text:
Highlight—Lessons on Parasitism from the Curious Dicyemida

The incredible diversity of life forms on the planet led Charles Darwin to note: “From so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.” In order to gain a true understanding of the power and limitations of evolution to produce such “endless forms,” it is important to study a variety of organisms from across the tree of life. Luckily, next-generation sequencing technologies have enabled in-depth genomic analysis of virtually any organism, expanding the scope of powerful evolutionary analyses beyond model organisms. For example, take the decidedly unusual animal, the Dicyemida. Dicyemida are microscopic organisms consisting of only 30 cells each (Fig. 1). They are parasites that live inside the renal sacs of cephalopods like the squid and octopus, deriving nutrients from the host’s urine. Despite a relatively complex lifecycle, dicyemids lack differentiated tissues such as a digestive tract and circulatory system and thus represent one of the most extreme cases of body plan reduction among animals. Because of their small size and simplified tissues, they were once thought to represent a link between single-celled and multicellular organisms. However, their true phylogenetic position was long considered controversial and was only resolved in 2017 when PhD student Tsai-Ming Lu at Okinawa Institute of Science and Technology Graduate University, along with Drs Miyuki Kanda, Noriyuki Satoh, and Hidetaka Furuya, used transcriptomic data to definitively place Dicyemida within the group Spiralia, along with segmented worms, flatworms, and mollusks (Lu et al. 2017). Now, the same group of researchers has extended their analysis of this lineage by sequencing the complete genome of Dicyema japonicum. The results are published in this issue of Genome Biology and Evolution in the article, “Dicyemid mesozoans: a unique parasitic lifestyle and a reduced genome” (Lu et al. 2019), providing further insight into this odd organism and revealing convergent patterns that are shared among parasites in general. Parasitism has been observed in 15 of the 35 animal phyla and is thought to have evolved independently over 200 times. While each parasitism event reflects a specific interaction between a host and parasite, parasites often possess certain features—such as a simplified body plan and complex lifecycle—that reflect the selective pressures commonly experienced by organisms that “make a living” off of other organisms. In order to understand the evolution of parasitism, the authors sought to explore the genomic innovations that make this lifestyle possible. According to the authors, “Decoding the dicyemid genome not only improves our understanding of the biology of dicyemids, such as their complicated lifecycle, but also provides us a rich resource for comparative studies to [better understand] the evolution of spiralians from a genomic perspective.” With these aims in mind, sequencing of the genome of D. japonicum revealed that, like other parasites, it has a reduced genome, with only 5,000 genes. This includes only four Hox genes, highly conserved genes crucial for delineating the animal body plan. In particular, the authors note, “Compared FIG. 1.—Confocal image showing the surface and cross-section of a rhombogen adult dicyemid and infusoriform dicyemid embryos developing inside a rhombogen. Image by Tsai-Ming Lu.

The incredible diversity of life forms on the planet led Charles Darwin to note: "From so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved." In order to gain a true understanding of the power and limitations of evolution to produce such "endless forms," it is important to study a variety of organisms from across the tree of life. Luckily, next-generation sequencing technologies have enabled in-depth genomic analysis of virtually any organism, expanding the scope of powerful evolutionary analyses beyond model organisms. For example, take the decidedly unusual animal, the Dicyemida.
Dicyemida are microscopic organisms consisting of only 30 cells each (Fig. 1). They are parasites that live inside the renal sacs of cephalopods like the squid and octopus, deriving nutrients from the host's urine. Despite a relatively complex lifecycle, dicyemids lack differentiated tissues such as a digestive tract and circulatory system and thus represent one of the most extreme cases of body plan reduction among animals. Because of their small size and simplified tissues, they were once thought to represent a link between single-celled and multicellular organisms. However, their true phylogenetic position was long considered controversial and was only resolved in 2017 when PhD student Tsai-Ming Lu at Okinawa Institute of Science and Technology Graduate University, along with Drs Miyuki Kanda, Noriyuki Satoh, and Hidetaka Furuya, used transcriptomic data to definitively place Dicyemida within the group Spiralia, along with segmented worms, flatworms, and mollusks (Lu et al. 2017). Now, the same group of researchers has extended their analysis of this lineage by sequencing the complete genome of Dicyema japonicum. The results are published in this issue of Genome Biology and Evolution in the article, "Dicyemid mesozoans: a unique parasitic lifestyle and a reduced genome" (Lu et al. 2019), providing further insight into this odd organism and revealing convergent patterns that are shared among parasites in general.
Parasitism has been observed in 15 of the 35 animal phyla and is thought to have evolved independently over 200 times. While each parasitism event reflects a specific interaction between a host and parasite, parasites often possess certain features-such as a simplified body plan and complex lifecycle-that reflect the selective pressures commonly experienced by organisms that "make a living" off of other organisms. In order to understand the evolution of parasitism, the authors sought to explore the genomic innovations that make this lifestyle possible. According to the authors, "Decoding the dicyemid genome not only improves our understanding of the biology of dicyemids, such as their complicated lifecycle, but also provides us a rich resource for comparative studies to [better understand] the evolution of spiralians from a genomic perspective." With these aims in mind, sequencing of the genome of D. japonicum revealed that, like other parasites, it has a reduced genome, with only 5,000 genes. This includes only four Hox genes, highly conserved genes crucial for delineating the animal body plan. In particular, the authors note, "Compared with reduced sets of genes in other spiralian parasites, dicyemids possess fewer genes and an extraordinarily shortened intron size" (38 bp). Reduced genomes tend to be widespread among parasites, allowing them to limit energy consumption by decreasing the amount of genetic material that must be maintained.
To determine how evolution drove the loss of genetic material, the researchers investigated which genes were retained or lost on a genome-wide scale. Rather than eliminating entire metabolic pathways, it appears that individual Dicyema genes were lost from a variety of pathways, resulting in streamlined and simplified pathways. With regard to the genes that were retained, the authors note that "the retention of some genes may indicate that their fundamental functions are shared by multiple pathways." However, this raises the questions of which pathways remain functional despite being incomplete and how dicyemids overcome the physiological gaps caused by lost genes.
Answering questions about the fine-scale changes that have occurred in various metabolic pathways will require further studies of dicyemid physiology. As pointed out by the authors, however, such studies are complicated by the unique biology of dicyemids: "In addition to operational difficulties caused by the tiny size of dicyemids, currently, methods to manipulate gene expression have not been established in dicyemids, so it is still difficult to execute functional tests." Despite the potential challenges, the genome sequence of this unusual parasite represents a resource that will be useful for future analyses, providing molecular information that could be used to reveal the ways in which this organism is similar to other spiralians and animal parasites, as well as the ways in which it is wholly unique.


extfieldsofstudy: Medicine Biology

text:
AtIRE1C, an unconventional isoform of the UPR master regulator AtIRE1, is functionally associated with AtIRE1B in Arabidopsis gametogenesis

Abstract The unfolded protein response (UPR), a highly conserved set of eukaryotic intracellular signaling cascades, controls the homeostasis of the endoplasmic reticulum (ER) in normal physiological growth and situations causing accumulation of potentially toxic levels of misfolded proteins in the ER, a condition known as ER stress. During evolution, eukaryotic lineages have acquired multiple UPR effectors, which have increased the pliability of cytoprotective responses to physiological and environmental stresses. The ER‐associated protein kinase and ribonuclease IRE1 is a UPR effector that is conserved from yeast to metazoans and plants. IRE1 assumes dispensable roles in growth in yeast but it is essential in mammals and plants. The Arabidopsis genome encodes two isoforms of IRE1, IRE1A and IRE1B, whose protein functional domains are conserved across eukaryotes. Here, we describe the identification of a third Arabidopsis IRE1 isoform, IRE1C. This protein lacks the ER lumenal domain that has been implicated in sensing ER stress in the IRE1 isoforms known to date. Through functional analyses, we demonstrate that IRE1C is not essential in growth and stress responses when deleted from the genome singularly or in combination with an IRE1A knockout allele. However, we found that IRE1C exerts an essential role in gametogenesis when IRE1B is also depleted. Our results identify a novel, plant‐specific IRE1 isoform and highlight that at least the control of gametogenesis in Arabidopsis requires an unexpected functional coordination of IRE1C and IRE1B. More broadly, our findings support the existence of a functional form of IRE1 that is required for development despite the remarkable absence of a protein domain that is critical for the function of other known IRE1 isoforms.

