MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes

The phosphatase inhibitor BVT-948 can be used to efficiently screen functional sexual development proteins in the malaria parasite Plasmodium berghei

BACKGROUND

Studying and discovering the molecular mechanism of Plasmodium sexual development is crucial for the development of transmission blocking drugs and malaria eradication. The aim of this study was to investigate the feasibility of using phosphatase inhibitors as a tool for screening proteins essential for Plasmodium sexual development and to discover proteins affecting the sexual development of malaria parasites.

METHODS

Differences in protein phosphorylation among Plasmodium gametocytes incubated with BVT-948 under in vitro ookinete culture conditions were evaluated using phosphoproteomic methods. Gene Ontology (GO) analysis was performed to predict the mechanism by which BVT-948 affected gametocyte-ookinete conversion. The functions of 8 putative proteins involved in Plasmodium berghei sexual development were evaluated. Bioinformatic analysis was used to evaluate the possible mechanism of PBANKA_0100800 in gametogenesis and subsequent sexual development.

RESULTS

The phosphorylation levels of 265 proteins decreased while those of 67 increased after treatment with BVT-948. Seven of the 8 genes selected for phenotype screening play roles in P. berghei sexual development, and 4 of these were associated with gametocytogenesis. PBANKA_0100800 plays essential roles in gametocyte-ookinete conversion and transmission to mosquitoes.

CONCLUSIONS

Seven proteins identified by screening affect P. berghei sexual development, suggesting that phosphatase inhibitors can be used for functional protein screening.

A proteogenomic analysis of cervical cancer reveals therapeutic and biological insights

Although the incidence of cervical cancer (CC) has been reduced in high-income countries due to human papillomavirus (HPV) vaccination and screening strategies, it remains a significant public health issue that poses a threat to women's health in low-income countries. Here, we perform a comprehensive proteogenomic profiling of CC tumors obtained from 139 Chinese women. Integrated proteogenomic analysis links genetic aberrations to downstream pathogenesis-related pathways and reveals the landscape of HPV-associated multi-omic changes. EP300 is found to enhance the acetylation of FOSL2-K222, consequently accelerating the malignant proliferation of CC cells. Proteomic stratification identifies three patient subgroups with distinct features in prognosis, genetic alterations, immune infiltration, and post-translational modification regulations. PRKCB is further identified as a potential radioresponse-related biomarker of CC patients. This study provides a valuable public resource for researchers and clinicians to delve into the molecular basis of CC, to identify potential treatments and to ultimately advance clinical practice.

zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation

Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.

The Crk4-Cyc4 complex regulates G2/M transition in Toxoplasma gondii

A versatile division of apicomplexan parasites and a dearth of conserved regulators have hindered the progress of apicomplexan cell cycle studies. While most apicomplexans divide in a multinuclear fashion, Toxoplasma gondii tachyzoites divide in the traditional binary mode. We previously identified five Toxoplasma CDK-related kinases (Crk). Here, we investigated TgCrk4 and its cyclin partner TgCyc4. We demonstrated that TgCrk4 regulates conventional G2 phase processes, such as repression of chromosome rereplication and centrosome reduplication, and acts upstream of the spindle assembly checkpoint. The spatial TgCyc4 dynamics supported the TgCrk4-TgCyc4 complex role in the coordination of chromosome and centrosome cycles. We also identified a dominant TgCrk4-TgCyc4 complex interactor, TgiRD1 protein, related to DNA replication licensing factor CDT1 but played no role in licensing DNA replication in the G1 phase. Our results showed that TgiRD1 also plays a role in controlling chromosome and centrosome reduplication. Global phosphoproteome analyses identified TgCrk4 substrates, including TgORC4, TgCdc20, TgGCP2, and TgPP2ACA. Importantly, the phylogenetic and structural studies suggest the Crk4-Cyc4 complex is limited to a minor group of the binary dividing apicomplexans.

Robust estimation of mean-variance relation

Accurate assessment of the mean-variance relation can benefit subsequent analysis in biomedical research. However, in most biomedical data, both the true mean and the true variance are unavailable. Instead, raw data are typically used to allow forming sample mean and sample variance in practice. In addition, different experimental conditions sometimes cause a slightly different mean-variance relation from the majority of the data in the same data set. To address these issues, we propose a semiparametric estimator, where we treat the uncertainty in the sample mean as a measurement error problem, the uncertainty in the sample variance as model error, and use a mixture model to account for different mean-variance relations. Asymptotic normality of the proposed method is established and its finite sample properties are demonstrated by simulation studies. The data application shows that the proposed method produces sensible results compared with methods either ignoring the uncertainty in the sample means or ignoring the potential different mean-variance relations.

