11.3D: Harmful Effects Associated with Abnormal Pattern-Recognition Receptor Responses, Variations in Innate Immune Signaling Pathways, and/or Levels of Cytokine Production - Biology

11.3D: Harmful Effects Associated with Abnormal Pattern-Recognition Receptor Responses, Variations in Innate Immune Signaling Pathways, and/or Levels of Cytokine Production - Biology

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Learning Objectives

  1. Describe how an overactive TLR-4 receptor can increase the risk of SIRS in a person if Gram-negative bacteria enter the bloodstream.
  2. Briefly describe two specific examples of how an improper functioning PRR can lead to an increased risk of a specific infection or disease.

The Ability of Pathogen-Associated Molecular Patterns or PAMPs to Trigger the Synthesis and Secretion of Excessive Levels of Inflammatory Cytokines and Chemokines

As learned in Unit 3 under sepsis and systemic inflammatory response syndrome (SIRS), during severe systemic infections with large numbers of bacteria present, high levels of cell wall PAMPs are released resulting in excessive cytokine production by the defense cells and this can harm the body (see Figure (PageIndex{10})). In addition, neutrophils start releasing their proteases and toxic oxygen radicals that kill not only the bacteria, but the surrounding tissue as well. Harmful effects include high fever, hypotension, tissue destruction, wasting, acute respiratory distress syndrome (ARDS), disseminated intravascular coagulation (DIC), and damage to the vascular endothelium. This can result in shock, multiple system organ failure (MOSF), and death.

Harmful Effects Associated with either an Overactive or an Underactive Innate Immune Response

There are a number of harmful effects that are known to occur as a result of either an overactive or an underactive innate immune response. This occurs as a result of people possessing different polymorphisms in the various genes participating in PRR signaling.

  • People born with underactive PRRs or deficient PRR immune signaling pathways are at increased risk of infection by specific pathogens due to a decrease innate immune response.
  • People born with overactive PRRs or deficient PRR immune signaling pathways are at increased risk of inflammatory damage by lower numbers of specific pathogens.

Examples include:

1. People with an underactive form of TLR-4, the toll-like receptor for bacterial LPS, have been found to be five times as likely to contract a severe bacterial infection over a five year period than those with normal TLR-4. People with overactive TLR-4 receptors may be more prone to developing SIRS from gram-negative bacteria.

2. Most people that die as a result of Legionnaire's disease have been found to have a mutation in the gene coding for TLR-5 that enables the body to recognize the flagella of Legionella pneumophila.

3. B-lymphocytes, the cells responsible for recognizing foreign antigens and producing antibodies against those antigens, normally don't make antibodies against the body's own DNA and RNA. The reason is that any B-lymphocytes that bind the body's own antigens normally undergo apoptosis, a programmed cell suicide. People with the autoimmune disease systemic lupus erythematosus have a mutation in a gene that signals the cell to undergo apoptosis. As a result, these B-cells are able to bind and engulf the body's own DNA and RNA and place them in an endosome or phagolysosome where the the DNA can be recognized by TLR-9 and the RNA by TLR-7. This, in turn, triggers those B-lymphocytes to make antibody molecules against the body's own DNA and RNA. Another gene error enables these B- cells to increase the expression of TLR-7.

4. TLR-4, MyD88, TLR-1 and TLR-2 have been implicated in the production of atherosclerosis in mice and some humans.

5. Mutations resulting in loss-of-function in the gene coding for NOD-2 that prevents the NOD-2 from recognizing muramyl dipeptide make a person more susceptible to Crohn's disease, an inflammatory disease of the large intestines. Mutations resulting in over-activation in the gene coding for NOD-2 can lead to an inflammatory disorder called Blau syndrome.

6. People with chronic sinusitis that does not respond well to treatment have decreased activity of TLR-9 and produce reduced levels of human beta-defensin 2, as well as mannan-binding lectin needed to initiate the lectin complement pathway.

7. Pathogenic strains of Staphylococcus aureus producing leukocidin and protein A, including MRSA, cause an increased inflammatory response. Protein A, a protein that blocks opsonization and functions as an adhesin, binds to cytokine receptors for TNF-alpha. It mimics the cytokine and induces a strong inflammatory response. As the inflammatory response attracts neutrophils to the infected area, the leukocidin causes lysis of the neutrophils. As a result, tissue is damaged and the bacteria are not phagocytosed.

8. People with chronic mucocutaneous candidiasis disease have a mutation either in the gene coding for IL-17F or the gene encoding IL-17F receptor. TH17 cells secrete cytokines such as IL-17 that are important for innate immunity against organisms that infect mucous membranes.

9. A polymorphism in the gene for TLR-2 makes individuals less responsive to Treponema pallidum and Borrelia burgdorferi and possibly more susceptible to tuberculosis and staphylococcal infections.

10. Polymorphisms in a gene locus called A20, a gene that helps to control inflammation, are considered as risk alleles for rheumatoid arthritis, systemic lupus erythematosus, psoriasis, type I diabetes, and Chron’s disease.

11. The innate immune response to Mycobacterium tuberculosis and the severity of tuberculosis depends on the response of TLRs 1/2, TLR 6, and TLR 9 to the bacterium. Polymorphisms in Toll-interacting protein (TOLLIP), a negative regulator of TLR signaling, influence the response of the patient to M. tuberculosis.

Exercise: Think-Pair-Share Questions

  1. What is the significance of underactive and overactive PRRs in innate immunity?

Therapeutic Possibilities

Researchers are now looking at various ways to either artificially activate TLRs in order to enhance immune responses or inactivate TLRs to lessen inflammatory disorders. Examples of agents being evaluated in clinical studies or animal studies include:

1. TLR activators to activate immune responses

a. Both TLR-4 and TLR-9 activators are being tried in early clinical trials as vaccine adjuvants to improve the immune response to vaccines. TLR-9 activators are being tried as an adjuvant for the hepatitis B and anthrax vaccines and a TLR-4 activator is being tried as an adjuvant for the vaccine against the human papillomaviruses that cause most cervical cancer.
b. Both TLR-7 and TLR-9 activators are being tried in early clinical trials as an antiviral against hepatitis C. Activation of these TLRs triggers the synthesis and secretion of type I interferons that block viral replication within infected host cells.
c. TLR-9 activators are being tried in early clinical trials as an adjuvant for chemotherapy in the treatment of lung cancer.
d. TLR-9 activators are being tried in early clinical trials to help in the treatment and prevention of allergies and asthma. Activation of TLR-9 in macrophages and other cells stimulates these cells to kill TH2 cells, the subclass of T-helper lymphocytes responsible for most allergies and asthma.

2. TLR inhibitors to suppress immune responses

a. General TLR inhibitors might one day be used to treat autoimmune disorders.
b. A TLR-4 inhibitor, a mimic of the endotoxin from the gram-negative cell wall, is being tried in early clinical trials to block or reduce the death rate from Gram-negative sepsis and SIRS.
c. TLR-4, TLR-2, and MyD88 inhibitors might possibly one day lessen atherosclerotic plaques and the risk of heart disease.

Of course using TLR activators or TLR inhibitors to turn up or turn down immune responses also carries risks. Trying to suppress harmful inflammatory responses may also result in increased susceptibility to infections; trying to activate immune responses could lead to SIRS or autoimmune disease.


  1. In severe bacterial infections, pathogen-associated molecular patterns or PAMPs can trigger the synthesis and secretion of excessive levels of inflammatory cytokines and chemokines leading to systemic inflammatory response syndrome or SIRS.
  2. People born with underactive PRRs or deficient PRR immune signaling pathways are at increased risk of infection by specific pathogens due to a decrease innate immune response.
  3. People born with overactive PRRs or deficient PRR immune signaling pathways are at increased risk of inflammatory damage by lower numbers of specific pathogens.
  4. Researchers are now looking at various ways to either artificially activate underactive PRRs in order to enhance immune responses, or inactivate overactive PRRs to lessen inflammatory disorders.

11.3D: Harmful Effects Associated with Abnormal Pattern-Recognition Receptor Responses, Variations in Innate Immune Signaling Pathways, and/or Levels of Cytokine Production - Biology

Please note that Internet Explorer version 8.x is not supported as of January 1, 2016. Please refer to this support page for more information.


Alcohol use disorders (AUDs) are among the most common pathologies that affect the central nervous system (CNS). Fetal exposure to ethanol is known to cause long-term cognitive impairment and brain deficits [1, 2] that are commonly referred to as Fetal Alcohol Spectrum Disorders (FASDs). Despite a wide array of epidemiological studies that have investigated the genetic predisposition to develop AUDs, the cellular underpinnings and the pathophysiology of AUDs remain elusive in the CNS [3].

Ethanol is known to act as a powerful epigenetic disruptor and is potentially able to interfere with cellular metabolism and differentiation. In particular, neuroinflammation and oxidative damage of mitochondria and cellular proteins are thought to contribute to the progression of neurological disorders initiated by alcohol abuse [4]. Ethanol can initiate an innate immune-like response in the CNS [5] via two main receptors and their respective signaling pathways: the membrane bound toll-like receptors (TLRs) [6] and the cytoplasmic NOD-like receptor family, pyrin domain containing 3 (NLRP3). NLRP3 forms intracellular danger-sensing multi-protein platforms called inflammasomes [5]. Activation of both the TLR- and NLRP3-mediated pathways in mammals is correlated with aging [7, 8] and cellular insults, including ethanol exposure [5]. NLRP3 can activate inflammatory caspases, e.g. Caspase-1 (Casp1), which accelerates the aging process through the impairment of autophagy, thus eventually leading to cell death [9]. On the other hand, ethanol has also been shown to induce the activation of Caspase-3 (Casp3)-dependent apoptosis and necrosis in vivo [10]. However, whether ethanol exposure activates these cellular inflammatory pathways in human cells is not clear.

Given the inaccessibility of human neural tissue, human induced pluripotent stem (iPS) cell-derived neurons and neural progenitor cells (NPCs) represent powerful tools for testing the effects of ethanol on both early brain development and neuronal differentiation in vitro [11]. The inconclusive and controversial findings of previous studies exploring the effects of ethanol on NPCs could be attributed to varying culture methods, approaches, as well as model systems [3, 12, 13]. Furthermore, the intrinsic variability between iPS cell lines derived from different individuals can also contribute to the incongruities in these studies [14].

In order to model the pathogenesis of AUDs with limited intrinsic variability, we have focused our analysis on the effects of alcohol on cells derived from the same individual at three different stages: pluripotency (i.e. iPS cells), neurogenesis (i.e. NPCs), and terminal differentiation (i.e. post-mitotic neurons) [11]. In accordance with previous studies on postmortem human brains [15, 16], we show that neither acute (24 hours) nor prolonged (7 days) exposure to 70mM ethanol affects the proliferation or self-renewal of iPS cells or NPCs, but most likely impacts terminal differentiation and neuronal function. More importantly, we show an alteration of the mitochondrial pattern, as well as activation of the NLRP3 inflammasome pathway in these cells in response to ethanol exposure [5, 17]. This finding is consistent with the development of a remarkable neuroinflammatory environment in the brains of patients with a history of long-term alcohol dependence [4, 11, 18].

