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The dental proteome of Homo antecessor.

ABSTRACT: 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.

SUBMITTER: Welker F

PROVIDER: S-EPMC7582224 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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The dental proteome of Homo antecessor.

Welker Frido F Ramos-Madrigal Jazmín J Gutenbrunner Petra P Mackie Meaghan M Tiwary Shivani S Rakownikow Jersie-Christensen Rosa R Chiva Cristina C Dickinson Marc R MR Kuhlwilm Martin M de Manuel Marc M Gelabert Pere P Martinón-Torres María M Margvelashvili Ann A Arsuaga Juan Luis JL Carbonell Eudald E Marques-Bonet Tomas T Penkman Kirsty K Sabidó Eduard E Cox Jürgen J Olsen Jesper V JV Lordkipanidze David D Racimo Fernando F Lalueza-Fox Carles C Bermúdez de Castro José María JM Willerslev Eske E Cappellini Enrico E

Nature 20200401 7802

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 debated<sup>1-5</sup>. 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 challenge<sup>6-8</sup>. Her ...[more]

PMID: 32269345

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Project description:BackgroundHuman T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported.ResultsIn this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses.ConclusionsThe Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target.

Project description:Dental cementum (DC) covers the tooth root and has important functions in tooth attachment and position. DC can be lost to disease, and regeneration is currently unpredictable due to limited understanding of DC formation. This study used a model of experimentally-induced apposition (EIA) in mice to identify proteins associated with new DC formation. Mandibular first molars were induced to super-erupt for 6 and 21days after extracting opposing maxillary molars. Decalcified and formalin-fixed paraffin-embedded mandible sections were prepared for laser capture microdissection. Microdissected protein extracts were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), and the data submitted to repeated measure ANOVA test (RM-ANOVA, alpha=5%). A total of 519 proteins were identified, with 97 (18.6%) proteins found exclusively in EIA sites and 50 (9.6%) proteins exclusively expressed in control sites. Fifty six (10.7%) proteins were differentially regulated by RM-ANOVA (p<0.05), with 24 regulated by the exclusive effect of EIA (12 proteins) or the interaction between EIA and time (12 proteins), including serpin 1a, procollagen C-endopeptidase enhancer, tenascin X (TNX), and asporin (ASPN). In conclusion, proteomic analysis demonstrated significantly altered protein profile in DC under EIA, providing new insights on DC biology and potential candidates for tissue engineering applications.Dental cementum (DC) is a mineralized tissue that covers the tooth root surface and has important functions in tooth attachment and position. DC and other periodontal tissues can be lost to disease, and regeneration is currently unpredictable due to lack of understanding of DC formation. This study used a model of experimentally-induced apposition (EIA) in mice to promote new cementum formation, followed by laser capture microdissection (LCM) and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) proteomic analysis. This approach identified proteins associated with new cementum formation that may be targets for promoting cementum regeneration.

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The dental proteome of Homo antecessor.

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The dental proteome of Homo antecessor.

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