text:
Novel Immune Mechanisms in Hypertension and Cardiovascular Risk

Purpose of Review Hypertension is a common disorder with substantial impact on public health due to highly elevated cardiovascular risk. The mechanisms still remain unclear and treatments are not sufficient to reduce risk in majority of patients. Inflammatory mechanisms may provide an important mechanism linking hypertension and cardiovascular risk. We aim to review newly identified immune and inflammatory mechanisms of hypertension with focus on their potential therapeutic impact. Recent Findings In addition to the established role of the vasculature, kidneys and central nervous system in pathogenesis of hypertension, low-grade inflammation contributes to this disorder as indicated by experimental models and GWAS studies pointing to SH2B3 immune gene as top key driver of hypertension. Immune responses in hypertension are greatly driven by neoantigens generated by oxidative stress and modulated by chemokines such as RANTES, IP-10 and microRNAs including miR-21 and miR-155 with other molecules under investigation. Cells of both innate and adoptive immune system infiltrate vasculature and kidneys, affecting their function by releasing pro-inflammatory mediators and reactive oxygen species. Summary Immune and inflammatory mechanisms of hypertension provide a link between high blood pressure and increased cardiovascular risk, and reduction of blood pressure without attention to these underlying mechanisms is not sufficient to reduce risk.

Introduction
Hypertension (HT) is a common disorder with substantial impact on public health because of its potential sequelae of stroke, heart failure and kidney disease; subsequently, it is a major source of morbidity and mortality [1]. It affects 30% of adults in Europe and USAwith additional 30% at high risk of the disease [2], and importantly, its prevalence increases with age [3].
Despite the progress in its diagnosis and treatment, the aetiology of HT remains unclear and a matter of substantial debate. It is widely acknowledged that function of the vascular system, kidneys and sympathetic nervous system is critical for control and maintenance of blood pressure (BP) [1]. Vascular resistance, stiffness and remodelling as well endothelial dysfunction are hallmarks of HT [4][5][6][7]. Kidney transplantation from hypertensive donors rises BP in recipients in animal and human studies [8,9]. Sympathetic nervous system hyperactivity contributes to initiation, maintenance and progression of HT [10]. Furthermore, deletion of extracellular superoxide dismutase or administration of angiotensin II (Ang II) into circumventricular organs (CVO) raises blood pressure, while lesions of these regions prevent experimental HT [11,12].
suggest that chronic low-grade inflammation contributes to cardiovascular disease (CVD) [13••, 14], including HT [15]. For instance, C-reactive protein (CRP) is a prototypic marker of inflammation which is elevated in many CVD such as acute myocardial infraction, coronary artery disease and HT [13••, 16]. Vongpatanasin et al. have shown that modest elevations in CRP are sufficient not only to increase BP but also exaggerate response to Ang II [17]. Furthermore, human and animal studies emphasize impact of CRP on endothelial vasodilator function [18,19] via direct action on endothelial nitric oxide synthase (eNOS) and/or activation of vascular NADPH oxidases leading to reactive oxygen species (ROS) formation [19][20][21]. In addition to CRP, the inflammatory process involves a complex interplay between cells and proinflammatory cytokines. This process is strictly controlled by the immune system. Various cells of the innate and adaptive immune systems contribute to initiation and maintenance of inflammation. Furthermore, resolution of inflammation occurs by different mechanism in many CVD, including HT.

Link Between Immune Cells and Hypertension
The concept that the immune system contributes to hypertension is not new. Fifty years ago, the pioneering study of White and Grollman described the role of immunosuppressive therapy on BP levels in rats with kidney infarction [22]. Further studies have shown that the transfer of immune cells isolated from lymph nodes or spleen of hypertensive animals increases BP in normotensive recipients [23,24]. Moreover, thymectomy of deoxycorticosterone acate (DOCA)-salt-treated mice [25] or spontaneously hypertensive rats [26] attenuates experimental HT. Despite the above-mentioned studies showing the potential role of the immune system in HT, lack of advanced animal models and methodology did not allow for a more thorough understanding this phenomenon.

T Cells
In 2007, the landmark study by Guzik et al. demonstrated that HT induced by Ang II or DOCA salt is blunted in RAG1 −/− mice lacking functional lymphocytes, and the hypertensive response is restored by adoptive transfer of T but not B lymphocytes [27]. These results were subsequently confirmed by Crowley et al., who showed that T cells are essential for the full development of AngII-dependent HT in immunodeficient scid mice [28] and by Mattson et al., who demonstrated a similar phenomenon in RAG1 −/− rats [29]. In addition to this, mycophenolate immunosuppressive therapy which, inhibits T cell proliferation, reduces BP in Dahl salt-sensitive rats [30]. It is known that Ang II acts through the AT1 and AT2 receptors, both of which are present on the surface of T cells [26]. Furthermore, adoptive transfer of AT1 receptor deficient T cells into RAG1 −/− animals leads to a blunted hypertensive response in Ang II-induced hypertension [27]. Infusion of Ang II increases the percentage of circulating T cells with effector phenotype (CD69+, CD25+, CCR5+) in both in vivo and in vitro studies [27,31]. Additionally, T cells with effector phenotype accumulate in perivascular adipose tissue (PVAT) and kidneys, and affect endothelial function and vascular fibrosis [30-32, 33•]. Interestingly, a meta-analysis of GWAS data pointed polymorphisms in the SH2B3 gene as significant predictors of systolic and diastolic BP. This gene encodes for the lymphocyte adaptor protein, lnk [34]. Keeping with this, Saleh et al. have shown that the loss of lnk exacerbates Ang II-induced HT and its associated renal and vascular dysfunction. Moreover, lnk-deficient mice have increased T cell activation and cytokine production in comparison to wildtype animals [35]. Higher cytokine production by immunosenescent cytotoxic CD8+ T cells (CD28 null and positive for CD57) as well as augmentation of their number have been previously reported in hypertensive patients [36].
Among T cells, there is a distinct subset of regulatory cells. They express CD4 and CD25 surface markers and a forkhead transcription factor 3 (FOXP3). This unique subset possesses the capacity to suppress innate and adaptive immune responses [37]. Experimental HTis related to a decline in the number of Treg cells in in vitro and in vivo studies [38,39]. Recent studies have shown that adoptive transfer of Treg cells reduces blood pressure [40,41] and ameliorates endothelial function in Ang II-treated animals [39]. Further studies have shown this is accompanied by the attenuation of NADPH oxidase activity, which is critical in the development of vascular dysfunction [42,43].

B Cells
Clinical and experimental HT is associated with raised serum IgG, IgA or IgM antibodies produced by B cells [44]. Although, transfer of B cells did not restore HT in Ang IIinfused RAG1 −/− mice [27], B cell activation does appear to be dependent on highly specific interactions with T cells [45] which are absent in RAG1 −/− animals. Recently, Drummond's group has shown that Ang II infusion leads to increased production of antibodies by activated B cells. Genetic deficiency of B-cell-activating factor receptor, or pharmacological depletion of B cells, protects against BP elevation and the end organ sequelae of Ang II such as collagen deposition and aortic stiffness. These effects are restored by the adoptive transfer of B cells [46].

Monocytes and Macrophages
Monocytes and macrophages have been implicated in various models of experimental HT [47,48]. Ang II-induced HT is associated with an increased number of circulating monocytes [49,50], and their elimination leads to decreased severity of HT, associated reduction of vascular ROS generation and improvement of vascular function [49]. Monocytes are circulating precursors of macrophages, which accumulate in the PVAT, adventitia and kidneys during HT [4,50,51]. Infiltrating macrophages release pro-inflammatory mediators and produce free radicals via NOX2 NADPH oxidase that changes vascular homeostasis [52][53][54]. Macrophage colonystimulating factor (m-CSF) deficiency is associated with attenuated Ang II-induced HT, arterial remodelling, endothelial dysfunction, superoxide generation, NADPH oxidase activation and vascular inflammation [47]. Correspondingly, pharmacological blockade of macrophage CCR2 receptors, using INCB3344, prevents macrophage accumulation and reverses DOCA salt and Ang II-induced HT [50,51].
Toll-like receptors (TLRs) have an important role in the activation of macrophages and monocytes [55]. They provoke cytokine and chemokine production through activation of NF-κB (nuclear factor kappa B) [56]. TLR4 is upregulated in Ang II-induced HT. Anti-TLR4 antibody treatment normalises BP and reduces inflammation and vascular changes associated with HT through MyD88-dependent activation and JNK/NF-κB signalling pathway [57]. Similarly, neutralization of TLR4 reduces BP and augmented vascular contractility in adult spontaneously hypertensive rats [58]. Finally, upon activation, macrophages and monocytes can activate T cells via antigen presentation, expression of costimulatory ligands and release of mediators that modulate their function and/or chemotaxis [53,55].

Dendritic Cells
Evidence suggests that dendritic cells (DCs) play a role in the development of HT. DCs from hypertensive animals produce an increased amount of superoxide and a wide range of cytokines (IL-1β, IL-6, IL-23), which affect T cell polarization into the inflammatory phenotype [59]. Transfer of DCs from hypertensive donor mice into C57BL/6 mice results in the development of severe HT in response to sub-pressor dose of Ang II while having no effect in mice that received DCs from control animals [59]. Hypertensive mice have demonstrated higher levels of DCs with increased expression of costimulatory ligands CD80 and CD86, which are the hallmark of their activation [60]. Moreover, treatment with the pharmacological agent CTLA4-Ig (blocking B7-dependent costimulation) resulted in reduction of BP in both Ang II and DOCA salt-induced HT [60]. Chronic oxidative stress, associated with HT, leads to formation of immunogenic isoketal-protein adducts, which accumulate in DCs and promote T cell activation. Interestingly, increased isoketal adducts are also observed in immune cells of hypertensive patients [59].

