Advancing computational methods for mass spectrometry-based proteomics, metabolomics, and analysis of multi-omics datasets

Undoubtedly, the current century is witness to an unprecedented speed in advancements within biological sciences, which are owed to the immense technological progress in the analytical tools and methods utilized, and to the dawn of the fast developing fields of omics and bioinformatics. Omics allows the collection of holistic data on several different biomolecule classes, and bioinformatics makes it possible to explore and understand the vast amounts of data produced. The most mature omics fields, in terms of both hardware and software, are genomics and transcriptomics, based on next generation sequencing (NGS) technologies. With the introduction of electrospray ionization and high-resolution mass spectrometry, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), has made significant leaps for the fields of metabolomics and proteomics. One promising method for LC-MS/MS-based proteomics is data independent acquisition (DIA), which requires advanced data analysis algorithms. MaxDIA, within the MaxQuant software for the processing of LC-MS/MS-based proteomics data, is introduced here. It comes with an accurate false discovery rate estimation of the peptide and protein identification based on measured and predicted spectrum libraries. When compared to the state of the art, MaxDIA also delivers comprehensive proteome coverages and lower coefficients of variation in protein quantification. Bioinformatics tools for the analysis of metabolomics data generally follow the same principles and steps as proteomics software, but due the huge numbers of metabolites and immense complexity of metabolomics data, much work is still needed to bring metabolomics software to the level of maturity of their proteomics equivalents. MaxQuant is a time tested and widely accepted software for the processing of proteomics data, which was first recognized for its cutting-edge nonlinear recalibration for reaching superior precursor mass accuracy, which helps significantly improve peptide identifications. Here, following this direction, a new algorithm within MaxQuant for improving mass accuracy in metabolomics data is introduced, which utilizes a novel metabolite library-based mass recalibration algorithm. The many types of omics data available today present a great opportunity for developing approaches to combine such data in order to infer new knowledge, often termed multi-omics studies. A robust approach to this end is to utilize prior knowledge on the relationships of the various major biomolecules in question, which are often depicted in network structures where the nodes of the network depict biomolecules and the edges correspond to an interaction. To implement this approach, Metis is introduced, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. This thesis includes four publications, the first of which is a review article on computational metabolomics as a part of the introduction, listed below: 1. Hamzeiy, Hamid, and Jürgen Cox. 2017. “What Computational Non-Targeted Mass Spectrometry-Based Metabolomics Can Gain from Shotgun Proteomics.” Current Opinion in Biotechnology 43: 141–46. https://doi.org/10.1016/j.copbio.2016.11.014. 2. Sinitcyn, Pavel, Shivani Tiwary, Jan Rudolph, Petra Gutenbrunner, Christoph Wichmann, Şule Yllmaz, Hamid Hamzeiy, Favio Salinas, and Jürgen Cox. 2018. “MaxQuant Goes Linux.” Nature Methods 15 (6): 401. https://doi.org/10.1038/s41592-018-0018-y. 3. Pavel Sinitcyn, Hamid Hamzeiy, Favio Salinas Soto, Daniel Itzhak, Frank McCarthy, Christoph Wichmann, Martin Steger, Uli Ohmayer, Ute Distler, Stephanie Kaspar-Schoenefeld, Nikita Prianichnikov, Şule Yılmaz, Jan Daniel Rudolph, Stefan Tenzer, Yasset Perez-Riverol, Nagarjuna Nagaraj, Sean J. Humphrey and Jürgen Cox. “MaxDIA enables highly sensitive and accurate library-based and library-free data-independent acquisition proteomics.” Submitted to Nature Biotechnology, 2020 4. Hamid Hamzeiy, Daniela Ferretti, Maria S. Robles, and Jürgen Cox. “Perseus plugin ‘Metis’ for metabolic pathway-centered quantitative multi-omics data analysis supporting static and time-series experimental designs.” Submitted to Cell Systems, 202

