho-) proteomics. We identify doublecortin-like kinase 1 (Dclk1) as one of the most prominently regulated proteins in both datasets, and show that it undergoes a gradual transition between its short/long isoform during NSC to OPC differentiation. This is regulated by phosphorylation of its SP-rich region, resulting in inhibition of proteolytic Dclk1 long cleavage, and therefore Dclk1 short generation. Through interactome analyses of different Dclk1 isoforms by proximity biotinylation, we characterize their individual putative interaction partners and substrates. All data are available via ProteomeXchange with identifier PXD040652.

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Neuromelanin granules of the substantia nigra: proteomic profile provides links to tyrosine hydroxylase, stress granules and lysosomes

Neuromelanin is a black-brownish pigment, present in so-called neuromelanin granules (NMGs) in the cell bodies of dopaminergic neurons in the substantia nigra (SN) pars compacta. These neurons are lost in neurodegenerative diseases, such as Parkinson’s disease and dementia with Lewy bodies. Although it is known that lipids, proteins, and environmental toxins accumulate in NMGs, the function of NMGs has not yet been finally clarified as well as their origin and the synthesis of neuromelanin. We, therefore, isolated NMGs and surrounding SN tissue from control patients by laser microdissection and analyzed the proteomic profile by tandem mass spectrometry. With our improved workflow, we were able to (1) strengthen the regularly reported link between NMGs and lysosomes, (2) detect tyrosine hydroxylase to be highly abundant in NMGs, which may be related to neuromelanin synthesis and (3) indicate a yet undescribed link between stress granules (SGs) and NMGs. Based on our findings, we cautiously hypothesize, that SGs may be the origin of NMGs or form in close proximity to them, potentially due to the oxidative stress caused by neuromelanin-bound metals.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography−mass spectrometry, ion mobility or MS imaging approaches on MS¹ and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Transcriptome and Proteome Analysis in LUHMES Cells Overexpressing Alpha-Synuclein

LUHMES cells share many characteristics with human dopaminergic neurons in the substantia nigra, the cells, the demise of which is responsible for the motor symptoms in Parkinson's disease (PD). LUHMES cells can, therefore, be used bona fide as a model to study pathophysiological processes involved in PD. Previously, we showed that LUHMES cells degenerate after 6 days upon overexpression of wild-type alpha-synuclein. In the present study, we performed a transcriptome and proteome expression analysis in alpha-synuclein-overexpressing cells and GFP-expressing control cells in order to identify genes and proteins that are differentially regulated upon overexpression of alpha-synuclein. The analysis was performed 4 days after the initiation of alpha-synuclein or GFP overexpression, before the cells died, in order to identify processes that preceded cell death. After adjustments for multiple testing, we found 765 genes being differentially regulated (439 upregulated, 326 downregulated) and 122 proteins being differentially expressed (75 upregulated, 47 downregulated). In total, 21 genes and corresponding proteins were significantly differentially regulated in the same direction in both datasets, of these 13 were upregulated and 8 were downregulated. In total, 13 genes and 9 proteins were differentially regulated in our cell model, which had been previously associated with PD in recent genome-wide association studies (GWAS). In the gene ontology (GO) analysis of all upregulated genes, the top terms were “regulation of cell death,” “positive regulation of programmed cell death,” and “regulation of apoptotic signaling pathway,” showing a regulation of cell death-associated genes and proteins already 2 days before the cells started to die. In the GO analysis of the regulated proteins, among the strongest enriched GO terms were “vesicle,” “synapse,” and “lysosome.” In total, 33 differentially regulated proteins were associated with synapses, and 12 differentially regulated proteins were associated with the “lysosome”, suggesting that these intracellular mechanisms, which had been previously associated with PD, also play an important role in our cell model.

Upgrading bio-based acetone to diacetone alcohol by aldol reaction using Amberlyst A26-OH as catalyst

The aldol reaction of bio acetone in presence of a strongly basic ion exchange resin was carried out with and without the addition of water in a temperature range between − 30 °C and 45 °C. The conversion, selectivity and service time of the ion exchange resins were investigated in a stirred batch reactor and a continuous fixed bed reactor. For the batch experiments, both conversion and selectivity increased with decreasing temperature. Furthermore, the addition of water to the reaction medium has a positive effect on selectivity and catalyst service time of the resins. For the continuous flow experiments carried out in a fixed bed reactor, the selectivity towards diacetone alcohol is higher than in a batch reactor. This high selectivity is favored by a short contact time which inhibits as expected most of the consecutive reactions.

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.

The impact of the COVID-19 pandemic on non-COVID induced sepsis survival

BACKGROUND

The COVID-19 pandemic has taken a toll on health care systems worldwide, which has led to increased mortality of different diseases like myocardial infarction. This is most likely due to three factors. First, an increased workload per nurse ratio, a factor associated with mortality. Second, patients presenting with COVID-19-like symptoms are isolated, which also decreases survival in cases of emergency. And third, patients hesitate to see a doctor or present themselves at a hospital. To assess if this is also true for sepsis patients, we asked whether non-COVID-19 sepsis patients had an increased 30-day mortality during the COVID-19 pandemic.

