Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data

Proteomics Impact on Cell Biology to Resolve Cell Structure and Function

The acceleration of advances in proteomics has enabled integration with imaging at the EM and light microscopy levels, cryo-EM of protein structures, and artificial intelligence with proteins comprehensively and accurately resolved for cell structures at nanometer to subnanometer resolution. Proteomics continues to outpace experimentally based structural imaging, but their ultimate integration is a path toward the goal of a compendium of all proteins to understand mechanistically cell structure and function.

IgG glycomic profiling identifies potential biomarkers for diagnosis of echinococcosis

Echinococcosis caused by larval stage of the genus Echinococcus, is a serious and potentially fatal parasitic zoonosis distributed globally. The two types of the disease in human are cystic echinococcosis (CE) and alveolar echinococcosis (AE). As the biological and encysting characteristics of the parasite, early diagnosis remains to address. In the present study, we demonstrate the value of Immunoglobulin G (IgG) glycome as a potential diagnostic biomarker for echinococcosis. Serum IgG glycome profiles were analyzed by ultra-performance liquid chromatography in a cohort comprised of 127 echinococcosis patients, of them 98 were diagnosed as CE and 29 as AE. IgG N-glycome analysis in pretreatment serum of echinococcosis patients presents 25 glycans and 64 derived traits. Compared with IgG glycans of healthy control group, neutral glycans, fucosylation and agalactosylated N-glycans increased while sialylation and galactosylation decreased in echinococcosis patients. Combined with a machine-learning-based approach, we built three biomarker combinations to distinguish CE, AE and healthy controls. Meanwhile, galactosylation, sialylation and A2BG2S1 in IgG glycan profiles were evidently associated with different types of CE (from CE1 to CE5). Our findings suggest that the alterations in IgG N-glycome may be of value in CE and AE diagnosis and follow-up CE disease progress. The role of IgG N-glycans as diagnostic biomarker remains to be verified in future study.

Higher-order modular regulation of the human proteome

Operons are transcriptional modules that allow bacteria to adapt to environmental changes by coordinately expressing the relevant set of genes. In humans, biological pathways and their regulation are more complex. If and how human cells coordinate the expression of entire biological processes is unclear. Here, we capture 31 higher-order co-regulation modules, which we term progulons, by help of supervised machine-learning on proteomics data. Progulons consist of dozens to hundreds of proteins that together mediate core cellular functions. They are not restricted to physical interactions or co-localisation. Progulon abundance changes are primarily controlled at the level of protein synthesis and degradation. Implemented as a web app at www.proteomehd.net/progulonFinder, our approach enables the targeted search for progulons of specific cellular processes. We use it to identify a DNA replication progulon and reveal multiple new replication factors, validated by extensive phenotyping of siRNA-induced knockdowns. Progulons provide a new entry point into the molecular understanding of biological processes.

Proteome Analysis of Condensed Barley Mitotic Chromosomes

Proteins play a major role in the three-dimensional organization of nuclear genome and its function. While histones arrange DNA into a nucleosome fiber, other proteins contribute to higher-order chromatin structures in interphase nuclei, and mitotic/meiotic chromosomes. Despite the key role of proteins in maintaining genome integrity and transferring hereditary information to daughter cells and progenies, the knowledge about their function remains fragmentary. This is particularly true for the proteins of condensed chromosomes and, in particular, chromosomes of plants. Here, we purified barley mitotic metaphase chromosomes by a flow cytometric sorting and characterized their proteins. Peptides from tryptic protein digests were fractionated either on a cation exchanger or reversed-phase microgradient system before liquid chromatography coupled to tandem mass spectrometry. Chromosomal proteins comprising almost 900 identifications were classified based on a combination of software prediction, available database localization information, sequence homology, and domain representation. A biological context evaluation indicated the presence of several groups of abundant proteins including histones, topoisomerase 2, POLYMERASE 2, condensin subunits, and many proteins with chromatin-related functions. Proteins involved in processes related to DNA replication, transcription, and repair as well as nucleolar proteins were found. We have experimentally validated the presence of FIBRILLARIN 1, one of the nucleolar proteins, on metaphase chromosomes, suggesting that plant chromosomes are coated with proteins during mitosis, similar to those of human and animals. These results improve significantly the knowledge of plant chromosomal proteins and provide a basis for their functional characterization and comparative phylogenetic analyses.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Use of Mass Spectrometry to Study the Centromere and Kinetochore

