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Silvae
\"Statius' Silvae, thirty-two occasional poems, were written probably between 89 and 96 AD. Here the poet congratulates friends, consoles mourners, offers thanks, admires a monument or artistic object, and describes a memorable scene. The verse is light in touch, with a distinct pictorial quality. Statius gives us in these impromptu poems clear images of Domitian's Rome. Statius was raised in the Greek cultural milieu of the Bay of Naples, and his Greek literary education lends a sophisticated veneer to his ornamental verse. The role of the emperor and the imperial circle in determining taste is also readily apparent: the figure of the emperor Domitian permeates these poems.\"-- Publisher description.
Book
High-throughput proteomics: a methodological mini-review
Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other ‘omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.
Journal Article
Genomic insights into the Ixodes scapularis tick vector of Lyme disease
Ticks transmit more pathogens to humans and animals than any other arthropod. We describe the 2.1 Gbp nuclear genome of the tick, Ixodes scapularis (Say), which vectors pathogens that cause Lyme disease, human granulocytic anaplasmosis, babesiosis and other diseases. The large genome reflects accumulation of repetitive DNA, new lineages of retro-transposons, and gene architecture patterns resembling ancient metazoans rather than pancrustaceans. Annotation of scaffolds representing similar to 57% of the genome, reveals 20,486 protein-coding genes and expansions of gene families associated with tick-host interactions. We report insights from genome analyses into parasitic processes unique to ticks, including host 'questing', prolonged feeding, cuticle synthesis, blood meal concentration, novel methods of haemoglobin digestion, haem detoxification, vitellogenesis and prolonged off-host survival. We identify proteins associated with the agent of human granulocytic anaplasmosis, an emerging disease, and the encephalitis-causing Langat virus, and a population structure correlated to life-history traits and transmission of the Lyme disease agent.
Journal Article
The third Nero
\"In 90 A.D., following the Saturninus revolt in Germany, the Emperor Domitian has become more paranoid about traitors and dissenters around him. This leads to several senators and even provincial governors facing charges and being executed for supposed crimes of conspiracy and insulting the emperor. Wanting to root out all the supports of Saturninus from the Senate, one of Domitian's men offers to hire Flavia Alba to do some intelligence work. Flavia Alba ... would rather avoid any and all court intrigue, thank you very much. But she's in a bit of a bind: her wedding is fast approaching, her fiancâe's still recovering--slowly--from being hit by a lightning bolt, and she's the sole support of their household\"-- Provided by publisher.
Book
MSBooster: improving peptide identification rates using deep learning-based features
Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.
There is a need for accessible ways to improve peptide spectrum match rescoring with deep learning predictions in bottom-up proteomics. Here, the authors demonstrate robust gains in peptide/protein identifications across various experiments, from single cell proteomics to immunopeptidomics.
Journal Article
Synergistic biodegradation of aromatic-aliphatic copolyester plastic by a marine microbial consortium
The degradation of synthetic polymers by marine microorganisms is not as well understood as the degradation of plastics in soil and compost. Here, we use metagenomics, metatranscriptomics and metaproteomics to study the biodegradation of an aromatic-aliphatic copolyester blend by a marine microbial enrichment culture. The culture can use the plastic film as the sole carbon source, reaching maximum conversion to CO
2
and biomass in around 15 days. The consortium degrades the polymer synergistically, with different degradation steps being performed by different community members. We identify six putative PETase-like enzymes and four putative MHETase-like enzymes, with the potential to degrade aliphatic-aromatic polymers and their degradation products, respectively. Our results show that, although there are multiple genes and organisms with the potential to perform each degradation step, only a few are active during biodegradation.
The degradation of plastics by marine microbes is not well understood. Here, Meyer-Cifuentes et al. use a meta-omics approach to study the biodegradation of an aromatic-aliphatic copolyester blend by a marine microbial enrichment culture, showing that different degradation steps are performed by different microorganisms.
Journal Article
Hyodeoxycholic acid ameliorates nonalcoholic fatty liver disease by inhibiting RAN-mediated PPARα nucleus-cytoplasm shuttling
Nonalcoholic fatty liver disease (NAFLD) is usually characterized with disrupted bile acid (BA) homeostasis. However, the exact role of certain BA in NAFLD is poorly understood. Here we show levels of serum hyodeoxycholic acid (HDCA) decrease in both NAFLD patients and mice, as well as in liver and intestinal contents of NAFLD mice compared to their healthy counterparts. Serum HDCA is also inversely correlated with NAFLD severity. Dietary HDCA supplementation ameliorates diet-induced NAFLD in male wild type mice by activating fatty acid oxidation in hepatic peroxisome proliferator-activated receptor α (PPARα)-dependent way because the anti-NAFLD effect of HDCA is abolished in hepatocyte-specific
Pparα
knockout mice. Mechanistically, HDCA facilitates nuclear localization of PPARα by directly interacting with RAN protein. This interaction disrupts the formation of RAN/CRM1/PPARα nucleus-cytoplasm shuttling heterotrimer. Our results demonstrate the therapeutic potential of HDCA for NAFLD and provide new insights of BAs on regulating fatty acid metabolism.
Nonalcoholic fatty liver disease (NAFLD) is often linked to disrupted bile acid homeostasis. Here, the authors show hyodeoxycholic acid (HDCA) ameliorates nonalcoholic fatty liver disease by inhibiting the formation of RAN/CRM1/PPARα nuclear export heterotrimer, resulting in increased nuclear localization of PPARα and activated fatty acid oxidation.
Journal Article
AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides (
https://github.com/MannLabs/alphapeptdeep
). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition (
https://github.com/MannLabs/PeptDeep-HLA
).
Deep learning (DL) has been frequently used in mass spectrometry-based proteomics but there is still a lot of potential. Here, the authors develop a framework that enables building DL models to predict arbitrary peptide properties with only a few lines of code.
Journal Article