Material & methods


Female wild-type (TLR4_WT, TLR4+/+, WT) (Harlan Ibérica S.L., Barcelona) and TLR4 knockout (TLR4_KO, TLR4-/-, KO) mice were used, which were kindly provided by Dr. S. Akira (Osaka University, Japan) with C57BL/6J genetic backgrounds. Animals were kept under controlled light/dark (12 h/12 h) conditions at 23°C and 60% humidity. The animal experiments were carried out in accordance with the guidelines set out in the European Communities Council Directive (86/609/ECC) and Spanish Royal Decree 1201/2005, and were approved by the Ethical Committee of Animal Experimentation of CIPF (Valencia, Spain).

Alcohol treatment

For the chronic alcohol treatment, 44 (11 animals/group) 7-week-old WT (C57BL/6J) and TLR4-KO female mice were housed (4 animals/cage) and maintained with either water (WT and TLR4-KO control) or water containing 10% (v/v) alcohol. They were placed on a solid diet ad libitum for 5 months. During this period, daily food and liquid intake were similar for both the WT and TLR4-KO mice and the alcohol-treated/untreated groups. Body weight gain at the end of the 5-month period was similar in both the WT (C57BL/6J) and TLR4-KO mice treated with or without alcohol, as previously described [15]. The peak blood alcohol levels (BALs) detected in mice after the chronic ethanol treatment were around ≈125 mg/dl (range of 87–140 mg/dl) in the ethanol-treated WT mice, and ≈ 122 mg/dl (range of 98–135 mg/dl) in the ethanol-treated-KO. The use of females instead of males was based on our previous studies showing that females were more vulnerable to the effects of ethanol than males [15].

Cerebral cortex dissection

Mice were sacrificed by cervical, brains were removed and cerebral cortices were dissected following the mouse brain atlas coordinates instructions [18]. Brain cortices were weighed and immediately snap-frozen in liquid nitrogen. Samples were stored at -80°C until processed.

Total RNA isolation

The frozen cortex samples (100–200 mg) were used for the total and small RNA (sRNA) extractions. Briefly, 100–200 mg of tissue were disrupted with 1 ml of QIAzol (Qiagen, Maryland, USA), followed by the phenol chloroform method [19]. Total RNA and sRNA were isolated using the miRNeasy columns from the Qiagen Kit to obtain a separate sample for each RNA type. sRNAs were used for the deep sequencing protocol. Total RNA was employed for RT-qPCR to evaluate miRNAs and genes.

DNA isolation and genotyping

The genomic DNA from the WT and TLR4-KO mice was isolated using the commercial Maxwell 16 mouse tail DNA purification kit and the Maxwell 16 Instrument (Promega, Barcelona, Spain). Following the manufacturer’s instructions, DNA was collected in 300 μl of elution buffer. DNA was amplified with specific primers designed to differentiate WT and TLR4-KO strains. For genotyping purposes, three primers were designed according to previous studies [20]: primer “b”, which was recognized by both genotypes (WT and TLR4-KO) primer “a”, which was specific for the WT mice primer “c”, which was specific for the TLR4-KO mice. PCR was performed using 2 μl of DNA extract with master mix 2x PCR TaqNova-RED (Blirt, Gdańsk, Poland). The thermocycler (Eppendorf) program was 40 cycles: 30 seg 94 °C+ 90 seg 67 °C and 60 seg 74 °C. Amplicons were loaded in 1.5% agarose gel and visualized in BioRad. The employed primers are described in Table 1.

RNA quantity and quality determinations

The quantities of each total RNA sample were determined using NanoDrop ™ , and quantity and qualities were measured in an Agilent 2100 bioanalyzer. Total RNA integrity was analyzed by the RNA Nano6000 kit (Agilent Technologies, Santa Clara, CA, USA) and the sRNA kit was employed for sRNAs (Agilent Technologies, Santa Clara, CA, USA). The best nine samples for each condition were selected and combined to obtain three pooled samples, which gave 12 pooled sRNA samples. Briefly for each condition, nine animals were used and divided into three sample pools. With this approach, an attempt was made to minimize differences due to individuals and, in turn, to increase differences due to the studied variables. Then the sRNA profiles were measured again with the small-RNA kit (Agilent Technologies, Santa Clara, CA, USA) following the manufacturer’s instructions. Total RNA integrity was measured by the RNA Nano6000 kit (Agilent Technologies, Santa Clara, CA, USA).

Small RNA library preparation

First 100 ng of the sRNA fraction from the pooled cortex samples were used to prepare the sRNA libraries with the Truseq library prep Small RNA Sample Preparation kit (Illumina, San Diego, USA). These samples were employed for sequencing in HiSeq following the Illumina pooling manufacture’s guidelines. The cDNA from miRNAs was obtained by the Superscript II Reverse Transcriptase kit (Thermo Fisher Scientific, Carlsbad, CA, USA) and unique indices were introduced during PCR amplification for 15 cycles. The sRNA libraries were visualized and quantified in an Agilent 2100 bioanalyzer. A multiplexed pool was prepared that consisted of equimolar amounts of sRNA-derived libraries. Libraries were sequenced for 50 single read cycles in HiSeq2000 (Illumina).

Reverse transcription miRNA (miRNA-RT)

First of all, 500 ng of total RNA from cortical brain tissue were used. Samples were treated with DNase I (Invitrogen, Foster City, CA, USA) to avoid genomic DNA contamination. The retrotranscription reaction was run with specific RT-primers (1nM) (Integrated DNA Technologies, Inc.) for each analyzed miRNA using the High Capacity cDNA Reverse Transcription kit (Thermo Fisher Scientific, Foster City, CA, USA) following the manufacturer’s protocol the specific primers are detailed in Table 1. The reaction was carried out in an Eppendorf 5341 Master Cycler (Eppendorf AG, Hamburg, Germany) at 25 °C for 10 min, then at 40 °C for 1 h, and finally at 85 °C for 5 min to inactivate the enzyme. Total RNA was also converted into cDNA. Briefly 2 μg of total RNA from the cortical brain tissue were retrotranscribed with the High Capacity cDNA Reverse Transcription kit (Thermo Fisher Scientific, Foster City, CA, USA) following the manufacturer’s protocol.

Real-time quantitative PCR

RT-qPCR was performed in a Light Cycler ® 480 System (Roche, Mannheim, Germany). The reactions contained Light Cycler 480 SYBR Green I Master (2X) (Roche Applied Science, Mannheim, Germany), 5 μM of the forward and reverse primers, and 1 μL of cDNA. The amplification efficiency (E) of the primers was calculated from the plot of the Cq values against the cDNA input according to the equation E = [10(-1/slope)]. The relative expression ratio of a target/reference gene was calculated by the Pfaffl method [21]. Housekeeping cyclophilin-A (Ppia) was used as an internal control for messengers and RNAU6 for small RNAs. The primer gene sequence is detailed in Table 1.

Bioinformatics /pipelines analysis

The preprocessing of reads was done with Cutadapt (version 2.0) [22]. After removing adapters, the trimmed sequences were aligned against the reference genome (GRCm38.pp6) by Bowtie2 (v. [23]. As the genomic alignment of miRNAs is challenging given their size ranges (

21 nucleotides) [24], Bowtie2 was configured to increase sensitivity [25] (Fig 2A).

A read count was performed by custom R scripts, and these scripts used the library RSubread [26]. In order to annotate the reads aligned against miRNAs, the genome coordinates from the miRBase/gff3 file [27] were used, which allowed the mature miRNA of interest to be detected and annotated. After generating the count matrix of the six samples, the gene expression data were evaluated by multidimensional scaling and clustering methods to detect any abnormal patterns in the samples. Finally, the Top 10 most abundant miRNAs by read counts in the samples were annotated, along with their corresponding GO terms (Biological Process) from the QuickGO database [28] for descriptive purposes.

The count matrix was normalized by the TMM method (Trimmed Mean of M values). The differential expressions were analyzed by the Bioconductor package of edgeR [29]. P-values were corrected from the False Discovery Rate (FDR) as proposed by Benjamini and Hochberg [30].

Gene Set Enrichment Analysis (GSEA)

The bioinformatics functional analysis was performed using the mdgsa (Multi-Dimensional Gene Set Analysis) package [31], while Cluster-Profiler [32] was utilized for the graphics/results representation. This functional profiling included several steps:

First, miRNA was linked with its targets using TargetScan Mouse (Release 7.2: August 2018). Two types of miRNA-to-Gene lists were used: one inferred by bioinformatic methods and the other validated by the experimental assays.

Second, the differential expression results were used in the mdgsa package to transform the miRNA expression level into a gene level, which allowed the gene inferred differential inhibition score or index to be obtained. This transferred index contained the effect of multiple miRNAs against the same gene. By this approach, it was possible to obtain a miRNA regulation model and the effects of many miRNAs against their target.

Third, the inferred gene index was used in a univariate gene set analysis [33].

The above methodology allowed us to correlate a large set of genes with different functional annotations (GO terms, KEGG and Reactome pathways, etc.). In fact as we herein obtained a ranking of genes with differential inhibition indices, it was possible to determine if a functional annotation was inhibited in either the WT or the TLR4-KO group. The employed functional annotations were Gene Ontology (Biological Process) terms [34], the Kyoto Encyclopedia of Genes and Genomes (KEGG) [35] and the Reactome pathways database [36]. Multiple testing corrections were made with the FDR developed by Benjamini and Hochberg. Data representation was carried out with ClusterProfiler, a Bioconductor package.

Statistical methods

The SPSS, version 17.0, and the R version 3.4.3 software 40 were used for the validation analysis and bioinformatics, respectively. The RT-qPCR data were analyzed by a Student’s t-test when comparing TLR4_WT and TLR4_KO. A two-way ANOVA was used in the ethanol treatment experiments when comparing more than two groups. Differences at a value of P < 0.05 were considered statistically significant.

A product of nature

Some scientists outside China have studied the virus’s genome in detail and conclude that it emerged naturally rather than from a lab.

An analysis published in Nature Medicine on 17 March discusses several unusual features of the virus, and suggests how they likely arose from natural processes. For starters, when performing experiments that seek to genetically modify a virus, researchers have to use the RNA of an existing coronavirus as a backbone. If scientists had worked on the new coronavirus, it’s likely that they would have used a known backbone. But the study’s authors report that no known viruses recorded in the scientific literature could have served as a backbone to create SARS-CoV-2.