METHODS

This is a post hoc analysis of the SepsisDataNet.NRW study, a multicentric, prospective study that includes septic patients fulfilling the SEPSIS-3 criteria. Within this study, we compared the 30-day mortality and disease severity of patients recruited pre-pandemic (recruited from March 2018 until February 2020) with non-COVID-19 septic patients recruited during the pandemic (recruited from March 2020 till December 2020).

RESULTS

Comparing septic patients recruited before the pandemic to those recruited during the pandemic, we found an increased raw 30-day mortality in sepsis-patients recruited during the pandemic (33% vs. 52%, p = 0.004). We also found a significant difference in the severity of disease at recruitment (SOFA score pre-pandemic: 8 (5 - 11) vs. pandemic: 10 (8 - 13); p < 0.001). When adjusted for this, the 30-day mortality rates were not significantly different between the two groups (52% vs. 52% pre-pandemic and pandemic, p = 0.798).

CONCLUSIONS

This led us to believe that the higher mortality of non-COVID19 sepsis patients during the pandemic might be attributed to a more severe septic disease at the time of recruitment. We note that patients may experience a delayed admission, as indicated by elevated SOFA scores. This could explain the higher mortality during the pandemic and we found no evidence for a diminished quality of care for critically ill sepsis patients in German intensive care units.

Laser Microdissection-Based Protocol for the LC-MS/MS Analysis of the Proteomic Profile of Neuromelanin Granules

Neuromelanin is a black-brownish pigment, present in so-called neuromelanin granules (NMGs) in dopaminergic neurons of the substantia nigra pars compacta. Besides neuromelanin, NMGs contain a variety of proteins, lipids, and metals. Although NMGs-containing dopaminergic neurons are preferentially lost in neurodegenerative diseases like Parkinson's disease and dementia with Lewy bodies, only little is known about the mechanism of NMG formation and the role of NMGs in health and disease. Thus, further research on the molecular characterization of NMGs is essential. Unfortunately, standard protocols for the isolation of proteins are based on density gradient ultracentrifugation and therefore require high amounts of human tissue. Thus, an automated laser microdissection (LMD)-based protocol is established here which allows the collection of NMGs and surrounding substantia nigra (SN) tissue using minimal amounts of tissue in an unbiased, automatized way. Excised samples are subsequently analyzed by mass spectrometry to decipher their proteomic composition. With this workflow, 2,079 proteins were identified of which 514 proteins were exclusively identified in NMGs and 181 in SN. The present results have been compared with a previous study using a similar LMD-based approach reaching an overlap of 87.6% for both proteomes, verifying the applicability of the revised and optimized protocol presented here. To validate current findings, proteins of interest were analyzed by targeted mass spectrometry, e.g., parallel reaction monitoring (PRM)-experiments.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

BIONDA: a free database for a fast information on published biomarkers

Summary

Because of the steadily increasing and already manually unmanageable total number of biomarker-related articles in biomedical research, there is a need for intelligent systems that extract all relevant information from biomedical texts and provide it as structured information to researchers in a user-friendly way. To address this, BIONDA was implemented as a free text mining-based online database for molecular biomarkers including genes, proteins and miRNAs and for all kinds of diseases. The contained structured information on published biomarkers is extracted automatically from Europe PMC publication abstracts and high-quality sources like UniProt and Disease Ontology. This allows frequent content updates.

Availability and implementation

BIONDA is freely accessible via a user-friendly web application at http://bionda.mpc.ruhr-uni-bochum.de. The current BIONDA code is available at GitHub via https://github.com/mpc-bioinformatics/bionda.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Advanced Fiber Type-Specific Protein Profiles Derived from Adult Murine Skeletal Muscle

Skeletal muscle is a heterogeneous tissue consisting of blood vessels, connective tissue, and muscle fibers. The last are highly adaptive and can change their molecular composition depending on external and internal factors, such as exercise, age, and disease. Thus, examination of the skeletal muscles at the fiber type level is essential to detect potential alterations. Therefore, we established a protocol in which myosin heavy chain isoform immunolabeled muscle fibers were laser microdissected and separately investigated by mass spectrometry to develop advanced proteomic profiles of all murine skeletal muscle fiber types. All data are available via ProteomeXchange with the identifier PXD025359. Our in-depth mass spectrometric analysis revealed unique fiber type protein profiles, confirming fiber type-specific metabolic properties and revealing a more versatile function of type IIx fibers. Furthermore, we found that multiple myopathy-associated proteins were enriched in type I and IIa fibers. To further optimize the assignment of fiber types based on the protein profile, we developed a hypothesis-free machine-learning approach, identified a discriminative peptide panel, and confirmed our panel using a public data set.

