DOI: 10.31038/JCRM.2022555

Abstract

The brain is a central key organ of the body containing the second highest lipid content only after adipose tissue. Lipids as the main structural components of biological membranes play important roles in a vast number of biological processes within the brain such as energy homeostasis, material transport, signal transduction, neurogenesis and synaptogenesis, providing a balanced cellular environment required for proper functioning of brain cells. Lipids and their metabolism are of great physiological importance in view of the crucial roles of lipids in brain development and function. Astrocytes are the most abundant glial cells in the brain and involved in various processes including metabolic homeostasis, blood brain barrier maintenance, neuronal support and crosstalk. Disturbances in lipid metabolism and astrocytic functions may lead to pathological alterations associated with numerous neurological diseases like Alzheimer’s Disease (AD) recognized as the most frequent cause of dementia leading to major progressive memory and cognitive deficits as well as Glioblastoma (GBM) known as the most aggressive malignant brain tumor with a poor prognosis. Herein, we not only review the level and role of altered lipid metabolism in correlation with astrocytic function and astrocyte-neuron crosstalk in AD and GBM, but also discuss important lipid-related metabolites and proteins participating in possible mechanisms of pathologically dysregulated lipid metabolism, offering potential therapeutic targets in targeted molecular therapies for AD and GBM.

Keywords

Astrocyte, Lipid metabolism, Alzheimer’s disease, Glioblastoma

Introduction

The brain is a central and pivotal organ highly enriched in lipids (constituting 50% to 60% of brain dry weight) [1], the major biomacromolecules characterized with poor water-solubility and good solubility in non-polar organic solvent, and is regarded with the second highest lipid content next to the adipose tissue [2]. Lipids are a class of fatty substances differing in overall structure, molecular weight, head group configuration, carbon-carbon bond formation and other factors, among which fatty acids, phospholipids, sphingolipids, sterol lipids and triglycerides are the five main brain lipid classes [3], serving as basic structural components of biological membranes and participating in a broad variety of physiological events, including chemical energy generation and storage, substance transport, cellular signaling, neural differentiation, axonal regeneration, synaptogenesis, synaptic plasticity and brain development [4-14].

The brain consists of neurons and non-neuronal cells such as glial and vascular epithelial cells, of which astrocytes represent the most abundant glial cells [15,16]. Astrocytes mediate diverse biological activities under physiological conditions, including structural and energy support for neurons [17,18], neuronal development and maintenance [19,20], formation, function and plasticity of synapses [21,22], modulation of synaptic transmission [22], metabolomic homeostasis [23] as well as integrity of the Blood-brain Barrier (BBB) [24,25] which is a semipermeable membrane regulating solute exchange between blood and brain parenchyma to maintain CNS homeostasis and function and partially separating local lipid metabolism of the brain from that of the body [25-33]. Apart from the well-known enzymatic capacity of glycogenesis and glycolysis [34-38], equipment of lipid metabolism also exists in astrocytes, providing membrane components for neurons and other glial cells [39,40] and playing fundamental roles in astrocyte function including membrane fluidity, energy generation and intercellular signaling. Emerging evidence has shown that astrocytic usage of lipids stored in droplets via mitochondrial β-oxidation fulfills crucial energy-providing and neuroprotective roles in the brain [18,41], whereby disruption in lipid metabolism, structure and function of astrocytes may lead to pathogenic mechanisms underlying an array of neurological diseases.

Lipid Classification in the Brain

Fatty Acids

As one of the most well-known lipid class, Fatty Acids (FAs), the essential monomeric constituents of all lipids, account for almost 20% of the energy source through oxidation, for which astrocytes as the major provider of fatty acid β-oxidation may be the essential place [42-44]. Additionally, fatty acids can also be utilized by astrocytes for producing ketone bodies under particular conditions (e.g. ischemia), serving as a substrate for neuronal energy production-related Tricarboxylic Acid (TCA) cycle [45]. Fatty acids permeate the Blood-brain Barrier (BBB) via passive (dissociation from albumin carriers, binditheng to luminal membrane which belong to endothelia cells, ATP-independent release and entrance into the cytosol) and/or protein-mediated transport (e.g. Fatty Acid Transport Proteins (FATPs), fatty acid translocase/CD36 (FAT/CD36), Fatty Acid Binding Proteins (FABPs) and caveolin-1) [46,47]. Fatty acids can be further divided into unsaturated and saturated fatty acids, from which the former subclass contains Monounsaturated Fatty Acids (MUFAs) and Polyunsaturated Fatty Acids (PUFAs), while the latter comprises palmitic acid, stearic acid and others [48,49]. PUFAs are highly enriched in the brain, with 3- to 4-fold level over other tissues [50,51]. What’s more, essential PUFAs play key roles in brain activity and development [52,53], in which the ω-3 Docosahexaenoic Acid (DHA) are particularly involved in synaptogenesis, neurogenesis and neuroprotection in the brain [54-57].

Phospholipids

As the most abundant constituent of major categories of membrane lipids [58,59], Phospholipids (PLs) generally consist of two hydrophobic tails of fatty acids differing in length and a backbone-attached hydrophilic phosphate group [60-62].

Phospholipids, which are synthesized in the mitochondria and Endoplasmic Reticulum (ER) tracing from diacylglycerol and phosphatidic acid, spontaneously aggregate into the formation of bimolecular layers in aqueous environments on account of configuration and amphipathic property [63]. Phospholipids can be classified into glycerophospholipids and phosphosphingolipids, of which glycerophospholipids are the prominent glycerol-based class of lipid molecules which can be further subclassified into subtypes such as Phosphatidic Acid (PA), Phosphatidylcholine (PC), Phosphatidylethanolamine (PE), Phosphatidylglycerol (PG), Phosphatidylinositol (PI) and Phosphatidylserine (PS) on the basis of variation in hydrophilic head groups and participate in a variety of physiological activities in the brain [59,64,65]. Moreover, fates of brain cells are influenced by exposure to different phospholipids, such as differentiation of neural cells into astrocytes was promoted and inhibited with PE and PC treatment, respectively [66].

Sphingolipids

Sphingolipids containing sphingoid bases (also known as long-chain bases) and a set of aliphatic amino alcohols that includes sphingosine are mainly synthesized in Endoplasmic Reticulum (ER). Sphingolipids comprise a large group of lipid molecules through compounding with different functional groups, such as ceramide (functional group of single hydrogen atoms) and Sphingomyelin (SM) (functional groups including phosphocholine) with regards to structural composition, functioning as building blocks of membranes (e.g. lipid rafts) [67] and playing fundamental roles in formation and regulation of synapse structure and function [68], cell recognition, signal transmission and inflammatory regulation of astrocytes [69-72]. Besides, sphingolipid metabolites have also been discovered to exert regulatory roles in autophagy, cancer cell growth, response to DNA damage and inflammation [73-75].

Sterol Lipids

Sterol lipids include numerous organic molecules, of which cholesterol with four hydrocarbon rings is the main part. Cholesterol can be synthesized in ER by all nucleated cells, while over 70% of total body cholesterol are provided by the diet [76], namely the cholesterol absorbed in the gut transfers into the liver and then spreads through the body. What is noteworthy is that the brain, unlike other organs, makes its own cholesterol because of effective prevention of peripheral cholesterol exchange between brain tissue and plasma cholesterol-carrying lipoproteins by the BBB [77-79]. In brain tissue, de novo synthesis of cholesterol is mainly performed in astrocytes which are considered as the main cholesterol producer in the brain [80], though the majority of sterol is synthesized in oligodendrocytes in developing brain and has an association with myelination [81] and oligodendrocytes, besides, cholesterol can also be synthesized in many other cell types [82-84]. Apart from de novo synthesis [85], brain cells are able to acquire cholesterol from neighboring cells through the absorption of cholesterol-laden lipoproteins (e.g. Apolipoprotein E (APOE)) in a receptor-mediated way [86,87], in which lipoprotein synthesis for cholesterol transport occurs in astrocytes [88]. With abundant existence in myelin and lipid membranes [81], cholesterol fulfills vital roles in the brain, including BBB integrity, organization of lipid rafts (discrete microdomains present in the external leaflet of plasma membrane), regulation of cell membrane flexibility (through interaction with neighbouring phospholipids) and localization and activity of diverse membrane proteins (e.g. membrane receptor and transporter proteins), axonal guidance, formation and maintenance of synapses and dendrites, synaptic membranerelated fluidity and ion channel function, glucose transport, intracellular signaling and other important neuronal functions [84,89-103].

Triglycerides

As the major form of FA deposition and the optimal form of FA triesters of glycerol, Triglycerides (TGs) are essential ingredients of glycerolipid synthesis by assembling with other glycerol molecules [104]. Triglycerides mainly generated in the adipose tissue and liver can reach other tissues with the package into lipoproteins containing a hydrophilic exterior and a hydrophobic lipid core, including chylomicrons, Very-lowdensity Lipoproteins (VLDL), low-density lipoproteins (LDL), very-high-density lipoproteins (VHDL) and high-density lipoproteins (HDL) only which can cross the BBB [105-108]. Additionally, apolipoprotein E (ApoE) and apolipoprotein J (ApoJ), the most abundant apolipoproteins synthesized in astrocytes, serve as receptor ligands on HDL [109-111] and play fundamental roles in lipid metabolism-associated structural support, enzyme activity and substrate delivery [110,112-114].

Astrocyte-Neuron Coupling of Lipid Metabolism

In humans, the brain representing, on average, merely 2% of total body weight consumes approximately and over 20% of energy substrates during quiet waking and diverse tasks, respectively [115,116], which depends on relatively efficient metabolic coupling between astrocytes and neurons. Physiologically, astrocytes are considered primarily as glycolytic cells with a large enzymatic capacity for glycolysis [115,117,118], whereas neurons are predominantly oxidative [119-121]. Besides the glucose metabolism in which astrocytes participate in the delivery of blood-derived glucose to neurons as an obligatory energy fuel, glycogen storage and activitydependent L-lactate production as a metabolic substrate for neurons during aerobic glycolysis [115,122-124], astrocytes-neuron coupling of lipid metabolism has also been suggested to occur as a response to neuronal activity in protection of neurons from lipotoxicity [125,126]. This is a mechanism proposing that L-lactate-derived de novo synthesis of free fatty acids (FFAs) in overstimulated neurons is triggered during astrocyte-neuron L-lactate shuttle (ANLS), resulting in excess FFAs in association to lipotoxicity-related reactive oxygen species (ROS) and lipid peroxidation chain reaction [127], peroxidized FAs with devastating effects [127] are then transferred from hyperactive neurons to astrocytes via apolipoprotein E-positive lipid particles, where they are directly stored in lipid droplets (LDs) [125,126,128] which are dynamic organelles possessing a core of neutral lipids (e.g. cholesterol esters (CEs) and triacylglycerides (TAGs)), influencing fatty acid breakdown for energy production [129] and buffering excess FFAs to prevent lipid accumulation [130] as well as utilized as an energy substrate in β-oxidation [126] (Figure 1).