To enter cells, coronaviruses use a ‘receptor binding domain’ (RDB) to latch onto a receptor on the cell’s surface. SARS-CoV-2’s RBD has sections that are unlike those in any other coronavirus. Although experimental evidence — and the sheer size of the pandemic — shows that the virus binds very successfully to human cells, the authors note that computer analyses of its unique RBD parts predict that it shouldn’t bind well. The authors suggest that as a result, no one trying to engineer a virus would design the RBD in this way — which makes it more likely that the feature emerged as a result of natural selection.

The authors also point to another unusual feature of SARS-CoV-2, which is also part of the mechanism that helps the virus to work its way into human cells, known as the furin cleavage site. The authors argue that natural processes can explain how this feature emerged. Indeed, a similar site has been identified in a closely-related coronavirus, supporting the authors claim that the components of SARS-CoV-2 could all have emerged from natural processes.

The analyses show that it is highly unlikely that SARS-CoV-2 was made or manipulated in a lab, says Kristian Andersen, a virologist at Scripps Research in La Jolla, California, and the lead author of the paper. “We have a lot of data showing this is natural, but no data, or evidence, to show that there’s any connection to a lab,” he says.

But several scientists say that although they do not believe that the virus escaped from the lab, analyses are limited in what they can reveal about its origin.

There is unlikely to be a characteristic sign that a genome has been manipulated, says Jack Nunberg, a virologist at the University of Montana in Missoula, who does not believe the virus came from a lab. If, for instance, scientists had added instructions for a furin cleavage site into the virus’s genome, “there is no way to know whether humans or nature inserted the site”, he says.

In the end, it will be very difficult, or even impossible, to prove or disprove the theory that the virus escaped from a lab, says Milad Miladi, who studies RNA evolution at the University of Freiburg in Breisgau, Germany. And despite scientists such as Shi warning the world that a new infectious respiratory disease would emerge at some point, “unfortunately, little was done to prepare for that,” he says. Hopefully governments will learn and be better prepared for the next pandemic, he says.

Classification and structure:


Adams, C.L., Grierson, A.M., Mowat, A.M., Harnett, M.M., and Garside, P. (2004). Differences in the kinetics, amplitude, and localization of ERK activation in anergy and priming revealed at the level of individual primary T cells by laser scanning cytometry. J. Immunol., 173, 1579-1586.

Alonso, A., Rahmouni, S., Williams, S., van Stipdonk, M., Jaroszeqski, L., Godzik, A., Abraham, R.T., Schoenberg, S.P., and Mustelin, T. (2003). Tyrosine phosphorylation of VHR phosphatase by ZAP-70. Nat. Immunol., 4, 44-48.

Altman, A. and Deckert, M. (1999). The function of small GTPases in signaling by immune recognition and other leukocyte receptors. Adv. Immunol., 72, 1-99.

Antov, A., Yang, L., Vig, M., Baltimore, D., and Van Parijs, L. (2003). Essential role for STAT5 signaling in CD4+CD25+ regulatory cell homeostasis and the maintenance of self-tolerance. J. Immunol., 171, 3435-3441.

Bachmaier, K., Krawczyk, C., Kozieradzki, I., Kong, Y., Sasaki, T., Oliveira-Santos, A., Mariathasan, S., Bouchard, D., Wakeham, A., Itie, A., Le, J., Ohash-Sarosi, I., Nishina, H., Lipkowitz, S., and Penninger, J. (2000). Negative regulation of lymphocyte activation and autoimmunity molecular adaptor Cbl-b. Nature, 403, 211-216.

Blasini, A.M., Brundula, V, Paris, M., Rivas, L., Salazar, S., Stekman, I.L., and Rodríguez, M.A. (1998a). Protein tyrosine kinase in T lymphocytes from patients with systemic lupus erythe-matosus. J. Autoimmun., 11, 387-393.

Blasini, A.M., Chacon, R., Riera, R., Stekman, I.L., and Rodriguez, M.A. (1998b). Abnormal pattern of tyrosine phosphorylation in unstimulated peripheral blood T lymphocytes from patients with systemic lupus erythematosus. Lupus, 7, 515-523.

Blasini, A.M. and Rodríguez, M.A. (2004). Altered signaling triggered by ligation of CR/CD3 receptor in T lymphocytes from patients with systemic lupus erythematosus: The road from anergy to autoimmunity. Int. Rev. Immunol., 23, 265-272.

Brundula, V, Rivas, L., Blasini, A.M., Paris, M., Salazar, S., Stekman, I.L., and Rodríguez, M.A. (1999). Diminished levels of TCR Z chain in peripheral blood (PB) T lymphocytes from patients with systemic lupus erythematosus (SLE). Arthritis Rheum., 42, 1908-1916.

Cannons, J.L. and Schwartzberg, P.L. (2004). Fine-tuning lymphocyte regulation: What's new with tyrosine kinases and phosphatases? Curr. Opin. Immunol., 16, 296-303.

Carpino, N., Turner, S., Mekala, D., Takahashi, Y., Zang, H., Geiger, T.L., Doherty, P., and Ihle, J.N. (2004). Regulation of ZAP-70 activation and TCR signaling by two related proteins, Sts-1 and Sts-2. Immunity, 20, 37-46.

Cedeño, S., Cifarelli, D.F., Blasini, A.M., Paris, M., Placeres, F., Alonso, G., and Rodriguez, M.A. (2003). Defective activity of ERK-1 and ERK-2 mitogen-activated protein kinases in peripheral blood T lymphocytes from patients with systemic lupus erythematosus. Potential role of altered coupling of Ras nucleotide exchange factor hSos to adaptor protein Grb2. Clin. Immunol., 106, 41-49.

Cheng, F., Wang, H.W., Cuenca, A., Huang, M., Ghansah, T., Brayer, J., Kerr, W., Takeda, K., Akira, S., Schoenberger, S., Yu, H., Jove, R., and Sotomayor, E. (2003). A critical role for Stat3 signaling in immune tolerance. Immunity, 19, 425-436.

Chikuma, S. and Bluestone, J.A. (2002). CTLA-4: Acting at the synapse. Mol. Intervention, 2, 205-208.

Combadiere, B., Freedman, M., Chen, L., Shores, E.W., Love, P., and Lenardo, M.J. (1996). Qualitative and quantitative contributions of the T cell receptor Z chain to mature T cell apopto-sis. J. Exp. Med., 183, 2109-2117.

Cope, A.P. (2002). Studies of T-cell activation in chronic inflammation. Arthritis Res., 4, S197-S211.

Curtsinger, J.M., Lins, D.C., and Mescher, M. (2003). Signal 3 determines tolerance versus full activation of naive CD8 T cells: Dissociating proliferation and development of effector function. J. Exp. Med., 197, 1141-1151.

Datta, S.K. (2003). Major peptide autoepitopes for nucleosome-centered T and B cell interaction in human and murine lupus. Ann. NY Acad. Sci., 987, 79-90.

De Lafaille, M.A.C. and Lafaille, J.J. (2002). CD4+ regulatory T cells in autoimmunity and allergy. Curr. Opin. Immunol., 14, 771-778.

Deng, C., Lu, Q., Zhang, Z., Rao, T., Attwood, J., Yung, R., and Richardson, B. (2003). Hydralazine may induce autoimmunity by inhibiting extracellular-regulated signal-regulated kinase pathway signaling. Arthritis Rheum., 48, 746-756.

Emlen, W., Niebur, J., and Richard, K. (1994). Accelerated in vitro apoptosis of lymphocytes from patients with systemic lupus erythematosus. J. Immunol., 152, 3685-3692.

Fields, P.E., Gajewski, T.F., and Fitch, F.W (1996). Blocked Ras activation in anergic CD4+ T cells. Science, 271, 1276-1278.

Fujii, S., Liu, K., Smith, C., Bonito, A., and Steinman, R. (2004). The linkage of innate to adaptive immunity via maturing dendritic cells in vivo requires CD40 ligation in addition to antigen presentation and CD80/86 costimulation. J. Exp. Med., 199, 1607-1618.

Graninger, W.B., Steiner, C.W., Graninger, M.T., Aringer, M., and Smolen, J.S. (2000). Cytokine regulation of apoptosis and Bcl-2 expression in lymphocytes of patients with systemic lupus ery-thematosus. Cell Death Differ., 7, 966-972.

Gringhuis, S., Leow, A., Papendrecht-van der Voort, E., Remans, P., Breedveld, F., and Verweij, C. (2000). Displacement of linker for activation of T cells from the plasma membrane due to redox balance alterations results in hyporesponsiveness of synovial fluid T lymphocytes in rheumatoid arthritis. J. Immunol., 164, 2170-2179.

Hilliard, B.A., Mason, N., Xu, L., Sun, J., Lamhamedi-Cherradi, S.E., Liou, H.C., Hunter, C., and Chen, Y.H. (2002). Critical roles of c-Rel in autoimmune inflammation and helper T cell differentiation. J. Clin. Invest., 110, 843-850.

Hron, J.D., Caplan, L., Gerth, A.J., Schwartzberg, P.L., and Peng, S.L. (2004). SH2D1A regulates T-dependent humoral autoimmunity. J. Exp. Med., 200, 261-266.

Jacobelli, J., Andres, P.G., Boisvert, J., and Krummel, M.F. (2004). New views of the immunological synapse: Variations in assembly and function. Curr. Opin. Immunol., 16, 345-352.

Jury, E.C., Kabouridis, P.S., Flores-Boija, F., Mageed, R.A., and Isenberg, D.A. (2004). Altered lipid raft-associated signaling and ganglioside expression in T lymphocytes from patients with systemic lupus erythematosus. J. Clin. Invest., 113, 176-187.

Kammer, G.M., Laxminarayana, D., and Khan, I.U. (2004). Mechanisms of deficient type I protein kinase A activity in lupus T lymphocytes. Int. Rev. Immunol., 23, 225-244.

Komai-Koma, M., Jones, L., Ogg, G., Xu, D., and Liew, F. (2004). TLR2 is expressed on activated T cells as a costimulatory receptor. Proc. Natl. Acad. Sci. USA, 101, 3029-3034.

Kowanetz, K., Cresette, N., Haglund, K., Schmidt, M.H., Heldin, C.H., and Dikie, I. (2004). Suppressors of T-cell receptor signaling sts-1 and sts-2 bind to cbl and inhibit endocytosis of receptor tyrosine kinases. J. Biol. Chem., 279, 32786-32795.

Krawczyk, C., Bachmaier, K., Sasaki, T., Jones, G.R., Snapper, B.S., Bouchard, D., Kozieradzki, I., Ohashi, S.P., Alt, W.F., and Penninger, M.J. (2000). Cbl-b is a negative regulator of receptor clustering and raft aggregation in T cells. Immunity, 13, 463-473.

