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Mitochondria

Mitochondria are organelles found within the cytoplasm of most eukaryotic cells, including those of plants and animals.
They are the primary site of cellular respiration, producing the majority of a cell's supply of adenosine triphosphate (ATP) through the process of oxidative phosphorylation.
Mitochondria have their own genetic material and ribosomes, and they can replicate independently within the cell.
Mitochondrial dysfunction has been implicated in a variety of diseases, including neurodegenerative disorders, metabollic diseases, and cancer.
Reseach into mitochondrial structure, function, and role in disease is an active area of biomedical investigation.

Most cited protocols related to «Mitochondria»

Strategies for Efficient Model Selection in Phylogenetics

When using ModelFinder, it is important to remember that it optimizes the likelihood of the tree and model, given the data, whenever it searches for the optimal values of parameters considered. Therefore, it is possible that the search algorithms may become trapped in local optima. To reduce the chance of this occurring, we strongly recommend model selection be repeated many times for each data set, as noted above. Doing so may entail using much more computing time, especially when long, species-rich alignments are considered or the advanced search option of ModelFinder is used. Therefore, when the alignment is very long, we recommend the following set of strategies to reduce the amount of time used on model selection:

If the computational resources allow distributed computing, invoke the –nt x option to spread the processes over x threads;

If the data are characters encoded by a specific type of genome (e.g., mitochondrial), invoke the –msub source option to limit the search to this specific type of data;

If the optimal model turns out to include the R₁₀ model of RHAS, we recommend the analysis be rerun with both the –cmin x and –cmax y options invoked (e.g., –cmin 8, –cmax 20). Doing so will ensure that PDF models with k = 8, 9, … , 20 are considered (i.e., lower values of k are ignored). The program will stop when the optimal value of k has been found, even if this value turns out to be 10.

Use the default search option to find the optimal model of SE. Having identified this model, use the advanced search option with the optimal substitution model selected (e.g., –mset LG) to search for the optimal model of RHAS. While there is no guarantee that this approach will identify the optimal model of SE, our experience suggests that the choice of RHAS model is highly influenced by the topology of the tree while that of the substitution model is not.

Kalyaanamoorthy S., Minh B.Q., Wong T.K., von Haeseler A, & Jermiin L.S. (2017). ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates. Nature methods, 14(6), 587-589.

Publication 2017

Character Genome Mitochondria Trees

Strategies for Efficient Model Selection in Phylogenetics

If the computational resources allow distributed computing, invoke the –nt x option to spread the processes over x threads;

If the data are characters encoded by a specific type of genome (e.g., mitochondrial), invoke the –msub source option to limit the search to this specific type of data;

Kalyaanamoorthy S., Minh B.Q., Wong T.K., von Haeseler A, & Jermiin L.S. (2017). ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates. Nature methods, 14(6), 587-589.

Publication 2017

Character Genome Mitochondria Trees

Great Ape Genomic Variation Analysis

We sequenced to a mean coverage of 25X (Illumina HiSeq 2000) a total of 79 great ape individuals, representing 10 subspecies and four genera of great apes from a variety of populations across the African continent and Southeast Asia. SNPs were called using GATK^{12 (link)} after BWA^{28 (link)} mapping to the human genome (NCBI Build 36) using relaxed mapping parameters. Samples combined by species were realigned around putative indels. SNP calling was then performed on the combined individuals for each species. For indels, we used the GATK Unified Genotyper to produce an initial set of indel candidates applying several quality filters and removing variants overlapping segmental duplications and tandem repeats. We also removed groups of indels clustering within 10 bp to eliminate possible artifacts in problematic regions. Conservative allelic imbalance filters were used to eliminate false heterozygotes that may affect demographic analyses, some of which are sensitive to low levels of contamination. We estimate that the application of this filter resulted in a 14% false negative rate for heterozygotes. Our multispecies study design facilitated this assessment of contamination, which may remain undetected in studies focused on assessing diversity within a single species. The amount of cross-species contamination was estimated from the amount of non-endogenous mitochondrial sequence present in an individual. Because we wished to compare patterns of variation between and within species, we report all variants with respect to coordinates of the human genome reference. For FRAPPE analyses, we used MAF0.06 (human, orangutan, and bonobo) and 0.05 (chimpanzee and gorilla) to remove singletons. For most of the analyses, we only used autosomal markers, except in the X/A analysis. To determine the amount of inbreeding, we calculated the heterozygosity genome-wide in windows of 1 Mbp with 200 kbp sliding windows. We then clustered together the neighboring regions to account for runs of homozygosity. For the PSMC analyses, we called the consensus bases using SAMtools^{29 (link)}. Underlying raw sequence data is available through the SRA (PRJNA189439/SRP018689). Data generated in this work are available from http://biologiaevolutiva.org/greatape/. A complete description of the material and methods is provided in the Supplementary Note.

