Bioinformatic approaches to metabolic pathways analysis

Transcriptional regulatory mechanisms and signaling networks in cancer

Cancer is a general term that refers to a wide range of illnesses that are characterized by the development of aberrant cells that have the capacity to divide uncontrollably, invade, and harm healthy tissue. It is caused by both genetic and epigenetic changes that suppress abnormal proliferation and prevent cells from surviving outside of their normal niches. Complex protein networks are responsible for the development of a suitable environment via multiple cells signaling pathways. The study of these pathways is essential for analysing network context and developing novel cancer therapies. Transcription factors (TFs) are actively involved in gene expression and maintain the combinatorial on-and-off states of the gene. In addition, the TFs regulate cell identity and state; these TFs cooperate to establish cell-type-specific gene expression. In this chapter, we describe the number of transcription factors and their role in the progression of cancer. The knowledge of transcriptional factors and their network is crucial for emphasizing the specific transcriptional addiction and for designing new anticancer therapies.

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

Understanding the Role of the Antioxidant Drug Erdosteine and Its Active Metabolite on Staphylococcus aureus Methicillin Resistant Biofilm Formation

Increasing numbers of researches have suggested that some drugs with reactive oxygen species (ROS)-mediated mechanisms of action modulate biofilm formation of some pathogenic strains. However, the full contribution of ROS to biofilm development is still an open question. In this paper, the correlations between the antioxidant drug Erdosteine (Er) and its active Metabolite I (Met I), ROS and biofilm development of two strains of methicillin resistant Staphylococcus aureus are presented. Experiments revealed that Er and Met I at 2 and 5 mg/L increased up to three orders of magnitude the number of biofilm-dwelling cells, while the content of ROS within the biofilms was reduced above the 87%, with a major effect of Met I in comparison to Er. Comparative proteomics showed that, 5 mg/L Met I modified the expression of 30% and 65% of total proteins in the two strains respectively. Some proteins involved in cell replication were upregulated, and a nitric oxide-based mechanism is assumed to modulate the biofilm development by changing quorum sensitive pathways. Additionally, several proteins involved in virulence were downregulated in the presence of Met I, suggesting that treated cells, despite being greater in number, might have lost part of their virulence.

Short Report: Using Targeted Urine Metabolomics to Distinguish Between Manganese Exposed and Unexposed Workers in a Small Occupational Cohort

Objectives: Despite the widespread use of manganese (Mn) in industrial settings and its association with adverse neurological outcomes, a validated and reliable biomarker for Mn exposure is still elusive. Here, we utilize targeted metabolomics to investigate metabolic differences between Mn-exposed and -unexposed workers, which could inform a putative biomarker for Mn and lead to increased understanding of Mn toxicity. Methods: End of shift spot urine samples collected from Mn exposed (n = 17) and unexposed (n = 15) workers underwent a targeted assay of 362 metabolites using LC-MS/MS; 224 were quantified and retained for analysis. Differences in metabolite abundances between exposed and unexposed workers were tested with a Benjamini-Hochberg adjusted Wilcoxon Rank-Sum test. We explored perturbed pathways related to exposure using a pathway analysis. Results: Seven metabolites were significantly differentially abundant between exposed and unexposed workers (FDR ≤ 0.1), including n-isobutyrylglycine, cholic acid, anserine, beta-alanine, methionine, n-isovalerylglycine, and threonine. Three pathways were significantly perturbed in exposed workers and had an impact score >0.5: beta-alanine metabolism, histidine metabolism, and glycine, serine, and threonine metabolism. Conclusion: This is one of few studies utilizing targeted metabolomics to explore differences between Mn-exposed and -unexposed workers. Metabolite and pathway analysis showed amino acid metabolism was perturbed in these Mn-exposed workers. Amino acids have also been shown to be perturbed in other occupational cohorts exposed to Mn. Additional research is needed to characterize the biological importance of amino acids in the Mn exposure-disease continuum, and to determine how to appropriately utilize and interpret metabolomics data collected from occupational cohorts.

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.

