Obesity-Related Neuroinflammation: Magnetic Resonance and Microscopy Imaging of the Brain

Obesity is a major risk factor of Alzheimer’s disease and related dementias. The principal feature of dementia is a loss of neurons and brain atrophy. The mechanistic links between obesity and the neurodegenerative processes of dementias are not fully understood, but recent research suggests that obesity-related systemic inflammation and subsequent neuroinflammation may be involved. Adipose tissues release multiple proinflammatory molecules (fatty acids and cytokines) that impact blood and vessel cells, inducing low-grade systemic inflammation that can transition to tissues, including the brain. Inflammation in the brain—neuroinflammation—is one of key elements of the pathobiology of neurodegenerative disorders; it is characterized by the activation of microglia, the resident immune cells in the brain, and by the structural and functional changes of other cells forming the brain parenchyma, including neurons. Such cellular changes have been shown in animal models with direct methods, such as confocal microscopy. In humans, cellular changes are less tangible, as only indirect methods such as magnetic resonance (MR) imaging are usually used. In these studies, obesity and low-grade systemic inflammation have been associated with lower volumes of the cerebral gray matter, cortex, and hippocampus, as well as altered tissue MR properties (suggesting microstructural variations in cellular and molecular composition). How these structural variations in the human brain observed using MR imaging relate to the cellular variations in the animal brain seen with microscopy is not well understood. This review describes the current understanding of neuroinflammation in the context of obesity-induced systemic inflammation, and it highlights need for the bridge between animal microscopy and human MR imaging studies.

Introduction
Obesity is a major risk factor of Alzheimer's disease and related dementias (ADRD) [1,2]. It is common, affecting >40% of the adult US population [3], and it increases the risk by 1.6-fold [1,2]; consequently, >13% of the ADRD incidence in the population is attributable to obesity [1,2]. Considering that the prevalence of obesity is still rising [3], this number is expected to increase in the near future. Dementia has a long prodromal phase, developing progressively over decades before the clinical diagnosis [4][5][6]. An early identification of individuals at risk and the implementation of effective strategies for prevention and early treatment are essential [2], as delaying symptom onset by just 1 year could lower the prevalence of ADRD by >9 million cases in the USA in the next 40 years [2].
The principal feature of dementia is the loss of neurons and brain atrophy [7][8][9]. The mechanistic links between obesity and the neurodegenerative processes of dementias are not fully understood, but several lines of evidence suggest that obesity-related systemic inflammation and subsequent neuroinflammation may be involved [10][11][12]. Adipose tissue adipocytes and macrophages release multiple proinflammatory molecules [13][14][15][16] that enter the systemic circulation, where they impact blood cells and endothelial cells lining the vessels, and promote the transition of this low-grade inflammation from the circulation to tissues, including the brain.
Inflammation in the brain-neuroinflammation-is one of the key elements of the pathobiology of neurodegenerative disorders [17][18][19]. On a cellular level, neuroinflammation is characterized by the activation of microglia, resident immune cells in the brain. Microglia alter their structure and function in response to the pro-inflammatory signals from the circulation; other cells forming the brain parenchyma, including astrocytes, oligodendrocytes, and neurons, also alter their structure and function in response to pro-inflammatory signals [20][21][22][23][24][25][26][27][28][29][30][31]. Collectively, neuroinflammation creates a tissue environment that becomes less neurotrophic and more pro-apoptotic; in the long-term, this may contribute to neurodegeneration. Most of these observations have been made in animal studies using microscopy imaging, which has the capacity to examine detailed cellular variations (at 'micrometer' to 'nanometer' resolutions) in (mostly) pre-selected regions of the brain. Such studies are much less feasible in humans, as microscopy imaging is (largely) limited to post mortem samples, which are sparse. In vivo studies of the human brain are mainly conducted with magnetic resonance (MR) imaging, which has the capacity to assess volumetric variations of neuroanatomical structures (at 'millimeter' resolution), and variations in tissue MR properties across the whole brain [32]. These human MR imaging studies have shown that adiposity is associated with variations in volumes, as well as in tissue MR-based properties. How these structural variations in the human brain observed with MR imaging relate to the cellular variations in the animal brain seen with microscopy is not well understood. In an attempt to start bridging this gap, here, we will first describe the key findings of human studies of the brain in obesity, as assessed with in vivo MR imaging, and second, we will describe the main observations in animal studies of the brain in obesity, as examined with ex vivo microscopy imaging. We will begin each part with some basic methodological considerations. We will focus this minireview on the cerebrum, which is the largest part of the brain and which exhibits changes during the preclinical and clinical course of AD (leading ultimately to brain atrophy) [7][8][9]33]. Note that the hypothalamus is outside the scope of this minireview, as it has been reviewed in the context of obesity previously [34]. MR imaging exploits the magnetization properties of hydrogen nuclei within the imaged tissue, such as the brain. Most often, MR imaging is based on radiofrequency signals generated by hydrogens, which are present in great abundance in biological tissues and have a high gyromagnetic ratio that makes the signal strong. The nucleus of hydrogen consists of one proton. Hydrogen protons are charged particles normally spinning with random orientation. In an MR scanner, a strong (static) magnetic field is present (e.g., 3.0 T), and hydrogen protons become aligned with this magnetic field (i.e., they become magnetized in the longitudinal direction). The direction of magnetization can be changed (or flipped) by an external radio frequency (RF) energy (RF pulse). The angle at which the magnetization is changed (e.g., 90 • ) or flipped (e.g., 180 • ) is determined by the duration and strength of the RF pulse. For example, a RF pulse inducing transverse magnetization is when the direction of tissue magnetization is at a 90 • angle with respect to the direction of the magnetic field. When the RF pulse is terminated, hydrogen protons return to the original alignment with the magnetic field of the scanner through various relaxation processes, during which they emit RF energy. After a certain period following the initial RF, the emitted signals are measured. Different types of images can be created by different sequences of RF pulses. These sequences are characterized by repetition time (TR), which is the time between successive RF pulses applied, and time to echo (TE), which is the time between the delivery of the RF pulse and the receipt of the echo signal. The relaxation processes through which excited hydrogen protons realign with the magnetic field of the scanner are characterized by T1-and T2-relaxation times. The T1-relaxation time is the time in which the longitudinal magnetization re-grows (after the RF pulse). The T2-relaxation time is the time in which the transverse magnetization dissipates (after the RF pulse). The relaxation processes differ for hydrogen protons in water molecules in extracellular and intracellular spaces (characterized by higher molecular motion) vs. hydrogen protons in macromolecules in semi-solid cell structures (characterized by lower molecular motion). This phenomenon is key in creating tissue contrasts on MR images, visualizing brain structures differing by molecular composition (high content of water (e.g., cerebrospinal fluid) vs. macromolecules (e.g., fatty myelin and lipid composition of cell membranes)).

Modalities Employed
(i) T1-weighted (T1W) imaging uses sequences with short TE and TR, and the signal is mainly determined by the T1-relaxation properties. This imaging modality is commonly used to assess volumes, such as those of the whole brain and its different subregions, including regions the cerebral cortex or the hippocampus ( Figure 1). (ii) T2-weighted (T2W) imaging uses sequences with longer TE and TR times, and the signal is mainly determined by the T2-relaxation properties; this modality is often used to visualize small lesions in white matter, including white matter hyperintensities, and quantify their volume. (iii) T2W Fluid Attenuated Inversion Recovery (FLAIR) imaging uses sequences that are similar to those of T2W imaging, except that the TE and TR times are longer. This modality is also often utilized to visualize and quantify white matter hyperintensities. (iv) Diffusion tensor imaging (DTI) is a T2-relaxation time-based imaging technique that assesses the diffusion of water molecules in tissue. It assesses the quantity and directionality of water diffusion within so-called diffusion tensor (conceptualized as a three-dimensional ellipsoid). The diffusion tensor is described using eigenvectors and eigenvalues, with the largest eigenvector being oriented in parallel with neuronal axons. DTI can provide insights into the architecture and integrity of myelinated axons, and the fiber tracts that these axons form. In myelinated axons, water molecules move along the axonal tracts, whereas in axons with disrupted myelin, water molecules can move also radially [35]. The main measures of DTI (characteristics of diffusion tensors) are fractional anisotropy (FA), radial diffusivity (RD), and mean diffusivity (MD). In general, decreased FA, increased RD, and increased MD are considered as indicators of myelin disruption and axonal damage. (v) Magnetization transfer ratio (MTR) imaging is also a T2-relaxation time-based modality; it generates contrast on MR images using the process of magnetization transfer between hydrogen protons bound to water and hydrogen protons bound to macromolecules [36][37][38]. Magnetization transfer is facilitated by the difference in molecular motion between the two pools of hydrogen protons [39]. In white matter, MTR is commonly assumed to index myelin content and structural properties [40], but, especially in gray matter, it may also index variations in lipid content within cell membranes [41,42] or cytosol (e.g., lipid droplets). (vi) Normalized T1W signal intensity (nT1W-SI) is a measure derived using the T1W MR imaging technique described above; it is determined as the signal intensity of a brain region of interest normalized by the signal intensity of the whole brain [43]. This metric may also index variations in myelin and lipid content within the cell membranes [41,42] and cytosol (e.g., lipid droplets).

