Protein is very important for almost all living creatures because it participates in most complicated and essential biological processes. Determining the functions of given proteins is one of the most essential problems in protein science. Such determination can be conducted through traditional experiments. However, the experimental methods are always time-consuming and of high costs. In recent years, computational methods give useful aids for identification of protein functions. This study presented a new multi-label classifier for identifying functions of mouse proteins. Due to the number of functional types, which were termed as labels in the classification procedure, a label space partition method was employed to divide labels into some partitions. On each partition, a multi-label classifier was constructed. The classifiers based on all partitions were integrated in the proposed classifier. The cross-validation results proved that the proposed classifier was of good performance. Classifiers with label partition were superior to those without label partition or with random label partition.
Citation: Xuan Li, Lin Lu, Lei Chen. Identification of protein functions in mouse with a label space partition method[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3820-3842. doi: 10.3934/mbe.2022176
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Protein is very important for almost all living creatures because it participates in most complicated and essential biological processes. Determining the functions of given proteins is one of the most essential problems in protein science. Such determination can be conducted through traditional experiments. However, the experimental methods are always time-consuming and of high costs. In recent years, computational methods give useful aids for identification of protein functions. This study presented a new multi-label classifier for identifying functions of mouse proteins. Due to the number of functional types, which were termed as labels in the classification procedure, a label space partition method was employed to divide labels into some partitions. On each partition, a multi-label classifier was constructed. The classifiers based on all partitions were integrated in the proposed classifier. The cross-validation results proved that the proposed classifier was of good performance. Classifiers with label partition were superior to those without label partition or with random label partition.
COX-2 | Cyclooxygensae-2 |
GPCR | G-Protein Coupled Receptor |
HUVEC | Human Umbilical Vein Endothelial Cells |
LPS | Lipopolysaccharide |
NF-ΚB | Nuclear Factor-Kappa B |
OxPL | Oxidized Phospholipid |
PAF | Platelet-Activating Factor |
PAF-AH | PAF-Acetylhydrolase |
PAF-R | PAF-Receptor |
PAMP | Pathogen Associated Molecular Pattern |
PMN | Polymorphonuclear Leukocytes |
PUFA | Polyunsaturated Fatty Acid |
TLR | Toll-Like Receptor |
A meticulously concerted network consisting of monocytes, macrophages and polymorphonuclear leukocytes (PMNs) make up the innate immune system that is highly sensitive to microbial invasion/injury and responds by synthesizing a variety of inflammatory mediators [1]. Among the various pathogen associated molecular patterns (PAMPs) recognized by Toll-like receptors (TLRs), lipopolysaccharide (LPS)—a major membrane lipoglycan of Gram-negative bacteria is of special importance and is most thoroughly studied [2,3]. The pro-inflammatory effect of LPS is conveyed via Toll-like receptor-4 (TLR-4) [4], leading to the downstream activation of NF-ΚB, the master regulator for the induction of a repertoire of inflammatory genes [5,6]. The NF-ΚB mediated inflammatory genes are known to play a vital role in the initiation and amplification of many systemic inflammatory diseases [7,8,9,10]. A key lipid mediator involved in these events is PAF, an autocoid, usually present in low levels in quiescent cells but either expressed or secreted by innate immune cells upon appropriate stimulation [11]. PAF synthesis in response to inflammatory stimuli is rapid and vigilantly regulated owing to its multi-faceted function and unique ability to activate immune cells at sub-picomolar concentrations [11]. Considerably, dysregulated PAF signaling is evidently the underlying cause for a host of inflammatory diseases [12,13]. PAF exerts its action via platelet activating factor receptor (PAF-R)—a typical GPCR [14] that specifically recognizes the sn-1 ether/ester bond and a short sn-2 moiety of this unusual phospholipid. However, PAF homologs with ester bond at sn-1position are several fold less potent than their ether counter parts [11]. The functional requirement to activate PAF-R allows several structural analogs of PAF termed OxPLs to substantially activate the PAF-R with varying potencies [15,16,17,18], although a separate receptor for the selected OxPLs has also been suggested [19]. More recently, OxPLs with a slightly longer sn-2 residue up to 9 carbon length such as KHdiA-GL PAF (7-carbon long), KOdiA-LPAF (8-carbon long) and HAz-LPAF (9-carbon long) have been shown to bind PAF-R and invoke responses that mimic PAF [20].
Endogenously, these bioactive lipid species are generated by unregulated non-enzymatic oxidation of polyunsaturated fatty acid (PUFA) residues by reactive oxygen species (ROS) and H2O2 [16]. Since PUFAs are targets for oxidation and mostly esterified at sn-2 position of the glycerol backbone of alkyl/acyl phosphatidylcholines, their oxidation yields truncated phospholipids with short sn-2 residues, making them appropriate PAF-R ligands. Relevantly, OxPLs mimic many of the biological effects of PAF and contribute to inflammatory pathophysiology [16,17,18,19,20,21,22,23], hence are collectively referred as "PAF-mimetics or PAF-like lipids". Oxidation of phospholipids such as 1-palmitoyl-2-arachidonoyl-sn-glycerol-3-phosphocholine (PAPC) generates a series of OxPAPC, predominantly 1-palmitoyl-2-oxovaleroyl-sn-glycero-3-phosphocholine (POVPC) and 1-palmitoyl-2-glutaroyl-sn-glycerol-3-phosphocholine (PGPC) [17,24]. Additionally, fragmentation of hexadecyl-arachidonoyl-phosphatidylcholine yields butanoyl/butenoyl analogs of PAF (C-4 PAF) that are only ten to hundred folds less potent than PAF [15]. Such truncated phospholipids can activate PMNs, monocytes, eosinophils as well as platelets and are validated to be the major components of oxidized low density lipoprotein (OxLDL) that promotes atherogenesis [16,25,26] in atherosclerotic plaque [27], smokers and alcoholic blood [28,29], inflamed tissues [30] and in models of cutaneous inflammation [31].
