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Down-Regulation

Down-Regulation: A biological process where the expression or activity of a gene, cell surface receptor, or other molecule is reduced or inhibited.
This can occur through various mechanisms, such as decreased transcription, accelerated mRNA or protein degradation, or allosteric modulation of receptor activity.
Down-regulation plays a crucial role in maintaining homeostasis and regulating cellular responses to stimuli.
Understanding the mechanisms and effects of down-regulation is essential for researchers studying cellular signaling, gene expression, and the development of therapies targeting these processes.
PubCompare.ai's AI-driven platform can enhance reproducibility and research accuracy in down-regulation studies by helping researchers locate the best protocols from literature, pre-prints, and patentts using intelligent comparissons to identify the most effective approaches, streamlining the research workflow and ensuring the accuracy of findings.

Most cited protocols related to «Down-Regulation»

We used both the 3ʹUTR and coding sequence of the terminal exon to determine the change in reads in 3ʹUTR of the longest (full-length) isoform. As 3ʹUTR reads can change with expression level changes, such as transcription up- or downregulation, 3ʹUTR signals were compared to that from the last exon’s coding sequence (LECDS). If the 3ʹUTR reads are significantly decreased or increased relative to those from the LECDS, the 3ʹUTR is called as shortening or lengthening, respectively. The significance of this read change was detected using a Fisher’s Exact test followed by Benjamini–Hochberg (BH) multiple testing with an adjusted P-value ≤ 0.01. This provides an accurate read-out of the net change in 3ʹUTR expression in a gene’s transcript and can be used based on total RNA-seq without specialized poly(A) mapping.
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Publication 2020
3' Untranslated Regions Down-Regulation Exons Gene Expression Open Reading Frames Poly A Protein Isoforms Transcription, Genetic Whole Transcriptome Sequencing
The goal of URA is to identify molecules upstream of the genes in the dataset that potentially explain the observed expression changes. Since it is a priori unknown which causal edges in the master network are applicable to the experimental context, we use a statistical approach to determine and score those regulators whose network connections to dataset genes as well as associated regulation directions are unlikely to occur in a random model. In particular, we define an overlap P-value measuring enrichment of network-regulated genes in the dataset, as well as an activation Z-score which can be used to find likely regulating molecules based on a statistically significant pattern match of up- and down-regulation, and also to predict the activation state (either activated or inhibited) of a putative regulator.
Here, we consider transcription and expression (T) edges only by looking at the subgraph and defining the subset of genes that are regulated by at least one edge in

A potential regulator r can be any node in V that is either a gene, protein family, complex, microRNA, or chemical. For a particular given regulator we define the set of downstream regulated genes as

For each the sign of v is defined as regulation direction of v under the assumption that r is activated, which is given by the regulation direction of the connecting edge, as