Metabolites-Enabled Survival of Crucian Carps Infected by Edwardsiella tarda in High Water Temperature

Temperature is one of the major factors that affect the outbreak of infectious disease. Lines of evidences have shown that virulence factors can be controlled by thermo-sensors in bacterial pathogens. However, how temperature influences host's responses to the pathogen is still largely unexplored, and the study of this might pave the way to develop strategies to manage pathogenic bacterial infection. In the present study, we show that finfish Carassius carassius, the crucian carp that is tolerant to a wide range of temperatures, is less susceptible to bacterial infection when grown in 20°C than in 30°C. The different responses of C. carassius to bacterial infection could be partially explained by the distinct metabolisms under the specific temperatures: C. carassius shows elevated tricarboxylic acid cycle (TCA cycle) but decreased taurine and hypotaurine metabolism as well as lower biosynthesis of unsaturated fatty acids at 30°C. The decreased abundance of palmitate, threonine, and taurine represents the most characteristic metabolic feature. Consistently, exogenous palmitate, threonine, or taurine enhances the survival of C. carassius to bacterial infection at 30°C in a dose-dependent manner. This effect could be attributed to the inhibition on the TCA cycle by the three metabolites. This notion is further supported by the fact that low concentration of malonate, a succinate dehydrogenase inhibitor, increases the survival of C. carassius at 30°C as well. On the other hand, addition of the three metabolites rescued the decreased expression of pro-inflammatory cytokines including TNF-α1, TNF-α2, IL-1β1, IL-1β2, and lysozyme at 30°C. Taken together, our results revealed an unexpected relationship between temperature and metabolism that orchestrates the immune regulation against infection by bacterial pathogens. Thus, this study shed light on the modulation of finfish physiology to fight against bacterial infection through metabolism.

Transcriptomic characterization of signaling pathways associated with osteoblastic differentiation of MC-3T3E1 cells

Bone remodeling involves the coordinated actions of osteoclasts, which resorb the calcified bony matrix, and osteoblasts, which refill erosion pits created by osteoclasts to restore skeletal integrity and adapt to changes in mechanical load. Osteoblasts are derived from pluripotent mesenchymal stem cell precursors, which undergo differentiation under the influence of a host of local and environmental cues. To characterize the autocrine/paracrine signaling networks associated with osteoblast maturation and function, we performed gene network analysis using complementary “agnostic” DNA microarray and “targeted” NanoString nCounter datasets derived from murine MC3T3-E1 cells induced to undergo synchronized osteoblastic differentiation in vitro. Pairwise datasets representing changes in gene expression associated with growth arrest (day 2 to 5 in culture), differentiation (day 5 to 10 in culture), and osteoblast maturation (day 10 to 28 in culture) were analyzed using Ingenuity Systems Pathways Analysis to generate predictions about signaling pathway activity based on the temporal sequence of changes in target gene expression. Our data indicate that some pathways involved in osteoblast differentiation, e.g. Wnt/β-catenin signaling, are most active early in the process, while others, e.g. TGFβ/BMP, cytokine/JAK-STAT and TNFα/RANKL signaling, increase in activity as differentiation progresses. Collectively, these pathways contribute to the sequential expression of genes involved in the synthesis and mineralization of extracellular matrix. These results provide insight into the temporal coordination and complex interplay between signaling networks controlling gene expression during osteoblast differentiation. A more complete understanding of these processes may aid the discovery of novel methods to promote osteoblast development for the treatment of conditions characterized by low bone mineral density.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Inhibition of C298S mutant of human aldose reductase for antidiabetic applications: Evidence from in silico elementary mode analysis of biological network model