Findings
Before we describe the MR imaging characteristics of the brain in obesity, we will do so for a typical brain.

MR Structure of a Typical Brain
On a neuroanatomical MR image (Figure 1), the cerebrum consists of grey and white matter, with the grey mater including the cerebral cortex, which is a 2 to 4 mm thick outer layer of the cerebrum, and individual subcortical structures, such as the hippocampus [44], and the white matter, which is located underneath the cortex, including structures such as the corpus callosum [44].

Findings
Before we describe the MR imaging characteristics of the brain in obesity, we will do so for a typical brain.

MR Structure of a Typical Brain
On a neuroanatomical MR image (Figure 1), the cerebrum consists of grey and white matter, with the grey mater including the cerebral cortex, which is a 2 to 4 mm thick outer layer of the cerebrum, and individual subcortical structures, such as the hippocampus [44], and the white matter, which is located underneath the cortex, including structures such as the corpus callosum [44].

MR Structure of the Brain in Obesity
A growing body of research over the last decade has shown that higher body adiposity (and/or systemic inflammation) is associated-mostly cross-sectionally-with variations in brain structure. Much of this research has used body mass index (BMI) as an index of adiposity (body weight in kg/height in m 2 ), which is an indirect measure of adiposity determined not only by fat mass, but also by muscle and bone mass. Only a few studies have examined body fat more directly (with MRI or computerized tomography), and have quantified the amount of visceral fat (i.e., intra-abdominal fat) [43,[45][46][47][48][49]. To assess visceral vs. non-visceral fat (mostly subcutaneous fat, i.e., the fat under the skin) may be important, as visceral fat (vs. subcutaneous fat) produces more proinflammatory molecules [21,[50][51][52][53], and individuals with a normal BMI but high visceral fat are at higher risk of developing systemic inflammation [50].
The adiposity-brain structure research has been reviewed in detail elsewhere [54]. Here, we highlight only the main findings. Thus, using T1W imaging, most studies have shown that a higher adiposity is associated with a lower brain volume, lower cortical grey volume, and/or lower cortical thickness [45,[54][55][56][57][58], and that the associations with cortical thickness are particularly strong in the frontal, temporal, and parietal cortices [54,56]. A higher adiposity is also associated with lower volumes of the hippocampus [57]. Using T2W and T2-FLAIR imaging, a higher adiposity is associated with higher volumes of white matter hyperintensities [59], which are a marker of small vessel disease [59]. Regarding tissue MR properties, several studies have also shown that a higher adiposity is associated with altered white matter properties, as indicated by lower FA and higher MD on DTI [56,60,61], and higher nT1WSI and MTR [43,47,62].
Some of the above studies suggest that the adiposity-brain structure relationships are stronger for visceral fat (accumulating intra-abdominally) than for fat stored elsewhere in the body (mainly subcutaneously) [45,62]. Some studies also suggest that at least a part of the observed adiposity-brain structure relationships may be mediated by systemic inflammation [45,47,62]. Further, it is becoming evident that the adiposity-brain structure relationships do only not exist during adulthood, but also during childhood and adolescence [63], and that they vary across the lifespan [64] and length of exposure (cumulating effects of sustained obesity) [65]. Further, excess adiposity induces not only systemic inflammation, but also metabolic abnormalities, such as insulin resistance and dyslipidemia [66,67], which may potentiate the effects of and/or contribute to the development of neuroinflammation. Thus, e.g., the proinflammatory cytokine TNFα, the production of which is increased by 'obese' adipose tissue, plays a key role in the development of insulin resistance [68], which exists not only in the periphery, but also in the brain [69], where it has been associated with lower cortical thickness in the regions that overlap with those demonstrating a lower thickness in association with higher adiposity [70][71][72][73][74][75][76]. Regarding dyslipidemia, a higher adiposity increases the blood concentrations of fatty acids that are enriched in saturated and monounsaturated fatty acids, which can directly stimulate neuroinflammation [67], and that are depleted in polyunsaturated fatty acids, which are essential building blocks of neuronal cell membranes and may inhibit neuroinflammation [77,78]. Consistently, higher blood levels of polyunsaturated fatty acids (incorporated in circulating triacylglycerols) are associated with higher cortical thickness [79]. Finally, many of the observed adiposity-brain structure relationships have been associated with impaired cognitive functioning [80].
Of note, the adiposity-brain structure relationships may exist not only in the direction from obesity to brain structure but also in the direction from brain structure to obesity (via, e.g., the brain regulation of eating behavior [81,82], reviewed recently elsewhere [54]).
In contrast to human research, only a few studies have investigated adiposity-brain structure relationships using brain MR imaging in animal models (Figure 1). Thus, in one mouse study of diet-induced obesity (in response to chronic high-fat diet feeding), a higher adiposity was associated with lower volumes of the frontal, temporal, and parietal cortices [83]. In another mouse study of diet-induced obesity, a higher adiposity was associated with higher MD on DTI of the cerebrum [84]. Several types of microscopy can be employed to image brain cells and their organelles. Most commonly, these include optical (light) microscopy [85], which involves magnifying the image of the object (a tissue sample) by passing light (photons) through it or reflecting light off it, and then examining this light through a single or multiple lens. Less commonly used types of microscopy include electron microscopy, which utilizes an electron beam instead of light, and electromagnets instead of traditional lenses; as the electron beam has a far smaller wavelength than light, the image resolution can increase from micrometers to nanometers [86].

Microscopy Imaging of the Brain in Obesity
Regarding optical microscopy, a variety of methods exist, with fluorescence microscopy being one of the most often used, and it is undergoing, at present, major advancements that are aimed to increase the speed of image acquisition, image resolution, and the volume of imaged tissue. Fluorescence microscopy exploits the capacity of some molecules to fluoresce, that is, to absorb and emit light. For the purpose of microscopy, such fluorescent molecules (probes) can be attached to (or integrated into) different cell structures and imaged. In a confocal microscope, which is the work horse of current fluorescence microcopy, the illumination and detection optics are focused on the same diffraction-limited spot in the sample, which is the only spot imaged by the detector during a confocal scan (while out-of-focus light is rejected), allowing for high-resolution image acquisition in thick tissues [87,88]. To generate a complete image, the spot is moved over the sample, and data are collected point by point (laser-point scanning) [87]. Acquiring high-resolution stacks of such images (optical sectioning) enables 3D reconstructions of tissue samples [87]. A higher speed of image acquisition is achieved by more recently developed light-sheet fluorescence microscopy [89], which illuminates a thin laminar volume (with a laminar laser beam, termed a light sheet) rather than a single point. A further reduction of out-of-focus light and greater tissue penetration is achieved with two-photon microscopy, which uses two low-energy excitation photons rather than one high-energy excitation photon [90].
These microscope developments have been complemented by substantive innovations in sample preparation, such as optical tissue clearing (OTC) [91][92][93]. OTC methods remove lipids (delipidation), pigments (decolorization), and calcium phosphate (decalcification), and they aim to match the refractive indices of the tissue sample and imaging media, reaching an almost complete level of transparency [91]. This reduces light scattering (and thus optical background) and, as such, allows for the acquisition of high-resolution images of large sections, or even the whole rodent brain [94][95][96]. Currently, hydrogel-based tissue-clearing methods [94,[97][98][99] are becoming the most commonly used methods. They include 'cleared lipid-extracted acryl-hybridized rigid immunostaining/in situ hybridization-compatible tissue hydrogel' (CLARITY) [100] and 'stabilization to harsh conditions via intramolecular epoxide linkages to prevent degradation' (SHIELD) [101]. CLARITY and SHIELD allow for a uniform delipidation of the tissue, and due to the presence of the hydrogel, also the stabilization and preservation of a large assortment of cell biomolecules through covalent linkages [98]. These preserved tissues can then withstand multiple rounds of antibody staining, stripping, and reprobing, vastly increasing the amount of cellular level information that can be gained from a single sample.

Modalities Employed
Microscopy methods applied to the brain in obesity include: (i) electron microscopy for assessing disruption in myelin membranes [29], (ii) bright-field light microscopy for evaluating neuronal dendritic spine density [20], (iii) confocal fluorescence microscopy for assessing cell-body size and ramifications of microglia [20,28], and (iv) light-sheet microscopy with tissue clearing for examining the blood-brain barrier across the whole brain for uptake of peripherally administered leptin [102].

Findings
Before we describe observations from microscopy imaging of the brain in obesity, we will describe the cellular composition of the cerebral parenchyma for a typical brain.