Similarly we define the weight associated with v to be

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Publication 2013
Down-Regulation Gene Regulatory Networks Genes MicroRNAs Proteins Transcription, Genetic
We undertook a systematic evaluation of the performance of Palantir in comparison to widely-used trajectory inference algorithms such as Monocle2, Diffusion Pseudotime (DPT), Partition based Graph Abstraction (PAGA- based on DPT), Slingshot, FateID, and Monocle 2.
We first compared the algorithms by evaluating their setup: the prior biology knowledge required as input and the diversity of outputs provided by each algorithm using the following criteria:
Supplementary Fig. 17a summarizes the characteristics of the different algorithms according to the criteria outline above:
Thus, Palantir uses minimal a priori biological information to (a) automatically determine the different terminal states, (b) generate a unified pseudo-time ordering to compare gene expression trends across lineages and (c) identify continuous branch probabilities and differentiation potential for each cell.
We next used the CD34+ human bone marrow data (replicate 1) as a benchmark to compare the results of the different algorithms. Due to the varied nature of the different outputs, we evaluated the ability of the algorithm to determine known and well established features of human hematopoiesis such as (a) identification of the different lineages represented in the data, with emphasis on less frequent populations such as megakaryocytes, cDCs and pDCs, which are more subtle and challenging to infer (b) recovering known expression trends of key genes across multiple lineages. We choose well-studied canonical genes across the different lineages, whose expression dynamics are known and can thus serve as ground truth. The following canonical genes, representing a broad spectrum of gene expression dynamics, were chosen for this evaluation:
Supplementary Fig. 17b shows the results of this comparison for the different algorithms. Palantir and DPT were able to identify the megakaryocyte lineages, whereas PAGA and Slingshot included these cells to be part of the erythroid lineage. Palantir was the only algorithm able to recover the distinction between the two DC lineages. Comparing the expression trends, all algorithms except Monocle 2 recovered the downregulation of CD34 across all lineages. Palantir recovers the known gene expression trends across all lineages (Fig. 2). While PAGA, DPT and Slingshot identify the trends in the larger lineages, PAGA (and DPT) suffer from a loss in resolution in gene expression trends and Slingshot does not provide a unified ordering of cells to compare gene expression trends across lineages. FateID with the default clustering using RaceID failed to identify any correct lineages and gene expression trends, whereas FateID with a preprocessing procedure and clustering followed in Palantir identifies correct expression trends in only the monocyte and CLP lineages. Monocle 2 could not recover the key hematopoietic lineages or expression trends from the CD34+ bone marrow data. See Supplementary Note 6 for a detailed description of the different algorithms and their performance.
Publication 2019
Biopharmaceuticals Bone Marrow Cells chenodeoxycholate sulfate conjugate Differentiations, Cell Diffusion DNA Replication Down-Regulation Gene Expression Genes Hematopoiesis Hematopoietic System Homo sapiens Megakaryocytes Monocytes PDC protein, human
We undertook a systematic evaluation of the performance of Palantir in comparison to widely-used trajectory inference algorithms such as Monocle2, Diffusion Pseudotime (DPT), Partition based Graph Abstraction (PAGA- based on DPT), Slingshot, FateID, and Monocle 2.
We first compared the algorithms by evaluating their setup: the prior biology knowledge required as input and the diversity of outputs provided by each algorithm using the following criteria:
Supplementary Fig. 17a summarizes the characteristics of the different algorithms according to the criteria outline above:
Thus, Palantir uses minimal a priori biological information to (a) automatically determine the different terminal states, (b) generate a unified pseudo-time ordering to compare gene expression trends across lineages and (c) identify continuous branch probabilities and differentiation potential for each cell.
We next used the CD34+ human bone marrow data (replicate 1) as a benchmark to compare the results of the different algorithms. Due to the varied nature of the different outputs, we evaluated the ability of the algorithm to determine known and well established features of human hematopoiesis such as (a) identification of the different lineages represented in the data, with emphasis on less frequent populations such as megakaryocytes, cDCs and pDCs, which are more subtle and challenging to infer (b) recovering known expression trends of key genes across multiple lineages. We choose well-studied canonical genes across the different lineages, whose expression dynamics are known and can thus serve as ground truth. The following canonical genes, representing a broad spectrum of gene expression dynamics, were chosen for this evaluation:
Supplementary Fig. 17b shows the results of this comparison for the different algorithms. Palantir and DPT were able to identify the megakaryocyte lineages, whereas PAGA and Slingshot included these cells to be part of the erythroid lineage. Palantir was the only algorithm able to recover the distinction between the two DC lineages. Comparing the expression trends, all algorithms except Monocle 2 recovered the downregulation of CD34 across all lineages. Palantir recovers the known gene expression trends across all lineages (Fig. 2). While PAGA, DPT and Slingshot identify the trends in the larger lineages, PAGA (and DPT) suffer from a loss in resolution in gene expression trends and Slingshot does not provide a unified ordering of cells to compare gene expression trends across lineages. FateID with the default clustering using RaceID failed to identify any correct lineages and gene expression trends, whereas FateID with a preprocessing procedure and clustering followed in Palantir identifies correct expression trends in only the monocyte and CLP lineages. Monocle 2 could not recover the key hematopoietic lineages or expression trends from the CD34+ bone marrow data. See Supplementary Note 6 for a detailed description of the different algorithms and their performance.
Publication 2019
Biopharmaceuticals Bone Marrow Cells chenodeoxycholate sulfate conjugate Differentiations, Cell Diffusion DNA Replication Down-Regulation Gene Expression Genes Hematopoiesis Hematopoietic System Homo sapiens Megakaryocytes Monocytes PDC protein, human
Consistent gene expression changes were identified between 44 stage A and 61 stage D CRCs from this study and 42 stage A and 62 stage D CRCs from expO. For the expO dataset, separate comparisons were performed for primary stage D cancers and distant metastases to identify gene expression maintained during metastatic spread. For each cohort, MAS5.0-calculated signal intensities were normalized using the quantile normalization procedure implemented in robust multiarray analysis (RMA) (17 , 18 (link)) and the normalized data were log transformed (base 2). Probe sets which were not expressed or probe sets which showed a low variability across samples were excluded. Expression values were required to be above the median of all expression measurements in at least 25% of samples, and the interquartile range across the samples on the log scale was required to be at least 0.5. Genes mapping to sex chromosomes were excluded as cases were not matched by gender. A total of 6716 gene probes passed these filtering steps in all three sample sets.
Differentially expressed genes were identified using Significance Analysis of Microarrays (SAM) with a Wilcoxon rank-sum test and a false discovery rate (FDR) of 10% (19 (link)). Separate lists were generated for genes significantly up- or down-regulated in stage A CRCs as compared to stage D CRCs for each of the three comparisons. For differentially expressed genes identified repeatedly between cohorts, consistency of up- or down-regulation was assessed using Pearson’s chi-squared test.
Publication 2009
Calcibiotic Root Canal Sealer Down-Regulation Gene Expression Genes Genes, vif Microarray Analysis Neoplasm Metastasis Staging, Cancer Strains