Curtailing the potency of this extended family of PAF and related OxPLs in circulation is primarily achieved by the hydrolysis of their sn-2 moiety by a family of related anti-inflammatory enzymes—"PAF-acetylhydrolase (PAF-AH)", of which the plasma form is thoroughly characterized [32]. The exceptionally extended substrate specificity of PAF-AH thus terminates the inappropriate signaling of OxPLs that are likely to circumvent other cellular controls [23]. Decrease in circulating PAF-AH levels together with the susceptibility of PAF-AH deficient subjects to asthma and other inflammatory diseases, nevertheless explains the very anti-inflammatory nature of this enzyme [23].
Studies have shown that OxPLs upregulate the expression of pro-inflammatory cytokines including IL-6 [22], IL-8 [33] and chemokines such as Monocyte chemoattractant protein-1 (MCP-1) [34] and inducible cyclooxygenase-2 (COX-2) [33]. Further evidence implicating the upregulation of COX-2 by OxPLs comes from the study by Pontsler et al. [35], where oxidized alkyl phospholipids derived from OxLDL enhanced COX-2 induced prostaglandin E2 (PGE2) in human monocytes via peroxisome proliferator activated receptor gamma (PPARγ), a receptor highly expressed in atherosclerotic plaques [36]. Presence of OxLDL along with PPARγ and COX-2 [37] in atherosclerotic plaques probably suggests their synergistic participation in atherogenesis. Additionally, phospholipids derived from OxLDL induce intracellular calcium transients and also cause PMNs to adhere to activated endothelium [38]. Furthermore, OxPLs have also been identified to promote TNF-α-induced cell death [39].
Despite, substantial evidences affirming the pro-inflammatory roles of PAF and OxPLs, few conflicting reports claiming the inhibitory actions of OxPLs on LPS-induced effects [40,41,42,43], questions the very anti-inflammatory nature of PAF-AH and pro-inflammatory properties of PAF and OxPLs. We examined the influence of the three representative OxPLs-POVPC, PGPC and C-4 PAF on LPS-induced effects in neutrophils and monocytes, that express both functional PAF-R and TLR-4, and human umbilical vein endothelial cells (HUVECs) that lack canonical PAF-R [19], but still express functional TLR-4. We show that PAF and related OxPLs do not suppress, but rather amplify LPS-induced expression of NF-ΚB mediated inflammatory readouts in these isolated cell types in vitro. Oxidatively generated phospholipids are thus likely to exaggerate LPS-induced effects and thereby intensify the progression of inflammation.
LPS from Escherichia coli O111:B4 was purchased both from List Biological Laboratories, Inc. (Campbell, CA) and from Sigma Chemicals Co. (St. Louis, MO). Dextran and MCDB/12 medium were from Sigma Chemicals Co. (St. Louis, MO). PAF (C16), LysoPAF, C-4 PAF and Lyso PC were obtained from Avanti Polar Lipids (Alabaster, AL). Hanks Balanced Salt Solution (HBSS) was purchased from HiMedia (Mumbai, India) and also from Cleveland Clinic Media Core (Cleveland, OH). Human serum albumin was from Baxter Healthcare (Glendale, CA). rPAF-AH was from ICOS Corp. (Bothell, WA) and Ficoll-Paque was obtained from GE Healthcare Bio-Sciences(Uppsala, Sweden). Sterile tissue culture plates were purchased from Nest Biotech Co. (Jiangsu, China). COX-2 specific monoclonal antibody, arachidonic acid, NS-398, POVPC, PGPC and PGE2 ELISA kit were from Cayman chemicals (AnnArbor, MI) and β-Actin antibody was from MP Biomedicals (SantaAna, CA). Secondary antibodies were from Biosource (Camarillo, CA). Protease inhibitor cocktails-Roche Complete Mini was from Roche (Basel, Switzerland), Protease Arrest was from GenoTechnology, Inc. (St. Louis, MO), and Halt Protease was from Pierce (Rockford, IL). M199 was from Mediatech, Inc. (Herndon, VA), BCA protein assay kit was from Pierce (Rockford, IL) and Immobilon-P was from Millipore Corp. (Bradford, MA). Fura 2-AM was from Santa Cruz Biotechnology (Dallas, TX). Human IL-8 Duoset ELISA kit was obtained from R & D Systems (Minneapolis, MN). Human umbilical vein endothelial cells (HUVEC) were kindly provided by Dr. Paul DiCorleto (Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH).
Approval for the use of isolated human cells was obtained from the Institutional Human Ethical Clearance Committee (IHEC) of the University of Mysore, India (No. IHEC-UOM No. 104Ph.D/2015-16). Venous blood was drawn from healthy human volunteers (non-smokers) with informed consent as per the guidelines of IHEC, University of Mysore, Mysuru, and PMNs were isolated by dextran sedimentation and centrifugation over Ficoll as described by Zimmerman et al. [44]. The isolated PMNs were resuspended in HBSS/A (HBSS containing 0.5% human serum albumin) or in M199. Mononuclear cells were collected at the inter phase of the Ficoll gradient, washed and resuspended in HBSS/A and used as monocyte enriched cell population.
