SOD1 Antibodies

Superoxide dismutase [Cu-Zn] (SOD1)

Destroys radicals which are normally produced within the cells and which are toxic to biological systems. .

Gene Information

Human
Gene Name:	SOD1
Uniprot:	P00441
Entrez:	6647

Family

Belongs to:
Cu-Zn superoxide dismutase family

Alternative Names

ALS; ALS1; amyotrophic lateral sclerosis 1 (adult); Cu; Cu/Zn superoxide dismutase; CuZn SOD; Cu-Zn SOD; EC 1.15.1.1; homodimer; hSod1; indophenoloxidase A; Ipo1; IPOA; SOD; SOD, cytosolic; SOD, Soluble; SOD1; superoxide dismutase [Cu-Zn]; Superoxide dismutase 1; superoxide dismutase 1, soluble; Zn superoxide dismutase, EC 1.15.1.110superoxide dismutase, cystolic

Molecular Weight

Mass (kDA):
15.936 kDA

Genomic Context

Human
Location:	21q22.11
Sequence:	21; NC_000021.9 (31659693..31668931)

Subcellular Localizations

Cytoplasm. Mitochondrion. Nucleus. Predominantly cytoplasmic; the pathogenic variants ALS1 Arg-86 and Ala-94 gradually aggregates and accumulates in mitochondria.

Diseases

• Amyotrophic Lateral Sclerosis
• Sclerosis
• Primary Lateral Sclerosis
• Neurodegenerative Disorders
• Nerve Degeneration
• Motor Neuron Disease
• Atrophy
• Nervousness
• Muscular Atrophy
• Ischemia
• Inflammation
• Weakness
• Alzheimer's Disease

• Malignant Neoplasms
• Down Syndrome
• Neuroblastoma
• Neurotoxicity Syndromes
• Abnormal Degeneration
• Parkinson Disease
• Dementia

Interacting Proteins

• CCS
• Chga
• Chgb
• DYNC2LI1
• EEF1D

• GAPDH
• Hspa5
• PRDX5

Pathways

• Pathogenesis
• Cell Death
• Transport
• Neuron Death
• Aging
• Localization
• Neuroprotection
• Muscle Atrophy
• Secretion
• Cell Proliferation
• Translation
• Autophagy
• Immune Response

• Electron Transport
• Protein Oxidation
• Innervation
• Cell Growth
• Inflammatory Response
• Rna Interference
• Dna Repair

PTMs

• Phosphorylation
• Oxidation
• Ubiquitination
• Cleavage
• Reduction
• Nitration
• Dephosphorylation
• Glycosylation
• Nitrosylation

• Glutathionylation
• Biotinylation
• Methylation
• Hydroxylation
• Sumoylation
• Demethylation
• Deamination
• Amidation
• Acetylation

• Myristoylation

Research Areas

• Cancer
• Diabetes Research
• Mitochondrial Fusion Proteins
• Neuronal Cell Markers
• Neuroscience

• Tumor Suppressors

For details, visit:
bosterbio.com/bosterbio-gene-info-cards/SOD1

Boster Bio - Best Uses Of The SOD1 Marker

Boster Bio can accept your results for different reasons. You can use it to identify species, to identify specific samples, or for specific applications. Scientists around the world can also receive credit for their products. Continue reading to learn more about the most widely used applications of the SOD1 marker and what you can accomplish with it. You'll be amazed at the possibilities! These tips will aid you in making the most of your marker.

The concentrations of SOD1 peptides in CSF of SOD1 rats

Boster Bio's SOD1 peptide showed a high affinity to the hSOD1 Oligomers. The strongest binding was evident in the disordered monomeric version. Both aSOD1143 and aSOD165-72 showed a similar pharmacokinetic profile and half-life in CSF samples from SOD1 rats.