Natural Killer Cells
Studies of Taherzadeh et al. have shown that NK gene complex is an important determinant to genetically determined sensitivity to develop HT and associated vascular remodelling in L-NAME-induced HT in mice [61]. An increased number of NK cells are also observed in the circulation of pregnant hypertensive rats [62]. Moreover, depletion of NK cells leads to protection from Ang II-induced vascular dysfunction [63].

Neutrophils
There are inconsistent results from studies investigating the role of neutrophils in HT. Pharmacological depletion of this subpopulation of leukocytes was associated with a significant fall in systolic BP in vivo and an attenuation in phenylephrineinduced vasoconstriction [64]. Conversely, selective depletion of circulating neutrophils protected against oxidative stress but not against the development of Ang II-induced HT [65]. Similarly, restoration of neutrophils in LysM iDTR -depleted mice with monocytes did not restore pathophysiological action of Ang II [49].

Cytokines as Key Mediators in Hypertension
During progression of HT, immune cells accumulate in target organs, of which kidneys and the vasculature are particularly vulnerable [4, 33•, 66] (Figure 1). These cells produce potent cytokines that affect vascular and renal function, which are essential for the development of HT. In recent years, numerous cytokines with a crucial role in HT have been reported.

Tumour Necrosis Factor Alpha
Tumour necrosis factor alpha (TNF-α) is produced by many cell types including immune cells, vascular cells and adipocytes [67]. Various studies have shown that HT is associated with elevated production of TNF-α by different immune cells and a subsequent rise is observed in the circulation [27,62,68,69]. Blockade of AT1 receptors in patients with HT results in a significant reduction of circulating levels of TNF-α. Mice lacking TNF-α gene or mice treated with etanercept (TNF-α antagonist) do not develop HT in response to Ang II [27,68]. Replacement therapy with recombinant TNF-α restores action of Ang II [68]. Stimulation of endothelial cells with TNF-α decrease eNOS expression [70] by destabilisation of eNOS mRNA [71], which impairs ability of ECs to produce NO. TNF-α activates NF-κB and NADPH oxidase [70], which play an important role in the induction of oxidative stress and overexpression of both chemokines and adhesion molecules [68].

Interferon Gamma
Another pro-inflammatory cytokine produced by various immune cells which plays a role in HT is interferon gamma (IFN-γ) [63,72]. Experimental HT is associated with increased production of IFN-γ by activated T cells and NK cells [4,35,63]. The knock down of IFN-γ results in a blunted increase of Ang II-induced murine BP [35]. In contrast, this phenomenon is not observed in IFN-γ receptor-1-deficient mice [73]. Loss of lnk exacerbates production of IFN-γ by CD8+ lymphocytes as well as enhances impairment of endothelial-dependent relaxation as compared to wild-type mice [35]. Interestingly, incubation of aortic segments with IFN-γ ex vivo promotes endothelial dysfunction that is partially reversed by preincubation with PEG-SOD [4], which ameliorates oxidative stress in vasculature [74]. IFN-γ also has a strong impact on superoxide production via upregulation of the expression and activity of NADPH oxidases in human aortic smooth muscle cells [75]. It acts directly on VSMC to induce their proliferation and apoptosis [76,77]. Furthermore, neutralization of IFN-γ biologic action prevents outward vascular remodelling of human coronary arteries induced by allogenic T cells in SCID/beige mice [78]. IFN-γ affects the RAS system as well as sodium-proton-exchanger type 3 transporter in the kidneys, which leads to increased production of angiotensinogen and modulation of sodium absorption, respectively [79,80].

Interleukin 6
Interleukin 6 (IL-6) is produced by a variety of cells, including DCs, macrophages, monocytes, T cells and vascular cells [81]. High levels of IL-6 correlate with increased BP and may be an independent risk factor for HT [81,82]. In addition to this, the IL-6 level is reduced after treatment with Ang II-receptor blockade [83]. The increase of IL-6 is also observed in many models of experimental HT [84][85][86] strongly suggesting the essential role of IL-6 in HT. Treatment of mice with IL-6 not only increases vascular AT1 receptor expression but also induces vasoconstriction, oxidative stress and impairs endothelial function [87]. IL-6 mediates elevation of superoxide production and endothelial impairment by affecting NO-cGMP signalling pathway [88]. Furthermore, IL-6 has been reported to play an important role in VSMC migration and proliferation leading to vascular medial hypertrophy [89,90]. Mice lacking IL-6 are protected against the action of Ang II and stressinduced HT [81,85,91]. Moreover, IL-6 promotes polarization of CD4+ T cells to produce IL-17 [92].

Interleukin 17
Interleukin 17 (IL-17) is produced mainly by the unique subpopulation of CD4+ cells called TH17. Additionally, production of IL-17 was reported in γ/δ cells, subsets of CD8+ T cells, some B cells and NK cells [93]. Several reports indicate that IL-17 contributes to CVD [94][95][96]. Plasma levels of IL-17 are increased in humans and animals with HT [97][98][99][100]. Administration of recombinant IL-17 in mice causes a modest elevation of BP in the absence of other hypertensive stimuli [98]. Furthermore, genetic deletion or pharmacological blockade of IL-17 protects animals against Ang II and DOCA salt-induced HT, oxidative stress and endothelial dysfunction [97,100]. Nguyen et al. have shown that IL-17 induces phosphorylation of Thr495 on the eNOS in Rho/Rhokinase-dependent manner leading to decrease in NO production and impairment of Ach-induced relaxation in ex vivo studies [98]. Moreover, IL-17 activates ECs as demonstrated by elevated expression of adhesion molecules and other immune cells chemoattractants [101]. Mice lacking IL-17 are protected against perivascular inflammation [100]. Recently, Harrison's group has shown that T cells, and especially TH17 cells, play crucial role in enhanced collagen deposition in adventitia and aortic stiffening in experimental HT [72]. It is evident that Il-17 induces expression of mRNA for collagens in p38MAP-kinase-dependent fashion leading to excessive collagen deposition and loss of aortic compliance [72]. Finally, IL-17 can induce or synergize effect of other proinflammatory cytokines [102] leading to perturbation between pro-and anti-inflammatory factors.

Interleukin 10
In contrast to previously mentioned cytokines, interleukin 10 (IL-10) possesses anti-inflammatory properties. IL-10 not only suppresses production of TNF-α, IFN-γ and IL-6 by various immune cells [103], but also blocks the activity of proinflammatory transcription factors such as NF-κB [104]. It is produced by T cells, mainly Treg, DCs and macrophages [105,106]. The IL-10 -627C/C polymorphism, associated with increased expression of IL-10, reduces an incidence of HT in Russian Tatars [107]. Hypertensive patients treated with AT1 receptor blockers or ACE inhibitors are characterized by an elevated serum IL-10 level [108]. Furthermore, there is much evidence that IL-10 blunts high BP in experimental models of HT, including preeclampsia [42,109,110]. Mice lacking IL-10 exhibit enhanced HT, endothelium dysfunction and increased superoxide production in response to Ang II compared with wild-type animals [63]. Similarly, incubation of the IL-10 −/− vessels with Ang II reduces their relaxation and enhances superoxide production as compared to wild-type vessels ex vivo [111]. On the other hand, administration of IL-10 or an antioxidant can restore Ang II-induced endothelial dysfunction [112]. IL-10, acting on ECs, upregulates expression, activity and phosphorylation of eNOS [113] and further inhibits activation of p38 MAP-kinase, which stimulates production of pro-inflammatory cytokines and regulates NADPH oxidase activity [114,115].

Clinical Evidence Linking Immune System and Hypertension
As stated above, experimental HT is associated with activated immune cells and their depletion/reduction very often results in normalization of BP. Based on the fact that immunosuppressive therapy is not currently clinically justified in patients with HT, we cannot countercheck these experimental observations directly. However, there is increasing evidence supporting the role of an immune component in the pathogenesis of HT in humans.
The third National Health and Nutrition Examination Survey (NHANES III) analysed data from 5626 participants and revealed a higher number of circulating leukocytes in hypertensive than in normotensive participants as well as the correlation between their number and systolic BP [116]. Increased BP is also observed in patients after infusion of allo-activated T cells during cancer treatment [117]. Conversely, HIV-infected patients have lower incidence of abnormally high BP [118]. Furthermore, immunosuppressive agents, which are not nephrotoxic, can reduce the prevalence of clinical HT [119,120]. Reduction of BP with combination of telmisartan and rosuvastatin is related to a decrease of TH17/Treg ratio and of proinflammatory cytokines in hypertensive patients with carotid atherosclerosis [121]. Also, hereditary neutropenia, observed in Yemenites, causes small but statistically significant decline in systolic and diastolic BP in comparison to non-Yemenites population [64].
Increased BP in humans is associated with elevation of many pro-inflammatory mediators produced by immune cells such as TNF-α, IFN-γ, IL-6, IL-17 and decrease of antiinflammatory cytokines like IL-10 [66,69,81,82,98]. This is also supported by the fact that polymorphisms of TNF-α and IL-6 are associated with human HT [122,123].
Importantly, pro-inflammatory cytokines, mentioned above, serve not only as markers of inflammation but also confer risk for many CVD.

