Open access peer-reviewed chapter - ONLINE FIRST

Th17/IL-17, Immunometabolism and Psoriatic Disease: A Pathological Trifecta

Written By

Seema Chhabra, Smrity Sahu, Keshav Sharma, Maryada Sharma, Lekha Rani, Ranjana Minz and Sunil Dogra

Submitted: December 2nd, 2021 Reviewed: January 12th, 2022 Published: April 5th, 2022

DOI: 10.5772/intechopen.102633

IntechOpen
Psoriasis Edited by Shahin Aghaei

From the Edited Volume

Psoriasis [Working Title]

Associate Prof. Shahin Aghaei

Chapter metrics overview

17 Chapter Downloads

View Full Metrics

Abstract

The burgeoning arena of immunometabolism provides evidence of how cellular, as well as local (tissue)/systemic metabolic pathways, are playing an important role in controlling immunity and inflammation. An intricate and elaborate network of various metabolic circuits specifically glycolysis, fatty acid oxidation and synthesis and amino acid metabolism precisely generate metabolites that rewire the immune response. Psoriasis is a chronic progressive self-perpetuated “IL-17-centric” inflammatory disease characterized by the co-existence of autoimmune and autoinflammatory pathways. Metabolic responses, governed by oxygen levels, nutrient availability, growth factors, cytokines, AMP/ATP ratios and amino acids, play a pivotal role in programming Th17 cell fate determination. Understanding the intricate interactions and complex interplay of molecular mechanisms responsible for Th17 cell metabolic rewiring, an important determinant of Th17 cell plasticity and heterogeneity, holds the potential to reshape psoriatic therapeutics in ways currently unimagined. This chapter entails with most recent updates on major cellular and systemic metabolic pathways regulating differentiation of Th17 cells as well their cross-talk with intracellular signaling mediators and also sheds light on how dysregulation of these pathways can be responsible for immune impairment and development of psoriatic disease. A better understanding of these metabolic processes could unveil an intriguing leverage point for therapeutic interventions to modulate metabolic programming and Th17 cell responses in this multi-systemic inflammatory disease.

Keywords

  • IL-17
  • Th17 cells
  • immune-metabolism
  • psoriasis
  • psoriatic disease

1. Introduction

Environment-driven metabolic adaptations perform important roles in regulating the immune system. Specific metabolic pathways control T-cell activation/proliferation/differentiation and regulate the switch towards either pro- or anti-inflammatory responses: it, therefore, seems rational that metabolic trepidations can alter self-immune tolerance [1]. Aberrant metabolic pathways constitute a molecular snapshot of the cellular processes that are exaggerated during disease pathogenesis [2]. This immune-metabolic interactome can orchestrate the choreography of interleukin (IL)-17-producing T helper (Th17) cells-induced pathogenicity in psoriatic patients, manifested as a ‘psoriatic march’, ultimately resulting in the development of a variety of psoriasis-associated co-morbidities [3]. Metabolic anomalies influencing the T regulatory cells (Treg)/Th17 axis play a paramount role in the pathophysiology of psoriasis, so it is imperative to understand the close linkage between metabolic pathways and immune cell function: this may unveil specific interventional targets and suggest indirect dietary styles and repositioning of metabolic drugs that beneficially impact the abnormal T-cell metabolism [4].

Advertisement

2. Th17 cells

Faced with any antigenic stimulus, either an intracellular or extracellular pathogen or any tissue homeostatic alteration, naïve CD4+ T-cells respond via activation, proliferation, and finally differentiation into specialized T-effector cell subsets which are specifically programmed to deal with the offending agent/s. One such specialized T-effector cell subset is comprised of Th17 cells, best known as a host-defensive effector T-cell subset at barrier mucosal tissues (intestine, lung, skin) with a prime role in providing immunity against fungi and other extracellular pathogens and in sustaining gut barrier integrity by transdifferentiating into Th1-like or Treg-like cells [5]. Retinoic acid-related orphan receptor-gamma (RORγt), a signature ligand-dependent transcription factor for Th17 cells has been characterized as the molecular orchestrator of Th17 cell program. RORγt belongs to a subfamily of nuclear receptors, encoded by the master switch gene RORA-C(or NR1F1-3) [6]. A variety of transcriptional regulators of RORγt, as well as other transcription factors that either interact with RORγt or bind the promoter or the intergenic regions of the IL-17gene locus, play a crucial role in the generation of Th17 cells (Figure 1) [7].

Figure 1.

IL-17 induced “Psoriasogenicity”: psoriatic march. (A) A constellation of regulatory factors including hypoxia-inducible factor (HIF)-1α (with recruited factor p300 having histone acetyltransferase activity), runt-related transcription factor (RUNX1), basic leucine zipper ATF-like transcription factor (BATF)- Jun B heterodimer, nuclear factor of activated T cells (NFAT), p65 NF-kB subunit, and signal transducer and activator of transcription (STAT) 3 act as co-operators ofRORCgene, enhancing its expression and resulting in increased Th17- lineage-specific transcription factor RORγt. (B) RORγt binds to ROR response elements (RORE) located in CNS2 (conserved non-coding sequences) of theIL-17gene, and globally controls its transcription. The effect of transcriptional regulator RUNX1 (binding to CNS2 region ofIL-17) is also dependent on RORγt; HIF-1α (coactivator for RORγt) physically associates with RORγt promotingIL-17expression without direct binding onIL-17locus. Nuclear protein inhibitor of kB (IκB)ζ, is another regulator, that also binds CNS2 elements in theIL-17locus. Various other transcription regulators that promoteIL-17gene expression by binding to its promoter include BATF, JunB, interferon regulatory factor (IRF)4, STAT3, Kruppel-like factor (KLF) 4. (C) IL-17 leads to induction of various genes encoding for inflammatory mediators, chemokines, antimicrobial peptides, and the osteoclastogenic factor RANKL. This leads to the wide-spread biological effects of IL-17, affecting a variety of cell types/tissues including keratinocytes, endothelial cells, fibroblasts, epithelial cells, and bone.

Th17 cells exhibit much superior plasticity compared to other T-cell subsets and epitomize a highly functionally diverse effector T cell population and also display stem cell-associated features [8]. Transforming growth factor (TGF)-β1 and IL-6 induced non-pathogenic/anti-inflammatory Th17 cells have been shown to play an important role in supporting cellular and organismal metabolic homeostasis as well [9]. However, Th17 cells are also recognized for their pathogenicity against the host, due to their association with several autoimmune diseases including psoriasis, multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, and diabetes mellitus. TGF-β3-induced, IL-23- dependent, functionally distinct pathogenic Th17 cells are characterized by different molecular, biochemical, and metabolic profiles (Table 1), conferring a proinflammatory phenotype to this effector T-cell subset [10].

ParameterNon-pathogenic Th17 cellsPathogenic Th17 cells
Polarizing cytokineTGF-β1/IL-6TGF-β3 + IL-6
IL-1β + IL-6 + IL-23
Master transcription factorRORγtRORγt
Upregulated expression of other relevant genes important for Th17 cell heterogeneityIL-10, Ahr, cMafIL-23, T-bet, CSF2
CD5L expressionPositiveNegative
Cytokines secretedIL-17, IL-10, IL-9IL-17, IFN-γ, GM-CSF
Metabolic profileGlycolytic ↑Glycolytic ↑↑↑
Glutaminolytic ↑Glutaminolytic ↑↑↑
Lipogenic (FAS)↑↑↑Lipogenic (FAS) ↑↑↑
Cholesterol biosynthesis↑Cholesterol biosynthesis ↑↑↑
Biochemical profile:
  • Cholesterol & it’s derivatives

LowHigh
  • PUFA levels

HighLow
  • SFA/MUFA levels

LowHigh
  • α-KG, 2-HG levels

High ↑High ↑↑↑
Functional profileAnti-inflammatory/regulatoryPro-inflammatory
Homeostasis/microbiota homeostasisAutoimmunity
Tissue repair/barrier integrityFibrosis
AdipogenesisTumorigenic
Thermogenesis

Table 1.

Differences between non-pathogenic and pathogenic Th17 cells.

Ahr, aryl hydrocarbon receptor; c-maf, c-musculoaponeurotic fibrosarcoma expression; csf2, colony stimulating factor; T-bet, T-box expressed in T cells; PUFA, polyunsaturated fatty acids; SFA, saturated fatty acids; MUFA, monounsaturated fatty caids; α-KG, alpha-ketoglutarate; 2-HG, 2-hydroxyglutarate.

Th17 cell evolution towards pro-inflammatory vs. anti-inflammatory or homeostatic phenotype is determined not only by a set of specific polarizing cytokines (TGF-β3 + IL-23 or IL-1β + IL-6 + IL-23 vs.TGF-β1 + IL-6) but also by a dynamically changing metabolic milieu comprised by a variety of metabolites viz. fatty acids, phospholipids, cholesterol intermediates, oxysterols and amino acids [11, 12, 13, 14, 15, 16]. These metabolites drive Th17 plasticity by changing the latter’s epigenetic landscape by serving as substrates for chromatin-modifying enzymes [17]. This remarkable metabolic heterogeneity can hugely influenceTh17 cell lineage plasticity and their effector function, thereby impacting the course of Th17-associated autoimmune inflammatory diseases, including psoriasis [18].

Advertisement

3. Psoriasis/psoriatic disease

Psoriasis is a progressive self-sustained and self-perpetuated inflammatory disease driven by the coexisting autoimmune and autoinflammatory pathways that, while primarily presenting with cutaneous involvement, also manifests as seronegative inflammatory arthritis with synovitis, enthesitis, dactylitis, and spondylitis [19]. It is quite heterogeneous in nature, characterized by a dynamic interplay of the individual’s genetic landscape, tissue-specific immune micro-environments, metabolite/immune-metabolite signature, host-microbiome interactions, and biomechanical stressors [20, 21].