Human aldose reductase (hAR) is the key enzyme in sorbitol pathway of glucose utilization and is implicated in the etiology of secondary complications of diabetes, such as, cardiovascular complications, neuropathy, nephropathy, retinopathy, and cataract genesis. It reduces glucose to sorbitol in the presence of NADPH and the major cause of diabetes complications could be the change in the osmotic pressure due to the accumulation of sorbitol. An activated form of hAR (activated hAR or ahAR) poses a potential obstacle in the development of diabetes drugs as hAR-inhibitors are ineffective against ahAR. The therapeutic efficacy of such drugs is compromised when a large fraction of the enzyme (hAR) undergoes conversion to the activated ahAR form as has been observed in the diabetic tissues. In the present study, attempts have been made to employ systems biology strategies to identify the elementary nodes of human polyol metabolic pathway, responsible for normal metabolic states, followed by the identification of natural potent inhibitors of the activated form of hAR represented by the mutant C298S for possible antidiabetic applications. Quantum Mechanical Molecular Mechanical docking strategy was used to determine the probable inhibitors of ahAR. Rosmarinic acid was found as the most potent natural ahAR inhibitor and warrants for experimental validation in the near future.

Translating in vitro ligand bias into in vivo efficacy

It is increasingly apparent that ligand structure influences both the efficiency with which G protein-coupled receptors (GPCRs) engage their downstream effectors and the manner in which they are activated. Thus, 'biased' agonists, synthetic ligands whose intrinsic efficacy differs from the native ligand, afford a strategy for manipulating GPCR signaling in ways that promote beneficial signals while blocking potentially deleterious ones. Still, there are significant challenges in relating in vitro ligand efficacy, which is typically measured in heterologous expression systems, to the biological response in vivo, where the ligand is acting on natively expressed receptors and in the presence of the endogenous ligand. This is particularly true of arrestin pathway-selective 'biased' agonists. The type 1 parathyroid hormone receptor (PTH₁R) is a case in point. Parathyroid hormone (PTH) is the principal physiological regulator of calcium homeostasis, and PTH₁R expressed on cells of the osteoblast lineage are an established therapeutic target in osteoporosis. In vitro, PTH₁R signaling is highly sensitive to ligand structure, and PTH analogs that affect the selectivity/kinetics of G protein coupling or that engage arrestin-dependent signaling mechanisms without activating heterotrimeric G proteins have been identified. In vivo, intermittent administration of conventional PTH analogs accelerates the rate of osteoblastic bone formation, largely through known cAMP-dependent mechanisms. Paradoxically, both intermittent and continuous administration of an arrestin pathway-selective PTH analog, which in vivo would be expected to antagonize endogenous PTH₁R-cAMP signaling, also increases bone mass. Transcriptomic analysis of tissue from treated animals suggests that conventional and arrestin pathway-selective PTH1R ligands act in largely different ways, with the latter principally affecting pathways involved in the regulation of cell cycle, survival, and migration/cytoskeletal dynamics. Such multi-dimensional in vitro and in vivo analyses of ligand bias may provide insights into the physiological roles of non-canonical arrestin-mediated signaling pathways in vivo, and provide a conceptual framework for translating arrestin pathway-selective ligands into viable therapeutics.

Informatic deconvolution of biased GPCR signaling mechanisms from in vivo pharmacological experimentation

Ligands possessing different physico-chemical structures productively interact with G protein-coupled receptors generating distinct downstream signaling events due to their abilities to activate/select idiosyncratic receptor entities ('receptorsomes') from the full spectrum of potential receptor partners. We have employed multiple novel informatic approaches to identify and characterize the in vivo transcriptomic signature of an arrestin-signaling biased ligand, [D-Trp(12),Tyr(34)]-bPTH(7-34), acting at the parathyroid hormone type 1 receptor (PTH1R), across six different murine tissues after chronic drug exposure. We are able to demonstrate that [D-Trp(12),Tyr(34)]-bPTH(7-34) elicits a distinctive arrestin-signaling focused transcriptomic response that is more coherently regulated, in an arrestin signaling-dependent manner, across more tissues than that of the pluripotent endogenous PTH1R ligand, hPTH(1-34). This arrestin-focused response signature is strongly linked with the transcriptional regulation of cell growth and development. Our informatic deconvolution of a conserved arrestin-dependent transcriptomic signature from wild type mice demonstrates a conceptual framework within which the in vivo outcomes of biased receptor signaling may be further investigated or predicted.

