The emergence of proteome-wide technologies: systematic analysis of proteins comes of age

CyanoTag: Discovery of protein function facilitated by high-throughput endogenous tagging in a photosynthetic prokaryote

Despite their importance to aquatic ecosystems, global carbon cycling, and sustainable bioindustries, the genomes of photosynthetic bacteria contain large numbers of uncharacterized genes. Here, we develop high-throughput endogenous fluorescent protein tagging in the cyanobacterium Synechococcus elongatus PCC 7942. From 400 targets, we successfully tag over 330 proteins corresponding to >10% of the proteome. We use this collection to determine subcellular localization, relative protein abundances, and protein-protein interaction networks, providing biological insights into diverse processes-from photosynthesis to cell division. We build a high-confidence protein-protein interaction map for the major components of photosynthesis, associating previously uncharacterized proteins with different complexes and processes. In response to light changes, we visualize, on second timescales, the reversible formation, growth, and fusion of puncta by two Calvin cycle proteins, suggesting that biomolecular condensation provides spatiotemporal control of the Calvin cycle in cyanobacteria. We envision that these insights, cell lines, and optimized methods will facilitate rapid advances in cyanobacteria biology and, more broadly, all photosynthetic life.

Detection of >400 Cluster of Differentiation Biomarkers and Pathway Proteins in Single Immune Cells by Cyclic Multiplex In Situ Tagging for Single-Cell Proteomic Studies

The identification and characterization of immune cell subpopulations are critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced single-cell genomics and transcriptomics for immune cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in protein format. Current single-cell proteomic technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology to simultaneously measure >400 proteins, a scale of >10 times than similar technologies. Such an ultrahigh multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both peripheral blood mononuclear cells (PBMCs) and T cells are analyzed by the CycMIST technology, and almost the entire spectrum of cluster of differentiation (CD) surface markers has been measured. The landscape of fluctuation of CD protein expression in single cells has been uncovered by our technology. Further study found T cell activation signatures and protein-protein networks. This study represents the highest multiplexity of single immune cell marker measurement targeting functional proteins. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses under various disease conditions.

Scaling up stem cell production: harnessing the potential of microfluidic devices

Stem cells are specialised cells characterised by their unique ability to both self-renew and transform into a wide array of specialised cell types. The widespread interest in stem cells for regenerative medicine and cultivated meat has led to a significant demand for these cells in both research and practical applications. Despite the growing need for stem cell manufacturing, the industry faces significant obstacles, including high costs for equipment and maintenance, complicated operation, and low product quality and yield. Microfluidic technology presents a promising solution to the abovementioned challenges. As an innovative approach for manipulating liquids and cells within microchannels, microfluidics offers a plethora of advantages at an industrial scale. These benefits encompass low setup costs, ease of operation and multiplexing, minimal energy consumption, and the added advantage of being labour-free. This review presents a thorough examination of the prominent microfluidic technologies employed in stem cell research and explores their promising applications in the burgeoning stem cell industry. It thoroughly examines how microfluidics can enhance cell harvesting from tissue samples, facilitate mixing and cryopreservation, streamline microcarrier production, and efficiently conduct cell separation, purification, washing, and final cell formulation post-culture.

The concept of Big Four: Road map from snakebite epidemiology to antivenom efficacy

Snake envenomation is a life-threatening disease caused by the injection of venom toxins from the venomous snake bite. Snakebite is often defined as the occupational or domestic hazard mostly affecting the rural population. India experiences a high number of envenoming cases and fatality due to the nation's diversity in inhabiting venomous snakes. The Indian Big Four snakes namely Russell's viper (Daboia russelii), spectacled cobra (Naja naja), common krait (Bungarus caeruleus), and saw-scaled viper (Echis carinatus) are responsible for majority of the snake envenoming cases and death. The demographic characteristics including occupation, stringent snake habitat management, poor healthcare facilities and ignorance of the rural victims are the primary influencers of high mortality. Biogeographic venom variation greatly influences the clinical pathologies of snake envenomation. The current antivenoms against the Big Four snakes are found to be less immunogenic against the venom toxins emphasizing the necessity of alternative approaches for antivenom generation. This review summarizes the burden of snake envenomation in India by the Big Four snakes including the geographic distribution of snake species and biogeographic venom variation. We have provided comprehensive information on snake venom proteomics that has aided the better understanding of venom induced pathological features, summarized the impact of current polyvalent antivenom therapy highlighting the need for potential antivenom treatment for the effective management of snakebites.

The alphavirus nonstructural protein 2 NTPase induces a host translational shut-off through phosphorylation of eEF2 via cAMP-PKA-eEF2K signaling