Other Risk Factors Affecting Development of Hypertension and Their Link with Immune System
Physiological stress is a significant factor for many CVD [124]. Moreover, sympathetic nervous system participates in development of HT [11,12]. Marvar et al. have demonstrated that repeated daily stress elevates BP in mice. This was associated with activation of the immune system i.e. increase of circulating T cells expressing CD69 and CD44 high markers.
Moreover, Ang II-infused mice exposed to chronic stress displayed greater BP than non-stressed animals. In contrast, repeated stress did not affect BP in T and B cells depleted RAG1−/− mice [125].
Another risk factor for HT is obesity, which is also associated with chronic inflammation, vascular remodelling and endothelial dysfunction [126,127]. Cells of both the innate and adoptive immune system reside in adipose tissue and participate in these pathophysiological changes [128]. Moreover, immune cells produce cytokines that can modulate expression of classical adipokines. Production of anti-inflammatory adiponectin can be inhibited by pro-inflammatory mediators such as TNF-α, IL-6 and IL-17. At the same time, proinflammatory cytokines can induce production of leptin which is critical in the development of obesity [33•].
Some studies have shown an association between air pollution exposure and the risk of HT [129]. Exposure to PM 2.5 (particulate matter <2.5 μm) and ozone increases blood pressure in humans [130]. Similarly, PM 2.5 exposure in mice induces endothelial dysfunction and mild elevation in BP, which is associated with increased levels of inflammatory cytokines in blood (TNF-α, IL-6). In addition to this, there is also an increased number of activated macrophages in adipose tissue and increased vascular adhesion of monocytes [131]. Finally, pollutant-induced oxidative stress may affect vascular function [132].

Micro-RNA as Novel Immune Players in Hypertension
Micro-RNAs (miRNAs) are implicated in the intricate control of genes throughout the body; therefore, it is understandable that they can be associated with immune mechanisms of hypertension (Fig. 1). MiRNAs are 18-22 nucleotides in length and do not code for a gene but may affect expression of other target genes. This is achieved through a negative regulation via binding of its complex (the RNA-induced silencing complex) to the 3′ untranslated region (3′ UTR) of its target messenger RNA (mRNA). Sixty percent of protein coding genes have miRNA target sites in their 3′ UTR, and a single miRNA may impact the expression of multiple mRNAs. MiRNAs are involved in both the cellular and clinical manifestations of various CVD diseases such as HT [133], coronary artery disease [134] and cardiac hypertrophy with subsequent heart failure [135]. Further to this, miRNAs are implicated in the immune system in the context of chronic immune disorders [136][137][138][139]. Two miRNAs are discussed, which are considered to be pro-inflammatory in nature and demonstrated associations with chronic inflammatory arthritides as well as with cardiovascular manifestations of HT.

miR-21
Micro-RNA 21 is an extensively investigated miR in the context of the cardiovascular system [140•]. MiR-21 is elevated in peripheral blood mononuclear cells (PBMCs) of hypertensive patients and is associated with the degree of LVH detected in this group [141]. In coronary artery disease, McDonald et al. demonstrated that eliminating miR-21 reduces in stent restenosis via anti-inflammatory effects of M2 macrophages, lowering number of CD3+ T cells and a reduction of Ly6c+cells level [134] (which differentiate to become the pro-inflammatory M1 phenotype). It is widely accepted that nitric oxide synthase is a marker for M1 macrophages and subsequently M1 cells produce ROS. This response was likely evolved to kill pathogens in the micro environment. That being said, it is now known that ROS elevate BP and activate pro-fibrotic signalling pathways as reviewed in many excellent reviews including this piece by Montezano and Touyz [142]. It is noteworthy that iNOS is not expressed by macrophages in humans; however, a clinical study has shown a relationship between elevated miR-21 and endothelial dysfunction with an associated reduction in NO and eNOS levels in hypertensive patients [143]. miR-21 can modulate T cell responses including exerting influence on cytokine production [144]. Not only miR-21 is induced by T cell activation and enhances the T cell immune response [145] but also miR-21 has been shown to induce proliferation of CD4+ Tcells in murine models of systemic lupus erythematosus (SLE) and in patients with SLE [146,147]. Relating to the production of IL-17 from CD4+ cells, previously mentioned in this work, this could link miR-21 to the increased cardiovascular risk observed in patients with lupus and other inflammatory arthritides. Further to this, miR-21 could serve as a biomarker for CVD in this patient group. miR-21 is a positive regulator of the expression of the transcription factor, FOXP3 [148]. As previously mentioned, Treg cells express FOXP3 and there is an association between a lower number of Treg cells and HT [38,39]. This conflicts with the previously understood concepts that miR-21 is associated with pathological processes seen in elevated BP suggesting the full mechanism is not yet known, thus bringing together the development of HT, inflammatory cell lines, the coronary arterial disease sequelae of HT and miR-21.

miR-155
The endothelium is the single-cell surface layer lining the entire vascular tree, and inflammation, the response to injury, is observed in the endothelium in HT. The endothelium is the source of eNOS, which underpins much of the vascular sequelae of HT. miR-155 reduces eNOS expression [149]. Mir-155 is regarded as a pro-inflammatory miR and is necessary for normal B cell and T cell function. In miR-155 KO mice, it has been shown that it is implicated in the regulation of many genes including chemokines and cytokines [150,151]. Further to this, TNF-α increases miR-155 expression with subsequent negative regulation of eNOS [133,152]. TNF-α has also been shown to increase miR-155 expression in macrophages and monocytes during an inflammatory response [153] helping to Fig. 1 Immune mechanism of hypertension explain this phenomenon in HT. In short, the effects of TNF-α discussed earlier in this paper are closely mediated by miR-155. MiR-155 has been shown to regulate the AT1 receptor in rat cardiomyocytes [154], rat aortic adventitial-derived fibroblasts [155] and in human PBMCs where it was also found to correlate with BP [151]. Interestingly, rare allele of rs5186 polymorphism, located in the AT1 receptor, blocks adhesion of mir-155 to the AT1 receptor 3′ UTR [156]. This observation may explain that rs5186 was a risk factor for HT in several epidemiological studies, although overall, the clinical effect of this polymorphism is unclear and further research, taking into account different subpopulations of patients with HT, are needed to verify its role in HT [157]. MiR-155 level in aortic tissue of adult spontaneously hypertensive rats negatively correlates with the blood pressure [158]. This has translated into a human study that examined miR levels in PBMCs and demonstrated lower circulating pro-inflammatory cytokines including TNF-α in patients who had downregulation of miR-155 [159]. It has been shown that the 5p strand of miR-155 is upregulated in fibroblasts in the synovial fluid taken from patients with rheumatoid arthritis [160], suggesting that miR-155 plays an important role in a chronic inflammatory response. In summary, miR-155 is another miR, which provides an association between inflammatory arthritis and HT. Moreover, miR-155 is associated with hypertension and is implicated in a pro-inflammatory phenotype. It is likely that these effects may have an impact in either HT onset or severity.

Conclusions
In summary, hypertension is associated with significant activation of immune and inflammatory systems and shares several functional differences with other immune-mediated diseases. Low-grade inflammation is prominent and further understanding of specific cytokine and chemokine milieu, similarities and differences between hypertension pathogenesis and atherosclerosis will shed a light on possible new therapeutic strategies to limit vascular and renal complications of HT. Indeed, in experimental models, immune-targeted therapies prevent vascular and renal damage and can alleviate hypertension. Evidence in humans is now urgently needed. Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


extfieldsofstudy: Biology Medicine

text:
The substitution rate of HIV-1 subtypes: a genomic approach

Abstract HIV-1M causes most infections in the AIDS pandemic. Its genetic diversity is defined by nine pure subtypes and more than sixty recombinant forms. We have performed a comparative analysis of the evolutionary rate of five pure subtypes (A1, B, C, D, and G) and two circulating recombinant forms (CRF01_AE and CRF02 AG) using data obtained from nearly complete genome coding sequences. Times to the most recent common ancestor (tMRCA) and substitution rates of these HIV genomes, and their genomic partitions, were estimated by Bayesian coalescent analyses. Genomic substitution rate estimates were compared between the HIV-1 datasets analyzed by means of randomization tests. Significant differences in the rate of evolution were found between subtypes, with subtypes C and A1 and CRF01_AE displaying the highest rates. On the other hand, CRF02_AG and subtype D were the slowest evolving types. Using a different molecular clock model for each genomic partition led to more precise tMRCA estimates than when linking the same clock along the HIV genome. Overall, the earliest tMRCA corresponded to subtype A1 (median = 1941, 95% HPD = 1943–55), whereas the most recent tMRCA corresponded to subtype G and CRF01_AE subset 3 (median = 1971, 95% HPD = 1967–75 and median = 1972, 95% HPD = 1970–75, respectively). These results suggest that both biological and epidemiological differences among HIV-1M subtypes are reflected in their evolutionary dynamics. The estimates obtained for tMRCAs and substitution rates provide information that can be used as prior distributions in future Bayesian coalescent analyses of specific HIV-1 subtypes/CRFs and genes.