Changes in intracellular Ca2+ flux in isolated PMNs in response to POVPC and PGPC were measured according to Marathe et al. [38]. Briefly, isolated PMNs (2.5 × 106 cells/mL) were incubated with Fura 2-AM (1 μM) at 37 ℃ for 45 min in dark. A concentration-dependent change in Ca2+ flux in these cells in response to indicated concentrations of POVPC and PGPC were measured at 340 and 380 nm with emission collected at 510 nm. In parallel experiments, PMNs were pre-treated with a PAF-R antagonist, WEB-2086 (10 μM) for 30 min and then tested for POVPC (1 μM) or PGPC (1 μM) stimulated calcium transients or POVPC and PGPC were pre-treated with rPAF-AH (10 ng/mL) for 30 min and then used to stimulate PMNs. Subsequently, to check if these cells were responsive, they were stimulated with 1 nM/1 μM PAF at indicated time.
Human PMNs (5 × 106 cells/mL) were suspended in M199 medium and pre-treated with or without PAF or OxPLs (0.1 μM) for 30 min followed by LPS (100 ng/mL) for 3 hours at 37 ℃. In few experiments, we added 20 μM exogenous arachidonic acid before the cell harvest for measuring PGE2. In other set of experiments, PMNs were treated with NS-398 (10 μM) before harvesting the cells. PMNs were treated with a combination of three protease inhibitor cocktail (Protease arrest, Roche Complete mini and Halt Protease) containing EDTA (10 mM) and DTT (5 mM) 30 min prior to cell harvest (This combination of these three protease inhibitors substantially control proteolysis in this protease loaded cell type). Cells were centrifuged at 300 × g for 5 min at 37 ℃ and media were aspirated and used to measure PGE2 as described below. Cell pellet was lysed with lysis buffer containing protease inhibitor cocktail. The proteins were resolved on a 7.5% SDS-PAGE and transferred to an Immobilon-P membrane by Bio-Rad semidry transfer apparatus. The membrane was blocked with 5% non-fat dry milk and probed with monoclonal COX-2 specific antibody at 1:1000 dilution and β-actin antibody at 1:20,000 dilution. Appropriate horseradish peroxidase conjugated secondary antibody was used and blots were developed by chemiluminescence (Bio-Rad) according to manufacturer's instructions. In a few experiments, monocytes (5 × 106 cells/mL) were stimulated with or without PAF or PAF-like lipids (0.1 μM) for 30 min followed by LPS (100 ng/mL) for 18 hours at 37 ℃. Culture supernatants were aspirated for ELISA analysis while cellular contents were lysed in the presence of protease inhibitor cocktail and blotted for COX-2 expression as described above. When HUVECs were used for COX-2 expression, they were plated overnight in 6-well plates in MCDB/12 media supplemented with 15% foetal bovine serum. The next day, cells were washed twice with PBS (pH 7.4) containing low serum (2%) and pre-incubated with or without indicated lipids for 30 min followed by LPS for 4 hours. Culture supernatants were used for PGE2 ELISA as described below. HUVECs were washed twice with PBS and then with ice cold cell lysis buffer containing protease inhibitors. Cellular materials were scraped and removed. The lysates were kept on ice for 30 min and then centrifuged at 4 ℃ for 5 min at 10,000 × g. Protein content of the supernatant was quantitated using BCA protein assay kit and blotted for COX-2 as described above.
The culture supernatants of stimulated human PMNs, monocytes or HUVECs from their respective media containing arachidonic acid (10 μM), added just before harvest, were aspirated, followed by an immediate acidification to pH 3.0 with formic acid. The prostaglandins were selectively extracted with ethyl acetate and dried under nitrogen. The residue was then resuspended in buffer provided in the ELISA kit and was used according to manufacturer's instructions.
Human PMNs were suspended in M199 media containing HEPES (25 mM, pH 7.2) and pre-incubated with POVPC, PGPC or C-4 PAF (0.1 μM) for 30 min followed by LPS (100 ng/mL) for 3 hours. Culture supernatants were collected and used either directly in ELISA or concentrated using Amicon Ultra 5,000 MWCO filters (Millipore). IL-8 protein levels were quantitatively assessed by ELISA according to the manufacturer's protocol. The quiescent PMNs had undetectable IL-8 on ELISA.
PAF-R agonists are potent inducers of rapid intracellular Ca2+ mobilization where, as low as 1 pM PAF causes rapid and significant increase in intracellular Ca2+ in PMNs [15]. Using isolated PMNs, we checked the ability of POVPC and PGPC to directly activate PAF-R and cause changes in intracellular Ca2+ levels. Both POVPC and PGPC efficiently provoked intracellular Ca2+ flux in a concentration–dependent way (0.01–1 μM) (Figure 1) that could be inhibited by pre-treating the PMNs for 30 min with PAF-R antagonist, WEB-2086 (10 μM). The response of POVPC and PGPC was also effectively abolished by pre-incubating them with selective phospholipase A2, PAF-AH (10 ng/mL). Subsequent addition of PAF (1 nM–1 μM) to these cells showed that the cells were still responsive to PAF. This implies that OxPLs-POVPC and PGPC appear to signal via PAF-R, but are however relatively weak agonists of PAF-R when compared to PAF.