A number of paths have led to the present list of centromere proteins, which is essentially complete for constitutive structural proteins, but still may be only partial if we consider the many other proteins that briefly visit the centromere and kinetochore to fine-tune the chromatin and adjust other functions. Elegant genetics led to the description of the budding yeast point centromere in 1980. In the same year was published the serendipitous discovery of antibodies that stained centromeres of human mitotic chromosomes in antisera from CREST patients. Painstaking biochemical analyses led to the identification of the human centromere antigens several years later, with the first yeast proteins being described 6 years after that. Since those early days, the discovery and cloning of centromere and kinetochore proteins has largely been driven by improvements in technology. These began with expression cloning methods, which allowed antibodies to lead to cDNA clones. Next, functional screens for kinetochore proteins were made possible by the isolation of yeast centromeric DNAs. Ultimately, the completion of genome sequences for humans and model organisms permitted the coupling of biochemical fractionation with protein identification by mass spectrometry. Subsequent improvements in mass spectrometry have led to the current state where virtually all structural components of the kinetochore are known and where a high-resolution map of the entire structure will likely emerge within the next several years.

Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations

The human species is becoming increasingly obese. Dapsone, which is extensively used across the globe for dermatological disorders, arachnid bites, and for treatment of several bacterial, fungal, and parasitic diseases, could be affected by obesity. We performed a clinical experiment, using optimal design, in volunteers weighing 44-150 kg, to identify the effect of obesity on dapsone pharmacokinetic parameters based on maximum-likelihood solution via the expectation-maximization algorithm. Artificial intelligence-based multivariate adaptive regression splines were used for covariate selection, and identified weight and/or age as predictors of absorption, systemic clearance, and volume of distribution. These relationships occurred only between certain patient weight and age ranges, delimited by multiple hinges and regions of discontinuity, not identified by standard pharmacometric approaches. Older and obese people have lower drug concentrations after standard dosing, but with complex patterns. Given that efficacy is concentration-dependent, optimal dapsone doses need to be personalized for obese patients.

Compositional Dynamics: Defining the Fuzzy Cell

Proteomic studies find many proteins in unexpected cellular locations. Can functional components of organelles be distinguished from biochemical artefacts or misguided cellular sorting? The clue might reside in compositional changes that follow biological challenges and that can be decoded by machine learning.

Proteomics Analysis with a Nano Random Forest Approach Reveals Novel Functional Interactions Regulated by SMC Complexes on Mitotic Chromosomes

Packaging of DNA into condensed chromosomes during mitosis is essential for the faithful segregation of the genome into daughter nuclei. Although the structure and composition of mitotic chromosomes have been studied for over 30 years, these aspects are yet to be fully elucidated. Here, we used stable isotope labeling with amino acids in cell culture to compare the proteomes of mitotic chromosomes isolated from cell lines harboring conditional knockouts of members of the condensin (SMC2, CAP-H, CAP-D3), cohesin (Scc1/Rad21), and SMC5/6 (SMC5) complexes. Our analysis revealed that these complexes associate with chromosomes independently of each other, with the SMC5/6 complex showing no significant dependence on any other chromosomal proteins during mitosis. To identify subtle relationships between chromosomal proteins, we employed a nano Random Forest (nanoRF) approach to detect protein complexes and the relationships between them. Our nanoRF results suggested that as few as 113 of 5058 detected chromosomal proteins are functionally linked to chromosome structure and segregation. Furthermore, nanoRF data revealed 23 proteins that were not previously suspected to have functional interactions with complexes playing important roles in mitosis. Subsequent small-interfering-RNA-based validation and localization tracking by green fluorescent protein-tagging highlighted novel candidates that might play significant roles in mitotic progression.