In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein-level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge.

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline , it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organismspecific composite data sets. A combination of protein digestion, liquid chromatography and tandem mass spectrometry (LC-MS/MS) 1 , often referred to as shotgun proteomics, has become a robust and powerful proteomics technology. Protein samples are digested into peptides, typically using trypsin. The resulting peptides are then separated and subjected to mass spectrometric (MS) analysis, whereby a subset of the available precursor ions are sampled by the MS instrument, isolated and further fragmented in the gas phase to generate fragment ion spectra (MS/MS spectra). From these spectra, the peptides and then the proteins present in the sample and, in conjunction with quantification strategies, their relative or absolute quantities can be determined (1).The volume of data generated in proteomic experiments has been growing steadily over the past decade. This has been aided by the rapid progress made in several facets of proteomics technology, including improved sample preparation and labeling techniques and faster, more sen...

The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) has standardized data submission and dissemination of mass spectrometry proteomics data worldwide since 2012. In this paper, we describe the main developments since the previous update manuscript was published in Nucleic Acids Research in 2017. Since then, in addition to the four PX existing members at the time (PRIDE, PeptideAtlas including the PASSEL resource, MassIVE and jPOST), two new resources have joined PX: iProX (China) and Panorama Public (USA). We first describe the updated submission guidelines, now expanded to include six members. Next, with current data submission statistics, we demonstrate that the proteomics field is now actively embracing public open data policies. At the end of June 2019, more than 14 100 datasets had been submitted to PX resources since 2012, and from those, more than 9 500 in just the last three years. In parallel, an unprecedented increase of data re-use activities in the field, including ‘big data’ approaches, is enabling novel research and new data resources. At last, we also outline some of our future plans for the coming years.

Recycling of spent lithium-ion batteries (LIBs) has attracted significant attention in recent years due to the increasing demand for corresponding critical metals/materials and growing pressure on the environmental impact of solid waste disposal. A range of investigations have been carried out for recycling spent LIBs to obtain either battery materials or individual compounds. For the effective recovery of materials to be enhanced, physical pretreatment is usually applied to obtain different streams of waste materials ensuring efficient separation for further processing. Subsequently, a metallurgical process is used to extract metals or separate impurities from a specific waste stream so that the recycled materials or compounds can be further prepared by incorporating principles of materials engineering. In this review, the current status of spent LIB recycling is summarized in light of the whole recycling process, especially focusing on the hydrometallurgy. In addition to understanding different hydrometallurgical technologies including acidic leaching, alkaline leaching, chemical precipitation, and solvent extraction, the existing challenges for process optimization during the recycling are critically analyzed. Moreover, the energy consumption of different processes is evaluated and discussed. It is expected that this research could provide a guideline for improving spent LIB recycling, and this topic can be further stimulated for industrial realization.

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Amelogenins make up a class of proteins associated with the formation of mineralized enamel in vertebrates, possess highly conserved N-and C-terminal sequence regions, and represent an interesting model protein system for understanding biomineralization and protein assembly. Using bioinformatics, we report here the identification of molecular traits that classify 12 amelogenin proteins as members of the intrinsically disordered or unstructured protein family (IDPs), a group of proteins that normally exist as unfolded species but are capable of transformation to a folded state as part of their overall function. Using biophysical techniques (CD and NMR), we follow up on our bioinformatics studies and confirm that one of the amelogenins, recombinant porcine rP172, exists in an extended, unfolded state in the monomeric form. This protein exhibits evidence of conformational exchange between two states, and this exchange may be mediated by Pro residues in the sequence. Although the protein is globally unfolded, we detect the presence of local residual secondary structure [α-helix, extended β-strand, turn/loop, and polyproline type II (PPII)] that may serve several functional roles within the enamel matrix. The extended, labile conformation of rP172 amelogenin is compatible with the known functions of amelogenin in enamel biomineralization, i.e., self-assembly, associations with other enamel matrix proteins and with calcium phosphate biominerals, and interaction with cell receptors. It is likely that the labile structure of this protein facilitates interactions of amelogenin with other macromolecules or with minerals for achievement of internal protein stabilization.The formation of inorganic compounds by organisms (biomineralization) is a substantial scientific puzzle. The ability of cells to employ proteins to control nucleation, crystal morphology, polymorphism, and the material properties of living tissues requires precise molecular control and efficient mechanisms (1). One such protein, amelogenin, is found in mammalian tooth enamel, one of the most highly mineralized materials of vertebrates (1-3). Amelogenin is essential for normal enamel development and is capable of protein selfassociation, forming supramolecular assemblies under defined conditions in the laboratory (4-6). These supramolecular assemblies (nanospheres) are believed to exert control over the † This work was supported by funding from the Department of Energy (DE-FG02-03ER46099) and the National Institute of Dental and Craniofacial Research (DE-013414 morphology, organization, and directionality of hydroxyapatite crystal growth (7,8). Primary sequence analysis of 26 mammalian lineages indicates that the N-terminus (Tyr-rich) and Cterminus (charged) of amelogenin are highly conserved, whereas variations occur in the central regions (9). Amelogenin sequence mutations lead to defective enamel crystal formation and organization (10,11), and deletion of the conserved terminal domains leads to the formation of ill-defined enamel crystals, high...

Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include mass spectrometry to define protein sequence, protein:protein interactions, and protein post-translational modifications. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative mass spectrometry proteomics. It supports all major operating systems and instrument vendors via open data formats. Here we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of tandem mass spectrometry datasets, as well as some major upcoming features.