Prado-Martinez J., Sudmant P.H., Kidd J.M., Li H., Kelley J.L., Lorente-Galdos B., Veeramah K.R., Woerner A.E., O’Connor T.D., Santpere G., Cagan A., Theunert C., Casals F., Laayouni H., Munch K., Hobolth A., Halager A.E., Malig M., Hernandez-Rodriguez J., Hernando-Herraez I., Prüfer K., Pybus M., Johnstone L., Lachmann M., Alkan C., Twigg D., Petit N., Baker C., Hormozdiari F., Fernandez-Callejo M., Dabad M., Wilson M.L., Stevison L., Camprubí C., Carvalho T., Ruiz-Herrera A., Vives L., Mele M., Abello T., Kondova I., Bontrop R.E., Pusey A., Lankester F., Kiyang J.A., Bergl R.A., Lonsdorf E., Myers S., Ventura M., Gagneux P., Comas D., Siegismund H., Blanc J., Agueda-Calpena L., Gut M., Fulton L., Tishkoff S.A., Mullikin J.C., Wilson R.K., Gut I.G., Gonder M.K., Ryder O.A., Hahn B.H., Navarro A., Akey J.M., Bertranpetit J., Reich D., Mailund T., Schierup M.H., Hvilsom C., Andrés A.M., Wall J.D., Bustamante C.D., Hammer M.F., Eichler E.E, & Marques-Bonet T. (2013). Great ape genetic diversity and population history. Nature, 499(7459), 471-475.

Publication 2013

Allelic Imbalance Genome Genome, Human Gorilla gorilla Heterozygote HIVEP1 protein, human Homo sapiens Homozygote INDEL Mutation Mitochondria Negroid Races Pan paniscus Pan troglodytes Pongidae Pongo pygmaeus Segmental Duplications, Genomic Tandem Repeat Sequences

RNA-Seq Data Integration and Normalization

To allow for consistent comparison across datasets, all read mapping was carried out using TopHat 1.1.0 [29 (link)] with supplied annotations and the --no-novel-juncs option set, except for the SOLiD datasets, which were only available in a pre-aligned form with mapping by BioScope 1.2.1. All expression estimation and bias correction were done using Cufflinks 0.9.3 with the same annotation and reference sequence as TopHat. In the case of strand-specific libraries, the correct --library-type option was used as per the Cufflinks manual. For the mouse dataset in the NanoString experiment, the RefSeq refGene annotation for assembly NCBI37/mm9 was used, and was downloaded from the UCSC Genome Browser. For all human datasets, the RefSeq refGene annotation for assembly NCBI36/hg18 [30 (link)] was used, and was downloaded from the UCSC Genome Browser. The only filtering was to remove non-chromosomal and 'random' contigs. After quanti cation with Cufflinks, the subset of transcripts with 1-to-1 mappings to the TaqMan qRT-PCR probes were selected (as listed in the supplement to [16 (link)]) to be used in the correlation tests. All yeast datasets used the Ensembl Saccharomyces cerevisiae annotation, release 59, which was downloaded from the Ensembl website [31 (link)]. Mitochondrial, non-coding, and ribosomal RNA sites were masked in the annotation. All remaining transcripts were used in our correlation tests.

Roberts A., Trapnell C., Donaghey J., Rinn J.L, & Pachter L. (2011). Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biology, 12(3), R22.

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Publication 2011

Chromosomes Dietary Supplements DNA Library Genome Homo sapiens Mice, House Mitochondria Ribosomal RNA Saccharomyces cerevisiae

Identifying RNA Editing Sites Using PREP

As mentioned, the PREP suite of programs identifies potential sites of RNA editing based on the evolutionary principle that editing increases protein conservation among species. This is a fundamental quality of RNA editing in plants that was noticed upon its discovery in 1989 (19–21 ) and has been repeatedly observed in nearly all subsequent studies. Full details of the PREP-Mt methodology have been published previously (16 (link)). Essentially, all three programs perform the same series of steps: (i) an input sequence is translated using the standard genetic code; (ii) the translated sequence is aligned to a set of homologous proteins; (iii) the alignment is examined column-by-column to determine if an editing event could increase the similarity of the input sequence to the sequences in the pre-defined alignment. An edit site is predicted if a C-to-U change in a codon causes it to produce an amino acid that is found in more of the homologous proteins than the amino acid coded for by the unedited codon. If a cutoff value is specified by the user, the score of the edited version of the codon must also be >C.
The major difference between each server is in the set of homologous proteins used for comparison to the input sequence. For PREP-Aln, the protein homologs derive from the RNA-tagged sequences in the input file provided by the user. PREP-Aln pulls out all of the DNA sequences from the input alignment, and then builds the homologous protein alignment by translating the RNA sequences remaining in the input alignment. PREP-Aln then compares each of the pulled DNA sequences to the translated RNA alignment. For PREP-Mt and PREP-Cp, the set of homologous proteins is determined by the user when the gene name parameter is specified. These alignments of known mitochondrial or chloroplast proteins have been pre-generated from data available in GenBank and literature sources. The mitochondrial alignments were described previously and consist predominantly of six species with widespread transcriptomic sequence data (Figure 2A), and three species (Marchantia polymorpha, Chara vulgaris, Chaetosphaeridium globosum) that lack RNA editing (16 (link)). To create the chloroplast alignments, chloroplast genomes from seed plants whose transcriptomes have been extensively examined for editing (Figure 2B) were downloaded from GenBank. The known positions of edit sites were used to reconstruct mature, edited RNA sequences and these sequences were translated using the standard genetic code. Homologous proteins were aligned with ClustalW and manually adjusted when necessary to produce a collection of 35 alignments representing all chloroplast genes with evidence for editing in at least one of the seed plants in this study (Figure 2B).
Figure 2.