Background
HIV is a retrovirus of the genus Lentivirus and is characterized by a very high genetic diversity. There exist two types of HIV, HIV-1 and HIV-2. The former causes the AIDS pandemics and comprises four phylogenetically distinct groups: M, N, O, and P. Groups N and O are found almost exclusively in West-Central Africa (Hahn et al. 2000). Only two strains from group P have been reported so far, both in Cameroon (Plantier et al. 2009;Vallari et al. 2011). HIV-1 group M is the main driver of the HIV pandemics. Within this group, there exist nine subtypes (denoted as A, B, C, D, F, G, H, J, and K) and at least sixty-one circulating recombinant forms (Kuiken et al. 2012).
High mutation and substitution rates favor the genetic diversity of HIV. These result from three main causes: (1) polymerization errors of the reverse transcriptase (Roberts et al. 1988), (2) genetic recombination that produces viral chimeras (Temin 1993), and (3) an explosive within-host proliferation and a large, and still growing, number of infected persons that lead to very large population sizes (Pennings et al. 2014). Although some of these factors may facilitate a fast pace of evolutionary change V C The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com (Moya et al., 2004), other factors act in the opposite direction. For instance, Simon-Loriere et al. (2013) showed that gene overlapping, which affects all genes in the HIV genome, is negatively correlated to the rate of evolution due to a reduction in the number of synonymous substitutions, although it would be less relevant in cases of terminal gene overlaps, which are the predominant type in HIV. Genetic bottlenecks during transmission also act slowing the pace of evolution in this virus, because many mutations accumulated within a host are lost after transmission. Because adaptive changes at the within-host level are lost or reverted after transmission, higher intra-host than interhost substitution rates of HIV-1 are regularly reported (Alizon & Fraser 2013;Duchê ne et al. 2014;Lin et al. 2015). The speed of spread of HIV in an epidemic also influences its substitution rate (Maljkovic Berry et al. 2007). Hence, differences in selective pressures, mutation rates, replication capacity, and/or epidemic dynamics may explain differences in substitution rates among HIV-1 subtypes.
There are important differences in the prevalence of the different subtypes around the world. Subtype C is the most prevalent group of HIV-1, occurring mainly in Africa (which presents the highest diversity of HIV-1) and Asia, accounting for almost 50 per cent of the infections. Subtype B is the main HIV-1 group in Western and Central Europe, the Americas, and Australia. It is also common in different countries of Southeast Asia, Northern Africa, the Middle East, and among South African and Russian men who have sex with men. Subtypes A, D, F, G, H, J, and K display their highest prevalence in Sub-Saharan Africa. It is important to mention the increasing prevalence of circulating recombinant forms, especially CRF01_AE and CRF02_AG, which cause most of the infections in South-East Asia and Western Africa, respectively (Buonaguro et al. 2007).
Differences among subtypes in the intensity of selection have also been reported (Choisy et al. 2004). Its importance on the differential pace at which HIV-1M subtypes evolve has been addressed by analyses of partial genes (Abecasis et al. 2009;Wertheim et al. 2012), thus ignoring the differences in mutation rates and/or selective constraints that are known to exist between genomic regions (Geller et al. 2015).
Here, we present a comparative analysis of the evolution of the main HIV-1 subtypes using Bayesian coalescent reconstructions. The primary goal of our study was to compare the substitution rates of HIV-1 subtypes from a genomic perspective, by using near-full viral coding sequences, which should be more informative for the inference of the substitution rates and diversification dates than the individual genomic regions used so far.

Datasets
Full coding-region sequences (CDS) were retrieved from the Los Alamos HIV Sequence Database, LANL (http://www.hiv.lanl.gov/), on October 2015. Independent datasets were obtained for subtypes A1, B, C, D and G, and the CRF01_AE and CRF02_AG circulating recombinant forms. Although subtype F1 was initially considered, it was excluded from the study due to the low number of sequences retrieved. The criteria for the selection of these sequences were (1) removal of problematic sequences (defined in LANL as sequences with a high proportion of non-ACTG characters or stop codons, presenting hypermutations, deletions or being either contaminants, synthetic constructs or reverse complements); (2) only one sequence per patient was used; and (3) sequences with large deletions or undetermined regions (>5% of the sequence length) were excluded. We also removed sequences without a known sampling date. In order to exclude recombinant or incorrectly subtyped sequences, the retained sequences were re-subtyped with the Comet HIV-1 (http://comet. retrovirology.lu) and REGAv3 HIV-1 subtyping tools (Pineda-Peña et al. 2013). All the sequences were also analyzed with five recombination detection methods implemented using the RDP4 software, RDP, Geneconv, Bootscan, Maxchi, and Chimaera (Smith 1992;Padidam et al. 1999;Martin & Rybicki 2000;Posada 2002;Martin et al. 2005Martin et al. , 2015. Sequences in which at least one method suggested recombination, with a P value <0.05, were considered for exclusion. In order to remove redundant sequences, alignments of the concatenated sequences were processed with CD-HIT (Huang et al. 2010) using a similarity threshold at 0.98. One sequence from each of the clusters found at this level was retained for further analysis.
Due to the high number of B, C, and CRF01_AE sequences that fulfilled the selection criteria and the computational limitations associated with the analysis of large genomic datasets, three different subsets (each with n ¼ 100 sequences) for each of these HIV-1 groups were generated by random sampling with replacement from the original data. These subsets also allowed to check the robustness of the estimates obtained for these subtypes. To reduce uncertainty in the estimates of evolutionary parameters (Wilkinson et al. 2015), eleven early subtype B sequences, corresponding to samples obtained between 1978 and 1983 (Worobey et al. 2016), were also included in all the subtype B subsets.

Molecular clock signal analysis
We checked the clock-likeness of each dataset by performing linear regression analyses between the parameters 'root-to-tip divergence' and 'sampling date' with TempEST (Rambaut et al. 2016). For each subtype and CRF, a tree reconstructed with Fasttree2.1 (Price et al. 2010) was used as input, and the root was chosen as the branch that maximized the coefficient of correlation (R), under the assumption of a strict molecular clock.

Evolutionary analyses
Times to the most recent common ancestor (tMRCA) and genomic substitution rate estimates of each HIV-1 subtype and CRF were obtained by independent Bayesian Markov Chain Monte Carlo (MCMC) coalescent analyses, as implemented in BEAST v1.8.1 (Drummond & Rambaut 2007). Initially, the same partition tree and clock models were applied to all gene regions. All the analyses were performed with the HKYþC (four categories) substitution model, combined with either an uncorrelated lognormal relaxed or the strict molecular clock model and three different demographic models (Bayesian Skyline Plot, and exponential or logistic demographic change). The best demographic model was chosen as that with the lowest Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC) (AICM) (Baele et al. 2012). We repeated the coalescent analyses using the GTRþC model, obtaining identical tMRCA and substitution rate results (data not shown).
For each viral group, we also estimated the tMRCA and substitution rate of each gene partition by repeating the BEAST analyses by assigning a different molecular clock model to each gene.
At least two independent runs of BEAST were performed for each alignment, with MCMC chain lengths ranging between 30 Â 10 6 and 20 Â 10 7 states. Convergence of the estimated parameters was confirmed with Tracer (http://tree.bio.ed.ac.uk/ software/tracer/) checking that effective sample sizes were larger than 200 for all the parameters.
Because substitutions in external branches may include recent, deleterious mutations leading to overestimates of the actual substitution rates, we compared the genomic substitution rates in internal and external branches for each subtype/CRF, using a Perl script (available upon request), to estimate the substitution rate (mean substitution rate) parameter independently for internal and external branches. As the estimates for internal and external branches were almost identical (data not shown), tMRCA and substitution rate estimates reported in this work correspond to values estimated from both external and internal branches.

Pairwise comparisons of genomic substitution rates
We tested whether the genomic substitution rate distributions estimated from BEAST were significantly different among subtypes/CRFs comparing pairwise posterior distributions by means of randomization tests, in which P-values were calculated by counting the number of times that one substitution rate was lower than the other, considering 5,000 tree states chosen randomly (with replacement) of each subtype/CRF. The obtained value (v) divided by 5,000 (number of comparisons) was considered as the probability that the compared values belong to different distributions (Abecasis et al. 2009). P-values were obtained as P ¼ 1 À v, and were adjusted with the false discovery rate method (Benjamini & Hochberg 1995). These comparisons were performed using an in-house R script available upon request (R Core Team 2014).

Datasets
We initially retrieved 2,399 HIV-1M sequences from LANL. After applying the filtering criteria detailed above, 862 different sequences were kept for further analysis: 96 were subtype A1, 248 subtype B, 234 subtype C, 45 subtype D, 32 subtype G, 177 CRF01_AE, and 30 CRF02_AG. As mentioned in the Materials and methods section, three different random subsets (each of n ¼ 100) were obtained for each of the subtypes B and C and CRF01_AE. Information on HIV-1 subtype/CRF, country of origin, sampling year, and accession number of the sequences used in each dataset is provided in Supplementary File S1.

Molecular clock signal analyses
The clock-like signal present in the analyzed datasets was evaluated by calculating the correlation coefficients (R) between the root-to-tip divergence and sampling date. R ranged between 0.50 (CRF02_AG) and 0.90 (subtype G) ( Table 1). The possible existence of over-dispersion of the HIV-1 molecular clock, which could be a major limitation for our comparisons, was rejected by ensuring that plots produced in the linear regression analyses of root-to-tip divergence versus sampling date for the concatenates did not present large dispersed clouds of points around the regression line. Residual mean squared values, which estimate the variance of the rates, were lower than 2 Â 10 À4 for all the subtypes (Table 1).