Krishnan, S., Nambiar, M.P., Warke, V.G., Fisher, C.U., Mitcell, J., Delaney, N., and Tsokos, G.C. (2004). Alterations in lipid raft composition and dynamics contribute to abnormal T cell responses in systemic lupus erythematosus. J. Immunol., 172, 7821-7831.

Lawson, B.R., Baccala, R., Song, J., Croft, M., Kono, D.H., and Theofilopoulos, A.N. (2004). Deficiency of cyclin kinase inhibitor p21 (WAF-1/CIP-1) promotes apoptosis of activated/memory T cells and inhibits spontaneous systemic autoimmunity. J. Exp. Med., 199, 547-557.

Lee, K.M., Chuang, E., Griffin, M., Khattri, R., Hong, D.K., Zhang, W., Straus, D., Samelson, L., Thompson, C., and Bluestone, J. (1998). Molecular basis of T cell inactivation by CTLA-4. Science, 282, 2263-2266.

Leo, A., Wienands, J., Baier, G., Horejsi, V, and Schraven, B. (2002). Adapters in lymphocyte signaling. J. Clin. Invest., 109, 301-309.

Li, W., Whaley, C.D., Mondino, A., and Mueller, D.L. (1996). Blocked signal transduction to the ERK and JNK protein kinases in anergic CD4+ T cells. Science, 271, 1272-1275.

Liossis, S.N.C., Ding, X.Z., Dennis, G.J., and Tsokos, G.C. (1998). Altered pattern of TCR/CD3-mediated protein-tyrosyl phosphorylation in T cells from patients with systemic lupus erythematosus. Deficient expression of the T cell receptor y chain. J Clin. Invest., 101, 1448-1457.

Liu, B., Dai, J., Zheng, H., Stoilova, D., Sun, S., and Li, Z. (2003). Cell surface expression of an endo-plasmic reticulum resident heat shock protein gp96 triggers Myd88-dependent systemic autoimmune diseases. Proc. Natl. Acad. Sci. USA, 100, 15842-15829.

Liu, M.F., Wang, C.R., Fung, L.L., and Wu, C.R. (2004). Decreased CD4+CD25+ T cells in peripheral blood of patients with systemic lupus erythematosus. Scand. J. Immunol., 59, 198-202.

Ludviksson, B.R., Gray, B., Strober, W., and Ehrhardt, R.O. (1997). Dysregulated intra-thymic development in the IL-2-deficient mouse leads to colitis-inducing thymocytes. J. Immunol., 158, 104-111.

Malek, T.R., Porter, B.O., Codias, E.K., Sciberlli, P., and Yu, A. (2000). Normal lymphoid homeosta-sis and lack of lethal autoimmunity in mice containing mature T cells with severely impaired IL-2 receptors. J. Immunol., 15, 2905-2914.

Matache, M., Stefanescu, M., Onu, A., Szegli, G, Barel, M., Tanseanu, S., Matei, I., Boullie, S., and Frade, R. (1996). Tyrosine phosphorylation in peripheral lymphocytes from patients with systemic lupus erythematosus. Autoimmunity, 24, 217-228.

Millar, D.G., Garza, K.M., Odermat, B., Elford, A.R., Ono, N., Li, Z., and Ohashi, P.S. (2003). Hsp 70 promotes antigen-presenting cell function and converts cell tolerance to autoimmunity in vivo. Nat. Med., 9, 1469-1476.

Mirshahidi, S., Ferris, L.C., and Sadegh-Nasseri, S. (2004). The magnitude of TCR engagement is a critical predictor of T cell anergy or activation. J. Immunol., 172, 5346-5355.

Moody, J.L. and Jirik, F.R. (2004). Compound heterozygosity for Pten and SHIP augments T-cell dependent humoral immune responses and cytokine production by CD4+ T cells. Immunology, 112, 404-412.

Moretta, A. and Bottino, C. (2004). Regulated equilibrium between opposite signals: A general paradigm for T cell function? Eur. J. Immunol., 34, 2084-2088.

Mustelin, T., Alonso, A., Bottini, N., Huynh, H., Rahmouni, S., Nika, K., Louis-dit-Sully, C., Tautz, L., Togo, S., Bruckner, S, Mena-Duran, A., and al-Khouri, A.M. (2004). Protein tyrosine phosphatases in T cell physiology. Mol. Immunol., 41, 687-700.

Nishibori, T., Tanabe, Y., Su, L., and David, M. (2004). Impaired development of CD4+CD25+ regulatory T cells in the absence of STA1: Increased susceptibility to autoimmune disease. J. Exp. Med., 199, 25-34.

Oelke, K. and Richardson, B. (2004). Decreased T cell ERK pathway signaling may contribute to the development of lupus through effects on DNA methylation and gene expression. Int. Rev. Immunol., 23, 315-331.

Ohashi, P.S. and DeFranco, A.L. (2002). Making and breaking tolerance. Curr. Opin. Immunol., 14, 744-759.

Pugliese, A. (2003). Peptide-based treatment for autoimmune diseases: Learning how to handle a double-edge sword. J. Clin. Invest., 111, 1280-1282.

Quaratino, S., Duddy, L., and Londei, M. (2000). Fully competent dendritic cells as inducers of T cell anergy in autoimmunity. Proc. Natl. Acad. Sci. USA, 97, 10911-10916.

Ramsdell, F. and Ziegler, S.F. (2003). Transcription factors in autoimmunity. Curr. Opin. Immunol., 15, 718-724.

Rathemell, J.C., Elstrom, R.L., Cinalli, R.M., and Thompson, C.B. (2003). Activated Akt promotes increased resting T cell size, CD28-independent T cell growth, and development of autoimmu-nity and lymphoma. Eur. J. Immunol., 33, 2223-2232.

Sakaguchi, N., Takahashi, T., Hata, H., Nomura, T., Tagami, T., Yamazaki, S., Sahikama, T., Matsutani, T., Negishi, I., Nakatsuru, S., and Sakaguchi, S. (2003). Altered thymic T-cell selection due to a mutation of the ZAP-70 gene causes autoimmune arthritis in mice. Nature, 426, 454-460.

Salojin, K.V, Zhang, J., Cameron, M., Gill, B., Arreaza, G., Ochi, A., and Delovitch, T.L. (1997). Impaired plasma membrane targeting of Grb2-murine son of sevenless (mSos) complex and differential activation of the Fyn-T cell receptor (TCR)-Z-Cbl pathway mediate T cell hyporespon-siveness in autoimmune nonobese diabetic mice. J. Clin. Invest., 186, 887-897.

Salojin, K.V, Zhang, J., Madrenas, J., and Delovitch, T.L. (1998). T-cell anergy and altered T-cell receptor signaling: Effects on autoimmune disease. Immunol. Today, 19, 468-473.

Schade, A.E. and Levine, A.D. (2004). Cutting edge: Extracellular signal-regulated kinases 1/2 function as integrators of TCR signal strength. J. Immunol., 172, 5828-5832.

Scheinecker, C., Zwölfer, B., Köller, M., Männer, G., and Smolen, J.S. (2000). Alterations of dendritic cells in systemic lupus erythematosus. Arthritis Rheum., 44, 856-865.

Seibl, R., Kyburz, D., Lauener, R.P., and Gay, S. (2004). Pattern recognition receptors and their involvement in the pathogenesis of arthritis. Curr. Opin. Rheumatol., 16, 411-418.

Shevach, E.M. (2000). Regulatory T cells in autoimmunity. Annu. Rev. Immunol., 18, 423-449.

Sloan-Lancaster, J. and Allen, P. (1996). Altered peptide ligand-induced partial T cell activation: Molecular mechanisms and role in T cell biology. Annu. Rev. Immunol., 14, 1-27.

Snow, J.W., Abraham, N., Na, M.C., Herndier, B.G., Pastuszak, A.W., and Goldsmith, M.A. (2003). Loss of tolerance and autoimmunity affecting multiple organ STAT5A/5B-deficient mice. J. Immunol., 171, 5042-5050.

Solomou, E.E., Juang, Y.T., Gourley, M.F., Kammer, G.M., and Tsokos, G.C. (2001). Molecular basis of deficient IL-2 production in T cells from patients with systemic lupus erythematosus. J. Immunol., 166, 4216-4222.

Stefanova, I., Hemmer, B., Vergami, M., Martin, R., Biddison, W.E., and Germain, R.N. (2003). TCR ligand discrimination is enforced by competing ERK positive and SHP-1 negative feedback pathways. Nat. Immunol., 4, 248-254.

Suzuki, H., Kundig, T.M., Furlonger, C., Wakeman, A., Timms, E., Matsuyama, T., Schmits, R., Simard, J.J., Ohashi, P.S., Griesser, H., Taniguchi, T., Paige, C.J., and Mak, T.W. (1995). Deregulated T cell activation and autoimmunity in mice lacking interleukin-2 receptor ß. Science, 9, 1472-1476.

Takahashi, T., Tanagmi, T., Yamazaki, S., Uede, T., Shimizu, J., Sakaguchi, N., Mak, T, and Sakaguchi, S. (2000). Immunological self-tolerance maintained by CD4+CD25+ regulatory T cells constitu-tively expressing cytotoxic T lymphocyte-associated antigen 4. J. Exp. Med., 192, 303-310.

Thomas, R. (2004). Signal 3 and its role in autoimmunity. Arthritis Res. Ther., 6, 26-27.

Thomas, S., Preda-Pais, A., Casares, S., and Brumeanu, T.D. (2004). Analysis of lipid rafts in T cells. Mol. Immunol., 41, 399-409.

Treisman, R. (1996). Regulation of transcription by MAP kinase cascades. Curr. Opin. Cell Biol., 8, 205-215.

Tsokos, G.C., Nambiar, M.P., Tenbrock, K., and Juang, Y.T. (2003). Rewiring the T-cell: Signaling defects and novel prospects for the treatment of SLE. Trends Immunol., 24, 259-263.

Turley, S.J. (2002). Dendritic cells: Inciting and inhibiting autoimmunity. Curr. Opin. Immunol., 14, 765-770.

Ueda, H., Howson, J.M.M., Esposito, L., Heward, J., Snook, H., Chamberlain, G., Rainbow, D., Hunter, K., Smith, A., Di Genova, G., Herr, M., Dahlman, I., Payne, F., Smyth, D., Lowe, C., Twells, R., Howlett, S., Healy, B., Nutland, S., Rance, H., Everett, V., Smink, L., Lam, A., Cordell, H., Walker, N., Bordin, C., Hulme, J., Motzo, C., Cucca, F., Hess, J., Metzker, M., Rogers, J., Gregory, S., Allahabadia, A., Nithiyananthan, R., Tuomilehto-Wolf, E., Tuomilheto, J., Bingley, P., Gillespie, K., Undlien, D., Ronningen, K., Guja, C., Ionescu-Tirgoviste, C., Savage, D., Maxwell, A., Carson, D., Patterson, C., Franklyn, J., Clayton, D., Peterson, L., Wicker, L., Todd, J., and Gough, S. (2003). Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease. Nature, 423, 506-511.