Seed plants with extensive editing data for (A) mitochondrial genes and (B) chloroplast genes. For each species is listed the number of genes with editing information, along with the number of edited (Pos) and unedited (Neg) cytidines found in those genes. The chronogram shows evolutionary relationships and approximate divergence times for species. Divergence times are listed in millions of years (MYA) and were taken from published analyses (22 ,23 (link)). Species in black were used to generate the sets of homologous protein alignments and to optimize the cutoff value. Species in red were used for the unseen tests only.

, & Mower J.P. (2009). The PREP suite: predictive RNA editors for plant mitochondrial genes, chloroplast genes and user-defined alignments. Nucleic Acids Research, 37(Web Server issue), W253-W259.

Publication 2009

Amino Acids Biological Evolution Chara Chloroplast Proteins Chloroplasts Codon Cytidine Gene Expression Profiling Genes Genes, Chloroplast Genes, Mitochondrial Genetic Code Genome, Chloroplast Marchantia Mitochondria Plant Embryos Plants Proteins RNA Sequence SET protein, human Transcriptome

Most recents protocols related to «Mitochondria»

Restoring Mitochondrial Function in Friedreich's Ataxia Neurons

Not available on PMC !

Example 2

The next experiments asked whether inhibition of the same set of FXN-RFs would also upregulate transcription of the TRE-FXN gene in post-mitotic neurons, which is the cell type most relevant to FA. To derive post-mitotic FA neurons, FA(GM23404) iPSCs were stably transduced with lentiviral vectors over-expressing Neurogenin-1 and Neurogenin-2 to drive neuronal differentiation, according to published methods (Busskamp et al. 2014, Mol Syst Biol 10:760); for convenience, these cells are referred to herein as FA neurons. Neuronal differentiation was assessed and confirmed by staining with the neuronal marker TUJ1 (FIG. 2A). As expected, the FA neurons were post-mitotic as evidenced by the lack of the mitotic marker phosphorylated histone H3 (FIG. 2B). Treatment of FA neurons with an shRNA targeting any one of the 10 FXN-RFs upregulated TRE-FXN transcription (FIG. 2C) and increased frataxin (FIG. 2D) to levels comparable to that of normal neurons. Likewise, treatment of FA neurons with small molecule FXN-RF inhibitors also upregulated TRE-FXN transcription (FIG. 2E) and increased frataxin (FIG. 2F) to levels comparable to that of normal neurons.

It was next determined whether shRNA-mediated inhibition of FXN-RFs could ameliorate two of the characteristic mitochondrial defects of FA neurons: (1) increased levels of reactive oxygen species (ROS), and (2) decreased oxygen consumption. To assay for mitochondrial dysfunction, FA neurons an FXN-RF shRNA or treated with a small molecule FXN-RF inhibitor were stained with MitoSOX, (an indicator of mitochondrial superoxide levels, or ROS-generating mitochondria) followed by FACS analysis. FIG. 3A shows that FA neurons expressing an NS shRNA accumulated increased mitochondrial ROS production compared to EZH2- or HDAC5-knockdown FA neurons. FIG. 3B shows that FA neurons had increased levels of mitochondrial ROS production compared to normal neurons (Codazzi et al., (2016) Hum Mol Genet 25(22): 4847-485). Notably, inhibition of FXN-RFs in FA neurons restored mitochondrial ROS production to levels comparable to that observed in normal neurons. In the second set of experiments, mitochondrial oxygen consumption, which is related to ATP production, was measured using an Agilent Seahorse XF Analyzer (Divakaruni et al., (2014) Methods Enzymol 547:309-54). FIG. 3C shows that oxygen consumption in FA neurons was ˜60% of the level observed in normal neurons. Notably, inhibition of FXN-RFs in FA neurons restored oxygen consumption to levels comparable to that observed in normal neurons. Collectively, these preliminary results provide important proof-of-concept that inhibition of FXN-RFs can ameliorate the mitochondrial defects of FA post-mitotic neurons.