Computertomography-Based Prediction of Complete Response Following Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer

Therapeutic strategies for patients with locally advanced rectal cancer (LARC) who are achieving a pathological complete response (pCR) after neoadjuvant radio-chemotherapy (neoCRT) are being increasingly investigated. Recent trials challenge the current standard therapy of total mesorectal excision (TME). For some patients, the treatment strategy of “watch-and-wait” seems a preferable procedure. The key factor in determining individual treatment strategies following neoCRT is the precise evaluation of the tumor response. Contrast-enhanced computer tomography (ceCT) has proven its ability to discriminate benign and malign lesions in multiple cancers. In this study, we retrospectively analyzed the ceCT based density of LARC in 30 patients, undergoing neoCRT followed by TME. We compared the tumors´ pre- and post-neoCRT density and correlated the results to the amount of residual vital tumor cells in the resected tissue. Overall, the density decreased after neoCRT, with the highest decrease in patients achieving pCR. Densitometry demonstrated a specificity of 88% and sensitivity of 68% in predicting pCR. Thus, we claim that ceCT based densitometry is a useful tool in identifying patients with LARC who may benefit from a “watch-and-wait” strategy and suggest further prospective studies.

Small RNAs as biomarkers to differentiate benign and malign prostate diseases: An alternative for transrectal punch biopsy of the prostate?

Prostate cancer (PCa) is the most common cancer and the third most frequent cause of male cancer death in Germany. MicroRNAs (miRNA) appear to be involved in the development and progression of PCa. A diagnostic differentiation from benign prostate hyperplasia (BPH) is often only possible through transrectal punch biopsy. This procedure is described as painful and carries risks. It was investigated whether urinary miRNAs can be used as biomarkers to differentiate the prostate diseases above. Therefore urine samples from urological patients with BPH (25) or PCa (28) were analysed using Next-Generation Sequencing to detect the expression profile of total and exosomal miRNA/piRNA.

79 miRNAs and 5 piwi-interacting RNAs (piRNAs) were significantly differentially expressed (adjusted p-value < 0.05 and log2-Fc > 1 or < -1). Of these, 6 miRNAs and 2 piRNAs could be statistically validated (AUC on test cohort > = 0.7). In addition, machine-learning algorithms were used to identify a panel of 22 additional miRNAs, whose interaction makes it possible to differentiate the groups as well. There are promising individual candidates for potential use as biomarkers in prostate cancer. The innovative approach of applying machine learning methods to this kind of data could lead to further small RNAs coming into scientific focus, which have so far been neglected.

MaCPepDB: A Database to Quickly Access All Tryptic Peptides of the UniProtKB

Protein sequence databases play a crucial role in the majority of the currently applied mass-spectrometry-based proteomics workflows. Here UniProtKB serves as one of the major sources, as it combines the information of several smaller databases and enriches the entries with additional biological information. For the identification of peptides in a sample by tandem mass spectra, as generated by data-dependent acquisition, protein sequence databases provide the basis for most spectrum identification search engines. In addition, for targeted proteomics approaches like selected reaction monitoring (SRM) and parallel reaction monitoring (PRM), knowledge of the peptide sequences, their masses, and whether they are unique for a protein is essential. Because most bottom-up proteomics approaches use trypsin to cleave the proteins in a sample, the tryptic peptides contained in a protein database are of great interest. We present a database, called MaCPepDB (mass-centric peptide database), that consists of the complete tryptic digest of the Swiss-Prot and TrEMBL parts of UniProtKB. This database is especially designed to not only allow queries of peptide sequences and return the respective information about connected proteins and thus whether a peptide is unique but also allow queries of specific masses of peptides or precursors of MS/MS spectra. Furthermore, posttranslational modifications can be considered in a query as well as different mass deviations for posttranslational modifications. Hence the database can be used by a sequence query not only to, for example, check in which proteins of the UniProt database a tryptic peptide can be found but also to find possibly interfering peptides in PRM/SRM experiments using the mass query. The complete database contains currently 5 939 244 990 peptides from 185 561 610 proteins (UniProt version 2020_03), for which a single query usually takes less than 1 s. For easy exploration of the data, a web interface was developed. A REST application programming interface (API) for programmatic and workflow access is also available at https://macpepdb.mpc.rub.de.

Quantitative analysis of proteome dynamics in a mouse model of asthma

BACKGROUND

Asthma is an inflammatory disease of the respiratory system, and a major factor of increasing health care costs worldwide. The molecular actors leading to the development of chronic asthma are not fully understood and require further investigation.

OBJECTIVE

The aim of this study was to monitor the proteome dynamics during asthma development from early inflammatory to late fibrotic stages.

METHODS

A mouse asthma model was used to analyse the lung proteome at four time points during asthma development (0 weeks = control, 5, 8 and 12 weeks of treatment, n = 6 each). The model was analysed using lung function tests, immune cell counting and histology. Furthermore, a multi-fraction mass spectrometry-based proteome analysis was performed to achieve a comprehensive coverage and quantification of the lung proteome.

