Ikzf2 Antibodies

Zinc finger protein Helios (Ikzf2)

Associates with Ikaros at centromeric heterochromatin.

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Gene Information

Mouse
Gene Name:	Ikzf2
Uniprot:	P81183
Entrez:	22779

Family

Belongs to:
Ikaros C2H2-type zinc-finger protein family

Alternative Names

Helios; IKAROS family zinc finger 2 (Helios); Ikaros family zinc finger protein 2; IKZF2; MGC34330; subfamily 1A, 2 (Helios); zinc finger DNA binding protein Helios; zinc finger protein Helios; ZNF1A2; ZNFN1A2

Molecular Weight

Mass (kDA):
59.401 kDA

Genomic Context

Mouse
Location:	1|1 C3
Sequence:	1;

Tissue Specificity

Restricted to the T-cell lineage. Abundant in thymus, low expression in bone marrow and brain and no detectable expression in spleen, liver, kidney or muscle.

Diseases

• Leukemia
• Malignant Neoplasms
• Lymphoma
• Adult T-cell Lymphoma/leukemia
• Acute Lymphocytic Leukemia
• Myeloid Leukemia, Chronic
• Carcinoma
• Myeloid Leukemia
• Autoimmune Diseases
• Decreased Immunologic Activity [pe]
• Precursor B-cell Lymphoblastic Leukemia-lymphoma
• Leukemogenesis
• Blast Phase

• Autoimmunity
• Burkitt Lymphoma
• Diabetic Nephropathy
• Hereditary Hemorrhagic Telangiectasia
• Kidney Neoplasm
• Neoplasms
• Renal Cell Carcinoma

Pathways

• Pathogenesis
• Cell Activation
• T Cell Activation
• Localization
• Lymphocyte Differentiation
• Secretion

PTMs

• Phosphorylation

• Cleavage

For details, visit:
bosterbio.com/bosterbio-gene-info-cards/Ikzf2

Boster Bio: Best Uses Of The IKZF2 Marker

In 1993, Steven Boster created his first product, earning himself the nickname, "he who converts science in the lavatory". He developed dozens of primary antibodies and eventually became the largest antibody catalog in China. By the late 90s, Boster had developed a proprietary ELISA platform, PicoKine(tm). The patented technology makes high-sensitivity ELISA kits available in the form of a reagent.

IKZF2

The Boster Bio: IKZF2 gene infographics provide basic gene information and a clear representation of the implication of this marker. Genes represented in this data set are mouse and human, with a search bar to locate a specific gene. Best uses of the IKZF2 marker:

Pbx3

A recent study demonstrated that the Ikzf2 marker can bind directly to the ICOS promoter in Th cells. This finding could provide valuable insight into the molecular mechanism of ICOS-induced Th cell activation. ChIP is also useful for studying the specific interactions between DNA-associated proteins and DNA. The multiprotein complex can regulate gene transcription and chromatin structure. The IKZF2 marker is useful for the study of chromatin architecture and gene expression in different biological systems.

In this study, IKZF2 was found to be expressed in LN in two cohorts. This cohort consisted of patients with LN, and the two groups were compared according to the IKZF2 marker expression. In both groups, IKZF2 expression was significantly downregulated. Moreover, it was associated with disease grade. However, despite its potential benefits, the IKZF2 gene marker has yet to be widely used in clinical research.

As of now, there is no direct correlation between IKZF2 expression and the expression of other immune cells. However, it has been found that IKZF2 is correlated with ICOS in Th cells of patients with SLE. In addition, studies of Th cells from SLE patients show decreased expression of bcl-6. These findings suggest that IKZF2 is an important regulatory factor in the development and differentiation of early lymphocytes. In addition, the protein has a zinc finger DNA-binding domain and a C-terminal protein-interaction domain, which makes it an effective target.

Previous research has highlighted IKZF2 expression in LN. Previous studies showed that IKZF2 expression was significantly downregulated in LN samples and in controls. It was even listed in the top 10 DEGs. These findings have helped identify IKZF2 as a potential diagnostic marker. However, there is still a long way to go to fully exploit IKZF2 for kidney diseases research.

The IKZF2 marker is a transcription factor that can bind to the promoter of the ICOS gene. It is a transcription factor that controls the expression of ICOS in CD4+ T cells. Its expression in Th cells is important for the regulation of host immunity. This study used animals, and the animal study was approved by the Institutional Animal Care and Use Committee at Guangzhou Medical University.

Machine learning

A recent paper published in Nature Biotechnology shows that a machine learning algorithm based on the IKZF2 marker can distinguish between different types of images and identify them using a graphical user interface. The researchers were able to do this by using a set of evaluation data that was held out from the training data. The evaluation data is then used to test the machine learning algorithm's accuracy and build a model that can be used with various datasets. The researchers believe that this approach can prove successful in several cases.

While machine learning can be very helpful in improving the performance of human workers, it is also capable of unlocking new business opportunities. However, it is important to remember that this technology is not a silver bullet. It can be easily spooked, undermined, and fail to recognize tasks that humans can do effortlessly. For example, one might adjust the metadata on an image so that it confuses the computer and mistakes the dog for an ostrich.

The team also identified thirteen genes in the Rhapsody data that showed differential expression in a Treg. They used this information to train a Recursive Feature Elimination algorithm and later applied it to the SVM and LR machine learning algorithms. As a result, the researchers were able to discriminate between responding Tregs and non-responding Tconvs using just 12 markers.

There are two types of machine learning algorithms: supervised and unsupervised. With supervised learning, you can specify the input and output data. Unsupervised learning, on the other hand, is based on algorithms that scan data sets for meaningful connections. In other words, the algorithms scan the data to identify patterns in it. And the more data there is, the more accurate the program will be. If you're interested in applying machine learning to your work, start researching today. The possibilities are endless! You'll be glad you did.

In addition to predicting cancer risk by analyzing IKZF2 gene expression, ML algorithms have also been applied to epigenetics and genetics data. Machine learning algorithms can be used as adjunct information, much like a stethoscope was used to diagnose a disease, and the results from them can be correlated with the characteristics of the patient. The results obtained by this method have significant implications in the field of genetics and epigenetics.

In the study, researchers observed that a minimal set of 27 genes provided discrimination between the four cell populations. These genes were validated using single-cell multiplex qPCR and flow cytometry. In addition to these genes, they also identified a novel marker, TRIM. This marker was strongly expressed in non-responding T-cells. Therefore, it's time for a broader use of ML in clinical research.