Most recents protocols related to «Down-Regulation»

Example 17

To further validate the activity of the DMPK siRNAs, many of the sequences that showed the best activity in the initial screen were selected for a follow-up evaluation in dose response format. Once again, two human cell lines were used to assess the in vitro activity of the DMPK siRNAs: first, SJCRH30 human rhabdomyosarcoma cell line; and second, Myotonic Dystrophy Type 1 (DM1) patient-derived immortalized human skeletal myoblasts. The selected siRNAs were transfected in a 10-fold dose response at 100, 10, 1, 0.1, 0.01, 0,001, and 0.0001 nM final concentrations or in a 9-fold dose response at 50, 5.55556, 0.617284, 0.068587, 0.007621, 0.000847, and 0.000094 nM final concentrations. The siRNAs were formulated with transfection reagent Lipofectamine RNAiMAX (Life Technologies) according to the manufacturer's “forward transfection” instructions. Cells were plated 24 h prior to transfection in triplicate on 96-well tissue culture plates, with 8500 cells per well for SJCRH30 and 4000 cells per well for DM1 myoblasts. At 48 h (SJCRH30) or 72 h (DM1 myoblasts) post-transfection cells were washed with PBS and harvested with TRIzol® reagent (Life Technologies). RNA was isolated using the Direct-zol-96 RNA Kit (Zymo Research) according to the manufacturer's instructions. 10 μl of RNA was reverse transcribed to cDNA using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems) according to the manufacturer's instructions. cDNA samples were evaluated by qPCR with DMPK-specific and PPIB-specific TaqMan human gene expression probes (Thermo Fisher) using TaqMan® Fast Advanced Master Mix (Applied Biosystems). DMPK values were normalized within each sample to PPIB gene expression. The quantification of DMPK downregulation was performed using the standard 2−ΔΔCt a method. All experiments were performed in triplicate, with Tables 16A-B, 17A-B, and 18A-B presenting the mean values of the triplicates as well as the calculated IC50 values determined from fitting curves to the dose-response data by non-linear regression.

TABLE 16A
sense strandSEQantisense strandSEQ
sequence (5′-3′)IDsequence (5′-3′)ID
ID #1Passenger Strand (PS)NO:Guide Strand (GS)NO:
535GGGCGAGGUGUCGUGCUUA9349UAAGCACGACACCUCGCCC12053
584GACCGGCGGUGGAUCACGA9398UCGUGAUCCACCGCCGGUC12102
716AUGGCGCGCUUCUACCUGA9530UCAGGUAGAAGCGCGCCAU12234
1028CAGACGCCCUUCUACGCGA9842UCGCGUAGAAGGGCGUCUG12546
1276UUUCGAAGGUGCCACCGAA10090UUCGGUGGCACCUUCGAAA12794
1825UGCUCCUGUUCGCCGUUGA10639UCAACGGCGAACAGGAGCA13343
1945CCCUAGAACUGUCUUCGAA10759UUCGAAGACAGUUCUAGGG13463
2529CUUCGGCGGUUUGGAUAUA11343UAUAUCCAAACCGCCGAAG14047
2558GUCCUCCGACUCGCUGACA11372UGUCAGCGAGUCGGAGGAC14076
2628CCGACAUUCCUCGGUAUUA11442UAAUACCGAGGAAUGUCGG14146
2636CCUCGGUAUUUAUUGUCUA11450UAGACAAUAAAUACCGAGG14154
119mer position in NM_001288766.1