Perseus plugin “Metis” for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs

We introduce Metis, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. Data from different omics types are connected through reactions of a genome-scale metabolic-pathway reconstruction. Metabolite concentrations connect through the reactants, while transcript, protein, and protein post-translational modification (PTM) data are associated through the enzymes catalyzing the reactions. Supported experimental designs include static comparative studies and time-series data. As an example for the latter, we combine circadian mouse liver multi-omics data and study the contribution of cycles of phosphoproteome and metabolome to enzyme activity regulation. Our analysis resulted in 52 pairs of cycling phosphosites and metabolites connected through a reaction. The time lags between phosphorylation and metabolite peak show non-uniform behavior, indicating a major contribution of phosphorylation in the modulation of enzymatic activity.publishedVersio

Gap Junctions: The Claymore for Cancerous Cells

Introduction: Gap junctions play an important role in the cell proliferation in mammalian cells as well as carcinogenesis. However, there are controversial issues about their role in cancer pathogenesis. This study was designed to evaluate genotoxicity and cytotoxicity of Carbenoxolone (CBX) as a prototype of inter-cellular gap junction blocker in MCF7 and BT20 human breast cancer cells. Methods: The MCF7and BT20 human breast cancer cell lines were cultivated, and treated at designated confluency with different doses of CBX. Cellular cytotoxicity was examined using standard colorimetric assay associated with cell viability tests. Gene expression evaluation was carried out using real time polymerase chain reaction (PCR). Results: MCF7 and BT20 cells were significantly affected by CBX in a dose dependent manner in cell viability assays. Despite varying expression of genes, down regulation of pro- and anti-apoptotic genes was observed in these cells. Conclusion: Based upon this investigation, it can be concluded that CBX could affect both low and high proliferative types of breast cancer cell lines and disproportionate down regulation of both pre- and anti-apoptotic genes may be related to interacting biomolecules, perhaps via gap junctions

Nanoscaled aptasensors for multi-analyte sensing

Introduction: Nanoscaled aptamers (Aps), as short single-stranded DNA or RNA oligonucleotides, are able to bind to their specific targets with high affinity, upon which they are considered as powerful diagnostic and analytical sensing tools (the so-called "aptasensors"). Aptamers are selected from a random pool of oligonucleotides through a procedure known as "systematic evolution of ligands by exponential enrichment". Methods: In this work, the most recent studies in the field of aptasensors are reviewed and discussed with a main focus on the potential of aptasensors for the multi-analyte detection(s). Results: Due to the specific folding capability of aptamers in the presence of analyte, aptasensors have substantially successfully been exploited for the detection of a wide range of small and large molecules (e.g., drugs and their metabolites, toxins, and associated biomarkers in various diseases) at very low concentrations in the biological fluids/samples even in presence of interfering species. Conclusion: Biological samples are generally considered as complexes in the real biological media. Hence, the development of aptasensors with capability to determine various targets simultaneously within a biological matrix seems to be our main challenge. To this end, integration of various key scientific dominions such as bioengineering and systems biology with biomedical researches are inevitable

Citoprotektivni učinci silafibrata, novosintetiziranog silikoniranog derivata klofibrata protiv acetaminofenom izazvane toksičnosti u izoliranim hepatocitima štakora

