Diverse Roles of Mitochondria in Immune Responses: Novel Insights Into Immuno-Metabolism

Lack of immune system cells or impairment in differentiation of immune cells is the basis for many chronic diseases. Metabolic changes could be the root cause for this immune cell impairment. These changes could be a result of altered transcription, cytokine production from surrounding cells, and changes in metabolic pathways. Immunity and mitochondria are interlinked with each other. An important feature of mitochondria is it can regulate activation, differentiation, and survival of immune cells. In addition, it can also release signals such as mitochondrial DNA (mtDNA) and mitochondrial ROS (mtROS) to regulate transcription of immune cells. From current literature, we found that mitochondria can regulate immunity in different ways. First, alterations in metabolic pathways (TCA cycle, oxidative phosphorylation, and FAO) and mitochondria induced transcriptional changes can lead to entirely different outcomes in immune cells. For example, M1 macrophages exhibit a broken TCA cycle and have a pro-inflammatory role. By contrast, M2 macrophages undergo β-oxidation to produce anti-inflammatory responses. In addition, amino acid metabolism, especially arginine, glutamine, serine, glycine, and tryptophan, is critical for T cell differentiation and macrophage polarization. Second, mitochondria can activate the inflammatory response. For instance, mitochondrial antiviral signaling and NLRP3 can be activated by mitochondria. Third, mitochondrial mass and mobility can be influenced by fission and fusion. Fission and fusion can influence immune functions. Finally, mitochondria are placed near the endoplasmic reticulum (ER) in immune cells. Therefore, mitochondria and ER junction signaling can also influence immune cell metabolism. Mitochondrial machinery such as metabolic pathways, amino acid metabolism, antioxidant systems, mitochondrial dynamics, mtDNA, mitophagy, and mtROS are crucial for immune functions. Here, we have demonstrated how mitochondria coordinate to alter immune responses and how changes in mitochondrial machinery contribute to alterations in immune responses. A better understanding of the molecular components of mitochondria is necessary. This can help in the development of safe and effective immune therapy or prevention of chronic diseases. In this review, we have presented an updated prospective of the mitochondrial machinery that drives various immune responses

Quadruple Negative Breast Cancers (QNBC) Demonstrate Subtype Consistency among Primary and Recurrent or Metastatic Breast Cancer

PURPOSE: Despite the availability of current standards of care treatments for triple negative breast cancer (TNBC), many patients still die from this disease. Quadruple negative tumors, which are TNBC tumors that lack androgen receptor (AR), represent a more aggressive subtype of TNBC; however, the molecular features are not well understood. METHODS: Immunohistochemistry of estrogen receptor (ER), progesterone receptor (PR), HER2, and AR was determined in 244 primary and 630 recurrent/metastatic site biopsies. Expression was correlated with a panel of 25 cancer-related genes and proteins by IHC and in situ hybridization (ISH). RESULTS: We observed that 80.2% (65 of 81) of primary TNBC tumors and 75.7% (159 of 210) of recurrent/metastatic TNBC tumors are QNBC. Bivariate fit analysis demonstrated that QNBC (n = 224) significantly (P < .03) correlated with younger aged patients at initial biopsy compared to AR positive TNBC patients (n = 51). In paired primary tissue samples and primary to recurrent/metastatic samples, at least 70% Luminal, HER2 enriched, and QNBC subtype did not change molecular profile. But, TNBC seems to be the “unstable” subtype. Within the total cohort, discordance in molecular profiles was identified in both synchronous (20%) and asynchronous (21%) intra-individual analyses. Irrespective of sample type, (Synchronous or Asynchronous), QNBC demonstrated higher concordant than TNBC. IHC and ISH results of the cancer related genes, demonstrated that gene/protein expression differ by molecular profile: TNBC (HR-/HER2-, AR+) and QNBC (HR-/HER2-, AR-). IHC in metastatic tumors, showed that the percentage of tumors positive of EGFR were higher, while PTEN and TLE3 were lower in QNBC compared to TNBC. CONCLUSION: Standard treatment of Breast Cancer (BC) relies on reliable assessment by IHC analysis of ER, PR, and HER2. Our analyses suggest that the heterogeneity of TNBC is at least partially associated with the presence or absence of AR expression, suggesting that QNBC should be considered as a clinically relevant BC subtype. IHC analysis of AR appears to be a practical assay to determine the most aggressive TNBC subtypes and identifies tumors that could benefit from available targeted therapies