Chikungunya virus (CHIKV) is a reemerging alphavirus. Since 2005, it has infected millions of people during outbreaks in Africa, Asia, and South/Central America. CHIKV replication depends on host cell factors at many levels and is expected to have a profound effect on cellular physiology. To obtain more insight into host responses to infection, stable isotope labeling with amino acids in cell culture and liquid chromatography-tandem mass spectrometry were used to assess temporal changes in the cellular phosphoproteome during CHIKV infection. Among the ~3,000 unique phosphorylation sites analyzed, the largest change in phosphorylation status was measured on residue T56 of eukaryotic elongation factor 2 (eEF2), which showed a >50-fold increase at 8 and 12 h p.i. Infection with other alphaviruses (Semliki Forest, Sindbis and Venezuelan equine encephalitis virus (VEEV)) triggered a similarly strong eEF2 phosphorylation. Expression of a truncated form of CHIKV or VEEV nsP2, containing only the N-terminal and NTPase/helicase domains (nsP2-NTD-Hel), sufficed to induce eEF2 phosphorylation, which could be prevented by mutating key residues in the Walker A and B motifs of the NTPase domain. Alphavirus infection or expression of nsP2-NTD-Hel resulted in decreased cellular ATP levels and increased cAMP levels. This did not occur when catalytically inactive NTPase mutants were expressed. The wild-type nsP2-NTD-Hel inhibited cellular translation independent of the C-terminal nsP2 domain, which was previously implicated in directing the virus-induced host shut-off for Old World alphaviruses. We hypothesize that the alphavirus NTPase activates a cellular adenylyl cyclase resulting in increased cAMP levels, thus activating PKA and subsequently eukaryotic elongation factor 2 kinase. This in turn triggers eEF2 phosphorylation and translational inhibition. We conclude that the nsP2-driven increase of cAMP levels contributes to the alphavirus-induced shut-off of cellular protein synthesis that is shared between Old and New World alphaviruses. MS Data are available via ProteomeXchange with identifier PXD009381.

Genomic medicine in Africa: a need for molecular genetics and pharmacogenomics experts

The large-scale implementation of genomic medicine in Africa has not been actualized. This overview describes how routine molecular genetics and advanced protein engineering/structural biotechnology could accelerate the implementation of genomic medicine. By using data-mining and analysis approaches, we analyzed relevant information obtained from public genomic databases on pharmacogenomics biomarkers and reviewed published studies to discuss the ideas. The results showed that only 68 very important pharmacogenes currently exist, while 867 drug label annotations, 201 curated functional pathways, and 746 annotated drugs have been catalogued on the largest pharmacogenomics database (PharmGKB). Only about 5009 variants of the reported ∼25,000 have been clinically annotated. Predominantly, the genetic variants were derived from 43 genes that contribute to 2318 clinically relevant variations in 57 diseases. Majority (∼60%) of the clinically relevant genetic variations in the pharmacogenes are missense variants (1390). The enrichment analysis showed that 15 pharmacogenes are connected biologically and are involved in the metabolism of cardiovascular and cancer drugs. The review of studies showed that cardiovascular diseases are the most frequent non-communicable diseases responsible for approximately 13% of all deaths in Africa. Also, warfarin pharmacogenomics is the most studied drug on the continent, while CYP2D6, CYP2C9, DPD, and TPMT are the most investigated pharmacogenes with allele activities indicated in African and considered to be intermediate metaboliser for DPD and TPMT (8.4% and 11%). In summary, we highlighted a framework for implementing genomic medicine starting from the available resources on ground.

Spatial proteomics reveals subcellular reorganization in human keratinocytes exposed to UVA light

The effects of UV light on the skin have been extensively investigated. However, systematic information about how the exposure to ultraviolet-A (UVA) light, the least energetic but the most abundant UV radiation reaching the Earth, shapes the subcellular organization of proteins is lacking. Using subcellular fractionation, mass-spectrometry-based proteomics, machine learning algorithms, immunofluorescence, and functional assays, we mapped the subcellular reorganization of the proteome of human keratinocytes in response to UVA light. Our workflow quantified and assigned subcellular localization for over 1,600 proteins, of which about 200 were found to redistribute upon UVA exposure. Reorganization of the proteome affected modulators of signaling pathways, cellular metabolism, and DNA damage response. Strikingly, mitochondria were identified as one of the main targets of UVA-induced stress. Further investigation demonstrated that UVA induces mitochondrial fragmentation, up-regulates redox-responsive proteins, and attenuates respiratory rates. These observations emphasize the role of this radiation as a potent metabolic stressor in the skin.

Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images

MOTIVATION

The development of microscopic imaging techniques enables us to study protein subcellular locations from the tissue level down to the cell level, contributing to the rapid development of image-based protein subcellular location prediction approaches. However, existing methods suffer from intrinsic limitations, such as poor feature representation ability, data imbalanced issue, and multi-label classification problem, greatly impacting the model performance and generalization.

RESULTS

In this study, we propose MSTLoc, a novel multi-scale end-to-end deep learning model to identify protein subcellular locations in the imbalanced multi-label immunohistochemistry (IHC) images dataset. In our MSTLoc, we deploy a deep convolution neural network to extract multi-scale features from the IHC images, aggregate the high-level features and low-level features via feature fusion to sufficiently exploit the dependencies amongst various subcellular locations, and utilize Vision Transformer (ViT) to model the relationship amongst the features and enhance the feature representation ability. We demonstrate that the proposed MSTLoc achieves better performance than current state-of-the-art models in multi-label subcellular location prediction. Through feature visualization and interpretation analysis, we demonstrate that as compared with the hand-crafted features, the multi-scale deep features learnt from our model exhibit better ability in capturing discriminative patterns underlying protein subcellular locations, and the features from different scales are complementary for the improvement in performance. Finally, case study results indicate that our MSTLoc can successfully identify some biomarkers from proteins that are closely involved with cancer development. For the convenient use of our method, we establish a user-friendly webserver available at http://server.wei-group.net/ MSTLoc.