Thank you for submitting to Plant Direct. All required reviews have been returned and we have now finished our evaluation of your manuscript. In light of the reviewers' and editor's comments, further revisions are needed before the paper can be accepted for publication in Plant Direct.
Please revise your manuscript in accord with the reviewers' comments (pasted below). The specific concerns that need to be addressed to meet the Plant Direct criteria are: Reviewer #1 suggests to provide detailed information about how to differentiate splice variants of bZIP60 in qPCR. Figure 2b did not match with its legend. Reviewer #2 was concerned about Non-Mendelian segregation pattern in Table 1. Both reviewers pointed out that statistical analysis in some assays were missing, e.g. Figure 2e and 2f. If these concerns can be fully addressed, we would not send the revised manuscript back for another round of review. The reviewers also made several other helpful suggestions.
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Thank you very much for giving us an opportunity to review your work. I look forward to receiving the next version. This manuscript characterizes a novel IRE1 isoform in Arabidopsis. IRE1C which lacks the lumenal sensor domain present in other IRE1 proteins, is demonstrated to have a role in gametogenesis, and a potential role in the UPR. Overall this work was well conceived and carried out, and the data support the conclusions. However, there are some minor issues that should be addressed.
1. Please provide more information about how the spliced bZIP60 was differentiated from the unspliced version in qPCR.
We have used specific primers for the spliced and unspliced forms that we used in earlier work (Moreno et al. 2012. PLoS One). We added the information and the citation. Thank you for raising this point.
2. In the discussion, the authors claim that this isoform exists only in Arabidopsis. Please provide data or citation to support this claim.
We appreciate the reviewer's comment about this. To confirm our claim, we performed the phylogenetic analysis on IRE1C with more stringent parameters, and we found a potential IRE1C isoform in Capsella rubella. We have added a phylogenetic tree in the supplemental figures, and have edited the text in discussion.
3. Please provide quantification and statistical analysis of the aborted pollen phenotype.
Done. Thank for the suggestion, 4. Figure legends refer to "histograms", these are not histograms they are bar or column graphs. Please correct.
You are correct. Done.
5. An earlier paper, (not referenced in manuscript; Moreno, Mukhtar, et al., 2012 Plos One) showed that ire1a and ire1b single mutants were more sensitive to ER stress than WT. Please address this discrepancy. Please also indicate which alleles were used in the current study. 6. The manuscript needs careful proofreading.

Reviewer #2:
The manuscript about AtIRE1C by Pu et al. described the function study of the AtIRE1C gene. As the title suggested, the authors drew the conclusion that AtIRE1C is "an unconventional isoform of the UPR master regulator 1 AtIRE1" and "functionally associated with AtIRE1B in Arabidopsis gametogenesis". While the authors did show some interesting results such as impaired seed development in heterozygous mutants, the experimental results presented in this manuscript are questionable and cannot support their claim.
My biggest concern or confusion is the absence of WT in the progenies of some of the crosses (Table 1). For instance, when +/b+/c was pollenated by itself or WT pollens, its progenies have no WT. It is indeed intriguing, but the authors did not explain clearly why this happened. In classic genetics, this usually indicates those two genes/alleles may not separate independently during meiosis or they are located very close to each other. However, this should not be the case: ireb and irec were located on different chromosomes. This led the inspection and suspicion on how the genotyping was done, especially for irec mutant. The Salk_204405 was used as the irec knockout. Without clear description of which exact primers were used for identifying the insertion and WT, it is hard to judge if the PCR was set up correctly for genotyping. With the limited information provided in Table S1 (two SALK_204405-LP primers), my suspicion/explanation would be the authors did not use a T-DNA specific primer? That might be a bold guess of what happened.
We apologize for the typo on the primer list. Primers used for genotyping including the LB primers have been added to the list, and the use for each primers have also been added to the list. We have also added supplemental Figure S3 as example of our genotyping. Given the controls included in the work, we do not believe there are issues with our genotyping method, and that the abnormal segregation is due to meiosis defects.
Other comments: Line 161-162, "as detailed below", there is no detail about T-DNA indetification.
Text has been edited for clarity.
Line 177, most references are mentioned as this line where two authors "(Meng, Ruberti et al. 2017)" were mentioned, a new style?
We've edited the reference style.
Line 192, Table S1, primer sets are not clear.

Amended to make it clear. Thank you.
Line 239-243, it would be better to show the sequence alignment instead of percentage of identical AA residue.
We've edited the text for clarity.
Line 312-315, the results are odd, not explained well.
We've added more details such as the expected segregation ratio in the text, and we have also expanded Table 1 for clarity. We think this suggestion was particulalry helpful. Thank you.
Line 388-389, the statement of "nearly no expression IREB" seemed a little conflicting with the lethal phenotype of complete loss of IREB (line 104-105). The ireb mutant used in this study is probably more of a knockdown instead of a knockout.
Yes, we indicated that our ire1b mutant is a functional knockdown. As discussed in an earlier publication (Chen and Brandizzi 2012, The Plant Journal), the allele used in this work may lack a functional RNAse domain.
Line 463-464, the authors concluded that IREC does not play a role in roots, why not investigate the expression pattern in other tissues/organs? Especially in siliques/seeds, or gametophyte as the authors suggested where IREC plays a critical role when IRECB is compromised.
Done. qRT-PCR results of IRE1C expression in different tissues/organs are shown in Figure S4.
Line 479-484, the explanation of the absence of WT is very vague, not convincing.
We have expanded the text in the discussion for better explanation.
From Figure 1f, the authors may get more information by investigating the IREC expression and function in silique/seed development. Figure S4.

Done. See
Any statistical analysis for Figure 2f?

Done.
Table S1 has two SALK_204405-LP primers. The authors should be more clear about how those primers were used. There is no T-DNA primer listed?
We apologize for our mistakes. The table has been updated.


Soluble epoxide hydrolase inhibition enhances anti-inflammatory and antioxidative processes, modulates microglia polarization, and promotes recovery after ischemic stroke

Background Ischemic stroke triggers inflammatory responses and oxidative stress in the brain, and microglia polarization affects the degree of neuroinflammation. It has been reported that the inhibition of soluble epoxide hydrolase (sEH) activity protects brain tissue. However, the anti-inflammatory and antioxidative effects of sEH inhibition in the ischemic brain are not fully understood. This study aimed to investigate the effects of a selective sEH inhibitor, 12-(3-adamantan-1-yl-ureido)-dodecanoic acid (AUDA), after ischemic stroke. Methods Adult male rats with middle cerebral artery occlusion (MCAO) were administered with AUDA or a vehicle. Behavioral outcome, infarct volume, microglia polarization, and gene expression were assessed. Results Rats treated with AUDA showed better behavioral outcomes and smaller infarct volumes after MCAO. After AUDA treatment, a reduction of M1 microglia and an increase of M2 microglia occurred at the ischemic cortex of rats. Additionally, there was an increase in the mRNA expressions of antioxidant enzymes and anti-inflammatory interleukin-10, and pro-inflammatory mediators were decreased after AUDA administration. Heme oxygenase-1 was mainly expressed by neurons, and AUDA was found to improve the survival of neurons. Conclusion The results of this study provided novel and significant insights into how AUDA can improve outcomes and modulate inflammation and oxidative stress after ischemic stroke.

Introduction
Stroke is a common neurological disease caused by the interruption of blood supply to the brain, and it can lead to neurological deficits. 1 It is the second leading cause of death worldwide, with tremendous resulting economic and social burdens on the human population. 2,3 Stroke can be classified as hemorrhagic or ischemic, wherein approximately 80% of all patients fall into the latter category. 4,5 After an ischemic stroke, the mechanisms of cerebral damage are primary ischemia and secondary neuroinflammation. 6 In the initial period, ischemia deprives the brain of oxygen, glucose, and other vital substances which are essential for brain cells. 7,8 Subsequently, ischemic stroke leads to cerebral tissue injury by the development of inflammation. After stroke, neuroinflammation rapidly triggers microglia activation and then promotes the infiltration of circulating inflammatory cells, including monocyte/macrophages, T lymphocytes, and neutrophils. 9 In the acute phase (minutes to hours), pro-inflammatory cytokines and chemokines and reactive oxygen species (ROS) are released from the injured brain tissue. 10 In the subacute phase (hours to days), a high expression of matrix metalloproteinases (MMPs) and ROS promotes neuroinflammation by causing a disruption of the blood-brain barrier and neuronal death. 9 Since there is a longer therapeutic time window for stroke-related cerebral inflammation than for the primary ischemic injury, there has been important experimental research in recent years focusing on such neuroinflammation. 11 Acute ischemic stroke not only induces a rapid increase of ROS production but also impairs the antioxidant system. Overabundant ROS causes cellular injury, inflammation, and death via lipid peroxidation and the oxidation of protein, RNA and DNA. 12 Superfluous ROS can be scavenged by some enzymatic antioxidants such as heme oxygenase-1 (HO-1), superoxide dismutases (SODs), and catalases (CATs). These enzymatic antioxidants are regulated by nuclear factor erythroid 2-related factor 2 (Nrf2), one of the critical regulators of endogenous antioxidant defense. Stimulating the Nrf2-mediated antioxidant process protects against cerebral ischemia. 13,14 Thus, reducing ROS production and inducing antioxidant defense are reasonable therapeutic strategies.
Residential microglia are the primary immune cells in the brain, activated by cerebral ischemia. 15 Upon activation, microglia undergo a morphologic change from the resting state of long ramifications and small soma to an activated state with a rounded amoeboid shape. Once activated, the cells develop polarized activation with distinct physiological roles: M1 and M2 polarization. 16,17 M1 or the classic activation drives the microglia toward a pro-inflammatory phenotype to produce pro-inflammatory mediators such as tumor necrosis factor-α, interleukin (IL)-1β, IL-6, monocyte chemoattractant protein-1 (MCP-1), ROS, and proteolytic enzymes MMP-9, which leads to blood-brain barrier breakdown. [18][19][20][21][22] In addition, these microglia express high levels of inducible nitric oxidase synthase (iNOS) for the production of nitric oxide. 22,23 By contrast, M2 or the alternatively activated microglia execute the effect of neuroprotection by secreting neurotrophic factors and anti-inflammatory cytokines such as IL-10. 24 The balance of polarization toward M1 or M2 can affect the severity of inflammation after experimental stroke, and the modulation of microglia to the M2 type has been reported to have a beneficial role in ischemic stroke. 25 Epoxyeicosatrienoic acids (EETs), which are synthesized from arachidonic acid by cytochrome P450 epoxygenases, have been proven to be beneficial in several disease models. In the cardiovascular system, EETs exhibit vasodilation effects, anti-inflammatory actions and antimigratory actions on vascular smooth muscle cells. 26 In brain tissue, EETs also possess an anti-inflammatory effect and can protect the brain from ischemic injury. 27 In vivo, EETs are rapidly metabolized into inactive dihydroxyeicosatrienoic acids (DHET) by soluble epoxide hydrolase (sEH), thereafter decreasing its anti-inflammatory effect. 26 Genetic studies of EPHX2 (which encodes the cytosolic and peroxisomal sEH) have demonstrated the association of several variants with the risk of ischemic stroke. The R287Q and G860A variants which are associated with reduced enzyme activity and protein stability were linked to lowered risk for ischemic stroke. [28][29][30] Deletion of the sEH gene and pharmacological inhibition of sEH have both been reported to reduce infarct size after experimental ischemic stroke. [31][32][33][34][35][36] In mice models with intracerebral hemorrhage (ICH) and bilateral carotid artery stenosis (BCAS), the activation of microglia was decreased by an sEH inhibitor. 37,38 In our previous study, we had demonstrated that M1 monocyte/macrophages were reduced and rotarod road running time was significantly increased by an sEH inhibitor after experimental stroke. 39 However, it remains unclear how the sEH inhibitor contributes to antiinflammatory and antioxidative effects in ischemic stroke. In the present study, we used a middle cerebral artery occlusion (MCAO) model in rats to investigate whether the pharmacological inhibition of sEH by 12-(3-adamantan-1-yl-ureido)-dodecanoic acid (AUDA) can enhance anti-inflammatory and antioxidant processes, modulate the polarization of microglia activation in the ischemic cortex and improve neurological function. The molecular mechanism underlying the AUDA-elicited beneficial process is also delineated.