3.1 Psoriasis and metabolic syndrome

Psoriasis has long been recognized as a chronic immune-mediated multi-systemic inflammatory disorder, associated with numerous comorbidities viz.Crohn’s disease, depression, cardiovascular disease, and metabolic syndrome [22]. A concept of ‘psoriatic march’ was postulated by Boehncke et al. for this inflammatory cascade to highlight the series of systemic inflammatory effects and their relationship with obesity and metabolic syndrome [23]. Metabolic syndrome represents a spectrum of metabolic complications encompassing obesity, hypertension, type 2 diabetes, insulin resistance, atherogenic dyslipidemia and non-alcoholic fatty liver disease (NAFLD) [21]. A recent metanalysis of 63 studies encompassing 15,939 psoriasis patients and 103,984 controls reported a significant association of psoriasis and metabolic syndrome (Odds ratio: 2.077; 95% confidence interval: 1.84–2.34) emphasizing regular monitoring of psoriatic patients for metabolic syndrome complications, including increased fasting plasma glucose levels, raised triglyceride levels, lowered high density cholesterol (HDL) levels, hypertension, and waist circumference [24]. Concurrent occurrence of these metabolic complications in psoriatic patients increases their risk of developing micro and macrovascular adversities contributing to significant morbidity and mortality [25, 26]. Increasing evidence demonstrates a complex interplay among immune cells with attendant aberrant immune dysfunction (such as is seen in psoriasis) and altered cellular and systemic metabolic axes across various organ systems, including adipose tissue, in triggering both local and systemic inflammation [27].

3.2 Th17/IL-17: crucial players in psoriasis

Psoriasis is considered as a “IL-17 centric” disease with a preponderance of pathogenic Th17 cells [28]. Psoriatic Th17 cells produce high levels of IL-17 (A to F), IL-26, IL-29 and IL-22 that synergistically act as transcriptional enhancers of many keratinocyte-expressed genes. IL-26 is linked with increased vascularisation while IL-29 regulates the expression of antiviral proteins [29, 30]. IL-17 induced inflammatory effects are not only limited to cutaneous plaques but also to more distant alterations in numerous different cell types that are responsible for producing systemic inflammatory effects and psoriasis-associated co-morbidities [31]. (Figure 1) IL-17 mediated psoriasogenicity is also linked in part to its synergism with other cytokines such as tumor necrosis factor (TNF)-α, IL-22, IL-23, IL-1β, IL-6, TGF-β across various organ systems [32].

Advertisement

4. Immunometabolism

A multifaceted, complex regulation of immune networks both depend on, and influences, cellular and local/systemic metabolic environment. This intricate, dynamic interplay between immunity and metabolism, i.e., “immunometabolism” outlines the metabolic patterning of immune cells and maintains metabolic homeostasis (local/systemic) but can also result in metabolic disorders dominated by deranged immune cells [33, 34]. In other words, immunometabolism can be defined as a molecular and biochemical intertwining of metabolism and immunology in all organisms that accounts for the physiological functioning of the immune system in different metabolic conditions in health and disease [35]. Immune response/inflammation can modulate cellular and tissue/systemic metabolism and vice-versa. Therefore, there are 2 dimensions to immunometabolism: the first is “cellular immunometabolism”, which includes the intracellular metabolism of a variety of immune cells under different states of activation, polarization, proliferation, and differentiation and the 2nd dimension is tissue/systemic immunometabolism, which explores the influences of immune cells and their products on local and systemic metabolism across various settings/organs [36]. Thus, the immune system, which can be prompted by the metabolic status of the body can, in turn, have significant consequences on cellular and systemic metabolic homeostasis or disarray.

We will cover these two dimensions of psoriasis-associated immune-metabolism separately:

  1. Cellular immunometabolism

    The impact of changes in major cellular metabolic pathways on differentiation of Th17 cells, the “signature” cells in psoriasis.

  2. Tissue/systemic immunometabolism

    The influence of the resultant Th17 response on metabolism across various tissues or organs, especially white adipose tissue (visceral and cutaneous) culminating in metabolic syndrome and other psoriasis-associated co-morbidities.

    Before delving deeper into these, we will brush up on immune-metabolic signaling pathways and discuss a basic outline of the major metabolic pathways used by immune cells.

4.1 Metabolic regulation of immune cells

Innate as well as adaptive immune cells have immense malleability to actively respond to different metabolic demands and diverse metabolic microenvironments viadynamic regulation of intracellular metabolism in health and disease [37]. Extracellular/environmental cues such as partial pressure of oxygen, oxidative stress, organ-specific pH, nutrient gradients, and disease-dependent fluctuations of the metabolic environment, can shift the metabolic homeostasis of immune cells. Body fluids such as blood and lymph, and the nervous system represent communication conduits for inter-organ coordination; distal communication is executed by the usual messenger molecules (hormones, neurotrophic peptides, cytokines, chemokines, and metabolites) while organelle communication within the cell is carried out by intracellular, spatially organized metabolic processes [38]. These communication networks at various levels orchestrate and harmonize responses to environmental cues/immunological challenges. The signals are sensed by metabolic serine/threonine kinases viz. phosphoinositide 3 kinase (PI3K)—protein kinase A, G and C (AGC) kinases (Akt), mechanistic/mammalian target of rapamycin (mTOR) C1/C2, and energy stress pathway kinases, i.e.,liver kinase (LK) B1–5′AMP-activated protein kinase (AMPK) in co-ordination with metabolic transcription factors like hypoxia-inducible factor (HIF)-1α, cellular-myelocytomatosis oncogene (c-MYC) and associated nutrient signaling networks. These integrate available environmental information to synchronize cellular proliferation by metabolic revamping [39]. mTOR has been recognized as a molecular orchestrator of immune cell metabolism and catalyzes aerobic glycolysis and anabolic metabolism after stimulation by proinflammatory agents [40]. AMPK, a sensitive radar for decreased cellular nutrients and energy, stimulates catabolic pathways by impeding mTORC1 activity and primarily facilitates T cell adaptation to situations of low nutrient availability as seen during malnutrition, starvation and in hypoxic microenvironments associated with chronic inflammatory conditions [41]. AMPK signaling promotes cell survival and also triggers autophagy (as an energy-preserving mechanism) and mitochondrial biogenesis. HIF-1α, regulated by growth factor signals, oxygen levels, and reactive oxygen species (ROS), rapidly increases in an mTOR-dependent manner and binds to hypoxia response elements (HRE) located in the promoter region of various target genes, this leads to their activation by opening the chromatin structure. Thus, the mTOR/HIF-1α axis is associated with the initiation and development of a “pro-inflammatory” metabolic signature while activation of AMPK favors the generation of an anti-inflammatory/tolerogenic response.

These intertwined and reciprocal PI3K-Akt/mTORC/HIF-1α/c-MYC and LKB1–AMPK immunometabolic signaling networks crosstalk viadirect reciprocal antagonisms, such as between mTORC1 and AMPK or Akt and LKB1 to regulate metabolism to meet context-specific and cell-specific functional needs [39]. These pathways are also mutually influenced by metabolites and nutrients generated as a consequence of these kinase dependent metabolic signaling, constituting ‘bidirectional metabolic signalling’ e.g. amino acid availability promoting mTORC1 signaling while low cellular glucose, low glutamine and elevated adenosine monophosphate (AMP) and adenosine diphosphate (ADP) concentrations can activate the AMPK pathway; metabolites generated from mitochondria-associated metabolism, i.e. α- ketoglutarate (α-KG), 2- hydroxyglutarate (2-HG) and acetyl-CoA, can influence transcription of a variety of genes lying in these immunometabolic signaling networks [42].

Figure 2 explains how T- cells, under the influence of environmental signals, renew their metabolic equipment to employ metabolic pathways that regulate and propel function of these immune cells.

Figure 2.

Signaling pathways regulating immunometabolism of immune cells. T-cell receptor ligation and CD28 costimulatory signals regulate immune-metabolic signaling pathways, i.e., mTORC1/C2 and LKB1-AMPK signaling. These signaling pathways are intertwined and crosstalk via direct reciprocal antagonism, i.e., AMPK directly inhibits mTORC1 while Akt suppresses LKB1 activity. (Left) Nutrient replete conditions activate PI3K-Akt/mTOR signaling that skew metabolic programming towards anabolism-associated processes such as glycolysis, fatty acid synthesis, and glutaminolysis supporting proliferation, differentiation, and heightened immune responses executed by effector immune cells (Th1, Th2, Th17 cells). (Right) Energy stress (increased AMP/ADP:ATP ratio), oxidative stress (increased reactive oxygen species, ROS), nutrient (glucose, glutamine) deprivation during malnutrition/starvation promotes LKB1-AMPK signaling, activating mitochondria-driven oxidative metabolism (citric acid cycle, oxidative phosphorylation, fatty acid oxidation), catabolic programs (autophagy/mitophagy), mitochondrial biogenesis, and inhibiting anabolic programs ultimately resulting in cellular quiescence.

4.2 Synopsis of metabolic modules used by immune cells

There are 7 fundamental inter-linked and co-regulated metabolic modules employed by immune cells to meet their energy demands. These include glycolysis, pentose phosphate pathway (PPP), citric acid cycle/Krebs cycle, mitochondrial oxidative phosphorylation (OXPHOS)/electron transport chain, fatty acid synthesis (FAS), fatty acid oxidation (FAO), and amino-acid metabolic pathways.

4.2.1 Glycolysis

Once glucose enters the cells through glucose transporters (GLUT), it is rapidly catabolized to pyruvate in a sequential enzymatic process generating a variety of macromolecules needed for different biosynthetic pathways (PPP, de novoFAS and amino acid (AA) Hypoxia synthesis pathways) as well as for maintaining cellular redox equilibrium (nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide hydrogen, NAD+/NADH), thereby supporting anabolic growth. Pyruvate either gets reduced to lactate (vialactate dehydrogenase, LDH) by engaging in cytosolic aerobic glycolysis (Warburg effect) in the presence of oxygen, generating 4 molecules of adenosine triphosphate (ATP)/unit of glucose or can enter the mitochondrial matrix (through citrate-pyruvate shuttle system) where it gets oxidized and decarboxylated to acetyl- coenzyme A (CoA) (viapyruvate dehydrogenase, PDH) to enter into Kreb’s cycle and undergo mitochondrial OXPHOS generating NADH/reduced flavin adenine dinucleotide (FADH2) and a total of 36 molecules of ATP/unit of glucose. Though ATP generation via glycolysis is far less as compared to OXPHOS, activated and effector immune cells rely on glycolysis because of its 100 times faster rate of ATP production, its proficiency to generate a range of biosynthetic intermediates needed for cell growth, as well as its ability to provide both these facilities in oxygen-poor conditions [43]. The mitochondrial enzyme PDH, a key bifurcation enzyme in the choice between glycolytic and mitochondrial oxidative metabolism, in its active dephosphorylated form catalyzes the movement of pyruvate from the cytoplasm to mitochondria for Kreb’s cycle. Because of its crucial bifurcating function, PDH is under tight regulation of PDH kinases (PDHKs) that inactivate PDH via phosphorylation, and PDH phosphatases (PDHPs) that dephosphorylate and activate phosphorylated-PDH.