Brain Cells in a Typical Brain
The human brain contains approximately 85 billion neurons and about the same number of glial cells [103]. Of the glial cells,~10% or less are microglia, 19-40% are astrocytes, and 45-75% are oligodendrocytes [104] (Figure 2). Neurons are the principal cells of the brain; their degeneration and loss are the primary features of AD and related dementias [105]. A typical neuron consists of a cell body, multiple branching dendrites (dendritic arbor), and a single axon; dendrites have small projections, spines. Axons form a network that totals 176,000 km in length in the adult brain [106]. The speed of conduction along the axon depends on two of its structural properties: axon diameter and myelin sheath. The former is determined by an axonal cytoskeleton constituted by neurofilaments, and the latter insulate axons along their length, except for the nodes of Ranvier, for fast (saltatory) conduction of action potentials [107]. The great majority of axons are short (<3 mm, intra-cortical axons); longer (3-30 mm) and very long (>30 m) axons exit (efferent) and enter (afferent) the cortex, forming the bulk of white matter [108]. Grey matter contains neuronal cell bodies, dendrites, and short axons (and some parts of the longer ones). Microglia are the resident immune cells of the brain. They are cells with multiple ramified processes that continuously survey the surrounding parenchyma [109]. When microglia detect cellular debris, apoptotic cells, foreign material, or pro-inflammatory cytokines, they undergo graded 'activation', during which their cell bodies enlarge and their ramified processes shorten [110]. 'Resting' microglia are responsible for scavenging, whereas 'activated' microglia are responsible for cytotoxicity, antigen presentation, and tissue repair. Both resting and activated microglia have the capacity for phagocytosis and extracellular signaling [111]. Astrocytes are glial cells with multiple supporting actions. In gray matter, astrocytes have short and thick primary branches that divide into thinner secondary branchlets and tertiary leaflets. The leaflets have characteristic endfeet that encase blood vessels and ensheath thousands of synapses; the processes of a single astrocyte can contact up to 2 million synapses in the human brain [112]. In white matter, astrocytes have long, thin, and mostly unbranched processes [113] that are aligned with axons, mostly at the nodes of Ranvier [114]. Astrocytes support neurons by releasing neurotrophic factors [115]. They also store and supply energy to neurons-they take up glucose to synthesize and store glycogen, which is used later as an energy source when needed [116]. Astrocytes play a major role in the coupling of neuronal activity to blood flow [117][118][119], and they participate in the formation and elimination of synapses [120]. Astrocytes maintain the fluid and pH homeostasis of the synaptic interstitial fluid via the expression of water channels and ion transporters [121]. Oligodendrocytes are glial cells that form myelin sheaths, provide trophic support to neurons, and modulate neuronal excitability [122]. Myelin sheaths are essentially cell membranes of multiple oligodendrocyte processes that are tightly packed (40 or more lipid bilayers) around neuronal axons [123,124]. Myelin sheaths are lipid-rich, containing 70-85% lipids and only 15-30% proteins (which is unlike other cell membranes, which contain approximately equal amounts of lipids and proteins) [125]. The brain parenchyma is separated from the blood circulation by the blood-brain barrier (Figure 2), which consists of endothelial cells, pericytes, and astrocytic endfeet. The blood-brain barrier regulates the transport of molecules (and immune cells) to the brain parenchyma [126].

Brain Cells in Obesity
Based mainly on preclinical studies of diet-induced obesity (in mice and rats) in selected brain regions (e.g., parts of the cerebral cortex or hippocampus), obese (vs. lean) animals show neurons with reduced spine density and disrupted myelin, and a higher presence of activated microglia and reactive astrocytes (both with enlarged soma size and reduced ramifications) [20,22,[25][26][27][28][29][30][31] (Figure 2). These cellular changes may develop due to the release of-by an expanding adipose tissue-multiple pro-inflammatory molecules (saturated and mono-unsaturated FAs and cytokines, such as IL-6 and TNFα) into the circulation, where they generate a hyperlipidemic, pro-inflammatory milieu that disrupts the functional and structural integrity of the blood-brain barrier; consequently, more lipids, proinflammatory molecules, and immune cells enter the brain parenchyma, and promote the development of neuroinflammation [26,27,127,128]-microglia and astrocytes become more proinflammatory (and cytotoxic) and less homeostatic (and neurotrophic). Both microglia and astrocytes increase the production of proinflammatory cytokines and reactive oxygen species, which impair mitochondrial function, damage DNA, proteins, and cell membranes, and promote apoptosis in surrounding cells, including neurons and oligodendrocytes; at the same time, they reduce the phagocytosis of cell and myelin debris and the production of neurotrophic factors that are required for the differentiation of oligodendrocytes and new myelin production, and for neuron survival [129][130][131][132][133][134]. Finally, in obesity and under inflammatory conditions, microglia, astrocytes, and neurons accumulate lipid droplets (LDs) in their cytoplasm [135,136]. Among others [137][138][139][140], LDs sequester excess lipids from the cytoplasm to prevent lipotoxicity, as excess lipids can disrupt the integrity of cell membranes [141]. Reactive oxygen species upregulate lipid synthesis in neurons that transfer excess fatty acids to astrocytes, where they are absorbed by LDs [142,143]. Lipid-laden astrocytes produce proinflammatory factors, such as IL-6 and TNFα [135,136]. Under inflammatory conditions, microglia accumulate LDs [142,144], which are accompanied by compromised phagocytic function. Thus, a number of cellular brain changes have been observed in obesity, but how these changes relate to the observed volumetric and tissue MR properties remains to be elucidated.

Brain Cells in Obesity
Based mainly on preclinical studies of diet-induced obesity (in mice and rats) in selected brain regions (e.g., parts of the cerebral cortex or hippocampus), obese (vs. lean) animals show neurons with reduced spine density and disrupted myelin, and a higher presence of activated microglia and reactive astrocytes (both with enlarged soma size and reduced ramifications) [20,22,[25][26][27][28][29][30][31] (Figure 2). These cellular changes may develop due to the release of-by an expanding adipose tissue-multiple pro-inflammatory molecules (saturated and mono-unsaturated FAs and cytokines, such as IL-6 and TNFα) into the circulation, where they generate a hyperlipidemic, pro-inflammatory milieu that disrupts the functional and structural integrity of the blood-brain barrier; consequently, more lipids, proinflammatory molecules, and immune cells enter the brain parenchyma, and promote the development of neuroinflammation [26,27,127,128]-microglia and astrocytes become more proinflammatory (and cytotoxic) and less homeostatic (and neurotrophic). Both microglia and astrocytes increase the production of proinflammatory cytokines and reactive oxygen species, which impair mitochondrial function, damage DNA, proteins, and cell membranes, and promote apoptosis in surrounding cells, including neurons and oligodendrocytes; at the same time, they reduce the phagocytosis of cell and myelin debris and the production of neurotrophic factors that are required for the differentiation of oligodendrocytes and new myelin production, and for neuron survival [129][130][131][132][133][134]. Finally, in obesity and under inflammatory conditions, microglia, astrocytes, and neurons accumulate lipid droplets (LDs) in their cytoplasm [135,136]. Among others [137][138][139][140], LDs sequester excess lipids from the cytoplasm to prevent lipotoxicity, as excess lipids can disrupt the integrity of cell membranes [141]. Reactive oxygen species upregulate lipid synthesis in neurons that transfer excess fatty acids to astrocytes, where they are absorbed by LDs [142,143]. Lipidladen astrocytes produce proinflammatory factors, such as IL-6 and TNFα [135,136]. Under inflammatory conditions, microglia accumulate LDs [142,144], which are accompanied by compromised phagocytic function. Thus, a number of cellular brain changes have been observed in obesity, but how these changes relate to the observed volumetric and tissue MR properties remains to be elucidated.

Conclusions and Future Directions
Human brain MR imaging studies show that a higher body adiposity is associated with lower volumes of the cerebral grey matter (including the cortex and hippocampus) and the alteration of tissue MR properties of the cerebral white matter. Brain MR imaging studies in animals are sparse, but the few that have been conducted provide similar results. Brain microscopy in animal studies shows that diet-induced obesity results in morphological changes of multiple cell types, including microglia, astrocytes, oligodendrocytes, and neurons, but how these cellular changes seen with microscopy relate to the changes in volumes and tissue MR properties observed with MR imaging is not clear at present. Clearly, there is a need for a bridge between animal microscopy and human MR imaging studies. Fast advancing microscopy (with tissue clearing and multiplex immunohistochemistry) that has the capacity to image the entire rodent brain, combined with MR imaging, may provide such a bridge. Towards that end, some novel approaches have been developed to infer cell biology behind the MR imaging data by combining such data with in silico data available in mRNA and protein brain atlases [70,79,145].