AVAILABILITY AND IMPLEMENTATION

http://server.wei-group.net/ MSTLoc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ancient Faunal History Revealed by Interdisciplinary Biomolecular Approaches

Starting four decades ago, studies have examined the ecology and evolutionary dynamics of populations and species using short mitochondrial DNA fragments and stable isotopes. Through technological and analytical advances, the methods and biomolecules at our disposal have increased significantly to now include lipids, whole genomes, proteomes, and even epigenomes. At an unprecedented resolution, the study of ancient biomolecules has made it possible for us to disentangle the complex processes that shaped the ancient faunal diversity across millennia, with the potential to aid in implicating probable causes of species extinction and how humans impacted the genetics and ecology of wild and domestic species. However, even now, few studies explore interdisciplinary biomolecular approaches to reveal ancient faunal diversity dynamics in relation to environmental and anthropogenic impact. This review will approach how biomolecules have been implemented in a broad variety of topics and species, from the extinct Pleistocene megafauna to ancient wild and domestic stocks, as well as how their future use has the potential to offer an enhanced understanding of drivers of past faunal diversity on Earth.

Using "-omics" Data to Inform Genome-wide Association Studies (GWASs) in the Osteoporosis Field

PURPOSE OF REVIEW

Osteoporosis constitutes a major societal health problem. Genome-wide association studies (GWASs) have identified over 1100 loci influencing bone mineral density (BMD); however, few of the causal genes have been identified. Here, we review approaches that use "-omics" data and genetic- and systems genetics-based analytical strategies to facilitate causal gene discovery.

RECENT FINDINGS

The bone field is beginning to adopt approaches that are commonplace in other disease disciplines. The slower progress has been due in part to the lack of large-scale "omics" data on bone and bone cells. This is however changing, and approaches such as eQTL colocalization, transcriptome-wide association studies (TWASs), network, and integrative approaches are beginning to provide significant insight into the genes responsible for BMD GWAS associations. The use of "-omics" data to inform BMD GWASs has increased in recent years, leading to the identification of novel regulators of BMD in humans. The ultimate goal will be to use this information to develop more effective therapies to treat and ultimately prevent osteoporosis.

Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks

Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.

Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer

BACKGROUND

Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied.

RESULTS

In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations.

CONCLUSIONS

Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.

Proteomic Analysis of Human Immune Responses to Live-Attenuated Tularemia Vaccine

Francisella tularensis (F. tularensis) is an intracellular pathogen that causes a potentially debilitating febrile illness known as tularemia. F. tularensis can be spread by aerosol transmission and cause fatal pneumonic tularemia. If untreated, mortality rates can be as high as 30%. To study the host responses to a live-attenuated tularemia vaccine, peripheral blood mononuclear cell (PBMC) samples were assayed from 10 subjects collected pre- and post-vaccination, using both the 2D-DIGE/MALDI-MS/MS and LC-MS/MS approaches. Protein expression related to antigen processing and presentation, inflammation (PPARγ nuclear receptor), phagocytosis, and gram-negative bacterial infection was enriched at Day 7 and/or Day 14. Protein candidates that could be used to predict human immune responses were identified by evaluating the correlation between proteome changes and humoral and cellular immune responses. Consistent with the proteomics data, parallel transcriptomics data showed that MHC class I and class II-related signals important for protein processing and antigen presentation were up-regulated, further confirming the proteomic results. These findings provide new biological insights that can be built upon in future clinical studies, using live attenuated strains as immunogens, including their potential use as surrogates of protection.

Spatial proteomics: a powerful discovery tool for cell biology

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

Single-cell analysis targeting the proteome

The existence of cellular heterogeneity and its central relevance to biological phenomena provides a strong rationale for a need for analytical methods that enable analysis at the single-cell level. Analysis of the genome and transcriptome is possible at the single-cell level, but the comprehensive interrogation of the proteome with this level of resolution remains challenging. Single-cell protein analysis tools are advancing rapidly, however, and providing insights into collections of proteins with great relevance to cell and disease biology. Here, we review single-cell protein analysis technologies and assess their advantages and limitations. The emerging technologies presented have the potential to reveal new insights into tumour heterogeneity and therapeutic resistance, elucidate mechanisms of immune response and immunotherapy, and accelerate drug discovery.

Comparative quantitative proteomics of osmotic signal transduction mutants in Botrytis cinerea explain mutant phenotypes and highlight interaction with cAMP and Ca2+ signalling pathways

Signal transduction (ST) is essential for rapid adaptive responses to changing environmental conditions. It acts through rapid post-translational modifications of signalling proteins and downstream effectors that regulate the activity and/or subcellular localisation of target proteins, or the expression of downstream genes. We have performed a quantitative, comparative proteomics study of ST mutants in the phytopathogenic fungus Botrytis cinerea during axenic growth under non-stressed conditions to decipher the roles of two kinases of the hyper-osmolarity pathway in B. cinerea physiology. We studied the mutants of the sensor histidine kinase Bos1 and of the MAP kinase Sak1. Label-free shotgun proteomics detected 2425 proteins, 628 differentially abundant between mutants and wild-type, 270 common to both mutants, indicating independent and shared regulatory functions for both kinases. Gene ontology analysis showed significant changes in functional categories that may explain in vitro growth and virulence defects of both mutants (secondary metabolism enzymes, lytic enzymes, proteins linked to osmotic, oxidative and cell wall stress). The proteome data also highlight a new link between Sak1 MAPK, cAMP and Ca²⁺ signalling. This study reveals the potential of proteomic analyses of signal transduction mutants to decipher their biological functions. TEXT-VULGARISATION: The fungus Botrytis cinerea is responsible for grey mold disease of hundreds of plant species. During infection, the fungus has to face important changes of its environment. Adaptation to these changing environmental conditions involves proteins of such called signal transduction pathways that regulate the production, activity or localisation of cellular components, mainly proteins. While the components of such signal transduction pathways are well known, their role globally understood, the precise impact on protein production remains unknown. In this study we have analysed and compared the global protein content of two Botrytis cinerea signal transduction mutants - both avirulent - to the pathogenic parental strain. The data of 628 differential proteins between mutants and wild-type, showed significant changes in proteins related to plant infection (secondary metabolism enzymes, lytic enzymes, proteins linked to osmotic, oxidative and cell wall stress) that may explain the virulence defects of both mutants. Moreover, we observed intracellular accumulation of secreted proteins in one of the mutants suggesting a potential secretion defect.