COX-2 is an inducible enzyme expressed by a variety of cells in response to various stimuli such as endotoxins, interleukins, growth factors and many more [45,46]. Human leukocytes such as PMNs and monocytes are known to express both PAF-R [38] and TLR-4 and possess inducible COX-2 [47]. Hence, we examined the expression of COX-2 in response to PAF and PAF-like lipids in human PMNs, monocytes and also in cells that do not express canonical PAF-R, yet possess functional TLR-4 such as umbilical vein endothelial cells [19]. Stimulation of PMNs (Figure 2A) and HUVECs (Figure 2C) with PAF or OxPLs (0.1 μM) for 30 min expressed minute or no COX-2, while monocytes expressed considerable amount of COX-2 (Figure 2E). Alternatively, we assessed the effect of pre-activation of PAF-R on LPS-induced COX-2 expression in these cell types. Pre-incubation of PMNs, monocytes and HUVECs with PAF or PAF-like lipids (0.1 μM), for 30 min followed by LPS (100 ng/mL) exposure for indicated time points did not suppress expression of
LPS-induced COX-2 (Figure 2A, C and E). Stimulation of all the three cell types to LPS (100 ng/mL) alone for indicated time points showed appreciable amount of COX-2 expression.
COX-2 expression results in the synthesis of several prostaglandins from arachidonic acid that is released from membrane phospholipids during an inflammatory insult [48]. Hence, we measured one of the major products, PGE2 in the supernatants of stimulated PMNs, monocytes and HUVECs by ELISA. All the three cell types secreted PGE2 upon stimulation with LPS (100 ng/mL), while prior exposure of the cells to specific COX-2 inhibitor NS-398 (10 μM) resulted in reduction of LPS-induced PGE2 release (Figure 2B, D and F) suggesting, that the induced COX-2 was responsible for the PGE2 being produced. Subsequently, pre-activation of PAF-R in these cell types by PAF or OxPLs (0.1 μM) augmented LPS (100 ng/mL)-induced production of PGE2 levels (Figure 2B, D and F). Although, corresponding increase in COX-2 expression is difficult to see in these myeloid cells loaded with proteases. However, production of PGE2 was increased in all the cell types. As the substrate arachidonic acid is limiting, it is necessary to add exogenous arachidonic acid to see measurable amount of eicosanoids.
LPS is a potent inducer of IL-8 synthesis in a multitude of cell types [49]. Apparently, PAF-R activation in PMNs stimulates the synthesis of various pro-inflammatory cytokines, most important among them being IL-8 [50,51]. This occurs via mTOR pathway as demonstrated previously by Yost et al. [51]. We examined the effect of PAF and OxPLs on LPS-induced IL-8 secretion in PMNs. Stimulating PMNs with LPS (100 ng/mL) for 30 min resulted in significant increase in IL-8 levels that was efficiently blocked by IL-10, while 0.1 μM of PAF, PGPC, POVPC and C-4 PAF induced minute or no IL-8. However, pre-activation of PAF-R in PMNs with PAF (0.1 μM) or OxPLs (0.1 μM) for 30 min followed by stimulation with LPS (100 ng/mL) for 3 hours, resulted in enhancement of LPS-induced IL-8 secretion. As expected, anti-inflammatory cytokine IL-10 inhibited LPS-induced IL-8 production.
PAF and related OxPLs are predominantly formed during inflammatory conditions. Unlike, biosynthetic PAF, the formation of oxidized alkyl/acyl phosphatidylcholines is unregulated and accumulates during oxidative insult [16]. The signaling events initiated by such modified lipids profoundly modulate the immune cells to a pro-inflammatory phenotype. Oxidative modification of lipids is the primary and deadly event in atherosclerosis, where, high levels of OxLDL bearing OxPLs constitutes the fatty streaks found in atherosclerotic lesions in humans and experimental animals [33]. Previously, OxPLs were believed to be mediators of chronic inflammation. Of late, a remarkable role for OxPLs has also been suggested in an array of infectious diseases [52] that is consistent with the fact that ROS generated as a host defence mechanism during infection contributes to the formation of OxPLs. Indeed, increased serum PAF levels have been observed in septic patients and animal models of endotoxemia [53]. Consequently, exogenous PAF-AH administration to hydrolyze PAF and related lipids and blocking PAF-R using specific PAF-R antagonists have been shown to protect animals from endotoxemia [54,55]. A sense of ambiguity opens up with studies claiming the inhibitory actions of OxPLs on LPS-induced effects, both in vivo [40,41] and in vitro [42,43]. Correspondingly, significant efforts have been directed to inhibit the hydrolysis of PAF using darapladib (a specific PAF-AH inhibitor) to retain PAF in circulation and thereby reduce inflammation underlying atherosclerosis [56], but in vain [57]. Besides, use of oxidized phospholipids to treat sepsis [58] is unlikely to show a positive outcome since, OxPLs themselves are shown to impair phagocytosis in Wiskott-Aldrich syndrome protein (WASP) family verprolin-homologous protein1 (WAVE1) dependent manner and diminish outcome in sepsis [59]. Additionally, our recent study indicates that intraperitoneal (i.p.) administration of a minute amount of PAF (5 μg/mouse) is lethal to Swiss albino mice [60].