TABLE 16B
IC50
ID #1qPCR2qPCR3qPCR4qPCR5qPCR6qPCR7qPCR8(nM)
535111.9105.4106.382.436.729.535.70.165
58490.590.284.767.838.025.828.30.190
71688.985.281.962.032.619.320.30.181
102888.581.883.061.332.727.331.50.127
127687.085.084.066.140.534.036.40.150
182585.185.983.769.136.225.225.00.259
194585.081.774.444.922.917.717.20.070
252983.381.875.350.624.617.517.70.103
255884.381.174.345.423.413.311.80.088
262885.384.079.559.830.323.525.10.140
263686.386.974.344.019.812.413.00.070
2SJCRH30; 0.0001 nM; % DMPK mRNA
3SJCRH30; 0.001 nM; % DMPK mRNA
4SJCRH30; 0.01 nM; % DMPK mRNA
5SJCRH30; 0.1 nM; % DMPK mRNA
6SJCRH30; 1 nM; % DMPK mRNA
7SJCRH30; 10 nM; % DMPK mRNA
8SJCRH30; 100 nM; % DMPK mRNA

TABLE 17A
sense strandSEQantisense strandSEQ
sequence (5′-3′)IDsequence (5′-3′)ID
ID #1Passenger Strand (PS)NO:Guide Strand (GS)NO:
2600CAAUCCACGUUUUGGAUGA11414UCAUCCAAAACGUGGAUUG14118
2636CCUCGGUAUUUAUUGUCUA11450UAGACAAUAAAUACCGAGG14154
2675CCCCGACCCUCGCGAAUAA11489UUAUUCGCGAGGGUCGGGG14193
2676CCCGACCCUCGCGAAUAAA11490UUUAUUCGCGAGGGUCGGG14194
2679GACCCUCGCGAAUAAAAGA11493UCUUUUAUUCGCGAGGGUC14197
2680ACCCUCGCGAAUAAAAGGA11494UCCUUUUAUUCGCGAGGGU14198
2681CCCUCGCGAAUAAAAGGCA11495UGCCUUUUAUUCGCGAGGG14199
2682CCUCGCGAAUAAAAGGCCA11496UGGCCUUUUAUUCGCGAGG14200
119mer position in NM_001288766.1

TABLE 17B
IC50
ID #1qPCR2qPCR3qPCR4qPCR5qPCR6qPCR7(nM)
2600107.5107.6108.1106.3103.172.731.31
263681.181.174.047.225.711.50.073
267588.188.384.364.638.120.70.151
267688.978.984.472.744.935.60.204
267984.087.382.753.331.413.50.091
268087.485.385.168.544.539.60.110
268187.085.477.649.626.516.00.061
268282.483.977.150.827.331.10.047
2SJCRH30; 0.000094 nM; % DMPK mRNA
3SJCRH30; 0.000847 nM; % DMPK mRNA
4SJCRH30; 0.007621 nM; % DMPK mRNA
5SJCRH30; 0.068587 nM; % DMPK mRNA
6SJCRH30; 0.617284 nM; % DMPK mRNA
7SJCRH30; 5.55556 nM; % DMPK mRNA