RESULTS

At early stages, the mice showed predominant eosinophilic inflammation of the airways, which disappeared at later stages and was replaced by marked airway hyper-reactivity and fibrosis of the airways. 3325 proteins were quantified with 435 proteins found to be significantly differentially abundant between the experimental groups (ANOVA p-value ≤.05, maximum fold change ≥1.5). We applied hierarchical clustering to identify common protein abundance profiles along the asthma development and analysed these clusters using gene ontology annotation and enrichment analysis. We demonstrate the correlation of protein clusters with the course of asthma development, that is eosinophilic inflammation and fibrotic remodelling of the airways.

CONCLUSIONS AND CLINICAL RELEVANCE

Proteome analysis revealed proteins that were previously described to be important during asthma chronification. Moreover, we identified additional proteins previously not described in the context of asthma. We provide a comprehensive data set of a long-term mouse model of asthma that may contribute to a better understanding and allow new insights into the progression and development of chronic asthma. Data are available via ProteomeXchange with identifier PXD011159.

Functional metagenomics of the thioredoxin superfamily

Environmental sequence data of microbial communities now makes up the majority of public genomic information. The assignment of a function to sequences from these metagenomic sources is challenging because organisms associated with the data are often uncharacterized and not cultivable. To overcome these challenges, we created a rationally designed expression library of metagenomic proteins covering the sequence space of the thioredoxin superfamily. This library of 100 individual proteins represents more than 22,000 thioredoxins found in the Global Ocean Sampling data set. We screened this library for the functional rescue of Escherichia coli mutants lacking the thioredoxin-type reductase (ΔtrxA), isomerase (ΔdsbC), or oxidase (ΔdsbA). We were able to assign functions to more than a quarter of our representative proteins. The in vivo function of a given representative could not be predicted by phylogenetic relation but did correlate with the predicted isoelectric surface potential of the protein. Selected proteins were then purified, and we determined their activity using a standard insulin reduction assay and measured their redox potential. An unexpected gel shift of protein E5 during the redox potential determination revealed a redox cycle distinct from that of typical thioredoxin-superfamily oxidoreductases. Instead of the intramolecular disulfide bond formation typical for thioredoxins, this protein forms an intermolecular disulfide between the attacking cysteines of two separate subunits during its catalytic cycle. Our functional metagenomic approach proved not only useful to assign in vivo functions to representatives of thousands of proteins but also uncovered a novel reaction mechanism in a seemingly well-known protein superfamily.

Establishing a Custom-Fit Data-Independent Acquisition Method for Label-Free Proteomics

Data-independent acquisition (DIA) has recently developed as a powerful tool to enhance the quantification of peptides and proteins within a variety of sample types, by overcoming the stochastic nature of classical data-dependent approaches, as well as by enabling the identification of all peptides detected in a mass spectrometric event. Here, we describe a workflow for the establishment of a sample-fitting DIA method using Spectronaut Pulsar X (Biognosys, Switzerland).

The PRIDE database and related tools and resources in 2019: improving support for quantification data

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

CalibraCurve: A Tool for Calibration of Targeted MS-Based Measurements

Targeted proteomics techniques allow accurate quantitative measurements of analytes in complex matrices with dynamic linear ranges that span up to 4-5 orders of magnitude. Hence, targeted methods are promising for the development of robust protein assays in several sensitive areas, for example, in health care. However, exploiting the full method potential requires reliable determination of the dynamic range along with related quantification limits for each analyte. Here, a software named CalibraCurve that enables an automated batch-mode determination of dynamic linear ranges and quantification limits for both targeted proteomics and similar assays is presented. The software uses a variety of measures to assess the accuracy of the calibration, namely precision and trueness. Two different kinds of customizable graphs are created (calibration curves and response factor plots). The accuracy measures and the graphs offer an intuitive, detailed, and reliable opportunity to assess the quality of the model fit. Thus, CalibraCurve is deemed a highly useful and flexible tool to facilitate the development and control of reliable SRM/MRM-MS-based proteomics assays.

Let me infuse this for you - A way to solve the first YPIC challenge

In a common proteomics analysis today, the origins of our sample in the vial are known and therefore a database dependent approach to identify the containing peptides can be used. The first YPIC challenge though provided us with 19 synthetic peptides, which together formed an English sentence. For the identification of these peptides, a de-novo approach was used, which brought us together with an internet search engine to the hidden sentence. But only having the sentence was not sufficient for us, we also wanted to identify as many as possible of the spectra in our data. Therefore, we created and refined a database approach from the de-novo method and finally could identify the peptide-sentence with a good overlap.

Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition

Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.