Methods Animals
Ten-week-old male Wistar Kyoto (WKY) rats weighing 200-300 g were obtained from BioLASCO (Taipei, Taiwan). Female rats were not used to avoid any influences of sex hormones. The rats lived in clean cages with a 12-hr light/dark cycle at a constant temperature, and had free access to water and food. All animal experiments were approved by the Institutional Animal Care and Utilization Committee (IACUC) of National Yang-Ming University, Taipei, Taiwan (approval ID: 1050315), and animal care was performed in compliance with the guidelines of IACUC and the United States National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. Rats were randomly assigned to sham-operated, vehicle, and AUDA treatment groups and allocation concealment was performed. The experimenters were blind to the experimental conditions and blinding was further extended through outcome assessment. All postmortem examinations were conducted blindly with pseudonymization.

Focal cerebral ischemia with MCAO
Focal cerebral ischemia with MCAO was achieved by the procedures which we previously described. 40 Briefly, rats were anesthetized with Zoletil (30 mg/kg) and Xylazine (8 mg/kg) by intraperitoneal (i.p.) injection. Bilateral common carotid arteries (CCAs) were identified through a vertical incision on the midline of the neck. The temporal muscle was retracted, and the right middle cerebral artery (MCA) was exposed through a craniotomy on the right temporal bone drilled by a cutting burr. The dura matter was cut, and the MCA was permanently occluded by suture ligation between the inferior cerebral vein and the olfactory tract. Subsequently, the bilateral CCAs were occluded temporarily for 90 mins by a vessel clamp. Rats in the sham group were subjected to identical anesthesia and surgical procedure without MCAO. During MCAO, body temperature was maintained with a pad. Only rats receiving successful MCAO were included for further treatment. Rats with massive bleeding during MCAO, rats that died during or after MCAO, and rats with no neurological deficits after MCAO were excluded. The mortality rate after MCAO was less than 10%.

Administration of sEH inhibitor
Rats were randomly assigned to sham-operated, vehicle, and AUDA treatment groups. Intraperitoneal injection of AUDA (20 mg/kg, Cayman Chemicals, MI, USA) or dimethyl sulfoxide was performed every 24 hrs, beginning 2 hrs after MCAO until 1 day before the rats were sacrificed.

Animal behavioral test
Neurological function assessment was performed with the beam walk test.
The beam walk test was utilized to evaluate fine motor coordination and function, especially on the hindlimb. Rats escaped a strange cage by walking along a narrowed beam (25×1100 mm) to enter its own cage at the opposite end of the beam. A score of 7 was given when animals traversed the beam with two or less footslips; 6 was given when animals traversed the beam with less than 50% footslips; 5 was given for more than 50% but less than 100% footslips; 4 was given for 100% footslips; 3 was given for traversal with the affected limb extended and not reaching the surface of the beam; 2 was given when the animal was able to balance on the beam but not traverse it; and 1 was given when the animal could not balance on the beam. Three trials were recorded for each testing day and footslip percentage and score were used to qualify motor performance (n=6/group).

Quantification of infarct volume
At 1 and 3 days after MCAO, rats (n=6/group) were subjected to transcardial perfusion with phosphate-buffered saline (PBS) after anesthetization with Zoletil and Xylazine by intraperitoneal injection. The rats were decapitated and their brains were quickly removed from the skull. For infarction size determination, brains were sliced into 2-mm-thick coronal sections, stained with 2% 2,3,5-triphenyltetrazolium chloride (TTC; Sigma, St. Louis, MO, USA) solution for 15 mins at room temperature (RT) without light, and then fixed with 4% paraformaldehyde (PFA). The unstained region represented the infarcted area, and the non-infarcted area revealed a red color on TTC-stained sections. To perform edema correction, the infarcted area was determined by subtracting the non-infarcted area in the ipsilateral hemisphere from the contralateral hemisphere. Infarct volumes were calculated as the summation of infarcted areas on each brain slice by ImageJ software (The National Institute of Health, Bethesda, MD, USA).

Microglia isolation
At 1 and 3 days after MCAO, the rats were anesthetized and subjected to transcardial perfusion with PBS. After decapitation, each rat brain was harvested and divided into two hemispheres: the ischemic and non-ischemic sides. Cerebral cortex was subjected to enzymatic digestion and mechanical dissociation as suggested by the manufacturer (Neural Dissociation Kit and gentleMACS™ Dissociator, Miltenyi Biotec, San Diego, CA, USA). PBS (10x) was added to the original Percoll (Sigma Chemical Co, St Louis, MO, USA) at a ratio of 1:9 and this solution was considered as a 100% Percoll solution. Furthermore, 30% and 70% Percoll solutions were prepared with HBSS (without phenol red) and 37% Percoll solution with HBSS (with phenol red). The cell pellet was resuspended in 4 mL of 37% Percoll solution. Subsequently, 4 mL 70% Percoll solution was added to the bottom of tube by syringe and 4 mL 30% Percoll solution was added to the top layer. The discontinuous Percoll gradient of 30%, 37%, and 70% was centrifuged at 500 g for 30 mins, and a thin white layer was observed between 37% and 70% Percoll solutions. The layer was considered to be the collection of microglia/macrophages. The white layer was then transferred and washed in three times the volume of HBSS at 600 g for 7 mins. Finally, the cell pellet was washed using PBS and the whole population of microglia/ macrophages was collected.

Flow cytometric analysis of microglia polarization
At 3 days after MCAO, the rats (n=4/group) were anesthetized and subjected to transcardial perfusion with PBS. Following the isolation protocol, microglia/ macrophages were recovered from the interphase, blocked with 0.5% bovine serum albumin and 2% NHS and stained for surface markers using fluorophore-conjugated primary antibody at 4°C for a desig- . Appropriate negative and compensation controls were performed. Data were acquired on a FACSCanto II flow cytometer (BD Biosciences, San Jose, CA, USA) and analyzed using BD FACSDiva Software (BD Biosciences, San Jose, CA, USA). Ten thousand events were recorded for each sample. Cells were gated using forward and sideward scatter characteristics to exclude cell debris. Microglia were recognized as a CD11b + CD45 int subpopulation. After gating microglia, the M1 and M2 type microglia were identified as CD86 + and CD206 + cells, respectively.

Reverse-transcription quantitative polymerase chain reaction (PCR)
At 1 day after MCAO, total RNA was extracted from the ischemic cortex tissue (n=4/group) and microglia (n=3/ group), respectively, using E.Z.N.A. ® HP Total RNA Kit (Omega Bio-tek Inc., Norcross, GA, USA). Then, they were reverse-transcribed to cDNA using a Thermo Scientific Revert Aid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific Inc., Waltham, MA, USA). Reverse-transcription quantitative PCR was performed on an ABI QuantStudio 3 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) using SYBR green PCR Master Mix (Vazyme, Nanjing, China) with the appropriate primers as noted in Table S1. Relative expression levels of these mRNAs were calculated as fold changes vs sham using the 2 −ΔΔCT method. Tissue samples were normalized to Rpl13a and microglia were normalized to GAPDH. The expression levels of mRNAs were calculated as fold changes vs control. Melting curves were routinely performed to determine the specificity of the PCR reaction.