TABLE 18A
sense strandSEQantisense strandSEQ
sequence (5′-3′)IDsequence (5′-3′)ID
ID #1Passenger Strand (PS)NO:Guide Strand (GS)NO:
584GACCGGCGGUGGAUCACGA9398UCGUGAUCCACCGCCGGUC12102
716AUGGCGCGCUUCUACCUGA9530UCAGGUAGAAGCGCGCCAU12234
1265UUUACACCGGAUUUCGAAA10079UUUCGAAAUCCGGUGUAAA12783
1297AUGCAACUUCGACUUGGUA10111UACCAAGUCGAAGUUGCAU12815
1945CCCUAGAACUGUCUUCGAA10759UUCGAAGACAGUUCUAGGG13463
1960CGACUCCGGGGCCCCGUUA10774UAACGGGGCCCCGGAGUCG13478
2529CUUCGGCGGUUUGGAUAUA11343UAUAUCCAAACCGCCGAAG14047
2530UUCGGCGGUUUGGAUAUUA11344UAAUAUCCAAACCGCCGAA14048
2531UCGGCGGUUUGGAUAUUUA11345UAAAUAUCCAAACCGCCGA14049
2554CCUCGUCCUCCGACUCGCA11368UGCGAGUCGGAGGACGAGG14072
2628CCGACAUUCCUCGGUAUUA11442UAAUACCGAGGAAUGUCGG14146
2629CGACAUUCCUCGGUAUUUA11443UAAAUACCGAGGAAUGUCG14147
2681CCCUCGCGAAUAAAAGGCA11495UGCCUUUUAUUCGCGAGGG14199
119mer position in NM_001288766.1

TABLE 18B
IC50
ID #1qPCR2qPCR3qPCR4qPCR5qPCR6qPCR7(nM)
58490.877.097.771.945.029.70.228
71696.582.577.064.643.333.90.080
126568.580.968.057.137.525.70.146
129771.467.269.453.540.525.40.171
194571.862.341.729.822.415.30.006
196063.065.462.145.831.128.30.068
252963.558.749.231.122.921.90.017
253069.366.753.143.238.824.50.016
253169.972.457.340.235.425.60.018
255468.270.151.243.032.117.30.043
262869.767.962.538.431.617.10.042
262972.165.669.042.134.413.70.078
268182.491.587.655.529.319.60.084
2DM1 myoblasts; 0.000094 nM; % DMPK mRNA
3DM1 myoblasts; 0.000847 nM; % DMPK mRNA
4DM1 myoblasts; 0.007621 nM; % DMPK mRNA
5DM1 myoblasts; 0.068587 nM; % DMPK mRNA
6DM1 myoblasts; 0.617284 nM; % DMPK mRNA
7DM1 myoblasts; 5.55556 nM; % DMPK mRNA

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Patent 2024
Cell Lines Cells DNA, Complementary Down-Regulation Gene Expression Homo sapiens Lipofectamine Myoblasts Myoblasts, Skeletal Myotonic Dystrophy NM-107 Patients PPIB protein, human Reverse Transcription Rhabdomyosarcoma RNA, Messenger RNA, Small Interfering Tissues Transfection trizol

Example 24

For groups 1-12, see study design in FIG. 32, the 21mer Atrogin-1 guide strand was designed. The sequence (5′ to 3′) of the guide/antisense strand was UCGUAGUUAAAUCUUCUGGUU (SEQ ID NO: 14237). The guide and fully complementary RNA passenger strands were assembled on solid phase using standard phospharamidite chemistry and purified over HPLC. Base, sugar and phosphate modifications that are well described in the field of RNAi were used to optimize the potency of the duplex and reduce immunogenicity. Purified single strands were duplexed to get the double stranded siRNA described in figure A. The passenger strand contained two conjugation handles, a C6-NH2 at the 5′ end and a C6-SH at the 3′ end. Both conjugation handles were connected to siRNA passenger strand via phosphodiester-inverted abasic-phosphodiester linkers. Because the free thiol was not being used for conjugation, it was end capped with N-ethylmaleimide.

For groups 13-18 see study design in FIG. 32, a 21mer negative control siRNA sequence (scramble) (published by Burke et al. (2014) Pharm. Res., 31(12):3445-60) with 19 bases of complementarity and 3′ dinucleotide overhangs was used. The sequence (5′ to 3′) of the guide/antisense strand was UAUCGACGUGUCCAGCUAGUU (SEQ ID NO: 14228). The same base, sugar and phosphate modifications that were used for the active MSTN siRNA duplex were used in the negative control siRNA. All siRNA single strands were fully assembled on solid phase using standard phospharamidite chemistry and purified over HPLC. Purified single strands were duplexed to get the double stranded siRNA. The passenger strand contained two conjugation handles, a C6-NH2 at the 5′ end and a C6-SH at the 3′ end. Both conjugation handles were connected to siRNA passenger strand via phosphodiester-inverted abasic-phosphodiester linker. Because the free thiol was not being used for conjugation, it was end capped with N-ethylmaleimide.