Conflicts of Interest:
The authors declare no commercial or financial relationships that could be construed as a potential conflict of interest.


s2fieldsofstudy: Biology Medicine

text:
An Immune-Related Gene Signature for Predicting Neoadjuvant Chemoradiotherapy Efficacy in Rectal Carcinoma

Background Locally advanced rectal cancers (LARC) show a highly variable response to neoadjuvant chemoradiotherapy (nCRT), and the impact of the tumor immune response in this process is poorly understood. This study aimed to characterize the immune-related gene expression profiles (GEP), pathways, and cell types associated with response or resistance to neoadjuvant chemoradiotherapy. Methods The transcriptomic and clinical data of Rectal carcinoma from the Gene Expression Omnibus database and Immune-related genes (IRGs) from ImmPort were downloaded to identify the differentially expressed immune-related genes (DEIRGs) between responder and non-responder to neoadjuvant chemoradiotherapy. Gene set enrichment analyses were performed to uncover significantly enriched GO terms and KEGG pathways. Immune cell infiltration was estimated from RNA-sequencing data using ImmuCellAI. Afterward, we constructed an immune-related gene-based predictive model (IRGPM) by Support Vector Machine and validated it in an external cohort. Result A 15-gene signature (HLA-DPB1, HLA-DQA1, CXCL9, CXCL10, TAP2, INHBB, BMP2, CD74, IL33, CCL11, CXCL11, DEFB1, HLA-DPA1, CCN3, STAT1) was identified as DEIRGs and found to be significantly associated with nCRT outcomes. Gene set enrichment analyses indicated that the 15 genes play active roles in inflammation-related biological processes. In addition, ImmuCellAI revealed that CD4 naive T cells, Tex, Th1 were significantly up-regulated (p=0.035, p=0.02, p=0.0086, respectively), while Tfh were significantly down-regulated (p=0.015) in responder subgroup. Finally, a novel predictive model was developed by SVM based on DEIRGs with an AUC of 80% (internal validation) and 73.5% (external validation). Conclusion Our team conducted a genomic study of the relationship between gene expression profile and response to nCRT in LARC. Our data suggested that the DEIRGs signature could help predict the efficacy of nCRT. And a DEIRGs‐based SVM model was developed to monitor the outcomes of nCRT in LARC.

INTRODUCTION
Rectal cancer (RC) is one of the most common malignancies, amounts to one-third of colorectal cancer, which is viewed as the third leading cancer and the second cancer-related mortality in men and women worldwide (1). And the majority of the patients are diagnosed with invasive stage, should be referred for multidisciplinary treatment. For patients with locally advanced rectal cancer (LARC), neoadjuvant chemoradiotherapy (nCRT) followed by radical surgery with total mesorectal excision (TME) has become the standard of care according to the clinical practice guideline of NCCN (National Comprehensive Cancer Network) (2). As was demonstrated in previous research, nCRT has emerged as an indispensable component of curative therapy, which offers a higher probability to downsize and downstage tumors, to enhance tumor resectability and sphincter preservation, and to improve local control rate (3)(4)(5)(6). However, LARC exhibits heterogeneity in response to nCRT, varying from nonresponse to complete pathologic response (7). Given the spectrum of response, tailored decisions should be made individually by physicians. However, there is a lack of effective methods to select rectal cancer patients who would or would not have a benefit from nCRT. Numerous efforts have been made, but none has currently reached the clinic (8).
The behavior of a tumor is not only governed by the epithelial component but also by the tumor environment (9). As an essential component of the tumor microenvironment, the tumor immune microenvironment (TIME) serves a vital role in tumor progression and treatment outcome, has gradually acquired accumulative attention. Mounting evidence indicates that characterizing TIME could enable the identification of new prognostic and predictive biomarkers and the possibility to guide first-line treatment algorithms (10). Some features of TIME have been quantified and incorporated into an index called 'immunoscore,' which can be used as a strong predictor of patient survival in colorectal carcinoma, providing information more robust than the standard AJCC/ UICC-TNM staging system and microsatellite instability (11)(12)(13)(14). Therefore, the contexture of TIME has the potential to predict response to nCRT, which has been confirmed in a recent study (15). But the preliminary findings need to be further corroborated in large patient cohorts. Herein, it is urgent to construct an immunerelated gene signature closely related to TIME, aiming to predict nCRT efficacy.
This study aimed to characterize the immune-related gene expression profiles (GEP), pathways, and cell types associated with response or resistance to neoadjuvant chemoradiotherapy. Initially, we mainly focused on significant immune genes associated with poor tumor response through data mining in the Gene Expression Omnibus (GEO) databases. We found that differentially expressed immune-related genes (DEIRGs) were significantly associated with the efficacy of nCRT. Then, Gene set enrichment analyses were performed to uncover greatly enriched GO terms and KEGG pathways. Immune cell infiltration was estimated from RNA-sequencing data using ImmuCellAI. Finally, an immune-related gene-based prognostic model (IRGPM) was constructed by Support Vector Machine and validated in an external database.

Data Acquisition
The publicly available mRNA expression profiles and corresponding clinical information were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi. nlm.nih.gov/geo/), with accession numbers GSE87211, GSE45404, and GSE35452. The gene expression profiles (ID, GSE87211) were previously produced using the Agilent-026652 Whole Human Genome Microarray 4x44K v2 (Probe Name version). Specimens in GSE87211, including 203 tumor samples and 160 mucosa control samples, were obtained from 243 patients. All patients were staged as locally advanced (cUICCII/III/IV) and treated according to the CAO/ARO/AIO-94 or the CAO/ARO/AIO-04 trial (16). The other two microarray datasets (ID, GSE45404, and GSE35452) were previously produced using Affymetrix HGU133 Plus 2.0 GeneChips (Affymetrix, Santa Clara, Ca).
The specimens in the GSE45404 were obtained from 42 patients with LARC (91% and 88% of patients were clinically staged as T3-4 and lymph nodes positive, respectively). 38 (90%) patients received a total dose of radiotherapy higher than 50 Gy, and 15 out of these cases (36%), drugs other than 5-FU were administered (n=11, Oxaliplatin; n=4, Carboplatin). For 33 (79%) patients, 5-FU was administered by continuous venous infusion. The median (range) interval time between the completion of nCRT and surgery was 46 (30-66) days. The tumor response to nCRT was defined as the tumor regression grade (TRG) and was scored following the criteria proposed by Mandard et al. (17).
The specimens in the GSE35452 were obtained from 46 patients with LARC. All patients received a total dose of 50.4Gy of radiation, given in 28 fractions over six weeks. Tegafur-uracil (300-500mg/day) and leucovorin (75mg/day) was given concomitantly with radiotherapy. Standardized curative resection was performed six weeks after the completion of nCRT. Response to nCRT was determined by histopathological examination of surgically resected specimens based on a semiquantitative classification system defined by the Japanese Society for Cancer of the Colon and Rectum (18).

Screen Genes of Differential Expression
The rectal cancer-related gene expression profile was identified in the GSE87211 database. And GSE45404 database was used to select genes associated with nCRT efficacy by comparing responders and non-responders. We utilized the R package 'limma' to screen the differentially expressed genes (19). Those genes with P-value < 0.05 and the expression ratio lg |fold change| > 0.5 were picked out for further evaluation. A Benjamini-Hochberg FDR of 0.1 was used to identify highly significant associations to account for multiple hypothesis testing.

Generation of DEIRGs
1793 IRGs were retrieved from 17 categories after excluding the duplicates from the Immunology Database and Analysis Portal (ImmPort) website (https://www.immport.org) (20). The overlapping DEGs in GSE87211 and GSE45404 datasets were intersected with immune-related genes from ImmPort to obtain the target genes defined as the differentially expressed immunerelated genes (DEIRGs).

Gene Set Enrichment Analysis
Gene set enrichment analysis (GSEA), also called functional enrichment analysis, is a popular calculation method applied to analyze the slight changes in the expressions of genes that belong to a critical pathway. So GSEA can be used to understand the molecular mechanisms of complex disorders, lead to the discovery of new suspect genes and biological pathways, and help provide molecular evidence for the association of biological pathways with diseases (21). In our study, Gene set enrichment analysis was performed and illustrated by 'Clusterprofiler' (22) and 'Pathview' (23) packages in R. Gene Ontology (GO) classification, including GO-BP (biological process), GO-MF (molecular function), and GO-CC (cellular component), was used to uncover the functions of intersecting genes and further test the biological links in DEGs. GO terms with significant P values were selected according to the BP, CC, and MF results. The pathways with the following criteria were significantly enriched: nominal p-value < 0.05, false discovery rate (FDR) q-value < 0.25.

Estimation of Immune Cell Infiltration
To explore the associations between nCRT efficacy and immune cell infiltration, we employed Immune Cell Abundance Identifier (ImmuCellAI) (24), a valuable resource for comprehensive analysis of tumor-infiltrating immune cells. ImmuCellAI is a tool to estimate the abundance of 24 immune cells from gene expression datasets, including RNA-Seq and microarray data. It can be applied to estimate the difference in immune cell infiltration in diverse groups. The core algorithm of ImmuCellAI includes three main steps: 1) reference expression matrix and marker gene preparation, 2) enrichment score calculation, and 3) compensation matrix correction. The reference expression profiles of the immune cells were obtained from GEO, and marker genes per immune cell type were obtained from the literature and analytical methods. For each queried sample, the enrichment score of the total expression deviation of the signal gene sets was calculated and assigned to each immune cell type by the ssGSEA algorithm. The compensation matrix and least square regression were implemented to correct the bias caused by the shared marker genes among different immune cell types.

Construction of an SVM Classifier
Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM), is a powerful classification tool that has been used for cancer genomic classification or subtyping (25). SVM aims to create a decision boundary between two classes that enables the prediction of labels from one or more feature vectors (26). because our classification problem was nonlinear, the polynomial kernel was adopted, the formula as follows: To evaluate the predictive accuracy of DEIRGs for "N" and "NR" samples, a supervised SVM classifier was constructed and trained via the SVM function in the 'e1071' package of the R software.