Substitution rate and tMRCA estimates and comparisons
Genomic substitution rate estimates of each HIV-1 subtype and CRF were obtained by Bayesian MCMC coalescent analyses, as implemented in BEAST. The best-fitting demographic and molecular clock models for each HIV-1 subtype/CRF are shown in Table 2, and dated phylogenetic trees obtained from the near full CDS of each dataset under the best-fitting demographic and clock model are shown in Supplementary File S1.
Bayesian coalescent analyses were also performed, unlinking the molecular clock models of the different genomic partitions. Median tMRCAs estimated from this approach were very similar to those obtained when the same clock model was used (largest difference ¼ 6 years, for CRF02_AG). However, 95% HPDs were more precise (narrower) than when applying the same molecular clock model along the whole CDS (Table 3). The 95% HPDs of tMRCAs estimated unlinking the molecular clock models were narrower than 15 years for all datasets, with the only exceptions of CRF02_AG (27 years). However, when the same clock model was applied to the whole CDS, 95% HPDs narrower than 15 years were obtained only for subtypes B (B-1 and B-2 datasets), C (all three datasets), G, and CRF1_AE (all three datasets). Regarding the tMRCA estimates obtained from the different subsets of subtypes B and C and CRF01_AE, the largest difference between medians of the same HIV-1 variant was found for the random subsets C-1 and C-3 (3 years). Overall, the earliest tMRCA corresponded to subtype A1 (median ¼ 1941, 95% HPD ¼ 194395% HPD ¼ -1955, whereas the most recent tMRCAs corresponded to subtype G and CRF01_AE subset 3 (median ¼ 1971, 95% HPD ¼ 1967-1975and median ¼ 1972, 95% HPD ¼ 1970-1975. In all cases, the substitution rates of the 5 0 half of HIV-1 genome (gag, pol, and vif) were lower than those of the 3 0 half (vprto-vpu, env, and nef). Specifically, pol presented the lowest substitution rate and env the highest in all HIV-1 subtypes/CRFs (Table 3).

Discussion
We have estimated and compared the genomic substitution rates of different HIV-1 subtypes (A1, B, C, D, and G) and CRFs (CRF01_AE and CRF02_AG). To obtain representative datasets of the publicly available genomes for each HIV-1 variant, we included sequences from the most complete geographical, temporal, and genetic range as possible and removed epidemiologically related variants, including those obtained from the same patient.
Our analyses were performed using tip-dates of heterochronous samples as the only calibration method. They have revealed differences in the substitution rates between the analyzed subtypes and CRFs. Subtypes C and A1 and CRF01_AE presented the fastest substitution rates among the studied HIV-1 datasets, with CRF02_AG and subtype D being the slowest evolving groups.
As expected, substitution rate estimates for the different subtypes and CRFs differed from previous analyses working only with partial pol and/or env regions (Abecasis et al. 2009;Wertheim et al. 2012  For subtypes B, C, and CRF01_AE, only the sub-dataset with highest molecular clock signal was subjected to model comparison. b AICM value obtained using the best-fitting demographic model. and env sequences of up to 799 and 931 nt, respectively, and found that for these two genes subtype G and CRF02_AG had the highest substitution rate and subtype D had the lowest rate. On the other hand, Wertheim et al. (2012) analyzed complete pol sequences and found subtype B to be evolving faster than subtypes D and C. These incongruences between different studies can be explained by the different genomic regions analyzed, focusing on the substitution rates of short genomic regions, but ignoring differences in selective constraints or mutation rates that exist along the whole HIV CDS (Geller et al. 2015). This could explain why the genomic substitution rate of the subsets from subtype B were very similar to that estimated recently from complete coding regions by Worobey et al. (2016). The discrepancies between works could also be explained by differences in the size of the analyzed datasets. Although larger datasets have been analyzed previously for CFR02_AG and subtype G (Abecasis et al. 2009), and for subtype D (Wertheim et al. 2012), we have analyzed larger datasets than previous works for the remaining HIV-1 groups. All the HIV-1 subtypes and CRFs analyzed displayed a similar pattern in the estimated substitution rates along their genomes: genes gag, pol, and vif presented lower rates than the vpr-to-vpu segment and env and nef genes, with pol and env presenting the slowest and fastest substitution rates, respectively. Li et al. (2015) found higher levels of amino acid diversity in the proteins encoded by tat, rev, vpu, env, and nef genes, and associated their higher levels of variability to different factors. Firstly, a higher variability might be associated with the presence of CD4 T-cell and antibody epitopes, which would favor diversifying selective pressures. Secondly, these proteins were found to present higher numbers of HIV-human associations, which may lead these proteins to present a higher structural flexibility.
Using nearly complete genome coding regions, the 95% HPD intervals obtained for the tMRCA of each subtype and gene were in most cases in agreement with previous estimates (Abecasis et al. 2009;Gray et al. 2009;Yebra et al. 2016). However, tMRCA estimates for subtypes B and C were discordant with respect to those obtained previously (Gilbert et al. 2007;Faria et al. 2014;Worobey et al. 2016). These works estimated the tMRCA of subtype B to have occurred in the 60 s. Faria et al. (2014) also estimated the tMRCA of subtype C to have occurred in the late 30 s. The most plausible reasons for such discrepancies is that the aforementioned studies analyzed older sequences, obtained from samples existing in the geographical locations from which these subtypes initially diversified, such as Kinshasa (Democratic Republic of the Congo) and the Caribbean. Indeed, the tMRCA of our subtype C datasets are more similar to that obtained by Wilkinson et al. (2015) for the origin of the southern African epidemic (median: year 1960), the geographic region from which most of our sequences were obtained. It is also noteworthy that, although the tMRCA that we estimated for subtype B differed from that reported by Worobey et al (2016), the genomic substitution rates (as well as the rates for gag, pol, and env) that they reported are very similar to our estimates. This highlights that, although the tMRCA that we obtained for subtype B may not represent the actual diversification date of this HIV-1 group, the substitution rate estimates that we have obtained are robust.
tMRCA estimated for CRF02_AG presented the broadest 95% HPD among all the HIV subtypes/CRFs analyzed in this work, probably because it was also the dataset with the lowest molecular clock signal. However, the median tMRCA obtained for this CRF was only 8 years older than that estimated by Yebra et al. (2016) in West Africa (median tMRCA between 1962 and 1963 as Table 3. Estimates (median and 95% HPD lower and upper limit) for the tMRCA, and for the substitution rates (Â 10e À3 ) of each genomic partition of the HIV-1 datasets analyzed, as obtained using the best-fitting demographic and molecular clock models.
Dataset ( estimated from PR and gp41, respectively). These tMRCA estimates obtained for CRF02_AG suggest an earlier origin than that of HIV-1 subtype G, which was supposed to be one of its parental subtypes. These estimates support previous results revealing that, indeed, CRF02_AG is the parent of subtype G, which is not an actual pure subtype although it remains classified as one (Abecasis et al. 2007).
Overall, analyzing nearly complete coding regions has produced more precise tMRCA and substitution rate estimates than previous analyses (Abecasis et al. 2009;Gray et al. 2009;Wertheim et al. 2012), especially when different molecular clock models were used for the different gene partitions comprising the CDS. However, it is noteworthy that for some HIV-1 groups (subtypes D and G and CRF02_AG) the number of available genomes was much lower than for the others. This work aimed at obtaining the most representative CDS datasets as possible, and the analyzed datasets represent the currently available genomes in public databases for each HIV-1 subtype/CRF. However, it is possible that the limited number of complete CDS sequences analyzed for subtypes D and G and CRF02_AG may have affected our estimates. Despite this potential caveat, estimating tMRCA and substitution rates from independent datasets for each genome partition would lack the statistical power that confers the information present in the different genome partitions in BEAST. Furthermore, it might introduce another bias, arising from sampling differences between genomic regions from the same HIV subtype/CRF.
Another bias in the estimates may result from the time dependence of the substitution rates. Rates can be very different when analyzing viral datasets from different timescales, with shorter timescales associated with higher rates (Duchê ne et al. 2014;Aiewsakun & Katzourakis 2015. Meyer et al. (2015) assessed the effect of time dependence of the substitution rate of influenza during the 2009 pandemic outbreak, and found that at least 9 months of temporal divergence were necessary to obtain precise estimates for long-term values. In our work, we have used datasets with similar sampling times, which ranged between 21 and 31 years. For this reason, such phenomenon should not introduce a bias in our comparisons.
In conclusion, we have estimated and compared the tMRCAs and genomic substitution rates of the main HIV-1M subtypes and CRFs from a genomic perspective, using the longest possible non-overlapping coding regions. The results obtained show that substitution rates differ significantly among HIV-1 subtypes and CRFs and that the accuracy of the estimated evolutionary parameters increases when independent molecular clock models are applied to each genomic partition. The results obtained provide information that can be used as prior distributions in future Bayesian coalescent analyses of specific HIV-1 subtypes/CRFs and genes, given that the substitution rates of HIV-1 vary among subtypes/CRFs and genomic regions.

Supplementary data
Supplementary data are available at Virus Evolution online.


text:
Metabolic and transcriptomic analysis of Huntington’s disease model reveal changes in intracellular glucose levels and related genes

Huntington’s Disease (HD) is a neurodegenerative disorder caused by an expansion in a CAG-tri-nucleotide repeat that introduces a poly-glutamine stretch into the huntingtin protein (mHTT). Mutant huntingtin (mHTT) has been associated with several phenotypes including mood disorders and depression. Additionally, HD patients are known to be more susceptible to type II diabetes mellitus (T2DM), and HD mice model develops diabetes. However, the mechanism and pathways that link Huntington’s disease and diabetes have not been well established. Understanding the underlying mechanisms can reveal potential targets for drug development in HD. In this study, we investigated the transcriptome of mHTT cell populations alongside intracellular glucose measurements using a functionalized nanopipette. Several genes related to glucose uptake and glucose homeostasis are affected. We observed changes in intracellular glucose concentrations and identified altered transcript levels of certain genes including Sorcs1, Hh-II and Vldlr. Our data suggest that these can be used as markers for HD progression. Sorcs1 may not only have a role in glucose metabolism and trafficking but also in glutamatergic pathways affecting trafficking of synaptic components.