Antibody siRNA Conjugate Synthesis Using Bis-Maleimide (BisMal) Linker

Step 1: Antibody Reduction with TCEP

Antibody was buffer exchanged with 25 mM borate buffer (pH 8) with 1 mM DTPA and made up to 10 mg/ml concentration. To this solution, 4 equivalents of TCEP in the same borate buffer were added and incubated for 2 hours at 37° C. The resultant reaction mixture was combined with a solution of BisMal-siRNA (1.25 equivalents) in pH 6.0 10 mM acetate buffer at RT and kept at 4° C. overnight. Analysis of the reaction mixture by analytical SAX column chromatography showed antibody siRNA conjugate along with unreacted antibody and siRNA. The reaction mixture was treated with 10 EQ of N-ethylmaleimide (in DMSO at 10 mg/mL) to cap any remaining free cysteine residues.

Step 2: Purification

The crude reaction mixture was purified by AKTA Pure FPLC using anion exchange chromatography (SAX) method-1. Fractions containing DAR1 and DAR2 antibody-siRNA conjugates were isolated, concentrated and buffer exchanged with pH 7.4 PBS.

Anion Exchange Chromatography Method (SAX)-1.

Column: Tosoh Bioscience, TSKGel SuperQ-5PW, 21.5 mm ID×15 cm, 13 um

Solvent A: 20 mM TRIS buffer, pH 8.0; Solvent B: 20 mM TRIS, 1.5 M NaCl, pH 8.0; Flow Rate: 6.0 ml/min

Gradient:

a.% A% BColumn Volume
b.10001
c.81190.5
d.505013
e .40600.5
f.01000.5
g.10002

Anion Exchange Chromatography (SAX) Method-2

Column: Thermo Scientific, ProPac™ SAX-10, Bio LC™, 4×250 mm

Solvent A: 80% 10 mM TRIS pH 8, 20% ethanol; Solvent B: 80% 10 mM TRIS pH 8, 20% ethanol, 1.5 M NaCl; Flow Rate: 0.75 ml/min

Gradient:

a.Time% A% B
b.0.09010
c.3.009010
d.11.004060
e.14.004060
f.15.002080
g.16.009010
h.20.009010

Step-3: Analysis of the Purified Conjugate

The purity of the conjugate was assessed by analytical HPLC using anion exchange chromatography method-2 (Table 22).

TABLE 22
SAX retention% purity
Conjugatetime (min)(by peak area)
TfR1-Atrogin-1 DAR19.299
TfR1-Scramble DAR18.993

In Vivo Study Design

The conjugates were assessed for their ability to mediate mRNA downregulation of Atrogin-1 in muscle (gastroc) in the presence and absence of muscle atrophy, in an in vivo experiment (C57BL6 mice). Mice were dosed via intravenous (iv) injection with PBS vehicle control and the indicated ASCs and doses, see FIG. 32. Seven days post conjugate delivery, for groups 3, 6, 9, 12, and 15, muscle atrophy was induced by the daily administration via intraperitoneal injection (10 mg/kg) of dexamethasone for 3 days. For the control groups 2, 5, 8, 11, and 14 (no induction of muscle atrophy) PBS was administered by the daily intraperitoneal injection. Groups 1, 4, 7, 10, and 13 were harvested at day 7 to establish the baseline measurements of mRNA expression and muscle weighted, prior to induction of muscle atrophy. At three days post-atrophy induction (or 10 days post conjugate delivery), gastrocnemius (gastroc) muscle tissues were harvested, weighed and snap-frozen in liquid nitrogen. mRNA knockdown in target tissue was determined using a comparative qPCR assay as described in the methods section. Total RNA was extracted from the tissue, reverse transcribed and mRNA levels were quantified using TaqMan qPCR, using the appropriately designed primers and probes. PPIB (housekeeping gene) was used as an internal RNA loading control, results were calculated by the comparative Ct method, where the difference between the target gene Ct value and the PPIB Ct value (ΔCt) is calculated and then further normalized relative to the PBS control group by taking a second difference (ΔΔCt).

Quantitation of tissue siRNA concentrations was determined using a stem-loop qPCR assay as described in the methods section. The antisense strand of the siRNA was reverse transcribed using a TaqMan MicroRNA reverse transcription kit using a sequence-specific stem-loop RT primer. The cDNA from the RT step was then utilized for real-time PCR and Ct values were transformed into plasma or tissue concentrations using the linear equations derived from the standard curves.