An Unbiased Proteomics Method to Assess the Maturation of Human Pluripotent Stem Cell-Derived Cardiomyocytes

RATIONALE

Human pluripotent stem cell (hPSC)-derived cardiomyocytes exhibit the properties of fetal cardiomyocytes, which limits their applications. Various methods have been used to promote maturation of hPSC-cardiomyocytes; however, there is a lack of an unbiased and comprehensive method for accurate assessment of the maturity of hPSC-cardiomyocytes.

OBJECTIVE

We aim to develop an unbiased proteomics strategy integrating high-throughput top-down targeted proteomics and bottom-up global proteomics for the accurate and comprehensive assessment of hPSC-cardiomyocyte maturation.

METHODS AND RESULTS

Utilizing hPSC-cardiomyocytes from early- and late-stage 2-dimensional monolayer culture and 3-dimensional engineered cardiac tissue, we demonstrated the high reproducibility and reliability of a top-down proteomics method, which enabled simultaneous quantification of contractile protein isoform expression and associated post-translational modifications. This method allowed for the detection of known maturation-associated contractile protein alterations and, for the first time, identified contractile protein post-translational modifications as promising new markers of hPSC-cardiomyocytes maturation. Most notably, decreased phosphorylation of α-tropomyosin was found to be associated with hPSC-cardiomyocyte maturation. By employing a bottom-up global proteomics strategy, we identified candidate maturation-associated markers important for sarcomere organization, cardiac excitability, and Ca2+ homeostasis. In particular, upregulation of myomesin 1 and transmembrane 65 was associated with hPSC-cardiomyocyte maturation and validated in cardiac development, making these promising markers for assessing maturity of hPSC-cardiomyocytes. We have further validated α-actinin isoforms, phospholamban, dystrophin, αB-crystallin, and calsequestrin 2 as novel maturation-associated markers, in the developing mouse cardiac ventricles.

CONCLUSIONS

We established an unbiased proteomics method that can provide accurate and specific assessment of the maturity of hPSC-cardiomyocytes and identified new markers of maturation. Furthermore, this integrated proteomics strategy laid a strong foundation for uncovering the molecular pathways involved in cardiac development and disease using hPSC-cardiomyocytes.

Recent advances in mass spectrometry (MS)-based glycoproteomics in complex biological samples

Protein glycosylation plays a key role in various biological processes and disease-related pathological progression. Mass spectrometry (MS)-based glycoproteomics is a powerful approach that provides a system-wide profiling of the glycoproteome in a high-throughput manner. There have been numerous significant technological advances in this field, including improved glycopeptide enrichment, hybrid fragmentation techniques, emerging specialized software packages, and effective quantitation strategies, as well as more dedicated workflows. With increasingly sophisticated glycoproteomics tools on hand, researchers have extensively adapted this approach to explore different biological systems both in terms of in-depth glycoproteome profiling and comparative glycoproteome analysis. Quantitative glycoproteomics enables researchers to discover novel glycosylation-based biomarkers in various diseases with potential to offer better sensitivity and specificity for disease diagnosis. In this review, we present recent methodological developments in MS-based glycoproteomics and highlight its utility and applications in answering various questions in complex biological systems.

Proteomic studies of bone and skeletal health outcomes

Proteins are an essential part of essentially all biological processes, and there is enormous variation in protein forms and concentrations that is not reflected in DNA or RNA. Recently there have been rapid advances in the ability to measure protein sequence, modification and concentration, particularly with methods based in mass spectrometry. Global measures of proteins in tissues or in the circulation provide a broad assessment of the proteome that can be extremely useful for discovery, and targeted proteomic measures can yield specific and sensitive assessments of specific peptides and proteins. While most proteomic measures are directed at the detection of consensus peptide sequences, mass spectrometry based proteomic methods also allow a detailed examination of the peptide sequence differences that result from genetic variants and that may have important effects on protein function. In evaluating proteomic data, a number of analytical considerations are important, including an understanding of missing data, the challenge of multiple testing and replication, and the use of rapidly evolving methods in systems biology. While proteomics has not yet had a major impact in skeletal research, interesting recent research has used these approaches in the study of bone cell biology and the discovery of biomarkers of skeletal disorders. Proteomics can be expected to have an increasing influence in the study of bone biology and pathophysiology.