Validation Analysis
The efficacy of the SVM model was further verified in the GSE45404 dataset (internal validation set) and GSE35452 dataset (external validation set), respectively. Receiver operating characteristic (ROC) curves were used to evaluate the sensitivity and specificity of the predicting model by the 'pROC' package in R. The area under the curve (AUC) ≥ 0.7 indicated a good differentiation ability. To more detailedly confirm the prediction value of the SVM classifier model, an SVM with a confusion matrix was constructed.

Statistical Analysis
Most statistical analyses were performed using R software (Version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria). Graphs related to R statistical analyses were drawn using the ggplot2 package in R.

RESULT An Immune-Related Signature of Chemo-Radio Resistance in LARC
The flowchart of the procedures in this study is shown in Figure 1. Three independent datasets were enrolled in our study ( Table 1). Initially, we focused on gene expression profiles (GEP) of rectal carcinoma to reveal the underlying mechanism of rectal cancer. DEGs analysis was performed between the tumor and normal tissue in the GSE87211 cohort. A total of 7941 deregulated genes were identified, which were defined as RC-related DEGs. A volcano plot and heatmap were generated to show the distribution of the DEGs (Figures 2B, C). Besides, the principal component analysis (PCA) was applied to analyze the whole gene expression, and the 2D-PCA plot showed significant differences between rectal cancer and normal tissue ( Figure 2A).
Then, we integrated gene expression signatures to search for differences in gene expression linked to nCRT response and obtain information on the molecular mechanisms involved in differential nCRT efficacy. DEGs analysis between responders and non-responders in the GSE45404 cohort was performed. A total of 73 upregulated and 40 downregulated genes were identified, defined as nCRT-related DEGs. A volcano plot and heatmap were generated to show the distribution of the DEGs (Figures 3B, C). Auxiliary, the principal component analysis (PCA) was applied to analyze the whole gene expression, and the difference between responders and non-responders was not significant in the 2D-PCA plot ( Figure 3A).
To obtain a robust gene signature to predict nCRT efficacy, GO enrichment analysis of biological processes was conducted in the overlapping DEGs in GSE87211 and GSE45404 datasets, which demonstrated "immune-related responses" were the most frequently used biological terms in biological processes (Supplementary Figure 1). In this regard, an immune-related gene signature may be able to predict chemo-radio resistance. To address the issue, the 58 overlapping DEGs were intersected with 1793 immune-related genes from ImmPort to obtain the target genes that were defined as the differentially expressed immunerelated genes (DEIRGs) (Figure 4). In the end, 15 DEIRGs were generated, and the details were shown in Table 2.

GSEA Analysis of DEIRGs
GO, and KEGG functional enrichment analyses were performed on the fifteen selected genes. KEGG pathway analysis showed that the DEIRGs were mainly enriched in cytokine-cytokine receptor interactions ( Figure 5A). Concerning biological processes, the DEGs were significantly enriched in leukocyte migration, myeloid leukocyte migration, leukocyte chemotaxis, and cell chemotaxis ( Figure 5B). Meanwhile, the most enriched terms in the aspect of molecular function were cytokine receptor binding, receptor-ligand activity, and signaling receptor activator activity ( Figure 5D). Enrichment analysis of cellular compartment and the corresponding distributions are shown in Figure 5C.

Immune Cell Infiltration Between Responders and Non-Responders
Given that the expression of immune genes was closely associated with nCRT efficacy, it is necessary to investigate the role of TIME on chemo-radio resistance. Twenty-four immune cell fractions were generated using a ssGSEA algorithm to confirm the correlation between TIME and nCRT efficacy. As shown in Figure 6, CD4 naive T cells, Tex (exhausted T cells), Th1 (type 1 CD4+ T helper cells) were significantly up-regulated (p=0.035, p=0.02, p=0.0086, respectively), while Tfh (T follicular helper cells) were significantly down-regulated (p=0.015) in responder subgroup of GSE45404 cohort. However, this difference in the distribution ratio of immune cells infiltration was not observed in GSE35452 (Supplementary Figure 2).

Construction of Predictive Model and External Validation
SVM, a method for building a classifier, aims to create a decision boundary, which is orientated in such a way that it is as far as possible from the closest data points from each of the classes. In our study, we addressed the outcome of nCRT as a two-class classification problem ("R" or "NR") and the above 15 DEIRGs as features. The polynomial kernel was adopted, and the SVM classifier was developed using the GSE45404 dataset. We performed a set of experiments in the training cohort of the first 10 patients, tested the classifier with the remaining 32 samples. As a result, 80% prediction accuracy was found to be achieved using this developed SVM classifier (Kernel=polynomial, cost=1/32, degree=2, gamma =0.125, coef0 = 2) in GSE45404 dataset ( Figure 7A). Furthermore, SVM with confusion matrix analysis also suggested an 80% prediction accuracy for response samples (sensitivity) and 80% prediction accuracy for nonresponse samples (specificity) ( Figure 7C).
To further confirm the classification reliability of the above DEIRGs, another microarray dataset GSE35452 was collected and underwent the SVM model analysis. As expected, the SVM exhibited a 73.5% prediction accuracy in the external cohort of 46 samples ( Figure 7B). The confusion matrix suggested an 83.3% sensitivity, a 63.6% specificity, a 71.4% positive predictive value (PPV), and a 77.8 negative predictive value (NPV) ( Figure 7D). These findings all proved an outstanding predictive value of this 15 DEIRGs signature.

DISCUSSION
Response to nCRT varies among patients, and pathological complete response is associated with a better outcome. However, there is a lack of effective methods to select rectal cancer patients who would or would not have a benefit from nCRT. The utility of clinicopathological and radiological features is limited due to a lack of adequate sensitivity and specificity. Although the relationship of gene expression profile and response to nCRT has been well studied (27)(28)(29), Predictors of outcome have not been systematically explored in the context of spontaneous tumor immunity. To our knowledge, the predictive ability of the immune gene signature per se is confirmed for the first time. This study sorted out the most influential immune-related genes affecting patients' nCRT outcomes with a public database and bioinformatical method. Based on this immune gene signature, we generated an SVM classifier capable of stratifying patients with LARC. Notably, satisfactory AUCs of the SVM classifier imply that the LARC response to nCRT may be affected by the immune system. This conclusion echoes a series of ongoing phase II clinical trials combining nCRT and immunotherapy in LARC patients, such as the VOLTAGE-A trial in Japan, which have achieved better pCR rates (30). Our research found that the DEIRGs and DEIRGs-based SVM model have the potential to be a reference for whether or not to combine immunotherapy in LARC patients.
HLA-DPA1, HLA-DQA1, and HLA-DPB1 belong to the HLA class II play a central role in the immune system by presenting peptides derived from extracellular proteins. TAP is essential for peptide delivery from the cytosol to the lumen of the endoplasmic reticulum in the major histocompatibility complex (MHC) class I antigen-presenting pathway (31). TAP expression level in tumor cells defines the nature and processing of MHC class I peptides for recognition by tumor-specific cytotoxic T lymphocytes (32). CD74 associates with class II major histocompatibility complex (MHC) and is a vital chaperone that regulates antigen presentation for immune response. The high expression of these genes indicates an active antigen presentation environment, which is conducive to the presentation of antigens generated during radiotherapy and chemotherapy to tumor killer cells, thereby activating antitumor immunity and suggesting better tumor efficacy. Some studies confirmed our conjecture. Restoration of the expression of TAP2 may increase tumor-specific immune responses and survival (33). Expression of CD74 associated with favorable survival for stage III melanoma (34).
CXCL9, CXCL10, CXCL11 are interferon-stimulated chemokines that attract T cells. The chemokine encoded by the CCL11 gene displays the chemotactic activity of eosinophils and shows anti-cancer characteristics in colorectal carcinoma (35). STAT1 signaling promotes T helper 1 (Th1) differentiation and interleukin-12 receptor (IL-12) expression, and thus, in some context, has a role in the anti-tumor immune response (36,37). Overexpression of these genes may better recruit T cells, thereby activating more robust anti-tumor immune responses.
The five genes downregulated in the R group were involved in tumor progression to varying degrees. INHBB and BMP2 are TGF-b (transforming growth factor-beta) related genes. TGF-b is central to immune suppression within the tumor microenvironment, and recent studies have revealed roles in tumor immune evasion and poor responses to cancer immunotherapy (38). in vivo IL33 is involved in the maturation of Th2 cells and activating mast cells, basophils, eosinophils, and natural killer cells. IL33 recruited macrophages into the cancer microenvironment and stimulated them to produce prostaglandin E2, which supported colon cancer stemness and tumor growth (39). CCN3 is a member of the CNN family, which plays crucial roles in CRC progression, including cell migration, invasion, adhesion, and distal metastasis (40). DEFB1 is an antimicrobial peptide implicated in the resistance of epithelial surfaces to microbial colonization, which decreased in colon cancer specimens (41).
Given that Immune-related genes expressed differentially in the subgroups of neoadjuvant radiotherapy and chemotherapy, we suggested that there may be different tumor immune microenvironments between the subgroups. Our data revealed a significant difference in the distribution of some immune cells between the responder group and the non-responder group in GSE45404. In the responder group, CD4+ naive T cells, Th1(type    (42,43). Previous studies have shown that Pre-nCRT CD8+TILs, CD4+TILs, and MDSC-TILs are sensitive predictors of response to CRT, and high CD8+TILs are associated with a better prognosis (44). We did not observe this phenomenon. In addition, the results of GSE45404 could not be confirmed in GSE35452. Perhaps limited by the limited sample size, the differences observed in GSE45404 are not generalizable. Or different nCRT schemes and different assessment protocols between GSE45404 and GSE35452 databases caused this result. In this regard, we revised the paper. There were several limitations in our study that should be acknowledged. First, our study was based on publicly available data sets, and it was not possible to obtain complete clinical information (like pCR, recurrence, survival) and demographic data for each patient. Because of insufficient data, our model cannot be used to predict pCR, survival time, and prediction of disease recurrence. Second, as it was a retrospective study, the potential bias relating to unbalanced clinical-pathological features and treatment heterogeneity like not wholly same nCRT scheme and assessment protocol cannot be ignored. Prospective studies are further required to validate the results. Third, among the 15 DEIRG, there were 3 HLA class II genes.  These are highly variable genes, and their expression couldn't be appropriately addressed using microarrays. This factor affected the predictive performance of our model. Forth, Specimens used in this study were obtained from biopsy, and samples from the biopsy could not reflect the tumor because of tumor heterogeneity. Finally, we have not yet added experiments to explore the mechanism behind the immune-related gene signature, and experimental studies on these immune genes are greatly needed. Even so, our findings might provide some reference value for further research in the functional roles of these immune-related genes.