Veillete, A. (2004). SLAM family receptors regulate immunity with and without SAP-related adaptors. J. Exp. Med., 199, 1175-1178.

Waldner, H., Collins, M., and Kuchroo, VK. (2004). Activation of antigen-presenting cells by microbial products breaks self tolerance and induces autoimmune disease. J. Clin. Invest., 113, 990-997.

Williams, N. (1996). T cell inactivation linked to Ras block. Science, 271, 1234.


1. Dugger BN, Dickson DW. Pathology of Neurodegenerative Diseases. Cold Spring Harb Perspect Biol (2017) 9. doi:ꀐ.1101/cshperspect.a028035

2. Erkkinen MG, Kim M-O, Geschwind MD. Clinical Neurology and Epidemiology of the Major Neurodegenerative Diseases. Cold Spring Harb Perspect Biol (2018) 10. doi:ꀐ.1101/cshperspect.a033118

3. GBD 2016 Neurology Collaborators. Global, Regional, and National Burden of Neurological Disorders, 1990-2016: A Systematic Analysis for the Global Burden of Disease Study 2016. Lancet Neurol (2019) 18:459�. doi:ꀐ.1016/S1474-4422(18)30499-X

4. Verkhratsky A, Parpura V, Pekna M, Pekny M, Sofroniew M. Glia in the Pathogenesis of Neurodegenerative Diseases. Biochem Soc Trans (2014) 42:1291�. doi:ꀐ.1042/BST20140107

5. Phatnani H, Maniatis T. Astrocytes in Neurodegenerative Disease. Cold Spring Harb Perspect Biol (2015) 7. doi:ꀐ.1101/cshperspect.a020628

6. Allen NJ, Eroglu C. Cell Biology of Astrocyte-Synapse Interactions. Neuron (2017) 96:697�. doi:ꀐ.1016/j.neuron.2017.09.056

7. Oberheim NA, Takano T, Han X, He W, Lin JHC, Wang F, et al. Uniquely Hominid Features of Adult Human Astrocytes. J Neurosci Off J Soc Neurosci (2009) 29:3276�. doi:ꀐ.1523/JNEUROSCI.4707-08.2009

8. Kimelberg HK. Supportive or Information-Processing Functions of the Mature Protoplasmic Astrocyte in the Mammalian CNS? A Critical Appraisal. Neuron Glia Biol (2007) 3:181𠄹. doi:ꀐ.1017/S1740925X08000094

9. Vasile F, Dossi E, Rouach N. Human Astrocytes: Structure and Functions in the Healthy Brain. Brain Struct Funct (2017) 222:2017�. doi:ꀐ.1007/s00429-017-1383-5

10. Haim LB, Rowitch DH. Functional Diversity of Astrocytes in Neural Circuit Regulation. Nat Rev Neurosci (2017) 18:31�. doi:ꀐ.1038/nrn.2016.159

11. Sofroniew MV, Vinters HV. Astrocytes: Biology and Pathology. Acta Neuropathol (Berl) (2010) 119:7�. doi:ꀐ.1007/s00401-009-0619-8

12. Yang Y, Vidensky S, Jin L, Jie C, Lorenzini I, Frankl M, et al. Molecular Comparison of GLT1+ and ALDH1L1+ Astrocytes In Vivo in Astroglial Reporter Mice. Glia (2011) 59:200𠄷. doi:ꀐ.1002/glia.21089

13. Lin C-CCJ, Yu K, Hatcher A, Huang T-W, Lee HK, Carlson J, et al. Identification of Diverse Astrocyte Populations and Their Malignant Analogs. Nat Neurosci (2017) 20:396�. doi:ꀐ.1038/nn.4493

14. Batiuk MY, Martirosyan A, Wahis J, de Vin F, Marneffe C, Kusserow C, et al. Identification of Region-Specific Astrocyte Subtypes At Single Cell Resolution. Nat Commun (2020) 11:1220. doi:ꀐ.1038/s41467-019-14198-8

15. Zeisel A, Hochgerner H, Lönnerberg P, Johnsson A, Memic F, van der Zwan J, et al. Molecular Architecture of the Mouse Nervous System. Cell (2018) 174:999�.e22. doi:ꀐ.1016/j.cell.2018.06.021

16. Molofsky AV, Deneen B. Astrocyte Development: A Guide for the Perplexed. Glia (2015) 63:1320𠄹. doi:ꀐ.1002/glia.22836

17. Fossati G, Matteoli M, Menna E. Astrocytic Factors Controlling Synaptogenesis: A Team Play. Cells (2020) 9. doi:ꀐ.3390/cells9102173

18. Araque A, Parpura V, Sanzgiri RP, Haydon PG. Tripartite Synapses: Glia, the Unacknowledged Partner. Trends Neurosci (1999) 22:208�. doi:ꀐ.1016/s0166-2236(98)01349-6

19. Daneman R, Prat A. The Blood-Brain Barrier. Cold Spring Harb Perspect Biol (2015) 7:a020412. doi:ꀐ.1101/cshperspect.a020412

20. Iadecola C. The Neurovascular Unit Coming of Age: A Journey Through Neurovascular Coupling in Health and Disease. Neuron (2017) 96:17�. doi:ꀐ.1016/j.neuron.2017.07.030

21. Iliff JJ, Wang M, Liao Y, Plogg BA, Peng W, Gundersen GA, et al. A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med (2012) 4:147ra111. doi:ꀐ.1126/scitranslmed.3003748

22. Jessen NA, Munk ASF, Lundgaard I, Nedergaard M. The Glymphatic System: A Beginner’s Guide. Neurochem Res (2015) 40:2583�. doi:ꀐ.1007/s11064-015-1581-6

23. Anderson MA, Ao Y, Sofroniew MV. Heterogeneity of Reactive Astrocytes. Neurosci Lett (2014) 565:23𠄹. doi:ꀐ.1016/j.neulet.2013.12.030

24. Liddelow SA, Guttenplan KA, Clarke LE, Bennett FC, Bohlen CJ, Schirmer L, et al. Neurotoxic Reactive Astrocytes are Induced by Activated Microglia. Nature (2017) 541:481𠄷. doi:ꀐ.1038/nature21029

25. Rothhammer V, Quintana FJ. Control of Autoimmune CNS Inflammation by Astrocytes. Semin Immunopathol (2015) 37:625�. doi:ꀐ.1007/s00281-015-0515-3

26. Morita M, Ikeshima-Kataoka H, Kreft M, Vardjan N, Zorec R, Noda M. Metabolic Plasticity of Astrocytes and Aging of the Brain. Int J Mol Sci (2019) 20. doi:ꀐ.3390/ijms20040941

27. Dienel GA. Brain Glucose Metabolism: Integration of Energetics With Function. Physiol Rev (2019) 99:949�. doi:ꀐ.1152/physrev.00062.2017

28. Dienel GA, Rothman DL. Reevaluation of Astrocyte-Neuron Energy Metabolism With Astrocyte Volume Fraction Correction: Impact on Cellular Glucose Oxidation Rates, Glutamate-Glutamine Cycle Energetics, Glycogen Levels and Utilization Rates vs. Exercising Muscle, and Na+/K+ Pumping Rates. Neurochem Res (2020) 45:2607�. doi:ꀐ.1007/s11064-020-03125-9

29. Bélanger M, Allaman I, Magistretti PJ. Brain Energy Metabolism: Focus on Astrocyte-Neuron Metabolic Cooperation. Cell Metab (2011) 14:724�. doi:ꀐ.1016/j.cmet.2011.08.016

30. Love S, Miners JS. Cerebrovascular Disease in Ageing and Alzheimer’s Disease. Acta Neuropathol (Berl) (2016) 131:645�. doi:ꀐ.1007/s00401-015-1522-0

31. Carmignoto G, Gómez-Gonzalo M. The Contribution of Astrocyte Signalling to Neurovascular Coupling. Brain Res Rev (2010) 63:138�. doi:ꀐ.1016/j.brainresrev.2009.11.007

32. Kisler K, Nelson AR, Montagne A, Zlokovic BV. Cerebral Blood Flow Regulation and Neurovascular Dysfunction in Alzheimer Disease. Nat Rev Neurosci (2017) 18:419�. doi:ꀐ.1038/nrn.2017.48

33. Michels L, Warnock G, Buck A, Macauda G, Leh SE, Kaelin AM, et al. Arterial Spin Labeling Imaging Reveals Widespread and Aβ-Independent Reductions in Cerebral Blood Flow in Elderly Apolipoprotein Epsilon-4 Carriers. J Cereb Blood Flow Metab Off J Int Soc Cereb Blood Flow Metab (2016) 36:581�. doi:ꀐ.1177/0271678X15605847

34. Sperling RA, Bates JF, Chua EF, Cocchiarella AJ, Rentz DM, Rosen BR, et al. fMRI Studies of Associative Encoding in Young and Elderly Controls and Mild Alzheimer’s Disease. J Neurol Neurosurg Psychiatry (2003) 74:44�. doi:ꀐ.1136/jnnp.74.1.44

35. Borghammer P, Chakravarty M, Jonsdottir KY, Sato N, Matsuda H, Ito K, et al. Cortical Hypometabolism and Hypoperfusion in Parkinson’s Disease is Extensive: Probably Even At Early Disease Stages. Brain Struct Funct (2010) 214:303�. doi:ꀐ.1007/s00429-010-0246-0

36. Guan J, Pavlovic D, Dalkie N, Waldvogel HJ, O�rroll SJ, Green CR, et al. Vascular Degeneration in Parkinson’s Disease. Brain Pathol Zurich Switz (2013) 23:154�. doi:ꀐ.1111/j.1750-3639.2012.00628.x

37. Rosengarten B, Dannhardt V, Burr O, Pöhler M, Rosengarten S, Oechsner M, et al. Neurovascular Coupling in Parkinson’s Disease Patients: Effects of Dementia and Acetylcholinesterase Inhibitor Treatment. J Alzheimers Dis JAD (2010) 22:415�. doi:ꀐ.3233/JAD-2010-101140

38. Camandola S, Mattson MP. Brain Metabolism in Health, Aging, and Neurodegeneration. EMBO J (2017) 36:1474�. doi:ꀐ.15252/embj.201695810

39. Lee H, Pienaar IS. Disruption of the Blood-Brain Barrier in Parkinson’s Disease: Curse or Route to a Cure? Front Biosci Landmark Ed (2014) 19:272�. doi:ꀐ.2741/4206