Introduction
Huntington's Disease (HD) is a progressive, autosomal dominant neurodegenerative disease that produces physical, mental and emotional changes due to loss of critically important brain neurons. The genetic basis for HD is an expansion of cytosine-adenine-guanine (CAG) repeats in the huntingtin (HTT or IT15) gene that leads to the formation of a prolonged polyglutamine (polyQ) tract in the N-terminal region of the mutant huntingtin protein (mHTT). The wild type huntingtin protein (HTT) is important for the intracellular transport and trafficking of proteins, organelles and vesicles. The expansion of glutamine (>36 repeats) produces a gain of function mHTT that affects the healthy function of cellular machinery, ultimately resulting in neurotoxicity and detrimental cell lethality in the brain [1].
Considerable research efforts have been made to elucidate the molecular and cellular mechanisms underlying HD pathology. The proposed mechanisms through which mHTT causes neurodegeneration include mutant protein aggregation, vesicle association, elevated oxidative stress, excitotoxicity, mitochondrial and transcriptional dysregulation [2,3]. Evidence for several of these mechanisms has been found in post-symptomatic disease models or post-mortem brain samples. Controversially, during the pre-symptomatic stage of HD, cellular architecture and morphology have been found to be disrupted but neurodegeneration has been found to be minimal or absent [4,5]. These studies have been limited to immunohistochemistry, fluorescence staining and immunoblot analysis, and provided static information of HD [6,7]. However, little is known at the early stages of the disease about the dynamics of metabolic and transcriptomic changes, which could be instrumental not only for HD therapy for diagnosed patients but also for at-risk individuals. Striatum, a subcortical part of the forebrain, is the most vulnerable part of the brain in HD, almost disappearing during the course of disease. One of the major clinical symptoms of HD is the loss of the medium spiny neurons in the striatum [8]. Therefore, although genome-wide gene expression studies have been conducted on targeted affected tissues in the post mortem human brain, it has proven difficult to detect specific changes in the human striatum [9].
To expand our understanding of changes in the transcriptional landscape of the affected cells, mRNA expression profiling was performed on cells isolated from the striatum of rat embryos expressing wild type and mutant HTT.
As shown by Lin and Beal, detecting changes in carbohydrates can be instrumental for the development of useful therapies that slow down HD progression and reduce its severity at the early stages of the disease [10]. Recent evidence suggests that, similar to other neurodegenerative diseases, mice exhibit increased deregulation in brain energy metabolism at the late stages of HD [11]. A super-family of glucose transporter genes, the GLUT gene family, is responsible for the transport and uptake of glucose [12,13]. The GLUT family includes 12 genes, each encoding one GLUT protein. Various members of this gene family were detected in the brain, although glucose transporter protein type I exclusively mediates transport across the blood-brain barrier [14,15]. GLUT1, initially thought to be the only glucose transporter protein expressed in the brain, supports the basal metabolic needs of proliferating cells [16]. GLUT2 is primarily GLUT protein expressed in the pancreatic β-cells, liver and kidneys. In the pancreas, GLUT2 is believed to act in conjunction with glucokinase in the glucose-sensing mechanism. GLUT4 is the insulin-responsive receptor and is primarily expressed in the heart, adipose tissue, and skeletal muscle, where it is responsible for the reduction in the glucose levels in the post-prandial rise of glycaemia. Insulin stimulates the translocation of intracellular vesicles containing GLUT4 to the plasma membrane, resulting in a 10-to 20-fold increase in glucose transport [17].
Current assessment methods for intracellular glucose levels in HD models are limited and performed with a combination of techniques including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), uptake assays, and electrophysiology [18]. The invasiveness, low sensitivity, and specificity of these techniques prevent longitudinal study of biological model cells. Other major limitations of these techniques are the complex sample preparation methods during which cellular concentration of glucose can be altered. Therefore, accurate real time detection of glucose starting from the early and going to the later stages of the disease may provide biochemical evidence that can inform the design of novel drugs and therapeutic regimens. We recently reported the use of nanopipette technology for intracellular measurements [19,20].
Importantly, because nanosensors are minimally invasive and not destructive to the cell, it is possible to take repeated measurements of the same cell. In this work, we employed glucose nanosensors for intracellular glucose measurements in HD cell models (ST14A) at different time points to investigate glucose metabolism over time. In parallel, we conducted transcriptomic analysis to understand temporal changes in RNA expression in model HD cells. We report impaired glucose metabolism in single rat striatum mHTT cells using our nondestructive nanoglucose sensor. This is the first report determining the intracellular glucose levels in mHTT cells to be in the range of 0.8-1.5 mM. A 2.5 fold decrease in intracellular glucose levels in HD cells indicated an altered glucose metabolism that was further investigated by transcriptomic study. Significant differences were seen in glucose uptake and glucose homeostasis genes. Interestingly, there was an increased expression of Sorcs1 in mHTT cells which has been previously associated with late onset of Alzheimer's disease, another neurodegenerative Article No~e00381 disorder. Overexpression of Sorcs1 was confirmed by performing RT-qPCR which corroborates Sorcs1 can be used as a biomarker for HD progression.

Glucose nanosensor fabrication
Glucose nanosensors with an outside diameter of 1.00 mm and an inside diameter of 0.70 mm were fabricated from quartz capillaries (Sutter Instrument, Novato, CA) using a P-2000 laser puller (Sutter Instrument, Novato, CA) as described elsewhere [19]. Briefly, to create glucose nanosensors, the nanopipettes were modified as follows: Pulled nanopipettes were backfilled with a 15 μl solution containing 10 mM Ferrocene prepared in 100 mM PBS (pH 7, supplemented with 0.1 M KCl). An Ag/AgCl electrode was placed into the nanopipette as a working electrode while another Ag/AgCl electrode was immersed in the cell media as a reference/counter electrode. Nanopipettes were modified with PLL by backfilling the nanopipette interior. Then nanopipette surface was treated with a 10% (v/v) solution of glutaraldehyde (GA) for 30 min. GOx (900 mU) was then reacted with the activated nanopipette walls. All glucose nanosensors were calibrated in DMEM with standard glucose concentrations.

Cell culture
The Huntington's disease model cell lines that were used in this work are both striatal cell lines derived from rat embryos and obtained from Coriell Institute cell repository. The two cell lines express human HTT fragments of 15 and 120 polyglutamine repeats representing wild type (ST14A-Q15) and mHTT disease model (ST14A-Q120), respectively. All cells were cultivated and maintained in DMEM (supplemented with 10% fetal bovine serum, antibiotics) with various glucose concentrations. DMEM was supplemented with 1 nM insulin when needed to study effects of insulin.

Intracellular glucose measurement
The intracellular glucose measurement setup consists of an inverted microscope Olympus IX 70 with Spot Insight CMOS camera to image cells. The nanosensors are fixed to a microscope by a pipette holder (Axon Instruments). The holder is connected to an Axopatch 700B amplifier (Molecular Devices) for current measurement, an MP-285 micromanipulator (Sutter Instrument) for coarse control of the nanopipette positioning in the X, Y, and Z directions, a Nanocube piezo actuator (Physik Instrument) for fine control in the X, Y, and Z directions, and a PCIe-7851R Field Programmable Gate Array (FPGA) (National Instruments) for hardware control of the system. The system is operated using custom-coded software written in LabVIEW. Current-clamp technique has been used at 1nA with signal filter at 1 kHz. The signal was further digitized by an Axon Instruments Digidata 1322A. The data were then recorded by a LabVIEW 9.0 home-made software, as described previously [21]. While in the medium, a fixed potential of 500 mV was applied to the glucose nanosensor. This biased potential generates an ion current through the liquid-liquid interface that is used as the input into a feedback loop analyzed by the scanning ionic conductance microscope. When a 5-10% current decrease is detected, the glucose sensor is penetrated into the target cell up to 0.8 μm at 100mm/s. The sensor is maintained inside the target cell for a pre-defined time of 60 seconds and then withdrawn to the initial position. Each condition was tested on a minimum of 9 cells. Three consecutive readings were recorded for each individual target cell to ensure reproducibility and robustness. In order to investigate underlying molecular mechanism of these observed changes, a detailed transcriptome study was performed.

RNA isolation from HD cells
RNA extraction and purification were performed using RNeasy Mini kit from Qiagen. Prior to RNA isolation, HD cells were collected with conventional trypsin treatment and counted using Bio-Rad automated cell counter. A minimum of 5 × 10 5 cells were used for RNA isolation. Samples were stabilized by treatment with RNAlater (Ambion). After RNA isolation, nucleic acid quantity and quality were checked using NanoDrop (Thermo Fisher Scientific).
2.6. RNA-seq library preparation, qRT-PCR and sequencing 2.6.1. cDNA synthesis Extracted total RNA was converted to cDNA using the Smart-seq2 protocol and the cDNA was preamplified as described by a previoud study [22]. Briefly, the cDNA product was mixed with KAPA HiFi HotStart ReadyMix (2X, KAPA Biosystems), ISPCR primer (10uM) and nuclease-free water (Gibco) and PCR amplified as follows: 98°C 3 min, then 12 cycles of (98°C 15 s, 67°C 20 s, 72°C Article No~e00381 6 min), with a final extension at 72°C for 5 min. The cDNA was cleaned up using a 1X ratio of AMPure XP beads (Beckman Coulter).

Quantitative real-time PCR
Real time PCR experiments were performed using 500 ng of reverse transcribed RNA and analyzed with SYBR green qPCR master mix (kapa Biosystems) using an Mx3000P instrument (Stratagene). The relative quantification of Sorcs1 expression levels was performed using a previously described method [23]. For these experiments actb was used as a reference gene. Sorcs1 and actb specific primer sequences designed in previous studies were used (Table 1).