Results

The data are summarized in FIG. 33-FIG. 35. The Atrogin-1 siRNA guide strands were able to mediate downregulation of the target gene in gastroc muscle when conjugated to an anti-TfR mAb targeting the transferrin receptor, see FIG. 33. Increasing the dose from 3 to 9 mg/kg reduced atrophy-induced Atrogin-1 mRNA levels 2-3 fold. The maximal KD achievable with this siRNA was 80% and a tissue concentration of 40 nM was needed to achieve maximal KD in atrophic muscles. This highlights the conjugate delivery approach is able to change disease induce mRNA expression levels of Atrogin-1 (see FIG. 34), by increasing the increasing the dose. FIG. 35 highlights that mRNA down regulation is mediated by RISC loading of the Atrogin-1 guide strands and is concentration dependent.

Conclusions

In this example, it was demonstrated that a TfR1-Atrogin-1 conjugates, after in vivo delivery, mediated specific down regulation of the target gene in gastroc muscle in a dose dependent manner. After induction of atrophy the conjugate was able to mediate disease induce mRNA expression levels of Atrogin-1 at the higher doses. Higher RISC loading of the Atrogin-1 guide strand correlated with increased mRNA downregulation.

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Patent 2024
Acetate Anions Antibody Formation Antigens Atrophy Biological Assay Borates Buffers Carbohydrates Chromatography Complementary RNA Complement System Proteins Cysteine Dexamethasone Dinucleoside Phosphates DNA, Complementary Down-Regulation Ethanol Ethylmaleimide Freezing Genes Genes, Housekeeping High-Performance Liquid Chromatographies Immunoglobulins Injections, Intraperitoneal maleimide MicroRNAs Mus Muscle, Gastrocnemius Muscle Tissue Muscular Atrophy Nitrogen Obstetric Delivery Oligonucleotide Primers Pentetic Acid Phosphates Plasma PPIB protein, human Prospective Payment Assessment Commission Real-Time Polymerase Chain Reaction Retention (Psychology) Reverse Transcription RNA, Messenger RNA, Small Interfering RNA-Induced Silencing Complex RNA Interference Sodium Chloride Solvents Stem, Plant STS protein, human Sulfhydryl Compounds Sulfoxide, Dimethyl TFRC protein, human Tissues Transferrin tris(2-carboxyethyl)phosphine Tromethamine

Example 4

FIG. 6—(A) VLC-PUFA and elovanoids ELV1 and ELV2 mediated effect on Bid upregulation in ARPE-19 cells under stress. This figure displays the downregulation of the proapoptotic protein of the Bcl2 family Bid by western blot analysis by VLC-PUFA and elovanoids in RPE cells in culture under oxidative-stress. Results indicate that upregulated Bid protein by OS, as evident from the figure, was inhibited by both elovanoids and VLC-PUFA. It is interesting to see that the sodium salts of the elovaniod precursors are more effective than the methyl ester forms. (B) VLC-PUFA and ELV1 and ELV2 compounds mediated upregulation of Bid in ARPE-19 cells under stress. This Figure shows the quantification of Bid downregulation.

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Patent 2024
Apoptosis Inducing Proteins bcl-2 Gene Bid Protein Cells Down-Regulation Esters Inflammation Neurodegenerative Disorders Oxidative Stress Polyunsaturated Fatty Acids Salts Sodium Therapeutics Up-Regulation (Physiology) Western Blot

Example 6

4 mm2 cartilage explants were taken from non-lesion areas of OA patient's knee articular cartilage (n=5) and randomly assigned to different experimental treatment conditions (4 explants per treatment group). After a 24 h equilibration period the explants were treated with BMP-7 (1 nM) or the 12-mer peptide according to SEQ ID NO: 16 (10 nM) for 24 h. Hypertrophic gene expression was determined via qRT-PCR and normalized for 28S rRNA levels. After treatment with BMP-7 or the 12 mer we observed a downregulation of pro-hypertrophic genes, such as Col10a1 (FIG. 10A) and MMP13 (FIG. 10B). These results are in line with the effects described above and show the BMP-7 mimicking bioactivity of the peptides according to the invention.