Figure 1: Astrocyte-Neuron coupling of lipid metabolism. Excess fatty acids produced in hyperactive neurons are transferred via lipid particles associated with APOE to astrocytes, where fatty acids are delivered to lipid droplets after endocytosis of neuron-derived lipid particles, detoxified as a means of neuron protection under conditions of enhanced activity as well as consumed by mitochondrial oxidation (e.g. β-oxidation). FAs, fatty acids; APOE, apolipoprotein E; LDs, lipid droplets.

AD

With the worldwide increase in longevity, Alzheimer’s disease (AD) as the most common form of senile dementia is rapidly becoming a major health problem [131,133]. AD is a devastating irreversible neurodegenerative disease clinically defined by memory loss, neuropsychiatric abnormalities, cognitive impairment, behaviour deficits and progressive decline of self-care capacity [134-136] as well as pathologically characterized by extracellular amyloid-ß (Aβ) plaques and intracellular neurofibrillary tangles (NFTs) composed of hyperphosphorylated microtubuleassociated protein tau [137-139]. Moreover, accumulation of lipid granules in glia, besides notorious Aβ deposition and tau aggregates, was noticed with the examination of Auguste Deter’s brain (the first described AD patient), initially establishing a possible involvement of perturbations of lipid metabolism in AD pathology [140,141]. Altered lipid metabolism has also been further described with important roles in AD pathogenesis [142-151].

Recent AD pathology-related lipidome studies have demonstrated changes in content of numerous lipids (Table 1). Substantial differences in fatty acid levels were observed in AD brain tissues [152,153], including a decrease in levels of docosahexaenoic acid (DHA) present in frontal cortex gray matter [154] and hippocampus [155] to which damage correlates with impaired learning and memory [156], suggesting a dysregulation of fatty acid metabolism and may potentially marking this neurodegenerative disease [157]. Cholesterol accumulation observed in senile plaques and influenced brain regions from AD patients [158] has been reported in association with region-specific synapse loss [159]. A causal relationship between hypercholesterolemia and dysfunctional cholinergic system, cognitive impairments and pathology of amyloid and tau protein has been also demonstrated [160,161], further supporting important roles of disturbed cholesterol metabolism in AD. What’s more, detection of elevated cholesterol esters was performed in lipid raft-like mitochondria-associated ER membranes (MAMs) [162] of which hyperactivity leads to cholesterol retention and synapse loss and correlates with cognitive deficits [163] and in which accumulated cleaved products of Amyloid Precursor Protein (APP) cause mitochondrial dysfunction, interruption of cellular lipid homeostasis and membrane lipid alterations generally observed in AD pathogenesis [164,165]. Mitochondrial dysfunction, accompanied with increased oxidative stress, in neurons induces a lipid transfer to nearby astrocytes in which lipid droplets accumulate, in turn, mitochondrial dysfunction in glial cells can be caused by accumulation of peroxidated lipids and oxidative stress, contributing to neurodegenerative processes [166-168]. Growing evidence has supported nonnegligible roles of phospholipids and sphingolipids in AD pathogenesis and progression, with studies reporting that phospholipids and sphingolipids, together with acylglycerols, fatty acids and sterol lipids, present significant content changes in AD brain tissues [154,169-175].

Table 1: Summary of lipid changes in AD

PC, phosphatidylcholine; PE, phosphatidylethanolamine; PS, phosphatidylserine; CM, ceramides; SM, sphingomyelin; TG, triglyceride; AA, arachidonic acid; ALA, alpha-linolenic acid; DHA, docosahexaenoic acid; DPA, docosapentaenoic acid; EPA, eicosapentaenoic acid; LA, linoleic acid; OA, oleic acid; SA, stearic acid; PG, prostaglandin; CSF, cerebral spinal fluid; FCx, frontal cortex; HPC, hippocampus; MFG, medial frontal gyrus;↑; increased from control ↓; decreased from control.

APOE

In comparison with early-onset familial AD (EOFAD), late-onset AD (LOAD) accounts for approximately 95% of all AD cases [200,201], in which genetic predisposition, after aging, plays major roles in the onset of AD. As the strongest risk factor for LOAD, apolipoprotein E (APOE) is the main lipoprotein in the brain and plays pivotal roles in brain lipid metabolism, membrane remodelling and neuronal growth and repair [202-206]. APOE mainly produced by astrocytes is released into extracellular space where essential lipids (e.g. cholesterol) are delivered to neurons adopting APOE-bound cargo through APOE receptors expressed on the neuronal surface [202]. In addition to the capacities of Aβ binding and influencing Aβ aggregation and clearance [204,207], APOE participates in indirect regulation of Aβ metabolism through interactions with receptors (e.g. low-density lipoprotein receptorrelated protein 1 (LRP1)) [206,208-213]. Critical and isoform-specific role of APOE has also been demonstrated in formation of intraparenchymal Aβ deposits in amyloid precursor protein (APP) transgenic mice [214-217]. APOE exists with 3 different alleles namely APOEε2, APOEε3 and APOEε4, translating to 3 protein isoforms termed APOE2, APOE3 and APOE4, of which APOE4 present in approximately 14% of worldwide populations [205,218] is the most prevalent genetic risk factor for AD [219-222]. A single amino acid difference between APOE3 and APOE4 (Cys 112 Arg) brings about a conformational change influencing the binding to Aβ, lipids and apolipoprotein receptors [223]. APOEε2 considered as a protective genetic factor associated with reduced risk for AD and late age at onset [219,224] has been reported to orchestrate differences in lipidome and transcriptome profiles of postmortem AD brain [218,225]. Conversely, APOEε4 markedly elevates AD risk [219,224], in which heterozygous and homozygous APOEε4 allele may increase AD risk by 3 and 12 times, respectively [223], accelerates disease course and worsens brain pathology [226-228]. A correlation between APOE4 genotype and increased expression of Serpina3n, a gene expressed by astrocytes and considered as a strong marker of reactive and aged astrocytes in the brain [229,230], has been reported with a possible contribution to the pathogenic role of APOE4 in AD [231]. Higher APOE4 level in Cerebral Spinal Fluid (CSF) of AD patients compared with that of control individuals has been connected to accelerated Aβ oligomer accumulation [232]. APOE4 may retard Aβ clearance and favour Aβ deposition via binding to Aβ after specific fragmentation [205,223]. APOE4 was reported to trap ATP-binding cassette transporters A1 (ABACA1) (a regulator of APOE4 lapidation in protection from lipidpoor ApoE4 aggregation) in late rather than recycling endosomes and alter ABACA1 membrane trafficking in astrocytes, which might result in reduced Aβ degradation [233]. Insufficient Aβ clearance also affects accumulation in synaptic cleft, contributing to disruption of hippocampal long-term synaptic plasticity related to learning and memory abilities [234]. APOE4 is internalized in APOE receptors such as low-density lipoprotein receptor-related protein 1 (LRP1) which is also a member of Aβ receptors including very low-density lipoprotein receptor (VLDLR) and apolipoprotein E receptor 2 (APOER2) [209]. Additionally, APOE4-induced reduction of dendritic spine density in mice [234,235] is consistent with pathological changes (dendritic spine density reduction and synapse loss) observed in brain tissues from AD APOEε4-carriers [236]. APOE4 causes widespread AD phenotypes-associated cellular and molecular alterations in brain cells derived from human induced pluripotent stem cells (iPSCs), among which increased Aβ secretion as well as impaired Aβ uptake and cholesterol accumulation occurred in neurons and astrocytes, respectively [237]. Astrocytic lipid metabolism is influenced by APOE4 [237,238], in which increased fatty acid unsaturation and lipid droplet (LD) accumulation were found in APOE4-expressing human iPSC-derived astrocytes, which can be restored to basal state through supplementation of culture medium with choline (a soluble phospholipid precursor) [238]. Furthermore, APOE4 can also impair astrocyte-neuron coupling of fatty acid metabolism via decreased fatty acid (FA) sequestering in LDs, reduced LD transport efficiency and lowered FA oxidation, resulting in lipid accumulation in astrocytes and hippocampus, diminished abilities of astrocytes in neuronal lipid elimination and FA degradation, accelerated lipid dysregulation and increased AD risk [239].

ACSBG1 and ACSL6

Cellular accumulation and activation of fatty acids (FAs) either synthesized de novo or taken up from diets require the ATP-dependent reaction catalyzed by acyl-CoA synthetases (ACSs), a family of enzymes initiating FA metabolism-related reactions through ligation to coenzyme A (CoA) [240]. ACS enzyme family contain various members differing in distribution and fatty acid substrate preference [241], among which only two show specific enrichment in the brain, ACSBG1 and ACSL6 [242,243], suggesting their potentially particular roles in modulation of brain fatty acid metabolism. ACSBG1, almost exclusively expressed in astrocytes, have preferences for a wide range of substrates containing long-chain saturated and unsaturated fatty acids [244,245]. ACSBG1 knockdown in vitro results in decreased ACS enzymatic activity and FA oxidation [245], indicating its participation in astrocytic FA oxidation, however, clear roles of ACSBG1 in brain function and/or dysfunction still remain poorly understood. ACSL6 showing high expression in the brain was reported to be downregulated in age-related neurodegenerative diseases [246,247] and in direct correlation with neurite outgrowth [248-252]. With high substrate preference for docosahexaenoic acid (DHA) of which low levels are associated with AD pathophysiology [253], ACSL6 has been revealed with key roles in regulating DHA incorporation into neuronal membranes using Acsl6 deficient mice with significant reduction in DHA-containing phospholipids and impaired memory [254,255]. Critical roles of ACSL6 in brain DHA retention and neuroprotection are further supported by findings that ACSL6 depletion led to markedly reduced levels of brain membrane phospholipid DHA, spatial memory deficits, hyperlocomotion, increased cholesterol biosynthesis and age-related neuroinflammation [256]. What’s noteworthy is that astrocyte-specific depletion had minimal influence on membrane lipid composition [256] in consideration of ACSL6 enrichment in astrocytes [240,257-261], possibly due to the expression of a DHA-nonpreferring variant [251,262-267] and enrichment of Y-gate domain rather than DHA-preferring F-gate domain in astrocytes [251].

ATAD3A

ATPase family AAA-domain containing protein 3A (ATAD3A), a nuclear-encoded mitochondrial membrane-anchored protein belonging to the AAA+-ATPase protein family and simultaneously interacting with inner and outer mitochondrial membranes, is implicated in a variety of biological processes including stability maintenance of mitochondrial DNA (mtDNA), regulation of mitochondrial dynamics and cholesterol metabolism [268-270]. ATAD3A deficiency led to neurodegenerative phenotypes in association with cholesterol elevation, downregulated expression of cholesterol metabolism-related genes [269], optic atrophy and axonal neuropathy [271]. Oligomerization and accumulation of ATAD3A at MAMs, lipid raft-like ER subdomain rich in sphingomyelin and cholesterol [272] and associated with diverse metabolic functions such as lipid metabolism, mitochondrial function and calcium homeostasis [273-277], have been discovered in both mouse models and postmortem human brain tissues of Alzheimer’s disease [278]. Aberrantly oligomerized ATAD3A leads to cholesterol accumulation via expression inhibition of cholesterol clearancemediating cytochrome P450 family 46 subfamily A member 1 (CYP46A1) located on MAMs of which deficiency correlates with cholesterol disturbance, amyloid aggregation and cognitive impairments [279], AD-like MAM hyperconnectivity (e.g. impaired MAM integrity) [277] as well as synapse loss [278]. MAM-resident cholesterol imbalance facilitates amyloidogenic APP cleavage [165], in turn, retention of APP proteolytic fragments at MAMs interrupts cholesterol trafficking and homeostasis [280]. Additionally, blocking ATAD3A oligomerization by heterozygous knockout or pharmacological inhibition treated with DA1 peptide has been reported in causal relationship with cholesterol turnover normalization, MAM integrity enhancement, APP processing suppression, synapse loss mitigation and ultimate reduction of AD-like neuropathology and cognitive impairments [278], further revealing a role of ATAD3A in AD pathology and suggesting a potential therapeutic strategy of retarding AD progression through manipulation of abnormal ATAD3A oligomerization.

