The multifunctional autophagy pathway in the human malaria parasite, Plasmodium falciparum.

Autophagy is a catabolic pathway typically induced by nutrient starvation to recycle amino acids, but can also function in removing damaged organelles. In addition, this pathway plays a key role in eukaryotic development. To date, not much is known about the role of autophagy in apicomplexan parasites and more specifically in the human malaria parasite Plasmodium falciparum. Comparative genomic analysis has uncovered some, but not all, orthologs of autophagy-related (ATG) genes in the malaria parasite genome. Here, using a genome-wide in silico analysis, we confirmed that ATG genes whose products are required for vesicle expansion and completion are present, while genes involved in induction of autophagy and cargo packaging are mostly absent. We subsequently focused on the molecular and cellular function of P. falciparum ATG8 (PfATG8), an autophagosome membrane marker and key component of the autophagy pathway, throughout the parasite asexual and sexual erythrocytic stages. In this context, we showed that PfATG8 has a distinct and atypical role in parasite development. PfATG8 localized in the apicoplast and in vesicles throughout the cytosol during parasite development. Immunofluorescence assays of PfATG8 in apicoplast-minus parasites suggest that PfATG8 is involved in apicoplast biogenesis. Furthermore, treatment of parasite cultures with bafilomycin A 1 and chloroquine, both lysosomotropic agents that inhibit autophagosome and lysosome fusion, resulted in dramatic morphological changes of the apicoplast, and parasite death. Furthermore, deep proteomic analysis of components associated with PfATG8 indicated that it may possibly be involved in ribophagy and piecemeal microautophagy of the nucleus. Collectively, our data revealed the importance and specificity of the autophagy pathway in the malaria parasite and offer potential novel therapeutic strategies

Understanding the need for digital twins’ data in patient advocacy and forecasting oncology

Digital twins are made of a real-world component where data is measured and a virtual component where those measurements are used to parameterize computational models. There is growing interest in applying digital twins-based approaches to optimize personalized treatment plans and improve health outcomes. The integration of artificial intelligence is critical in this process, as it enables the development of sophisticated disease models that can accurately predict patient response to therapeutic interventions. There is a unique and equally important application of AI to the real-world component of a digital twin when it is applied to medical interventions. The patient can only be treated once, and therefore, we must turn to the experience and outcomes of previously treated patients for validation and optimization of the computational predictions. The physical component of a digital twins instead must utilize a compilation of available data from previously treated cancer patients whose characteristics (genetics, tumor type, lifestyle, etc.) closely parallel those of a newly diagnosed cancer patient for the purpose of predicting outcomes, stratifying treatment options, predicting responses to treatment and/or adverse events. These tasks include the development of robust data collection methods, ensuring data availability, creating precise and dependable models, and establishing ethical guidelines for the use and sharing of data. To successfully implement digital twin technology in clinical care, it is crucial to gather data that accurately reflects the variety of diseases and the diversity of the population

Determining Protein Complex Connectivity Using a Probabilistic Deletion Network Derived from Quantitative Proteomics

Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex

Combinatorial depletion analysis to assemble the network architecture of the SAGA and ADA chromatin remodeling complexes

A combinatorial depletion strategy is combined with biochemistry, quantitative proteomics and computational approaches to elucidate the structure of the SAGA/ADA complexes. The analysis reveals five connected functional modules capable of independent assembly

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92060/1/pmic7070.pd

Amino Acid Patterns around Disulfide Bonds

Disulfide bonds provide an inexhaustible source of information on molecular evolution and biological specificity. In this work, we described the amino acid composition around disulfide bonds in a set of disulfide-rich proteins using appropriate descriptors, based on ANOVA (for all twenty natural amino acids or classes of amino acids clustered according to their chemical similarities) and Scheffé (for the disulfide-rich proteins superfamilies) statistics. We found that weakly hydrophilic and aromatic amino acids are quite abundant in the regions around disulfide bonds, contrary to aliphatic and hydrophobic amino acids. The density distributions (as a function of the distance to the center of the disulfide bonds) for all defined entities presented an overall unimodal behavior: the densities are null at short distances, have maxima at intermediate distances and decrease for long distances. In the end, the amino acid environment around the disulfide bonds was found to be different for different superfamilies, allowing the clustering of proteins in a biologically relevant way, suggesting that this type of chemical information might be used as a tool to assess the relationship between very divergent sets of disulfide-rich proteins

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

The WHHERE coactivator complex is required for retinoic acid-dependent regulation of embryonic symmetry

Bilateral symmetry is a striking feature of the vertebrate body plan organization. Vertebral precursors, called somites, provide one of the best illustrations of embryonic symmetry. Maintenance of somitogenesis symmetry requires retinoic acid (RA) and its coactivator Rere/Atrophin2. Here, using a proteomic approach we identify a protein complex, containing Wdr5, Hdac1, Hdac2 and Rere (named WHHERE), which regulates RA signaling and controls embryonic symmetry. We demonstrate that Wdr5, Hdac1, and Hdac2 are required for RA signaling in vitro and in vivo. Mouse mutants for Wdr5 and Hdac1 exhibit asymmetrical somite formation characteristic of RA-deficiency. We also identify the Rere-binding histone methyltransferase Ehmt2/G9a, as a RA coactivator controlling somite symmetry. Upon RA treatment, WHHERE and Ehmt2 become enriched at RA target genes to promote RNA polymerase II recruitment. Our work identifies a protein complex linking key epigenetic regulators acting in the molecular control of embryonic bilateral symmetry

Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/

Reinvestigation of aminoacyl-TRNA synthetase core complex by affinity purification-mass spectrometry reveals TARSL2 as a potential member of the complex

10.1371/journal.pone.0081734PLoS ONE812-POLN