CONCLUSION
Overall, our data revealed that the DEIRGs signature could help predict the outcomes of nCRT, and the DEIRGs-based SVM model could monitor the efficacy of nCRT in LARC. These  findings may suggest that immune response plays a vital role in the therapeutic effects of chemoradiation.

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.


RNA Interference: Promising Approach to Combat Plant Viruses

Plant viruses are devastating plant pathogens that severely affect crop yield and quality. Plants have developed multiple lines of defense systems to combat viral infection. Gene silencing/RNA interference is the key defense system in plants that inhibits the virulence and multiplication of pathogens. The general mechanism of RNAi involves (i) the transcription and cleavage of dsRNA into small RNA molecules, such as microRNA (miRNA), or small interfering RNA (siRNA), (ii) the loading of siRNA/miRNA into an RNA Induced Silencing Complex (RISC), (iii) complementary base pairing between siRNA/miRNA with a targeted gene, and (iv) the cleavage or repression of a target gene with an Argonaute (AGO) protein. This natural RNAi pathway could introduce transgenes targeting various viral genes to induce gene silencing. Different RNAi pathways are reported for the artificial silencing of viral genes. These include Host-Induced Gene Silencing (HIGS), Virus-Induced Gene Silencing (VIGS), and Spray-Induced Gene Silencing (SIGS). There are significant limitations in HIGS and VIGS technology, such as lengthy and time-consuming processes, off-target effects, and public concerns regarding genetically modified (GM) transgenic plants. Here, we provide in-depth knowledge regarding SIGS, which efficiently provides RNAi resistance development against targeted genes without the need for GM transgenic plants. We give an overview of the defense system of plants against viral infection, including a detailed mechanism of RNAi, small RNA molecules and their types, and various kinds of RNAi pathways. This review will describe how RNA interference provides the antiviral defense, recent improvements, and their limitations.

Introduction
The world population is increasing rapidly and is expected to be double within the next 50 years, creating a massive demand for worldwide food production [1]. One way to increase food production is to develop new crops with an enhanced resistance to various biotic stress factors, such as viruses, bacteria, and fungi, which are currently a significant threat to agriculture.
Viruses are infectious pathogens, and plant virus epidemics can have devastating consequences to crop yield and quality. Every year viruses cause damage to the quality and quantity of crops. It is difficult to measure the actual losses caused by viruses worldwide, but according to an estimate, about $60 billion in losses were estimated to be caused by viral diseases [2]. For example, Potato leaf roll Polerovirus caused annual losses of about $100 million in the USA [2,3]. Moreover, Barley yellow dwarf virus (BYDV) damaged a variety of cereals such as rice, barley, maize, and oat and caused an economic loss of about $12 million per year [2,4]. The cassava crop is one of the staple crops for more than 500 million people globally. However, Cassava mosaic begomovirus caused annual losses of 25 million tons in Sri Lanka, Africa, and India, which often cause famine in these countries [5,6]. Moreover, the sugarcane mosaic virus affects the sugarcane crops every year and causes up to 20% losses when the disease incidence reaches 50% [7].

Development of Resistance against Viral Infection Inside the Host Cell
Plant viruses are a significant threat to crops and cause approximately 10-15% economic loss [8]. To encounter the virus attack, the host comprises various barriers, including (i) physical means such as cuticles, wax deposition, and the thickening of the cell wall, which assist in the blockage of virus transmission through insect vectors, (ii) passive resistance in which the host deprives the virus of vital components required for its life cycle, (iii) pattern-triggered immunity, (iv) effector-triggered immunity, (v) an RNA-silencing mechanism [9].

Effector-Triggered Immunity (ETI) against Virus Infection
ETI has been considered the second line of the defense system. The response is initiated when effector proteins (released by plant pathogens that suppress the PTI response) are recognized by ETI [13]. Programmed cell death (PCD) is commenced at the site of the infection to inhibit the virus' spread. The intracellular immune response is related to nucleotide-binding/Leucine-rich repeat (NLR) proteins [20,21]. NLR proteins are divided into two categories: a toll-like interleukin-1 receptor (TIR) and an N-terminal coiledcoil, referred to as TNLs or CNLs [22]. Previous studies suggest that CNLs formed a resistosome complex (calcium-permeable ion channel), which directly activates ETI [21,23]. However, TNLs are activated with NADase activity, which converts into a holoenzyme. The complexes trigger Enhanced Diseased Susceptibility (EDS1) and are facilitated by helper NLRs, named RNLs [24][25][26][27]. ETI provides resistance against virus infection through hypersensitive response (HR), which is enhanced by phytohormone production [28]. ETI provides local immunity at the site of infection, but it also provides resistance against systemic infection, classified as systemic acquired resistance (SAR) [9].

Gene Silencing
Gene silencing is the natural defense system in eukaryotes which provides resistance against a virus infection and regulates endogenous gene expression [29][30][31]. The initial concept of gene silencing was given by Sanford and Johnston when they reduced the pathogenicity inside plants by expressing the core genetic constituents of pathogens [29]. There are two types of gene silencing inside plant cells that ultimately downregulate the transcript level. The first occurs at a transcriptional level known as transcriptional gene silencing (TGS). In contrast, the second type of silencing is post-transcriptional gene silencing (PTGS), which degrades the accumulated mRNA in cells [32].

Transcriptional Gene Silencing (TGS)
TGS is the form of epigenetics gene silencing that causes the methylation of promoter DNA sequences. It produces a heterochromatin area around the gene which is ultimately responsible for gene transcription repression [33,34] Post-transcriptional gene silencing is the RNA interference (RNAi) phenomenon. With the advent of technology, the RNA silencing mechanism has been identified in plants [35]. Subsequently, RNA silencing/gene silencing has been utilized to generate successful resistance against various plant viruses. RNAi is triggered by two types of small RNA that are microRNAs (miRNAs) and small interfering RNAs (siRNAs). These small RNAs are produced by the precursor of a stem-loop structure or through long double-stranded RNA [36,37]. The siRNA and miRNA silencing phenomena follow the same biochemical pathway inside the cell.

General Mechanism of RNA-Induced Gene Silencing
Gene silencing is triggered by small RNA (sRNAs) molecules. sRNAs are small interfering RNA (siRNA) and microRNA (miRNA), depending on the origin and processing. The silencing process is initiated when long dsRNAs are cleaved into small fragments (21-25 nts) of sRNAs with the aid of the Dicer (DCL) enzyme inside the cytoplasm [38][39][40]. Next, these sRNAs duplexes unwind themselves and are loaded into the RISC. The thermodynamic stability determines the polarity of the strands as the 5' end of the less stable strand is selected as the guide strand, and the second strand is the passenger strand, which is later discarded. Guide strands integrate into the RISC and activate it. An essential member of the RISC is the Argonaute (AGO) protein. After loading the guide strand, the AGO protein, by its endonucleolytic action, mediates the repression or degradation of the targeted mRNA [41,42]. Cleavage of targeted mRNA is initiated from 10-12 nts away from the region's center, recognized by the guide sRNA [39]. The mechanism is accomplished by amplifying sRNA molecules through RNA-dependent RNA polymerase (RDRs) enzymes. RDRs produced double-stranded RNA, which are further cleaved and processed by DCLs and continued in the next round of RNA silencing [43] (Figure 1).