In the present study, we examined the influence of POVPC, PGPC and C-4 PAF on LPS-induced effects on isolated human immune cells viz. monocytes and neutrophils, and also non immune cell-HUVECs. We show that POVPC and PGPC dose-dependently invoke intracellular calcium transients in human PMNs due to PAF-R activation (Figure 1). This is consistent with our previous data where we show C-4 PAF to signal via PAF-R [15]. PMNs respond quickly to agonists like PAF that cause rapid change in intracellular calcium levels that in turn causes their adhesion to activated endothelium [38]. Analogously, OxPLs derived from minimally modified OxLDL have been previously shown to increase monocyte-endothelial interactions, although there appears to be a difference in the action of PGPC and POVPC on leukocyte and endothelium via different receptors [61]. Moreover, bacterial endotoxins such as LPS and lipoproteins stimulate PMNs to synthesize PAF and PAF-like oxidized phospholipids [62], suggesting their role in amplifying endotoxin responses. Thus, LPS-induced PAF/OxPLs may further synergistically upregulate inflammatory genes including IL-8 and COX-2. We next questioned, if pre-activation of PAF-R influenced LPS-induced NF-ΚB signaling in isolated cells. Interestingly, as opposed to Eligini and her co-workers [42], where they find OxPOPC to inhibit LPS-induced COX-2 expression in human macrophages, OxPLs in our study did not suppress LPS-induced IL-8 as well as COX-2 (Figure 2A, C and E) but rather augmented these readout as seen by increase in PGE2 levels (Figure 2B, D and F). However, macrophages secrete PAF-AH [63] and this may have contributed to the hydrolysis of OxPOPC thereby nullifying the effects of the lipid in their study. Also, PAF and OxPLs themselves were unable to significantly induce IL-8 and COX-2 in our study.
The reason behind OxPLs exerting anti-inflammatory effects against LPS is believed to be due to the inhibition of interaction of LPS with LPS binding protein (LBP) and CD14 [40], vital components of the LPS-signaling complex. The authors of this study employed sterile LPS that limit the understanding the role of OxPLs in complex clinical settings like sepsis, where multiple mediators are often involved. Interestingly, the priming effect of PAF and OxPLs on LPS-induced signaling in vascular cells observed in our studies indicates the detrimental effects exerted by them. This is in accordance with, Knapp and co-workers [22] where they observed OxPAPC to rather render CD14–/– mice susceptible to E.coli-induced sepsis. In fact, OxPLs were found to increase mortality of mice intraperitoneally injected with viable E. coli cells and additionally promote bacterial growth. Besides, OxPLs also upregulated pro-inflammatory cytokine IL-6 and TNF-α and strongly attenuated the phagocytosing capacity of PMNs and macrophages [22]. Further, OxPLs are recognized to contribute to air way injury and inflammation in asthma and related pulmonary disorders [64] where, scavenger receptors on alveolar macrophages such as macrophage receptor with collagenous structure (MARCO) and scavenger receptor AI/Ⅱ (SRA-Ⅰ/Ⅱ) are primarily responsible to clear oxidatively modified lipids and thereby limit lung inflammation [65]. CD36 [66] and lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) [67] are other scavenging receptors identified to play a role in clearing OxPLs in circulation and their enhanced expression is observed in pro-atherogenic conditions.
Considering the ill effects of PAF and OxPLs in inflammatory conditions, the credibility of PAF-AH appears to have a positive impact. Decreased levels of PAF-AH has been observed in a number of diseases such as asthma, coronary heart disease, stroke, systemic lupus erythematosus and Crohn's disease and is suggested to be a potential risk marker rather than risk factor [23]. Exogenous administration of rPAF-AH promotes bacterial clearance in septic mice [68]. The beneficial role of PAF-AH has also been demonstrated in vitro, where endothelial cells were protected from undergoing apoptosis when exposed to OxLDL pre-treated with PAF-AH [69]. In the present study, we show PAF and OxPLs do not impair LPS-induced pro-inflammatory responses but rather amplify (increased PGE2 levels) LPS-mediated signaling in PMNs, monocytes and HUVECs, implying the pro-inflammatory nature of this class of bioactive lipids.
PAF and related PAF-like lipids do not oppose LPS signaling but indeed participate in aggravating inflammation, thus contributing to progression of inflammatory diseases. With our findings and other substantial reports suggesting the involvement of PAF and OxPLs in inflammatory disorders, using them as treatment option against sepsis is questionable. This invites a better understanding of the role of PAF and OxPLs in complex inflammatory disorders like sepsis to be advantageous for therapeutic interventions. Moreover, inhibiting PAF-AH in inflammatory scenario including CVD needs to be revisited [23].
The authors gratefully acknowledge the technical assistance of Jessica M Cemete, Stacy Macecevic, Manisha Sharma, Vidyanath Thirumala of Cleveland Clinic Foundation, USA. Dr. Paul Di Corleto (Cleveland Clinic Foundation, Lerner Research Institute, Cleveland, OH) is greatly acknowledged for providing HUVECs.
This work was supported by University Grants Commission Non-NET Fellowship (UGC-NonNETFS)—[F. 87-1-2012(SU-1)2] to SPJ; Basic Science Research fellowship [F.7-366/2012] to LCL; UGC-Major Research Project [F.No: 41-128/2012-13] to GMK, Vision Group of Science and Technology (VGST) [VGST/K-FIST(2010-11)/GRD-36/2013-14], Government of Karnataka, UGC-Special Assistance Program (UGC-SAP) [F3-14/2012(SAPII)].
The authors have no financial conflict of interest.