4.2.2 Citric acid cycle/Krebs cycle

Kreb’s cycle serves as an important node for multiple nutrient inputs as it integrates fatty acid (FA) and AA metabolism with that of glucose by generating a variety of metabolic biosynthetic intermediates required for FA and AA synthesis and by metabolizing other substrates such as glutamine via glutaminolysis or FAs via β-oxidation. Kreb’s cycle results in the generation of different epigenetic-regulating metabolites, e.g., α-KG, 2-HG and acetyl-CoA to calibrate T-cell function [42].

4.2.3 PPP

The PPP provides important precursor molecules for nucleotide synthesis thereby contributing to cell growth. It also generates reducing equivalents of NADPH needed for the maintenance of a favorable cellular redox environment.

4.2.4 Amino acid metabolism

AAs have a significant impact on immune cell metabolism. They contribute to glycolysis by increasing translocation of GLUT1/GLUT4 (by leucine and isoleucine) to the cell surface and also by activating a glycolytic enzyme pyruvate kinase muscle enzyme 2, PKM2 (by serine). Glutaminolysis, i.e., conversion of glutamine into glutamate, is a basic and widespread metabolic process linking OXPHOS, redox regulation, and biosynthetic pathways (protein, nucleotides and branched chain FAs [44]. Glutamate, the first product of glutamine decomposition, can either aid in de novosynthesis of glutathione (GSH) to balance oxidative stress, or get converted into α-KG and enter the Kreb’s cycle to generate ATP, mitochondrial ROS, and biosynthetic precursors. Hence, the varied end-products of glutamate, by generating counteracting metabolites, i.e., GSH and ROS, enable meticulous synchronization of metabolic flux. AA-derived metabolic products like α-KG and 2-HG assist in chromatin remodeling by affecting histone and DNA modifications [42].

4.2.5 Lipid metabolism

FAs and cholesterol, important building materials for cell membranes, are energy-dense substrates, and are required for post-translational modifications, thereby modulating T-cell proliferation and differentiation. This ability to guide post-translational modifications (by serving as ligands for several transcription factors) varies with the length of their carbon atom chains (short-chain FAs with less than 6 carbon atoms vs. long-chain FAs with more than 12 carbon atoms) and their degree of saturation (polyunsaturated fatty acids, PUFA vs. saturated fatty acids, SFA/monounsaturated fatty acids, MUFA).

4.2.5.1 Fatty acid synthesis

FAS is chiefly mediated by the enzyme acetyl-CoA carboxylase 1 (ACC1), catalyzing the rate-limiting step in FA biosynthesis, i.e., the carboxylation reaction of acetyl-CoA to malonyl-CoA in the cellular cytoplasm. Acetyl-CoA needed for de novoFAS is made available in the cytoplasm through the citrate-pyruvate shuttle that first transfers pyruvate (final glycolytic product) from the cytoplasm into mitochondria to form acetyl-CoA and then transfers a substantial portion of citrate (one of the intermediates generated in the Kreb’s cycle by the combination of acetyl-CoA with oxaloacetate) from the mitochondria into the cytosol. In the cytoplasm, citrate lyase generates cytosolic acetyl-CoA from citrate that subsequently powers downstream FA, cholesterol, and lipid biosynthesis [45, 46]. Another important enzyme participating in the initial steps of SFA/MUFA/PUFA generation is fatty acid synthase (FASN), a multienzyme complex that operates downstream of ACC1 and mediates the conversion of acetyl-CoA and malonyl-CoA to saturated long-chain FAs [47]. CD5 antigen-like (CD5L) protein, a member of the scavenger receptor cysteine-rich superfamily inhibits the de novosynthesis of SFA through direct binding to FASN, thus helping maintain the intracellular lipidome saturation by modulating PUFA versus SFA levels. CD5L is more than a general inhibitor as it regulates the quantity as well as the quality of fatty acids being generated inside the cell, fine-tuning the FA composition in T cells, causing elevation of PUFA and alterations in specific lipid species, including cholesterol metabolites, guiding post-translational modifications [13].

Acetyl-CoA is a central intermediate in lipid metabolism. In addition to FAS, cytosolic acetyl-CoA can be catalyzed in the mevalonate-cholesterol synthetic pathway, generating cholesterol and its derivatives (desmosterol, 4α-carboxy, 4β-methyl-zymosterol, oxysterols viz.7β, 27-dihydroxy-cholesterol <7β, 27–OHC > and 7α, 27–OHC, 20α-OHC, 22R-OHC, 25-OHC) [15, 48]. All these lipid biosynthetic processes are delicately regulated by coordinated actions of sterol response element binding proteins (SREBPs), the transcription factors which activate all genes necessary for lipid synthesis with SREPB1 inducing genes involved in de novolipogenesis whereas SREBP2 activating genes necessary for cholesterol synthesis and uptake [49].

4.2.5.2 Fatty acid oxidation

Long-chain free FAs enter the metabolizing cells viaspecific transport proteins (SLC27) where they are acted upon by long-chain fatty acid-CoA ligase resulting in a fatty acyl-adenylate, which then reacts with free coenzyme A (CoA) in the presence of acyl-CoA synthetase (ACS) to give an acyl-CoA molecule. Acyl-CoA enters the mitochondria through carnitine transporter, which itself is directed by 3 enzymes including carnitine palmitoyl transferase I (CPT I) located on the cytosolic faces of the outer and inner mitochondrial membranes, and carnitine-acylcarnitine translocase (CAT) and carnitine palmitoyl transferase II (CPT II) located on the interior face of the inner mitochondrial membrane. In the mitochondrial matrix, beta-oxidation cuts the long-chain FAs (now in the form of acyl-CoA molecules) into a series of two-carbon acetate units, which, combined with CoA, form acetyl CoA. This is how acetyl-CoA is added to the cycle, which will be dissipated as carbon dioxide and water, releasing a substantial quantity of energy - captured in the form of ATP—with each beta oxidative cut of the acyl-CoA molecule yielding 5 ATP molecules. It has been calculated that complete β-oxidation of a single palmitate molecule can potentially yield over 100 ATP molecules.

Effector and regulatory/tolerogenic immune cells employ different metabolic modules to fulfill their energy requirements. Activated immune cells and effector immune cell subsets including Th1, Th2, Th17 cells and M1 macrophages upregulate glucose and AA transporters to increase their uptake and rely on aerobic glycolysis, glutaminolysis, PPP and FAS to support pro-inflammatory cytokine secretion while regulatory cells including T regs, memory T cells and M2 macrophages predominantly utilize FAO and OXPHOS to meet their ATP requirements [43].

Advertisement

5. Cellular immunometabolism of Th17 cells, their development and pathogenicity: how intracellular metabolism plays a fundamental role in determining plasticity of Th17 cells

Within Th17 cell subset, depending on the presence of further local stimulatory cues (metabolites), there exists substantial functional and molecular heterogeneity determining the generation of pathogenic or non-pathogenic Th17 cells [18]. Due to the shared developmental requirement of TGF-β and due to functional and physical interaction of master transcriptional factors, i.e. RORγt and Foxp3 regulating Th17 and Treg respectively, these cells are capable of transdifferentiating into each other. The reciprocal metabolic cues are fundamental in shaping the relative proportions of Th17 vs.T reg cells and non-pathogenic vs.pathogenic Th17 cells, affecting Th17 cell plasticity and pathogenicity. Essentially, the active Th17 cells utilize the faster ATP-producing, oxygen-independent pathways, while the Treg cells utilize the more efficient, if slower, oxidative pathways.

Metabolically, Th17 cells are characterized by “glycolytic-lipogenic-glutaminolytic”anabolic phenotype with highly active PPP, ensuring the availability of biosynthetic precursors [50].

5.1 Glycolysis

T cell receptor (TCR) ligation and CD28 co-stimulatory signals induce PI3K dependent phosphorylation of Akt that activates key metabolic regulator mTOR (selective role of mTORC1 but not mTORC2 in Th17 differentiation) leading to increased glycolysis (Figure 3). Under Th17-polarizing conditions, the PI3K-Akt/mTORC1/HIF-1α/c-MYC axis activates a series of reactions shifting the Th17/Treg cell balance in favor of Th17 cells. HIF-1α drives Th17 differentiation while simultaneously suppressing Treg induction viaits differential interaction with transcription factors RORγt and Foxp3 causing transactivation of the former and proteasomal degradation of the latter [51]. HIF-1α doubly enhances this response: firstly, it binds to hypoxia response element (HRE) located in the proximal region of the RORCgene promoter (Figure 1) and secondly, it might physically associate with RORγt transcription factor, serving as a coactivator for RORγt, thereby increasing IL-17gene expression without direct DNA binding on the IL-17gene locus [7]. HIF-1α is an essential facilitator of the acquisition of Th17 glycolytic metabolism as shown in Figure 3 as it enhances expression of a series of glycolytic enzymes including GLUT1 (central glucose transporter on T cells) leading to robust glucose uptake, hexokinase 2 (HK), pyruvate kinase muscle enzyme (PKM2) and lactate dehydrogenase (LDH) causing a shift to aerobic glycolysis. The enzyme PDHK1 that inactivates the PDH enzyme, has been identified as an important player in selective regulation of Th17 cell differentiation and inflammation as evidenced by higher levels of PDHK1 expression on Th17 cells [52]. The transcription factor, inducible cAMP early repressor (ICER, an endogenous repressor of cAMP-responsive element {CRE})-mediated gene transcription, plays a vital role in deciphering Th17 cell biology. It has been shown to be overexpressed in Th17 cells, binds to and suppresses PDHPs, reducing PDH activity thereby enhancing glycolysis, and subsequently increasing Th17 differentiation [53]. Therefore, activation of glycolytic pathways contributes to the differentiation of pro-inflammatory Th17 cells that exhibit enhanced pathogenicity in the context of autoimmune responses.