Mitochondrial dysfunction results in reduced levels of several mitochondrial Fe-S proteins, such as aconitase 2 (ACO2), iron-sulfur cluster assembly enzyme (ISCU) and NADH:ubiquinone oxidoreductase core subunit S3 (NDUFS3), and lipoic acid-containing proteins, such as pyruvate dehydrogenase (PDH) and 2-oxoglutarate dehydrogenase (OGDH), as well as elevated levels of mitochondria superoxide dismutase (SOD2) (Urrutia et al., (2014) Front Pharmacol 5:38). Immunoblot analysis is performed using methods known in the art to determine whether treatment with an FXN-RF shRNA or a small molecule FXN-RF inhibitor restores the normal levels of these mitochondrial proteins in FA neurons.

US11873494B2. Genetic and pharmacological transcriptional upregulation of the repressed FXN gene as a therapeutic strategy for Friedreich ataxia (2024-01-16). University of Massachusetts [US]. Inventors: Michael R. Green [US], Minggang Fang [US].

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Patent 2024

Aconitate Hydratase Biological Assay Cells Cloning Vectors Enzymes EZH2 protein, human frataxin Genets HDAC5 protein, human Histone H3 Immunoblotting Induced Pluripotent Stem Cells inhibitors Iron Ketoglutarate Dehydrogenase Complex Mitochondria Mitochondrial Inheritance Mitochondrial Proteins MitoSOX NADH NADH Dehydrogenase Complex 1 NEUROG1 protein, human Neurons Oxidoreductase Oxygen Consumption Proteins Protein Subunits Psychological Inhibition Pyruvates Reactive Oxygen Species Repression, Psychology Seahorses Short Hairpin RNA Sulfur sulofenur Superoxide Dismutase Superoxides Thioctic Acid Transcription, Genetic

Cytotoxicity of Plant Stem Cell Extracts

Not available on PMC !

Example 7

The MTT Cell Proliferation assay determines cell survival following apple stem cell extract treatment. The purpose was to evaluate the potential anti-tumor activity of apple stem cell extracts as well as to evaluate the dose-dependent cell cytotoxicity.

Principle: Treated cells are exposed to 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT). MTT enters living cells and passes into the mitochondria where it is reduced by mitochondrial succinate dehydrogenase to an insoluble, colored (dark purple) formazan product. The cells are then solubilized with DMSO and the released, solubilized formazan is measured spectrophotometrically. The MTT assay measures cell viability based on the generation of reducing equivalents. Reduction of MTT only occurs in metabolically active cells, so the level of activity is a measure of the viability of the cells. The percentage cell viability is calculated against untreated cells.

Method: A549 and NCI-H520 lung cancer cell lines and L132 lung epithelial cell line were used to determine the plant stem cell treatment tumor-specific cytotoxicity. The cell lines were maintained in Minimal Essential Media supplemented with 10% FBS, penicillin (100 U/ml) and streptomycin (100 μg/ml) in a 5% CO₂at 37 Celsius. Cells were seeded at 5×10³cells/well in 96-well plates and incubated for 48 hours. Triplicates of eight concentrations of the apple stem cell extract were added to the media and cells were incubated for 24 hours. This was followed by removal of media and subsequent washing with the phosphate saline solution. Cell proliferation was measured using the MTT Cell Proliferation Kit I (Boehringer Mannheim, Indianapolis, IN) New medium containing 50 μl of MTT solution (5 mg/ml) was added to each well and cultures were incubated a further 4 hours. Following this incubation, DMSO was added and the cell viability was determined by the absorbance at 570 nm by a microplate reader.

In order to determine the effectiveness of apple stem cell extracts as an anti-tumor biological agent, an MTT assay was carried out and IC50 values were calculated. IC50 is the half maximal inhibitory function concentration of a drug or compound required to inhibit a biological process. The measured process is cell death.

Results: ASC-Treated Human Lung Adenocarcinoma Cell Line A549.

TABLE 7

Results of cytotoxicity of apple stem cell extract on lung cancer cell

line A549 as measured by MTT assay (performed in triplicate).

Values of replicates are % of cell death.

Concentration*replicatereplicatereplicateMean of% Live

(μg/ml)123replicatesSDSEMCells

25093.1890.8690.3491.461.510.878.54

10086.8885.1885.6985.920.870.5014.08

5080.5879.4981.0480.370.800.4619.63

2574.2873.8176.3974.831.380.7925.17

12.567.9868.1371.7569.282.131.2330.72

6.2561.6762.4567.1063.742.931.6936.26

3.12555.3756.7762.4558.203.752.1641.80

1.56249.0751.0857.8052.654.572.6447.35

0.78142.7745.4053.1547.115.403.1252.89

Results: ASC-Treated Human Squamous Carcinoma Cell Line NCI-H520.