[1] |
R. Milo, What is the total number of protein molecules per cell volume? A call to rethink some published values, Bioessays, 35 (2013), 1050-1055. https://doi.org/10.1002/bies.201300066 doi: 10.1002/bies.201300066
![]() |
[2] |
Z. C. Üretmen Kagıalı, A. Şentürk, N. E. Özkan Küçük, M. H. Qureshi, N. Özlü, Proteomics in cell division, Proteomics, 17 (2017), 1600100. https://doi.org/10.1002/pmic.201600100 doi: 10.1002/pmic.201600100
![]() |
[3] |
M. J. Mughal, R. Mahadevappa, H. F. Kwok, DNA replication licensing proteins: Saints and sinners in cancer, Semin. Cancer Biol., 58 (2019), 11-21. https://doi.org/10.1016/j.semcancer.2018.11.009 doi: 10.1016/j.semcancer.2018.11.009
![]() |
[4] |
D. Davidi, R. Milo, Lessons on enzyme kinetics from quantitative proteomics, Curr. Opin. Biotechnol., 46 (2017), 81-89. https://doi.org/10.1016/j.copbio.2017.02.007 doi: 10.1016/j.copbio.2017.02.007
![]() |
[5] |
S. F. Altschul, W. Gish, W. Miller, E. W. Myers, D. J. Lipman, Basic local alignment search tool, J. Mol. Biol., 215 (1990), 403-410. https://doi.org/10.1016/S0022-2836(05)80360-2 doi: 10.1016/S0022-2836(05)80360-2
![]() |
[6] |
C. J. Sigrist, L. Cerutti, E. De Castro, P. S. Langendijk-Genevaux, V. Bulliard, A. Bairoch, et al., PROSITE, a protein domain database for functional characterization and annotation, Nucleic Acids Res., 38 (2010), D161-D166. https://doi.org/10.1093/nar/gkp885 doi: 10.1093/nar/gkp885
![]() |
[7] |
R. D. Finn, J. Mistry, B. Schuster-Böckler, S. Griffiths-Jones, V. Hollich, T. Lassmann, et al., Pfam: clans, web tools and services, Nucleic Acids Res., 34 (2006), D247-D251. https://doi.org/10.1093/nar/gkj149 doi: 10.1093/nar/gkj149
![]() |
[8] |
Y. Ye, A. Godzik, FATCAT: a web server for flexible structure comparison and structure similarity searching, Nucleic Acids Res., 32 (2004), W582-W585. https://doi.org/10.1093/nar/gkh430 doi: 10.1093/nar/gkh430
![]() |
[9] |
L. Hu, T. Huang, X. Shi, W. C. Lu, Y. D. Cai, K. C. Chou, Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties, PLoS One, 6 (2011), e14556. https://doi.org/10.1371/journal.pone.0014556 doi: 10.1371/journal.pone.0014556
![]() |
[10] |
G. Huang, C. Chu, T. Huang, X. Kong, Y. Zhang, N. Zhang, et al., Exploring mouse protein function via multiple approaches, PLoS One, 11 (2016), e0166580. https://doi.org/10.1371/journal.pone.0166580 doi: 10.1371/journal.pone.0166580
![]() |
[11] |
X. Wang, Y. Wang, Z. Xu, Y. Xiong, D. Q. Wei, ATC-NLSP: Prediction of the classes of anatomical therapeutic chemicals using a network-based label space partition method, Front. Pharmacol., 10 (2019), 971. https://doi.org/10.3389/fphar.2019.00971 doi: 10.3389/fphar.2019.00971
![]() |
[12] |
X. Wang, X. Zhu, M. Ye, Y. Wang, C. D. Li, Y. Xiong, et al., STS-NLSP: A network-based label space partition method for predicting the specificity of membrane transporter substrates using a hybrid feature of structural and semantic similarity, Front. Bioeng. Biotech., 7 (2019), 306. https://doi.org/10.3389/fbioe.2019.00306 doi: 10.3389/fbioe.2019.00306
![]() |
[13] |
A. Ruepp, O. N. Doudieu, J. van den Oever, B. Brauner, I. Dunger-Kaltenbach, G. Fobo, et al., The mouse functional genome database (MfunGD): functional annotation of proteins in the light of their cellular context, Nucleic Acids Res., 34 (2006), D568-D571. https://doi.org/10.1093/nar/gkj074 doi: 10.1093/nar/gkj074
![]() |
[14] |
V. D. Blondel, J. L. Guillaume, R. Lambiotte, E. Lefebvre1, Fast unfolding of communities in large networks, J. Stat. Mech-Theory E., 2008 (2008), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008 doi: 10.1088/1742-5468/2008/10/P10008
![]() |
[15] | G. Tsoumakas, I. Vlahavas, Random k-Labelsets: An ensemble method for multilabel classification, in European conference on machine learningmachine learning, (2007), 406-417. https://doi.org/10.1007/978-3-540-74958-5_38 |
[16] |
C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn., 20 (1995), 273-297. https://doi.org/10.1007/BF00994018 doi: 10.1007/BF00994018
![]() |
[17] |
L, Breiman, Random forests, Mach. Learn., 45 (2001), 5-32. https://doi.org/10.1023/A:1010933404324 doi: 10.1023/A:1010933404324
![]() |
[18] |
M. Ashburner, S. Lewis, On ontologies for biologists: the Gene Ontology-untangling the web, in Novartis Foundation Symposia (eds. N. Foundation), Wiley Online Library, 247 (2002), 66-80. https://doi.org/10.1002/0470857897.ch6 doi: 10.1002/0470857897.ch6
![]() |
[19] |
E. Camon, M. Magrane, D. Barrell, D. Binns, W. Fleischmann, P. Kersey, et al., The gene ontology annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro, Genome Res., 13 (2003), 662-672. https://doi.org/10.1101/gr.461403 doi: 10.1101/gr.461403