Technological development of multidimensional liquid chromatography-mass spectrometry in proteome research

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for high-throughput proteomic research. Limited by the peak capacity, the separation performance of conventional single-dimensional LC hampers the development of proteomics. Combining different separation modes orthogonally, multidimensional liquid chromatography (MDLC) with high peak capacity was developed to address this challenge. MDLC has evolved rapidly since its establishment, and the progress of proteomics has been greatly facilitated by the advent of novel MDLC-MS-based methods. In this paper, we will review the advances of MDLC-MS-based methodologies and technologies in proteomics studies, from different perspectives including novel application scenarios and proteomic targets, automation, miniaturization, and the improvement of the classic methods in recent years. In addition, attempts regarding new MDLC-MS models are also mentioned together with the outlook of MDLC-MS-based proteomics methods.

The Plasmodium falciparum Nuclear Protein Phosphatase NIF4 Is Required for Efficient Merozoite Invasion and Regulates Artemisinin Sensitivity

Artemisinin resistance in Plasmodium falciparum has been associated with a mutation in the NLI-interacting factor-like phosphatase PfNIF4, in addition to the mutations in the Kelch13 protein as the major determinant. We found that PfNIF4 was predominantly expressed at the schizont stage and localized in the nuclei of the parasite. To elucidate the functions of PfNIF4 in P. falciparum, we performed PfNIF4 knockdown (KD) using the inducible ribozyme system. PfNIF4 KD attenuated merozoite invasion and affected gametocytogenesis. PfNIF4 KD parasites also showed significantly increased in vitro susceptibility to artemisinins. Transcriptomic and proteomic analysis revealed that PfNIF4 KD led to the downregulation of gene categories involved in invasion and artemisinin resistance (e.g., mitochondrial function, membrane, and Kelch13 interactome) at the trophozoite and/or schizont stage. Consistent with PfNIF4 being a protein phosphatase, PfNIF4 KD resulted in an overall upregulation of the phosphoproteome of infected erythrocytes. Quantitative phosphoproteomic profiling identified a set of PfNIF4-regulated phosphoproteins with functional similarity to FCP1 substrates, particularly proteins involved in chromatin organization and transcriptional regulation. Specifically, we observed increased phosphorylation of Ser2/5 of the tandem repeats in the C-terminal domain (CTD) of RNA polymerase II (RNAPII) upon PfNIF4 KD. Furthermore, using the TurboID-based proteomic approach, we identified that PfNIF4 interacted with the RNAPII components, AP2-domain transcription factors, and chromatin-modifiers and binders. These findings suggest that PfNIF4 may act as the RNAPII CTD phosphatase, regulating the expression of general and parasite-specific cellular pathways during the blood-stage development.

Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure

Several algorithms for the normalization of proteomic data are currently available, each based on a priori assumptions. Among these is the extent to which differential expression (DE) can be present in the dataset. This factor is usually unknown in explorative biomarker screens. Simultaneously, the increasing depth of proteomic analyses often requires the selection of subsets with a high probability of being DE to obtain meaningful results in downstream bioinformatical analyses. Based on the relationship of technical variation and (true) biological DE of an unknown share of proteins, we propose the “Normics” algorithm: Proteins are ranked based on their expression level–corrected variance and the mean correlation with all other proteins. The latter serves as a novel indicator of the non-DE likelihood of a protein in a given dataset. Subsequent normalization is based on a subset of non-DE proteins only. No a priori information such as batch, clinical, or replicate group is necessary. Simulation data demonstrated robust and superior performance across a wide range of stochastically chosen parameters. Five publicly available spike-in and biologically variant datasets were reliably and quantitively accurately normalized by Normics with improved performance compared to standard variance stabilization as well as median, quantile, and LOESS normalizations. In complex biological datasets Normics correctly determined proteins as being DE that had been cross-validated by an independent transcriptome analysis of the same samples. In both complex datasets Normics identified the most DE proteins. We demonstrate that combining variance analysis and data-inherent correlation structure to identify non-DE proteins improves data normalization. Standard normalization algorithms can be consolidated against high shares of (one-sided) biological regulation. The statistical power of downstream analyses can be increased by focusing on Normics-selected subsets of high DE likelihood.

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Normics is a tool for the normalization of proteomic data based on existing algorithms.

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Specifically addresses data with high shares of differential expression.

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Combines variance and data-inherent correlation structure.

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Provides a ranking of differential expression likelihood.

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Enables normalization based on the most stable proteins.