A generally applicable lightweight method for calculating a value structure for tools and services in bioinformatics infrastructure projects

Sustainable noncommercial bioinformatics infrastructures are a prerequisite to use and take advantage of the potential of big data analysis for research and economy. Consequently, funders, universities and institutes as well as users ask for a transparent value model for the tools and services offered. In this article, a generally applicable lightweight method is described by which bioinformatics infrastructure projects can estimate the value of tools and services offered without determining exactly the total costs of ownership. Five representative scenarios for value estimation from a rough estimation to a detailed breakdown of costs are presented. To account for the diversity in bioinformatics applications and services, the notion of service-specific ‘service provision units’ is introduced together with the factors influencing them and the main underlying assumptions for these ‘value influencing factors’. Special attention is given on how to handle personnel costs and indirect costs such as electricity. Four examples are presented for the calculation of the value of tools and services provided by the German Network for Bioinformatics Infrastructure (de.NBI): one for tool usage, one for (Web-based) database analyses, one for consulting services and one for bioinformatics training events. Finally, from the discussed values, the costs of direct funding and the costs of payment of services by funded projects are calculated and compared.

Digital droplet PCR-based chimerism analysis for monitoring of hematopoietic engraftment after allogeneic stem cell transplantation

INTRODUCTION

Allogeneic hematopoietic stem cell transplantation (alloHSCT) is a curative approach for multiple hematologic diseases. The success of alloHSCT is evaluated by analyzing the proportion of living donor cells in blood and bone marrow samples of the recipient (chimerism analysis). To monitor the engrafted cells, donor's individual genetic markers are analyzed in peripheral blood and bone marrow samples, usually by using short tandem repeat (STR) analysis. An alternative method to measure chimerism is based on insertion and deletion markers (InDels) analyzed by digital droplet PCR (ddPCR); however, this approach is rarely evaluated in clinical practice.

METHODS

In this study, we examined the usefulness of ddPCR-based chimerism analysis against the standard STR analysis in samples around day+30 after alloHSCT in clinical practice using peripheral blood and bone marrow samples.

RESULTS

The median absolute difference between ddPCR and STR analysis was 0.55% points for bone marrow chimerisms and 0.25% points for peripheral blood chimerisms, respectively, including variation in the range of maximum 2% for both methods. The results of every single sample gave the same clinical message.

CONCLUSION

According to our data, chimerism analysis by ddPCR has an excellent correlation with STR-based analyses. Due to its fast and easy applicability, the ddPCR technique is suitable for chimerism monitoring in clinical practice.

Proteomics Standards Initiative Extended FASTA Format

Mass-spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs) in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI extended FASTA format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backward compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff .

Noninvasive diagnosis of urothelial cancer in urine using DNA hypermethylation signatures-Gender matters

Urothelial cancer (UCa) is the most predominant cancer of the urinary tract and noninvasive diagnosis using hypermethylation signatures in urinary cells is promising. Here, we assess gender differences in a newly identified set of methylation biomarkers. UCa-associated hypermethylated sites were identified in urine of a male screening cohort (n = 24) applying Infinium-450K-methylation arrays and verified in two separate mixed-gender study groups (n = 617 in total) using mass spectrometry as an independent technique. Additionally, tissue samples (n = 56) of mixed-gender UCa and urological controls (UCt) were analyzed. The hypermethylation signature of UCa in urine was specific and sensitive across all stages and grades of UCa and independent on hematuria. Individual CpG sensitivities reached up to 81.3% at 95% specificity. Albeit similar methylation differences in tissue of both genders, differences were less pronounced in urine from women, most likely due to the frequent presence of squamous epithelial cells and leukocytes. Increased repression of methylation levels was observed at leukocyte counts ≥500/μl urine which was apparent in 30% of female and 7% of male UCa cases, further confirming the significance of the relative amounts of cancerous and noncancerous cells in urine. Our study shows that gender difference is a most relevant issue when evaluating the performance of urinary biomarkers in cancer diagnostics. In case of UCa, the clinical benefits of methylation signatures to male patients may outweigh those in females due to the general composition of women's urine. Accordingly, these markers offer a diagnostic option specifically in males to decrease the number of invasive cystoscopies.

Monocytes-neutrophils-ratio as predictive marker for failure of first induction therapy in AML

INTRODUCTION

Acute myeloid leukemia (AML) is, if untreated, a fatal hematologic neoplasia. Failure of the first induction chemotherapy is a hallmark for a poor prognosis. Early recognition of therapy failure is crucial for planning further therapies. Therefore, international guidelines recommend a bone marrow biopsy around day 14 after the beginning of induction therapy. Hypocellular bone marrow on day 14 is still gold standard for therapy assessment and further therapy strategy. Despite this, non-invasive ways for the evaluation of induction therapy were looked for in the past years.

METHODS

We collected peripheral blood cell counts and routine laboratory values of patients treated with "7 + 3" induction therapy. Ratios of absolute cell counts of monocytes and neutrophils (MNR) were calculated daily, and the values were compared in patients with failure of the first induction therapy and patients with therapy response.