Statistical analyses
The statistical analyses were conducted by researchers blinded to the experimental design and conditions. Data are expressed as mean ± the standard error of the mean. Independent samples t-test or the Mann-Whitney U test was used to analyze the difference between the two groups. For multiple comparisons, one-way analysis of variance was performed followed by Tukey's post hoc test. All statistical analyses were carried out using the SPSS 24.0 software package (SPSS, Inc, Chicago, IL, USA). Results were considered significant at P<0.05.

sEH inhibitor treatment promoted functional recovery
In the beam walk test, the rats treated with AUDA had a better mean score compared with the vehicle-treated group at 1 day (6.17 vs 4.83, P=0.003) and 3 days (6.58 vs 5.92, P=0.011) after MCAO ( Figure 1A). Footslips mean percentage was also improved after AUDA administration at 1 day (28.7% vs 73.9%, P=0.001) and 3 days (32.4% vs 15.0%, P=0.003) after MCAO ( Figure 1B). The effect of AUDA was better at 1 day than that at 3 days after MCAO. In groups treated with vehicle, beam walk test performance at 1 day after surgery was the worst of all groups, but improved with time (all P<0.001) (Figure 1).

sEH inhibitor reduced infarct volume after MCAO
The ischemic region revealed a pale color by the use of TTC stain, and only the cortex was shown to be involved (Figure 2A, B). At 1 day after MCAO, the mean infarct volume of the vehicle control group was 200.3 mm, 3 larger than the 126.6 mm 3 of the group treated with AUDA (P=0.003). At 3 days after surgery, the mean infarct volumes of the group with vehicle and AUDA treatment were 157.7 and 122.1 mm 3 , respectively (P=0.008). The effect of decreasing the infarct area by AUDA existed at both 1 and 3 days after MCAO. With time, the infarct size also became smaller in the group receiving vehicle treatment (P=0.005) ( Figure 2C).

sEH inhibitor modulated the microglia polarization
Ischemic stroke triggers serious inflammatory responses. Microglia, which are major cellular contributors, polarize into either M1 classical activation or M2 alternative activation in response to brain alterations elicited by ischemic insults. Upon the further inspection of microglia with a confocal microscope, M1 type microglia/macrophages with coexpression of CD86 and Iba-1 were obviously seen in the ischemic border of rats with vehicle treatment, more so than in the ischemic region of rats with AUDA treatment at 1 and 3 days after MCAO (P=0.041 and 0.002, respectively) ( Figure 3A, C). Meanwhile, there were significantly fewer M2 type microglia/macrophages with coexpression of CD206 and Iba-1 in the ischemic border of vehicle-treated rats than in the AUDA-treated rats at 3 days after MCAO (P=0.005) ( Figure 3B, C). At 1 day after MCAO, CD206 + /Iba-1 + double positive microglia/macrophages were rarely found in both groups. No CD86 + /Iba-1 + or CD206 + /Iba-1 + double positive microglia/macrophages were found in the corresponding area of the non-ischemic hemisphere. In the ischemic border area, the activated microglia presented with an amoeboid shape; however, the microglia in the non-ischemic hemisphere remained in a resting state and had small soma with long ramifications (Figure 3A, B).
CD86, CD206, and Iba-1 are expressed in both activated microglia and macrophages. To further clarify the effect of AUDA on microglia polarization, flow cytometry was used to distinguish microglia from macrophages. Flow cytometry revealed the microglia polarization toward the M2 type after sEH inhibition at 3 days after surgery. In the CD11b + CD45 int microglia population from the ischemic region, the percentage of CD86 + M1 microglia decreased from 51.0±4.8% to 35.6±3.4% after the administration of AUDA (P=0.041). In contrast, the percentage of CD206 + M2 microglia increased from 22.0±5.2% to 41.6±3.7% after AUDA treatment (P=0.022) (Figure 4).

sEH inhibitor regulated mRNA expression of pro-and anti-inflammatory cytokines and antioxidant enzymes
The microglia polarization shift was obvious at 3 days, but functional recovery was significant from 1 day after MCAO. Thus, in the first 24 hrs, there may exist some protective effects induced by AUDA treatment. To clarify the effect of AUDA at 1 day after MCAO, ischemic cortex tissue and microglia of the cortex were collected and analyzed by reverse-transcription quantitative PCR. In a previous study, microglia/monocytes were harvested by Percoll gradient. The authors found a slight increase but no significant difference in monocyte counts 24 hrs after MCAO surgery in ischemic brain compared with sham braincases. This means the majority of cells collected by Percoll gradient are microglia at 24 hrs after MCAO. 41 The contribution of microglia in brain tissue can be demonstrated through this method at 1 day after MCAO. The mRNA expression of pro-inflammatory mediators including IL-1β, IL-6, iNOS, MCP-1, and   MMP-9 was attenuated in ischemic cortex (P=0.012, 0.021, 0.044, 0.040, and 0.041, respectively). Besides, the level of mRNA of anti-inflammatory IL-10 was increased at 1 day after MCAO (P=0.040) in ischemic cortex. Upon further inspection of microglia in the ischemic area, AUDA administration decreased the mRNA level of IL-1β, IL-6, iNOS, MCP-1, and MMP-9 at 1 day after MCAO (P=0.045, 0.033, 0.041, 0.035, and 0.007, respectively). The gene expression pattern of these pro-inflammatory mediators between the cortex and microglia was similar and AUDA-regulated proinflammatory mediators in cortex were partially contributed by microglia ( Figure 5A).
After AUDA treatment, the mRNA level of antioxidant enzymes including CAT, HO-1, and SOD-1 was upregulated in ischemic cortex at 1 day after MCAO (P=0.033, 0.016, and 0.012, respectively). The critical transcription factor for controlling antioxidant enzymes, Nrf2, was also upregulated in ischemic cortex at 1 day after MCAO (P=0.011). In rats receiving AUDA treatment, peroxisome proliferator-activated receptor-γ (PPAR-γ) was upregulated in ischemic cortex at 1 day after MCAO (P=0.032) ( Figure 5B). However, these five antioxidant-associated gene expressions were not altered by AUDA at 1 day after MCAO in the microglia of ischemic cortex ( Figure 5B). Thus, microglia were not the target cells for the AUDA-regulated antioxidant effect. Among the antioxidant-associated genes in cortex tissue, HO-1 accounted for the most upregulated gene after AUDA treatment. Coexpression of HO-1 and Iba-1 in microglia was rare and there was no difference between vehicle and AUDA-treated groups (P=0.274) ( Figure 6A, C). Double-labeling immunofluorescence showed that HO-1 was colocalized with NeuN-positive neurons. More neuronal cells with coexpression of HO-1 and NeuN were found in rats receiving AUDA treatment than in rats receiving vehicle treatment at 1 day after MCAO (P=0.012) ( Figure 6B, C). NeuN gene expression in the ischemic area was significantly increased in AUDA-treated rats compared with the vehicle group (P=0.049) ( Figure 6D).