MicroRNA-224 Induces G1/S Checkpoint Release in Liver Cancer

Profound changes in microRNA (miR) expression levels are frequently found in liver cancers compared to the normal liver. In this study, we evaluate the expression of miR-224 in human HCC and CCA, as well as its downstream targets and affected pathways. We show that miR-224 is upregulated in a large cohort of human CCA, similar to its upregulation in human HCC. For the purpose of studying the roles of miR-224 in HCC and CCA, we enforced miR-224 expression in cells. mRNA arrays followed by Ingenuity Pathway Analysis (IPA)-identified putative molecules and pathways downstream of miR-224. Phenotypically, we report that enforced expression of miR-224 increases the growth rate of normal cholangiocytes, CCA cell lines, and HCC cell lines. In addition, we identified, in an unbiased fashion, that one of the major biologic processes affected by miR-224 is Gap1 (G1) to Synthesis (S) transition checkpoint release. We next identified p21, p15, and CCNE1 as downstream targets of miR-224 and confirmed the coordinated downregulation results in the increased phosphorylation of Retinoblastoma (Rb) with resulting G1/S checkpoint release. Our data suggest that miR-224 is a master regulator of cell cycle progression, and that its overexpression results in G1/S checkpoint release followed by accelerated cell growth.

Exploring G protein-coupled receptor signaling networks using SILAC-based phosphoproteomics

The type 1 parathyroid hormone receptor (PTH1R) is a key regulator of calcium homeostasis and bone turnover. Here, we employed SILAC-based quantitative mass spectrometry and bioinformatic pathways analysis to examine global changes in protein phosphorylation following short-term stimulation of endogenously expressed PTH1R in osteoblastic cells in vitro. Following 5min exposure to the conventional agonist, PTH(1-34), we detected significant changes in the phosphorylation of 224 distinct proteins. Kinase substrate motif enrichment demonstrated that consensus motifs for PKA and CAMK2 were the most heavily upregulated within the phosphoproteome, while consensus motifs for mitogen-activated protein kinases were strongly downregulated. Signaling pathways analysis identified ERK1/2 and AKT as important nodal kinases in the downstream network and revealed strong regulation of small GTPases involved in cytoskeletal rearrangement, cell motility, and focal adhesion complex signaling. Our data illustrate the utility of quantitative mass spectrometry in measuring dynamic changes in protein phosphorylation following GPCR activation.

The effects of aging on the BTBR mouse model of autism spectrum disorder

Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder characterized by alterations in social functioning, communicative abilities, and engagement in repetitive or restrictive behaviors. The process of aging in individuals with autism and related neurodevelopmental disorders is not well understood, despite the fact that the number of individuals with ASD aged 65 and older is projected to increase by over half a million individuals in the next 20 years. To elucidate the effects of aging in the context of a modified central nervous system, we investigated the effects of age on the BTBR T + tf/j mouse, a well characterized and widely used mouse model that displays an ASD-like phenotype. We found that a reduction in social behavior persists into old age in male BTBR T + tf/j mice. We employed quantitative proteomics to discover potential alterations in signaling systems that could regulate aging in the BTBR mice. Unbiased proteomic analysis of hippocampal and cortical tissue of BTBR mice compared to age-matched wild-type controls revealed a significant decrease in brain derived neurotrophic factor and significant increases in multiple synaptic markers (spinophilin, Synapsin I, PSD 95, NeuN), as well as distinct changes in functional pathways related to these proteins, including “Neural synaptic plasticity regulation” and “Neurotransmitter secretion regulation.” Taken together, these results contribute to our understanding of the effects of aging on an ASD-like mouse model in regards to both behavior and protein alterations, though additional studies are needed to fully understand the complex interplay underlying aging in mouse models displaying an ASD-like phenotype.