RESULTS

54 patients were included, 12 of which had failure of first induction therapy. The MNR following therapy was highly correlated with the bone marrow results. With the right cut-off, the MNR provides a valid and reliable tool for identification of patients with failure of first induction therapy with a sensitivity of 83.3% and a specificity of 87.8% on day 18.

CONCLUSIONS

We propose a novel and non-invasive method for detection of failure of first induction therapy in patients with de novo AML and "7 + 3" induction therapy. The MNR is free of cost since the required cell counts are performed routinely for each patient undergoing intensive chemotherapy.

mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics

Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.

Integrated Fourier Transform Infrared Imaging and Proteomics for Identification of a Candidate Histochemical Biomarker in Bladder Cancer

Histopathological differentiation between severe urocystitis with reactive urothelial atypia and carcinoma in situ (CIS) can be difficult, particularly after a treatment that deliberately induces an inflammatory reaction, such as intravesical instillation of Bacillus Calmette-Guèrin. However, precise grading in bladder cancer is critical for therapeutic decision making and thus requires reliable immunohistochemical biomarkers. Herein, an exemplary potential biomarker in bladder cancer was identified by the novel approach of Fourier transform infrared imaging for label-free tissue annotation of tissue thin sections. Identified regions of interest are collected by laser microdissection to provide homogeneous samples for liquid chromatography-tandem mass spectrometry-based proteomic analysis. This approach afforded label-free spatial classification with a high accuracy and without interobserver variability, along with the molecular resolution of the proteomic analysis. Cystitis and invasive high-grade urothelial carcinoma samples were analyzed. Three candidate biomarkers were identified and verified by immunohistochemistry in a small cohort, including low-grade urothelial carcinoma samples. The best-performing candidate AHNAK2 was further evaluated in a much larger independent verification cohort that also included CIS samples. Reactive urothelial atypia and CIS were distinguishable on the basis of the expression of this newly identified and verified immunohistochemical biomarker, with a sensitivity of 97% and a specificity of 69%. AHNAK2 can differentiate between reactive urothelial atypia in the setting of an acute or chronic cystitis and nonmuscle invasive-type CIS.

Protein Biomarker Discovery Using Human Blood Plasma Microparticles

Cells shed into the extracellular space a population of membranous vesicles of plasma membrane origin called microparticles (MP). Given the fact that MP are abundantly present in body fluids including plasma, rich in cell-type or disease-specific proteins and formed in conditions of stress and injury, they have been extensively investigated as biomarkers in various diseases. With the advancement in the mass spectrometry-based proteome analysis, the knowledge of the protein composition of plasma MP (PMP) has been intensively expanded, which aids the discovery of novel diagnostic target proteins. However, the lack of standardized and accurate protocols for PMP isolation limits the implementation of PMP as biomarkers in clinical settings. Here, we describe in detail a robust protocol for PMP isolation from human blood plasma via ultracentrifugation followed by label-free quantitative proteome analysis of PMP.

Protein variability in cerebrospinal fluid and its possible implications for neurological protein biomarker research

Cerebrospinal fluid is investigated in biomarker studies for various neurological disorders of the central nervous system due to its proximity to the brain. Currently, only a limited number of biomarkers have been validated in independent studies. The high variability in the protein composition and protein abundance of cerebrospinal fluid between as well as within individuals might be an important reason for this phenomenon. To evaluate this possibility, we investigated the inter- and intraindividual variability in the cerebrospinal fluid proteome globally, with a specific focus on disease biomarkers described in the literature. Cerebrospinal fluid from a longitudinal study group including 12 healthy control subjects was analyzed by label-free quantification (LFQ) via LC-MS/MS. Data were quantified via MaxQuant. Then, the intra- and interindividual variability and the reference change value were calculated for every protein. We identified and quantified 791 proteins, and 216 of these proteins were abundant in all samples and were selected for further analysis. For these proteins, we found an interindividual coefficient of variation of up to 101.5% and an intraindividual coefficient of variation of up to 29.3%. Remarkably, these values were comparably high for both proteins that were published as disease biomarkers and other proteins. Our results support the hypothesis that natural variability greatly impacts cerebrospinal fluid protein biomarkers because high variability can lead to unreliable results. Thus, we suggest controlling the variability of each protein to distinguish between good and bad biomarker candidates, e.g., by utilizing reference change values to improve the process of evaluating potential biomarkers in future studies.

Direct-acting antivirals-based therapy decreases hepatic fibrosis serum biomarker microfibrillar-associated protein 4 in hepatitis C patients

Background/Aims

An estimated 80 million people worldwide are infected with viremic hepatitis C virus (HCV). Even after eradication of HCV with direct acting antivirals (DAAs), hepatic fibrosis remains a risk factor for hepatocarcinogenesis. Recently, we confirmed the applicability of microfibrillar-associated protein 4 (MFAP4) as a serum biomarker for the assessment of hepatic fibrosis. The aim of the present study was to assess the usefulness of MFAP4 as a biomarker of liver fibrosis after HCV eliminating therapy with DAAs.