FoxO3

Forkhead box O transcription factor 3 (FoxO3) belonging to the forkhead box (FOX) family sharing an evolutionarily conserved forkhead DNA-binding domain composed of 80 to 100 amino acids [281]and possessing single nucleotide polymorphisms (SNPs) associated with human longevity [282,283] functions as a mediator of biological processes promoting lifespan and preventing aging-related diseases [284,285], of which alterations are involved in carcinoma, cardiovascular and neurodegenerative diseases [283,286-289]. FoxO3 plays a pivotal role in quiescence maintenance of neural stem cells (NSCs) in the brain, removal of which induces NSC differentiation and consequent NSC pool reduction [290-293]. Apart from capacities for neuronal survival promotion or neuronal apoptosis mediation [294,295], FoxO3 has also been shown with astrocyte proliferation controlling through inhibiting inflammatory cytokines (e.g. TNF-α and IL-1β) mediating reactive astrogliosis in neurodegenerative diseases [296-299]. Conditional knockout of FoxO3 in astrocytes was reported to impair consumption of excess fatty acids [300] which are cytotoxic and destructive to mitochondrial function [301]. FoxO3 reduction in aged mice was found to be specific to the cortex rather than the hippocampus, where FoxO3 deficiency caused cortical astrogliosis and dysregulated lipid metabolism [300]. In addition, lipid dysregulation, mitochondrial dysfunction together with Aβ uptake impairment were also observed in cultured astrocytes deficient in FoxO3, which could be reversed by astrocytic FoxO3 overexpression [300], potentially supporting the concept that FoxO3 elevation in astrocytes may retard or restore cortical astrogliosis and AD-associated impairments.

GSAP

Under typical conditions, Amyloid-β (Aβ) peptides as the products of body’s cholesterol disturbance are cleaved from amyloid precursor protein (APP) which may occur in two cellular pools, namely lipid raft-associated pool preferentially favouring APP cleavage by β- and γ-secretase as well as non-raft pools where cleavage is performed by α-secretase in a non-amyloidogenic pathway [302] and rapidly eliminated to maintain normal Aβ levels [303]. γ‐secretase activating protein (GSAP) was first reported for its regulatory roles in γ-secretase activity and specificity and its significant and selective enhancement of Aβ production through interactions with γsecretase and amyloid precursor protein carboxy‐terminal fragment (APP-CTF) [304]. Significantly upregulated GSAP level has been demonstrated in both AD mouse models and postmortem brain tissues from AD patients [305-307]. Single-nucleotide polymorphisms (SNPs) at the GSAP locus have been shown association with AD diagnosis [308,309], of which one SNP was found to correlate with GSAP expression and AD risk [310]. Genetic knockdown and pharmacological inhibition of GSAP suppress Aβ generation and deposition and tau phosphorylation in AD mouse models [304,305,311]. Apart from the promotion of APP-CTF partitioning into Aβ production-favoring lipid rafts, GSAP has also been shown to be enriched in mitochondria-associated membranes (MAMs), an intracellular domain where amyloidogenic APP processing responsible for dysregulated lipid metabolism is performed [312,313]. GSAP depletion lowers APP-CTF accumulation in lipid rafts, reduces ER-mitochondrial contacts elevated in AD [313-316], and alters lipid profiles in a direction opposite to AD pathogenesis (e.g. GSAP depletion-raised levels of phosphatidylethanolamine (PE) and phosphatidylinositol (PI) showing consistent reduction in human AD brain) [310,317]. What’s more, interactions between GSAP and multiple components related to ER-associated degradation (ERAD) regulating mitochondrial function through MAM and participating in AD pathogenesis have also been revealed, further supporting crucial roles of GSAP in attenuating AD-associated pathogenic process.

Glioblastoma

Glioma as a malignant primary brain tumor originating from astrocytes or other glial cells accounts for approximately 80% of all malignant brain tumors [318], of which glioblastoma (GBM) is the most aggressive type of brain tumor known with a 5-year survival rate below 5% [319-321]. Metabolic reprogramming has been recognized as a fundamental hallmark for carcinogenesis and progression of multiple tumors including GBM [322-324], through which tumor cells meet the high-energy demands of rapid proliferation [325]. Except for the representative metabolic feature named the Warburg effect, a phenomenon in which GBM cells rely on glycolysis for energy production under oxygen-sufficient and oxygen-insufficient conditions [323,325-327], GBM cells can also be fueled by fatty acid oxidation (FAO) as an alternative crucial energy resource to meet high-energy consumption in GBM aggressiveness [328-332], of which inhibition negatively impacted GBM proliferation and progression [333]. Oxidation of fatty acids is achieved by two major pathways, namely enzymatic oxidation mediated by peroxidases (e.g cyclooxygenase (COX), cytochrome P450 (CYP450), lipoxygenase (LOX) and phospholipase A2 (PLA2)) [334] as well as nonenzymatic self-catalyzed peroxidation (Figure 2A) of which 4-hydroxynonenal (4HNE) is an end-product showing elevated expression proportional to the grade of brain tumor malignancy [335-337]. Moreover, lipid metabolism reprogramming in association with numerous pathophysiological processes such as tumor proliferation and development [338-343] has been further evidenced with the observation of large amounts of lipid droplets (LDs) in GBM [344-346] and other tumors [347-354]. Neutral lipid core of a single LD includes cholesteryl esters and triglycerides (TGs) composed of glycerol molecules with triple hydroxyl groups esterified by fatty acids [355-358]. TGs have been demonstrated to serve as an important energy reservoir for supporting GBM cell survival, in which LDs were rapidly broken down by GBM cells via autophagy, a pivotal cellular process degrading damaged organelles and protein aggregates and recycling nutrients via hydrolysis of cytoplasmic components to ultimately maintain cellular homeostasis [359-362], to release stored fatty acids for producing energy upon energetic stress like glucose deprivation (Figure 2B), in turn, inhibition of FAO or autophagy led to LD retention and significant potentiation of GBM cell death [363], suggesting that LDs may play critical roles in regulating GBM growth and limitation of LD usage might be indispensable in GBM treatment. What’s more, cholesterol metabolism in GBM is different from that in healthy brain tissues where nearly all brain cholesterol is synthesized de novo [364-366]. Contrary to normal astrocytes mainly synthesizing cholesterol from glucose or glutamine [367,369] and converting excess cholesterol to oxysterol as an endogenous ligand of liver X receptors (LXRs) to consequently trigger efflux of surplus cholesterol via ATPbinding cassette transporter A1 (ABCA1) and suppression of cholesterol uptake by low-density lipoprotein receptors (LDLRs) [370-374], GBM cells are insufficient to de novo synthesize cholesterol and thus dependent on exogenously supplied cholesterol for survival through upregulated LDLR expression [364,375] (Figure 2C), in which LXR agonists could induce GBM cell death by lowering intracellular cholesterol content via ABCA1-dependent cholesterol efflux and LDLR inhibition [364]. Additionally, intracellular cholesterol level has been revealed to be involved in resistance against GBM cell death induced by temozolomide (TMZ), a blood-brain barrier (BBB) penetrant chemotherapy agent currently used in the standard therapy for patients with GBM [376,377]. Furthermore, sphingomyelins (SMs), an important group of phospholipids in cell membranes, together with their hydrolysis by sphingomyelinases (SMase) are crucial to effects of radio- and chemotherapy [378,381]. Ceramides which are generated by SMase-mediated SM hydrolysis caused by TMZ and radiation can induce cell apoptosis [382-384], which can be evaded through conversion of ceramides to sphingosine-1-phosphate (S1P) (Figure 3) [385-387] linked to tumor grade and implicated in GBM aggressive phenotypes [383,388].

Figure 2: A. Scheme of non-enzymatic self-catalyzed lipid peroxidation. Abstraction of allylic hydrogen from PUFA induces lipid radical formation and initiates a chain reaction of lipid peroxidation, which is followed by conjugated diene-yielding molecular rearrangement. Conjugated dienes, in presence of molecular oxygen, are transformed to lipid peroxyl radical abstracting allylic hydrogen from another PUFA, forming lipid hydroperoxide and another lipid radical. Lipid hydroperoxide can be further catalyzed and transformed to lipid alkoxyl radical and lipid peroxyl radical. Lipid peroxidation is terminated when non-radical products are formed because of interaction with antioxidants. Reaction between two lipid peroxyl radicals or two lipid alkoxyl radicals will consequently form a peroxide-bridged lipid dimer, while lipid dimers can be formed by reaction between lipid hydroperoxides and lipid radicals. PUFA, polyunsaturated fatty acids. B. Schematic model of LDs hydrolysis maintaining GBM cell survival. GBM cells mainly utilize glucose to produce energy under glucose-rich conditions, while LDs can be rapidly broken down after autophagy activation upon glucose starvation, released FAs then enter mitochondria for energy production. FAs, fatty acids; LDs, lipid droplets; GBM, glioblastoma. C. Astrocytes are relied upon by neurons and GBM cells to provide de novo synthesized cholesterol. Neurons and GBM cells take up astrocyte-secreted cholesterol in APOE-containing lipoproteins. Following cholesterol uptake mediated by LDLR, oxysterol and cholesterol derivatives produced in neurons are physiological agonists for LXR of which activation leads to dimerization with RXR and subsequent elevation in ABCA1 expression. LXR activation also inhibits LDLR expression, resulting in decreased cholesterol uptake and regulating intracellular cholesterol level. On the contrary, mechanisms surveilling and regulating cholesterol are disrupted in GBM cells, in which oxysterol and cholesterol derivatives cannot activate LXR inducing intracellular cholesterol accumulation. ABCA1, ATP-binding cassette transporter A1; APOE, apolipoprotein E; GBM, glioblastoma; LDLR, low-density lipoprotein receptor; LXR, liver X receptor; RXR, retinoid X receptor.

