Proteomic Analysis Reveals Distinct Metabolic Differences Between Granulocyte-Macrophage Colony Stimulating Factor (GM-CSF) and Macrophage Colony Stimulating Factor (M-CSF) Grown Macrophages Derived from Murine Bone Marrow Cells*

Macrophages are crucial in controlling infectious agents and tissue homeostasis. Macrophages require a wide range of functional capabilities in order to fulfill distinct roles in our body, one being rapid and robust immune responses. To gain insight into macrophage plasticity and the key regulatory protein networks governing their specific functions, we performed quantitative analyses of the proteome and phosphoproteome of murine primary GM-CSF and M-CSF grown bone marrow derived macrophages (GM-BMMs and M-BMMs, respectively) using the latest isobaric tag based tandem mass tag (TMT) labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Strikingly, metabolic processes emerged as a major difference between these macrophages. Specifically, GM-BMMs show significant enrichment of proteins involving glycolysis, the mevalonate pathway, and nitrogen compound biosynthesis. This evidence of enhanced glycolytic capability in GM-BMMs is particularly significant regarding their pro-inflammatory responses, because increased production of cytokines upon LPS stimulation in GM-BMMs depends on their acute glycolytic capacity. In contrast, M-BMMs up-regulate proteins involved in endocytosis, which correlates with a tendency toward homeostatic functions such as scavenging cellular debris. Together, our data describes a proteomic network that underlies the pro-inflammatory actions of GM-BMMs as well as the homeostatic functions of M-BMMs.Macrophages are a heterogeneous population of immune cells that are essential for the initiation and resolution of pathogen- or tissue damage-induced inflammation (1). They show remarkable plasticity that allows them to respond efficiently to environmental signals and change their phenotype and physiology upon cytokine and microbial signaling (2). These changes can give rise to populations of cells with distinct functions that are phenotypically characterized by the production of pro-inflammatory and anti-inflammatory cytokines (3). Among the growth factors that affect macrophage activation states, two cytokines that appear to be important in controlling the functions of macrophage lineage populations in inflammatory conditions are granulocyte-macrophage colony stimulating factor (GM-CSF)¹ and macrophage colony stimulating factor (M-CSF) (4). These CSFs are critical to the proper maintenance of steady-state macrophage development, although with different roles. GM-CSF has a role in inducing emergency hematopoiesis not in steady state, and influences the pathogenesis of various inflammatory as well as autoimmune diseases (5). In this line, in vitro generated GM-CSF grown macrophages are now considered as pro-inflammatory macrophages that display a robust immune responses upon LPS stimulation compared with M-CSF grown macrophages (6). In contrast, M-CSF contributes the maintenance of most resident macrophages including osteoclast in vivo and is known to affect homeostatic anti-inflammatory characteristics of macrophages. M-CSF grown macrophages are widely accepted as in vitro-generated macrophage sources because they showed relatively homogenous and stable macrophage phenotypes (7). A number of genomic studies have been performed to analyze macrophage activation in response to pro-inflammatory/anti-inflammatory stimuli. However, to date, there have been no clear reports on the global proteomic differences that govern the functional characteristics of differently differentiated or activated macrophages (8–11). To fully elucidate what enables pro-inflammatory macrophages to be poised for rapid and robust immune responses requires assessing their global intracellular proteomic network signatures.There has long been an appreciation, especially in the cancer field, for how changes in cellular activation coincide with alterations in cellular metabolic states (12, 13). Importantly, over the last couple of years it is becoming increasingly clear that immune cell activation is also coupled to profound changes in cellular metabolism and that their fate and function are metabolically regulated (14). In line with this, in this study, we found that GM-CSF grown macrophages have a higher glycolytic capacity through up-regulated glycolytic enzymes, as well as high lipid/nitrogen compound biosynthetic enzymes compared with M-CSF grown macrophages. They produce robust inflammatory cytokines upon TLR ligand stimulation only when sufficient glucose is available.Here we performed a quantitative analysis of the proteome/phosphoproteome of primary GM-CSF and M-CSF grown macrophages using the latest isobaric tag based TMT labeling and LC-MS/MS (15). This proteomic approach with high throughput technology is the first attempt to show the fundamental differences between primary GM-CSF and M-CSF grown macrophages and finally reveals that innate cellular anabolic metabolism paves the way for inducing robust immune responses. In this study, we describe individual differentially expressed proteins in the total network maps of GM-CSF and M-CSF grown macrophages and predict how they are specifically involved in initiating inflammation or resolution.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]     