![]() |
[20] |
K. C. Chou, Y. D. Cai, Using functional domain composition and support vector machines for prediction of protein subcellular location, J. Biol. Chem., 277 (2002), 45765-45769. https://doi.org/10.1074/jbc.M204161200 doi: 10.1074/jbc.M204161200
![]() |
[21] |
K. C. Chou, Y. D. Cai, Predicting protein structural class by functional domain composition, Biochem, Bioph. Res. Co., 321 (2004), 1007-1009. https://doi.org/10.1016/j.bbrc.2004.07.059 doi: 10.1016/j.bbrc.2004.07.059
![]() |
[22] |
L. Lu, Z. Qian, Y. D. Cai, Y. Li, ECS: an automatic enzyme classifier based on functional domain composition, Comput. Biol. Chem., 31 (2007), 226-232. https://doi.org/10.1016/j.compbiolchem.2007.03.008 doi: 10.1016/j.compbiolchem.2007.03.008
![]() |
[23] |
H. Zhou, Y. Yang, H. B. Shen, Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features, Bioinformatics, 33 (2017), 843-853. https://doi.org/10.1093/bioinformatics/btw723 doi: 10.1093/bioinformatics/btw723
![]() |
[24] | L. Chen, K. Y. Feng, Y. D. Cai, K. C. Chou, H. P. Li, Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition, BMC Bioinformatics, 11 (2010), 293. https://doi.org/10.1186/1471-2105-11-293 |
[25] |
M. Blum, H. Y. Chang, S. Chuguransky, T. Grego, S. Kandasaamy, A. Mitchell, et al., The InterPro protein families and domains database: 20 years on, Nucleic Acids Res., 49 (2021), D344-D354. https://doi.org/10.1093/nar/gkaa977 doi: 10.1093/nar/gkaa977
![]() |
[26] | T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, Preprint, arXiv: 1301.3781v3. |
[27] |
K. W. Church, Word2Vec, Nat. Lang. Eng., 23 (2017), 155-162. https://doi.org/10.1017/S1351324916000334 doi: 10.1017/S1351324916000334
![]() |
[28] | B. Perozzi, R. Al-Rfou, S. Skiena, Deepwalk: Online learning of social representations, in 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2014), 701-710. https://doi.org/10.1145/2623330.2623732 |
[29] | A. Grover, J. Leskovec, node2vec: Scalable feature learning for networks, in 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2016), 855-864. https://doi.org/10.1145/2939672.2939754 |
[30] |
H. Cho, B. Berger, J. Peng, Compact integration of multi-network topology for functional analysis of genes, Cell Syst., 3 (2016), 540-548. https://doi.org/10.1016/j.cels.2016.10.017 doi: 10.1016/j.cels.2016.10.017
![]() |
[31] |
H. Liu, B. Hu, L. Chen, L. Lu, Identifying protein subcellular location with embedding features learned from networks, Curr. Proteomics, 18 (2021): 646-660. https://doi.org/10.2174/1570164617999201124142950 doi: 10.2174/1570164617999201124142950
![]() |
[32] |
X. Zhang, L. Chen, Z. H. Guo, H. Liang, Identification of human membrane protein types by incorporating network embedding methods, IEEE Access, 7 (2019), 140794-140805. https://doi.org/10.1109/ACCESS.2019.2944177 doi: 10.1109/ACCESS.2019.2944177
![]() |
[33] | X. Pan, L. Chen, M. Liu, Z. Niu, T. Huang, Y. D. Cai, Identifying protein subcellular locations with embeddings-based node2loc, IEEE ACM Trans. Comput. Bi., 2021 (2021). https://doi.org/10.1109/TCBB.2021.3080386 |
[34] |
X. Pan, H. Li, T. Zeng, Z. Li, L. Chen, T. Huang, et al., Identification of protein subcellular localization with network and functional embeddings, Front. Genet., 11 (2021), 626500. https://doi.org/10.3389/fgene.2020.626500 doi: 10.3389/fgene.2020.626500
![]() |
[35] |
D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, et al., STRING v10: protein–protein interaction networks, integrated over the tree of life, Nucleic Acids Res., 43 (2015), D447-D452. https://doi.org/10.1093/nar/gku1003 doi: 10.1093/nar/gku1003
![]() |
[36] | H. Tong, C. Faloutsos, J. Pan, Fast random walk with restart and its applications, in Sixth International Conference on Data Mining, (2006), 613-622. https://doi.org/10.1109/ICDM.2006.70 |
[37] |
S. Kohler, S. Bauer, D. Horn, P. N. Robinson, Walking the interactome for prioritization of candidate disease genes, Am. J. Hum. Genet., 82 (2008), 949-958. https://doi.org/10.1016/j.ajhg.2008.02.013 doi: 10.1016/j.ajhg.2008.02.013
![]() |
[38] | G. Tsoumakas, I. Katakis, Multi-label classification: An overview. Int. J. Data Warehous., 3 (2007), 1-13. |
[39] | J. Read, P. Reutemann, B. Pfahringer, G. Holmes, MEKA: A multi-label/multi-target extension to WEKA, J. Mach. Learn. Res., 17 (2016), 1-5. |
[40] |
J. P. Zhou, L. Chen, Z. H. Guo, iATC-NRAKEL: An efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs, Bioinformatics, 36 (2020), 1391-1396. https://doi.org/10.1093/bioinformatics/btz757 doi: 10.1093/bioinformatics/btz757
![]() |