Figure 3: Sphingolipid metabolism in tumor progression. Sphingomyelin, after chemotherapy and radiation, is broken down into ceramide involved in blocking tumor progression. Ceramide can be converted by tumor cells to S1P (S1P can also be produced by astrocytes and other cells) exerting protumor effects including tumor proliferation, migration, invasion and angiogenesis. Involvement of S1P in tumor progression is specifically mediated by S1PRs (S1PR1-S1PR5) which can signal through phospholipase mechanisms. Each S1PR can couple to one or more GPCRs to signal through different phospholipases and induce phenotypes (e.g. angiogenesis, proliferation, migration and invasion). CDase, ceramidase; GPCRs, G protein-coupled receptors; SMase, sphingomyelinase; S1P, sphingosine-1-phosphate; S1PRs, S1P receptors.

S1PRs

GBM cells utilize exogenous source of S1P synthesized and exported by astrocytes and neuronal cells [389] and endogenous S1P production [390] for tumor progression. Involvement of S1P in tumor growth, migration, invasion, survival and angiogenesis [391-394] is specifically mediated by the family of G-protein coupled receptors named S1P receptors (S1PRs, S1PR1-S1PR5) [395-400]. S1PR1, S1PR2, S1PR3 and S1PR5 are expressed in human GBM cells [401-403], and elevated levels of S1PR1, S1PR2, and S1PR3 have been detected in brain tissues from GBM patients compared with healthy tissues, while only S1PR1 and S1PR2 showed significant association with GBM survival rates [401,402]. S1PRs are essential for mediating diverse S1P functions, whereas orientations in which they influence cell phenotypes still remain unclear. S1PR1 inhibition was reported to promote GBM cell proliferation, which collides with studies suggesting increased GBM proliferation by S1PR1-3, of which S1PR1 showed the strongest effects [402,404]. S1PR2 was shown to both reduce GBM migration through Rho/Rho kinase signaling pathway and participate in promoting GBM invasion [405,406]. In addition, S1PR5 has also been identified as an independent prognostic factor of GBM patients’ survival, aligning with reported role of S1PR5 in proliferation promotion [404,407]. Pharmacologically altered S1PR expression by fingolimod (FTY720), a sphingosine analogue leading to S1PR1 internalization, has been revealed to suppress astrocyte activation and change astrocytic secretion of C-X-C motif chemokine 5 (CXCL5) known to promote GBM proliferation and migration [408-410]. Furthermore, functions of individual S1P receptor subtypes are dependent upon activation of diverse downstream effector proteins, especially coupling to different G-proteins [399], such as binding of S1PR1, S1PR2 and S1PR5 with Gi, activation of Gq by S1PR2 and S1PR3 as well as signaling of S1PR2, S1PR3, and S1PR5 via G12/13 (Figure 3) [411], which alters signaling of phospholipases (particularly phospholipase C (PLC) cleaving proximal phosphodiester bonds of glycerophospholipids in production of phosphorylated headgroups and diacylglycerols [399,400]) and further activates downstream signaling molecules (e.g. extracellular signal-regulated kinase (ERK), phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MEK)) (Table 2). What’s noteworthy is that a S1PR-targeted liposomal drug delivery system, named S1P/JS-K/Lipo, capable of blood-brain tumor barrier (BBTB) penetration and enhanced tumor-targeted delivery has recently been described, efficiently delivering a nitric oxide (NO) prodrug (JS-K, O₂-(2,4-dinitrophenyl) 1-[(4-ethoxycarbonyl) piperazin-1-yl] diazen-1-ium-1,2-diolate) to GBM tissues via specific interactions with S1PRs highly expressed on GBM cells [412], representing a promising targeted approach for GBM therapy.

Table 2: Summary of S1PR-mediated effects in GBM

AKT, v-akt murine thymoma viral oncogene homolog; Cdc42, cell division control protein 42 homolog; ERK, extracellular signal-regulated kinase; JNK, c-Jun Nterminal kinase; MAPK, mitogen-activated protein kinase; MEK, mitogen-activated protein kinase; PI3K, phosphoinositide 3-kinase; PLC, phospholipase C; PLD, phospholipase D; PTEN, phosphatase and tensin homolog; PTX, pertussis toxin; Rac1, Ras-related C3 botulinum toxin substrate 1; ROCK, Rho-associated protein kinase.

FABP7

Fatty acid binding protein 7 (FABP7), a member of the multi-gene FABP family comprised of structurally related proteins with expression patterns specific to cell, tissue and development, binds to very long chain polyunsaturated fatty acids (VLCPUFAs) such as docosahexaenoic acid (DHA) with high affinity [422,423]. FABP7 abundant in astrocytes [424-426] is a lipid chaperone mediating cellular uptake, intracellular trafficking and subsequent oxidation of fatty acids (FAs), whose expression was reported to be elevated in GBM and GBM stem-like cells forming neurospheres (NS) and might accounting for GBM aggressiveness [427,428] and recurrence as well as associated with proliferation, migration and invasion of GBM cells, GBM histology and reduced survival time [429-434]. Under metabolic stresses (e.g. hypoxia), fatty acids are stored as lipid droplets (LDs) and subsequently oxidized in a FABP-dependent manner for energy production in GBM cells [435]. Slowcycling cells (SCCs), a subpopulation of GBM cells preferentially utilizing mitochondrial oxidative phosphorylation (OXPHOS), showing elevated lipid contents specifically metabolized under glucose deprivation and displaying enhanced capabilities of migration, invasion and chemoresistance, have been revealed with the characterization of higher FABP expression and larger LD amounts in cultured conditions of normal oxygen levels or nutrients [436]. Additionally, resistance of SCCs against deprived glucose or inhibited glycolysis could be restrained by FA uptake blocking via genetic deletion or pharmacological inhibition of FABP7 [436].

Moreover, promotion effects of FABP7 on GBM cell migration can be mitigated with DHA supplementation through specific and dramatic inhibition of DHA supplementation in culture medium on plasma membrane lipid order of FABP7expressing GBM cells which positively correlates with GBM cell migration as well as DHA supplementation-mediated disruption of nanodomains formed by FABP7 on GBM cell membranes [437], further suggesting a critical role of FABP7 in lipid metabolism in GBM cells.

SCD

Stearoyl-CoA desaturase (SCD) is an endoplasmic reticulum (ER)-localized delta-9 fatty acid desaturase forming carbon-carbon double bonds at the 9th to 10th position from the COOH-terminus of saturated fatty acids (SFAs), stearic acid and palmitic acid and thus generating monounsaturated fatty acids (MUFAs), oleic acid and palmitoleic acid, respectively [438,439], whose expression correlates with the ratio of MUFA to SFA in which a disequilibrium contributes to alterations in cell growth and differentiation [438-441]. SCD has 4 isoforms in mice (SCD1, SCD2, SCD3 and SCD4), while only two paralogs are expressed in human, namely SCD sharing approximately 85% amino acid identity with mouse SCDs and SCD5 unique to primates [440,442]. SCD has been described as a hypermethylated gene member contributing to the CpG island methylator phenotype which defines a distinct glioma subgroup [443]. SCD expression in GBM, in contrast to SCD upregulation often observed in multiple human tumors [444-447], was reported to be lower than normal brain tissues because of hypermethylation and monoallelic deletion together with phosphatase and tensin homolog (PTEN) frequently deleted in GBM [448] in a subset of GBM patients [449]. In addition, GBM cells without epigenetic and genetic changes mentioned above were revealed to express elevated SCD levels on which tumor cells rely for their survival [449]. SCD inhibition by CAY10566, an inhibitor with a modest BBB penetration ability, has been demonstrated to not only significantly suppress intracranial GBM growth, but also obviously affect tumor vasculature including nearly complete blocking of intratumoral bleeding and possible normalization of blood vessels, potentially allowing enhanced delivery of combinedly used antitumor drugs such as temozolomide (TMZ) [449,450].

Conclusions

The brain is highly enriched in lipids where they are crucial for multiple physiological processes ranging from maintenance of structural integrity and metabolic homeostasis to brain function and development. Metabolism of lipids is a complicated process in which a wide range of lipid-related effector proteins are involved and whose alteration is strongly associated with brain dysfunctions and diseases such as Alzheimer’s disease (AD) and glioblastoma (GBM). In this review, we throw light upon basic classes of lipids including fatty acids, phospholipids, sphingolipids, sterol lipids and triglycerides, of which dysregulated metabolism can be regarded as disease biomarkers. We also briefly discuss the role of lipids within the brain and altered lipid profile correlated with astrocytic function and astrocyte-neuron crosstalk in AD and GBM. Moreover, we have discussed lipid-related metabolites and proteins critical for disease-associated lipid dyshomeostasis and how these proteins together with lipids in correlation with astrocytic functions modulate disease pathogenesis and development, enlightening their therapeutic potential in preventing onset and progression of AD and GBM. However, there are still several lipids whose association with AD and GBM and availability as clinically valuable biomarkers for disease detection at early stages need further evaluation, which can be performed by newly-developed and improved techniques of gradually matured lipidomic platforms. What’s more, there remains much to be discovered about benefits and risks of manipulation of compounds affecting effector proteins involved in lipid metabolism, and further characterization of pathways in which important lipid-related proteins participate along with clinical studies will aid the understanding of pathogenesis mechanisms behind AD and GBM and identification of novel therapeutic targets to help ameliorate disease courses, facilitate disease treatments and consequently benefit patients.

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DOI: 10.31038/JCRM.2022554

Abstract

Colorectal Cancer (CRC) is a common malignant tumor with high mortality arising from adenomatous polyps of the large intestine. The rapid development of multiple immunofluorescence has led to the widespread application of a newly advanced technology called multiplex immunohistochemistry (mIHC), which enables the detection of multiple fluorescent proteins on a tumor tissue microarray (TMA) within the same temporal and spatial organization. Using this mIHC technology, we detected six tumor-associated proteins, including cluster of differentiation 4 (CD4), cluster of differentiation 8 (CD8), Pan-cytokeratin (P-CK), forkhead box P3 (FOXP3), programmed cell death 1 (PD1) as well as programmed death ligand-1 (PDL1) in cancer tissues and para-carcinomatous normal tissues from a cohort of 79 colorectal cancer patients. Results showed that, in CRC tissues, expression levels of P-CK and FOXP3 were upregulated while CD4 expression decreased significantly in comparison with adjacent normal tissues. What’s more, no significantly differential expression of CD8, PD1 or PDL1 was observed between cancer and normal tissues. FOXP3 expression was found to be correlated with tumor size (FOXP3 expression in tumor with volume >10 cm³ was significantly lower than that in tumor with volume ≤ 10 cm³), and reduced FOXP3 expression was associated with worse prognosis. P-CK expression in low-grade (Grade I-II) CRC patients was higher than that in advanced grade (Grade III-IV) patients, while association of P-CK expression with CRC prognosis was of no significance. In conclusion, FOXP3 and P-CK could be utilized as biopredictors of CRC (FOXP3 as a diagnostic and prognostic biomarker; P-CK as a diagnostic biomarker) for their differential expression patterns and clinicopathological correlation, while CD4, CD8, PD1 and PDL1 are more suitable for combined use.