Discussion
In this study, the administration of AUDA in rats improved behavior outcomes and decreased the infarct size after ischemic brain injury. Moreover, AUDA modulated microglia polarization to decrease M1 type and increase M2 type microglia. In ischemic brain tissue and microglia, AUDA decreased the mRNA expression of pro-inflammatory mediators including IL-1β, IL-6, iNOS, MCP-1, MMP-9, and increased the gene expression of anti-inflammatory cytokine IL-10. The gene expression of antioxidative enzymes including HO-1, CAT, and SOD-1 was also upregulated after AUDA treatment. This antioxidative process may be initiated by the activation of transcription factor PPAR-γ and Nrf2. Furthermore, HO-1, which was expressed mainly in neurons, was increased by AUDA and more neuronal cells survived in the ischemic environment after AUDA treatment. To the best of our knowledge, this is the first study to reveal that a selective sEH inhibitor modulates microglia activation toward the M2 neuroprotective type, as well as upregulates anti-inflammatory and antioxidant genes in a short time in rats with ischemic stroke.  To date, only one previous report has described that an sEH inhibitor improved functional outcome at 1 day after ischemic stroke in a rat model. 42 Our previous study has proved that an sEH inhibitor improved the performance of the rotarod test at 1 and 3 days after MCAO. 39 Our present study further utilized precise behavior tests including beam walk test to evaluate the effect of the sEH inhibitor. The rotarod test is a useful test to assess motor coordination and balance changes in rodents. 43 The beam walk test is a locomotor assessment and focuses on hindlimb functioning. 44 Using these two tools, we confirmed that the sEH inhibitor ameliorated the behavior outcome after ischemic stroke. However, in rats without sEH inhibitor treatment, we also observed that the behavior outcome improved with time.
Previous reports indicated that an sEH inhibitor reduced infarct size at 24 hrs after MCAO. 31,34,35,42 Our previous study indicated that inhibition of sEH can reduce infarct size at 3 days after MCAO. 39 Our present research further revealed that the effect of the sEH inhibitor on infarct size reduction existed on both 1 and 3 days after ischemic brain injury. However, there was no difference between rats receiving AUDA at 1 day and 3 days after MCAO. The reason for this may be associated with the concept that there is an area which is not able to be rescued in an acute phase. Another reason is that selfrecovery from brain injury in the young stroke animal model is fast. 45 These possible factors may cause AUDA effects at 3 days to be inferior to AUDA effects at 1 day after MCAO.
Inhibition of sEH can decrease the degradation of EET into DHET and increase the level of EET, which itself possesses an anti-inflammatory effect. Liu et al, reported that the administration of 14, 15-EET, and AUDA both inhibited microglia activation, reduced infarct volume and improved behavioral function after focal ischemia. 42 Most recent studies, as well as our present research, focused on the effect of sEH inhibitor, which has a more direct impact on anti-inflammation. Ischemic brain injury activates microglia and these cells develop an M1 or M2 phenotype. M1 microglia produce pro-inflammatory cytokines and increase the level of injury. However, M2 microglia secrete antiinflammatory cytokines and execute neuroprotection. 46 Thus, driving the microglia activation to the M2 phenotype can reduce neuroinflammation. Recently, the decrease of microglia activation by pharmacological inhibition of sEH was reported in both ICH and BCAS models. 37,38 Wu et al, revealed that an sEH inhibitor decreased M1 microglia and activated microglia at 1 day after ICH. However, if M2 microglia were affected, this was not mentioned and there was no conclusion regarding the modulation of microglia toward the M2 type by the sEH inhibitor. 38 At 4 weeks after BCAS, Chen et al, proved that an sEH inhibitor was able to decrease M1 microglia and increase M2 microglia, and thus microglia were activated toward the M2 type. 37 In our previous study, we also found M1 monocyte/macrophages were decreased by an sEH inhibitor. 39 In our present study, we first discovered that the sEH inhibitor modulates microglia polarization toward the M2 neuroprotective type in rats with MCAO in the acute stage. In this study focusing on gene expression in ischemic cerebral cortex and microglia, the mRNA level of pro-inflammatory cytokines IL-1β, IL-6, iNOS, MCP-1, and MMP-9 decreased and the anti-inflammatory cytokine IL-10 increased in rats receiving MCAO and treated by AUDA. This was compatible with the consequence of the modulation of microglia polarization to decrease the proportion of M1 type microglia.
Transcriptional regulators including PPAR-γ and CCAAT/enhancer-binding protein-β (C/EBP-β) have been reported to turn on the M2 gene and promote polarization in research on macrophages. 47,48 In microglia, there were no previous reports which discussed the molecular pathway in microglia polarization after sEH inhibitor treatment. Our result revealed that AUDA treatment increased the mRNA expression of C/EBP-β, but not PPAR-γ, in microglia at 3 days after MCAO (data not shown). C/EBPβ may modulate the microglia polarization toward the M2 phenotype in addition to producing anti-inflammatory cytokines and decreasing pro-inflammatory cytokines.
The modulation of microglia polarization became most obvious at 3 days after MCAO; however, the improvement of behavior outcome and infarct size was significant from 1 day after MCAO. Thus, there exists other protection mechanism in the acute stage and we proved that the antioxidant process plays an important role. HO-1 is an anti-inflammatory and antioxidant enzyme that can be induced by an inflammatory trigger or stress. 49 HO-1 has been reported to be beneficial in ischemic stroke, and its upregulation after sEH inhibition was also noted in diabetic spontaneously hypertensive rats and in platelet-derived growth factor-induced rat vascular smooth muscle cell proliferation. [50][51][52] M2 microglia triggered antioxidative responses including the upregulation of HO-1. 53,54 However, there has been no report describing the relationship between HO-1 and the sEH inhibitor in ischemic brain injury. In addition to HO-1, CAT and SOD1 are other antioxidant enzymes and all of these are downstream genes of Nrf2, a transcription factor regulating cellular antioxidant response. 55 PPAR-γ is a transcription factor known to play an anti-inflammatory and antioxidative role. Previous studies revealed that PPAR-γ transrepresses pro-inflammatory mediators such as IL-1ß, IL-6, interferon-γ (IFN-γ), iNOS, MCP-1, and MMP-9, and transactivates anti-inflammatory cytokines including IL-10. 56-59 PPAR-γ has also been reported to execute its antioxidative function by activating the gene expression of HO-1, CAT, and SOD1. 60 Nrf2 and PPAR-γ are linked by a positive feedback loop that maintains their expression and their target antioxidant genes during oxidative stress. 60,61 The increased expression of antioxidant enzymes and transcription factors was found in ischemic cerebral cortex but not microglia. Thus, we performed further experiments to clarify the origin of these antioxidant enzymes. Immunofluorescence staining revealed that HO-1 induced by AUDA was mainly produced by neurons and the neuron survival improved after AUDA treatment. It has been reported that the mRNA expression of NeuN is significantly reduced by ischemia. 62 Based on our results, the sEH inhibitor may execute anti-inflammatory and antioxidative functions to reduce neuronal injury partially via the PPAR-γ and Nrf2 pathway in ischemic brain tissue.

Conclusion
In summary, our present report indicated that applying the sEH inhibitor AUDA in rats with ischemic stroke can reduce the infarct size and improve behavior outcome at the acute stage. Furthermore, the inhibition of sEH modulates the polarization of microglia activation toward the M2 anti-inflammatory type. AUDA also reduces the mRNA expression of inflammatory mediators, promotes antioxidative genes and alleviates neuronal injury in ischemic brain tissue. These results suggest that sEH inhibitors may be a potentially promising treatment for ischemic stroke.


s2fieldsofstudy: Biology Medicine

text:
Serum protein signature of coronary artery disease in type 2 diabetes mellitus

Background Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers. Methods Using sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers. Results Our two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis. Conclusion We have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples. Electronic supplementary material The online version of this article (10.1186/s12967-018-1755-5) contains supplementary material, which is available to authorized users.

Background Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease associated with higher blood glucose levels as a result of insufficient insulin secretion, insulin action or both. T2DM is influenced by both host genetics and environmental factors including age, family history, diet and sedentary life style. The global burden of T2DM has been estimated at 425 million and it is going to increase 48% i.e., 625 million by the year 2045 [1]. T2DM is a major risk factor for developing micro and macro vascular complications [2]. Patients with T2DM have two to three-fold higher cardiovascular risk than non-diabetic subjects [1]. Persistently high blood glucose levels may cause vascular damage and develop vascular complications like coronary artery disease (CAD), which further lead to angina or myocardial infarction. Diabetic patients were unaware of the cardiovascular complications until they were hospitalized with angina or myocardial infarction. Early prediction or detection of the disease may prevent the disease progression by therapeutic intervention and management plan. Therefore, there is a need to find specific markers for the detection of different levels of diseases severity or progression of T2DM associated with CAD. Until now, most of the studies reported single or very few numbers of markers to understand the disease progression [3] and limited number of studies used multiple markers approaches giving consideration of different pathophysiological process, like lipoprotein metabolism, hormonal imbalance and inflammation [4]. Several prospective and randomized studies such as United Kingdom Prospective Diabetes Study (UKPDS), Action to Control Cardiovascular Risk in Diabetes (ACCORD), Action in Diabetes and Vascular Disease Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) and Veterans Administration Diabetes Trial (VADT) show that regulation of glucose has little or no effect on cardiovascular complication and mortality [5]. Canakinumab Anti-inflammatory Thrombosis Outcome Study (CANTOS) with cankanemib antiinflammatory therapy targeting IL-1beta shown reduced recurrent cardiovascular events and decreased IL-6 levels dose depended manner [6]. CANTOS study as well as previous literature demonstrated that inflammation plays pivotal role in the development and progression of many complex diseases such as hypertension, dyslipidaemia, diabetes, and cardiovascular diseases (CVD) [6,7]. Data indicates that inflammation could be the bridging link between T2DM and CVD. Chronic inflammation works through different mechanisms like endothelial dysfunction, oxidative stress, and insulin resistance [8]. Among different cells, macrophages and adipocytes play important role to induce inflammation in diabetes. Cytokines are cell-signaling molecules in inflammatory state and secretes from macrophages [9]. Similar to macrophages, adipocytes are also very sensitive to inflammation and secrete different adipokines such as leptin, adiponectin, resistin, and proinflammatory factors like TNF-α, IL-1β, IL-6, and PAI-1. Retained lipoproteins with other atherogenic factors that observed during atherosclerosis process, activates endothelial cells to recruit more immune cells like monocytes, which further differentiate into macrophages and activate other inflammatory signaling pathways [10]. In the process of atherosclerosis development, the expression of lipoprotein surface molecules such as apolipoproteins i.e., atherogenic (Apo-B) and anti-atherogenic (Apo-AI) molecules [11] may alter and cause coronary artery diseases [12]. We hypothesised that different cells like macrophages, adipocytes, and endothelial cells were communicating through metabolic hormones, cytokine/ chemokine, adipokine and apolipoproteins to regulate insulin sensitivity, lipid metabolism, and inflammation. Perturbation in signaling molecules homeostasis due to environmental or genetic changes may contribute in T2DM and CAD progression. Therefore, a set of markers perturbed at different stages of the disease progression, may be potential biomarkers for predicting diseases phenotypes. To identify potential biomarkers of T2DM with or without cardiovascular complication, different omics approaches targeting multiple proteins, metabolites, microRNAs and long non-coding RNAs are preferred [13]. In the present study, we measured protein markers in patients with T2DM, CAD, T2DM_CAD and healthy controls to identify a disease-specific panel through nonparametric based machine learning classification to distinct different stages of disease progression.