Figure 3.

Impact of glucose metabolism in Th17 cell differentiation: T-cell receptor (TCR) ligation and CD28 co-stimulation integrate phosphatidylinositol 3-kinase (PI3K)-Akt signaling, activating a kaleidoscope of metabolic pathways. mTORC1 activates glycolytic pathways in Th17 cells through activation of the transcription factors c-myc and hypoxia-inducible factor 1-alpha (HIF-1α), mediating multiple pathways. (1) HIF-1α enhances cellular glucose uptake by promoting membrane translocation of the glucose transporter 1 (GLUT1). (2) HIF-1α causes ubiquitination-mediated proteosomal degradation of Foxp3 thereby shifting the Th17/Treg cell balance towards Th17 cells. (3) HIF-1α inducesIL-17gene transcription. (4) HIF-1α enhances expression of genes encoding for key glycolytic enzymes hexokinase (HK2, rate-limiting enzyme of glycolysis), lactate dehydrogenase (LDH, converting pyruvate to lactate), and (4a) pyruvate kinase muscle enzyme 2 (PKM2), that catalyzes the final step of glycolysis producing pyruvate from phosphoenolpyruvate. PKM2 cab functionally exists as either a dimer or a tetramer, each exerting different functions: the cytoplasmic tetrameric configuration is associated with glycolytic activity while nuclear dimeric form interacts with transcription factors and histones, enabling post-translational modifications including theIL-17gene. (5) HIF-1α also induces pyruvate dehydrogenase kinase, PDHK1 enzyme that phosphorylates and inactivates pyruvate dehydrogenase (PDH) that catalyzes the mitochondrial oxidative conversion of pyruvate to acetyl-coA for launching Kreb’s cycle), thereby, nurturing aerobic glycolysis in Th17 cells.

5.2 Amino acid metabolism

Rapid AA import mediated by the amino acid transporters propels Th17 cell lineage specification by enhancing mTORC1 activity leading to enhanced protein biosynthesis and glycolysis. ICER binds to the IL-17gene promoter, enhancing its transcription. It enhances glutaminolysis through glutaminase induction and finally generates glutathione that supports Th17 cell steadiness by enhancing ROS-associated detoxification pathways, polarizing them towards a pathogenic phenotype [54]. Transamination of glutamate (catalyzed by the glutamate oxaloacetate transaminases (GOT)1/2), can epigenetically redirect Th17/T reg equilibrium towards Th17 cell destiny by generating epigenetic-regulating metabolites (α-KG and 2-HG). 2-HG, an inhibitor of α -KG-dependent histone/DNA demethylases, directly increases DNA methylation at CpG islands at the Foxp3gene locus leading to its transcriptional repression (Figure 4). This is how increased levels of 2-HG in Th17 cells lead to blockade of Treg cell lineage commitment. Th17 cells are characterized by an abundance of α-KG and 2-HG. This highlights the importance of the glutamate-GOT1/2-α-KG-2-HG axis in guiding Th17 cell destiny. GOT1 also contributes to increased mTORC1 signaling by suppressing AMPK activation [55].

Figure 4.

Glutamine and methionine potentiating Th17 cell differentiation by impacting epigenetic landscape: (1) glutamine imported by the amino acid transporter, (alanine-serine-cysteine transporter, ASCT2) activated mTORC1/c-Myc signaling axis, enhancing glycolysis. (2) Glutaminase, transactivated by ICER binding, generates glutamate and finally glutathione. (3) glutathione causes reactive oxygen species (ROS) neutralization, enhancing pathogenic Th17 cell production. (4) glutamate is metabolized to α-KG [viaglutamate oxaloacetate transaminases, GOT1/2]. (5) GOT1/2 inhibits the AMPK pathway leading to enhanced mTORC1 signaling. (6) α-KG generates 2-HGvia.Isocitrate dehydrogenases 1/2(IDH1/2). (7) 2-HG impacts DNA methylation by inhibiting histone/DNA demethylases, e.g., Jumonji domain-containing demethylase (Jmjc) and ten-eleven translocation (TET1/2) methylcytosine dioxygenases (that demethylate the CpG islands on theFoxp3promoter) (8). This leads to heightened DNA methylation at CpG islands of theFoxp3gene locus and its transcriptional repression. (9) methionine-derived S-adenosyl methionine (SAM), by serving as a methyl donor, causes chromatin remodeling and helps maintain permissive H3K4me3 marks onIL-17,IFNG, andCSF2genes promoting their transcription, leading to increased pathogenic Th17 generation.

Methionine-derived S-adenosyl methionine (SAM) plays a crucial role in chromatin remodeling by serving as a co-factor for epigenome-modifying enzymes, maintaining permissive H3K4me3 marks on IL-17a, IFNG, and CSF2genes promoting their transcription leading to increased pathogenic Th17 generation [56].

In this way, amino acids regulate energy metabolism, redox balance, and impact the epigenetic landscape, modulating Th17 lineage heterogeneity and plasticity [42].

5.3 Lipogenesis

Rather than utilizing already-available exogenous FA for their lipid requirements, Th17 cells primarily engage in the ATP-costly process of de novoFAS for their proliferation and differentiation [45]. De novoFA and cholesterol synthesis promote activation-induced proliferation and differentiation of Th17 cells (Figure 5). Cholesterol precursors, as well as its derivatives, are essential for Th17 cell lineage commitment [57]. They enhance the transcriptional activity of RORγt by increasing co-activator recruitment leading to enhanced IL-17and IL-23gene transcription (Figure 5 Inset) CYP51 and CYP27A1, key mediators of the cholesterol biosynthesis pathway are the most highly upregulated genes in Th17 cells [15]. An upregulation of cholesterol biosynthesis and simultaneous downregulation of cholesterol metabolism and efflux during Th17 differentiation leads to the accumulation of the cholesterol precursors desmosterol and its sulphate conjugates. Th17-polarizing milieu upregulates expression of SREBP1and SREBP2, FASN, 3- hydroxy-3-methylglutaryl-CoA reductase (HMGCR, the rate-limiting enzyme in the mevalonate–cholesterol pathway) as well as expression of enzymes involved in citrate-pyruvate shuttle system leading to enhanced cholesterol synthesis and rapid de novoFAS from glucose. This metabolic alteration is again under partial control of mTORC1 activation. In addition, to enhancing glycolysis, mTOR signaling fabricates a closely-interacting loop between glycolysis and lipogenesis, “the glycolytic-lipogenic pathway” in Th17 development [58] .

Figure 5.

Lipid metabolism as a central controller of Th17 cell differentiation: T-cell receptor (TCR) ligation and CD28 co-stimulation activate PI3K-Akt/mTORC1 signaling affecting lipid metabolism. (1) increased expression of sterol response element-binding proteins (SREBP2),CYP51and3-hydroxy-3-methylglutaryl-CoA reductase(HMGCR) enhances cholesterol biosynthesis. (2) this leads to accumulation of the cholesterol precursors desmosterol, its sulphate conjugates as well as oxysterols [with participation of CYP27A1]. (3) these precursors and derivatives serve as endogenous RORγt agonists, enhancing pathogenic Th17 cell production. The ligand-binding domain (LBD) of RORγt binds to these agonistic ligands including CD5L-dependent displacing the co-repressors and recruiting co-activator proteins, causing enhancedIL-17gene expression. DNA-binding domain (DBD) of RORγt binds to ROR response elements (RORE) located in CNS2 (conserved non-coding sequences) ofIL-17gene, and globally controls its transcription. (4) HMGCR also contributes towards protein geranylation by generating mevalonate. (5) increased expression ofSREBPIstimulatesde novofatty acid synthesis by enhancing the expression ofacetyl-CoA carboxylase 1(ACC1) andfatty acid synthase(FASN) genes. (6) CD5 antigen-like (CD5L) protein inhibits thede novosynthesis of saturated and monounsaturated fatty acids (SFA/MUFA) causing elevation of polyunsaturated fatty acids (PUFA) that results in enhanced RORγt binding to theIL-10locus leading to its transactivation and production of non-pathogenic Th17 cells. (7) loss of CD5L elevates intracellular SFA/MUFA levels causing enhanced RORγt binding to theIL-17αandIL-23loci leading to their transactivation and production of pathogenic Th17 cells.

5.3.1 Type of fatty acids governing pathogenicity of T cells

Cellular lipid composition influences both generation as well as pathogenicity of Th17 cells. CD5Lgene, encoding CD5L protein, a “negative” regulator of Th17 pathogenicity; has been recognized as a critical molecular switch of Th17 cell’s function (pathogenic versus non-pathogenic phenotype) though it does not affect Th17 differentiation [59]. Loss of CD5L transforms non-pathogenic Th17 cells into disease-inducing pathogenic ones by modulating intracellular lipidome saturation (PUFA versus SFA lipid balance) and by elevating intracellular free cholesterol, thereby regulating the quality and/or quantity of available ligands for RORγt [13]. PUFA regulates the ligand-dependent function of RORγt: in the absence of CD5L/PUFA (Figure 5), RORγt binding to the IL-17and IL-23loci is increased leading to transactivation of both these genes, while binding to IL-10locus is decreased leading to its downregulation. Thus, the balance of lipid saturation contributes to CD5L-dependent regulation of Th17 cells by regulating the RORγt genomic binding and Th17-cell transcriptome.

Dietary LCFAs enhance Th1 and Th17 cell differentiation and also alter the composition of the gut microbiome whereas SCFAs (derived from diet/intestine/microbiota) promote Treg cell formation, demonstrating unique phenotypes driven by different fatty acids [60].

The same glycolytic-lipogenic-glutaminolytic metabolic axis co-ordinated by mTORC1/HIF-1α, plays an equally important role in controlling the generation as well as the “pathogenicity” of Th17 cells. These findings highlight how the generation of Tregs/non-pathogenic Th17/pathogenic Th17 cells is tightly linked to their metabolic state, offering potential new targets for the regulation of these two reciprocally regulated T cell subsets (Table 2). Thus, it is quite clear that the generation of Th17 cells is entwined with complex and intricate intracellular metabolic adaptations.