Keywords

Colorectal cancer (CRC), Multiplex immunohistochemistry (mIHC), Tumor tissue microarray (TMA), Diagnosis, Prognosis, Biomarker

Introduction

Colorectal Cancer (CRC), one of the major causes of morbidity and mortality worldwide, is the second most common type of cancer in women and the third most common type of cancer in men, accounting for over 9% of all cancer incidence and causing death for more than 600,000 cases all over the world per year [1-4]. CRC is widely believed to develop in a multi-step process from Aberrant Crypt Foci (ACF), through benign and precancerous lesions (adenomas), to malignant tumors (adenocarcinomas) over an extended period of time [5]. Treatment of CRC usually comprises surgical resection of the primary tumors in patients followed by chemotherapy, radiotherapy and/or immunotherapy for advanced stages (stage III and IV) [6]. Despite advances in detection and available therapeutic strategies, the clinical outcomes for CRC remain poor due to tumor recurrence, metastasis, and resistance to radio-/chemo-therapy [7,8].

Early diagnosis of CRC is of importance for its significant impacts on cancer management, prognosis, recurrence and survival [9-11]. The 5-year survival rate could rise up to 90% in CRC patients who were diagnosed in the early stage, but unfortunately, the great majority of CRC cases had developed to an advanced stage at the time of diagnosis with a low survival rate around 8-9% [12,13]. Invasive techniques used for CRC diagnosis including endoscopic and radiological imaging suffered from poor patient compliance [14]. In addition, tumor markers such as carbohydrate antigen 19-9 (CA 19-9) and Carcinoembryonic Antigen (CEA) commonly used in clinical circumstance have the problems of unsatisfactory sensitivity and specificity, resulting in limited clinical application in CRC diagnosis, prognosis and survival [15]. Thus, the development of noninvasive and accurate screening tools for early detection and precise staging of CRC are of great importance and significance.

Conventional Immunohistochemistry (IHC) is a diagnostic technique widely used in the field of tissue pathology. However, IHC suffers from a number of limitations such as relatively high interobserver variability and limited labelling of a single marker per tissue section, resulting in missed opportunities of important diagnostic and prognostic information [16-18]. By contrast, multiplex Immunohistochemistry (mIHC), allowing simultaneous detection of multiple markers on a single tissue section, has emerged as a promising technology for its capability of provision of high throughput multiplex immunohistochemical staining and standardized quantitative analysis of highly reproducible and efficient tissue studies, as well as comprehensive study of cellular component, marker expression patterns, relative spatial distribution of multiple cell types and cell‐cell interactions, which are of benefit to diagnostic accuracy [19-21].

In light of this, we analyzed the expression levels and potential clinicopathological prognosis values of six tumor-associated proteins including cluster of differentiation 4 (CD4), cluster of differentiation 8 (CD8), Pan-cytokeratin (P-CK), forkhead box P3 (FOXP3), programmed cell death 1 (PD1) and programmed death ligand-1 (PDL1) in colorectal cancer, relying on 7-color fluorescent multiplex immunostaining of tumor tissue microarray (TMA) from a cohort of 79 cancer patients.

Materials and Methods

Information for Patients

The HColA180Su17 tumor Tissue Microarray (TMA) (Outdo, Shanghai, China) consisted of paired colorectal adenocarcinoma tissues and adjacent normal tissues derived from 79 colorectal cancer patients. These patients underwent surgery from Jun. 2006 to Apr. 2007, and the follow-up information was available from Sep. 2007 to Jul. 2015. The study was conducted under the approval of the Institutional Ethics Committee and all procedures were performed according to relevant guidelines and regulations for research. The clinicopathological characteristics of 79 cancer patients were summarized in Table 1.

Table 1: Clinicopathological characteristics of a cohort of 79 colorectal cancer patients

Preparation of Tissue Microarray (TMA)

Tissue Microarray (TMA) was made on basis of pathological diagnosis of each tissue. Briefly, formalin-fixed and paraffin-embedded samples were identified as well as the specimens were reviewed with hematoxylin and eosin stain by an independent surgical pathologist in order to confirm the presence of colorectal cancer and adjacent normal tissues [22]. For the formation of TMA, core cylinders (1 mm) were punched from each of circled areas and stored in a recipient paraffin block after circling of at least two representative tumor areas from each block by the pathologist. At last, consecutive TMA sections (6 mm thick) were cut and placed onto poly-L-lysinecoated slides for subsequent analysis [23].

Fluorescent mIHC of TMA

For multiplex Immunohistochemistry (mIHC) staining, antibodies for CD4, CD8, PCK, FOXP3, PD1 and PDL1were optimized by concentration and application order, meanwhile, a spectral library was built based on the single-stained slides [24]. The multiplex immunofluorescence staining and multispectral imaging of these six proteins were obtained on a TMA slide using Opal Polaris 7 Color Manual IHC Detection Kit (cat NEL861001KT, Akoya, US). In brief, the slide was deparaffinized by xylene for 10 min for three times, followed by 100% ethanol, 95% ethanol, 85% ethanol, and 75% ethanol for 5 min, respectively. After rinsing in distilled water for 3 min, slide was pretreated with 100 ml citric acid solution (pH6.0/pH9.0) for antigen retrieval with microwaving (15 min on 20% power after 45 s on 100% power) and transferred to a slide jar containing 1xTBST to mix well. Afterwards, the slide was blocked in 10% blocking solution for 10 min, stained respectively with primary antibody against CD4, CD8, P-CK, FOXP3, PD1 or PDL1 for 1 h at room temperature, washed with 1xTBST for 3 min twice and incubated with polymer HRPanti-mouse/rabbit IgG secondary antibody for 10 min at room temperature. The slide was covered by Tyramide (TSA)-conjugated fluorophore (TSA Fluorescence Kits, Panovue, Beijing, China) at 1:100 dilution and incubated for 10 min at room temperature, washed with 1xTBST for 3 min twice  for next staining procedure. Furthermore, the process was repeated by microwave heat-treating the slide for antigen retrieval for every additional marker in mIHC assay, followed by one primary antibody staining during each cycle ordered as CD4, CD8, P-CK, FOXP3, PD1 and PDL1, respectively, and then downstream procedures as mentioned above. After labelling of all these human antigens, cell nucleus were counterstained with 4′,6diamidino-2-phenylindole (DAPI) (Sigma-Aldrich, US). Detailed information about primary antibodies was summarized in Table 2.

Table 2: Primary antibodies used for mIHC staining

Multispectral Imaging

The stained slide was scanned using the Vectra Polaris (Akoya, US) to obtain multispectral images, which precisely captures the fluorescent spectra from 420 to 720 nm (at 20-nm wavelength intervals) with identical exposure time. Next, the scans were combined into a single stack image with high contrast and accuracy

Scoring Multispectral Images

InForm Tissue Analysis Software (Akoya, US) was used in batch analysis of experimental multispectral images [25]. Firstly, images of single-stained and unstained sections were used to respectively extract the fluorescent spectrum of each fluorescein and autofluorescence of tissues. Secondly, the extracted images were used in establishment of a spectral library for multispectral unmixing by InForm image analysis software. Finally, using this established spectral library, gain of reconstructed images of sections with removed autofluorescence was fulfilled. In order to score multispectral images, three to six representative regions of interest for imaging (200×) from each case were selected. A few representative multispectral images were then loaded into analysis software to build an algorithm for segmenting tissues and cells. Next, two tissue categories of STROMA and TUMOR were trained in accordance with intensity of DAPI signals, these detected tissue compartments were selected and quantified for each stained target proteins, and corresponding number of positive and total cells were counted as well. 4-bin (0, 1+, 2+, 3+) scoring system was used for quantification of expression levels of target proteins by calculating H-score (a score which was calculated using the percentage in each bin and ranges from 0 to 300) with cell stains. Results of H-score were shown by the positive rate of cells in each bin, including four levels (0~1, 1~2, 2~3, 3~) so as to measure and categorize protein expression levels into negative, low, medium and high levels, respectively. Generally, H-score with 0~1 and 1~2 (0, 1+) were considered as low expression level, while score with 2~3 and 3~ (2+, 3+) were considered as high expression level.

Statistical Analysis

The significance of experimental data from patient specimens was determined by the Mann-Whitney U test. The Kaplan-Meier test was used to assess overall survival (OS) rates, and survival curves were plotted by the log-rank test. *P<0.05 was considered as statistically significant, **P<0.01 and ***P<0.0001 were considered as strongly significant. Statistics software GraphPad Prism version 8 was used for all statistical analyses.

Results

Demographics

A following-up for the cohort of 79 CRC patients was performed from 2008 to 2015 for the evaluation of a seven-year survival. Among these eight clinicopathological characteristics including gender, age, tumor size, T stage, N stage, M stage, TNM stage and pathological grade, the survival was associated with three of them, namely N stage, TNM stage as well as pathological grade. The results showed that prognosis of patients with negative lymph nodes (N0), early TNM stage (TNM I-II) and low pathological grade (Grade I-II) were significantly better than those with positive lymph nodes (N1-2), late TNM stage (TNM 3-4) and advanced pathological grade (Grade III-IV) (P<0.05, Figure 1 and Table 3).

Figure 1: Overall survival (OS) rates of clinicopathological characteristics analyzed by Kaplan-Meier test. A. Lymph Node (N Stage), B. TNM Stage, C. Pathological Grade as clinical prognostic factors in cancer tissues in a cohort of 79 CRC patients. Orange dotted line: Overall survival (OS) rates as 50%.

Table 3: Prognostic clinicopathological characteristics of a cohort of 79 colorecta cancer patients

Fluorescent mIHC Profile on TMA Slides Derived from Colorectal Cancer Patients

In order to obtain multiple fluorescent images, the TMA slides were trained according to intensity of DAPI signals before the selection of detected tissue compartments for each stained target proteins on slides. All six antibodies of CD4, CD8, P-CK, FOXP3, PD1 and PDL1 were then performed ahead of the quantification of protein expression level by scoring system to calculate H-score based on cell fluorescence. In detected tissue compartments and cells, images of monochromatic proteins were shown in the upper row ordered as DAPI, CD4, CD8, P-CK, FOXP3, PD1 as well as PDL1 (Figure 2). In addition, merged images of the multispectral fluorescence of these target proteins and DAPI were displayed at the bottom of the figure. The selected images displayed tumor (Figure 2A) and adjacent normal (Figure 2B) tissues, respectively.

Figure 2: Mono- and multi-chromatic mIHC profile of colorectal cancer and adjacent normal tissues. A, B. Representative images of monochromatic and multispectral fluorescence in tissues from colorectal cancer and adjacent normal areas. Small images in the upper row displayed selected tissue compartments stained by DAPI, CD4, CD8, P-CK, FOXP3, PD1 and PDL1. Large images at the bottom showed a merged multispectral fluorescence from DAPI, CD4, CD8, P-CK, FOXP3, PD1 and PDL1.