TABLE 8

Results of cytotoxicity of apple stem cell extract on lung cancer

cell line NCI-H520 measured by MTT assay (performed in triplicate).

Values of replicates are % of cell death.

Concen-%

tration*replicatereplicatereplicateMean ofLive

(μg/ml)123replicatesSDSEMcell

25088.2889.2987.7388.430.790.4611.57

10078.1379.1978.1378.480.610.3521.52

5067.9869.0968.5468.540.560.3231.46

2557.8358.9958.9458.590.660.3841.41

12.547.6848.8949.3448.640.860.5051.36

6.2537.5338.7939.7538.691.110.6461.31

3.12527.3728.6930.1528.741.390.8071.26

1.56217.2218.5920.5618.791.680.9781.21

0.781 7.07 8.4810.96 8.841.971.1491.16

Results: ASC-treated Lung Epithelial Cell Line L132.

TABLE 9

Results of cytotoxicity of apple stem cell extract on

lung epithelial cell line L132 as measured by MTT assay

(performed in triplicate). Values of replicates are % of cell death.

Concen-rep-rep-rep-Mean%

tration*licatelicatelicateofLive

(μg/ml)123replicatesSDSEMcell

25039.5142.5244.0342.022.301.3357.98

10032.9334.4433.6933.690.750.4466.31

5030.6028.9430.5230.020.940.5469.98

2527.9627.8127.1327.630.440.2572.37

12.525.6225.5525.4025.520.120.0774.48

6.2523.1320.8718.6120.872.261.3179.13

3.12513.3411.0811.8312.081.150.6687.92

1.562 6.56 7.31 9.57 7.811.570.9192.19

0.781 8.06 4.30 3.54 5.302.421.4094.70

Summary Results: Cytotoxicity of Apple Stem Cell Extracts.

TABLE 10

IC50 values of the apple stem cell extracts on the on the target

cell lines as determined by MTT assay.

Target Cell

LineIC50

A54912.58

NCI-H52010.21

L132127.46

Apple stem cell extracts killed lung cancer cells lines A549 and NCI-H520 at relatively low doses: IC50s were 12.58 and 10.21 μg/ml respectively as compared to 127.46 μg/ml for the lung epithelial cell line L132. Near complete anti-tumor activity was seen at a dose of 250 μg/ml in both the lung cancer cell lines. This same dose spared more than one half of the L132 cells. See Tables 7-10. The data revealed that apple stem cell extract is cytotoxic to lung cancer cells while sparing lung epithelial cells. FIG. 6 shows a graphical representation of cytotoxicity activity of apple stem cell extracts on lung tumor cell lines A549, NCIH520 and on L132 lung epithelial cell line (marked “Normal”). The γ-axis is the mean % of cells killed by the indicated treatment compared to unexposed cells. The difference in cytotoxicity levels was statistically significant at p≤05.

Example 9

The experiment of Example 7 was repeated substituting other plant materials for ASC. Plant stem cell materials included Dandelion Root Extract (DRE), Aloe Vera Juice (AVJ), Apple Fiber Powder (AFP), Ginkgo Leaf Extract (GLE), Lingonberry Stem Cells (LSC), Orchid Stem Cells (OSC) as described in Examples 1 and 2. The concentrations of plant materials used were nominally 250, 100, 50, 25, 6.25, 3.125, 1.562, and 0.781 μg/mL. These materials were tested only for cells the human lung epithelial cell line L132 (as a proxy for normal epithelial cells) and for cells of the human lung adenocarcinoma cell line A549 (as a proxy for lung cancer cells).

A549 cells lung cancer cell line cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Cancer Cell Line A549 Cells.

TABLE 11

Triplicate results of cell death of DRE-treated

A549 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates.

Concentration%

(μg/mL)-DRE-Live

treated A549% of cell deathMeanSDSEMcell

25080.4376.4074.8477.232.891.6722.77

10067.6075.2663.7768.885.853.3831.12

5065.3262.9459.9462.732.701.5637.27

2556.8357.9748.1454.315.383.1145.69

6.2555.5949.6949.1751.483.572.0648.52

3.12551.7648.4545.3448.523.211.8551.48

1.56243.6944.0036.0241.244.522.6158.76

0.78137.4726.1919.5727.749.055.2372.26

AVJ-Treated Lung Cancer Cell line A549 Cells.

TABLE 12

Triplicate results of cell death of AVJ-treated

A549 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates.

Concentration%

(μg/mL)-AVJ-treatedLive

A549% of cell deathMeanSDSEMcell

25076.8178.1675.8876.951.140.6623.05

10076.4075.2673.7175.121.350.7824.88

5065.3266.1559.9463.803.371.9536.20

2550.1048.4556.6351.734.322.5048.27

6.2547.5246.3846.1746.690.720.4253.31

3.12539.8638.6143.7940.752.701.5659.25

1.56232.4019.7730.5427.576.823.9472.43

0.78120.5015.6332.1922.778.514.9277.23

AFP-Treated Lung Cancer Cell line A549 Cells.

