Down-Regulation
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»
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
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:
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:
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.
Example 24
For groups 1-12, see study design in
For groups 13-18 see study design in
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:
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:
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).
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
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
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.
Example 4
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 (
Example 6
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More about "Down-Regulation"
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.