Inclusion and exclusion criteria for selection of study subjects
Group 1: Control (CT, n = 26) subjects had no prior history of T2DM, hypertension, coronary artery diseases or any other cardiovascular diseases, and were not taking medication for any chronic medical condition. Fasting blood glucose, HbA1c and blood chemistry were normal. Group 2: Type 2 diabetes (T2DM, n = 53) subjects with HbA1c levels ≥ 6.5% as per American Diabetes Association (ADA) guidelines with proven history of T2DM and no other complications. Group 3: Coronary artery diseases (CAD, n = 21) subjects were diagnosed based on positive medical history (myocardial infarction, angina pectoris and coronary artery bypass graft) and/or ischemic changes on a conventional 12-lead ECG, which included ST-segment depression or Q-wave changes [14]. Coronary artery disease subjects were identified in inpatient setting of cardiac catheterization unit in Mediciti hospital by the cardiologist. This group had no prior history of T2DM. Group 4: Type 2 diabetes with coronary artery diseases (T2DM_CAD, n = 27) subjects were coronary artery disease as defined for group 3 but patient had HbA1c levels ≥ 6.5% and prior history of T2DM. Information regarding demographic, clinical, and angiographic data was also collected from all patients. Fasting samples were collected from the patients prior to the percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG). Clinical or laboratory evidence of chronic diseases conditions like liver failure, renal failure (serum creatinine levels > 1.5 mg/dl), type 1 diabetes, cancer, thyroid disease and pregnancy subjects were excluded from the study.

Statistical analysis, classification and visualization
The majority of statistical analysis and visualizations were performed using the various modules of the R programming interface. In order to obtain the fold change, we used the median values of each protein marker obtained across all the healthy controls (referred to as the 'control median'). The fold change of a given marker for a given patient was then obtained as the log-ratio of the value of the marker in that patient divided by the control-median corresponding to that marker. The various modules used for this purpose were randomForest (Random forest classifications), dudi.pca of the ade4 package (for performing Principal Component Analysis), dunn.test (for performing post hoc dunn's tests of pairwise comparisons across cohorts), and kruskal.test (for identifying the significantly different markers for different cohorts). Only p < 0.05 were considered as significant. We used twostage approach to select the specific markers ( Table 2). In the first step, we selected all those protein features that were significantly different between two groups at a nominal p-value < 0.05. Then, in the second step, on this subset, we applied a Benjamini-Hochberg correction and selected those features with corrected false discovery rate (fdr) p-value < 0.15. Correlations between markers, and between clinical parameters and protein markers were obtained using kendall's tau (corr function of R) and spearman correlation was used to find the correlation. R value 0.3 was set as threshold and significance was considered as p < 0.05. The different modules for visualizations were heatmap.2 (for heatmap), Between Class Analysis is a specialized form of 'supervised' Principal Component Analysis (PCA), with respect to the instrumental variable (in this the class). It provided better resolution and provides a better analysis for marker identification as compared to the PCA factoextra (for PCA biplot showing the association of the markers with different patient marker profiles), ggcorrplot (for plotting the correlations as heatmaps) and s.class function of the ade4 package (for visualization of the class-based resolution of the patient marker profiles obtained using the PCA). In-house codes were written in Perl for computing the pair-wise variations among patient marker profiles using the J-divergence measure [16].

Results
A total 127 subjects were randomly selected and enrolled in the study (Table 1). Clinical and biochemical characteristics were represented in the Table 1. Male and female subject's ratio was not matching in the study groups as male subjects were more prone to CAD than females. Fasting blood glucose and glycated haemoglobin (HbA1c) levels were significantly (p < 0.001) increased in T2DM and T2DM_CAD groups as compared to control.

Serum protein markers levels in study groups
Levels of 45 protein markers in the serum samples of enrolled subjects were measured. Four cytokines IL-2, IL-7, IL-15 and MIP-1α were excluded from out of 46 protein markers due to detection limits of the present assay. Fold change of the various clinically significant markers across all the individuals belonging to the three different disease states i.e., T2DM, CAD and T2DM_ CAD represented in heat map. The median fold change in each disease cohort versus the control medians of each marker is also shown (Fig. 1).

Table 1 Clinical and biochemical variables in study groups
Data are mean (SD) and median (Q1-Q3) for normally distributed and non-normally distributed variables respectively BMI Body mass index, FBS fasting blood sugar, HbA1c glycated hemoglobin, eGFR estimated glomerular filtration rate, FRS Framingham Coronary Heart Disease Risk Score in 10 years, ASCVD estimate risk score for atherosclerotic cardiovascular disease in 10 years. Anti-platelet and Statin therapy was given to the all the patients for the prophylaxis for the CAD event a p < 0.05 compared to control Hypertension history -25 16 14 Patients were on hypertensive drugs -18 13 14 Patients were on anti-diabetes medication -34 -17 Patients were on anti-platelet and statin therapy --25 26 were significantly (fdr corrected p-value < 0.15) increased in the T2DM_CAD group as compared to T2DM group.
In the present study, we also compared protein markers alteration in T2DM_CAD groups with CAD group, metabolic hormones i.e., insulin, GIP and GLP-1 levels were significantly (fdr corrected p-value < 0.10) increased where IP-10 levels were decreased in T2DM_CAD as compared with CAD group.

Serum protein marker levels in T2DM and CAD group compared with control group
Our data also indicated that metabolic hormone i.e., GIP, cytokine i.e., GM-CSF and apolipoprotein i.e., Apo-AI levels were significantly (fdr corrected p-value < 0.10) decreased while lipocalin-2 was significantly increased in T2DM group as compared with control group (fdr corrected p-value < 0.10). Metabolic hormone such as ghrelin and adipokines i.e., resistin, PAI-I, adipsin, and lipocalin-2 levels were significantly (fdr corrected p-value < 0.10) increased in CAD group and similarly cytokines eotaxin, IP-10, PDGF-BB levels, and apolipoprotein such as Apo-CII levels were significantly (fdr corrected p-value < 0.10) increased in CAD group as compared with control group. Metabolic hormones i.e., GIP and leptin levels were significantly (fdr corrected p-value < 0.05) decreased in CAD group as compared to control group (Table 2).

Network analysis
Further, we conducted protein-protein interaction (PPI) network analysis using significantly (p ≤ 0.05) altered proteins in each disease using STRING 10.5 database. STRING database provides PPIs from experimental interactions from different sources combining text and data mining approaches. We constructed diseasespecific PPI networks based on high confidence score threshold (STRING score ≥ 0.7). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to assign related gene categories into their associated pathways, through the STRING interface. KEGG pathway enrichment analysis was performed and results with multiple testing corrections were used for further analysis. False discovery rate (FDR) threshold ≤ 1% was applied. KEGG pathway analysis sheet was submitted as Additional file 1. The important processes were colored using the STRING analysis tool tab. The networks were Fig. 1 Heatmap showing the fold change of the various clinically significant markers across all the individuals belonging to the three different disease states. In order to obtain the fold change, the median values of each clinical marker was obtained across all the healthy controls (referred to as the 'control median'). The fold change of a given marker for a given patient was then obtained as the log-ratio of the value of the marker in that patient divided by the control-median corresponding to that marker. Four distinct sets of correlated protein markers (CLs) are highlighted by dark blue, light blue, yellow and green boxes on heatmap. The median fold change in each disease cohort versus the control medians of each marker is also shown  downloaded and edited to highlight the upregulated and downregulated proteins. In T2DM group, three proteins were down-regulated and one protein lipocalin-2 was upregulated. PPI network showed that these molecules were involved in the cytokine-cytokine receptor signaling and Jak/Stat signaling (Fig. 2a). Similarly, in CAD group, lipocalin 2, ghrelin, PAI-I (serpine1), adipsin (CFD), resistin, PDGF-BB, CCL11, IP-10 and APO-CII were upregulated, and GIP and leptin were down regulated. The network revealed that these molecules were closely associated with cytokine-cytokine receptor signaling and chemokine signaling (Fig. 2b). T2DM_CAD group GM-CSF (CSF2) and PAI-I levels were down regulated and TNF-alpha, IL-1β, CCL-11, lipocalin 2, insulin, GLP-1, adiponectin and adipsin were upregulated. All these proteins are involved in the cytokine-cytokine receptor signaling, NF-kB signaling, insulin signaling and adipocytokine signaling (Fig. 2c). In the T2DM_CAD category, nine markers GLP-1, GIP, Insulin, IL-6, Apo E, Apo-AI, Apo-AII, TNF-α, IL-6, and adipsin were upregulated and leptin was down regulated. These proteins were involved in the cytokine-cytokine receptor signaling, Jak-Stat signaling, PI3K-Akt signaling, adipocytokine signaling and insulin signaling when compared with T2DM (Fig. 2d).

