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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


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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.

Summary

  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

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Background

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

Animals

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. 2.3.5.1) [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:


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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.



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