Metabolic adjustments favoring Th17 differentiationMetabolic adjustments impairing Th17 differentiation
Activation of PI3K/AKT-mTORC1 pathwayActivation of AMPK with 5-aminoimidazole-4-carboxamide riboside, AICAR (a direct activator) and metformin
Induction of transcription factors HIF-1α and c-MYC (c-MYC initiates the metabolic reprogramming while HIF-1α sustains it)Inhibition of glycolysis with 2-deoxyglucose (an inhibitor of all hexokinases)
Activation of glycolysis, pentose-phosphate pathway and glutaminolytic pathwayInhibition of hexokinase-2 with specific inhibitor 3-bromopyruvate
Selective expression of PDHK1 (inhibited PDH activity) directing pyruvate flow through LDH-mediated reactions to produce lactate (aerobic glycolysis)Inhibition of acetyl CoA carboxylase 1 with a pharmacological inhibitor soraphen A, inhibiting de novofatty acid synthesis
Overexpression of inducible cAMP early repressor (ICER) that suppresses expression of PDHPs leading to reduced PDH activity and enhanced glycolysisInhibition of 3- hydroxy-3-methylglutaryl-CoA reductase (HMGCR) with statins
A diet low in cholesterol and high in fibers can skew Th17 cells towards non-pathogenic anti-inflammatory phenotypeInhibition of glutamine oxaloacetate transaminase (GOT1/2) by aminooxyacetic acid that leads to decreased production of α-ketoglutarate and 2- hydroxyglutarate skewing Th17 differentiation to the inducible T regulatory cells (iTregs) lineage
A diet high in glucose/salt/ methionine can induce the generation of pathogenic pro-inflammatory Th17 cells

Table 2.

Comprehensive list of metabolic changes determining Th17 cell fate.

5.4 Crosstalk between IL-17 and cellular immunometabolism in psoriasis

IL-17 has the potential to temper the cellular metabolism in a variety of ways. IL-17 has already been shown to regulate metabolism in psoriatic keratinocytes by reprogramming the urea cycle resulting in excessive polyamine generation that facilitates self-RNA sensing by immune cells independent of RNA-binding proteins LL37 and HNRNPA1 (the proven autoantigens) ultimately leading to amplification of inflammatory circuits [61, 62].

IL-17 also induces intracellular cholesterol accumulation that facilitates NF-κB mediated up-regulation of CCL20, IL-8 and S100A7 expression in keratinocytes thereby further intensifying IL-17A induced psoriatic inflammation [63]. This highlights that how IL-17-induced metabolic alterations can actively participate in eliciting infiltration and activation of innate and adaptive immune cells and keratinocyte hyperproliferation leading to sustained inflammatory dermatoses.

5.4.1 Therapeutics in psoriasis

Depending upon disease severity (based on PASI scores), topical therapy (vitamin analogues, tars, corticosteroids, dithranol, and retinoids), phototherapy (UV-B or PUV-A), and systemic therapy are considered for treatment of psoriasis. Systemic therapeutic agents used in psoriasis include methotrexate, cyclosporine, retinoids and biologics including etanercept, adalimumab, efalizumab, and alefacept. Other approved biologics for psoriasis include anti-IL-23 antibodies (ustekinumab, guselkumab, tildrakizumab, mirikizumab, risankizumab) targeting only IL-23/IL-17 axis and anti-IL-17 antibodies including anti-IL-17A agents (ixekizumab and secukinumab), anti-IL-17 receptor molecules (brodalumab); or drugs targeting both IL-17A and IL-17F (bimekizumab) [64]. These antibodies have shown promising results in the treatment of psoriasis with IL-17 blockers showing much quicker clinical efficacy resulting in a 50% decline in PASI scores as early as 1.8 weeks as compared to IL-23 blockers [65, 66].

To decrease toxicity and enhance efficacy, a few anti-metabolites are currently being repurposed and investigated as potential therapeutic agents for the management of psoriasis [67]. Metformin, an antidiabetic drug, has the potential to exert an antipsoriatic effect via. AMPK activation [68]. Simvastatin, an HMG-CoA reductase inhibitor, in combination with steroids, has demonstrated positive clinical outcomes in psoriatic patients in terms of improved PASI and dermatological life quality index [69]. These examples provide insight to clinicians for investigating the safety and efficacy of existing anti-metabolite drugs to reposition them as effective psoriasis therapeutic agents.

Advertisement

6. Adipose tissue and systemic immunometabolism

One tissue that has recently become an area of intense metabolic research is adipose tissue. The adipose tissue is an important metabolic organ that regulates the balance of energy intake and consumption and is actively involved in the regulation of many systemic metabolic pathways including glucose and lipids. Adipose tissue also serves as an endocrine organ (secreting bioactive adipocytokines) and an important immune cell niche (secreting chemokines and cytokines, exerting beneficial or detrimental effects on immunometabolism) with visceral white adipose tissue as the major immune-metabolic communication hub. Adipose tissue also communicates with other organs including the liver and muscle and contributes to systemic metabolism viasecretion of a variety of bioactive molecules as mentioned above [70]. Adipose tissue (visceral as well as cutaneous) has recently emerged as an important node linking immunity/inflammation, obesity and a cluster of metabolic diseases including psoriasis. Adipose tissue is a highly dynamic tissue showing the extreme degree of plasticity and remodeling potential (by expansion or contraction of adipocytes in response to energy surplus or famine) that makes it a suitable centre for maintaining immune-metabolic homeostasis [71].

6.1 Adipose tissue and immune-metabolism: an interesting interlink

Besides metabolically active parenchymal adipocytes and preadipocytes, adipose tissue is also comprised of a diverse and malleable immune-landscape comprising both innate and adaptive immunocytes residing in special adipose niches [72]. This diversity of T cell pools in adipose tissue is the result of intracellular metabolic alterations that in turn influence systemic metabolism in innumerable ways. Adipose tissue-resident immune cells include T cells, B cells, macrophages, and dendritic cell subsets and other unconventional lymphocyte subtypes viz. invariant natural killer T (iNKT) cells, mucosal-associated invariant T (MAIT) cells, γδ T cells and innate lymphoid cells 2 (ILCs) with either stimulating or regulatory roles under different physiological or pathological conditions [73]. All these immunocytes work in close co-operation with preadipocytes, adipocytes, endothelial cells and stromal cells (fibroblasts) to maintain the immune and metabolic homeostasis of adipose tissue, providing a steady environment to maintain the normal systemic metabolism of an organism [74]. Extremely heterogeneous mesenchymal stromal cells (five subtypes numbered 1–5, defined by single-cell transcriptomics analysis and cytofluorimetric assessment of marker expression) in visceral white adipose tissue have been characterized as key orchestrators of metabolic-immunologic cross-talk by their ability to balance ‘immunocyte’numbers through secretion of IL-33 (subtypes 1–3) and ‘adipocyte’numbers/activities through regulation of adipocyte precursors (subtypes 4 and 5) [75]. The stromal cell-derived IL-33, a mechanosensitive chemokine, dampens aberrant inflammation by increasing Treg numbers. IL-33 has been recently shown to induce mitochondrial rewiring, thereby promoting differentiation of alternatively activated macrophages finally leading to resolution of inflammation [76]. Taken together, tight and well-balanced cooperation and coordination exist between parenchymal-stromal-immune cell populations that ultimately regulate adipose tissue homeostasis.

Intradermal adipocytes residing in “superficial subcutaneous adipose tissue” or “dermal white adipose tissue” of psoriatic skin secrete monocyte chemoattractant protein-1 (MCP-1) favoring macrophage recruitment via the C-C chemokine receptor 2 (CCR2) pathway and also release high levels of antimicrobial peptides, cathelicidin, contributing to the pathophysiology of psoriasis [77].

6.1.1 Adipocytokines and cytokines released by adipocytes

Adipocytes participate in the regulation of the immune system viasecretion of various cytokines – including IL-6 – and adipocytokines such as adiponectin and leptin. IL-6, an obligatory pro-inflammatory cytokine, drives naïve CD4+ T cells differentiation into Th17 lineage, and in association with IL-17A, regulates the differentiation of adipocytes and their capacity to secrete adipocytokines (especially leptin) and chemokines [78].

Adiponectin is an insulin-sensitizing anti-inflammatory adipocytokine that corrects insulin resistance and obesity-induced NAFLD (21). Leptin is a critical hormonal regulator of metabolism and an important signaling transducer that activates JAK2 kinase causing tyrosine phosphorylation of various downstream signaling proteins, e.g., STAT3, SHP2, IRS2, and PI3K, thereby regulating transcription of genes essential for energy intake and lipid metabolism [79, 80]. Leptin also affects various immune cells including dendritic cells (DCs), neutrophils, NK cells, T and B cells, through surface leptin receptors and regulates a variety of cellular biological processes involving chemokinesis, chemotactic responsiveness, cell migration, proliferation, cell survival (delayed apoptosis) and pro-inflammatory cytokine production [81, 82]. Resistin, another cytokine is also known as an adipose tissue-specific secretory factor (ADSF) also has the pro-inflammatory potential [83].

A disturbed balance of pro-inflammatory and anti-inflammatory cytokines and adipocytokines (hormones) can cause chronic adipose tissue inflammation resulting in obesity and associated metabolic complications.

6.2 Obesity, systemic immunometabolism and psoriasis

Obesity (fat and weight gain/body mass index (BMI) ≥ 35 kg/m2/increased abdominal fat mass) resulting from adipose tissue expansion and adipocyte hypertrophy, is a state of chronic systemic low-grade inflammation that accelerates obesity-related insulin resistance (IR), leading to the development of the metabolic syndrome, including diabetes mellitus (DM). Obesity increases the body’s vulnerability to a variety of immune diseases, such as psoriasis, by abnormally altering the whole biology of adipose tissue including stromal-driven regulation of immunocyte and adipocyte numbers. Obesity evokes extensive remodeling of adipose tissue morphology and function with alterations of both immune as well as stromal cell landscapes resulting in metabolic and/or immunologic aberrancies [84]. The number of pro-inflammatory immunocytes viz. CD8+ T cells, M1 macrophages, neutrophils, mast cells, and γδ T cells increases while the proportions of tolerogenic/immunosuppressive cells viz. Tregs, regulatory B cells (B regs), eosinophils, iNKT cells, alternatively activated macrophages and type 2 ILCs (copiously producing anti-inflammatory cytokines IL-10, IL-15, IL-2, IL-5, and IL-25) are either decreased or have impaired functional capacity. Various studies have already shown a strong “dose-dependent” relationship between PASI scores and obesity with improvement in disease severity as a result of weight loss in psoriatic patients [85, 86].