Library preparation and sequencing
The cDNA was diluted to ∼300 pg/ul and then processed with the Nextera XT DNA library preparation kit according to the manufacturer's protocol (Illumina).
These libraries were purified and size-selected with an average library size of 300-600 bp as determined by Bioanalyzer 2100 high-sensitivity DNA assay (Agilent).
Functional library concentration was determined with the KAPA Biosystems library quantification kit. The libraries were denatured after quantification and loaded on Illumina HiSeq 2000 for sequencing.

Gene expression analysis
HTSeq package was used to generate the gene matrix from the output sam file produced by the STAR alignment. A gtf file containing Ensembl identifiers as well Table 1. Sequences of primers used in this study.
β-Actin 5'-CACCCGCGAGTACAACCTTC-3' 5'-CCCATACCCACCATCACACC-3' [77] as gene names was used for rat and human genes. For the ERCC genes, the gtf file from Thermo Fisher Scientific was downloaded from the company website (www. thermofisher.com). The gene matrix was then subjected to DESeq2 differential expression analysis, as described [26,27,28,29]. Benjamini-Hochberg multiple testing adjustment was used as a method to deal with p-values that are low due to chance, and not because the measurement represents a significant value. False Discovery Rate values used were calculated by DESeq2 package. Hierarchical Clustering of differentially expressed genes was performed using the Pretty . Pearson coefficients were used as correlation coefficients for the Hierarchical Cluster Analysis heatmaps. The Panther classification system (www.pantherdb.org) was used to visualize the pathways in which differential genes were involved.

Statistical analysis
Analysis of Variance (ANOVA) and independent t-test were performed using Prism (GraphPad) software when indicated.

Results and discussion
The energy metabolism in presymptomatic and symptomatic HD individuals is known to be defective [30,31]. Reduced glucose uptake in the striatum and cortex of HD patients prior to the onset of clinical symptoms has been previously revealed by positron emission tomography (PET) scanning. Additionally, this hypometabolism demonstrated a high correlation with the onset and progression of HD, independent of the extent of CAG expansion [32]. Recently, Besson et al. reported that the increase of glucose transporters was beneficial to HD pathology in a Drosophila melanogaster model [33]. However, due to the destructive nature of previously employed intracellular glucose measurement methods, a direct interrogation of temporal changes at the transcriptomic and metabolic level could not have been studied. In this work, we took a step forward by deploying functionalized nanopipettes as glucose sensors to measure changes in intracellular glucose concentrations over time, and performed RNA sequencing to evaluate concomitant changes in expression of genes related to glucose metabolism. tool. We have recently demonstrated the use of this nanosensor in conjunction with a customized cell finder at normal and cancer cells [19].

Glucose nanosensors reveal temporal intracellular glucose changes at HD cells
Typical physiological concentrations of glucose in biological systems are in the low-micromolar range [34]; intracellular glucose levels in HD cells is unknown. More importantly, to our knowledge, there are no reports on the assessment of temporal changes in intracellular glucose concentration in HD models, an assessment that could illuminate one aspect of the cellular pathophysiology of HD. To achieve the goal of measuring intracellular glucose over time, we placed nanosensors in the cytoplasm of wild type (WT) (ST14A-Q15) and HD (ST14A-Q120) cells.
Cells underwent consecutive passages in fresh culture medium and glucose measurements were made at the end (day 3 (T1), day 6 (T2), day 11 (T3), day 16 (T4)) of each passage. In the initial experimental conditions, WT and HD cells were cultured in low glucose media (LGM) and high glucose media (HGM) containing 0.5X and 1X (4.5 g/L) glucose respectively. Cells were also cultured in glucose-free media but neither WT nor HD cells survived in this condition. Therefore, measurements could not be conducted in the absence of glucose. It is important to note that the regular culture condition of these cells does not require or include insulin.
Exposure of WT cells to low and high glucose media in the absence of insulin resulted in similar intracellular glucose levels as measured by glucose nanosensors T2 to T4, and the range was between 1.79 to and 2.58 mM. The most significant difference was observed for the T1 time point where intracellular levels were about 3 fold lower than those of T2-T4 in wild type cells. This low concentration can be due to either fast metabolism or rapid growth rate of young cells. When the glucose nanosensor was used on HD cells, we observed that intracellular levels of glucose were about 2.5 fold lower than that found WT cells for T3 and T4 (Figs. 1 and 2).
To compare glucose uptake in WT and HD cells, we exposed the cells to 1 nM insulin (normal non-fasting insulin level), as insulin regulates glucose uptake by activating glucose transporters 1 and 4 (GLUT1 and GLUT4) [35]. We saw a general increase in intracellular glucose levels for all time points for both cell lines ( Figs. 1 and 2). As expected, in HGM, intracellular glucose levels were much higher than in LGM. Notably, the effect of insulin was more pronounced at T3 and T4 suggesting that glucose uptake of HD cells is facilitated when insulin is present.
The average intracellular glucose concentrations and their variations for both cell types under all conditions are summarized in Table 2.

Assessing transcriptomics in rat HD cells
We integrated transcriptomics and metabolomics i.e., the metabolome provided phenotypic (glucose) measurements to which we anchored the global measurements of the transcriptome related to glucose pathways. In order to achieve this, RNA-seq transcriptomics was performed to complement the data obtained from glucose nanosensor measurements, using a similar set-up. RNA was extracted from [ ( F i g . _ 2 ) T D $ F I G ]  wild type (WT) (ST14A-Q15) and disease (HD) (ST14A-Q120) cells. The gene expression levels were assessed across four passages T1-T4.
First, to assess the sequencing by synthesis (SBS) method as a quantitative profiling tool, ERCC spike-in controls were included in the cDNA mix with the samples. ERCC spike-in controls showed Pearson correlation above 90% in all library-to-library comparisons. Also, a 90% Pearson correlation was observed in the expression of ERCC pseudo-genes to the ERCC starting concentrations provided by the ERCC manufacturer (Fig. 3 (A)). Although Spearman correlation was slightly lower (minimum 88%) (Fig. 3 (B)), Pearson correlation is a better parameter for our dataset because it assumes a linear relationship between ERCC gene expression measurements and ERCC starting concentration [36].
Comparisons of upregulated, downregulated and significantly different genes were made with the top 50 genes in a universe of 3213 differentially expressed genes. A recent RNAseq study in human post-mortem HD patients also showed 5480 genes differentially expressed, indicating a similar order of magnitude in the number of differentially expressed genes in rat and humans due to mHTT [9].

Glucose-related genes were differentially expressed in mHTT cells
Specific genes used for the analysis were involved in glycolysis, trichloroacetic acid cycle (TCA), pentose phosphate pathway (PPP) and glucose transport; accessed from a list of genes in glucose metabolism provided in Qiagen's website (www.qiagen.com). The pattern of expression of genes involved in these pathways in mutant and wildtype were plotted as a heatmap using the Pheatmap package available in R [75]. Seven genes in the glycolysis pathway, eleven genes in the TCA, two genes in the PPP, and one gene expressing a glucose transporter were found to be affected by mHTT in this study (Fig. 4).

Survival effect of HK-II and Glut1 on HD cells
Hexokinase catalyzes the first step of glycolysis and represents a regulatory enzyme [74]. Expression of hexokinase II (HK-II) was found to be upregulated in our rat striatal HD cells, a finding also observed by other authors [37]. An increased expression of hexokinase II has also been associated with other neurodegenerative disorders [38] (Fig. 4). It was demonstrated previously that alteration in HK-II gene expression has a direct impact on glucose metabolism [39].
Hexokinases (HKs) phosphorylate the glucose transported through glucose transporters (GLUTs) on the plasma membrane to produce glucose-6-phosphate (G-6P) (Berg et al., 2002). HK activity is inhibited by G-6P providing a feedback mechanism [40]. The reduced glucose content measured in mutant cells could be attributed to the increased expression of HK-II in HD cells. HK-II upregulation has been previously shown to be an important consequence of metabolic reprogramming in cancer [39].

[ ( F i g . _ 4 ) T D $ F I G ]
The expression of Glut1 (Slc2a1), the main class of glucose receptor involved in glucose transport from the blood-brain barrier to the CNS [15], was also upregulated in the mutant cell type (Fig. 4). A previous study in a hematopoietic cell line showed that increased glucose phosphorylation due to co-expression of HK-II and Glut1 has an anti-apoptotic effect. The protective effect was attributed to increase in NADPH activity through the pentose phosphate pathway eliciting an anti-apoptotic effect [41]. The mutant huntingtin protein causes neuronal dysfunction and eventual cell death. Some of the pathways found to be abnormal in Huntington's disease models are transcriptional impairment, neuronal excitotoxicity, oxidative damage, inflammation, apoptosis, and mitochondrial dysfunction. The anti-apoptic effect could slow down the disease progression in our HD rat cell model, similar to observations by the study of Johri and Beal [42].
In summary, our data suggest a compensation mechanism by which increased expression of HK-II and Glut1, may drive mutant cells to survive huntingtin mutation.
Insulin treatment is known to increase HK-II mRNA and protein in various cell types and the increase is blocked by inhibition of PI3K, an upstream kinase of Akt, as well as inhibition of mTORC1 [39]. mTORC1 is a protein that integrates signals to regulate cell growth and metabolism.
Genes coding for IDH1, ENO3, MDH2 and ALDOC proteins were also found to be differentially expressed (Fig. 4) in mHTT cells. Eno3 is a gene that codes the enolase version of the muscle. The deficiency of this protein correlates with glycogen storage disease XIII [43]. Together, the results of this glucose homeostasis targeted analysis suggest that glucose metabolism is altered in our HD model, in agreement with other studies [44].