[41] |
L. Chen, S. Wang, Y. H. Zhang, L. Li, Z. H. Xing, J. Yang, et al., Identify key sequence features to improve CRISPR sgRNA efficacy, IEEE Access, 5 (2017), 26582-26590. https://doi.org/10.1109/ACCESS.2017.2775703 doi: 10.1109/ACCESS.2017.2775703
![]() |
[42] |
J. P. Zhou, L. Chen, T. Wang, M. Liu, iATC-FRAKEL: A simple multi-label web-server for recognizing anatomical therapeutic chemical classes of drugs with their fingerprints only, Bioinformatics, 36 (2020), 3568-3569. https://doi.org/10.1093/bioinformatics/btaa166 doi: 10.1093/bioinformatics/btaa166
![]() |
[43] |
Y. H. Zhang, H. Li, T. Zeng, L. Chen, Z. Li, T. Huang, et al., Identifying transcriptomic signatures and rules for SARS-CoV-2 infection, Front. Cell Dev. Biol., 8 (2021), 627302. https://doi.org/10.3389/fcell.2020.627302 doi: 10.3389/fcell.2020.627302
![]() |
[44] |
Y. H. Zhang, Z. Li, T. Zeng, L. Chen, H. Li, T. Huang, et al., Detecting the multiomics signatures of factor-specific inflammatory effects on airway smooth muscles, Front. Genet., 11 (2021), 599970. https://doi.org/10.3389/fgene.2020.599970 doi: 10.3389/fgene.2020.599970
![]() |
[45] |
Y. Zhu, B. Hu, L. Chen, Q. Dai, iMPTCE-Hnetwork: a multi-label classifier for identifying metabolic pathway types of chemicals and enzymes with a heterogeneous network, Comput. Math. Method M., 2021 (2021), 6683051. https://doi.org/10.1155/2021/6683051 doi: 10.1155/2021/6683051
![]() |
[46] |
Y. Wang, Y. Xu, Z. Yang, X. Liu, Q. Dai, Using recursive feature selection with random forest to improve protein structural class prediction for low-similarity sequences, Comput. Math. Method M., 2021 (2021), 5529389. https://doi.org/10.1155/2021/5529389 doi: 10.1155/2021/5529389
![]() |
[47] | J. Platt, Fast training of support vector machines using sequential minimal optimization, MIT Press, 1998. |
[48] |
Y. Yang, L. Chen, Identification of drug-disease associations by using multiple drug and disease networks, Curr. Bioinform., 17 (2022), 48-59. https://doi.org/10.2174/1574893616666210825115406 doi: 10.2174/1574893616666210825115406
![]() |
[49] |
Y. Jia, R. Zhao, L. Chen, Similarity-based machine learning model for predicting the metabolic pathways of compounds, IEEE Access, 8 (2020), 130687-130696. https://doi.org/10.1109/ACCESS.2020.3009439 doi: 10.1109/ACCESS.2020.3009439
![]() |
[50] |
X. Zhao, L. Chen, J. Lu, A similarity-based method for prediction of drug side effects with heterogeneous information, Math. Biosci., 306 (2018), 136-144. https://doi.org/10.1016/j.mbs.2018.09.010 doi: 10.1016/j.mbs.2018.09.010
![]() |
[51] |
K. K. Kandaswamy, K. C. Chou, T. Martinetz, S. Möllera, P. N. Suganthand, S. Sridharan, et al., AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties, J. Theor. Biol., 270 (2011), 56-62. https://doi.org/10.1016/j.jtbi.2010.10.037 doi: 10.1016/j.jtbi.2010.10.037
![]() |
[52] |
Y. B. Marques, A. de Paiva Oliveira, A. T. Ribeiro Vasconcelos, F. R. Cerqueira, Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction, BMC Bioinformatics, 17 (2016), 474. http://dx.doi.org/10.1186/s12859-017-1508-0 doi: 10.1186/s12859-017-1508-0
![]() |
[53] |
G. Pugalenthi, K. Kandaswamy, K. C. Chou, S. Vivekanandan, P. Kolatkar, RSARF: Prediction of residue solvent accessibility from protein sequence using random forest method, Protein Peptide Lett., 19 (2011), 50-56. https://doi.org/10.2174/092986612798472875 doi: 10.2174/092986612798472875
![]() |
[54] |
M. Onesime, Z. Yang, Q. Dai, Genomic island prediction via chi-square test and random forest algorithm, Comput. Math. Method M., 2021 (2021), 9969751. https://doi.org/10.1155/2021/9969751 doi: 10.1155/2021/9969751
![]() |
[55] | M. Fernandez-Delgado, E. Cernadas, S. Barro, D. Amorim, Do we need hundreds of classifiers to solve real world classification problems?, J. Mach. Learn. Res., 15 (2014), 3133-3181. |
[56] | R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, in International Joint Conference on Artificial Intelligence, (1995), 1137-1145. |
[57] |
W. Chen, L. Chen, Q. Dai, iMPT-FDNPL: identification of membrane protein types with functional domains and a natural language processing approach, Comput. Math. Method M., 2021 (2021), 7681497. https://doi.org/10.1155/2021/7681497 doi: 10.1155/2021/7681497
![]() |
[58] |
J. Zhang, Q. Chen, B. Liu, iDRBP_MMC: Identifying DNA-binding proteins and RNA-binding proteins based on multi-label learning model and motif-based convolutional neural network, J. Mol. Biol., 432 (2020), 5860-5875. https://doi.org/10.1016/j.jmb.2020.09.008 doi: 10.1016/j.jmb.2020.09.008
![]() |
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