Global views of proteasome-mediated degradation by mass spectrometry

Introduction: Degradation of proteins by cellular proteasomes is critical for the fidelity of protein homeostasis and proper cell function. Indeed, perturbations in proteasome function, as well as the degradation of specific substrates, are associated with a variety of human diseases. Yet, monitoring and analyzing protein degradation in a high throughput manner in physiology and pathology remains limited. Areas covered: Here we discuss several of the recently developed mass spectrometry-based methods for studying proteasome-mediated cellular degradation and discuss their advantages and limitations. We highlight Mass Spectrometry Analysis of Proteolytic Peptides (MAPP), a method designed to purify and identify proteasome-cleaved cellular proteins as a novel approach in molecular and clinical profiling of human disease. Expert opinion: The recent improvement of proteomics technologies now offers an unprecedented ability to study disease in clinical settings. Expanding clinical studies to include the degradation landscape will provide a new resolution to complement the cellular proteome. In turn, this holds promise to provide both new disease targets and novel peptide biomarkers which will further enhance personalized proteomics.

Integrating -Omics Approaches into Human Population-Based Studies of Prenatal and Early-Life Exposures

PURPOSE OF REVIEW

We present the study design and methodological suggestions for population-based studies that integrate molecular -omics data and highlight recent studies that have used such data to examine the potential impacts of prenatal environmental exposures on fetal health.

RECENT FINDINGS

Epidemiologic studies have observed numerous relationships between prenatal exposures (smoking, toxic metals, endocrine disruptors) and fetal and early-life molecular profiles, though such investigations have so far been dominated by epigenomic association studies. However, recent transcriptomic, proteomic, and metabolomic studies have demonstrated their promise for the identification of exposure and response biomarkers. Molecular -omics have opened new avenues of research in environmental health that can improve our understanding of disease etiology and contribute to the development of exposure and response biomarkers. Studies that incorporate multiple -omics data from different molecular domains in longitudinally collected samples hold particular promise.

An Interaction Network of the Human SEPT9 Established by Quantitative Mass Spectrometry

Septins regulate the organization of the actin cytoskeleton, vesicle transport and fusion, chromosome alignment and segregation, and cytokinesis in mammalian cells. SEPT9 is part of the core septin hetero-octamer in human cells which is composed of SEPT2, SEPT6, SEPT7, and SEPT9. SEPT9 has been linked to a variety of intracellular functions as well as to diseases and diverse types of cancer. A targeted high-throughput approach to systematically identify the interaction partners of SEPT9 has not yet been performed. We applied a quantitative proteomics approach to establish an interactome of SEPT9 in human fibroblast cells. Among the newly identified interaction partners were members of the myosin family and LIM domain containing proteins. Fluorescence microscopy of SEPT9 and its interaction partners provides additional evidence that SEPT9 might participate in vesicle transport from and to the plasma membrane as well as in the attachment of actin stress fibers to cellular adhesions.

Molecular insights into cancer drug resistance from a proteomics perspective

INTRODUCTION

Resistance to chemotherapy and development of specific and effective molecular targeted therapies are major obstacles facing current cancer treatment. Comparative proteomic approaches have been employed for the discovery of putative biomarkers associated with cancer drug resistance and have yielded a number of candidate proteins, showing great promise for both novel drug target identification and personalized medicine for the treatment of drug-resistant cancer. Areas covered: Herein, we review the recent advances and challenges in proteomics studies on cancer drug resistance with an emphasis on biomarker discovery, as well as understanding the interconnectivity of proteins in disease-related signaling pathways. In addition, we highlight the critical role that post-translational modifications (PTMs) play in the mechanisms of cancer drug resistance. Expert opinion: Revealing changes in proteome profiles and the role of PTMs in drug-resistant cancer is key to deciphering the mechanisms of treatment resistance. With the development of sensitive and specific mass spectrometry (MS)-based proteomics and related technologies, it is now possible to investigate in depth potential biomarkers and the molecular mechanisms of cancer drug resistance, assisting the development of individualized therapeutic strategies for cancer patients.

Protection Mechanisms of Periphytic Biofilm to Photocatalytic Nanoparticle Exposure

Researchers are devoting great effort to combine photocatalytic nanoparticles (PNPs) with biological processes to create efficient environmental purification technologies (i.e., intimately coupled photobiocatalysis). However, little information is available to illuminate the responses of multispecies microbial aggregates against PNP exposure. Periphytic biofilm, as a model multispecies microbial aggregate, was exposed to three different PNPs (CdS, TiO₂, and Fe₂O₃) under xenon lamp irradiation. There were no obvious toxic effects of PNP exposure on periphytic biofilm as biomass, chlorophyll content, and ATPase activity were not negatively impacted. Enhanced production of extracellular polymetric substances (EPS) is the most important protection mechanism of periphytic biofilm against PNPs exposure. Although PNP exposure produced extracellular superoxide radicals and caused intracellular reactive oxygen species (ROS) accumulation in periphytic biofilm, the interaction between EPS and PNPs could mitigate production of ROS while superoxide dismutase could alleviate biotic ROS accumulation in periphytic biofilm. The periphytic biofilms changed their community composition in the presence of PNPs by increasing the relative abundance of phototrophic and high nutrient metabolic microorganisms (families Chlamydomonadaceae, Cyanobacteriacea, Sphingobacteriales, and Xanthomonadaceae). This study provides insight into the protection mechanisms of microbial aggregates against simultaneous photogenerated and nanoparticle toxicity from PNPs.