Obesity has been postulated to worsen psoriasis via.Its effect on the Treg/Th17 axis through various metabolites, adipocytokines like leptin and pro-inflammatory cytokines like IL-6, released by inflamed adipocytes [72, 87, 88] for example, higher leptin and resistin concentrations have been observed in obese, psoriatic patients [89, 90].

Adiponectin plays a crucial role in controlling psoriasiform dermatosis by reducing Th17 cell differentiation, restraining glycolysis in an AMPK dependent fashion, thus tightly regulating their nutritional demands and metabolic function [91, 92, 93]. Psoriasis patients with or without metabolic abnormalities exhibit significant hypoadiponectinemia (negatively corelated with psoriasis area severity index, PASI) and hyperleptinemia with leptin resistance (positively corelated with PASI), that contribute to the development of the metabolic syndrome.

The pro-inflammatory cytokine IL-17, a potential linker between metabolic syndrome and psoriasis, causes adipose tissue inflammation by mediating important interactions between adipose tissue and the immune system, leading to IR (the key component of metabolic syndrome) finally manifesting as obesity, DM, hypertension, NAFLD, and hyperlipidemia [21].

Thus, it can be inferred that obesity-driven immune and stromal landscape alterations in adipose tissue, in turn leading to disturbances in systemic metabolism might enhance Th17 differentiation and effector function, consequently leading to increased severity of psoriasis.

6.3 Nutritional metabolism and psoriasis

Beyond the adipose tissue, the diet or nutritional status, a.k.a. nutritional metabolism, also has a profound impact on the immune system by influencing the immune cell metabolic parameters; malnutrition is clearly associated with diminished immune function whereas a “Western” lifestyle/nutritional pattern rich in calories, fat, and salt, leads to a low-grade systemic inflammation thereby predisposing individuals to a variety of autoimmune diseases associated with metabolic complications, including psoriasis. Diet-associated obesity by increasing availability of extracellular lipids revamps cellular metabolism of innate as well as adaptive immune cells [94]. It is quite tantalizing to consider dietary interventions like fasting-mimicking diets and diets low in salt and calories re-stabilizing the immune-metabolism in psoriasis patients, potentially serving as viable substitutes or adjuvants to drugs directly targeting cellular metabolism.

Understanding the relationship between nutrition and metabolism and its impact on cellular/systemic immunometabolism in psoriasis is important to develop novel therapeutic strategies.

Advertisement

7. Metabolomics in psoriasis

Metabolites/biochemical intermediates/end-products, unique chemical fingerprints of proteomic or cellular metabolic pathways, are viewed as keystones of life as they provide vital communication signals that are necessary to sustain life. Metabolomics, a systematic study of the global metabolite profile in a biological system, i.e., cell, tissue, organ, or organism, provides an “instantaneous snapshot”/direct “functional readout of the physiological state”, capturing the metabolic perturbations driving physiological and disease states. The metabolome, highly dynamic like transcriptome and proteome, and a rapid indicator of biological status, refer to the complete set of diverse small-molecules (<1500 Da) such as sugars, nucleotides, amino acids and lipids in any biological system.

A large number of psoriatic disease-related metabolomic studies have been carried out in the recent past to identify metabolomic biomarkers associated with psoriatic disease [95]. The majority of studies have focused on identifying a metabolic profile that can be used for diagnosis of psoriasis and/or psoriatic arthritis while others have explored correlating the metabolome with different dimensions of psoriatic disease activity to improve clinical management. A variety of biological samples including peripheral blood (whole blood, plasma, serum, peripheral blood mononuclear cells), urine, and skin tissue (uninvolved skin, psoriatic skin, and corticosteroid treated psoriatic skin) have been researched in these metabolomic studies, revealing alterations predominantly in pathways associated with lipid and amino acid metabolism. To get more meaningful information, a few study-groups have compared metabolite concentrations in different biological milieu by examining metabolomes across multiple sample matrices. A variety of metabolites in the eicosapentaenoic, docosahexaenoic and arachidonic acid pathways are elevated in both the skin and the peripheral blood of psoriatic patients [96, 97]. Plasma and psoriatic skin choline levels correlated positively while citrulline levels across both sample matrices correlated negatively with disease activity scores [98].

A step further, Tarentini et al. integrated results of metabolomic profiling and cytokine/chemokines profiling from lesional skin and serum of psoriatic patients and identified immuno-metabolic clusters indicating biochemical pathways associated with the initial phases of psoriasis development, thus hinting at putative biomarkers of new-onset psoriasis [99].

Advertisement

8. Future perspectives and summary

It is intriguing to explore and validate metabolomic biomarkers that can accurately and reliably predict which psoriatic patients will develop psoriatic arthritis. Identifying metabolites that could differentiate psoriatic arthritis patients from patients with other inflammatory arthritides would be a great added advantage. The synovial fluid, being in direct contact with articular cartilage, bone and synoviocytes, is a very promising candidate for deciphering metabolomic information which can serve as a promising source of biomarkers for psoriatic arthritis.

Metabolic health and gut microbiome dysbiosis are emerging areas of intense investigation as the gut microbiome influences host immunity and metabolism by producing numerous compounds [100]. The connection between the microbiome and the metabolome of patients with heterogenous psoriatic disease holds the potential to highlight aberrant signaling pathways likely driving “psoriatic march”. This could pave the path for the development of clinically useful biomarkers for early recognition and management of comorbidities for this patient population [101].

Thus, immuno-metabolic reprogramming may be worth further exploration for the comprehension of its therapeutic potential in psoriasis. In future studies, it will be quite intriguing to define the interplay between IL-17–driven metabolic reprogramming and epigenetics/chromatin remodeling that are responsible for chronic, sustained transcriptional responses seen in psoriasis, which in turn modulate the activity of IL-17- related proinflammatory cytokine programs.

Integrating metabolomics with other high-throughput-omic technologies such as genomics, epigenomics, transcriptomics, and proteomics can unravel molecular, cellular, and functional signatures associated with psoriasis pathogenesis [102, 103]. The “omics” datasets, thus generated, can help construct predictive or diagnostic classifiers, grouping psoriatic patients based on their probability of developing systemic comorbidities or their likelihood to respond to a specific therapy [104]. This cutting-edge, systemic, and holistic approach will allow the clinicians to institute tailored, targeted, precision medicine, based on individual patient characteristics thereby maximizing efficacy and minimizing toxicity and at the same time overcoming the biggest challenge we face in achieving long-term, stable remission in psoriatic patients.

Advertisement

Conflict of interest

None.

