Jens Allmer

Algorithms for the de novo sequencing of peptides from tandem mass spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.

Computational Methods for MicroRNA Target Prediction

MicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.

Computational methods for ab initio detection of microRNAs

MicroRNAs are small RNA sequences of 18–24 nucleotides in length, which serve as templates to drive post-transcriptional gene silencing. The canonical microRNA pathway starts with transcription from DNA and is followed by processing via the microprocessor complex, yielding a hairpin structure. Which is then exported into the cytosol where it is processed by Dicer and then incorporated into the RNA-induced silencing complex. All of these biogenesis steps add to the overall specificity of miRNA production and effect. Unfortunately, their modes of action are just beginning to be elucidated and therefore computational prediction algorithms cannot model the process but are usually forced to employ machine learning approaches. This work focuses on ab initio prediction methods throughout; and therefore homology-based miRNA detection methods are not discussed. Current ab initio prediction algorithms, their ties to data mining, and their prediction accuracy are detailed.

Can MiRBase Provide Positive Data for Machine Learning for the Detection of MiRNA Hairpins

Experimental detection and validation of miRNAs is a tedious, time-consuming, and expensive process. Computational methods for miRNA gene detection are being developed so that the number of candidates that need experimental validation can be reduced to a manageable amount. Computational methods involve homology-based and ab inito algorithms. Both approaches are dependent on positive and negative training examples. Positive examples are usually derived from miRBase, the main resource for experimentally validated miRNAs. We encountered some problems with miRBase which we would like to report here. Some problems, among others, we encountered are that folds presented in miRBase are not always the fold with the minimum free energy; some entries do not seem to conform to expectations of miRNAs, and some external accession numbers are not valid. In addition, we compared the prediction accuracy for the same negative dataset when the positive data came from miRBase or miRTarBase and found that the latter led to more precise prediction models. We suggest that miRBase should introduce some automated facilities for ensuring data quality to overcome these problems.

EPO Mediates Neurotrophic, Neuroprotective, Anti-Oxidant, and Anti-Apoptotic Effects via Downregulation of miR-451 and miR-885-5p in SH-SY5Y Neuron-Like Cells

Erythropoietin (EPO) is a neuroprotective cytokine, which has been applied in several animal models presenting neurological disorders. One of the proposed modes of action resulting in neuroprotection is post-transcriptional gene expression regulation. This directly brings to mind microRNAs (miRNAs), which are small non-coding RNAs that regulate gene expression at the post-transcriptional level. It has not yet been evaluated whether miRNAs participate in the biological effects of EPO or whether it, inversely, modulates specific miRNAs in neuronal cells. In this study, we employed miRNA and mRNA arrays to identify how EPO exerts its biological function. Notably, miR-451 and miR-885-5p are downregulated in EPO-treated SH-SY5Y neuronal-like cells. Accordingly, target prediction and transcriptome analysis of cells treated with EPO revealed an alteration of the expression of genes involved in apoptosis, cell survival, proliferation, and migration. Low expression of miRNAs in SH-SY5Y was correlated with high expression of their target genes, vascular endothelial growth factor A, matrix metallo peptidase 9 (MMP9), cyclin-dependent kinase 2 (CDK2), erythropoietin receptor, Mini chromosome maintenance complex 5 (MCM5), B-cell lymphoma 2 (BCL2), and Galanin (GAL). Cell viability, apoptosis, proliferation, and migration assays were carried out for functional analysis after transfection with miRNA mimics, which inhibited some biological actions of EPO such as neuroprotection, anti-oxidation, anti-apoptosis, and migratory effects. In this study, we report for the first time that EPO downregulates the expression of miRNAs (miR-451 and miR-885-5p) in SH-SY5Y neuronal-like cells. The correlation between the over-expression of miRNAs and the decrease in EPO-mediated biological effects suggests that miR-451 and miR-885-5p may play a key role in the mediation of biological function.

Lithium protects against paraquat neurotoxicity by NRF2 activation and miR-34a inhibition in SH-SY5Y cells

Lithium is a mood stabilizing agent commonly used for the treatment of bipolar disorder. Here, we investigated the potential neuroprotective effect of lithium against paraquat toxicity and its underlying mechanisms in vitro. SH-SY5Y human neuroblastoma cells were treated with paraquat (PQ) 0.5 mM concentration after lithium pretreatment to test lithiums capability in preventing cell toxicity. Cell death was evaluated by LDH, WST-8, and tryphan blue assays. Apoptosis was analyzed using DNA fragmentation, Annexin V immunostaining, Sub G1 cell cycle analysis, and caspase-3 activity assays. BCL2, BAX, and NRF2 protein expression were evaluated by Western-blotting and the BDNF protein level was determined with ELISA. mRNA levels of BCL2, BAX, BDNF, and NRF2 target genes (HO-1, GCS, NQO1), as well as miR-34a expression were analyzed by qPCR assay. Functional experiments were done via transfection with NRF2 siRNA and miR-34a mimic. Lithium treatment prevented paraquat induced cell death and apoptosis. Lithium treated cells showed increased anti-apoptotic protein BCL2 and decreased pro-apoptotic protein BAX expression. Lithium exerted a neurotrophic effect by increasing BDNF protein expression. It also diminished reactive oxygen species production and activated the redox sensitive transcription factor NRF2 and increased its target genes expression. Knockdown of NRF2 abolished neuroprotective, anti-apoptotic, and anti-oxidant effects of lithium. Furthermore, lithium significantly decreased both basal and PQ-induced expression of miR-34a. Transfection of miR-34a specific mimic reversed neuroprotective, anti-apoptotic, and anti-oxidant effects of lithium against PQ-toxicity. Our results revealed two novel mechanisms of lithium neuroprotection, namely NRF2 activation and miR-34a suppression.