Atlantic salmon (Salmo salar L.) genetics in the 21st century: taking leaps forward in aquaculture and biological understanding

Atlantic salmon (Salmo salar L.) is among the most iconic and economically important fish species and was the first member of Salmonidae to have a high-quality reference genome assembly published. Advances in genomics have become increasingly central to the genetic improvement of farmed Atlantic salmon as well as conservation of wild salmon stocks. The salmon genome has also been pivotal in shaping our understanding of the evolutionary and functional consequences arising from an ancestral whole-genome duplication event characterising all Salmonidae members. Here, we provide a review of the current status of Atlantic salmon genetics and genomics, focussed on progress made from genome-wide research aimed at improving aquaculture production and enhancing understanding of salmonid ecology, physiology and evolution. We present our views on the future direction of salmon genomics, including the role of emerging technologies (e.g. genome editing) in elucidating genetic features that underpin functional variation in traits of commercial and evolutionary importance.

Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach

The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.

System biology approach intersecting diet and cell metabolism with pathogenesis of brain disorders

The surge in meals high in calories has prompted an epidemic of metabolic disorders around the world such that the elevated incidence of obese and diabetic individuals is alarming. New research indicates that metabolic disorders pose a risk for neurological and psychiatric conditions including stroke, Alzheimer's disease, Huntington's disease, and depression, all of which have a metabolic component. These relationships are rooted to a dysfunctional interaction between molecular processes that regulate energy metabolism and synaptic plasticity. The strong adaptive force of dietary factors on shaping the brain during evolution can be manipulated to transform the interaction between cell bioenergetics and epigenome with the aptitude to promote long-lasting brain healthiness. A thorough understanding of the association between the broad action of nutrients and brain fitness requires high level data processing empowered with the capacity to integrate information from a multitude of molecular entities and pathways. Nutritional systems biology is emerging as a viable approach to elucidate the multiple molecular layers involved in information processing in cells, tissues, and organ systems in response to diet. Information about the wide range of cellular and molecular interactions elicited by foods on the brain and cognitive plasticity is crucial for the design of public health initiatives for curtailing the epidemic of metabolic and brain disorders.

Gene Regulatory Network Inference from Perturbed Time-Series Expression Data via Ordered Dynamical Expansion of Non-Steady State Actors

The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, presents an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets.

An Organelle Correlation-Guided Feature Selection Approach for Classifying Multi-Label Subcellular Bio-Images

Nowadays, with the advances in microscopic imaging, accurate classification of bioimage-based protein subcellular location pattern has attracted as much attention as ever. One of the basic challenging problems is how to select the useful feature components among thousands of potential features to describe the images. This is not an easy task especially considering there is a high ratio of multi-location proteins. Existing feature selection methods seldom take the correlation among different cellular compartments into consideration, and thus may miss some features that will be co-important for several subcellular locations. To deal with this problem, we make use of the important structural correlation among different cellular compartments and propose an organelle structural correlation regularized feature selection method CSF (Common-Sets of Features) in this paper. We formulate the multi-label classification problem by adopting a group-sparsity regularizer to select common subsets of relevant features from different cellular compartments. In addition, we also add a cell structural correlation regularized Laplacian term, which utilizes the prior biological structural information to capture the intrinsic dependency among different cellular compartments. The CSF provides a new feature selection strategy for multi-label bio-image subcellular pattern classifications, and the experimental results also show its superiority when comparing with several existing algorithms.

Integrative Approaches to Understanding the Pathogenic Role of Genetic Variation in Rheumatic Diseases

The use of high-throughput omics may help to understand the contribution of genetic variants to the pathogenesis of rheumatic diseases. We discuss the concept of missing heritability: that genetic variants do not explain the heritability of rheumatoid arthritis and related rheumatologic conditions. In addition to an overview of how integrative data analysis can lead to novel insights into mechanisms of rheumatic diseases, we describe statistical approaches to prioritizing genetic variants for future functional analyses. We illustrate how analyses of large datasets provide hope for improved approaches to the diagnosis, treatment, and prevention of rheumatic diseases.

Nanoliter-Scale Oil-Air-Droplet Chip-Based Single Cell Proteomic Analysis

Single cell proteomic analysis provides crucial information on cellular heterogeneity in biological systems. Herein, we describe a nanoliter-scale oil-air-droplet (OAD) chip for achieving multistep complex sample pretreatment and injection for single cell proteomic analysis in the shotgun mode. By using miniaturized stationary droplet microreaction and manipulation techniques, our system allows all sample pretreatment and injection procedures to be performed in a nanoliter-scale droplet with minimum sample loss and a high sample injection efficiency (>99%), thus substantially increasing the analytical sensitivity for single cell samples. We applied the present system in the proteomic analysis of 100 ± 10, 50 ± 5, 10, and 1 HeLa cell(s), and protein IDs of 1360, 612, 192, and 51 were identified, respectively. The OAD chip-based system was further applied in single mouse oocyte analysis, with 355 protein IDs identified at the single oocyte level, which demonstrated its special advantages of high enrichment of sequence coverage, hydrophobic proteins, and enzymatic digestion efficiency over the traditional in-tube system.