ATR activity controls stem cell quiescence via the cyclin F-SCF complex

A key property of adult stem cells is their ability to persist in a quiescent state for prolonged periods of time. The quiescent state is thought to contribute to stem cell resilience by limiting accumulation of DNA replication–associated mutations. Moreover, cellular stress response factors are thought to play a role in maintaining quiescence and stem cell integrity. We utilized muscle stem cells (MuSCs) as a model of quiescent stem cells and find that the replication stress response protein, ATR (Ataxia Telangiectasia and Rad3-Related), is abundant and active in quiescent but not activated MuSCs. Concurrently, MuSCs display punctate RPA (replication protein A) and R-loop foci, both key triggers for ATR activation. To discern the role of ATR in MuSCs, we generated MuSC-specific ATR conditional knockout (ATRcKO) mice. Surprisingly, ATR ablation results in increased MuSC quiescence exit. Phosphoproteomic analysis of ATRcKO MuSCs reveals enrichment of phosphorylated cyclin F, a key component of the Skp1–Cul1–F-box protein (SCF) ubiquitin ligase complex and regulator of key cell-cycle transition factors, such as the E2F family of transcription factors. Knocking down cyclin F or inhibiting the SCF complex results in E2F1 accumulation and in MuSCs exiting quiescence, similar to ATR-deficient MuSCs. The loss of ATR could be counteracted by inhibiting casein kinase 2 (CK2), the kinase responsible for phosphorylating cyclin F. We propose a model in which MuSCs express cell-cycle progression factors but ATR, in coordination with the cyclin F–SCF complex, represses premature stem cell quiescence exit via ubiquitin–proteasome degradation of these factors.

Piriformospora indica and Azotobacter chroococcum Consortium Facilitates Higher Acquisition of N, P with Improved Carbon Allocation and Enhanced Plant Growth in Oryza sativa

The soil microbiome contributes to nutrient acquisition and plant adaptation to numerous biotic and abiotic stresses. Numerous studies have been conducted over the past decade showing that plants take up nutrients better when associated with fungi and additional beneficial bacteria that promote plant growth, but the mechanisms by which the plant host benefits from this tripartite association are not yet fully understood. In this article, we report on a synergistic interaction between rice (Oryza sativa), Piriformospora indica (an endophytic fungus colonizing the rice roots), and Azotobacter chroococcum strain W5, a free-living nitrogen-fixing bacterium. On the basis of mRNA expression analysis and enzymatic activity, we found that co-inoculation of plant roots with the fungus and the rhizobacterium leads to enhanced plant growth and improved nutrient uptake compared to inoculation with either of the two microbes individually. Proteome analysis of O. sativa further revealed that proteins involved in nitrogen and phosphorus metabolism are upregulated and improve nitrogen and phosphate uptake. Our results also show that A. chroococcum supports colonization of rice roots by P. indica, and consequentially, the plants are more resistant to biotic stress upon co-colonization. Our research provides detailed insights into the mechanisms by which microbial partners synergistically promote each other in the interaction while being associated with the host plant.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Assessing the statistical training in animal science graduate programs in the United States: survey on statistical training

Statistical analysis of data and understanding of experimental design are critical skills needed by animal science graduate students (ASGS). These skills are even more valuable with the increased development of high-throughput technologies. The objective of this study was to evaluate the perceived statistical training of U.S. ASGS. A survey with 38 questions was shared across U.S. universities, and 416 eligible ASGS from 43 universities participated in this study. The survey included questions on the demographics and overall training, graduate education on statistics, and self-assessment on statistics and career path of ASGS. Several analyses were performed: relationship between perceived received education (PRE; i.e., how ASGS evaluated their graduate education in statistics) and perceived knowledge (PK; i.e., how ASGS evaluated their knowledge in statistics from their education); ranking of statistical topics based on PRE, PK, and confidence in performing statistical analyses (CPSA); cluster analysis of statistical topics for PRE, PK, and CPSA; and factors (demographic, overall training, interest in statistics, and field of study) associated with the overall scores (OS) for PRE, PK, and CPSA. Students had greater (P < 0.05) PRE than PK for most of the statistical topics included in this study. The moderate to high repeatability of answers within statistical topics indicates substantial correlations in ASGS answers between PRE and PK. The cluster analysis resulted in distinct groups of "Traditional" and "Nontraditional" statistical topics. ASGS showed lower (P < 0.05) scores of PRE, PK, and CPSA in "Nontraditional" compared with "Traditional" statistical methods. Several factors were associated (P < 0.05) with the OS of PRE, PK, and CSPA. In general, factors related to greater training and interest in statistics of ASGS were associated with greater OS, such as taking more credits in statistics courses, having additional training in statistics outside the classroom, knowing more than one statistics software, and more. This study provided comprehensive information on the perceived level of education, knowledge, and confidence in statistics in ASGS in the United States. Although objective measurements of their training in statistics are needed, the current study suggests that ASGS have limited statistical training on topics of major importance for the current and future trends of data-driven research in animal sciences.