Determination of Significant Markers by Fluorescent mIHC in Colorectal Cancer Patients

In a cohort of 79 colorectal patients, comparison of the expression levels of CD4, CD8, P-CK, FOXP3, PD1 and PDL1 were performed between tumor and paracarcinomatous normal tissues for the exploration of cancer associated potential biomarker. As shown in Figure 3 for monochromatic proteins, expressions of P-CK and FOXP3 were upregulated while CD4 expression decreased significantly in cancer tissues compared with adjacent normal tissues (Figure 3A, P<0.05; Figure 3C, P<0.001; Figure 3D, P<0.01). As shown in Figure 4 for bi- as well as multi-chromatic combinations, except that the expression levels of bichromatic CD4/P-CK, CD8/P-CK and P-CK/FOXP3, trichromatic CD4/CD8/P-CK, multichromatic CD4/CD8/P-CK/FOXP3 and CD4/CD8/P-CK/FOXP3/PD1/PDL1 were of significant differences (Figure 4A to 4F, P<0.001), differential expressions were not observed in other combinations in cancer tissues. As to the compared expression levels of single, double or multiple stained combinations of these six target proteins, all data were analyzed by Mann-Whitney U test and the P values were shown in Table 4.

Figure 3: Comparing expression levels of monochromatic target proteins based on H-scores by mIHC from tumor versus normal tissues in a cohort of 79 colorectal cancer patients. A to F. Differential expression patterns of single stained proteins including CD4 (3A. P<0.05), CD8 (3B, P=0.915), P-CK (3C, P<0.001), FOXP3 (3D, P<0.01), PD1 (3E, ns. P=0.231) and PDL1 (3F, P=0.511).

Figure 4: Comparing expression levels of di- and multi-chromatic target proteins based on H-scores by mIHC from tumor versus normal tissues in a cohort of 79 colorectal cancer patients. A to F. Comparing expression patterns of combination of double and multiple stained proteins including CD4/P-CK (3A. P<0.001), CD8/PC-K (3B, P<0.001), P-CK/FOXP3 (3C, P<0.05), CD4/CD8/P-CK (3D, P<0.001), CD4/CD8/P-CK/FOXP3 (3E, P<0.001), and CD4/CD8/P-CK/FOXP3/PD1/PDL1 (3F, P<0.001).

Table 4: Differential expression of mIHC markers in cancer vs. normal tissues

Correlation between Six Proteins and Clinicopathological Characteristics

Statistic analyses were performed by Mann-Whitney U test to explore the correlation between six proteins (CD4, CD8, P-CK, FOXP3, PD1 and PDL1) and eight cancer related clinicopathological factors (gender, age, tumor size, T stage, lymph node, metastasis, TNM stage, pathological grade). FOXP3 and P-CK were found to be correlated with tumor size and pathological grade, respectively , even though most of the correlations were of no significance (Table 5). Among which, expression of FOXP3 in tumor with volume>10 cm³ (N=65) was significantly lower than that in tumor with volume≤10 cm³ (N=14) (Figure 5A, P<0.01), and expression of P-CK in low pathological grade (Grade I-II) (N=66) was higher than that in advanced grade (Grade III-IV) (N=13) (Figure 5B, P<0.05).

Table 5: Correlation between mIHC target proteins and clinicopathological characteristics

Figure 5: Significant correlations between two target proteins and clinicopathological characteristics in colorectal cancer tissues. A. The expression level of FOXP3 significantly declined in larger tumor (V>10 cm³) in comparison with smaller tumor (V≤10 cm³). B. The expression level of P-CK in pathological grade I and II was significantly higher than that in advanced grade III and IV. Data on the graph was displayed as mean ± SD (*P<0.05, **P<0.01).

Association of Prognosis Markers with Clinical Outcomes

For purpose of prognosis potential of these six proteins in CRC, expression of each protein was divided into low-expressed group (H-score 0 to 1+) and high-expressed group (H-score 2+ to 3+) on the basis of H-score representation calculated by the fluorescence intensity from three to six representative regions of each sample. Association of low-/high-level protein expression with the seven-year overall survival (OS) status of 79 CRC patients were analyzed by Kaplan-Meier test. Compared with CD4 (Figure 6A, P=0.122), CD8 (Figure 6B, P=0.905), P-CK (Figure 6C, P=0.406), PD1 (Figure 6E, P=0.582) and PDL1 (Figure 6F, P=0.156), FOXP3 was the only one with statistically significant association with CRC prognosis (Figure 6D, P<0.05). CRC patients with high-level FOXP3 expression (N=6) seemed to have a longer OS time than those with low-level expression (N=73) (Figure 6D, P<0.05), which supported the tumor-growth potential of low-expressed FOXP3 observed in our study (Figure 5A, P<0.01).

Figure 6: Overall survival (OS) rates with differential expression levels of CD4 and FOXP3 analyzed by Kaplan-Meier test. The low- and high-expression of A. CD4 and B. FOXP3 were associated with a status of seven-year survival in cancer tissues in a cohort of 79 CRC patients. Orange dotted line: half of OS rates as 50%.

Discussion

In this study, we performed multiplex immunohistochemistry analysis on six target proteins to explore the correlation of these molecules with colorectal cancer. FOXP3 (forkhead box P3, also named IPEX, PIDX) is a member of the forkhead box (FOX) family of transcription factors consisting of an evolutionarily conserved group of transcriptional regulators whose dysfunction has been associated with human malignant neoplasias [26-35]. FOXP3 is mainly expressed in regulatory T (Treg) cells, and it has also been found in other cells such as B lymphocytes [36-42]. FOXP3 was described as an important molecular actor involved in the development and function of Treg cells playing essential roles in the regulation of autoimmunity, infection and tumor environment [36-38]. FOXP3 is considered as a molecule at the crossroads of tumorigenesis and immunity for its bilateral role of cancer promotor or suppressor [42-46]. Furthermore, the role of FOXP3 in the biogenesis, development as well as clinical prognosis of colorectal cancer is still not completely understood, thereinto, infiltration of FOXP3⁺ Treg cells indicated favorable prognosis in some but not all studies [47-57]. Our results showed that the expression level of FOXP3 was not only significantly upregulated in tumor tissues (Figure 3D, P<0.01), but also associated with tumor size (Table 5, P<0.01). Additionally, FOXP3 expression in CRC patients with tumor volume>10 cm³ was significantly lower than that in patients with tumor volume≤10 cm³ (Figure 5A, P<0.01), indicating a tumor growth potential of FOXP3 with low expression in CRC, consistently, reduced expression of FOXP3 was associated with worse prognosis (Figure 6D, P<0.05). All these results showed that FOXP3 could be applied as a potential biomarker for CRC diagnosis and prognosis.

CD4, a membrane glycoprotein of T lymphocytes, is expressed not only in T lymphocytes, but also in B cells, macrophages as well as granulocytes. CD4 acts as a coreceptor with the T-cell receptor on T lymphocytes in recognition of antigens displayed by antigen-presenting cells in the context of class II major histocompatibility complex (MHC) molecules, and functions to initiate or augment the early phase of T-cell activation. Similarly, CD8, a cell surface glycoprotein found on most cytotoxic T lymphocytes mediating immune cell-cell interactions, acts as a coreceptor with the T-cell receptor on T lymphocytes to recognize antigens displayed by antigen-presenting cells in the context of class I MHC molecules. In general, CD4⁺ and CD8⁺ T cells identify antigens related to cancer cells and play significant roles in cancer immunology and immunotherapy [58,59]. Additionally, various studies have demonstrated that CD4⁺ and CD8⁺ T cells may also control tumor growth [60,61]. Increased levels of CD4⁺ and CD8⁺ T cells in colorectal tumor microenvironment were shown to correlate with improved response to chemoradiotherapy [62]. Results of prevenient studies suggested that levels of tumor infiltration by CD4⁺ and CD8⁺ T cells may be good predictive factors for patient clinical prognosis of various tumors including colorectal cancer, melanoma, oesophageal squamous cell carcinoma, ovarian cancer, pancreatic cancer and renal cancer [63-71]. Differently, compared with adjacent normal tissues, the expression levels of CD4 and CD8 in colorectal cancer tissues here decreased significantly and had no difference, respectively (Figure 3A and 3B), suggesting reduced immune infiltration of CD4⁺ and CD8⁺ T cells. What’s more, association of differential expression levels of CD4 or CD8 with clinical outcomes of CRC patients was of no significance (Figure 6A and 6B), and they had no significant association with tumor size or other clinicopathological characteristics (Table 5). Malignant tumors like CRC can cause the functional loss of antigen recognition, cell proliferation and activation of effector T cells, which is known as T cell exhaustion accompanied by the activation of multiple inhibitory receptors such as CTLA4 and PD1/PDL1 [72-74]. The decreased infiltration and effects on prognosis of CD4⁺ and CD8⁺ T cells observed here may be related to T cell exhaustion. Generally, the biological behaviors of cancers are influenced by the functional status of tumor-infiltrating immune cells whose roles in response to cancer are component- and stage-dependent. The CD4⁺ T cells consist of multiple morphologically and functionally distinctive subpopulations such as T regulatory (Treg) cells, T helper 1 (Th1), Th2, Th9, Th17 and follicular helper T (Tfh) cells, whose roles related to proinflammation and/or antiinflammation, activation and infiltration of CD8⁺ T cells in tumor microenvironment display tumor-promoting or tumor-suppressing effects in a case-dependent manner [75-87]. The limited tumor infiltration and functional potential of CD8⁺ T cells over served in this study might be partially due to a lack of CD4⁺ Th cell-mediated function.

PD1 (programmed cell death 1, also named PDCD1, CD279), an immunoinhibitory receptor belonging to the CD28 family that is expressed on activated T cells, is involved in T cell proliferation and functional regulation including those of effector CD8⁺ T cells, and also able to promote the differentiation of CD4⁺ T cells into T regulatory cells [88-90]. PD1 is expressed in many types of tumors and has demonstrated to play roles in anti-tumor immunity, safeguarding against autoimmunity as well as the inhibition of effective anti-tumor and anti-microbial immunity. Furthermore, PD1 interacts with ligand PDL1 (also named B7-H1, CD274) to form the PD1/PDL1 axis, an immune checkpoint which is usually up-regulated to help tumor cells avoid immune destruction in an immunosuppressive tumor microenvironment [91]. Overexpressed PDL1 can protect tumor cells by inhibiting the activity of PD1 expressing adjoining tumor-infiltrating effector CD4⁺/CD8⁺ T cells [92]. PDL1 is mainly expressed on the surface of antigen-representing and tumor cells in various types of cancer such as carcinomas of the adrenal cortex, bladder, brain, breast, colorectum, esophagus, gastrointestinal tract, kidney, liver, lung, ovary, pancreas, thymus, thyroid and urothelium [93]. Contradictory correlations of expression of PD1 and/or PDL1 with prognosis results and clinicopathological characteristics of colorectal cancer were observed (some investigations showed that overexpression of PD1 and/or PDL1 forecasted better prognosis, while others presented opposite results), even if PD1 and PDL1 may play oncogenic roles in colon cancer carcinogenesis [94-98]. Again, in our study, no significant difference was observed between expression of either PD1 or PDL1 in colorectal cancer tissues and those in normal tissues (Figure 3E and 3F), they also had no significant association with pathological grade or other characteristics (Table 5). Differential expression of PD1 or PDL1 was not statistically associated with CRC prognosis (Figure 6E and 6F). All these results indicated that PD1 and PDL1, in comparison to using alone, are more suitable for combination with other proteins for the application as potential biopredictors of CRC diagnosis.