TABLE 13

Triplicate results of cell death of AFP-treated

A549 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates.

Concentration%

(μg/mL)-AFP-treatedLive

A549% of cell deathMeanSDSEMcell

25086.1387.9986.6586.920.960.5613.08

10079.5081.0682.0980.881.300.7519.12

5073.6072.4671.3372.461.140.6627.54

2568.0167.7066.9867.560.530.3132.44

6.2560.8762.1160.7761.250.750.4338.75

3.12549.4851.7650.7250.661.140.6649.34

1.56240.0641.7247.0042.933.622.0957.07

0.78139.2337.7836.8537.961.200.6962.04

GLE-treated Lung Cancer Cell line A549 Cells.

TABLE 14

Triplicate results of cell death of GLE-treated

A549 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates.

Concentration%

(μg/mL)-GLE-treatedLive

A549% of cell deathMeanSDSEMcell

25088.4291.4990.4490.121.560.909.88

10084.3983.7783.1683.770.610.3516.23

5079.4781.5876.7579.272.421.4020.73

2573.6072.5471.4072.511.100.6327.49

6.2562.8963.6859.9162.161.991.1537.84

3.12550.1854.4751.8452.162.171.2547.84

1.56246.9344.3043.3344.851.861.0755.15

0.78139.5639.3940.9639.970.870.5060.03

LSC-treated lung cancer cell lines A549 cells.

TABLE 15

Triplicate results of cell death of LSC-treated

A549 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates.

Concentration

(μg/mL)% Live

LSC treated A549% of cell deathMeanSDSEMcell

25077.5478.8578.2078.200.650.3821.80

10077.1476.0476.5976.590.550.3223.41

5066.4268.5266.8267.251.120.6532.75

2559.8067.2264.1663.733.732.1536.27

6.2550.5348.8248.0749.141.260.7350.86

3.12541.1443.6042.7242.491.240.7257.51

1.56239.4739.7440.6139.940.600.3460.06

0.78138.5531.8336.7935.723.482.0164.28

OSC-treated Lung Cancer Cell line A549 Cells.

TABLE 16

Triplicate results of cell death of OSC-treated

A549 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates.

Concentration

(μg/mL)% Live

OSC-treated A549% of cell deathMeanSDSEMcell

25070.8465.5771.4969.303.251.8730.70

10048.8150.9157.2852.334.412.5547.67

5046.5949.6053.3349.843.381.9550.16

2538.7740.8136.5838.722.111.2261.28

6.2535.7440.7941.0539.193.001.7360.81

3.12534.5533.6837.0235.081.731.0064.92

1.56233.8633.4427.6331.643.482.0168.36

0.78121.3220.0034.8225.388.214.7474.62

L132 cells (“normal” lung epithelial cell line) cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 17

Triplicate results of cell death of DRE-treated

L132 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates.

Concentration% of %

(μg/mL)cellLive

DRE-treated L132deathMeanSDSEMcell

25086.6686.6186.6686.640.030.0213.36

10076.2977.3976.8476.840.550.3223.16

5065.9268.1767.0167.031.130.6532.97

2555.5458.9557.1957.231.700.9842.77

6.2545.1749.7347.3747.422.281.3252.58

3.12534.8040.5037.5437.612.851.6562.39

1.56224.4231.2827.7227.813.431.9872.19

0.78114.0522.0617.8918.004.012.3182.00

AVJ-Treated Lung Epithelial Cell Line L132 cells.

TABLE 18

Triplicate results of cell death of AVJ-treated

L132 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates

AFP-treated lung epithelial cell line L132 cells.

Concentration % of %

(μg/mL)cellLive

AVJ-treated L132deathMeanSDSEMcell

25057.0355.9353.6255.531.741.0044.47

10050.9949.7847.0449.272.031.1750.73

5044.9543.6340.4543.012.311.3456.99

2538.9137.4933.8636.752.601.5063.25

6.2532.8831.3427.2830.502.891.6769.50

3.12526.8425.1920.6924.243.181.8475.76

1.56220.8019.0514.1117.983.472.0082.02

0.78114.7612.90 7.5211.733.762.1788.27

AFP-Treated Lung Epithelial Cell Line L132 cells.

TABLE 19

Triplicate results of cell death of AFP-treated

L132 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates

AFP-treated lung epithelial cell line L132 cells.