Methods

MFAP4 was measured using an immunoassay in 50 hepatitis C patients at baseline (BL), the end-of-therapy (EoT), and the 12-week follow-up (FU) visit. Changes in MFAP4 from BL to FU and their association with laboratory parameters including alanine aminotransferase (ALT), aspartate aminotransferase (AST), platelets, the AST to platelet ratio index (APRI), fibrosis-4 score (FIB-4), and albumin were analyzed.

Results

MFAP4 serum levels were representative of the severity of hepatic fibrosis at BL and correlated well with laboratory parameters, especially APRI (Spearman correlation, R²=0.80). Laboratory parameters decreased significantly from BL to EoT. MFAP4 serum levels were found to decrease from BL and EoT to FU with high statistical significance (Wilcoxon p<0.001 for both).

Conclusions

Our findings indicate that viral eradication resulted in reduced MFAP4 serum levels, presumably representing a decrease in hepatic fibrogenesis or fibrosis. Hence, MFAP4 may be a useful tool for risk assessment in hepatitis C patients with advanced fibrosis after eradication of the virus.

Characterization of Cerebrospinal Fluid via Data-Independent Acquisition Mass Spectrometry

Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF ( n = 5), CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) and applied to three CSF DIA replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 min gradient. Compared to a conventional DDA method, our DIA approach increased the number of identified protein groups from 648 identifications in DDA to 1574 in DIA using a comprehensive spectral library generated with DDA measurements from five native CSF and 54 substantia nigra fractions. We also could show that a sample specific spectral library generated from native CSF only increased the identification reproducibility from three DIA replicates to 90% (77% with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA, over 60 brain-originated proteins could be identified compared to only 11 with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF. Data are available via ProteomeXchange with the identifiers PXD010698, PXD010708, PXD010690, PXD010705, and PXD009624.

Methods for Predictor Analysis of Repeated Measurements: Application to Psychiatric Data

Objectives: In schizophrenia research, little attention yet has been directed on methods for analyzing data from studies with repeated measurements over time. Motivation for this research stems from a project within the German Research Network on Schizophrenia, in which an algorithm is developed to guide prodrome-based early intervention strategies in stable first episode patients.

Methods: We present two different approaches for the analysis of correlated response data, the Generalized Estimating Equations (GEE) method and the Artificial Neural Network (ANN) approach. We illustrate the methods using the data of the A.N.I. study, which is one of the largest German multicenter treatment studies in regard to the long-term treatment of schizophrenia conducted between 1983 and 1989.

Results: The results of statistical model selection prior to GEE analysis and various data presentation methods for ANNs are presented. The primary goal of our evaluation is to investigate if the defined prodromes are valid predictors for relapse. Additionally, it is shown that both methods are applicable on a realistic data set.

Conclusions: It is concluded that both methods are suitable for predictor analysis especially since all variable time points of the patients are included instead of only selected, so that it can be assumed that results are not biased. With the GEE method a test of association for each predictor can be performed whereas with ANNs a general proposition can be made for pro-dromes depending on the type of data presentation. Using the A.N.I. data the prodrome ‘trouble sleeping’ seems to be the most informative predictor. Finally, the important differences of the two methods are discussed.

Comprehensive proteomic analysis of Penicillium verrucosum

Mass spectrometric identification of proteins in species lacking validated sequence information is a major problem in veterinary science. In the present study, we used ochratoxin A producing Penicillium verrucosum to identify and quantitatively analyze proteins of an organism with yet no protein information available. The work presented here aimed to provide a comprehensive protein identification of P. verrucosum using shotgun proteomics. We were able to identify 3631 proteins in an "ab initio" translated database from DNA sequences of P. verrucosum. Additionally, a sequential window acquisition of all theoretical fragment-ion spectra analysis was done to find differentially regulated proteins at two different time points of the growth curve. We compared the proteins at the beginning (day 3) and at the end of the log phase (day 12).

Proteomics Standards Initiative: Fifteen Years of Progress and Future Work

The Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) has now been developing and promoting open community standards and software tools in the field of proteomics for 15 years. Under the guidance of the chair, cochairs, and other leadership positions, the PSI working groups are tasked with the development and maintenance of community standards via special workshops and ongoing work. Among the existing ratified standards, the PSI working groups continue to update PSI-MI XML, MITAB, mzML, mzIdentML, mzQuantML, mzTab, and the MIAPE (Minimum Information About a Proteomics Experiment) guidelines with the advance of new technologies and techniques. Furthermore, new standards are currently either in the final stages of completion (proBed and proBAM for proteogenomics results as well as PEFF) or in early stages of design (a spectral library standard format, a universal spectrum identifier, the qcML quality control format, and the Protein Expression Interface (PROXI) web services Application Programming Interface). In this work we review the current status of all of these aspects of the PSI, describe synergies with other efforts such as the ProteomeXchange Consortium, the Human Proteome Project, and the metabolomics community, and provide a look at future directions of the PSI.

