A Systematic Evaluation of Semispecific Peptide Search Parameter Enables Identification of Previously Undescribed N-Terminal Peptides and Conserved Proteolytic Processing in Cancer Cell Lines

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the most commonly used technique in explorative proteomic research. A variety of open-source tools for peptide-spectrum matching have become available. Most analyses of explorative MS data are performed using conventional settings, such as fully specific enzymatic constraints. Here we evaluated the impact of the fragment mass tolerance in combination with the enzymatic constraints on the performance of three search engines. Three open-source search engines (Myrimatch, X! Tandem, and MSGF+) were evaluated concerning the suitability in semi- and unspecific searches as well as the importance of accurate fragment mass spectra in non-specific peptide searches. We then performed a semispecific reanalysis of the published NCI-60 deep proteome data applying the most suited parameters. Semi- and unspecific LC-MS/MS data analyses particularly benefit from accurate fragment mass spectra while this effect is less pronounced for conventional, fully specific peptide-spectrum matching. Search speed differed notably between the three search engines for semi- and non-specific peptide-spectrum matching. Semispecific reanalysis of NCI-60 proteome data revealed hundreds of previously undescribed N-terminal peptides, including cases of proteolytic processing or likely alternative translation start sites, some of which were ubiquitously present in all cell lines of the reanalyzed panel. Highly accurate MS2 fragment data in combination with modern open-source search algorithms enable the confident identification of semispecific peptides from large proteomic datasets. The identification of previously undescribed N-terminal peptides in published studies highlights the potential of future reanalysis and data mining in proteomic datasets.

Untargeted Metabolomics Analysis of Serum Metabolites in Zucker Diabetic Fatty Rats

Zucker Diabtic Fatty rats is a common model used for the studies related to type 2 diabetes. Metabolomics, proven by previous studies, can be used for the prediction of phenotype. This investigation held untargeted metabolomics analysis on the serum metabolites of the Zucker Diabetic Fatty rats, intended to gain insights in the study of the pathology of type 2 diabetes. LCMS method (liquid chromatography coupled with mass spectrometry) is an analytical chemistry technique used to separate and purify complex mixtures. After identifying metabolites using MS2 library spectrum matching, this investigation used Metabo Analyst to compare fatty rats and lean rats. Lastly, by applying over representation analysis, we identified seven metabolic pathways that are important for humans, which assists in discovery of therapeutic targets and the developments of diabetes biomarkers.

An Approach of Spectra Standardization and Qualitative Identification for Biomedical Materials Based on Terahertz Spectroscopy

Terahertz time-domain spectroscopy (THz-TDS) systems are widely used to obtain fingerprint spectra of many different biomedical substances, and thus the identification of different biological materials, medicines, or dangerous chemicals can be realized. However, the spectral data for the same substance obtained from different THz-TDS systems may have distinct differences because of differences in system errors and data processing methods, which leads to misclassification and errors in identification. To realize the exact and fast identification of substances, spectral standardization is the key issue. In this paper, we present detailed disposal methods and execution processes for the spectral standardization and substance identification, including feature extracting, database searching, and fingerprint spectrum matching of unknown substances. Here, we take twelve biomedical compounds including different biological materials, medicines, or dangerous chemicals as examples. These compounds were analyzed by two different THz-TDS systems, one of which is a commercial product and the other is our verification platform. The original spectra from two systems showed obvious differences in their curve shapes and amplitudes. After wavelet transform, cubic spline interpolation, and support vector machine (SVM) classification with an appropriate kernel function, the spectra from two systems can be standardized, and the recognition rate of qualitative identification can be up to 99.17%.

Improving Water Leaving Reflectance Retrievals from ABI and AHI Data Acquired Over Case 2 Waters from Present Geostationary Weather Satellite Platforms