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DOI: 10.31038/JCRM.2022553

Abstract

Hypothalamic hamartoma is a well-known neurological rare cause of central precocious puberty and gelastic seizures and may be asymptomatic for long period. It is rare and non-progressive tumor like congenital malformation. Precocious puberty defined as children attained the puberty before age of 8 years in girls and 9 years in boys. We present such a case of precocious puberty due to hypothalamic hamartoma in 5 year old boy and its radiological imaging findings.

Keywords

Hamartoma, hypothalamic, precocious, puberty, central cause

Introduction

Hypothalamic hamartoma is a rare non progressive tumor like well-organized congenital malformation of tuber cinereum in the floor of third ventricle [1-4]. It causes the classical spectrum or trait of gelastic seizures, precocious puberty and developmental delay or behavioral disorder [1,3]. It is most usual neurological cause of central precocious puberty.² This condition may be asymptomatic for long period, or may present with clinical appearance and symptoms of precocious puberty and complex partial seizures refectory to anticonvulsant drugs [1,2]. One third of patients with hypothalamic hamartoma can be present with precocious puberty and it is defined as children attained the puberty before age of 8 years in girls and 9 years in boys [1,4,5]. Furthermore precocious puberty is divided into gonadotropin releasing hormone (GnRH)-dependent/central and gonadotropin releasing hormone (GnRH)-independent / peripheral precocious puberty [4]. Contrast enhanced MRI is investigation of choice and play important role in diagnosis of hypothalamic hamartoma [2,3] It appears as well-defined non enhancing lesion in floor of third ventricle showing similar signal intensity to the grey matter of brain parenchyma [3,4,6]. Gold standard treatment option for isolated central precocious puberty due to hypothalamic hamartoma is long acting analogs agonists of GnRH (GnHas) with good efficacy and safety [4,7].

Case Report

5 Years old boy came in paediatric outpatient department of Liaquat National Hospital with complain of faster growth of child than expected according to his father. On further questioning child has also history of epileptic laughter (gelastic seizures) 1-2 times, 2-3 months back for which he has taken antiepileptic drugs from local doctor in periphery of Afghanistan.

On local examination there were axillary and pubic hair and enlargement of penis measured about 4-5 cm in length. This was classified as TANNER stage (sexual maturity rating) 04 (Figure 1). Neurological examination was normal with Glasgow coma scale (GCS) of 15/15. Electroencephalogram (EEG) was recommended to see the cause of previous history of epileptic seizures was negative. His baseline laboratory investigations turned out to be within normal limits. On the basis of clinical and examination diagnosis of precocious puberty was made. His endocrinology related laboratory investigations were deranged. Follicle Stimulating Hormone (FSH) 4.11 (N= 1.9 mIU/ml), Luteinizing Hormone (LH) 9.82 (N1.3 mIU/ml) Testosterone 1500 ng/dL and Testosterone/Estradiol T/E2 ratio 45 pg/ml. All these investigations are raised and not normally corresponding to the patient’s age. Thyroid Function Tests (TFT) were within the normal limit.

Figure 1: Enlarged penis and appearance of pubic hair not corresponding to the patient’s actual age

He underwent the contrast enhanced MRI brain which showed abnormal signal intensity mass arising from the tuber cinereum in the region of hypothalamus. It appeared iso-intense to grey matter on T1 and T2 weighted images and showed no post contrast enhancement. It measured about 0.9 x 1.1 cm. The lesion was very small in size and has no mass effect or compression over the adjacent brain parenchyma. This was diagnosed as hypothalamic hamartoma. (Figures 2 and 3). Patient also underwent the X-ray right hand and elbow according to the radiological protocol to see the exact age of patient which showed 15 years reported by the experienced radiologist (Figure 4). During hospital stay course patient was asymptomatic and discharged on long acting analogs agonists of GnRH (GnHas). Follow-up was recommended in outpatient department of endocrinology.

Figure 2: A Axial T2 weighted image and B sagittal T2 weighted image show abnormal signal intensity mass arising from the tuber cinereum in the region of hypothalamus. It appears iso-intense to the brain parenchyma (black arrow head)

Figure 3: A sagittal plain T1 weighted image and B sagittal contrast enhanced T1 weighted image show abnormal signal intensity mass arising from the tuber cinereum in the region of hypothalamus. It appears iso-intense to brain parenchyma and show no post contrast enhancement (White arrow head)

Figure 4: A anterior-posterior elbow, B lateral elbow and C anterior-posterior wrist X-rays show bone age of 15 years

Discussion

Previously Hypothalamic hamartoma used to be an uncommon finding and was estimated to occur in one person per one million population,¹ however with timely advancement in the field of Radiology and better clinical recognition, the early diagnosis has now become possible and its incidence has significantly decreased [1,2].

Most patients usually present in the first or second decade of life [1-3]. Precocious puberty is the most common presentation, however larger hamartomas are less likely to produce precocious puberty [2,3]. Other clinical presentations include developmental delay, attention deficit or hyperactivity disorder and anxiety [1-4]. A very specific feature of hypothalamic hamartoma is Gelastic seizure [1-3]. In our case, hamartoma measured up to 1cm on MRI brain (Figure 2) and patient was presented with complaints of both precocious puberty and laughing fits (Gelastic seizures).

On MRI, hypothalamic hamartomas produce soft tissue intensity masses which are isointense to grey matter on T1WI and hyperintense on T2WI [2,3]. They are homogeneous and sharply marginated by the surrounding CSF with no post contrast enhancement [2,3,6]. Calcification is rare and hemorrhage is not described in these lesions [2,3]. This classical appearance of soft tissue intensity mass in the region of hypothalamus was also observed in our patient (Figure 2).

Patients with central precocious puberty are usually managed conservatively [2], for progressive central precocious puberty, treatment with a depot GnRH agonist is suggested and is generally continued for 11 years [2-4]. However, surgery including resection or disconnection through craniotomy or trans-spheroidal approach can be helpful in cases of poorly controlled epilepsy [2-6], to our patient, we advised long acting analogs agonists of GnRH (GnHas) with a follow up in OPD.

Conclusion

Hypothalamic hamartomas are rare congenital malformation seen in floor of third ventricle. Precocious puberty is most usual presenting feature. MRI brain play important role in diagnosis of these lesions. Radiologist should aware of imaging findings of hypothalamic hamartoma and should examine the hypothalamic region properly in suspected cases as they can be easily missed due to small size. Bone age can be assessed by using the wrist X-ray and we can also observe the slowing of bone growth after treatment by X-ray wrist recommendation in every 6^th month.

References

1. Qasim BA, Mohammed AA (2020) Hamartoma of hypothalamus presented as precocious puberty and epilepsy in a 10-year-old girl. International Journal of Surgery Case Reports 77: 170-173. [crossref]
2. Mahmood R, Al Taei TH, Samah Al Obaidi MD (2019) Hamartoma of the Hypothalamus. Bahrain Medical Bulletin 41.
3. Kalekar T (2015) Hypothalamic Hamartomas: Two Cases. Open Journal of Medical Imaging 5: 20.
4. Sharma P, Acharya N, Guleria TC (2020) Hypothalamic hamartoma presenting as central precocious puberty: a rare case report. International Journal of Contemporary Pediatrics 7: 1634.
5. Mahajan ZA, Mehta SR (2020) Central precocious puberty: A case report. Medical Journal of Dr. DY Patil Vidyapeeth 13: 413.
6. Boyko OB, Curnes JT, Oakes WJ, Burger PC (1991) Hamartomas of the tuber cinereum: CT, MR, and pathologic findings. American Journal of Neuroradiology 12: 309-314. [crossref]
7. Eugster EA (2019) Treatment of central precocious puberty. Journal of the Endocrine Society 3: 965-972. [crossref]

DOI: 10.31038/JCRM.2022544

Abstract

There had been three Arizona COVID-19 Reopening Phases. On March 5, 2021, Arizona’s Reopening Phase 3 began. The state is the sixth largest in size of the United States 50 states and about the same size as Italy. There were four case surges — in the summer and fall 2021 with Delta variant, and the winter 2021-22 and summer 2022 with Omicron variants. This 18 months longitudinal study examined changes in the number of new COVID-19 cases, hospitalized cases, deaths, and vaccinations. There was an increase of more than 1.4 million cases during the study period. The data source used was from the Arizona Department of Health Services COVID-19 dashboard database. Even with the case surges, the new normal was low number of severe cases, manageable hospitalization numbers, and low number of deaths.

Keywords

COVID-19, Arizona returning to normal, Longitudinal study, Arizona and COVID-19

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is also known as COVID-19 (coronavirus). It is a respiratory disease (attacks primarily the lungs) that spreads by person to person through respiratory droplets (coughs, sneezes, and talks) and contaminated surfaces or objects. Since the virus first appears in Wuhan, China in December 2019, there has been more than 600 million cases in the world. On September 7, 2022, Johns Hopkins University [1] reports that there are 606,889,445 total COVID-19 cases and 6,507,958 deaths associated with the virus in the world. The United States has the highest total cases (95,020,855) and deaths (1,048,989) in the world [1].

A three prolong attack is used against the virus by encouraging the public to practice preventive health behaviors that reduces the risks of getting respiratory infections (e.g., coronavirus, flu, and cold), and using vaccines and therapeutics. The preventive health behaviors include, but not limited to, practicing physical and social distancing, washing hands frequently and thoroughly, and wearing face masks. Johns Hopkins reports that more than 12.18 billion vaccine doses have been administered in the world and the U.S. has administered more than 605 million vaccine doses (September 7, 2022) [1].

There has been three Arizona Reopening Phases. During Arizona’s Reopening Phase 2 winter surge in 2020, ABC and NBC News report that the state has the highest new cases per capital in the world [2,3]. On September 7, Arizona is ranked 12^th in total COVID-19 cases (2,258,040) and 11^th in total deaths (31,162) of the 50 U.S. states [1]. Arizona is the sixth largest in size (113,990 square miles / 295,233 square kilometers) of the U.S. 50 states and is about the same size as Italy (301,340 square kilometer) [4,5]. The state population estimate is 7,276,316 on July 1, 2021 [6].

A partnership between the U.S. federal government and each of the 50 states is required to address the COVID-19 pandemic [7,8]. The federal government provides the national guidance primarily through the Centers for Disease Control and Prevention (CDC) and needed logistical support (e.g., provide federal supplemental funding, needed medical personnel and resources, and other needed assistance). The states decide on what actions to take and when to carry out those actions; the state COVID-19 restrictions; when to carry out each reopening phase; and the state vaccination plan.

On March 5, 2021, Arizona Governor Douglas Ducey begin Reopening Phase 3 (final reopening phase) after the state had administered more than two million vaccine doses and several weeks of declining cases [9,10]. This eases more of the COVID-19 restrictions. As more people become vaccinated and those infected recovered and have immunity against the virus; the numbers of cases, hospitalizations, and deaths will be low; COVID-19 will be manageable; and the state returns to normal.