References

  1. 1. Wilson CS, Stocks BT, Hoopes EM, Rhoads JP, McNew KL, Major AS, et al. Metabolic preconditioning in CD4+ T cells restores inducible immune tolerance in lupus-prone mice. JCI Insight. 2021;6(19):e143245. Available from:https://pubmed.ncbi.nlm.nih.gov/34403367
  2. 2. Piranavan P, Bhamra M, Perl A. Metabolic targets for treatment of autoimmune diseases. Immunometabolism. 2020;2(2):e200012. DOI: 10.20900/immunometab20200012
  3. 3. Wagner A, Wang C, Fessler J, DeTomaso D, Avila-Pacheco J, Kaminski J, et al. Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell. 2021;184(16):4168-4185
  4. 4. Puniya BL, Amin R, Lichter B, Moore R, Ciurej A, Bennett SJ, et al. Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders. npj Systems Biology and Applications. 2021;7(1):1-18
  5. 5. Ueno A, Jeffery L, Kobayashi T, Hibi T, Ghosh S, Jijon H. Th17 plasticity and its relevance to inflammatory bowel disease. Journal of Autoimmunity. 2018;87:38-49
  6. 6. Ma H, Kang J, Fan W, He H, Huang F. ROR: Nuclear receptor for melatonin or not? Molecules. 2021;26(9):2693. DOI: 10.3390/molecules26092693
  7. 7. Capone A, Volpe E. Transcriptional regulators of T helper 17 cell differentiation in health and autoimmune diseases. Frontiers in Immunology. 2020;11:348. DOI: 10.3389/fimmu.2020.00348
  8. 8. Karmaus PWF, Chen X, Lim SA, Herrada AA, Nguyen T-LM, Xu B, et al. Metabolic heterogeneity underlies reciprocal fates of TH 17 cell stemness and plasticity. Nature. 2019;565(7737):101-105
  9. 9. Bechara R, McGeachy MJ, Gaffen SL. The metabolism-modulating activity of IL-17 signaling in health and disease. The Journal of Experimental Medicine. 2021;218(5):e20202191. DOI: 10.1084/jem.20202191
  10. 10. Lee Y, Awasthi A, Yosef N, Quintana FJ, Xiao S, Peters A, et al. Induction and molecular signature of pathogenic TH 17 cells. Nature Immunology. 2012;13(10):991-999
  11. 11. Michalek RD, Gerriets VA, Jacobs SR, Macintyre AN, MacIver NJ, Mason EF, et al. Cutting edge: Distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. Journal of Immunology. 2011;186(6):3299-3303
  12. 12. Haghikia A, Jörg S, Duscha A, Berg J, Manzel A, Waschbisch A, et al. Dietary fatty acids directly impact central nervous system autoimmunity via the small intestine. Immunity. 2015;43(4):817-829
  13. 13. Wang C, Yosef N, Gaublomme J, Wu C, Lee Y, Clish CB, et al. CD5L/AIM regulates lipid biosynthesis and restrains Th17 cell pathogenicity. Cell. 2015;163(6):1413-1427
  14. 14. Santori FR, Huang P, Van De Pavert SA, Douglass EF Jr, Leaver DJ, Haubrich BA, et al. Identification of natural RORγ ligands that regulate the development of lymphoid cells. Cell Metabolism. 2015;21(2):286-298
  15. 15. Hu X, Wang Y, Hao L-Y, Liu X, Lesch CA, Sanchez BM, et al. Sterol metabolism controls Th 17 differentiation by generating endogenous RORγ agonists. Nature Chemical Biology. 2015;11(2):141-147
  16. 16. Soroosh P, Wu J, Xue X, Song J, Sutton SW, Sablad M, et al. Oxysterols are agonist ligands of RORγt and drive Th17 cell differentiation. Proceedings of the National Academy of Sciences. 2014;111(33):12163-12168
  17. 17. Bantug GR, Hess C. The burgeoning world of immunometabolites: Th17 cells take center stage. Cell Metabolism. 2017;26(4):588-590
  18. 18. Papadopoulou G, Xanthou G. Metabolic rewiring: A new master of Th17 cell plasticity and heterogeneity. The FEBS Journal. Apr 2021. DOI: 10.1111/febs.15853 (Online ahead of print)
  19. 19. CEM G, Armstrong AW, Gudjonsson JE, JNWN B. Psoriasis. Lancet (London, England). 2021;397(10281):1301-1315
  20. 20. Ghoreschi K, Balato A, Enerbäck C, Sabat R. Therapeutics targeting the IL-23 and IL-17 pathway in psoriasis. Lancet (London, England). 2021;397(10275):754-766
  21. 21. Hao Y, Zhu Y, Zou S, Zhou P, Hu Y, Zhao Q, et al. Metabolic syndrome and psoriasis: Mechanisms and future directions. Frontiers in Immunology. 2021;12:711060. DOI: 10.3389/fimmu.2021.711060
  22. 22. Yan D, Blauvelt A, Dey AK, Golpanian RS, Hwang ST, Mehta NN, et al. New frontiers in psoriatic disease research, part II: Comorbidities and targeted therapies. The Journal of Investigative Dermatology. 2021;141(10):2328-2337
  23. 23. Boehncke W, Boehncke S, Tobin A, Kirby B. The ‘psoriatic march’: A concept of how severe psoriasis may drive cardiovascular comorbidity. Experimental Dermatology. 2011;20(4):303-307
  24. 24. Choudhary S, Pradhan D, Pandey A, Khan M, Lall R, Ramesh V, et al. The association of metabolic syndrome and psoriasis: A systematic review and meta-analysis of observational study. Endocrine, Metabolic & Immune Disorders—Drug Targets. 2020;20(5):703-717
  25. 25. Gottlieb AB, Chao C, Dann F. Psoriasis comorbidities. The Journal of Dermatological Treatment. 2008;19(1):5-21
  26. 26. Menter A, Griffiths CEM, Tebbey PW, Horn EJ, Sterry W, Council IP. Exploring the association between cardiovascular and other disease-related risk factors in the psoriasis population: The need for increased understanding across the medical community. Journal of the European Academy of Dermatology and Venereology. 2010;24(12):1371-1377
  27. 27. Bajaj S, Mandal S, Singh KG, Prajapati R. Metabolic diseases and associated complications in patients with psoriasis. The Journal of the Association of Physicians of India. 2020;68(10):44-46
  28. 28. Bianchi E, Vecellio M, Rogge L. Role of the IL-23/IL-17 pathway in chronic immune-mediated inflammatory diseases: Mechanisms and targeted therapies. Frontiers in Immunology. 2021;12:770275. DOI: 10.3389/fimmu.2021.770275
  29. 29. Itoh T, Hatano R, Komiya E, Otsuka H, Narita Y, Aune TM, et al. Biological effects of IL-26 on T cell–mediated skin inflammation, including psoriasis. The Journal of Investigative Dermatology. 2019;139(4):878-889
  30. 30. Wolk K, Witte K, Witte E, Raftery M, Kokolakis G, Philipp S, et al. IL-29 is produced by TH17 cells and mediates the cutaneous antiviral competence in psoriasis. Science Translational Medicine. 2013;5(204):204ra129
  31. 31. Krueger JG, Brunner PM. Interleukin-17 alters the biology of many cell types involved in the genesis of psoriasis, systemic inflammation and associated comorbidities. Experimental Dermatology. 2018;27(2):115-123
  32. 32. Binger KJ, Côrte-Real BF, Kleinewietfeld M. Immunometabolic regulation of interleukin-17-producing T helper cells: Uncoupling new targets for autoimmunity. Frontiers in Immunology. 2017;8:311. DOI: 10.3389/fimmu.2017.00311
  33. 33. Makowski L, Chaib M, Rathmell JC. Immunometabolism: From basic mechanisms to translation. Immunological Reviews. 2020;295(1):5-14
  34. 34. Galgani M, Bruzzaniti S, Matarese G. Immunometabolism and autoimmunity. Current Opinion in Immunology. 2020;67:10-17
  35. 35. Stathopoulou C, Nikoleri D, Bertsias G. Immunometabolism: An overview and therapeutic prospects in autoimmune diseases. Immunotherapy. 2019;11(9):813-829
  36. 36. Man K, Kutyavin VI, Chawla A. Tissue immunometabolism: Development, physiology, and pathobiology. Cell Metabolism. 2017;25(1):11-26
  37. 37. Mogilenko DA, Haas JT, L’homme L, Fleury S, Quemener S, Levavasseur M, et al. Metabolic and innate immune cues merge into a specific inflammatory response via the UPR. Cell. 2019;177(5):1201-1216
  38. 38. Lercher A, Baazim H, Bergthaler A. Systemic immunometabolism: Challenges and opportunities. Immunity. 2020;53(3):496-509
  39. 39. Saravia J, Raynor JL, Chapman NM, Lim SA, Chi H. Signaling networks in immunometabolism. Cell Research. 2020;30(4):328-342
  40. 40. Saxton RA, Sabatini DM. mTOR signaling in growth, metabolism, and disease. Cell. 2017;168(6):960-976
  41. 41. von Meyenn L, Bertschi NL, Schlapbach C. Targeting T cell metabolism in inflammatory skin disease. Frontiers in Immunology. 2019;10:2285. DOI: 10.3389/fimmu.2019.02285
  42. 42. Kelly B, Pearce EL. Amino assets: How amino acids support immunity. Cell Metabolism. 2020;32(2):154-175
  43. 43. O’Neill LAJ, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nature Reviews. Immunology. 2016;16(9):553-565
  44. 44. Lin W, Shen P, Song Y, Huang Y, Tu S. Reactive oxygen species in autoimmune cells: Function, differentiation, and metabolism. Frontiers in Immunology. 2021;12:635021. DOI: 10.3389/fimmu.2021.635021
  45. 45. Berod L, Friedrich C, Nandan A, Freitag J, Hagemann S, Harmrolfs K, et al. De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells. Nature Medicine. 2014;20(11):1327-1333
  46. 46. Thomas SP, Denu JM. Short-chain fatty acids activate acetyltransferase p300. eLife. 2021;10:e72171. DOI: 10.7554/eLife.72171
  47. 47. Young KE, Flaherty S, Woodman KM, Sharma-Walia N, Reynolds JM. Fatty acid synthase regulates the pathogenicity of Th17 cells. Journal of Leukocyte Biology. 2017;102(5):1229-1235
  48. 48. Cai F, Jin S, Chen G. The effect of lipid metabolism on CD4+ T cells. Mediators of Inflammation. Jan 2021. DOI: 10.1155/2021/6634532
  49. 49. Howie D, Ten Bokum A, Necula AS, Cobbold SP, Waldmann H. The role of lipid metabolism in T lymphocyte differentiation and survival. Frontiers in Immunology. 2018;8:1949. DOI: 10.3389/fimmu.2017.01949
  50. 50. Shan J, Jin H, Xu Y. T cell metabolism: A new perspective on Th17/treg cell imbalance in systemic lupus erythematosus. Frontiers in Immunology. 2020;11:1027. DOI: 10.3389/fimmu.2020.01027
  51. 51. Dang EV, Barbi J, Yang H-Y, Jinasena D, Yu H, Zheng Y, et al. Control of TH17/Treg balance by hypoxia-inducible factor 1. Cell. 2011;146(5):772-784
  52. 52. Gerriets VA, Kishton RJ, Nichols AG, Macintyre AN, Inoue M, Ilkayeva O, et al. Metabolic programming and PDHK1 control CD4+ T cell subsets and inflammation. The Journal of Clinical Investigation. 2015;125(1):194-207
  53. 53. Kono M, Yoshida N, Maeda K, Skinner NE, Pan W, Kyttaris VC, et al. Pyruvate dehydrogenase phosphatase catalytic subunit 2 limits Th17 differentiation. Proceedings of the National Academy of Sciences. 2018;115(37):9288-9293
  54. 54. Yoshida N, Comte D, Mizui M, Otomo K, Rosetti F, Mayadas TN, et al. ICER is requisite for Th17 differentiation. Nature Communications. 2016;7(1):1-13
  55. 55. Xu T, Stewart KM, Wang X, Liu K, Xie M, Ryu JK, et al. Metabolic control of TH 17 and induced T reg cell balance by an epigenetic mechanism. Nature. 2017;548(7666):228-233