Sip1, a Novel RS Domain-Containing Protein Essential for Pre-mRNA Splicing

Previous studies have shown that protein-protein interactions among splicing factors may play an important role in pre-mRNA splicing. We report here identification and functional characterization of a new splicing factor, Sip1 (SC35-interacting protein 1). Sip1 was initially identified by virtue of its interaction with SC35, a splicing factor of the SR family. Sip1 interacts with not only several SR proteins but also with U1-70K and U2AF65, proteins associated with 5′ and 3′ splice sites, respectively. The predicted Sip1 sequence contains an arginine-serine-rich (RS) domain but does not have any known RNA-binding motifs, indicating that it is not a member of the SR family. Sip1 also contains a region with weak sequence similarity to the Drosophila splicing regulator suppressor of white apricot (SWAP). An essential role for Sip1 in pre-mRNA splicing was suggested by the observation that anti-Sip1 antibodies depleted splicing activity from HeLa nuclear extract. Purified recombinant Sip1 protein, but not other RS domain-containing proteins such as SC35, ASF/SF2, and U2AF65, restored the splicing activity of the Sip1-immunodepleted extract. Addition of U2AF65 protein further enhanced the splicing reconstitution by the Sip1 protein. Deficiency in the formation of both A and B splicing complexes in the Sip1-depleted nuclear extract indicates an important role of Sip1 in spliceosome assembly. Together, these results demonstrate that Sip1 is a novel RS domain-containing protein required for pre-mRNA splicing and that the functional role of Sip1 in splicing is distinct from those of known RS domain-containing splicing factors.Pre-mRNA splicing takes place in spliceosomes, the large RNA-protein complexes containing pre-mRNA, U1, U2, U4/6, and U5 small nuclear ribonucleoprotein particles (snRNPs), and a large number of accessory protein factors (for reviews, see references 21, 22, 37, 44, and 48). It is increasingly clear that the protein factors are important for pre-mRNA splicing and that studies of these factors are essential for further understanding of molecular mechanisms of pre-mRNA splicing.Most mammalian splicing factors have been identified by biochemical fractionation and purification (3, 15, 19, 31–36, 45, 69–71, 73), by using antibodies recognizing splicing factors (8, 9, 16, 17, 61, 66, 67, 74), and by sequence homology (25, 52, 74).Splicing factors containing arginine-serine-rich (RS) domains have emerged as important players in pre-mRNA splicing. These include members of the SR family, both subunits of U2 auxiliary factor (U2AF), and the U1 snRNP protein U1-70K (for reviews, see references 18, 41, and 59). Drosophila alternative splicing regulators transformer (Tra), transformer 2 (Tra2), and suppressor of white apricot (SWAP) also contain RS domains (20, 40, 42). RS domains in these proteins play important roles in pre-mRNA splicing (7, 71, 75), in nuclear localization of these splicing proteins (23, 40), and in protein-RNA interactions (56, 60, 64). Previous studies by us and others have demonstrated that one mechanism whereby SR proteins function in splicing is to mediate specific protein-protein interactions among spliceosomal components and between general splicing factors and alternative splicing regulators (1, 1a, 6, 10, 27, 63, 74, 77). Such protein-protein interactions may play critical roles in splice site recognition and association (for reviews, see references 4, 18, 37, 41, 47 and 59). Specific interactions among the splicing factors also suggest that it is possible to identify new splicing factors by their interactions with known splicing factors.Here we report identification of a new splicing factor, Sip1, by its interaction with the essential splicing factor SC35. The predicted Sip1 protein sequence contains an RS domain and a region with sequence similarity to the Drosophila splicing regulator, SWAP. We have expressed and purified recombinant Sip1 protein and raised polyclonal antibodies against the recombinant Sip1 protein. The anti-Sip1 antibodies specifically recognize a protein migrating at a molecular mass of approximately 210 kDa in HeLa nuclear extract. The anti-Sip1 antibodies sufficiently deplete Sip1 protein from the nuclear extract, and the Sip1-depleted extract is inactive in pre-mRNA splicing. Addition of recombinant Sip1 protein can partially restore splicing activity to the Sip1-depleted nuclear extract, indicating an essential role of Sip1 in pre-mRNA splicing. Other RS domain-containing proteins, including SC35, ASF/SF2, and U2AF65, cannot substitute for Sip1 in reconstituting splicing activity of the Sip1-depleted nuclear extract. However, addition of U2AF65 further increases splicing activity of Sip1-reconstituted nuclear extract, suggesting that there may be a functional interaction between Sip1 and U2AF65 in nuclear extract.     

Proteomic Analysis of Microtubule-associated Proteins during Macrophage Activation