Figure 1.
Schematic representation of gene silencing mechanism inside the plant cell. (A) Gene silencing pathway via miRNA. miRNA gene is transcribed by RNA polymerase II. It results in the formation of stem-loop structure, which is termed primary microRNA/ pri-miRNA. Dicer enzyme (DCL1) cleaved the stem-loop portion in two steps, forming precursor-microRNA (pre-miRNA) and miRNA/miRNA * duplex. The miRNA/miRNA * duplex is methylated by HEN1 protein. Methylated duplex is detected by Agonuate (AGO1) protein of RNA-Induced Silencing Complex (RISC), which unwinds the duplex. Single-stranded AGO1 guided the methylated miRNA strands, which was hybridized with complementary mRNA. Targeted mRNA undergoes cleavage or translational repression. (B) Gene silencing pathway via siRNA. The inverted repeat sequence of hairpin RNA is transcribed by RNA polymerase III, which generates a double-stranded RNA (dsRNA) structure. dsRNA is cleaved by DCL4 to generate siRNA duplex. siRNA duplex is methylated by HEN1 protein. AGO1 protein detected the methylated duplex, which underwent unwinding of duplex DNA and complementary paired with targeted mRNA. It also results in the degradation/repression of the target gene [38][39][40][41][42][43].

Dicer
Dicer is a multiple-domain protein about ~220-kDa in size, which belongs to the RNase III class and generates sRNA from long dsRNAs (using ATP). Dicer comprises of an N-terminal RNA helicase C/ATPase domain, two RNase III domains (RNase III a, RNase III b), a Piwi-Argonaute-Zwille (PAZ) domain, a domain of unknown function 283 (DUF283), and dsRNA-binding domains (dsRBDs) [44,45]. Helicase unwinds the duplex with the aid of energy produced by hydrolyzing the nucleotide triphosphate. The PAZ domain is responsible for recognizing the end of dsRNA, while RNase III domains catalyze the cleavage of dsRNA [46]. After cleavage, dicer generates two nt overhang at the 3' It results in the formation of stem-loop structure, which is termed primary microRNA/pri-miRNA. Dicer enzyme (DCL1) cleaved the stem-loop portion in two steps, forming precursor-microRNA (pre-miRNA) and miRNA/miRNA * duplex. The miRNA/miRNA * duplex is methylated by HEN1 protein. Methylated duplex is detected by Agonuate (AGO1) protein of RNA-Induced Silencing Complex (RISC), which unwinds the duplex. Single-stranded AGO1 guided the methylated miRNA strands, which was hybridized with complementary mRNA. Targeted mRNA undergoes cleavage or translational repression. (B) Gene silencing pathway via siRNA. The inverted repeat sequence of hairpin RNA is transcribed by RNA polymerase III, which generates a double-stranded RNA (dsRNA) structure. dsRNA is cleaved by DCL4 to generate siRNA duplex. siRNA duplex is methylated by HEN1 protein. AGO1 protein detected the methylated duplex, which underwent unwinding of duplex DNA and complementary paired with targeted mRNA. It also results in the degradation/repression of the target gene [38][39][40][41][42][43].

Dicer
Dicer is a multiple-domain protein about~220-kDa in size, which belongs to the RNase III class and generates sRNA from long dsRNAs (using ATP). Dicer comprises of an N-terminal RNA helicase C/ATPase domain, two RNase III domains (RNase III a, RNase III b), a Piwi-Argonaute-Zwille (PAZ) domain, a domain of unknown function 283 (DUF283), and dsRNA-binding domains (dsRBDs) [44,45]. Helicase unwinds the duplex with the aid of energy produced by hydrolyzing the nucleotide triphosphate. The PAZ domain is responsible for recognizing the end of dsRNA, while RNase III domains catalyze the cleavage of dsRNA [46]. After cleavage, dicer generates two nt overhang at the 3' region in each strand [46,47]. DUF283 facilitates the hybridization between the opposite strands of nucleic acids [48]. Moreover, dsRNAs regulate the protein nucleo-cytoplasm distribution and bind with dsRNAs [49,50].

RNA-Induced Silencing Complex (RISC)
The RISC is the complex of ribonuclease proteins that degrade the targeted mRNA through sequence-specific nucleases. The RISC is a 250-500 kDa precursor complex that is activated with ATP into a 100 kDa activation complex [60,61]. The siRNA is a vital member of the RISC, guiding the complex to bind with targeted mRNA. Different protein components of the RISC have been identified, and a significant one is the Argonaute (AGO) protein. AGOs are about 100 kDa proteins that comprise three components: the N-terminal PAZ domain, while the P-element induced wimpy (PIWI) domain is in the mid and Cterminal [62]. The PAZ domain is required for the protein-protein interaction through its RNA-binding module. However, PIWI is required for the cleavage of targeted mRNA [63]. AGO proteins in plants are involved in DNA methylation, gene silencing, plant growth, and defense or immune response [32,[64][65][66]. AGO 4, 6, and 9 participate in transcriptional gene silencing (TGS), while AGO 1, 2, 3, 5, 7, and 10 play vital roles in post-transcriptional gene silencing (PTGS) [67]. AGO proteins are also involved in RNA-dependent DNA methylation (RdDM) pathways [68].

RNA-Dependent RNA Polymerase (RDR)
RDR is involved in the continuation of the RNAi process through the amplification of sRNA. This enzyme amplifies the single-stranded RNA molecules and converts them into a dsRNA form, cleaving them by DCLs to generate new sRNAs [69]. A crucial role of RDR proteins is to interact with RNAi machinery and provide defense against pathogens. RDR proteins in plants are identified as RDRγ [70], which is evolutionarily distributed into four subclasses (RDR1, 2, 3, and 6) [71,72]. Among them, RDR1, RDR2, and RDR6 are vital proteins that are involved in the defense against pathogens [73][74][75], the biosynthesis of siRNAs [54,76], DNA methylation, and epigenetic silencing [77].

Virus-Induced Gene Silencing (VIGS)
In virus-induced gene silencing (VIGS), the viral genome is manipulated by deleting the disease-causing genes and then the cloning of the modified viral genome cDNA into a binary vector. Viruses that lack gene silencing or weak suppressors are the potential target as VIGS vectors [78][79][80][81][82][83]. The targeted vector also contained multiple cloning sites (MCS) which can insert targeted gene fragments [78][79][80]. The specific silenced gene is cloned into the MCS of the binary vector [81]. The recombinant virus then enters the plant cells through an Agrobacterium-mediated transformation or DNA bombardment into the host cells. Once the recombinant virus enters the plant cell, RNA-dependent RNA polymerase (RdRp) transcribes the viral RNA and transgene [82]. Double-stranded RNAs (dsRNAs) are generated and further cleaved by Dicer into 21-25 nts long siRNA. These siRNAs further loaded into the RISC that targeted the complementary DNA [32,83]. More than 35 DNA or RNA viruses have been altered as VIGS vectors [84]. The Tobacco mosaic virus (TMV), Tomato golden mosaic virus (TGMV), and Potato virus X (PVX) belong to the first generation of the VIGS vector system. The first-generation VIGS vector caused the short-term silencing of endogenous gene expression and leaf chlorosis [85][86][87]. For the second-generation VIGS vectors, viruses responsible for milder symptoms were selected. Improvements were made in the targeted VIGS vectors, such as deleting pathogenicityrelated genes and adding inverted repeats for hairpin RNA formation [88,89]. These alterations result in fewer side effects in the host plants. Additionally, the first-generation VIGS vectors have a limited range of host susceptibility, except for the Bamboo mosaic virus (BaMV), which has a broad host range [90]. However, the most commonly used VIGS vector is the Tobacco rattle virus (TRV), which has the ability of a wide host range infection, with its systemic transmission including the meristem and minor virus infections in infected plants [91,92].

Host Induced Gene Silencing (HIGS)
Host-induced gene silencing is based on the plant's natural immune system, which utilizes RNA-induced silencing to defend the viral infection [93][94][95]. HIGS is further advanced to VIGS, which silences the pathogenic genes inside plants by expressing RNAi constructs, targeting the specific genes of the pathogen inside the host plant [94]. In HIGS, transgenic plants are generated by introducing an inverted repeat sequence inside the plant genome. Double-stranded RNAs are produced as small RNAs inside the transgenic plants, introduced either through Agrobacterium or VIGS. A homologous sequence to the gene or genes from pathogens, which is incorporated into plant genomes, expresses the siRNA of targeted genes and leads to the pathogen's gene silencing [96].
Previously, applying chemicals to virus-infected plants was the only source to limit virus infections. However, with the advent of RNAi technology, it has been established that HIGS is a powerful and efficient technology to control the virus spread and infection inside the plant cell. The development of efficient, resistant, and polycistronic miRNA and the fusion of multiple genes in hairpin RNA is efficiently successful [97,98]. Noticeably, infections against the Wheat streak mosaic virus (WSMV) were reduced using a coat protein and a full-length viral replicase (NIb) gene [99][100][101].

