Mass spectrometry based proteomics : data analysis and applications

Mass spectrometry (MS) based proteomics has become a widely used high throughput method to investigate protein expression and functional regulation. From being able to study only dozens of proteins, state-of-art MS proteomic techniques are now able to identify and quantify ten thousand proteins. Nevertheless, MS proteomics are facing problems investigating protein variants derived from alternative splicing, detecting peptides from novel coding sequences, identifying peptide variants from genetic changes and statistical analysis of quantitative proteome. The work present in this thesis start from these problems and contribute solutions to them. In standard shotgun proteomics studies, protein identifications are inferred from a list of identified peptides using Occam Razor’s rule, which outputs a minimum list of proteins sufficient to explain peptide evidences. The protein inference process creates a potential problem in protein level quantification, resulting mixture of quantitative signals from different splice variants if the inferred proteins do not correctly represent the peptide populations. Paper I present a tool to investigate splice variants using MS proteomics data. By clustering the quantitative pattern of peptides and showing their transcript positions, it is able to reveal splice variants specific peptides with different quantitative signal. The tool was applied to a comprehensive proteomics data of A431 cells treated with Gefitinib (EGFR inhibitor). For certain genes, we observed splice-variant-centric quantification differs from traditional proteincentric or gene-centric quantification, suggesting differentially regulated splice variants after Gefitinib treatment. Previously, MS proteomics has been used to refine genome annotation. However, the applications were limited to validate and confirm predicted gene models. In Paper II, we demonstrate an integrative genome annotation workflow that combines MS proteomics data and RNA-sequencing to perform evidence-based whole genome annotation of a newly sequenced commensal yeast. The workflow showed higher accuracy of protein coding gene annotation compared to conventional way of using only RNA-sequencing data. The study exemplifies that proteomics data used in combination with RNA-seq data is able to produce a more accurate and complete whole genome annotation. Paper III shows an integrative proteogenomics analysis workflow. Compared to standard proteomics which analyzes known proteins in reference database, proteogenomics aims to discover peptides from novel coding sequences and disease relevant mutations. To identify novel coding sequences in well annotated genomes, such as human, it is particular challenging due to several reasons. First, protein-coding sequences in the human genome consists of only 2%-3% of the total sequences. There are approximately one million peptides from known coding genes, and the novel peptides from undiscovered coding loci constitutes a minor part of the total peptide population. That means the vast majority of experimental spectra are produced from known peptides. Identification of peptides with MS proteomics technique relies on correct matching between experimental spectra to in silico generated spectra of the peptides in search space. Detecting of novel peptides requires correct spectra matching for both known and novel peptides, and the process is doomed to produce false positives. Previously, conservative criteria and manual curation has been applied to ensure the quality of findings. Paper III presents a workflow which improves the reliability of proteogenomics findings by automated extensive data curation and evidence searching in orthogonal data. In analysis of the proteomics data of a cancer cell line and five normal human tissues, the workflow successfully detected novel peptides from unknown coding regions and peptide variants from non-synonymous single nucleotide polymorphisms (nsSNPs) and mutations, with multiple sources of evidence provided. Moreover, our quantitative MS data indicated that certain pseudogenes and lncRNAs were expressed and translated in tissue-specific manner. Paper IV addresses the statistical analysis of quantitative proteomics. Currently, there is no consensus in the usage of statistical methods to analyze labelled and label-free proteomics data. One of the main reasons is the lack of statistical tool with high performance, ease to use, and broad applicability to various proteomics datasets. The presented statistical method, DEqMS, is a robust and universal tool to assess differential protein expression for quantitative MS proteomics. DEqMS takes into account the variance dependence on the number of peptides/PSMs used for protein quantification in statistical significance test. Compared to existing methods in several benchmarking datasets, DEqMS was demonstrated with both high statistical accuracy and general applicability. In summary, the work included in this thesis contributes with improved data interpretation and applications of MS proteomics data in analysis of splice variants, genome annotation, proteogenomics studies and statistical analysis of protein expression changes. Development of these methods facilitate a wide range of applications of MS proteomics data in the systems biology researc

New Results on Stability and Stabilization of Markovian Jump Systems with Partly Known Transition Probabilities

This paper investigates the problem of stability and stabilization of Markovian jump linear systems with partial information on transition probability. A new stability criterion is obtained for these systems. Comparing with the existing results, the advantage of this paper is that the proposed criterion has fewer variables, however, does not increase any conservatism, which has been proved theoretically. Furthermore, a sufficient condition for the state feedback controller design is derived in terms of linear matrix inequalities. Finally, numerical examples are given to illustrate the effectiveness of the proposed method

Dual brain stimulation enhances interpersonal learning through spontaneous movement synchrony

Abstract Social interactive learning denotes the ability to acquire new information from a conspecific—a prerequisite for cultural evolution and survival. As inspired by recent neurophysiological research, here we tested whether social interactive learning can be augmented by exogenously synchronizing oscillatory brain activity across an instructor and a learner engaged in a naturalistic song-learning task. We used a dual brain stimulation protocol entailing the trans-cranial delivery of synchronized electric currents in two individuals simultaneously. When we stimulated inferior frontal brain regions, with 6 Hz alternating currents being in-phase between the instructor and the learner, the dyad exhibited spontaneous and synchronized body movement. Remarkably, this stimulation also led to enhanced learning performance. These effects were both phase- and frequency-specific: 6 Hz anti-phase stimulation or 10 Hz in-phase stimulation, did not yield comparable results. Furthermore, a mediation analysis disclosed that interpersonal movement synchrony acted as a partial mediator of the effect of dual brain stimulation on learning performance, i.e. possibly facilitating the effect of dual brain stimulation on learning. Our results provide a causal demonstration that inter-brain synchronization is a sufficient condition to improve real-time information transfer between pairs of individuals