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Patent 2024
Aftercare Bone Morphogenetic Protein 7 Cartilage Cartilages, Articular Down-Regulation Gene Expression Genes, vif Hypertrophy Knee Joint MMP13 protein, human Peptides RNA, Ribosomal, 28S Therapies, Investigational

Example 6

FIG. 8—(A) Bcl-xL-upregulation by elovanoids ELV1 and ELV2 in ARPE-19 cells under stress. Bcl-xL is the antiapoptotic Bcl2 family protein. Like proapoptotic proteins Bid and Bim, the effect of elovaniod precursors on the antiapoptotic protein Bcl-xL was tested in this figure in RPE cells under OS. Results showed that elovaniod precursors were able to upregulate the Bcl-xL protein in RPE cells under stress, which is the opposite effect of Bid and Bim. (B) Effect of NPD1, ELV1 and ELV2 on Bax expression in LOX-D cells under stress. Proapoptotic Bax was tested in this figure. It is evident that elovaniod precursors downregulated the Bax upregulation by OS in RPE cells under OS, which is consistent with our inhibition of apoptosis experiments, as shown before. C) VLC-PUFA and elovanoids ELV1 and ELV2 mediated effect on Bax upregulation in ARPE-19 cells under stress. In this experiment, elovanoid precursors along with VLC-PUFA were tested on the downregulation of the Bax protein in RPE cells under stress.

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Patent 2024
Anastasis Apoptosis Inducing Proteins Apoptosis Inhibiting Proteins Bax Protein bcl-2 Gene BCL2L1 protein, human Cells Down-Regulation Inflammation Neurodegenerative Disorders Polyunsaturated Fatty Acids Somatostatin-Secreting Cells Therapeutics Transcriptional Activation

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Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.
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Lipofectamine RNAiMAX is a transfection reagent designed for efficient delivery of small interfering RNA (siRNA) and short hairpin RNA (shRNA) into a wide range of cell types. It is a cationic lipid-based formulation that facilitates the uptake of these nucleic acids into the target cells.
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Lipofectamine 3000 is a transfection reagent used for the efficient delivery of nucleic acids, such as plasmid DNA, siRNA, and mRNA, into a variety of mammalian cell types. It facilitates the entry of these molecules into the cells, enabling their expression or silencing.
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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
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Gonal-F is a recombinant human follicle-stimulating hormone (r-hFSH) produced by recombinant DNA technology. It is used as a fertility medication to stimulate follicular development and maturation in the ovary as part of an assisted reproductive technology (ART) program.
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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Opti-MEM is a cell culture medium designed to support the growth and maintenance of a variety of cell lines. It is a serum-reduced formulation that helps to reduce the amount of serum required for cell culture, while still providing the necessary nutrients and growth factors for cell proliferation.
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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Lipofectamine 2000 reagent is a cationic lipid-based transfection reagent used for the delivery of nucleic acids, such as DNA and RNA, into eukaryotic cells. It facilitates the uptake of these molecules by the cells, enabling efficient gene expression or gene silencing studies.
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DMEM (Dulbecco's Modified Eagle's Medium) is a cell culture medium formulated to support the growth and maintenance of a variety of cell types, including mammalian cells. It provides essential nutrients, amino acids, vitamins, and other components necessary for cell proliferation and survival in an in vitro environment.

More about "Down-Regulation"

Down-regulation is a crucial biological process where the expression or activity of a gene, cell surface receptor, or other molecule is reduced or inhibited.
This can occur through various mechanisms, such as decreased transcription, accelerated mRNA or protein degradation, or allosteric modulation of receptor activity.
Understanding the mechanisms and effects of down-regulation is essential for researchers studying cellular signaling, gene expression, and the development of therapies targeting these processes.
Researchers often use tools like Lipofectamine 2000, Lipofectamine RNAiMAX, and Lipofectamine 3000 to facilitate gene knockdown and study down-regulation.
The RNeasy Mini Kit is commonly used for RNA extraction, while Gonal-F and FBS are used in cell culture.
Opti-MEM and TRIzol reagent are also frequently employed in down-regulation experiments.
PubCompare.ai's AI-driven platform can enhance reproducibility and research accuracy in down-regulation studies by helping researchers locate the best protocols from literature, pre-prints, and patents using intelligent comparisons to identify the most effective approaches.
This streamlines the research workflow and ensures the accuracy of findings, making PubCompare.ai an invaluable tool for researchers working in this field.