Upregulated and downregulated genes in HD rat cell model
To further understand the perturbation initiated by huntingtin, we analyzed the most abundantly differentiated genes of our cell model in the Panther Classification System (www.pantherdb.org). The group of the top 50 upregulated genes was segmented after the pathway enrichment analysis of genes present using Panther Classification. Fig. 5 shows the heatmap and pie-chart analysis of the up-regulated genes. Fig. 5b  Alzheimer's diseases were also found in the Panther Classification. Igf1 is involved in two pathways: the MAPK pathway, related to cell development and differentiation, and the AKT/PKB pathway that controls insulin metabolic actions (Fig. 5) [45]. protein trafficking and transport of other cellular components, such as the amyloid beta plaques [47,48], and has been established as an important hallmark of Alzheimer's disease. Gene Sorcs1 was found upregulated in the mutant type in this study (Fig. 5a). A recent study suggested that SORCS1 has a novel regulatory mechanism and acts as a modulator of sortillin function [49]. Sortillin, also a member of the Vps 10 protein sorting receptor family, is involved in biological processes such as glucose and lipid metabolism. It has been proposed that dysfunction of SORCS1 protein may contribute to both the APP/Aβ disturbance underlying Alzheimer's disease (AD) and the insulin/glucose disturbance in Diabetes Mellitus (DM) [50].
To better investigate changes observed in mHTT model in this study, we compared transcriptomics data from other Huntington mice models and included some of the changes observed in AD. Out of 70 genes annotated in the Alzheimer's disease presinilin pathway, 26 genes were found altered in mHTT cells ( Fig. 5 and Fig. 6).
The presinilin pathway is dysregulated in AD, cleaving several single-transmembrane proteins within the membrane domain, including the amyloid precursor protein, Notch, ErbB4, E-cadherin, Nectin-1alpha, and CD44 (www.pantherdb. org). Notably, another important AD marker, gene Apoe, was affected in our cell model. Interestingly, Vldlr, an Apoe receptor was the top downregulated gene in our study (Fig. 7). As mentioned before, HK-II, a glycolysis enzyme, was affected by the mHTT protein. Besides the already established low-glucose metabolism characteristic of AD, it was observed that AD condition affects the regulation of 6phosphofructo-2-kinase in human patients, further illustrating common features between AD and HD [51].
Other pathways significantly affected in our mHTT model, including calcium signaling, glutamate receptor activity and synaptic transmission, were observed by other researchers in HD mice and human patients [52,53,54,55]. For instance, Achour and collaborators identified Gata2 transcription factor, a gene involved in establishment of tissue-specific enhancers, which was found to be affected in the present study, highly expressed in the striatum, and not cerebellum, of HD mice model [56]. Furthermore, genetic variations in intron 1 of Sorcs1 have been associated with Alzheimer's disease in several studies [57,58]. In order to confirm upregulation of Sorcs1 observed in RNA seq analysis, we stimulated cells with insulin as described in intracellular glucose measurements and performed gene specific PCR analysis (Fig. 8). Sorcs1 was found to be upregulated in mHTT compared to wild type as observed in RNA-seq data. To understand the effect of insulin and/or glucose on expression of Sorcs1 in mHTT cells, qRT-PCR was performed. The mHTT cells not treated with glucose and insulin showed the highest Sorcs1 expression (Fig. 9). Significant difference was observed when these mHTT cells (no insulin, no glucose) were compared to WT cells grown in all conditions tested. However, we observed no statistically significant difference in Sorcs1 expression in mutant cells treated with either insulin or glucose. Our results suggest that mHTT mutation was the only factor causing higher Sorcs1 expression.
However, more stringent and detailed analysis will enable better understanding of the effect of additional glucose and insulin on expression of Sorcs1.
In response to insulin, sortillins can transport GLUT4 to the membrane for uptake of glucose [59]. There are no previous studies demonstrating that the two proteins, SORCS1 and sortilin, can act identically. However, recent literature has indicated that the SORCS are sortilin-related CNS expressed proteins, and, as they belong to the same subgroup of VSP10 receptor family, these proteins are related [46,47,60]. Since SORCS1 and sortilins belong to the same family of proteins, we were [ ( F i g . _ 6 ) T D $ F I G ] interested in understanding whether increased Sorcs1 in cells with Huntington phenotype would affect Glut4 gene expression, thereby increasing glucose uptake.
As Glut4 expression was not significantly different in HD and normal cells in the RNA-Seq analysis, we performed protein expression studies. Our results showed that GLUT4 protein was expressed at lower level in rat HD cells ( Fig. 10A and B). [ ( F i g . _ 9 ) T D $ F I G ] one-way ANOVA. **P < 0.05 was considered statistically significant. [59]. Higher GLUT4 and/or higher metabolism could explain the increased intracellular glucose concentration in wild-type cells and a time-dependent study could give a better idea whether GLUT4 is indeed regulated by SORCS1 in addition to sortillin, as observed in the study by Hou and Pessin [59]. As GLUT4 levels were lower in mHTT cells, we became interested in analyzing GLUT1 levels, the main class of glucose receptor. Results showed higher GLUT1 levels in mHTT cells suggesting that GLUT1 could indeed compensate for the lack of Glut4 in mHTT cells (Fig. 10C).
The fine regulation of brain developed neutrophic factor (BDNF) by sortillin has been implicated in neuronal and tumor cell survival. Interestingly, BDNF protein was also shown as a diabetes and Huntington's disease marker [2,61]. In our cell model, following the trends described by Zuccato and collaborators, Bdnf levels were lower in mutant cells than in wild type. Furthermore, it has been demonstrated that BDNF protein is one of the molecules that is a target of organelle sorting mediated by SORCS1 [62]. BDNF, once transported to the striatum from the cortex, induces cholesterol synthesis and is related to synaptic plasticity and neuronal survival [63]. Sortillin has also been implicated in LDLcholesterol metabolism and VLDL secretion [64,65]. The very low density lipoprotein receptor (Vldlr) was found as the top downregulated gene in HD cells as compared to wild type cells in our data set (Fig. 7). VLDLR is an apolipoprotein [ ( F i g . _ 1 0 ) T D $ F I G ] Article No~e00381 E (APOE) receptor and the interaction of these two proteins has important implications in lipid metabolism [66]. Downregulation of Vldlr gene indicates that lipid metabolism is also impaired in mutant huntingtin striatal cells, as has been shown by another group [67]. Other genes that were found to be downregulated in this study are shown in Fig. 7a, along with the pathways they belonged to in Fig. 7b.
Together, these data suggest the involvement of Sorcs1, insulin, Glut4 and Glut1 in the HD condition, at least in our cell model, providing molecular candidates that support a decreased intracellular glucose concentration due to mutant huntingtin.
To our knowledge, no other group has reported the involvement of Sorcs1, an Alzheimer's disease and diabetes marker in a Huntington's disease model. Sorcs1 may not only be involved in impairment of glucose metabolism but also lipid metabolism in Huntington's disease.

Other pathways affected by mutant huntingtin
Using transcriptomics and metagenomics, we sought to understand how mHTT influences not only HD related pathways but also other major pathways. In order to understand the pathways affected, we classified differentially expressed genes according to their signaling pathways, with at least 10 genes observed per pathway in the Pantherdb classification system. 1145 genes (out of 3213) are represented in Fig. 6. The signaling pathway most significantly affected by mutated huntingtin in number of genes was the wingless-related (Wnt) signaling pathway (P00057), showing 65 disregulated genes. Beta-catenins, a canonical element from the Wnt pathway, forms a complex with GSK-3B; they regulate epidermal growth factor and insulin, as well as control cell fate. Other genes that were differentially expressed were important in the insulin/MAPK/PKB/IGF pathways (P00032 and P00033). 64 genes were impaired in the gonadotrophin-releasing hormone receptor (P06664). Dysregulation of inflammation mediated by the cytokines and chemokines pathway (P00031) supports the anti-inflammatory treatments suggested for human HD [9]. Glutamatergic pathways were also found to be affected in our study (P00037 and P00039). Glutamate ionotropic receptor AMPA type subunit 3 (gria 3), a gene in this pathway, was downregulated in our study (Fig. 7). Morton et al. also showed this gene to be downregulated in R2/6 mice, affecting synaptic plasticity [68]. A recent study showed SORCS1 regulating the AMPA receptors [47]. All the other pathways affected due to mutant huntingtin (containing 10 or more than 10 genes per pathway) are shown in Fig. 6

Conclusion
Correlation between HD and glucose blood levels has been extensively studied.
Although some studies do not show a correlation between HD and glucose metabolism, [3], other studies suggest the involvement of insulin resistance mediated by the huntingtin mutation and cross-talk between glucose metabolism and other metabolic pathways. Among these pathways, involvement of fatty-acid biosynthesis and cholesterol synthesis are described in HD progression [63,67,69,70,71,72,73]. Here, we showed impaired glucose metabolism in rat striatum cells using a functionalized nanopipette as a non-destructive intra-cellular glucose measurement technique. Impaired glucose metabolism along with altered expression of genes concerned with energy metabolism contributes to HD pathogenesis. A new additional target, SorCS1 was identified to be altered in HD cells along with previously reported transporter related genes, glut1 and glut4.
Increased levels of SorCS1 correlate positively with loss of the ability of the cell to sort proteins like glut4 properly. Further work on generating a knock-out mutant of