The researchers used transgenic animals expressing human SOD1 peptides (hSOD1G93A or hSOD1H46R) and adhered to standard animal care protocols to ensure animal welfare. In this study, the spinal cords from non-Tg SOD1WT rats and SOD1G93A-transgenic rats were homogenized cold PBS 1x supplemented with protease inhibitors. Cells were then eliminated by centrifugation at 1,000 g for 10 minutes and immunoblotting to measure peptide concentrations.

The peptide levels in CSF of SOD1-transgenic mouse were also found. The peptide levels were 9-fold greater than those in the control group. The SOD1 transgenic rats displayed a similar phenotype to D90A. The samples of patients with D90A were identical to the samples with D90A however the presymptomatic samples were slightly different from those suffering from the disease. The results from both animal models were correlated with the mRNA levels of spinal cord tissue.

The results suggest a new method to classify the stability and degradability of proteins. This method provides a high-sensitivity overview of protein processing and reveals the differences that could be important for identifying effective treatments. Future studies will employ this approach to determine how to improve the dose of drugs for SOD1 patients and the frequency of administration. These findings could also be applied to other neurological conditions related to protein misfolding.

The Boster Bio SOD1 protein was detected in the CSF of SOD1 rats in a previous study. It is connected to the activity in the CNS of the SOD1 protein. The peptide might help in maintaining BP levels in mammals. The function of the SOD protein is being studied in order to determine the role it plays in maintaining BP levels.

The LP3 is a peptide that was derived from VDAC1 and had a lower affinity to the SOD1 mutant than either the (1-26) or N-terminal peptide. It is believed that this peptide interacts with mutant SOD1 in a manner that mimics SOD1 degrading. It was also discovered that SOD1G93A interacts directly with voltage-dependent anion channels (VDAC) which regulate the metabolism of cells, energy levels growth, survival, and growth.

The results of these studies showed that aSOD1143–153 dramatically reduced premature disease transmission and spontaneous hSOD1 accumulation among mice who have aged. However it was unclear whether the mutant SOD1G93A blocked SOD1G85R. In this study, it was shown that aSOD1143-153 inhibited SOD1G85R channel aggregation in a bilayer-reconstituted VDAC1.

This study revealed that 10 mM of the N-terminal protein expressed in SOD1G93A expressing NMs increased neurite outgrowth. A concentration of 10 mM significantly increased neurite outgrowth. This result is also in agreement with the theory that the peptide can increase the longevity of MNs within mice with SOD1 deficiency.

Application for untargeted metabolomics data processing

For metabolomics without a target to be efficient, data processing techniques that are reliable and accurate are required. First, raw metabolite measurements are transformed into numerical formats to allow for statistical analysis downstream. To reduce the noise of measurement there are several steps when processing LCMS data. Feature detection is utilized for identifying metabolites of importance in the raw signal. The identification of compounds and their interpretation is used to determine biological relationships between metabolites.

Metabolite annotation is the most important step in LCMS-based metabolism. To determine the structure of unknown metabolites The metabolites are identified through MS/MS spectra. The selection of precursor ions for the analysis of metabolomics data is generally performed using targeted MS/MS strategies and data dependent acquisition strategies. However different statistical analyses could pick the same metabolite but with different levels of spectral similarity. Therefore, it is important to create a complete database of spectra from reference metabolisms.

Metabolomics experiments generate large amounts of data. To process this data efficiently sophisticated bioinformatic tools are needed to remove systematic bias and uncover bio-relevant findings. These tools are developed at various stages that include model validation and performance evaluation. To ensure their efficacy the metabolomics data processing tools must be validated before they are employed in clinical settings. The tools utilized in this process are listed below.

Metabolomics workflows need to be highly reproducible. High analytical reproducibility decreases the requirement for technical replicates and allows the use of smaller samples. High-resolution mass spectrometers with large dynamic ranges, as well as positive and negative ionization are essential for untargeted metabolism data processing. The pipeline for processing data must also be intuitive and user-friendly.