40. Carvey PM, Zhao CH, Hendey B, Lum H, Trachtenberg J, Desai BS, et al. 6-Hydroxydopamine-induced Alterations in Blood-Brain Barrier Permeability. Eur J Neurosci (2005) 22:1158�. doi:ꀐ.1111/j.1460-9568.2005.04281.x

41. Reeves BC, Karimy JK, Kundishora AJ, Mestre H, Cerci HM, Matouk C, et al. Glymphatic System Impairment in Alzheimer’s Disease and Idiopathic Normal Pressure Hydrocephalus. Trends Mol Med (2020) 26:285�. doi:ꀐ.1016/j.molmed.2019.11.008

42. Keir LHM, Breen DP. New Awakenings: Current Understanding of Sleep Dysfunction and its Treatment in Parkinson’s Disease. J Neurol (2020) 267:288�. doi:ꀐ.1007/s00415-019-09651-z

43. Pierre K, Pellerin L. Monocarboxylate Transporters in the Central Nervous System: Distribution, Regulation and Function. J Neurochem (2005) 94:1�. doi:ꀐ.1111/j.1471-4159.2005.03168.x

44. Pellerin L, Magistretti PJ. Glutamate Uptake Into Astrocytes Stimulates Aerobic Glycolysis: A Mechanism Coupling Neuronal Activity to Glucose Utilization. Proc Natl Acad Sci USA (1994) 91:10625𠄹. doi:ꀐ.1073/pnas.91.22.10625

45. Li B, Freeman RD. Neurometabolic Coupling Between Neural Activity, Glucose, and Lactate in Activated Visual Cortex. J Neurochem (2015) 135:742�. doi:ꀐ.1111/jnc.13143

46. Magistretti PJ, Chatton J-Y. Relationship Between L-Glutamate-Regulated Intracellular Na+ Dynamics and ATP Hydrolysis in Astrocytes. J Neural Transm Vienna Austria (2005) 1996:112. doi:ꀐ.1007/s00702-004-0171-6

47. Pellerin L, Bouzier-Sore A-K, Aubert A, Serres S, Merle M, Costalat R, et al. Activity-Dependent Regulation of Energy Metabolism by Astrocytes: An Update. Glia (2007) 55:1251�. doi:ꀐ.1002/glia.20528

48. Magistretti PJ. Role of Glutamate in Neuron-Glia Metabolic Coupling. Am J Clin Nutr (2009) 90:875S�S. doi:ꀐ.3945/ajcn.2009.27462CC

49. Marcus C, Mena E, Subramaniam RM. Brain PET in the Diagnosis of Alzheimer’s Disease. Clin Nucl Med (2014) 39:e413� quiz e423-426. doi:ꀐ.1097/RLU.0000000000000547

50. Vlassenko AG, Gordon BA, Goyal MS, Su Y, Blazey TM, Durbin TJ, et al. Aerobic Glycolysis and Tau Deposition in Preclinical Alzheimer’s Disease. Neurobiol Aging (2018) 67:95𠄸. doi:ꀐ.1016/j.neurobiolaging.2018.03.014

51. Bell SM, Burgess T, Lee J, Blackburn DJ, Allen SP, Mortiboys H. Peripheral Glycolysis in Neurodegenerative Diseases. Int J Mol Sci (2020) 21. doi:ꀐ.3390/ijms21238924

52. Meles SK, Renken RJ, Pagani M, Teune LK, Arnaldi D, Morbelli S, et al. Abnormal Pattern of Brain Glucose Metabolism in Parkinson’s Disease: Replication in Three European Cohorts. Eur J Nucl Med Mol Imaging (2020) 47:437�. doi:ꀐ.1007/s00259-019-04570-7

53. Leke R, Schousboe A. The Glutamine Transporters and Their Role in the Glutamate/GABA-Glutamine Cycle. Adv Neurobiol (2016) 13:223�. doi:ꀐ.1007/978-3-319-45096-4_8

54. Anlauf E, Derouiche A. Glutamine Synthetase as an Astrocytic Marker: its Cell Type and Vesicle Localization. Front Endocrinol (2013) 4:144. doi:ꀐ.3389/fendo.2013.00144

55. Rothman DL, De Feyter HM, de Graaf RA, Mason GF, Behar KL. 13c MRS Studies of Neuroenergetics and Neurotransmitter Cycling in Humans. NMR BioMed (2011) 24:943�. doi:ꀐ.1002/nbm.1772

56. Hertz L, Chen Y. Integration Between Glycolysis and Glutamate-Glutamine Cycle Flux may Explain Preferential Glycolytic Increase During Brain Activation, Requiring Glutamate. Front Integr Neurosci (2017) 11:18. doi:ꀐ.3389/fnint.2017.00018

57. Huang S, Tong H, Lei M, Zhou M, Guo W, Li G, et al. Astrocytic Glutamatergic Transporters are Involved in Aβ-Induced Synaptic Dysfunction. Brain Res (2018) 1678:129�. doi:ꀐ.1016/j.brainres.2017.10.011

58. Conway ME. Alzheimer’s Disease: Targeting the Glutamatergic System. Biogerontology (2020) 21:257�. doi:ꀐ.1007/s10522-020-09860-4

59. Iovino L, Tremblay ME, Civiero L. Glutamate-Induced Excitotoxicity in Parkinson’s Disease: The Role of Glial Cells. J Pharmacol Sci (2020) 144:151�. doi:ꀐ.1016/j.jphs.2020.07.011

60. Ioannou MS, Jackson J, Sheu S-H, Chang C-L, Weigel AV, Liu H, et al. Neuron-Astrocyte Metabolic Coupling Protects Against Activity-Induced Fatty Acid Toxicity. Cell (2019) 177:1522�. doi:ꀐ.1016/j.cell.2019.04.001

61. Qi G, Mi Y, Shi X, Gu H, Brinton RD, Yin F. Apoe4 Impairs Neuron-Astrocyte Coupling of Fatty Acid Metabolism. Cell Rep (2021) 34:108572. doi:ꀐ.1016/j.celrep.2020.108572

62. Castagnet PI, Golovko MY, Barceló-Coblijn GC, Nussbaum RL, Murphy EJ. Fatty Acid Incorporation is Decreased in Astrocytes Cultured From Alpha-Synuclein Gene-Ablated Mice. J Neurochem (2005) 94:839�. doi:ꀐ.1111/j.1471-4159.2005.03247.x

63. DiNuzzo M, Schousboe A. Brain Glycogen Metabolism. Cham, Switzerland: Springer International Publishing (2019). doi:ꀐ.1007/978-3-030-27480-1

64. Rahman B, Kussmaul L, Hamprecht B, Dringen R. Glycogen is Mobilized During the Disposal of Peroxides by Cultured Astroglial Cells From Rat Brain. Neurosci Lett (2000) 290:169�. doi:ꀐ.1016/s0304-3940(00)01369-0

65. Suzuki A, Stern SA, Bozdagi O, Huntley GW, Walker RH, Magistretti PJ, et al. Astrocyte-Neuron Lactate Transport is Required for Long-Term Memory Formation. Cell (2011) 144:810�. doi:ꀐ.1016/j.cell.2011.02.018

66. Bak LK, Walls AB, Schousboe A, Waagepetersen HS. Astrocytic Glycogen Metabolism in the Healthy and Diseased Brain. J Biol Chem (2018) 293:7108�. doi:ꀐ.1074/jbc.R117.803239

67. Gannon M, Che P, Chen Y, Jiao K, Roberson ED, Wang Q. Noradrenergic Dysfunction in Alzheimer’s Disease. Front Neurosci (2015) 9:220. doi:ꀐ.3389/fnins.2015.00220

68. Dringen R. Metabolism and Functions of Glutathione in Brain. Prog Neurobiol (2000) 62:649�. doi:ꀐ.1016/s0301-0082(99)00060-x

69. Scheiber IF, Mercer JFB, Dringen R. Metabolism and Functions of Copper in Brain. Prog Neurobiol (2014) 116:33�. doi:ꀐ.1016/j.pneurobio.2014.01.002

70. Rizor A, Pajarillo E, Johnson J, Aschner M, Lee E. Astrocytic Oxidative/Nitrosative Stress Contributes to Parkinson’s Disease Pathogenesis: The Dual Role of Reactive Astrocytes. Antioxid Basel Switz (2019) 8. doi:ꀐ.3390/antiox8080265

71. Dunn L, Allen GF, Mamais A, Ling H, Li A, Duberley KE, et al. Dysregulation of Glucose Metabolism is an Early Event in Sporadic Parkinson’s Disease. Neurobiol Aging (2014) 35:1111𠄵. doi:ꀐ.1016/j.neurobiolaging.2013.11.001

72. Sian J, Dexter DT, Lees AJ, Daniel S, Agid Y, Javoy-Agid F, et al. Alterations in Glutathione Levels in Parkinson’s Disease and Other Neurodegenerative Disorders Affecting Basal Ganglia. Ann Neurol (1994) 36:348�. doi:ꀐ.1002/ana.410360305

73. Baillet A, Chanteperdrix V, Trocmé C, Casez P, Garrel C, Besson G. The Role of Oxidative Stress in Amyotrophic Lateral Sclerosis and Parkinson’s Disease. Neurochem Res (2010) 35:1530𠄷. doi:ꀐ.1007/s11064-010-0212-5

74. Kim GH, Kim JE, Rhie SJ, Yoon S. The Role of Oxidative Stress in Neurodegenerative Diseases. Exp Neurobiol (2015) 24:325�. doi:ꀐ.5607/en.2015.24.4.325

75. Bandopadhyay R, Kingsbury AE, Cookson MR, Reid AR, Evans IM, Hope AD, et al. The Expression of DJ-1 (PARK7) in Normal Human CNS and Idiopathic Parkinson’s Disease. Brain J Neurol (2004) 127:420�. doi:ꀐ.1093/brain/awh054

76. Booth HDE, Hirst WD, Wade-Martins R. The Role of Astrocyte Dysfunction in Parkinson’s Disease Pathogenesis. Trends Neurosci (2017) 40:358�. doi:ꀐ.1016/j.tins.2017.04.001

77. Allaman I, Bélanger M, Magistretti PJ. Astrocyte-Neuron Metabolic Relationships: for Better and for Worse. Trends Neurosci (2011) 34:76�. doi:ꀐ.1016/j.tins.2010.12.001

78. Vicente-Gutierrez C, Bonora N, Bobo-Jimenez V, Jimenez-Blasco D, Lopez-Fabuel I, Fernandez E, et al. Astrocytic Mitochondrial ROS Modulate Brain Metabolism and Mouse Behaviour. Nat Metab (2019) 1:201�. doi:ꀐ.1038/s42255-018-0031-6

79. Vallerga CL, Zhang F, Fowdar J, McRae AF, Qi T, Nabais MF, et al. Analysis of DNA Methylation Associates the Cystine-Glutamate Antiporter SLC7A11 With Risk of Parkinson’s Disease. Nat Commun (2020) 11:1238. doi:ꀐ.1038/s41467-020-15065-7