Iron Metabolism and Brain Development in Premature Infants

Iron is important for a remarkable array of essential functions during brain development, and it needs to be provided in adequate amounts, especially to preterm infants. In this review article, we provide an overview of iron metabolism and homeostasis at the cellular level, as well as its regulation at the mRNA translation level, and we emphasize the importance of iron for brain development in fetal and early life in preterm infants. We also review the risk factors for disrupted iron metabolism that lead to high risk of developing iron deficiency and subsequent adverse effects on neurodevelopment in preterm infants. At the other extreme, iron overload, which is usually caused by excess iron supplementation in iron-replete preterm infants, might negatively impact brain development or even induce brain injury. Maintaining the balance of iron during the fetal and neonatal periods is important, and thus iron status should be monitored routinely and evaluated thoroughly during the neonatal period or before discharge of preterm infants so that iron supplementation can be individualized

Routes of administration for adeno-associated viruses carrying gene therapies for brain diseases

Gene therapy is a powerful tool to treat various central nervous system (CNS) diseases ranging from monogenetic diseases to neurodegenerative disorders. Adeno-associated viruses (AAVs) have been widely used as the delivery vehicles for CNS gene therapies due to their safety, CNS tropism, and long-term therapeutic effect. However, several factors, including their ability to cross the blood&ndash;brain barrier, the efficiency of transduction, their immunotoxicity, loading capacity, the choice of serotype, and peripheral off-target effects should be carefully considered when designing an optimal AAV delivery strategy for a specific disease. In addition, distinct routes of administration may affect the efficiency and safety of AAV-delivered gene therapies. In this review, we summarize different administration routes of gene therapies delivered by AAVs to the brain in mice and rats. Updated knowledge regarding AAV-delivered gene therapies may facilitate the selection from various administration routes for specific disease models in future research. &nbsp;</p

Fire facilitates warming-induced upward shifts of alpine treelines by altering interspecific interactions

Biotic interactions between trees and other plants may modulate the responses of alpine treelines to climate. Moderate disturbances could, therefore, accelerate upward shifts of alpine treelines as the climate warms by reducing the coverage of competitor plants and resetting interspecific interactions. Larch (Larix potaninii var. macrocarpa) treelines disturbed by fire on the southeastern Tibetan Plateau are good locales for testing this hypothesis. We characterized treelines in five large rectangular plots spanning undisturbed and fire-disturbed fir (Abies georgei) and larch treelines. The fires in the 1960s caused gaps in the reconstructed age structures of the larches during the 1970s but did not lead to downslope shifts in treeline position. Recruitment has instead increased since the 1980s within the disturbed larch treelines, with treelines shifting upward by 11-44 m. In contrast, the undisturbed larch and fir treeline positions remained mostly unchanged. We hypothesize that upslope shifts of alpine treelines are likely a consequence of climatic warming, but fire disturbances can accelerate these dynamics by altering interspecific interactions

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

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

Moisture-mediated responsiveness of treeline shifts to global warming in the Himalayas

Among forest ecosystems, the alpine treeline ecotone can be considered to be a simplified model to study global ecology and climate change. Alpine treelines are expected to shift upwards in response to global warming given that tree recruitment and growth are assumed to be mainly limited by low temperatures. However, little is known whether precipitation and temperature interact to drive long-term Himalayan treeline dynamics. Tree growth is affected by spring rainfall in the central Himalayan treelines, being good locations for testing if, in addition to temperature, precipitation mediates treeline dynamics. To test this hypothesis, we reconstructed spatiotemporal variations in treeline dynamics in 20 plots located at six alpine treeline sites, dominated by two tree species (birch, fir), and situated along an east-west precipitation gradient in the central Himalayas. Our reconstructions evidenced that treelines shifted upward in response to recent climate warming, but their shift rates were primarily mediated by spring precipitation. The rate of upward shift was higher in the wettest eastern Himalayas, suggesting that its ascent rate was facilitated by spring precipitation. The drying tendency in association with the recent warming trends observed in the central Himalayas, however, will likely hinder an upslope advancement of alpine treelines and promote downward treeline shifts if moisture availability crosses a critical minimum threshold. Our study highlights the complexity of plant responses to climate and the need to consider multiple climate factors when analyzing treeline dynamics

The Potential Role of Ferroptosis in Neonatal Brain Injury

Ferroptosis is an iron-dependent form of cell death that is characterized by early lipid peroxidation and different from other forms of regulated cell death in terms of its genetic components, specific morphological features, and biochemical mechanisms. Different initiation pathways of ferroptosis have been reported, including inhibition of system Xc-, inactivation of glutathione-dependent peroxidase 4, and reduced glutathione levels, all of which ultimately promote the production of reactive oxygen species, particularly through enhanced lipid peroxidation. Although ferroptosis was first described in cancer cells, emerging evidence now links mechanisms of ferroptosis to many different diseases, including cerebral ischemia and brain hemorrhage. For example, neonatal brain injury is an important cause of developmental impairment and of permanent neurological deficits, and several types of cell death, including iron-dependent pathways, have been detected in the process of neonatal brain damage. Iron chelators and erythropoietin have both shown neuroprotective effects against neonatal brain injury. Here, we have summarized the potential relation between ferroptosis and neonatal brain injury, and according therapeutic intervention strategies