The final step of data processing is to determine the metabolic patterns at the root and reducing other variables, and then processing the data. This is an essential part of the analysis process, since these features will provide the basis for classifiers based on the metabolomic information. One of the most popular methods of metabolomics that is supervised is partial least squares (PLS), which can be used as a regression or a binary classifier. This method reduces the dimensionality of data while conserving as much variability as is possible. This technique is useful in identifying outliers, related groups or trends.

Metabolomics software is readily available. These programs allow researchers to integrate their data with other omics data, including transcriptomics, genomics and protein sequencing. In addition, metabolomics software applications offer a range of methods and tools to process the metabolomics information. Here are some of the most frequently used software tools for metabolomics. You can pick the one that is most suitable for your requirements.

Automated spectraL processing system, also known as AlpsNMR provides high-level statistics analysis and interpretation of untargeted metabolomics data sets. AlpsNMR preprocesses Bruker and JDX metabolomics samples. These applications are both powerful and convenient. They also detect outliers in real-time. AlpsNMR can be combined together with other mass spectrometry information.

Comparison to unacetylated WT.apo-SOD1

We have studied two forms of apoSOD1: partially denatured and native. Both monomers displayed a similar degree of fibrillation in the presence of 5 milligrams in TCEP. In both forms, the sedimentation velocity experiments were carried out using an ultracentrifuge that is analytical. Unacetylated WT apo-SOD1 shows more the complexity of its conformation than a monomer comprising two thiols.

Electron microscopy proved that there were amyloid fibrils inside the apo WT–SOD1 AS-SOD1. As expected, the apo AS-SOD1 fibrils are smoother and less dense than those of the apo WT-SOD1. The apo WT intermediate, apo-SOD1, is on the other hand is more sausage-like than the apo AS SOD1 intermediate and does not have the dark amyloid hue. The greatest contrast is between WT SOD1 that is apo and AS at TCEP concentrations of 5 mM.

A examination of apo-SOD1 that was partially acylated with unacetylated WT p-SOD1 revealed similar melting temperatures. The DZformal of the unmodified WT-apoSOD1 protein was -4, and the DZformal for acylated WT-p-SOD1 was -2. In parentheses the melting temperatures of both proteins were measured in tandem.

Disulfide scrambling is a possibility to hinder fibrillation (apo AS-SOD1) is capable of creating sausage-like Oligomers when mixed in with C6/C111. The inherent disordered properties of the compound make it a suitable candidate for fibrillation. However fibrillation is more effective when the reductant is present. Disulfide scrambled AS–SOD1 reduced the amount of TCEP required to start fibrillation.

Similar studies were conducted using the G85R–SOD1*YFP transgenic model to identify the mouse model that expresses G85R–SOD1 gene below disease threshold. This model has been extensively employed to study prion-like seeding in the oligomeric SOD1.

To dissolve anhydrides in SOD1 solution, you will require a small amount IAA. This solvent is then washed out during centrifugation or buffer transfer. So the p values were only marginally different. In addition, the exact function of Ile112 and Phe20 in apo-SOD1 aggregates is still unclear.

Comparative acylation results in decreased nucleation and elongation of amyloid fibrils if there is an apo-SOD1 lysine residue. The mechanism responsible for this effect is electrostatic and results from increased resistance of the subunits. This is why acylated apo-SOD1 is more reactive than WT apoSOD1 unacetylated.

These findings led to the identification among other things, of the oxidation state for the zinc loop as well as four of the b-strands in apo-hSOD1. During the tests, FPOP modified the two B-strands the same way in both samples. This oxidation state is compatible both with local motions in the b-barrel and long-distance radical transfers. However, the FPOP modification led to an unfurling of a portion of the barrel's b-barrel, which enables these proteins to aggregate formation.

The SOD1 hydrophobic group participates in the earliest events of aggregation and in the event that SOD1 has a homologous hydrophobic group, it could also participate in the aggregation process. It could also play a role on the surface, which could lead to conclusions about its roles in various human diseases. The WT-apo-SOD1 that is not acetylated is and stable.