Author Correction: The dental proteome of Homo antecessor.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The dental proteome of Homo antecessor.

The phylogenetic relationships between hominins of the Early Pleistocene epoch in Eurasia, such as Homo antecessor, and hominins that appear later in the fossil record during the Middle Pleistocene epoch, such as Homo sapiens, are highly debated1-5. For the oldest remains, the molecular study of these relationships is hindered by the degradation of ancient DNA. However, recent research has demonstrated that the analysis of ancient proteins can address this challenge6-8. Here we present the dental enamel proteomes of H. antecessor from Atapuerca (Spain)9,10 and Homo erectus from Dmanisi (Georgia)1, two key fossil assemblages that have a central role in models of Pleistocene hominin morphology, dispersal and divergence. We provide evidence that H. antecessor is a close sister lineage to subsequent Middle and Late Pleistocene hominins, including modern humans, Neanderthals and Denisovans. This placement implies that the modern-like face of H. antecessor-that is, similar to that of modern humans-may have a considerably deep ancestry in the genus Homo, and that the cranial morphology of Neanderthals represents a derived form. By recovering AMELY-specific peptide sequences, we also conclude that the H. antecessor molar fragment from Atapuerca that we analysed belonged to a male individual. Finally, these H. antecessor and H. erectus fossils preserve evidence of enamel proteome phosphorylation and proteolytic digestion that occurred in vivo during tooth formation. Our results provide important insights into the evolutionary relationships between H. antecessor and other hominin groups, and pave the way for future studies using enamel proteomes to investigate hominin biology across the existence of the genus Homo.

Proteogenomic Analysis of Breast Cancer Transcriptomic and Proteomic Data, Using De Novo Transcript Assembly: Genome-Wide Identification of Novel Peptides and Clinical Implications.

We have carried out proteogenomic analysis of the breast cancer transcriptomic and proteomic data, available at The Clinical Proteomic Tumor Analysis Consortium resource, to identify novel peptides arising from alternatively spliced events as well as other noncanonical expressions. We used a pipeline that consisted of de novo transcript assembly, six frame-translated custom database, and a combination of search engines to identify novel peptides. A portfolio of 4,387 novel peptide sequences initially identified was further screened through PepQuery validation tool (Clinical Proteomic Tumor Analysis Consortium), which yielded 1,558 novel peptides. We considered the dataset of 1,558 validated through PepQuery to understand their functional and clinical significance, leaving the rest to be further verified using other validation tools and approaches. The novel peptides mapped to the known gene sequences as well as to genomic regions yet undefined for translation, 580 novel peptides mapped to known protein-coding genes, 147 to non-protein-coding genes, and 831 belonged to novel translational sequences. The novel peptides belonging to protein-coding genes represented alternatively spliced events or 5' or 3' extensions, whereas others represented translation from pseudogenes, long noncoding RNAs, or novel peptides originating from uncharacterized protein-coding sequences-mostly from the intronic regions of known genes. Seventy-six of the 580 protein-coding genes were associated with cancer hallmark genes, which included key oncogenes, transcription factors, kinases, and cell surface receptors. Survival association analysis of the 76 novel peptide sequences revealed 10 of them to be significant, and we present a panel of six novel peptides, whose high expression was found to be strongly associated with poor survival of patients with human epidermal growth factor receptor 2-enriched subtype. Our analysis represents a landscape of novel peptides of different types that may be expressed in breast cancer tissues, whereas their presence in full-length functional proteins needs further investigations.

Role for ribosome-associated quality control in sampling proteins for MHC class I-mediated antigen presentation.

Mammalian cells present a fingerprint of their proteome to the adaptive immune system through the display of endogenous peptides on MHC-I complexes. MHC-I-bound peptides originate from protein degradation by the proteasome, suggesting that stably folded, long-lived proteins could evade monitoring. Here, we investigate the role in antigen presentation of the ribosome-associated quality control (RQC) pathway for the degradation of nascent polypeptides that are encoded by defective messenger RNAs and undergo stalling at the ribosome during translation. We find that degradation of model proteins by RQC results in efficient MHC-I presentation, independent of their intrinsic folding properties. Quantitative profiling of MHC-I peptides in wild-type and RQC-deficient cells by mass spectrometry showed that RQC substantially contributes to the composition of the immunopeptidome. Our results also identify endogenous substrates of the RQC pathway in human cells and provide insight into common principles causing ribosome stalling under physiological conditions.

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.

The PRIDE database and related tools and resources in 2019: improving support for quantification data.

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

Secretome analysis of Glioblastoma cell line--HNGC-2.

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary malignant tumor of the central nervous system. We have carried out a deep analysis of the secretome of a rapidly proliferating and tumorigenic cell line HNGC-2, representing GBM, in an effort to identify proteins, which may be targeted in the plasma of GBM patients as markers for diagnosis and disease surveillance. Prefractionation of the proteins from the conditioned medium of HNGC-2 cells in SDS gels followed by LC-MS/MS analysis using an ESI-IT mass spectrometer (LTQ) led to a total of 996 protein identifications with ≥2 peptides each. Of them, 664 proteins were observed in the transcriptome of HNGC-2 cells. The dataset of 996 proteins was mapped to important functional groups, such as cellular assembly and organisation, DNA recombination and repair, and other classes. Actin cytoskeleton signalling, phosphatidyl inositol 3 kinase (PI3K/AKT) and integrin linked kinase (ILK) signalling pathways were seen as enriched pathways. Comparisons with the published secretome of cell lines from 12 different cancers, including GBM, revealed that 348 proteins shared a commonality with a secretome of at least one other cell line, 321 of which were found to contain signal sequences or transmembrane domains and 335 could be linked to a plasma membrane or extracellular localization. Through intergration of this data we arrived at a non-redundant list of 597 protein identifications with the potential for secretion either by classical secretory pathways or by non-secretory processes; 233 of them have been detected in cerebrospinal fluid or plasma as per the published literature, and 172 have been implicated in GBM or other cancers. The HNGC-2 secretome dataset could serve as a useful resource for designing a targeted investigation of GBM biomarkers in plasma.

Glioblastoma cell secretome: analysis of three glioblastoma cell lines reveal 148 non-redundant proteins.

Glioblastoma multiforme (GBM) is the most aggressive among human gliomas with poor prognosis. Study of tumor cell secretome - proteins secreted by cancer cell lines, is a powerful approach to discover potential diagnostic or prognostic biomarkers. Here we report, for the first time, proteins secreted by three GBM cell lines, HNGC2, LN229 and U87MG. Analysis of the conditioned media of these cell lines by LC-MS/MS using ESI-IT mass spectrometer (LTQ) resulted in the confident identification of 102, 119 and 64 proteins, respectively. Integration of the results from all the three cell lines lead to a dataset of 148 non-redundant proteins. Subcellular classification using Genome Ontology indicated that 42% of the proteins identified belonged to extracellular or membrane proteins, viz. Vinculin, Tenascin XB, SERPIN F1 and TIMP-1. 52 proteins matched with the secretomes of 11 major cancer types reported earlier whereas remaining 96 are unique to our study. 25 protein identifications from the dataset represent proteins related to GBM or other cancer tissues as per Human Protein Atlas; at least 22 are detectable in plasma, 11 of them being reported even in cerebrospinal fluid. Our study thus provides a valuable resource of GBM cell secretome with potential for further investigation as GBM biomarkers.