Acetaminophen (N-acetyl para amino phenol, APAP) is a widely used antipyretic and analgesic drug responsible for various drug-induced liver injuries. This study evaluated APAP-induced toxicity in isolated rat hepatocytes alongside the protective effects of silafibrate and N-acetyl cysteine (NAC). Hepatocytes were isolated from male Sprague-Dawley rats by collagenase enzyme perfusion via the portal vein. This technique is based on liver perfusion with collagenase after removing calcium ions (Ca2+) with a chelator. Cells were treated with different concentrations of APAP, silafibrate, and NAC. Cell death, reactive oxygen species (ROS) formation, lipid peroxidation, and mitochondrial depolarisation were measured as toxicity markers. ROS formation and lipid peroxidation occurred after APAP administration to rat hepatocytes. APAP caused mitochondrial depolarisation in isolated cells. Administration of silafibrate (200 μmol L-1) and/or NAC (200 μmol L-1) reduced the ROS formation, lipid peroxidation, and mitochondrial depolarisation caused by APAP. Cytotoxicity induced by APAP in rat hepatocytes was mediated by oxidative stress. In addition, APAP seemed to target cellular mitochondria during hepatocyte damage. The protective properties of silafibrate and/or NAC against APAP‑induced hepatic injury may have involved the induction of antioxidant enzymes, protection against oxidative stress and inflammatory responses, and alteration in cellular glutathione content.Acetaminofen (N-acetil-para-aminofenol, APAP) često je korišteni antipiretik i analgetik koji može izazvati oštećenja jetara. Na modelu izoliranih hepatocita štakora istražili smo toksične učinke APAP-a i protektivne učinke silafibrata i N-acetilcisteina (NAC). Hepatociti su izolirani iz mužjaka štakora soja Sprague-Dawley perfuzijom jetara i uvođenjem enzima kolagenaze putem portalne vene. Ta se tehnika zasniva na perfuziji jetara kolagenazom nakon uklanjanja kalcijevih iona (Ca2+) kelatorom. Stanice su tretirane različitim koncentracijama APAP-a, silafibrata i NAC-a. Kao markeri toksičnosti mjereni su smrt stanica, stvaranje reaktivnih kisikovih vrsta (ROS), lipidna peroksidacija i depolarizacija mitohondrija. Primjena APAP-a u štakora izazvala je stvaranje ROS-ova i lipidnu peroksidaciju. APAP je uzrokovao depolarizaciju mitohondrija u izoliranim stanicama. Primjena silafibrata (200 μmol L-1) i/ili NAC-a (200 μmol L-1) smanjila je stvaranje ROS-a, lipidnu peroksidaciju i depolarizaciju mitohondrija uzrokovanu APAP-om. Utvrdili smo da je citotoksičnost APAP-a posredovana oksidativnim stresom. Nadalje, čini se da su mitohondriji ciljni stanični organeli za oštećenja hepatocita izazvanih APAP-om. Moguće je da su protektivna svojstva silafibrata i/ili NAC-a protiv APAP‑om induciranog oštećenja jetara uključivala i indukciju antioksidacijskih enzima, zaštitu od oksidativnog stresa i upalnih odgovora te promjenu razine staničnoga glutationa

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification. The software platform MaxDIA streamlines analysis of data-independent acquisition proteomics

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.publishedVersio

Transcriptional profiling of HERV-K(HML-2) in amyotrophic lateral sclerosis and potential implications for expression of HML-2 proteins

Abstract Background Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder. About 90% of ALS cases are without a known genetic cause. The human endogenous retrovirus multi-copy HERV-K(HML-2) group was recently reported to potentially contribute to neurodegeneration and disease pathogenesis in ALS because of transcriptional upregulation and toxic effects of HML-2 Envelope (Env) protein. Env and other proteins are encoded by some transcriptionally active HML-2 loci. However, more detailed information is required regarding which HML-2 loci are transcribed in ALS, which of their proteins are expressed, and differences between the disease and non-disease states. Methods For brain and spinal cord tissue samples from ALS patients and controls, we identified transcribed HML-2 loci by generating and mapping HML-2-specific cDNA sequences. We predicted expression of HML-2 env gene-derived proteins based on the observed cDNA sequences. Furthermore, we determined overall HML-2 transcript levels by RT-qPCR and investigated presence of HML-2 Env protein in ALS and control tissue samples by Western blotting. Results We identified 24 different transcribed HML-2 loci. Some of those loci are transcribed at relatively high levels. However, significant differences in HML-2 loci transcriptional activities were not seen when comparing ALS and controls. Likewise, overall HML-2 transcript levels, as determined by RT-qPCR, were not significantly different between ALS and controls. Indeed, we were unable to detect full-length HML-2 Env protein in ALS and control tissue samples despite reasonable sensitivity. Rather our analyses suggest that a number of HML-2 protein variants other than full-length Env may potentially be expressed in ALS patients. Conclusions Our results expand and refine recent publications on HERV-K(HML-2) and ALS. Some of our results are in conflict with recent findings and call for further specific analyses. Our profiling of HML-2 transcription in ALS opens up the possibility that HML-2 proteins other than canonical full-length Env may have to be considered when studying the role of HML-2 in ALS disease