Spray Induced Gene Silencing (SIGS)
Spray-induced gene silencing (SIGS) is an additional advanced RNA silencing strategy for disease control. SIGS has been effectively used for monocots and dicots pathogen infections [96]. SIGS has been used for crop protection based on findings that plant pathogens can up-take dsRNA, which is applied externally [102,103]. This dsRNA then silences the targeted pathogen genes which is critical for disease improvement. SIGS is an eco-friendly and advanced strategy for pathogen control at pre-harvesting and post-harvesting stages and offers fewer off-target effects [104]. The topical application of dsRNA confers resistance against Alfalfa mosaic virus (AMV), Pepper mild mottle virus (PMMoV), and Tobacco etches virus (TEV) [105]. More research has been carried out using dsRNA, which provides resistance against various host plants' viral infections [106][107][108][109].
Previously, an RNA-based strategy targeting plant transgenes had been reported, mainly in Nicotiana benthamiana, Arabidopsis, and Oryza sativa. Carborundum/Silwet L-77 surfactant was sprayed on N. benthamiana leaves before 21 nt sRNAs targeted the PHY-TOENE DESATURASE (PDS) gene [110]. The exogenous application of dsRNA included syringe infiltration, irrigation methods, foliar spray, and an adjuvant with formulations.
Syringe infiltration is one of the simplest methods for transgene introduction in experimental plants. It was observed that dsRNA infiltration into leaf targeting the YFP transgene in Arabidopsis failed to develop RNAi. However, the introduction of dsRNA and a carrier complex constituting a co-polymer of lysine or histidine enhanced the efficacy of dsRNA delivery, which targeted the Chalcone Synthase Gene (CHS) and the YFP transgene, resulting in the decreased accumulation of targeted genes [111].
Mechanical inoculation with soft sterile brushes also facilitates the introduction of dsRNA. Additionally, gentle rubbing of the inoculum at the center of the leaf was also reported to deliver dsRNA to induce RNAi. dsRNAs targeting Neomycin phosphotransferase II (NPTII) and eGFP were transferred into transgenic Arabidopsis plants through soft brushes, reducing target genes and their protein levels [107]. Furthermore, the inoculation of dsRNA targeting the MYB1 transcription factor (TF) through gentle rubbing results in flattened epidermal cells instead of conical cells on the Dendrobium hybrid orchid plant [112]. However, the rub inoculation and infiltration method are unsuitable for greenhouse or field conditions. These constraints were overcome through the spray-based inoculation method. SIGS had been performed by the low-pressure spraying of siRNAs, which was previously reported to fail the GFP silencing. However, the problem was overcome by spraying siRNA at a high pressure, which successfully silenced the GFP. Further investigation is required to optimize the high-pressure spraying technology against targeted tissues [104].

Advantages of SIGS over HIGS and VIGS
RNAi is responsible for targeted mRNA removal from the transcriptome. It is suitable to modulate those plant traits that negatively impact the plant phenotype. For example, in the case of a plant-pathogen infection, the pathogen delivers effector proteins into infected plant tissue to hijack host proteins and suppress the host immune system. Effector proteins inside the target host cells critical for the host-pathogen interaction are often known as susceptibility factors [113].
RNAi mediated a broad range of biological processes such as plant growth, development, and the host-pathogen interaction [102,114]. HIGS provides the silencing of the targeted genes of pests and pathogens by generating transgenic plants [115]. However, using transgenes and genetically modified organisms (GMOs) in HIGS and VIGS may raise consumer concerns. However, SIGS is advantageous over other RNAi technologies as it does not require genetic modification. It is also beneficial, especially for those crops that are difficult to modify through transgene and gene editing. Therefore, SIGS is an eco-friendly strategy that can control plant diseases at the pre-harvest and post-harvest stages.

Gene Silencing for Plant Virus Resistance
Plant viruses are a significant threat to crops and cause approximately 10-15% of economic loss [8]. Plant viruses enter host plants through wounds or insect vectors, then multiply inside host cells, causing a transient and systemic spread [116]. Plants use various mechanisms to limit virus infection, including hypersensitive response (HR), systemic acquired resistance, and DNA methylation [100]. Virus resistance has been engineered using small RNA-based strategies such as artificial microRNA (amiRNA) and hairpin RNA (hpRNA) [117][118][119].

RNAi against Insect Vectors
Most plant viruses are transmitted to the host plants through insect vectors such as aphids. Therefore, controlling aphid vectors is another alternative means to block the virus spread. Virus transmission is acquired by an aphids-virus protein interaction through receptors distributed on the salivary glands and gut of the aphid vector [145]. This receptor-protein interaction is a potential target for controlling virus transmission through RNAi [146]. Similarly, cuticular proteins control the virus and vector interaction, enter the gut, and protect the virus degradation in insect hemolymph. A cuticular protein receptor in the acrostylet of the pea aphid is responsible for virus transmission in a circulative manner [147]. Additional research was accomplished by silencing the Rack1 gene of the midgut found in a green peach aphid which reduced nutrient uptake [148]. Previously, C002, an effector protein of the salivary gland, was selected as a suitable candidate for RNAi. This protein plays a crucial role in suppressing the plant defense system and detoxifying plant secondary metabolites [149]. Enzymes of MIF1, glutathione-S-transferase-1, and Armet, are suitable targets for RNAi and are involved in the feeding and growth of aphids [150,151]. Besides salivary proteins, other aphid enzymes are also essential targets for RNAi. For example, the alteration of the fatty acid biochemical pathway increased the aphids' mortality and decreased its fecundity [152].

HIGS against Plants Viruses
HIGS has been proven to be an efficient technology against plant virus resistance development. Previously, transgenic wheat lines were generated against the Wheat streak mosaic virus (WSMV) using a viral replicase (Nib) full-length gene [97,101]. Transgenic lines harboring hairpin RNA transgenes showed efficient resistance against WSMV (T5 generations). Moreover, HIGS was reported against the Triticum mosaic virus (TriMV), in which hairpin constructs were generated through the TriMV CP gene, which showed stable transgene resistance through T6 [153]. Additionally, RNAi-mediated transgene resistance was acquired against the Sugarcane mosaic virus (SCMV), which was accomplished by constructing hairpin RNA sequences against the Coat protein (CP) and Helper-component proteinase (Hc-Pro) genes. The fusion of both genes developed stable transgenic resistance against SCMV in a model rice plant [101].

VIGS against Plant Viruses
VIGS is a fast and efficient method to analyze gene functions in plants [89]. VIGS vectors can be designed from RNA and DNA viruses to silence targeted genes inside plant cells [154]. By far, TRV has a broad host range, such as Zea maize [155], Nicotiana benthamiana [156], Arabidopsis thaliana [156], Hyoscyamus niger [157], and Gerbera hybrids [158]. Another virus, the Apple latent spherical virus (ALSV), is also utilized as a VIGS vector with a broad spectrum. Up till now, ALSV has been used for several crops, such as Nicotiana spp., A. thaliana, S. lycopersicum, legumes, cucurbits, Malus, and Pyrus spp. [159,160]. Furthermore, the Citrus leaf blotch virus (CLBV) has been developed as a VIGS vector for N. benthamiana and Citrus [161]. The Cucumber mosaic virus (CMV) is an efficient vector to down-regulate the anthocyanin content in Glycine max by the silencing of the CHALCONE SYNTHASE (CHS) gene [162]. The Barley stripe virus (BSMV) and Foxtail mosaic virus (FoMV) are the vectors for monocots, previously used to silence the PHYTOENE DESATURASE (PDS) gene in Hordeum vulgare and Zea mays, respectively [163,164].

SIGS against Plant Viruses
In recent years, SIGS has been in practice to reduce the number of diseases caused by pathogens such as fungus, bacteria, and viruses [106,108,109]. SIGS was firstly reported to be against the Pepper mild mottle virus (PMMoV), Tobacco etch virus (TEV), and Alfalfa mosaic virus (AMV). The dsRNA-targeted viral replicase gene downregulated the viral infection in the infected host plant [105]. The effect of SIGS against various viruses is still under investigation [108].

Limitations of Gene Silencing
The major limitation of RNAi is the lengthy and expensive procedure, as it requires the generation of transgenic plants. These genetically modified plants also required significant regulations and public awareness issues. There are certain limitations in VIGS technology, despite its numerous advantages. VIGS most often results in random gene silencing throughout the infected plant, which could result in interpretations when the silencing is not related to the visible phenotype. The problem might be solved by adding a positive control in the VIGS vectors that facilitates the visibility of the silenced genes [89]. Moreover, VIGS is responsible for silencing non-target genes that are difficult to understand, especially when the genome sequence of the studied species is not available. This unintentional silencing will be solved as whole-genome sequences for most species become available [89]. HIGS technology has improved plant genomic content for resistance to various plant diseases. However, silencing the individual gene from the pathogen may not be enough to limit the disease, as partial silencing of mRNA does not assure the inactivation of the protein. This problem is solved by initially screening the silencing constructs through the transient system [96]. Another limitation of HIGS is the off-target effects that could be avoided using bioinformatic tools to screen off-targets. SIGS is another advanced and innovative technology to silence pathogenic genes, but the detailed mechanism of RNA molecule transportation is still under investigation [165].

Conclusions and Future Prospects
RNAi is an effective and robust technique to develop transgenic plants which are highly resistant to virus infection. RNAi technology offers avenues to generate a high-yield and resistant transgenic varieties. Recently, SIGS has emerged as an efficient strategy to develop virus resistance, although much work is underway to optimize SIGS technology against different plant viruses. With more research and scientific progress, all the limitations could be resolved in the future.
Author Contributions: All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement:
The authors confirm that the data supporting the findings of this study are available within the article.

Conflicts of Interest:
The authors declare no conflict of interest.