80. Khodagholi F, Shaerzadeh F, Montazeri F. Mitochondrial Aconitase in Neurodegenerative Disorders: Role of a Metabolism- Related Molecule in Neurodegeneration. Curr Drug Targets (2018) 19:973�. doi:ꀐ.2174/1389450118666170816124203

81. Chen H, Denton TT, Xu H, Calingasan N, Beal MF, Gibson GE. Reductions in the Mitochondrial Enzyme α-Ketoglutarate Dehydrogenase Complex in Neurodegenerative Disease - Beneficial or Detrimental? J Neurochem (2016) 139:823�. doi:ꀐ.1111/jnc.13836

82. Gibson GE, Starkov A, Blass JP, Ratan RR, Beal MF. Cause and Consequence: Mitochondrial Dysfunction Initiates and Propagates Neuronal Dysfunction, Neuronal Death and Behavioral Abnormalities in Age-Associated Neurodegenerative Diseases. Biochim Biophys Acta (2010) 1802:122�. doi:ꀐ.1016/j.bbadis.2009.08.010

83. Fernandez E, Bolaños JP. α-Ketoglutarate Dehydrogenase Complex Moonlighting: ROS Signalling Added to the List: An Editorial Highlight for “Reductions in the Mitochondrial Enzyme α-Ketoglutarate Dehydrogenase Complex in Neurodegenerative Disease - Beneficial or Detrimental?” J Neurochem (2016) 139:689�. doi:ꀐ.1111/jnc.13862

84. Gibson GE, Kingsbury AE, Xu H, Lindsay JG, Daniel S, Foster OJF, et al. Deficits in a Tricarboxylic Acid Cycle Enzyme in Brains From Patients With Parkinson’s Disease. Neurochem Int (2003) 43:129�. doi:ꀐ.1016/s0197-0186(02)00225-5

85. Bubber P, Haroutunian V, Fisch G, Blass JP, Gibson GE. Mitochondrial Abnormalities in Alzheimer Brain: Mechanistic Implications. Ann Neurol (2005) 57:695�. doi:ꀐ.1002/ana.20474

86. Tufekci KU, Civi Bayin E, Genc S, Genc K. The Nrf2/ARE Pathway: A Promising Target to Counteract Mitochondrial Dysfunction in Parkinson’s Disease. Park Dis (2011) 2011:314082. doi:ꀐ.4061/2011/314082

87. Stewart VC, Land JM, Clark JB, Heales SJ. Comparison of Mitochondrial Respiratory Chain Enzyme Activities in Rodent Astrocytes and Neurones and a Human Astrocytoma Cell Line. Neurosci Lett (1998) 247:201𠄳. doi:ꀐ.1016/s0304-3940(98)00284-5

88. Lopez-Fabuel I, Le Douce J, Logan A, James AM, Bonvento G, Murphy MP, et al. Complex I Assembly Into Supercomplexes Determines Differential Mitochondrial ROS Production in Neurons and Astrocytes. Proc Natl Acad Sci USA (2016) 113:13063𠄸. doi:ꀐ.1073/pnas.1613701113

89. Grünewald A, Kumar KR, Sue CM. New Insights Into the Complex Role of Mitochondria in Parkinson’s Disease. Prog Neurobiol (2019) 177:73�. doi:ꀐ.1016/j.pneurobio.2018.09.003

90. Lindström V, Gustafsson G, Sanders LH, Howlett EH, Sigvardson J, Kasrayan A, et al. Extensive Uptake of α-Synuclein Oligomers in Astrocytes Results in Sustained Intracellular Deposits and Mitochondrial Damage. Mol Cell Neurosci (2017) 82:143�. doi:ꀐ.1016/j.mcn.2017.04.009

91. Choi I, Kim J, Jeong H-K, Kim B, Jou I, Park SM, et al. PINK1 Deficiency Attenuates Astrocyte Proliferation Through Mitochondrial Dysfunction, Reduced AKT and Increased P38 MAPK Activation, and Downregulation of EGFR. Glia (2013) 61:800�. doi:ꀐ.1002/glia.22475

92. Schmidt S, Linnartz B, Mendritzki S, Sczepan T, L󼮾rt M, Stichel CC, et al. Genetic Mouse Models for Parkinson’s Disease Display Severe Pathology in Glial Cell Mitochondria. Hum Mol Genet (2011) 20:1197�. doi:ꀐ.1093/hmg/ddq564

93. Larsen NJ, Ambrosi G, Mullett SJ, Berman SB, Hinkle DA. DJ-1 Knock-Down Impairs Astrocyte Mitochondrial Function. Neuroscience (2011) 196:251�. doi:ꀐ.1016/j.neuroscience.2011.08.016

94. Mullett SJ, Di Maio R, Greenamyre JT, Hinkle DA. DJ-1 Expression Modulates Astrocyte-Mediated Protection Against Neuronal Oxidative Stress. J Mol Neurosci MN (2013) 49:507�. doi:ꀐ.1007/s12031-012-9904-4

95. Swerdlow RH. Mitochondria and Mitochondrial Cascades in Alzheimer’s Disease. J Alzheimers Dis JAD (2018) 62:1403�. doi:ꀐ.3233/JAD-170585

96. McAvoy K, Kawamata H. Glial Mitochondrial Function and Dysfunction in Health and Neurodegeneration. Mol Cell Neurosci (2019) 101:103417. doi:ꀐ.1016/j.mcn.2019.103417

97. Shigetomi E, Saito K, Sano F, Koizumi S. Aberrant Calcium Signals in Reactive Astrocytes: A Key Process in Neurological Disorders. Int J Mol Sci (2019) 20. doi:ꀐ.3390/ijms20040996

98. Agarwal A, Wu P-H, Hughes EG, Fukaya M, Tischfield MA, Langseth AJ, et al. Transient Opening of the Mitochondrial Permeability Transition Pore Induces Microdomain Calcium Transients in Astrocyte Processes. Neuron (2017) 93:587�.e7. doi:ꀐ.1016/j.neuron.2016.12.034

99. Parri HR, Gould TM, Crunelli V. Spontaneous Astrocytic Ca2+ Oscillations In Situ Drive NMDAR-mediated Neuronal Excitation. Nat Neurosci (2001) 4:803�. doi:ꀐ.1038/90507

100. Durkee CA, Araque A. Diversity and Specificity of Astrocyte-neuron Communication. Neuroscience (2019) 396:73𠄸. doi:ꀐ.1016/j.neuroscience.2018.11.010

101. Kuchibhotla KV, Lattarulo CR, Hyman BT, Bacskai BJ. Synchronous Hyperactivity and Intercellular Calcium Waves in Astrocytes in Alzheimer Mice. Science (2009) 323:1211𠄵. doi:ꀐ.1126/science.1169096

102. Delekate A, F࿌htemeier M, Schumacher T, Ulbrich C, Foddis M, Petzold GC. Metabotropic P2Y1 Receptor Signalling Mediates Astrocytic Hyperactivity In Vivo in an Alzheimer’s Disease Mouse Model. Nat Commun (2014) 5:5422. doi:ꀐ.1038/ncomms6422

103. Loaiza A, Porras OH, Barros LF. Glutamate Triggers Rapid Glucose Transport Stimulation in Astrocytes as Evidenced by Real-Time Confocal Microscopy. J Neurosci Off J Soc Neurosci (2003) 23:7337�. doi: 10.1523/JNEUROSCI.23-19-07337.2003

104. Porras OH, Ruminot I, Loaiza A, Barros LF. Na(+)-Ca(2+) Cosignaling in the Stimulation of the Glucose Transporter GLUT1 in Cultured Astrocytes. Glia (2008) 56:59�. doi:ꀐ.1002/glia.20589

105. Horvat A, Muhič M, Smolič T, Begić E, Zorec R, Kreft M, et al. Ca2+ as the Prime Trigger of Aerobic Glycolysis in Astrocytes. Cell Calcium (2021) 95:102368. doi:ꀐ.1016/j.ceca.2021.102368

106. Li X, Tao Y, Bradley R, Du Z, Tao Y, Kong L, et al. Fast Generation of Functional Subtype Astrocytes From Human Pluripotent Stem Cells. Stem Cell Rep (2018) 11:998�. doi:ꀐ.1016/j.stemcr.2018.08.019

107. Tcw J, Wang M, Pimenova AA, Bowles KR, Hartley BJ, Lacin E, et al. An Efficient Platform for Astrocyte Differentiation From Human Induced Pluripotent Stem Cells. Stem Cell Rep (2017) 9:600�. doi:ꀐ.1016/j.stemcr.2017.06.018

108. Janssen K, Bahnassawy L, Kiefer C, Korffmann J, Terstappen GC, Lakics V, et al. Generating Human Ipsc-Derived Astrocytes With Chemically Defined Medium for In Vitro Disease Modeling. Methods Mol Biol Clifton NJ (2019) 1994:31𠄹. doi:ꀐ.1007/978-1-4939-9477-9_3

109. Santos R, Vadodaria KC, Jaeger BN, Mei A, Lefcochilos-Fogelquist S, Mendes APD, et al. Differentiation of Inflammation-Responsive Astrocytes From Glial Progenitors Generated From Human Induced Pluripotent Stem Cells. Stem Cell Rep (2017) 8:1757�. doi:ꀐ.1016/j.stemcr.2017.05.011

110. Tchieu J, Calder EL, Guttikonda SR, Gutzwiller EM, Aromolaran KA, Steinbeck JA, et al. NFIA is a Gliogenic Switch Enabling Rapid Derivation of Functional Human Astrocytes From Pluripotent Stem Cells. Nat Biotechnol (2019) 37:267�. doi:ꀐ.1038/s41587-019-0035-0

111. Chandrasekaran A, Avci HX, Leist M, Kobolák J, Dinnyés A. Astrocyte Differentiation of Human Pluripotent Stem Cells: New Tools for Neurological Disorder Research. Front Cell Neurosci (2016) 10:215. doi:ꀐ.3389/fncel.2016.00215

112. Konttinen H, Gureviciene I, Oksanen M, Grubman A, Loppi S, Huuskonen MT, et al. Pparβ/δ-Agonist GW0742 Ameliorates Dysfunction in Fatty Acid Oxidation in PSEN1𹓩 Astrocytes. Glia (2019) 67:146�. doi:ꀐ.1002/glia.23534

113. Krencik R, Zhang S-C. Directed Differentiation of Functional Astroglial Subtypes From Human Pluripotent Stem Cells. Nat Protoc (2011) 6:1710𠄷. doi:ꀐ.1038/nprot.2011.405