Microfluidic-Mass Spectrometry Interfaces for Translational Proteomics

Interfacing mass spectrometry (MS) with microfluidic chips (μchip-MS) holds considerable potential to transform a clinician's toolbox, providing translatable methods for the early detection, diagnosis, monitoring, and treatment of noncommunicable diseases by streamlining and integrating laborious sample preparation workflows on high-throughput, user-friendly platforms. Overcoming the limitations of competitive immunoassays - currently the gold standard in clinical proteomics - μchip-MS can provide unprecedented access to complex proteomic assays having high sensitivity and specificity, but without the labor, costs, and complexities associated with conventional MS sample processing. This review surveys recent μchip-MS systems for clinical applications and examines their emerging role in streamlining the development and translation of MS-based proteomic assays by alleviating many of the challenges that currently inhibit widespread clinical adoption.

Protein-species quantitative venomics: looking through a crystal ball

In this paper we discuss recent significant developments in the field of venom research, specifically the emergence of top-down proteomic applications that allow achieving compositional resolution at the level of the protein species present in the venom, and the absolute quantification of the venom proteins (the term “protein species” is used here to refer to all the different molecular forms in which a protein can be found. Please consult the special issue of Jornal of Proteomics “Towards deciphering proteomes via the proteoform, protein speciation, moonlighting and protein code concepts” published in 2016, vol. 134, pages 1-202). Challenges remain to be solved in order to achieve a compact and automated platform with which to routinely carry out comprehensive quantitative analysis of all toxins present in a venom. This short essay reflects the authors’ view of the immediate future in this direction for the proteomic analysis of venoms, particularly of snakes.

Automated analysis of high-content microscopy data with deep learning

Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data.

Global analysis of gene expression reveals mRNA superinduction is required for the inducible immune response to a bacterial pathogen

The inducible innate immune response to infection requires a concerted process of gene expression that is regulated at multiple levels. Most global analyses of the innate immune response have focused on transcription induced by defined immunostimulatory ligands, such as lipopolysaccharide. However, the response to pathogens involves additional complexity, as pathogens interfere with virtually every step of gene expression. How cells respond to pathogen-mediated disruption of gene expression to nevertheless initiate protective responses remains unclear. We previously discovered that a pathogen-mediated blockade of host protein synthesis provokes the production of specific pro-inflammatory cytokines. It remains unclear how these cytokines are produced despite the global pathogen-induced block of translation. We addressed this question by using parallel RNAseq and ribosome profiling to characterize the response of macrophages to infection with the intracellular bacterial pathogen Legionella pneumophila. Our results reveal that mRNA superinduction is required for the inducible immune response to a bacterial pathogen.

Characterization of proteome dynamics during growth in oleate reveals a new peroxisome-targeting receptor

To optimally perform the diversity of metabolic functions that occur within peroxisomes, cells must dynamically regulate peroxisome size, number and content in response to the cell state and the environment. Except for transcriptional regulation little is known about the mechanisms used to perform this complicated feat. Focusing on the yeast Saccharomyces cerevisiae, we used complementary high-content screens to follow changes in localization of most proteins during growth in oleate. We found extensive changes in cellular architecture and identified several proteins that colocalized with peroxisomes that had not previously been considered peroxisomal proteins. One of the newly identified peroxisomal proteins, Ymr018w, is a protein with an unknown function that is similar to the yeast and human peroxisomal targeting receptor Pex5. We demonstrate that Ymr018w is a new peroxisomal-targeting receptor that targets a subset of matrix proteins to peroxisomes. We, therefore, renamed Ymr018w, Pex9, and suggest that Pex9 is a condition-specific targeting receptor that enables the dynamic rewiring of peroxisomes in response to metabolic needs. Moreover, we suggest that Pex5-like receptors might also exist in vertebrates.

Microfluidic Platforms for Yeast-Based Aging Studies

The budding yeast Saccharomyces cerevisiae has been a powerful model for the study of aging and has enabled significant contributions to our understanding of basic mechanisms of aging in eukaryotic cells. However, the laborious low-throughput nature of conventional methods of performing aging assays limits the pace of discoveries in this field. Some of the technical challenges of conventional aging assay methods can be overcome by use of microfluidic systems coupled to time-lapse microscopy. One of the major advantages is the ability of a microfluidic system to perform long-term cell culture under well-defined environmental conditions while tracking individual yeast. Here, recent advancements in microfluidic platforms for various yeast-based studies including replicative lifespan assay, long-term culture and imaging, gene expression, and cell signaling are discussed. In addition, emerging problems and limitations of current microfluidic approaches are examined and perspectives on the future development of this dynamic field are presented.

A Split-Ubiquitin Based Strategy Selecting for Protein Complex-Interfering Mutations

Understanding the topologies and functions of protein interaction networks requires the selective removal of single interactions. We introduce a selection strategy that enriches among a random library of alleles for mutations that impair the binding to a given partner protein. The selection makes use of a split-ubiquitin based protein interaction assay. This assay provides yeast cells that carry protein complex disturbing mutations with the advantage of being able to survive on uracil-lacking media. Applied to the exemplary interaction between the PB domains of the yeast proteins Bem1 and Cdc24, we performed two independent selections. The selections were either analyzed by Sanger sequencing of isolated clones or by next generation sequencing (NGS) of pools of clones. Both screens enriched for the same mutation in position 833 of Cdc24. Biochemical analysis confirmed that this mutation disturbs the interaction with Bem1 but not the fold of the protein. The larger dataset obtained by NGS achieved a more complete representation of the bipartite interaction interface of Cdc24.