Concentration

(μg/mL)% Live

AFP-treated L132% of cell deathMeanSDSEMcell

25056.1555.4357.1956.260.880.5143.74

10049.9548.2447.6448.611.200.6951.39

5043.7441.0538.0940.962.831.6359.04

2537.5433.8628.5433.324.532.6166.68

6.2531.3426.6718.9925.676.243.6074.33

3.12525.1419.489.4418.027.954.5981.98

1.56218.9412.2910.8714.034.312.4985.97

0.78112.73 5.10 6.81 8.214.002.3191.79

GLE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 20

Triplicate results of cell death of GLE-treated

L132 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates

AFP-treated lung epithelial cell line L132 cells.

Concentration

(μg/mL)% Live

GLE-treated L132% of cell deathMeanSDSEMcell

25084.4283.2083.0883.570.740.4316.43

10080.0579.2978.5979.310.730.4220.69

5072.7571.5974.1072.811.260.7227.19

2580.0581.8679.9980.631.060.6119.37

6.2568.2670.1368.2668.881.080.6231.12

3.12560.6263.0760.6261.441.410.8238.56

1.56248.0748.7748.8348.560.420.2451.44

0.78146.2745.5746.6746.170.560.3253.83

LSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 21

Triplicate results of cell death of LSC-treated

L132 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates

AFP-treated lung epithelial cell line L132 cells.

Concentration

(μg/mL)% Live

LSC-treated L132% of cell deathMeanSDSEMcell

25086.4185.8285.7686.000.350.2014.00

10081.2181.2779.9980.820.720.4219.18

5075.9674.7473.5174.741.230.7125.26

2574.7472.7571.4772.991.650.9527.01

6.2570.1368.3268.2668.901.060.6131.10

3.12554.0358.0553.4455.172.511.4544.83

1.56253.9751.9851.9852.641.150.6647.36

0.78146.7945.6244.9245.78 0.940.54 54.22

OSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 22

Triplicate results of cell death of OSC-treated

L132 cells measured by MTT assay.

Percentage of live cells calculated as 100% − Mean of triplicates

AFP-treated lung epithelial cell line L132 cells.

Concentration %

(μg/mL)Live

OSC-treated L132% of cell deathMeanSDSEMcell

25061.8462.3760.4461.551.000.5738.45

10054.1453.4452.1053.231.040.6046.77

5042.9442.3040.3241.851.370.7958.15

2535.9434.4833.3134.581.320.7665.42

6.2533.9632.6732.0332.890.980.5767.11

3.12527.4826.2026.7226.800.650.3773.20

1.562 9.80 7.29 7.35 8.151.430.8391.85

0.781 7.29 8.98 8.05 8.110.850.4991.89

Calculated values.

TABLE 23

Calculated IC50 doses (ug/mL) and therapeutic ratios

(IC50 for L132 cells/IC50 for A549 cells) for each

treatment material. Values greater than one indicate

that a material would be more selective in killing cancer

cells than normal cells. ASC results imported from

Example 8. These studies indicate that at least

some of the materials may be effective anti-cancer agents.

ASC has outstanding selectivity compared to other materials.

ASCDREAVJAFPGLELSCOSC

A549 12.589.82211.4811.9811.1 13.733.9

IC50

L132 127.4656.88 62.6682.6577.6369.26715.38

IC50

Ther.10.15.8 5.56.97.0 0.70.5

Ratio

US11872258B2. Oral cavity treatments (2024-01-16). None [US]. Inventors: Maryam Rahimi [US].

Full text: Click here

Patent 2024

14-3-3 Proteins 43-63 61-26 A549 Cells Action Potentials Adenocarcinoma of Lung Aloe Aloe vera Antineoplastic Agents Biological Assay Biological Factors Biological Processes Bromides Cardiac Arrest Cell Death Cell Extracts Cell Lines Cell Proliferation Cells Cell Survival Cytotoxin diphenyl DNA Replication Epistropheus Epithelial Cells Fibrosis Formazans Genetic Selection Ginkgo biloba Ginkgo biloba extract Homo sapiens Lingonberry Lung Lung Cancer Lung Neoplasms Malignant Neoplasms Mitochondria Mitochondrial Inheritance Neoplasms Neoplastic Stem Cells Oral Cavity PEG SD-01 Penicillins Pharmaceutical Preparations Phosphates Plant Cells Plant Leaves Plant Roots Plants Powder Psychological Inhibition Saline Solution SD 31 SD 62 SEM-76 Squamous Cell Carcinoma Stem, Plant Stem Cells Streptomycin Succinate Dehydrogenase Sulfoxide, Dimethyl Taraxacum Tetrazolium Salts

Mitochondrial Motility Regulation by Delta-2 Tubulin and Bortezomib

Not available on PMC !