One Step Forward, Two Steps Back; Xeno-MicroRNAs Reported in Breast Milk Are Artifacts

Background MicroRNAs (miRNAs) are short RNA sequences that guide post-transcriptional regulation of gene expression via complementarity to their target mRNAs. Discovered only recently, miRNAs have drawn a lot of attention. Multiple protein complexes interact to first cleave a hairpin from nascent RNA, export it into the cytosol, trim its loop, and incorporate it into the RISC complex which is important for binding its target mRNA. This process works within one cell, but circulating miRNAs have been described suggesting a role in cell-cell communication. Motivation Viruses and intracellular parasites like Toxoplasma gondii use miRNAs to manipulate host gene expression from within the cellular environment. However, recent research has claimed that a rice miRNA may regulate human gene expression. Despite ongoing debates about these findings and general reluctance to accept them, a recent report claimed that foodborne plant miRNAs pass through the digestive tract, travel through blood to be incorporated by alveolar cells excreting milk. The miRNAs are then said to have some immune-related function in the newborn. Principal Findings We acquired the data that supports their claim and performed further analyses. In addition to the reported miRNAs, we were able to detect almost complete mRNAs and found that the foreign RNA expression profiles among samples are exceedingly similar. Inspecting the source of the data helped understand how RNAs could contaminate the samples. Conclusion Viewing these findings in context with the difficulties foreign RNAs face on their route into breast milk and the fact that many identified foodborne miRNAs are not from actual food sources, we can conclude beyond reasonable doubt that the original claims and evidence presented may be due to artifacts. We report that the study claiming their existence is more likely to have detected RNA contamination than miRNAs.

Computational Prediction of MicroRNAs from Toxoplasma gondii Potentially Regulating the Hosts’ Gene Expression

MicroRNAs (miRNAs) were discovered two decades ago, yet there is still a great need for further studies elucidating their genesis and targeting in different phyla. Since experimental discovery and validation of miRNAs is difficult, computational predictions are indispensable and today most computational approaches employ machine learning. Toxoplasma gondii, a parasite residing within the cells of its hosts like human, uses miRNAs for its post-transcriptional gene regulation. It may also regulate its hosts’ gene expression, which has been shown in brain cancer. Since previous studies have shown that overexpressed miRNAs within the host are causal for disease onset, we hypothesized that T. gondii could export miRNAs into its host cell. We computationally predicted all hairpins from the genome of T. gondii and used mouse and human models to filter possible candidates. These were then further compared to known miRNAs in human and rodents and their expression was examined for T. gondii grown in mouse and human hosts, respectively. We found that among the millions of potential hairpins in T. gondii, only a few thousand pass filtering using a human or mouse model and that even fewer of those are expressed. Since they are expressed and differentially expressed in rodents and human, we suggest that there is a chance that T. gondii may export miRNAs into its hosts for direct regulation.

Existing bioinformatics tools for the quantitation of post-translational modifications

Mass spectrometry (MS)-based proteomics, by itself, is a vast and complex area encompassing various mass spectrometers, different spectra, and search result representations. When the aim is quantitation performed in different scanning modes at different MS levels, matters become additionally complex. Quantitation of post-translational modifications (PTM) represents the greatest challenge among these endeavors. Many different approaches to quantitation have been described and some of these can be directly applied to the quantitation of PTMs. The amount of data produced via MS, however, makes manual data interpretation impractical. Therefore, specialized software tools meet this challenge. Any software currently able to quantitate differentially labeled samples may theoretically be adapted to quantitate differential PTM expression among samples as well. Due to the heterogeneity of mass spectrometry-based proteomics; this review will focus on quantitation of PTM using liquid chromatography followed by one or more stages of mass spectrometry. Currently available free software, which either allow analysis of PTM or are easily adaptable for this purpose, is briefly reviewed in this paper. Selected studies, especially those related to phosphoproteomics, shall be used to highlight the current ability to quantitate PTMs.

Systematic computational analysis of potential RNAi regulation in Toxoplasma gondii

RNA interference (RNAi) is the mechanism through which RNA interferes with the production of other RNAs in a sequence specific manner. Micro RNA (miRNA) is a type of RNA which is transcribed as pri-miRNAs and processed to pre-miRNAs in the nucleus. These pre-miRNAs are then exported from the nucleus and processed in the cytoplasm to double stranded RNA with one strand providing target specificity..Toxoplasma gondii is a parasitic apicomplexan which causes several diseases. T. gondii is a good candidate for computational efforts with its small and publicly available genome files and extensive information about its gene structure. Although the existence of RNA interference in T. gondii is being debated, establishment of its complete potential RNAi regulatory network may be beneficial for further investigations into the topic.