  56. 56. Roy DG, Chen J, Mamane V, Ma EH, Muhire BM, Sheldon RD, et al. Methionine metabolism shapes T helper cell responses through regulation of epigenetic reprogramming. Cell Metabolism. 2020;31(2):250-266
  57. 57. Ladurner A, Schwarz PF, Dirsch VM. Natural products as modulators of retinoic acid receptor-related orphan receptors (RORs). Natural Product Reports. 2021;38(4):757-781. DOI: 10.1039/d0np00047g
  58. 58. Shen H, Shi LZ. Metabolic regulation of TH17 cells. Molecular Immunology. 2019;109:81-87
  59. 59. Gaublomme JT, Yosef N, Lee Y, Gertner RS, Yang LV, Wu C, et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell. 2015;163(6):1400-1412
  60. 60. Tan J, McKenzie C, Potamitis M, Thorburn AN, Mackay CR, Macia L. The role of short-chain fatty acids in health and disease. Advances in Immunology. 2014;121:91-119
  61. 61. Lou F, Sun Y, Xu Z, Niu L, Wang Z, Deng S, et al. Excessive polyamine generation in keratinocytes promotes self-RNA sensing by dendritic cells in psoriasis. Immunity. 2020;53(1):204-216
  62. 62. Bandyopadhyay M, Larregina AT. Keratinocyte-polyamines and dendritic cells: A bad duet for psoriasis. Immunity. 2020;53(1):16-18
  63. 63. Varshney P, Narasimhan A, Mittal S, Malik G, Sardana K, Saini N. Transcriptome profiling unveils the role of cholesterol in IL-17A signaling in psoriasis. Scientific Reports. 2016;6(1):19295. DOI: 10.1038/srep19295
  64. 64. Lebwohl M. Interleukin-23 blockade: Another breakthrough in the treatment of psoriasis. Lancet. 2019;394(10198):544-546. DOI: 10.1016/S0140-6736(19)31513-2
  65. 65. Papp KA, Lebwohl MG. Onset of action of biologics in patients with moderate-to-severe psoriasis. Journal of Drugs in Dermatology. 2017;17:247-250
  66. 66. Reich K, Gooderham M, Thaçi D, Crowley JJ, Ryan C, Krueger JG, et al. Risankizumab compared with adalimumab in patients with moderate-to-severe plaque psoriasis (IMMvent): A randomised, double-blind, active-comparator-controlled phase 3 trial. Lancet. 2019;394(10198):576-586
  67. 67. Jain H, Bhat AR, Dalvi H, Godugu C, Singh SB, Srivastava S. Repurposing approved therapeutics for new indication: Addressing unmet needs in psoriasis treatment. Current Research in Pharmacology and Drug Discovery. 2021;2:100041. DOI: 10.1016/j.crphar.2021.100041
  68. 68. Chang JE, Choi MS. A molecular perspective on the potential benefits of metformin for the treatment of inflammatory skin disorders. International Journal of Molecular Sciences. 2020;21(23):8960
  69. 69. Naseri M, Hadipour A, Sepaskhah M, Namazi MR. The remarkable beneficial effect of adding oral simvastatin to topical betamethasone for treatment of psoriasis: A double-blind, randomized, placebo-controlled study. Nigerian Journal of Medicine. 2010;19(1):58-61
  70. 70. Scheja L, Heeren J. The endocrine function of adipose tissues in health and cardiometabolic disease. Nature Reviews. Endocrinology. 2019;15(9):507-524
  71. 71. Winn NC, Cottam MA, Wasserman DH, Hasty AH. Exercise and adipose tissue immunity: Outrunning inflammation. Obesity. 2021;29(5):790-801
  72. 72. Wang Q, Wu H. T cells in adipose tissue: Critical players in immunometabolism. Frontiers in Immunology. 2018;9:2509. DOI: 10.3389/fimmu.2018.02509
  73. 73. Wang Q, Wang Y, Xu D. The roles of T cells in obese adipose tissue inflammation. Adipocytes. 2021;10(1):435-445
  74. 74. Lu J, Zhao J, Meng H, Zhang X. Adipose tissue-resident immune cells in obesity and type 2 diabetes. Frontiers in Immunology. 2019;10:1173. DOI: 10.3389/fimmu.2019.01173
  75. 75. Spallanzani RG, Zemmour D, Xiao T, Jayewickreme T, Li C, Bryce PJ, et al. Distinct immunocyte-promoting and adipocyte-generating stromal components coordinate adipose tissue immune and metabolic tenors. Science Immunology. 2019;4:eaaw3658. DOI: 10.1126/sciimmunol.aaw3658
  76. 76. Faas M, Ipseiz N, Ackermann J, Culemann S, Grüneboom A, Schröder F, et al. IL-33-induced metabolic reprogramming controls the differentiation of alternatively activated macrophages and the resolution of inflammation. Immunity. 2021;54(11):2531-2546
  77. 77. Chen SX, Zhang L-J, Gallo RL. Dermal white adipose tissue: A newly recognized layer of skin innate defense. The Journal of Investigative Dermatology. 2019;139(5):1002-1009
  78. 78. Al-Mansoori L, Al-Jaber H, Prince MS, Elrayess MA. Role of inflammatory cytokines, growth factors and adipokines in adipogenesis and insulin resistance. Inflammation. 2022;45(1):31-44
  79. 79. Rijnsburger M, Djuric N, Mulder IA, de Vries HE. Adipokines as immune cell modulators in multiple sclerosis. International Journal of Molecular Sciences. 2021;22(19):10845. DOI: 10.3390/ijms221910845
  80. 80. Gerriets VA, Danzaki K, Kishton RJ, Eisner W, Nichols AG, Saucillo DC, et al. Leptin directly promotes T-cell glycolytic metabolism to drive effector T-cell differentiation in a mouse model of autoimmunity. European Journal of Immunology. 2016;46(8):1970-1983
  81. 81. Su X, Zhang G, Cheng Y, Wang B. Leptin in skin disease modulation. Clinica Chimica Acta. 2021;516:8-14
  82. 82. Su X, Cheng Y, Chang D. The important role of leptin in modulating the risk of dermatological diseases. Frontiers in Immunology. 2021;11:593564. DOI: 10.3389/fimmu.2020.593564
  83. 83. Li Y, Yang Q, Cai D, Guo H, Fang J, Cui H, et al. Resistin, a novel host defense peptide of innate immunity. Frontiers in Immunology. 2021;12:699807. DOI: 10.3389/fimmu.2021.699807
  84. 84. Lee M-J, Wu Y, Fried SK. Adipose tissue remodeling in pathophysiology of obesity. Current Opinion in Clinical Nutrition and Metabolic Care. 2010;13(4):371-376
  85. 85. Langan SM, Seminara NM, Shin DB, Troxel AB, Kimmel SE, Mehta NN, et al. Prevalence of metabolic syndrome in patients with psoriasis: A population-based study in the United Kingdom. The Journal of Investigative Dermatology. 2012;132(3):556-562
  86. 86. Warren RB, Marsden A, Tomenson B, Mason KJ, Soliman MM, Burden AD, et al. Identifying demographic, social and clinical predictors of biologic therapy effectiveness in psoriasis: A multicentre longitudinal cohort study. The British Journal of Dermatology. 2019;180(5):1069-1076
  87. 87. Aune D, Snekvik I, Schlesinger S, Norat T, Riboli E, Vatten LJ. Body mass index, abdominal fatness, weight gain and the risk of psoriasis: A systematic review and dose–response meta-analysis of prospective studies. European Journal of Epidemiology. 2018;33(12):1163-1178
  88. 88. Hwang J, Yoo JA, Yoon H, Han T, Yoon J, An S, et al. The role of leptin in the association between obesity and psoriasis. Biomolecules & Therapeutics. 2021;29(1):11-21
  89. 89. Dopytalska K, Baranowska-Bik A, Roszkiewicz M, Bik W, Walecka I. The role of leptin in selected skin diseases. Lipids in Health and Disease. 2020;19(1):1-10
  90. 90. Yamazaki F. Psoriasis: Comorbidities. The Journal of Dermatology. 2021;48(6):732-740
  91. 91. Ruiyang B, Panayi A, Ruifang W, Peng Z, Siqi F. Adiponectin in psoriasis and its comorbidities: A review. Lipids in Health and Disease. 2021;20(1):1-11
  92. 92. Shibata S, Tada Y, Hau CS, Mitsui A, Kamata M, Asano Y, et al. Adiponectin regulates psoriasiform skin inflammation by suppressing IL-17 production from γδ-T cells. Nature Communications. 2015;6(1):1-14
  93. 93. Surendar J, Frohberger SJ, Karunakaran I, Schmitt V, Stamminger W, Neumann A-L, et al. Adiponectin limits IFN-γ and IL-17 producing CD4 T cells in obesity by restraining cell intrinsic glycolysis. Frontiers in Immunology. 2019;10:2555. DOI: 10.3389/fimmu.2019.02555
  94. 94. Michelet X, Dyck L, Hogan A, Loftus RM, Duquette D, Wei K, et al. Metabolic reprogramming of natural killer cells in obesity limits antitumor responses. Nature Immunology. 2018;19(12):1330-1340
  95. 95. Koussiouris J, Looby N, Anderson M, Kulasingam V, Chandran V. Metabolomics studies in psoriatic disease: A review. Metabolites. 2021;11(6):375. DOI: 10.3390/metabo11060375
  96. 96. Sorokin AV, Domenichiello AF, Dey AK, Yuan Z-X, Goyal A, Rose SM, et al. Bioactive lipid mediator profiles in human psoriasis skin and blood. The Journal of Investigative Dermatology. 2018;138(7):1518-1528
  97. 97. Sorokin AV, Norris PC, English JT, Dey AK, Chaturvedi A, Baumer Y, et al. Identification of proresolving and inflammatory lipid mediators in human psoriasis. Journal of Clinical Lipidology. 2018;12(4):1047-1060
  98. 98. Dutkiewicz EP, Hsieh K-T, Urban PL, Chiu H-Y. Temporal correlations of skin and blood metabolites with clinical outcomes of biologic therapy in psoriasis. Journal of Applied Laboratory Medicine. 2020;5(5):877-888
  99. 99. Tarentini E, Odorici G, Righi V, Paganelli A, Giacomelli L, Mirisola V, et al. Integrated metabolomic analysis and cytokine profiling define clusters of immuno-metabolic correlation in new-onset psoriasis. Scientific Reports. 2021;11(1):1-12
  100. 100. Lynch SV, Pedersen O. The human intestinal microbiome in health and disease. The New England Journal of Medicine. 2016;375(24):2369-2379
  101. 101. Chen L, Li J, Zhu W, Kuang Y, Liu T, Zhang W, et al. Skin and gut microbiome in psoriasis: Gaining insight into the pathophysiology of it and finding novel therapeutic strategies. Frontiers in Microbiology. 2020;11:589726. DOI: 10.3389/fmicb.2020.589726
  102. 102. Aydin B, Arga KY, Karadag AS. Omics-driven biomarkers of psoriasis: Recent insights, current challenges, and future prospects. Clinical, Cosmetic and Investigational Dermatology. 2020;13:611-625. DOI: 10.2147/CCID.S227896
  103. 103. Liu Y, Cui S, Sun J, Yan X, Han D. Identification of potential biomarkers for psoriasis by DNA methylation and gene expression datasets. Frontiers in Genetics. 2021;12:722803. DOI: h10.3389/fgene.2021.722803
  104. 104. Su W, Wei Y, Huang B, Ji J. Identification of hub genes and immune infiltration in psoriasis by bioinformatics method. Frontiers in Genetics. 2021;12:606065. DOI: 10.3389/fgene.2021.606065

Written By

Seema Chhabra, Smrity Sahu, Keshav Sharma, Maryada Sharma, Lekha Rani, Ranjana Minz and Sunil Dogra

Submitted: December 2nd, 2021 Reviewed: January 12th, 2022 Published: April 5th, 2022