EXAMPLE 4

To determine the effect bortezomib and delta-2 tubulin accumulation have on mitochondrial motility, DRG neurons were transduced with lentivirus to express wild-type tubulin or delta-2 tubulin. As shown in FIGS. 15A and B, DRG neurons that expressed delta-2 tubulin showed a significant reduction in the motility of the mitochondria in the neurons as compared to control neurons or neurons that expressed wild-type tubulin. Expression of delta-2 tubulin affected every state of mitochondrial movement analyzed except for the stationary state (STA) (FIG. 15C). The effect of CCP1 knockdown on mitochondrial movement in the presence of bortezomib was also analyzed. As shown in FIG. 15D, treatment with bortezomib greatly affected the movement of mitochondria in DRG neurons. However, the knockdown of CCP1 activity rescued the effects bortezomib had on the motility of the mitochondria (FIG. 15D-F).

US11874284B2. Delta-2-tubulin as a biomarker and therapeutic target for peripheral neuropathy (2024-01-16). THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK [US], UNIVERSITÀ DEGLI STUDI DI MILANO-BICOCCA [IT]. Inventors: Francesca Bartolini [US], Maria Elena Pero [US], Guido Cavaletti [IT].

Full text: Click here

Patent 2024

Bortezomib delta-Tubulin Lentivirus Mitochondria Mitochondrial Inheritance Motility, Cell Motor Neurons Movement Neurons Tubulin

Potentiation of Gluconeogenesis by LbNOX

Not available on PMC !

Example 6

As a result of its ability to elevate the intracellular ratio of NAD⁺ to NADH, LbNOX is also capable of potentiating gluconeogenesis in mammalian cells (e.g., human cells). The first step of gluconeogenesis from lactate is the conversion of lactate to pyruvate, which requires cytosolic NAD⁺. Gluconeogenesis from lactate was significantly increased when primary hepatocytes were transduced with either LbNOX or mitoLbNOX-containing adenovirus (FIG. 3D). The effect of LbNOX and mitoLbNOX on gluconeogenesis was commensurate to their effect on lactate/pyruvate ratio (FIG. 3B), suggesting that cytoplasmic and not mitochondrial NAD⁺/NADH is important for regulation of gluconeogenesis rate from lactate. These examples demonstrate the ability of water-forming NADH oxidases to control the rate of gluconeogenesis upon introducing these enzymes to mammalian cells.

US11866736B2. Protein prostheses for mitochondrial diseases or conditions (2024-01-09). The General Hospital Corporation [US]. Inventors: Vamsi Mootha [US], Denis Titov [US], Valentin Cracan [US], Zenon Grabarek [US].

Full text: Click here

Patent 2024

Adenovirus Vaccine Cells Cytoplasm Cytosol Enzymes Gluconeogenesis Hepatocyte Homo sapiens Lactates Mammals Mitochondria NADH Protoplasm Pyruvates Water-Splitting Enzyme of Photosynthesis

Modulation of Pyruvate Dehydrogenase by mitoLbNOX

Not available on PMC !

Example 5

LbNOX is also capable of modulating the phosphorylation state of pyruvate dehydrogenase complex (PDH), which is known to be regulated by NAD⁺/NADH based on in vitro studies. As shown in FIGS. 3C and 9D, mitoLbNOX was capable of inducing the dephosphorylation of PDH, thus confirming the physiological impact of compartment-specific perturbation of mitochondrial NAD⁺/NADH by mitoLbNOX. This example demonstrates that PDH activity is regulated by mitochondrial NAD⁺/NADH in vivo, and underscores the ability of water-forming NADH oxidases to modulate metabolic activity in a compartment-specific manner. This is the first time this activity has been shown in vivo.

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Patent 2024

Cells Figs Mammals Mitochondria NADH Phosphorylation physiology Pyruvate Dehydrogenase Complex Water-Splitting Enzyme of Photosynthesis

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More about "Mitochondria"

Mitochondria are the powerhouses of eukaryotic cells, responsible for generating the majority of a cell's supply of adenosine triphosphate (ATP) through the process of oxidative phosphorylation.
These organelles, found within the cytoplasm, possess their own genetic material and ribosomes, allowing them to replicate independently within the cell.
Mitochondrial dysfunction has been implicated in a variety of diseases, including neurodegenerative disorders, metabolic diseases, and cancer.
Research into mitochondrial structure, function, and role in disease is an active area of biomedical investigation.
Researchers can utilize various tools to study mitochondria, such as MitoTracker Red CMXRos, a fluorescent dye that specifically stains active mitochondria, and MitoSOX Red, a superoxide indicator that can be used to detect mitochondrial oxidative stress.
MitoTracker Green FM is another fluorescent dye that can be used to label mitochondria, while the Oxygraph-2k is a high-resolution respirometer that can measure mitochondrial oxygen consumption.
Mitochondria isolation kits, such as the Cell Mitochondria Isolation Kit, can be used to extract and purify mitochondria from cells, enabling researchers to study their structure and function in more detail.
MitoTracker Green and MitoTracker Red are also useful tools for visualizing and tracking mitochondria within living cells.
By leveraging these advanced techniques and tools, researchers can gain deeper insights into the role of mitochondria in health and disease, ultimately leading to the development of more effective treatments and therapies.