114. Oksanen M, Petersen AJ, Naumenko N, Puttonen K, Lehtonen Š, Gubert Olivé M, et al. Psen1 Mutant Ipsc-Derived Model Reveals Severe Astrocyte Pathology in Alzheimer’s Disease. Stem Cell Rep (2017) 9:1885�. doi:ꀐ.1016/j.stemcr.2017.10.016

115. Lin Y-T, Seo J, Gao F, Feldman HM, Wen H-L, Penney J, et al. Apoe4 Causes Widespread Molecular and Cellular Alterations Associated With Alzheimer’s Disease Phenotypes in Human Ipsc-Derived Brain Cell Types. Neuron (2018) 98:1141�.e7. doi:ꀐ.1016/j.neuron.2018.05.008

116. Chen C, Jiang P, Xue H, Peterson SE, Tran HT, McCann AE, et al. Role of Astroglia in Down’s Syndrome Revealed by Patient-Derived Human-Induced Pluripotent Stem Cells. Nat Commun (2014) 5:4430. doi:ꀐ.1038/ncomms5430

117. Fong LK, Yang MM, Dos Santos Chaves R, Reyna SM, Langness VF, Woodruff G, et al. Full-Length Amyloid Precursor Protein Regulates Lipoprotein Metabolism and Amyloid-β Clearance in Human Astrocytes. J਋iol Chem (2018) 293:11341�. doi:ꀐ.1074/jbc.RA117.000441

118. Yuan SH, Martin J, Elia J, Flippin J, Paramban RI, Hefferan MP, et al. Cell-Surface Marker Signatures for the Isolation of Neural Stem Cells, Glia and Neurons Derived From Human Pluripotent Stem Cells. PloS One (2011) 6:e17540. doi:ꀐ.1371/journal.pone.0017540

119. Sonninen T-M, Hämäläinen RH, Koskuvi M, Oksanen M, Shakirzyanova A, Wojciechowski S, et al. Metabolic Alterations in Parkinson’s Disease Astrocytes. Sci Rep (2020) 10:14474. doi:ꀐ.1038/s41598-020-71329-8

120. Aldana BI, Zhang Y, Jensen P, Chandrasekaran A, Christensen SK, Nielsen TT, et al. Glutamate-Glutamine Homeostasis is Perturbed in Neurons and Astrocytes Derived From Patient iPSC Models of Frontotemporal Dementia. Mol Brain (2020) 13:125. doi:ꀐ.1186/s13041-020-00658-6

121. Shaltouki A, Peng J, Liu Q, Rao MS, Zeng X. Efficient Generation of Astrocytes From Human Pluripotent Stem Cells in Defined Conditions. Stem Cells Dayt Ohio (2013) 31:941�. doi:ꀐ.1002/stem.1334

122. Ciavardelli D, Piras F, Consalvo A, Rossi C, Zucchelli M, Di Ilio C, et al. Medium-Chain Plasma Acylcarnitines, Ketone Levels, Cognition, and Gray Matter Volumes in Healthy Elderly, Mildly Cognitively Impaired, or Alzheimer’s Disease Subjects. Neurobiol Aging (2016) 43:1�. doi:ꀐ.1016/j.neurobiolaging.2016.03.005

123. Palomer X, Barroso E, Pizarro-Delgado J, Pe༚ L, Botteri G, Zarei M, et al. Pparβ/δ: A Key Therapeutic Target in Metabolic Disorders. Int J Mol Sci (2018) 19. doi:ꀐ.3390/ijms19030913

124. Paik M-J, Ahn Y-H, Lee PH, Kang H, Park CB, Choi S, et al. Polyamine Patterns in the Cerebrospinal Fluid of Patients With Parkinson’s Disease and Multiple System Atrophy. Clin Chim Acta Int J Clin Chem (2010) 411:1532𠄵. doi:ꀐ.1016/j.cca.2010.05.034

125. Manyam BV, Ferraro TN, Hare TA. Cerebrospinal Fluid Amino Compounds in Parkinson’s Disease. Alterations Due to Carbidopa/Levodopa. Arch Neurol (1988) 45:48�. doi:ꀐ.1001/archneur.1988.00520250054021

126. Sonnay S, Christinat N, Thevenet J, Wiederkehr A, Chakrabarti A, Masoodi M. Exploring Valine Metabolism in Astrocytic and Liver Cells: Lesson From Clinical Observation in TBI Patients for Nutritional Intervention. Biomedicines (2020) 8. doi:ꀐ.3390/biomedicines8110487

127. Thevenet J, De Marchi U, Domingo JS, Christinat N, Bultot L, Lefebvre G, et al. Medium-Chain Fatty Acids Inhibit Mitochondrial Metabolism in Astrocytes Promoting Astrocyte-Neuron Lactate and Ketone Body Shuttle Systems. FASEB J Off Publ Fed Am Soc Exp Biol (2016) 30:1913�. doi:ꀐ.1096/fj.201500182

128. Sonnay S, Chakrabarti A, Thevenet J, Wiederkehr A, Christinat N, Masoodi M. Differential Metabolism of Medium-Chain Fatty Acids in Differentiated Human-Induced Pluripotent Stem Cell-Derived Astrocytes. Front Physiol (2019) 10:657. doi:ꀐ.3389/fphys.2019.00657

129. Dong Y, Benveniste EN. Immune Function of Astrocytes. Glia (2001) 36:180�. doi:ꀐ.1002/glia.1107

130. Lee H-J, Kim C, Lee S-J. Alpha-Synuclein Stimulation of Astrocytes: Potential Role for Neuroinflammation and Neuroprotection. Oxid Med Cell Longev (2010) 3:283𠄷. doi:ꀐ.4161/oxim.3.4.12809

131. Haim BL, Carrillo-de Sauvage M-A, Ceyzériat K, Escartin C. Elusive Roles for Reactive Astrocytes in Neurodegenerative Diseases. Front Cell Neurosci (2015) 9:278. doi:ꀐ.3389/fncel.2015.00278

132. Lee H-J, Suk J-E, Patrick C, Bae E-J, Cho J-H, Rho S, et al. Direct Transfer of Alpha-Synuclein From Neuron to Astroglia Causes Inflammatory Responses in Synucleinopathies. J Biol Chem (2010) 285:9262�. doi:ꀐ.1074/jbc.M109.081125

133. Söllvander S, Nikitidou E, Brolin R, Srberg L, Sehlin D, Lannfelt L, et al. Accumulation of Amyloid-β by Astrocytes Result in Enlarged Endosomes and Microvesicle-Induced Apoptosis of Neurons. Mol Neurodegener (2016) 11:38. doi:ꀐ.1186/s13024-016-0098-z

134. Rostami J, Holmqvist S, Lindström V, Sigvardson J, Westermark GT, Ingelsson M, et al. Human Astrocytes Transfer Aggregated Alpha-Synuclein via Tunneling Nanotubes. J Neurosci Off J Soc Neurosci (2017) 37:11835�. doi:ꀐ.1523/JNEUROSCI.0983-17.2017

135. Tsunemi T, Ishiguro Y, Yoroisaka A, Valdez C, Miyamoto K, Ishikawa K, et al. Astrocytes Protect Human Dopaminergic Neurons From α-Synuclein Accumulation and Propagation. J Neurosci Off J Soc Neurosci (2020) 40:8618�. doi:ꀐ.1523/JNEUROSCI.0954-20.2020

136. Gu X-L, Long C-X, Sun L, Xie C, Lin X, Cai H. Astrocytic Expression of Parkinson’s Disease-Related A53T Alpha-Synuclein Causes Neurodegeneration in Mice. Mol Brain (2010) 3:12. doi:ꀐ.1186/1756-6606-3-12

137. Grünewald A, Rygiel KA, Hepplewhite PD, Morris CM, Picard M, Turnbull DM. Mitochondrial DNA Depletion in Respiratory Chain-Deficient Parkinson Disease Neurons. Ann Neurol (2016) 79:366�. doi:ꀐ.1002/ana.24571

138. West AP, Khoury-Hanold W, Staron M, Tal MC, Pineda CM, Lang SM, et al. Mitochondrial DNA Stress Primes the Antiviral Innate Immune Response. Nature (2015) 520:553𠄷. doi:ꀐ.1038/nature14156

139. Zhong Z, Liang S, Sanchez-Lopez E, He F, Shalapour S, Lin X-J, et al. New Mitochondrial DNA Synthesis Enables NLRP3 Inflammasome Activation. Nature (2018) 560:198�. doi:ꀐ.1038/s41586-018-0372-z

140. Smajic S, Prada-Medina CA, Landoulsi Z, Dietrich C, Jarazo J, Henck J, et al. Single-Cell Sequencing of the Human Midbrain Reveals Glial Activation and a Neuronal State Specific to Parkinson’s Disease. medRxiv (2020). doi:ꀐ.1101/2020.09.28.20202812

141. Russ K, Teku G, Bousset L, Redeker V, Piel S, Savchenko E, et al. Tnf-α and α-Synuclein Fibrils Differently Regulate Human Astrocyte Immune Reactivity and Impair Mitochondrial Respiration. Cell Rep (2021) 34:108895. doi:ꀐ.1016/j.celrep.2021.108895

142. Grazioli S, Pugin J. Mitochondrial Damage-Associated Molecular Patterns: From Inflammatory Signaling to Human Diseases. Front Immunol (2018) 9:832. doi:ꀐ.3389/fimmu.2018.00832

143. Motori E, Puyal J, Toni N, Ghanem A, Angeloni C, Malaguti M, et al. Inflammation-Induced Alteration of Astrocyte Mitochondrial Dynamics Requires Autophagy for Mitochondrial Network Maintenance. Cell Metab (2013) 18:844�. doi:ꀐ.1016/j.cmet.2013.11.005

Keywords: metabolism, astrocytes, neurons, neurodegeneration, Parkinson’s disease, Alzheimer’s disease

Citation: Mulica P, Grünewald A and Pereira SL (2021) Astrocyte-Neuron Metabolic Crosstalk in Neurodegeneration: A Mitochondrial Perspective. Front. Endocrinol. 12:668517. doi: 10.3389/fendo.2021.668517

Received: 16 February 2021 Accepted: 22 April 2021
Published: 07 May 2021.

Jeni Sideris, Astellas Pharma, United States
Tamas Kozicz, Mayo Clinic, United States

Copyright © 2021 Mulica, Grünewald and Pereira. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


  1. Kakinos

    You must say you are wrong.

  2. Kiran

    Of course, he is not human

  3. Grokazahn

    I apologise, but, in my opinion, you are mistaken. I can prove it. Write to me in PM, we will discuss.

  4. Adriaan

    What interesting message

  5. Digrel

    You are not right. I'm sure. Let's discuss this. Email me at PM, we'll talk.

  6. Kadmus

    I consider, that you are not right. I am assured. Write to me in PM, we will communicate.

  7. Shelton

    Amazing theme ....

Write a message