Novel CRK-Cyclin Complex Controls Spindle Assembly Checkpoint in Toxoplasma Endodyogeny

Opportunistic parasites of the Apicomplexa phylum use a variety of division modes built on two types of cell cycles that incorporate two distinctive mechanisms of mitosis: uncoupled from and coupled to parasite budding. Parasites have evolved novel factors to regulate such unique replication mechanisms that are poorly understood. Here, we have combined genetics, quantitative fluorescence microscopy, and global proteomics approaches to examine endodyogeny in Toxoplasma gondii dividing by mitosis coupled to cytokinesis. In the current study, we focus on the steps controlled by the recently described atypical Cdk-related kinase T. gondii Crk6 (TgCrk6). While inspecting protein complexes, we found that this previously orphaned TgCrk6 kinase interacts with a parasite-specific atypical cyclin, TgCyc1. We built conditional expression models and examined primary cell cycle defects caused by the lack of TgCrk6 or TgCyc1. Quantitative microscopy assays revealed that tachyzoites deficient in either TgCrk6 or the cyclin partner TgCyc1 exhibit identical mitotic defects, suggesting cooperative action of the complex components. Further examination of the mitotic structures indicated that the TgCrk6/TgCyc1 complex regulates metaphase. This novel finding confirms a functional spindle assembly checkpoint (SAC) in T. gondii. Measuring global changes in protein expression and phosphorylation, we found evidence that canonical activities of the Toxoplasma SAC are intertwined with parasite-specific tasks. Analysis of phosphorylation motifs suggests that Toxoplasma metaphase is regulated by CDK, mitogen-activated kinase (MAPK), and Aurora kinases, while the TgCrk6/TgCyc1 complex specifically controls the centromere-associated network. IMPORTANCE The rate of Toxoplasma tachyzoite division directly correlates with the severity of the disease, toxoplasmosis, which affects humans and animals. Thus, a better understanding of the tachyzoite cell cycle would offer much-needed efficient tools to control the acute stage of infection. Although tachyzoites divide by binary division, the cell cycle architecture and regulation differ significantly from the conventional binary fission of their host cells. Unlike the unidirectional conventional cell cycle, the Toxoplasma budding cycle is braided and is regulated by multiple essential Cdk-related kinases (Crks) that emerged in the place of missing conventional cell cycle regulators. How these novel Crks control apicomplexan cell cycles is largely unknown. Here, we have discovered a novel parasite-specific complex, TgCrk6/TgCyc1, that orchestrates a major mitotic event, the spindle assembly checkpoint. We demonstrated that tachyzoites incorporated parasite-specific tasks in the canonical checkpoint functions.

Protein-driven mechanism of multiorgan damage in COVID-19

We propose a new plausible mechanism by mean of which SARS-CoV-2 produces extrapulmonary damages in severe COVID-19 patients. The mechanism consist on the existence of vulnerable proteins (VPs), which are (i) mainly expressed outside the lungs; (ii) their perturbations is known to produce human diseases; and (iii) can be perturbed directly or indirectly by SARS-CoV-2 proteins. These VPs are perturbed by other proteins, which are: (i) mainly expressed in the lungs, (ii) are targeted directly by SARS-CoV-2 proteins, (iii) can navigate outside the lungs as cargo of extracellular vesicles (EVs); and (iv) can activate VPs via subdiffusive processes inside the target organ. Using bioinformatic tools and mathematical modeling we identifies 26 VPs and their 38 perturbators, which predict extracellular damages in the immunologic endocrine, cardiovascular, circulatory, lymphatic, musculoskeletal, neurologic, dermatologic, hepatic, gastrointestinal, and metabolic systems, as well as in the eyes. The identification of these VPs and their perturbators allow us to identify 27 existing drugs which are candidates to be repurposed for treating extrapulmonary damage in severe COVID-19 patients. After removal of drugs having undesirable drug-drug interactions we select 7 drugs and one natural product: apabetalone, romidepsin, silmitasertib, ozanezumab, procaine, azacitidine, amlexanox, volociximab, and ellagic acid, whose combinations can palliate the organs and systems found to be damaged by COVID-19. We found that at least 4 drugs are needed to treat all the multiorgan damages, for instance: the combination of romidepsin, silmitasertib, apabetalone and azacitidine.