The current generation of geostationary weather satellite instruments, such as the Advanced Baseline Imagers (ABIs) on board the US NOAA GOES 16 and 17 satellites and the Advanced Himawari Imagers (AHIs) on board the Japanese Himawari-8/9 satellites, have six channels located in the visible to shortwave IR (SWIR) spectral range. These instruments can acquire images over both land and water surfaces at spatial resolutions between 0.5 and 2 km and with a repeating cycle between 5 and 30 min depending on the mode of operation. The imaging data from these instruments have clearly demonstrated the capability in detecting sediment movements over coastal waters and major chlorophyll blooms over deeper oceans. At present, no operational ocean color data products have been produced from ABI data. Ocean color data products have been operationally generated from AHI data at the Japan Space Agency, but the spatial coverage of the products over very turbid coastal waters are sometimes lacking. In this article, we describe atmospheric correction algorithms for retrieving water leaving reflectances from ABI and AHI data using spectrum-matching techniques. In order to estimate aerosol models and optical depths, we match simultaneously the satellite-measured top of atmosphere (TOA) reflectances on the pixel by pixel basis for three channels centered near 0.86, 1.61, and 2.25 μm (or any combinations of two channels among the three channels) with theoretically simulated TOA reflectances. We demonstrate that water leaving reflectance retrievals can be made from ABI and AHI data with our algorithms over turbid case two waters. Our spectrum-matching algorithms, if implemented onto operational computing facilities, can be complimentary to present operational ocean versions of atmospheric correction algorithms that are mostly developed based on the SeaWiFS type of two-band ratio algorithm.

An integrative proteogenomics approach reveals peptides encoded by annotated lincRNA in the mouse kidney inner medulla

Long noncoding RNAs (lncRNAs) are intracellular transcripts longer than 200 nucleotides and lack protein-coding information. A subclass of lncRNA known as long intergenic noncoding RNAs (lincRNAs) are transcribed from genomic regions that share no overlap with annotated protein-coding genes. Increasing evidence has shown that some annotated lincRNA transcripts do in fact contain open reading frames (ORFs) encoding functional short peptides in the cell. Few robust methods for lincRNA-encoded peptide identification have been reported, and the tissue-specific expression of these peptides has been largely unexplored. Here we propose an integrative workflow for lincRNA-encoded peptide discovery and test it on the mouse kidney inner medulla (IM). In brief, low molecular weight protein fractions were enriched from homogenate of IMs and trypsinized into shorter peptides, which were sequenced by high resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). To curate a hypothetical lincRNA-encoded peptide database for peptide-spectrum matching following LC-MS/MS, we performed RNA-Seq on IMs, computationally removed reads overlapping with annotated protein-coding genes, and remapped the remaining reads to a database of mouse noncoding transcripts to infer lincRNA expression. Expressed lincRNAs were searched for ORFs by an existing rule-based algorithm, and translated ORFs were used for peptide-spectrum matching. Peptides identified by LC-MS/MS were further evaluated by using several quality control criteria and bioinformatics methods. We discovered three novel lincRNA-encoded peptides, which are conserved in mouse, rat, and human. The workflow can be adapted for discovery of small protein-coding genes in any species or tissue where noncoding transcriptome information is available.

PeptideWitch–A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data

PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

Quantitative profiling of microbial communities by de novo metaproteomics

ABSTRACTMetaproteomics has emerged as one of the most promising approaches for determining the composition and metabolic functions of complete microbial communities. Conventional metaproteomics approaches however, rely on the construction of protein sequence databases and efficient peptide-spectrum matching algorithms. Thereby, very large sequence databases impact on computational efforts and sensitivity. More recently, advanced de novo sequencing strategies—which annotate peptide sequences without the requirement for a database—have become (again) increasingly proposed for proteomics applications. Such approaches would vastly expand many metaproteomics applications by enabling rapid community profiling and by capturing unsequenced community members, which otherwise remain inaccessible for further interpretation. Nevertheless, because of the lack of efficient pipelines and validation procedures, those strategies have only rarely been employed for community proteomics.Here we report on a newly established de novo metaproteomics pipeline which was evaluated for its quantitative performance using synthetic and natural communities. Additionally, we introduce a novel validation strategy and investigate the actual content of community members within community proteomics data.

Katav-Ivanovsk earthquake on 04.09.2018, mb=5.4 (Urals)

The article summarizes the instrumental and macroseismic data obtained in the area of Katav-Ivanovsk earthquake, which occurred on September 4, 2018, in Chelyabinsk region, Russia. The earthquake was the strongest instrumentally recorded earthquake in the Urals (mb=5.4) and at the same time, it had the most seismic intensity among other earthquakes in Russia in 2018 (I0=6 points). The uniqueness of this event was given by the fact that after it for the first time for the Urals the aftershock process was recorded, the active stage of which lasted more than 1 year. Like the mainshock, some aftershocks had a significant macroseismic effect. The work contains the results of studies that allowed to determine the exact coordinates of the epicenter in conditions of lack of near stations using the relative location technique. New processing approaches also made it possible to estimate the depth of the focus through a function of phase spectrum matching. Finally, a considerable amount of macroseismic data formed the basis of the macroseismic field map.