Correlation among protein markers and clinical characteristics
The correlations between protein markers and clinical characteristics were shown in Fig. 3b. Our correlation

Machine learning classification methods for characterizing the disease groups
We used two supervised analysis methods to distinct disease groups based on the protein markers i.e., Between Class Analysis (BCA) and Principal Component Analysis (PCA). BCA performed between class analysis to distinct the diseases groups with marker profiles. Our BCA analysis revealed that distinct biomarkers profile for the T2DM_CAD and CAD groups. However, T2DM group has marker profiles, relatively similar to that of control group. BCA ordination plot shown in Fig. 4a. Subjects belonging to different groups are colored differently (as indicated in the figure legend) and connected with the centroid profiles of each group. Furthermore, principal component analysis (PCA) of the serum markers showed decent separation of samples from patients with CAD and T2DM_CAD from both controls and T2DM individuals based on the most decisive component of the dataset (Fig. 4b). Dimension 1 (Dim 1) of the PCA accounted for 28.3% variation, while Dimension (Dim 2) accounted for 13.9% variability. Further, we have presented within the group marker profile variations, which revealed that there is a high degree of variability in the markers within CAD and T2DM_CAD as compared to the control and T2DM group (Fig. 4c).
For each disease groups among control prediction (Fig. 5), we performed 100 iterations, where in each iteration; we trained a random forest classification model on 50% of the dataset (based on the protein profiles), and tested it on the rest 50% (2-fold cross validation; rather than the 90% to 10% training: testing ratio, thereby reducing the over-fitting aspect). In other words, each iteration involved the random forest classification trained on a different subset of control and diseased samples, and tested on a completely non-overlapping set. The two cross validation addresses that the models do not over-fit. Furthermore, the 100 iterations gave us the statistical power to explore the entire landscape of individuals (available in the current study) to judge the power of each classification strategy. The classification power of each feature was then computed as the mean feature rank across all the 100 iterations. Our random forest classifier approach is given predictive models to distinct the different diseases like T2DM, CAD, and T2DM_CAD respect to control group. We also found the random forest classifier to predict T2DM_CAD with respect to T2DM. Accuracy, variable importance score and median abundance of the markers for each disease states were shown in Fig. 5a. The iterative approach gave us the statistical power to not only compare the classification efficiency for the three diseases using the protein profiles (p < 3.4e−10 using Kruskal-Wallis H-test, CAD > T2DM_CAD > DM), but also validated the stability of the features for prediction across an entire landscape of subjects (Fig. 5b). Principal Coordinate Analysis for the 100 ranked feature importance profiles (obtained in each iteration) for each disease, indicates that the feature importance profiles are relatively similar to each other for a given disease and significantly from those of other diseases (PERMANOVA p < 2.8e−13). Briefly, Random Forest (RF) classifier classified T2DM group with respect to the control group by nine markers (IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP, Apo-B; accuracy 76%, sensitivity 72%, specificity 81%, AUC 0.72), CAD was predicted with respect to the control by 14 markers (resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII, IP-10; accuracy 86%, sensitivity 85%, specificity 87.5%, AUC 0.84); T2DM_CAD was predicted by 12 markers (insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6, and ghrelin; accuracy 92%, sensitivity 92.3%, specificity 90%, AUC 0.92); (Fig. 5c). T2DM_CAD was also classified well with respect to T2DM by nine markers (adiponectin, C-peptide, resistin, IL-1β, ghrelin, lipocalin-2, Apo-AII, IP-10, Apo-B; accuracy 85.7%, sensitivity 86.9%, specificity 78.5%, AUC 0.76) (6a-c). These all classifiers were considered as significant p < 0.05 and shown in Table 3.

Discussion
In the process of development of CAD in T2DM, multiple pathophysiologic processes including hyperglycemia, hyperinsulinemia, insulin resistance, dyslipidemia, chronic low-grade inflammation, oxidative stress, endothelial dysfunction, vascular calcification, and hypercoagulability were involved [17]. However, these all mechanisms were responsible together to alter the plasma levels of metabolic hormones, inflammatory mediators, adipokines and apolipoproteins in diabetes and associated cardiovascular disease complications [18]. These individual mechanisms and their interplay were not fully understood in the diabetic disease progression. In the present study, we were interested to find the simultaneous induction of various protein markers in diabetes and CAD based on the diseases mechanisms related to metabolic hormonal regulation, inflammation, adipogenesis, and atherogenesis. Our interest was to characterise T2DM by a panel of protein markers that accelerates CAD progression in Indian subjects. Identification of specific markers in Indian T2DM patients is very important considering the high prevalence of type 2 diabetes and its complications specially CAD in India.
The present study showed that metabolic hormones like GLP-1, C-peptide and insulin levels were significantly increased in T2DM_CAD as compared with control. Similarly, GLP-1 and C-peptide levels were significantly increased in TDM_CAD as compared with T2DM. Previously, it was reported that increased insulin and C-peptide levels were independently associated with increased risk of coronary artery disease in T2DM subjects. Interestingly, hyperinsulinemia accelerates atherosclerosis in diabetic patients than non-diabetic patients [19,20]. In contrast, GLP-1 other than metabolic regulation shows anti-atherosclerotic effect [21]. Increased GLP-1 levels in T2DM_CAD group that we observed in the present study might be the compensatory response to the atherosclerotic effect. In the present study leptin level were significantly lower in CAD patients but increased when diabetes is associated with CAD. Al-Daghri et al. reported the association of increased leptin levels with severity of both metabolic syndrome and diabetes associated with coronary heart diseases [22]. Many clinical reports suggest that leptin might play a key link between Fig. 6 a Classification area under the curves (AUCs) of random forest-based classifiers (trained on the marker profiles) for predicting the T2DM_CAD with respect to T2DM. For each disease state, classification accuracies were obtained after 100 iterations, where in each iteration, the model was trained on 50% of the data and validated/tested on the rest 50%. b Variable importance scores of the markers identified to be optimal for at least one of the three comparisons (T2DM v/s T2DM_CAD). c Fold change of the median abundances of the corresponding markers for each comparison (T2DM v/s T2DM_CAD) metabolism and inflammation, across different age categories, ranging from pediatric to geriatric patients with diabetes or other cardiovascular risk factors [23]. Further, leptin is able to induce production of C-reactive protein by endothelial cells [24]. The increase local availability of leptin in the vascular wall can in turn exert pro-atherothrombotic effects both on endothelial cells and smooth muscle cells [25,26]. Among all pro-inflammatory cytokines i.e., TNF-α and IL-1β levels were increased in T2DM_CAD subjects as compared with control subjects. As per the previous literature, TNF-α induces insulin resistance by inhibiting IRS-1 phosphorylation and GLUT-4 expression, and elevated in patients with heart failure, and myocardial ischemia reperfusion [27]. Pu et al. reported the association of increased TNF-α levels with CAD among T2DM patients [28]. Increased TNF-α and IL-1β levels along with hyperglycemia helps to develop atherosclerosis [29,30]. Similarly, another pro-inflammatory marker IL-6 increased in both CAD and T2DM_CAD groups. However, the significant increase was observed in T2DM_ CAD group as compared with T2DM. Increased IL-6 levels disturb the glucose metabolism and contribute to the development of insulin resistance. It is also reported earlier that IL-6 levels were positively correlated with CAD [31]. Present study showed that pro-inflammatory markers i.e., TNF-α, IL-1β, and IL-6 together induce chronic inflammation condition to promote CAD in diabetes. Recently, CANTOS study reported that treatment with canakinumab, a monoclonal antibody targeted against IL-1beta in myocardial infraction patients showed dose-dependent decrease in IL-6 levels [6]. Serum markers like PDGF-BB, IP-10, resistin and PAI-1 levels were significantly increased in the CAD group, therefore representing specific markers for the CAD in the absence of diabetes. All these parameters help to develop atherosclerosis as reported earlier [32][33][34][35][36][37].
In our study, GM-CSF levels decreased in T2DM and T2DM_CAD group as compared with control group. GM-CSF induces activation of monocytes/ macrophages and mediates differentiation to other states that participate in immune responses. Previously, researchers reported that GM-CSF protects from diabetes by increasing a tolerogenic dendritic cells population [38]. Recently, Al-Hassnawi et al. reported decreased GM-CSF levels in type 2 diabetes patients and found indirect association with blood glucose levels [39]. Present study also confirmed that GM-CSF could be used in the disease progressive marker for diabetes and diabetes with CAD. Similarly, eotaxin levels were significantly increased in CAD and T2DM_CAD group. Researchers previously reported that increased eotaxin levels were associated with CAD and coronary atherosclerosis [40]. Therefore, elevation of eotaxin levels might be more important for atherosclerosis development in diabetes subjects. Increased adipsin levels were observed in CAD and T2DM_CAD group while lipocolin-2 levels were increased in T2DM, CAD and T2DM_CAD groups. Researchers reported that adipsin promotes lipid accumulation and adipocyte differentiation, and improves beta cell function [41,42]. In previous literature, it was reported that increased lipocolin-2 in serum is positively correlated with insulin resistance and inflammation in T2DM patients [43,44].
The association of Apo-AI, Apo-AII and Apo-CII levels was reported with atherosclerotic occlusive disease, CAD and type 2 diabetes associated with CAD [45][46][47]. In the present study, Apo-AI, Apo-AII and Apo-CII levels