Quantitative proteome analysis of plasma microparticles for the characterization of HCV-induced hepatic cirrhosis and hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor and a leading cause of cancer-related deaths worldwide. Cirrhosis induced by hepatitis-C virus (HCV) infection is the most critical risk factor for HCC. However, the mechanism of HCV-induced carcinogenesis is not fully understood. Plasma microparticles (PMP) contribute to numerous physiological and pathological processes and contain proteins whose composition correlates to the respective pathophysiological conditions.

EXPERIMENTAL DESIGN

We analyzed PMP from 22 HCV-induced cirrhosis patients, 16 HCV-positive HCC patients with underlying cirrhosis and 18 healthy controls. PMP were isolated using ultracentrifugation and analyzed via label-free LC-MS/MS.

RESULTS

We identified 840 protein groups and quantified 507 proteins. 159 proteins were found differentially abundant between the three experimental groups. PMP in both disease entities displayed remarkable differences in the proteome composition compared to healthy controls. Conversely, the proteome difference between both diseases was minimal. GO analysis revealed that PMP isolated from both diseases were significantly enriched in proteins involved in complement activation, while endopeptidase activity was downregulated exclusively in HCC patients.

CONCLUSION

This study reports for the first time a quantitative proteome analysis for PMP from patients with HCV-induced cirrhosis and HCC. Data are available via ProteomeXchange with identifier PXD005777.

The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics

The first stable version of the Proteomics Standards Initiative mzIdentML open data standard (version 1.1) was published in 2012-capturing the outputs of peptide and protein identification software. In the intervening years, the standard has become well-supported in both commercial and open software, as well as a submission and download format for public repositories. Here we report a new release of mzIdentML (version 1.2) that is required to keep pace with emerging practice in proteome informatics. New features have been added to support: (1) scores associated with localization of modifications on peptides; (2) statistics performed at the level of peptides; (3) identification of cross-linked peptides; and (4) support for proteogenomics approaches. In addition, there is now improved support for the encoding of de novo sequencing of peptides, spectral library searches, and protein inference. As a key point, the underlying XML schema has only undergone very minor modifications to simplify as much as possible the transition from version 1.1 to version 1.2 for implementers, but there have been several notable updates to the format specification, implementation guidelines, controlled vocabularies and validation software. mzIdentML 1.2 can be described as backwards compatible, in that reading software designed for mzIdentML 1.1 should function in most cases without adaptation. We anticipate that these developments will provide a continued stable base for software teams working to implement the standard. All the related documentation is accessible at http://www.psidev.info/mzidentml.

A community proposal to integrate proteomics activities in ELIXIR

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on ‘The Future of Proteomics in ELIXIR’ that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes.

These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR’s existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper.

The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry

To have confidence in results acquired during biological mass spectrometry experiments, a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Standards Initiative has established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic technical necessities required to support extensive quality control for any type of mass spectrometry-based workflow. We want to emphasize that this is an open community effort, and we seek participation from all scientists with an interest in this field.

Annexin A10 optimally differentiates between intrahepatic cholangiocarcinoma and hepatic metastases of pancreatic ductal adenocarcinoma: a comparative study of immunohistochemical markers and panels

Discriminating intrahepatic cholangiocarcinoma (ICC) from hepatic metastases of pancreatic ductal adenocarcinoma (mPDAC) can be challenging. While pathologists might depend on clinical information regarding a primary tumor, their diagnosis will lead the patient either to potentially curative surgery (for ICC) or to palliation (for mPDAC). Beyond the validation of recently published potential biomarkers for PDAC (primary or metastatic) in a large cohort, we assessed diagnostic performance of the most promising candidates in the challenging task of discriminating metastatic PDAC (mPDAC) from ICC. In a training set of 87 ICC and 88 pPDAC, our previously identified biomarkers Annexin A1 (ANXA1), ANXA10, and ANXA13 were tested and compared with 11 published biomarkers or panels (MUCIN 1, Agrin, S100P, MUC5 AC, Laminin, VHL, CK 17, N-Cadherin, ELAC2, PODXL and HSPG2). Biomarkers with best results were further tested in an independent series of biopsies of 27 ICC and 36 mPDAC. Highest AUC values (between 0.72 and 0.84) for the discrimination between ICC and pPDAC were found in the training set for Annexin A1, Annexin A10, MUC5 AC, CK17, and N-Cadherin. These markers were further tested on an independent series of liver biopsies containing ICC or mPDAC. Diagnostic characteristics were evaluated for individual markers as well as for 3× panels. ANXA 10 showed the highest diagnostic potential of all single markers, correctly classifying 75% of mPDAC and 85% of ICC. Our results suggest that ANXA10 may be useful to differentiate between ICC and mPDAC, when only a tissue specimen is available.