Plurigon: three dimensional visualization and classification of high-dimensionality data

High-dimensionality data is rapidly becoming the norm for biomedical sciences and many other analytical disciplines. Not only is the collection and processing time for such data becoming problematic, but it has become increasingly difficult to form a comprehensive appreciation of high-dimensionality data. Though data analysis methods for coping with multivariate data are well-documented in technical fields such as computer science, little effort is currently being expended to condense data vectors that exist beyond the realm of physical space into an easily interpretable and aesthetic form. To address this important need, we have developed Plurigon, a data visualization and classification tool for the integration of high-dimensionality visualization algorithms with a user-friendly, interactive graphical interface. Unlike existing data visualization methods, which are focused on an ensemble of data points, Plurigon places a strong emphasis upon the visualization of a single data point and its determining characteristics. Multivariate data vectors are represented in the form of a deformed sphere with a distinct topology of hills, valleys, plateaus, peaks, and crevices. The gestalt structure of the resultant Plurigon object generates an easily-appreciable model. User interaction with the Plurigon is extensive; zoom, rotation, axial and vector display, feature extraction, and anaglyph stereoscopy are currently supported. With Plurigon and its ability to analyze high-complexity data, we hope to see a unification of biomedical and computational sciences as well as practical applications in a wide array of scientific disciplines. Increased accessibility to the analysis of high-dimensionality data may increase the number of new discoveries and breakthroughs, ranging from drug screening to disease diagnosis to medical literature mining.

Textrous!: extracting semantic textual meaning from gene sets

The un-biased and reproducible interpretation of high-content gene sets from large-scale genomic experiments is crucial to the understanding of biological themes, validation of experimental data, and the eventual development of plans for future experimentation. To derive biomedically-relevant information from simple gene lists, a mathematical association to scientific language and meaningful words or sentences is crucial. Unfortunately, existing software for deriving meaningful and easily-appreciable scientific textual ‘tokens’ from large gene sets either rely on controlled vocabularies (Medical Subject Headings, Gene Ontology, BioCarta) or employ Boolean text searching and co-occurrence models that are incapable of detecting indirect links in the literature. As an improvement to existing web-based informatic tools, we have developed Textrous!, a web-based framework for the extraction of biomedical semantic meaning from a given input gene set of arbitrary length. Textrous! employs natural language processing techniques, including latent semantic indexing (LSI), sentence splitting, word tokenization, parts-of-speech tagging, and noun-phrase chunking, to mine MEDLINE abstracts, PubMed Central articles, articles from the Online Mendelian Inheritance in Man (OMIM), and Mammalian Phenotype annotation obtained from Jackson Laboratories. Textrous! has the ability to generate meaningful output data with even very small input datasets, using two different text extraction methodologies (collective and individual) for the selecting, ranking, clustering, and visualization of English words obtained from the user data. Textrous!, therefore, is able to facilitate the output of quantitatively significant and easily appreciable semantic words and phrases linked to both individual gene and batch genomic data.

VennPlex--a novel Venn diagram program for comparing and visualizing datasets with differentially regulated datapoints

With the development of increasingly large and complex genomic and proteomic data sets, an enhancement in the complexity of available Venn diagram analytical programs is becoming increasingly important. Current freely available Venn diagram programs often fail to represent extra complexity among datasets, such as regulation pattern differences between different groups. Here we describe the development of VennPlex, a program that illustrates the often diverse numerical interactions among multiple, high-complexity datasets, using up to four data sets. VennPlex includes versatile output features, where grouped data points in specific regions can be easily exported into a spreadsheet. This program is able to facilitate the analysis of two to four gene sets and their corresponding expression values in a user-friendly manner. To demonstrate its unique experimental utility we applied VennPlex to a complex paradigm, i.e. a comparison of the effect of multiple oxygen tension environments (1–20% ambient oxygen) upon gene transcription of primary rat astrocytes. VennPlex accurately dissects complex data sets reliably into easily identifiable groups for straightforward analysis and data output. This program, which is an improvement over currently available Venn diagram programs, is able to rapidly extract important datasets that represent the variety of expression patterns available within the data sets, showing potential applications in fields like genomics, proteomics, and bioinformatics.

Mining disease fingerprints from within genetic pathways

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components ('fingerprints') of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ~77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways.

