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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.

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.

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.

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.

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.

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.

The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 10 5 -nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design. The ability to design functional sequences is central to protein engineering and biotherapeutics. Here the authors introduce a deep generative alignment-free model for sequence design applied to highly variable regions and design and test a diverse nanobody library with improved properties for selection experiments.

Food protein amyloid fibrils have superior technological, nutritional, sensorial, and physical properties compared to native monomers, but there is as yet insufficient understanding of their digestive fate and safety for wide consumption. By combining SDS-PAGE, ELISA, fluorescence, AFM, MALDI-MS, CD, microfluidics, and SAXS techniques for the characterization of β-lactoglobulin and lysozyme amyloid fibrils subjected to in-vitro gastrointestinal digestion, here we show that either no noticeable conformational differences exist between amyloid aggregates and their monomer counterparts after the gastrointestinal digestion process (as in β-lactoglobulin), or that amyloid fibrils are digested significantly better than monomers (as in lysozyme). Moreover, in-vitro exposure of human cell lines and in-vivo studies with C. elegans and mouse models, indicate that the digested fibrils present no observable cytotoxicity, physiological abnormalities in health-span, nor accumulation of fibril-induced plaques in brain nor other organs. These extensive in-vitro and in-vivo studies together suggest that the digested food amyloids are at least equally as safe as those obtained from the digestion of corresponding native monomers, pointing to food amyloid fibrils as potential ingredients for human nutrition. Food protein amyloid fibrils have superior properties but due to safety concerns, have not yet been used in foods. In-vitro and in-vivo studies here show that upon digestion they are safe and can be used as potential ingredients for human nutrition.

Lysine lactylation (Kla) links metabolism and gene regulation and plays a key role in multiple biological processes. However, the regulatory mechanism and functional consequence of Kla remain to be explored. Here, we report that HBO1 functions as a lysine lactyltransferase to regulate transcription. We show that HBO1 catalyzes the addition of Kla in vitro and intracellularly, and E508 is a key site for the lactyltransferase activity of HBO1. Quantitative proteomic analysis further reveals 95 endogenous Kla sites targeted by HBO1, with the majority located on histones. Using site-specific antibodies, we find that HBO1 may preferentially catalyze histone H3K9la and scaffold proteins including JADE1 and BRPF2 can promote the enzymatic activity for histone Kla. Notably, CUT&Tag assays demonstrate that HBO1 is required for histone H3K9la on transcription start sites (TSSs). Besides, the regulated Kla can promote key signaling pathways and tumorigenesis, which is further supported by evaluating the malignant behaviors of HBO1- knockout (KO) tumor cells, as well as the level of histone H3K9la in clinical tissues. Our study reveals HBO1 serves as a lactyltransferase to mediate a histone Kla-dependent gene transcription. The regulatory mechanism and functional consequence of lysine lactylation remain to be explored. Here, the authors identify HBO1 as a lysine lactyltransferase and suggest a potential role of HBO1 in tumorigenesis through H3K9la-mediated transcription regulation.

Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other’s partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome. Computational protein-protein interaction (PPI) prediction has the potential to complement experimental efforts to map interactomes. Here, the authors show that proteins tend to interact if one is similar to the other’s partners and that PPI prediction based on this principle is highly accurate.