The remainder of the paper examines Arizona Reopening Phase 3 (March 5, 2021 to September 7, 2022) looking at changes in the number of new COVID-19 cases, hospitalizations, and deaths.

Methods

This was an 18-months longitudinal study. The Arizona Department of Health Services (the state health department) COVID-19 dashboard database was the data source used. The study examined the changes in the numbers of new COVID-19 cases, hospitalized cases, deaths, and vaccines administered.

There were several data limitations. The COVID-19 case numbers represented the numbers of positive tests reported. When more than one test given to the same person (e.g., during hospitalization, at work, and mandatory testing), there were individual case duplications. Aggressive testing resulted in increases in false positive and false negative testing results. The case numbers did not include positive home testing results.

Delays in the data submitted to the state health department affected the timeliness of data reported and caused fluctuations in the number of cases, hospitalizations, deaths, and vaccinations. The state health department continued to adjust the reported numbers that may take more than a month to correct the numbers. The deaths associated with the coronavirus may cause by more than one serious underlying medical conditions, and the virus may not be the primary cause of death.

Results

A case could be mild (no symptoms), moderate (sick, but can recover at home), and severe (require hospitalization and/or result in death). There were four case surges during the Reopening Phase 3: 2 summers, 1 fall, and 1 winter. The 2022 cases (882,671) had already exceeded the 2021 total case numbers (838,836). Figure 1 shows the Arizona weekly COVID-19 cases during January 1, 2020 to September 10, 2022.

Figure 1: Arizona Weekly COVID-19 Cases: January 1, 2020 to September 10, 2022.
Source: Arizona Department of Health Services Arizona COVID-19 weekly Cases Graph*2022 cases as of September 14.

At the end of the 18 months of Arizona Reopening Phase (began March, 5, 2021), there were 1,432,921 COVID-19 cases, 59,091 case hospitalizations, and 14,839 deaths associated with the virus in Arizona (Table 1). There were higher percentages of hospitalizations, and deaths in the first 6 months of the first year than the following two 6-month periods.

Table 1: Arizona Reopening Phase 3 Total Numbers of COVID-19 Cases, Hospitalizations, and Deaths: March 7, 2021 to September 7, 2022

Source: Arizona Department of Health Services COVID-19 Dashboard.
Arizona 2021 population estimate is 7,276,316, July 1, 2021 – U.S. Census.

Table 2 tracks the weekly total and weekly numbers of COVID-19 cases, hospitalized cases, and deaths during the past 6 months (March 9 through September 7, 2022). The largest weekly numbers of cases (20,198) occurred on July 6, while hospitalizations (1,955) occurred on March 9. The largest weekly number of deaths was on March 16 (457).

Table 2: Arizona Total and Weekly Numbers of COVID-19 Cases, Hospitalizations, and Deaths

Source: Arizona Department of Health Services COVID-19 Dashboard.
Arizona 2021 population estimate is 7,285,370, July 1, 2021 – Arizona OEO.

Figures 2-4 compare the numbers of COVID-19 cases, hospitalized cases, and deaths by age groups for the three 6-month periods. A case could be mild, moderate, and severe. Most people recovered and did not require hospitalization. There was an increase of 1,432,921 cases during the 18 months. The 20-44 years age group had the largest number of cases (Figure 2). There were more females (52.8%) than males (47.2%) who got the virus on September 7, 2022.

Figure 2: Arizona Reopening Phase 3 COVID-19 Cases by Age Groups for Three 6-Month Periods.
Source: Arizona Department of Health Services COVID-19 Cases by Age Groups Statistics.

The percentages of total hospitalized cases (severe cases) decreased from 7 percent on March 6, 2021 to 5 percent on September 7, 2022. The case hospitalizations had increased by 59,091 during the study period. Seniors had the highest percent of the total hospitalizations (43.7% on September 7) and those under 20 years of age had the lowest percent (4.5%). Eighteen percent (18.1%) of seniors diagnosed with COVID-19 hospitalized, while 1.1 percent of those under 20 years of age hospitalized. There were more males (52.2%) than females (47.8%) hospitalized. Figure 3 shows the hospitalization numbers for each age group with the virus for the three 6-month periods.

The numbers of deaths had increased by 14,839 during the 18 months. The rates of fatalities per 100,000 population increased 227.05 to 433.50. As expected, seniors had the highest percent of total deaths (71.3% on September 7) and those under 20 years of age had the lowest percent (0.2) Eight percent (7.9%) of the seniors diagnosed with COVID-19 died, while 0.01 percent of those under 20 years of age died. There were more males (59%) than females (41%) who died. Figure 4 shows the numbers of deaths for each age group with the virus for the three 6-month periods.

The first U.S. COVID-19 vaccine, Pfizer/BioNTech Comirnaty, approved for emergency use authorization on December 11, 2020. In late December, Arizona began to administer vaccines. During Reopening Phase 3 (March 5, 2021 to September 7, 2022), there were 10,451,502 vaccine doses administered, and 3,813,974 fully vaccinated against the virus. Figure 5 shows the numbers of COVID-19 vaccines that were given in Arizona (persons fully vaccinated, persons receiving at least one dose, and total doses given) during the 18 months.

Initially, there were three vaccines available (Pfizer/BioNTech Comirnaty, Moderna Spikevax, and Johnson&Johnson Jcovden). Novavax Nuvaxivud became the fourth vaccine available in July 2022. The vaccines provided different levels of protection against COVID-19 and its variants. Those 65 years and older had the highest vaccination percentage, while those under 20 years of age had the lowest (Figure 6). It was expected the vaccination rates for this age group will increase with the approval of younger children vaccines use.

Figure 3: Arizona Reopening Phase 3 Hospitalized COVID-19 Cases by Age Groups for Three 6-Month Periods.
Source: Arizona Department of Health Services Hospitalized COVID-19 Cases by Age Groups Statistics.

Figure 4: Arizona Reopening Phase 3 COVID-19 Deaths by Age Groups for Three 6-Month Periods.
Source: Arizona Department of Health Services COVID-19 Deaths by Age Groups Statistics.

Figure 5: Arizona Reopening Phase 3 COVID-19 Vaccination Numbers: March 5, 2021 to September 7, 2022.
Source: Arizona Department of Health Services COVID-19 Vaccination Statistics.

Figure 6: Arizona COVID-19 Vaccination Percentages (at least one dose) by Age Groups on September 7, 2022.
Source: Arizona Department of Health Services COVID-19 Vaccinations by Age Group Statistics.

Discussion

The Arizona Governor began Reopening Phase 3 (final phase of reopening) after the state had administered more than two million vaccine doses and several weeks of declining cases on March 5, 2021 [9,10]. The state continued its efforts to vaccinate its population. The number of vaccine dosages administered had increase from 2,016,512 on March 5, 2021 to 12,468,014 on September 7, 2022. Sixty-two percent (4,525,048) of the state population were fully vaccinated. The largest numbers of fully vaccinated persons occurred in the week of April 17 to 23, 2021 (249,755) [9,10]. The pace of vaccination began to slow down in June.

Arizona case numbers had decreased in the spring and early summer 2021. At the end of June, the Arizona State Legislature and Governor had rescinded many of the state COVID-19 restrictions. The state used a three-pronged attack against the virus: (1) encourage preventive health behaviors, (2) increase vaccination numbers, and (3) use therapeutics. During the month of July, the highly contagious Delta variant appeared in the state and began the summer surge. Even with the increase vaccination efforts and other actions, they were not enough to stop the Delta variant. This resulted in the fall surge.

In December, the more contagious Omicron variant appeared in the state and began to surge. The Omicron variant surge in January 2022, and the cases remained high into early March. For more than two months in the spring, the cases were low. The state cases rose at the end May as the Omicron variants moved westward in the U.S. and began the summer surge. By late August, the cases declined.

It has been more than 32 months since the first COVID-19 cases appeared in Arizona on January 22, 2020; the state has not returned to pre-pandemic normal of zero cases and no face mask wearing. Most health facilities require both medical staff and patients wear masks. Many businesses require their staff wear masks and masking wearing is optional for customers, and they still have their virus protective glass/plastic barriers. There are signs of the public experiencing COVID fatigue (e.g., significant numbers did not wear masks during summer 2022 case surge and did not pay attention to the daily/weekly number of case increases).

Many still have anxiety/depression/stress associated with the virus. The causes for the mental anguish are the uncertainty of the virus, constant emergent of new variants, vaccine limitations, the lack control of the situation, and no end to the virus. There are persons who have not adapt to the new normal and have limited their interactions with people.

Overtime, the vaccines are not as effective against the later variants (Delta and Omicron) and Omicron subvariants as the original Alpha – breakthrough infections and wane over time. Even though the vaccines and their boosters reduce the risks in getting a severe case, one can still can get the virus. There has been very little increase in the vaccination rate in the last six months — the number of fully vaccinated percent increased only by 2.9 percent.

The Food and Drug Administration has approved both Pfizer and Moderna new bivalent COVID-19 vaccines that are effective against both the original virus and Omicron BA.4 and BA.5 variants on August 31, 2022. With the new vaccines, it is expected the vaccination rates will rise; the population immunity level will be high enough to keep the winter case surge lower than last year; and the state will move the closer to returning back pre-pandemic normal.

Conclusion

The vaccines, most of the Omicron variant cases are mild or moderate, and therapeutics have kept the number of hospitalizations and deaths low. Even with the occasion case surges, the state new normal are low number of severe cases, manageable hospitalization numbers, and low number of deaths.

References

1. Johns Hopkins University Coronavirus Resource Center, https://coronavirus.jhu.edu/.
2. Deliso, Meredith. “Arizona ‘hottest hot spot’ for COVID-19 as health officials warn of hospital strain: The state has the highest infections per capita globally, based on JHU data, ABC News, January 7, 2021, https://abcnews.go.com/US/arizona-hottest-hot-spot-covid-19-health-officials/story?id=75062175.
3. Chow, Denise and Joe Murphy. These three states have the worst Covid infection rates of anywhere in the world: Arizona currently has the highest per capita rate of new Covid-19 infections, with 785 cases per 100,000 people over the past seven days, followed closely by California and Rhode Island, NBC News, January 5, 2021 and updated on January 7, 2021, https://www.nbcnews.com/science/science-news/these-three-states-have-worst-covid-infection-rates-anywhere-world-n1252861.
4. Britannica, Arizona state, United States, https://www.britannica.com/place/Arizona-state.
5. My Life Elsewhere, Arizona is around the same size as Italy, https://www.mylifeelsewhere.com/country-size-comparison/arizona-usa/italy.
6. United States Census Bureau, Quick Facts, https://www.census.gov/quickfacts/AZ.
7. Eng H (2020) Arizona and COVID-19. Medical and Clinical Research 5: 175-178.
8. Eng H (2021) Arizona Reopening Phase 2: Rise and Fall of COVID-19 Cases. Medical and Clinical Research 6: 114-118.
9. Eng H (2021) Arizona Reopening Phase 3 and COVID-19: Returning to Normal. Medical and Clinical Research 6: 687-669.
10. Eng H (2022) Arizona Reopening Phase 3 and COVID-19: One Year Later. Journal of Clinical Research and Medicine 5: 1-7.