Trespassing cancer cells: 'fingerprinting' invasive protrusions reveals metastatic culprits

Metastatic cancer cells produce invasive membrane protrusions called invadopodia and pseudopodia, which play a central role in driving cancer cell dissemination in the body. Malignant cells use these structures to attach to and degrade extracellular matrix proteins, generate force for cell locomotion, and to penetrate the vasculature. Recent work using unique subcellular fractionation methodologies combined with spatial genomic, proteomic, and phosphoproteomic profiling has provided insight into the invadopodiome and pseudopodiome signaling networks that control the protrusion of invasive membranes. Here I highlight how these powerful spatial 'omics' approaches reveal important signatures of metastatic cancer cells and possible new therapeutic targets aimed at treating metastatic disease.

GIT2 acts as a potential keystone protein in functional hypothalamic networks associated with age-related phenotypic changes in rats

The aging process affects every tissue in the body and represents one of the most complicated and highly integrated inevitable physiological entities. The maintenance of good health during the aging process likely relies upon the coherent regulation of hormonal and neuronal communication between the central nervous system and the periphery. Evidence has demonstrated that the optimal regulation of energy usage in both these systems facilitates healthy aging. However, the proteomic effects of aging in regions of the brain vital for integrating energy balance and neuronal activity are not well understood. The hypothalamus is one of the main structures in the body responsible for sustaining an efficient interaction between energy balance and neurological activity. Therefore, a greater understanding of the effects of aging in the hypothalamus may reveal important aspects of overall organismal aging and may potentially reveal the most crucial protein factors supporting this vital signaling integration. In this study, we examined alterations in protein expression in the hypothalami of young, middle-aged, and old rats. Using novel combinatorial bioinformatics analyses, we were able to gain a better understanding of the proteomic and phenotypic changes that occur during the aging process and have potentially identified the G protein-coupled receptor/cytoskeletal-associated protein GIT2 as a vital integrator and modulator of the normal aging process.

Effective correction of experimental errors in quantitative proteomics using stable isotope labeling by amino acids in cell culture (SILAC)

Accurate and reliable quantitative proteomics in cell culture has been considerably facilitated by the introduction of the stable isotope labeling by amino acids in cell culture (SILAC), combined with high resolution mass spectrometry. There are however several major sources of quantification errors that commonly occur with SILAC techniques, i.e. incomplete incorporation of isotopic amino acids, arginine-to-proline conversion, and experimental errors in final sample mixing. Dataset normalization is a widely adopted solution to such errors, however this may not completely prevent introducing incorrect expression ratios. Here we demonstrate that a label-swap replication of SILAC experiments was able to effectively correct experimental errors by averaging ratios measured in individual replicates using quantitative proteomics and phosphoproteomics of ligand treatment of neural cell cultures. Furthermore, this strategy was successfully applied to a SILAC triplet experiment, which presents a much more complicated experimental matrix, affected by both incomplete labeling and arginine-to-proline conversion. Based on our results, we suggest that SILAC experiments should be designed to incorporate label-swap replications for enhanced reliability in expression ratios.

Central role of the EGF receptor in neurometabolic aging

A strong connection between neuronal and metabolic health has been revealed in recent years. It appears that both normal and pathophysiological aging, as well as neurodegenerative disorders, are all profoundly influenced by this "neurometabolic" interface, that is, communication between the brain and metabolic organs. An important aspect of this "neurometabolic" axis that needs to be investigated involves an elucidation of molecular factors that knit these two functional signaling domains, neuronal and metabolic, together. This paper attempts to identify and discuss a potential keystone signaling factor in this "neurometabolic" axis, that is, the epidermal growth factor receptor (EGFR). The EGFR has been previously demonstrated to act as a signaling nexus for many ligand signaling modalities and cellular stressors, for example, radiation and oxidative radicals, linked to aging and degeneration. The EGFR is expressed in a wide variety of cells/tissues that pertain to the coordinated regulation of neurometabolic activity. EGFR signaling has been highlighted directly or indirectly in a spectrum of neurometabolic conditions, for example, metabolic syndrome, diabetes, Alzheimer's disease, cancer, and cardiorespiratory function. Understanding the positioning of the EGFR within the neurometabolic domain will enhance our appreciation of the ability of this receptor system to underpin highly complex physiological paradigms such as aging and neurodegeneration.