Table 3 Classification performance of marker profile based on random-forest classifiers for different pairs of groups
For each pair of groups, the Random Forest classifications were obtained with 10-fold cross validation (there were 1000 iterations where in each iteration the classifiers were trained on 90% of the subjects, while the rest 10% were used for prediction). Top discriminatory marker features for each pair wise classification. Fisher's exact test were then performed on the confusion matrix, in order to judge the significance of the prediction profile VIS variable importance score, AUC area under curve, AUC is mentioned as median Optimal markers giving the separation more than > 1 VIS

Control vs T2DM
Total 9 markers i.e., IL-1beta, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP, Apo-B. were increased in CAD and T2DM_CAD group, however, significantly increased was observed only in CAD group. As per the previous literature increased Apo-AI shows cardio-protective effect and thus improvement of Apo-AI expression is considered as a potential therapeutic strategy to inhibit atheroma formation [3]. However, ApoA-I Milano product (MDCO-216) and wild-type ApoA-I product (CER-001) failed to promote regression of coronary atherosclerosis compared with placebo. Further another ApoA-I product, CSL112, recently entered a phase III cardiovascular outcomes trial [48]. Hope this study may come up with a fruitful result. Researchers reported that Apo-AII promotes insulin resistance and disturbs body fat homeostasis [49]. Increased Apo-AII levels promote development of atherosclerosis by disturbing the reverse cholesterol transport and antioxidant properties of HDL [49][50][51][52][53]. Similarly, Apo-CII plays an important role in triglyceride rich lipoprotein metabolism, and positively correlates with increased CAD and coronary heart diseases (CHD) [54]. Between class analysis (BCA) was performed to distinct the disease groups with marker profiles. Our BCA analysis revealed that distinct biomarkers profile was observed for the T2DM_CAD and CAD groups but no difference between control and T2DM group. Thus the distinct biomarker profile between groups is depends on high degree of glycaemia, duration of diabetes state and its complications. Principal component analysis (PCA) of the serum markers showed decent separation of samples with high degree variability with CAD and T2DM_CAD from controls and type 2 diabetes based on the most decisive component of the dataset.
We have also analysed our data using random forest classifier approach, which is a predictive model to distinct the different diseases like T2DM, CAD, and T2DM_CAD respect to control group, and classified T2DM_CAD group in respect to T2DM group. All these protein markers from random forest (RF) classifier were further used to made venny diagram to represent common and individual markers to distinct each disease group from other group (Fig. 7a). We found that GM-CSF, PAI-I and resistin were common classifiers for the T2DM, CAD, T2DM_CAD diseases. While IL-1β, glucagon and Apo-B are individual markers for type 2 diabetes, PDGF-BB, IL-13, eotaxin, Apo-E, and Apo-CII are the individual markers for the CAD group. Only four markers like insulin, adiponectin, Apo-AII and IL-6 are the individual markers for T2DM_CAD group. Six serum markers representing metabolic hormones (leptin, ghrelin) and adipokines (resistin, adipsin, PAI-1, lipocalin-2) are common between CAD and T2DM_CAD group while serum markers from inflammation, cytokines and apolipoproteins observed in both groups are completely different. Similarly, four serum markers representing Fig. 7 a Venny diagram represented common and unique protein markers from the RF classifier to distinct type 2 diabetes, CAD, and T2DM_CAD as compared with control. b Protein markers that responsible for development and progression of diabetes and associated coronary artery disease complication. Different pathological protein markers i.e., adipokines, cytokines, metabolic hormones and apolipoproteins (markers which were classified in RF classifier Table 3) may act as mediators in the initiation of insulin resistance, systemic inflammation, endothelial dysfunction and increase lipolysis and free fatty acids. Up arrow resembles upregulated proteins and down arrow resembles downregulated markers inflammation (GM-CSF), adipokines (resistin, PAI-1) and apolipoproteins (Apo B) are common between T2DM and T2DM_CAD groups while serum markers from metabolic hormones observed in both groups are completely different. The classifier analysis showed that few plasma markers from T2DM_CAD group were common with T2DM and CAD marker panels. These common plasma markers confirm the involvement of common pathologies among T2DM, CAD and T2DM_CAD.
Few plasma protein markers i.e., rantes, IL-13, glucagon and Apo B were picked up with the absence of statistically significant differences. Rantes (CCL5) an inflammatory marker, secreted by adipocyte and contributes to leukocyte infiltration. Previous researcher reported that increased levels of circulatory rantes observed in obesity, impaired glucose tolerance, type 2 diabetes, and coronary artery diseases [55,56]. In contrast, Podolec et al. reported that severe coronary atherosclerosis correlated with decreased rantes levels [57]. Our data suggested that rantes could be a marker for type 2 diabetes and type 2 diabetes with CAD. Similarly, anti-inflammatory molecule IL-13 is a classifier of CAD panel without any significant change. Decreased serum IL-13 levels in T2DM subjects play a role in impaired glucose uptake and metabolism [58]. Similarly, another study showed decreased IL-13 levels in patients with coronary artery disease subjects [59]. Metabolic hormone glucagon has emerged as one of the marker for the type 2 diabetes classifier; however, there was no significant difference. Metabolic hormones glucagon and insulin together regulate glucose production by stimulatory and inhibitory actions, respectively [60]. In our study, decreased glucagon levels, and increased insulin levels were observed in type 2 diabetes group. However, in T2DM_CAD group, both the levels were increased which shows impairment of the glucose regulation. Apolipoproteins like Apo-B and Apo-E were classifiers for the T2DM and CAD, respectively and Apo-B, was a classifier marker for the T2DM_ CAD. It is well known that Apo-B and Apo-E were risk factor for coronary artery diseases and cardiovascular mortality [61]. Researchers also reported that Apo-B is associated with the incident type 2 diabetes and better predictor for coronary artery diseases among diabetic patients [62,63]. As reported earlier our study also classified Apo-B a marker for T2DM and T2DM_CAD when compare to control and T2DM, respectively. Furthermore, all these significant protein markers in study groups compared with control group and T2DM_ CAD group compared with T2DM were analyzed using STRING database to find the possible cellular signaling pathways (Fig. 2). Circulatory protein markers with significant change in respect to control or T2DM were used to build a closely associated network of known pathways.
Network analysis revealed the complexity of T2DM_CAD that linked to several inflammatory and insulin resistance pathways like cytokine receptor, Jak-STAT, PI3K-Akt, adipocytokine and insulin signaling pathways (Fig. 2d). To understand the overall affected pathways throughout the disease progression starting from healthy to diabetesto-diabetes with CAD, we combined all significant circulatory markers to link with the crucial cellular proteins that could be affected during the disease progression (Fig. 2b). PTPN1 found from the network study (Fig. 2c) is linked to the common network pathway and modulates insulin resistance [64] through Jak-Stat, insulin receptor substrates (IRS1 and IRS2) and leptin signaling pathway [64]. All these proteins are already being explored as targets for the treatment of diabetes in diverse studies. Hyperinsulinemia that observed in T2DM and T2DM_ CAD groups, may down regulate IRS1 and IRS2 via p38-MAPK and triggers insulin resistance in liver and skeletal muscle. Alteration of leptin found in T2DM_CAD also effects phosphatidylinositol 3-kinase-Akt (PI3K-Akt) signaling pathway via Jak2 activation of IRS1 and IRS2 (Fig. 2d). Together these proteins contribute to the dysregulation of glucose and lipid metabolism, mitochondrial biogenesis, calcium-handling, fibrosis, and motor gene expression, culminating in cardiovascular complications. Pro-inflammatory marker IL-6 signaling via IL6R and STAT3 is a contributor for the vascular inflammation in vessel wall or fatty streak. Another pro-inflammatory marker i.e., IL-1β that belongs to IL-1 family, binds with the IL-1R type I (IL-1RI) and induces a downstream signal via numerous inflammatory kinases, such as Myd88, ERK, JNK and NF-κB leading to transcription of several inflammatory genes like cytokines and chemokines (Fig. 2c). Some signaling molecules specially, NF-kB also overlap with toll like receptors (TLRs) signaling [65]. Recently, the transcription factor high-mobility-group AT-hook 1 (HMGA1) has been linked to NF-kB activation, and involved in inflammation and in the pathogenesis of insulin resistance. It has been demonstrated that HMGA1 is associated to both the risk for diabetes and the risk of developing cardiovascular complications [66].
With the help of the network analysis, we were able to identify several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, AKT1, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis. Several adipokines like resistin, adiponectin, lipokolin-2 and IL-6 contribute to the development of insulin resistance, type 2 diabetes and cardiovascular diseases. We also found that apolipoproteins and cytokines were tightly connected with each other in the network and contribute to the development as well as progression of diabetes and diabetes with coronary artery diseases.