Deconstructing the molecular architecture of olfactory areas using proteomics

The anatomy of the olfactory system is highly complex, comprising a system of olfactory receptors, pathways for the transmission of olfactory information, and structures for the recognition, discrimination, and memorization of odors. During the last years, proteomics has emerged as a large-scale comprehensive approach to characterize and quantify specific olfactory-related proteomes in different biological conditions such as olfactory learning, neurodegeneration, and ageing between others. The current work reviews recent applications of proteomics to olfaction with particular focus on quantitative proteome profiling studies performed on olfactory areas from laboratory animal models as well as proteomic characterizations performed on specific brain structures and fluids involved in human smell. Finally, we will also discuss the potential application of proteomics to study global proteome dynamics and posttranslationally modified proteomes in order to unravel cell-signaling networks that occur from peripheral structures to olfactory cortical areas during odor processing.

Different RNA splicing mechanisms contribute to diverse infective outcome of classical swine fever viruses of differing virulence: insights from the deep sequencing data in swine umbilical vein endothelial cells

Molecular mechanisms underlying RNA splicing regulation in response to viral infection are poorly understood. Classical swine fever (CSF), one of the most economically important and highly contagious swine diseases worldwide, is caused by classical swine fever virus (CSFV). Here, we used high-throughput sequencing to obtain the digital gene expression (DGE) profile in swine umbilical vein endothelial cells (SUVEC) to identify different response genes for CSFV by using both Shimen and C strains. The numbers of clean tags obtained from the libraries of the control and both CSFV-infected libraries were 3,473,370, 3,498,355, and 3,327,493 respectively. In the comparison among the control, CSFV-C, and CSFV-Shimen groups, 644, 158, and 677 differentially expressed genes (DEGs) were confirmed in the three groups. Pathway enrichment analysis showed that many of these DEGs were enriched in spliceosome, ribosome, proteasome, ubiquitin-mediated proteolysis, cell cycle, focal adhesion, Wnt signalling pathway, etc., where the processes differ between CSFV strains of differing virulence. To further elucidate important mechanisms related to the differential infection by the CSFV Shimen and C strains, we identified four possible profiles to assess the significantly expressed genes only by CSFV Shimen or CSFV C strain. GO analysis showed that infection with CSFV Shimen and C strains disturbed ‘RNA splicing’ of SUVEC, resulting in differential ‘gene expression’ in SUVEC. Mammalian target of rapamycin (mTOR) was identified as a significant response regulator contributed to impact on SUVEC function for CSFV Shimen. This computational study suggests that CSFV of differing virulence could induce alterations in RNA splicing regulation in the host cell to change cell metabolism, resulting in acute haemorrhage and pathological damage or infectious tolerance.

A scalable strategy for high-throughput GFP tagging of endogenous human proteins

A central challenge of the postgenomic era is to comprehensively characterize the cellular role of the ∼20,000 proteins encoded in the human genome. To systematically study protein function in a native cellular background, libraries of human cell lines expressing proteins tagged with a functional sequence at their endogenous loci would be very valuable. Here, using electroporation of Cas9 nuclease/single-guide RNA ribonucleoproteins and taking advantage of a split-GFP system, we describe a scalable method for the robust, scarless, and specific tagging of endogenous human genes with GFP. Our approach requires no molecular cloning and allows a large number of cell lines to be processed in parallel. We demonstrate the scalability of our method by targeting 48 human genes and show that the resulting GFP fluorescence correlates with protein expression levels. We next present how our protocols can be easily adapted for the tagging of a given target with GFP repeats, critically enabling the study of low-abundance proteins. Finally, we show that our GFP tagging approach allows the biochemical isolation of native protein complexes for proteomic studies. Taken together, our results pave the way for the large-scale generation of endogenously tagged human cell lines for the proteome-wide analysis of protein localization and interaction networks in a native cellular context.

Translational regulation in blood stages of the malaria parasite Plasmodium spp.: systems-wide studies pave the way

The malaria parasite Plasmodium spp. varies the expression profile of its genes depending on the host it resides in and its developmental stage. Virtually all messenger RNA (mRNA) is expressed in a monocistronic manner, with transcriptional activation regulated at the epigenetic level and by specialized transcription factors. Furthermore, recent systems-wide studies have identified distinct mechanisms of post-transcriptional and translational control at various points of the parasite lifecycle. Taken together, it is evident that 'just-in-time' transcription and translation strategies coexist and coordinate protein expression during Plasmodium development, some of which we review here. In particular, we discuss global and specific mechanisms that control protein translation in blood stages of the human malaria parasite Plasmodium falciparum, once a cytoplasmic mRNA has been generated, and its crosstalk with mRNA decay and storage. We also focus on the widespread translational delay observed during the 48-hour blood stage lifecycle of P. falciparum-for over 30% of transcribed genes, including virulence factors required to invade erythrocytes-and its regulation by cis-elements in the mRNA, RNA-processing enzymes and RNA-binding proteins; the first-characterized amongst these are the DNA- and RNA-binding Alba proteins. More generally, we conclude that translational regulation is an emerging research field in malaria parasites and propose that its elucidation will not only shed light on the complex developmental program of this parasite, but may also reveal mechanisms contributing to drug resistance and define new targets for malaria intervention strategies. WIREs RNA 2016, 7:772-792. doi: 10.1002/wrna.1365 For further resources related to this article, please visit the WIREs website.