Classical activation of macrophages induces a wide range of signaling and vesicle trafficking events to produce a more aggressive cellular phenotype. The microtubule (MT) cytoskeleton is crucial for the regulation of immune responses. In the current study, we used a large scale proteomics approach to analyze the change in protein composition of the MT-associated protein (MAP) network by macrophage stimulation with the inflammatory cytokine interferon-γ and the endotoxin lipopolysaccharide. Overall the analysis identified 409 proteins that bound directly or indirectly to MTs. Of these, 52 were up-regulated 2-fold or greater and 42 were down-regulated 2-fold or greater after interferon-γ/lipopolysaccharide stimulation. Bioinformatics analysis based on publicly available binary protein interaction data produced a putative interaction network of MAPs in activated macrophages. We confirmed the up-regulation of several MAPs by immunoblotting and immunofluorescence analysis. More detailed analysis of one up-regulated protein revealed a role for HSP90β in stabilization of the MT cytoskeleton during macrophage activation.Microtubules (MTs)¹ are major structural components of the cytoskeleton that are intricately involved in cell morphology, motility, division, and intracellular organization and transport. The diverse roles of MTs are dependent on the polymer having the capacity to be both dynamic and static in nature. Individual MTs alternate between growing and shrinking by the rapid attachment and detachment of tubulin subunits at their ends (1, 2). Thus, MTs can continually reorganize and undergo cycles of growing, pausing, and shortening. A number of mechanisms exist to regulate this dynamic equilibrium and involve association of proteins with the MT lattice. MT-associated proteins (MAPs), such as MAP4 and tau, stabilize MTs by binding to the wall thus inhibiting MT disassembly (3, 4). Recently MT plus (+) end-binding proteins have been implicated in stabilizing MTs by associating with cortical proteins to tether the MT end to peripheral target sites (5–7). Stabilized MT subsets are biochemically distinct and acquire posttranslational modifications that can be used to differentiate them from dynamic subsets. For example, posttranslational modifications such as glutamylation (8), detyrosination (8, 9), and acetylation (10) occur on MTs that exhibit increased stability. Stabilized MTs have been implicated in MT transport by allowing increased binding of MT motors (11, 12). Numerous other MAPs have been shown to regulate MT form and function including control of MT nucleation and elongation, MT linkage to and movement of organelles, and modulation of MT growth to allow scaffolding of signal transduction events (13).The extensive MT network provides a large surface area to serve as a platform for the binding of a large number of proteins that is likely heavily influenced by local cellular events and cell type. Traditionally the term MAP referred to proteins that bind directly to tubulin within the MT polymer, and a lot of recent debate and controversy have surrounded the definition of a MAP (14, 15). In this and other reports the definition of MAPs is considered to also include proteins that indirectly or transiently interact with MTs, co-localize with MTs, or influence MT growth dynamics in some way (16). The advent of proteomics has allowed cytoskeleton researchers to resolve the spectrum of MAPs. To date, the MT proteome has been resolved by MS analysis in developmentally important animal and plant models including Xenopus laevis egg extracts (17), Drosophila melanogaster embryos (18), Artemia franciscana embryos (19), Arabidopsis suspension cells (20), and complex mammalian tissues such as rat brain (21). The MT proteome has also been described for specialized MT structures including mitotic spindles (22–24), centrosomes (25, 26), and cilia (27, 28).Macrophages are key regulators of the immune system connecting innate and specific immune responses. Lipopolysaccharide (LPS), an outer membrane component of Gram-negative bacteria, is a potent activator of monocytes and macrophages. LPS triggers the abundant secretion of many cytokines from macrophages including IL-1 (29), IL-6, (30), and tumor necrosis factor-α (31), which together contributes to the pathophysiology of septic shock. IFN-γ is a proinflammatory cytokine produced by the host in response to intracellular pathogens. IFN-γ binds to IFN-γ receptors on macrophages, and IFN-γ signaling induces the production and/or release of cytokines, like IL-1 or tumor necrosis factor-α, which enhance LPS-mediated effects (32). Thus, the synergy between LPS and inflammatory cytokines such as IFN-γ represents an important regulatory mechanism by which the host tackles a significant, ongoing infection before it activates potent effector responses (33). It has been demonstrated that LPS may cause changes in monocyte cytoskeleton and directly influence assembly of isolated MTs (34). Recently we observed that classical activation of murine resident peritoneal or RAW264.7 macrophages with a combination of IFN-γ and LPS induces an increase in stabilized cytoplasmic MTs (5). A significant effort has been made to unravel the importance of stable MTs in cellular processes over the past few years. With respect to macrophage function, stable MTs could potentially function as tracks for vesicle secretion of cytokines and matrix metalloproteinases necessary to effect the enhanced inflammatory response observed in classically activated macrophages. We recently demonstrated that stable MTs are important for cell spreading as well as the binding of large particles in activated macrophages (5). The stabilization of macrophage interphase MTs is uniquely rapid, thus serving as an ideal model for studying MAPs involved in MT modulation in mammalian cells.The focus of the present study was to identify the MT-associated proteins involved in altering and stabilizing MT structures and also to resolve the spectrum of proteins within the MT proteome of a mammalian cell. To achieve this goal, we used a proteomics approach involving a MAP purification technique based on MT co-sedimentation (35) followed by off-line fractionation and identification of MAPs using LC-MS/MS. Information provided by mass spectrometry analysis allowed us to analyze the changes in MAP abundance during activation of macrophages by IFN-γ/LPS. These studies also provided candidate proteins for selective molecular intervention for chronic inflammatory disorders.