Genome-Wide Binding Patterns of Thyroid Hormone Receptor Beta

<div><p>Thyroid hormone (TH) receptors (TRs) play central roles in metabolism and are major targets for pharmaceutical intervention. Presently, however, there is limited information about genome wide localizations of TR binding sites. Thus, complexities of TR genomic distribution and links between TRβ binding events and gene regulation are not fully appreciated. Here, we employ a BioChIP approach to capture TR genome-wide binding events in a liver cell line (HepG2). Like other NRs, TRβ appears widely distributed throughout the genome. Nevertheless, there is striking enrichment of TRβ binding sites immediately 5′ and 3′ of transcribed genes and TRβ can be detected near 50% of T3 induced genes. In contrast, no significant enrichment of TRβ is seen at negatively regulated genes or genes that respond to unliganded TRs in this system. Canonical TRE half-sites are present in more than 90% of TRβ peaks and classical TREs are also greatly enriched, but individual TRE organization appears highly variable with diverse half-site orientation and spacing. There is also significant enrichment of binding sites for TR associated transcription factors, including AP-1 and CTCF, near TR peaks. We conclude that T3-dependent gene induction commonly involves proximal TRβ binding events but that far-distant binding events are needed for T3 induction of some genes and that distinct, indirect, mechanisms are often at play in negative regulation and unliganded TR actions. Better understanding of genomic context of TR binding sites will help us determine why TR regulates genes in different ways and determine possibilities for selective modulation of TR action.</p></div

Links between TR Binding and Adm Transcription.

<p>A. Graph showing results of realtime PCR analysis of adm transcription in the B7B cells after six hours of T3 treatment +/−10 µg/ml CHX cotreatment of B7B cells. B. Patterns of TRβ binding peaks at the <i>adm</i> locus (UCSC Genome Browser), in similar format to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081186#pone-0081186-g004" target="_blank">Fig. 4</a>. TRβ binding events clustered into four regions (R1, R2, R3, R4), upstream and downstream of this transcript, as well as a substantial amount of binding immediately proximal to the transcriptional start site. C. Binding of TRβ was confirmed by realtime ChIP PCR analysis in B7B cells at the regions indicated (ChIP primers are depicted by horizontal bars in B). D. The proximal promoter region of <i>adm</i> (corresponding to R2) conferred T3-dependent increases in luciferase activity upon a standard reporter after transfection into B7B. E. Results of gel shift confirming direct TRβ binding to two putative response elements, designated TRE-1 and TRE-2 that were found in R2 at positions marked in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081186#pone-0081186-g006" target="_blank">Fig. 6B</a>. Individual lanes show shifts obtained with elements and RXRα-TRβ +/− competitor DNA or mutated versions of both elements. F. Luciferase reporter assays confirming that TRE-1 and TRE-2 confer T3 responsiveness on a reporter gene. (*P<0.05 by Student's T-Test).</p

Occurrence of binding motifs in TRβ-bound peaks.

<p>Occurrence of binding motifs in TRβ-bound peaks.</p

Links between TRβ Binding and regulatory events.

<p>A. Bar graph representing numbers of genes that display positive regulation (upper panel) or negative regulation (lower panel) that met statistical significance and an arbitrary +/−1.7-fold cut-off in an array-based analysis of TRβ-BioChIP cells +/−T3 or in TRβ-BioChIP cells versus parental cells that lack TRβ (THRB effect). B. Bar graph representing percentages of TRβ binding events within 1 KB, 5 KB or 25 KB of the TSS of T3 induced, TRβ induced or unaffected genes (upper panel) or T3 or TRβ repressed genes (lower panel). Progressively lighter shading in the bar graph columns represents increasing distance from the TSS.</p

Characterization of genomic binding events.

<p>A. Distributions of TRβ binding peaks across specific genomic regions in the absence (black) and presence (grey) of T3. B. Bar graph representing relative enrichment of TRβ-bound regions within genomic intervals specified. Gene-proximal regions, including promoter regions, 5′UTR regions and downstream regions were highly enriched in TRβ-bound regions of the genome. C. Frequency distribution plot of binding events in regions proximal to transcriptional start sites (TSS) +/−T3 (blue and red, respectively). The x-axis represents nucleotides upstream and downstream of the TSS, y-axis represents numbers of binding events.</p

Patterns of TRβ binding and transcriptional regulation.

<p>Heatmap depicting log2-transformed expression levels (left) and TRβ binding events within 1 KB, 5 KB or 25 KB of TSS (right) of genes that met statistical significance and an arbitrary +/−2.55-fold cut-off of gene induction in TRβ-BioChIP cells +/−T3. Columns reflect the average of three experimental samples. Expression values in heatmap are as indicated by color scale (bottom, green indicating −5.7-fold repression, red indicating 13-fold induction), and location of binding events within the indicated ranges are depicted by the presence or absence of black bars in the three right-most columns.</p

Characterization of TRβ binding near induced genes.

<p>A. Patterns of TRβ binding depicted at representations of individual target gene loci (LDLR, BCL3, NCOR2,ADSSL1 and SOX7). Blue bars represent genomic binding regions, and the vertical red lines represent peaks, as classified by QuEST. The horizontal black bars are regions analyzed by ChIP-PCR (locations of primer amplification). Observed binding patterns included 5′, 3′ and intronic binding events, as shown in genomic data tracks (UCSC Genome Browser). Putative regulatory elements, identified through sequence analysis of the genomic regions indicated, are depicted below bound regions in which they occur. B. QPCR of ChIP analysis confirming DNA binding in regulatory regions of genes. C. Realtime PCR analysis depicting enhancement of transcription of individual loci in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081186#pone-0081186-g004" target="_blank">Fig. 4A</a> by T3 in the presence of TRβ. (*P<0.05 by Student's T-Test).</p