RLN2 Human

What is RLN2 and what is its genomic location?

RLN2 (Human Relaxin-2) is a peptide hormone encoded by the RLN2 gene located on chromosome 9 at position 9p24 in humans. It is clustered with other related genes including RLN1, INSL4, and INSL6. The gene consists of two exons separated by an intron, with this genomic organization being significant for understanding its transcriptional regulation. The position of this intron matches one of the two introns found in insulin genes, reflecting their evolutionary relationship, although the second intron seen in insulin genes is not present in RLN2 .

How is RLN2 structurally related to other hormones?

RLN2 belongs to the Insulin/IGF/Relaxin superfamily that share similar structural characteristics despite sequence variations. The mature RLN2 hormone has a structure consisting of A and B chains connected by disulfide bridges, similar to insulin. The hormone is synthesized as a prohormone (preprorelaxin-2) containing three distinct regions designated A, B, and C chains. During post-translational processing, the C-peptide is cleaved, leaving the mature hormone composed of the A and B chains connected by disulfide bonds .

When investigating structure-function relationships, researchers should note that despite structural similarities within this hormone family, there is a remarkable lack of sequence homology between relaxin from different species, presenting challenges for cross-species research .

How is RLN2 transcribed and translated?

The RLN2 gene transcription produces preprorelaxin-2 mRNA. As shown in Figure 2 from reference material, Exon I encodes the signal peptide, the B Chain, and part of the C Chain, while Exon II encodes the remainder of the C Chain and the A chain of H2 relaxin . This transcript undergoes standard translation to produce the preprorelaxin-2 protein.

Post-translational processing involves:

• Removal of the signal peptide to form prorelaxin-2

• Proteolytic cleavage of the C peptide

• Formation of disulfide bonds between the A and B chains to create the mature RLN2 hormone

For researchers studying RLN2 expression, it's important to design primers and probes that can distinguish between the different processing stages or target specific regions of the transcript.

What evidence supports RLN2's role in tumorigenesis?

Substantial evidence indicates that RLN2 is involved in tumorigenesis through various mechanisms. Research findings supporting this include:

• Clear evidence that relaxin is involved in critical tissue and cellular functions which are important attributes of cancer development and growth

• Studies detecting high levels of RLN2 transcripts in various cancer cell lines, particularly prostate and breast cancer

• Functional studies showing RLN2 enhances key processes in cancer development including:

  • Promoting invasion and migration of cancer cells

  • Stimulating angiogenesis, essential for tumor growth

  • Influencing chemoresistance to anticancer drugs

When investigating RLN2's role in cancer, researchers should employ multiple approaches including gene expression analysis, protein detection methods, and functional assays to assess invasion, migration, and angiogenesis.

How does RLN2 influence cancer cell invasion and migration?

RLN2 promotes cancer cell invasion and migration through multiple mechanisms:

• Extracellular matrix remodeling: RLN2 has significant actions on connective tissue remodeling

• Signaling pathway activation: RLN2 activates signaling cascades that promote the invasive phenotype, including AKT pathways

Research has demonstrated that silencing RLN2 inhibits cell migratory and invasive ability in cancer cells such as MG-63 osteosarcoma cells, while RLN2 overexpression promotes migratory and invasive ability in U2-OS cells . These findings suggest that targeting RLN2 could be a strategy to reduce cancer invasiveness and metastasis.

To study these effects, researchers can utilize:

• Transfection with specific siRNAs (e.g., RLN2 siRNA1, siRNA2, siRNA3) to inhibit RLN2 expression

• Treatment with recombinant human RLN2 (B-29/A-24) to study overexpression effects

• Transwell invasion assays and angiogenesis assays

What signaling pathways does RLN2 interact with in cancer cells?

RLN2 interacts with several signaling pathways in cancer cells that mediate its effects on tumor progression:

• AKT/NF-κB pathway: Evidence shows a positive relation between RLN2 and p-AKT expression in tissues of osteosarcoma. Silencing RLN2 inhibits the activity of the AKT/NF-κB pathway, suggesting this is a key mechanism through which RLN2 influences cancer cell behavior .

• MAPK/ERK pathway: Some studies have found that RLN2 treatment results in rapid activation of MAPK (or ERK) kinase (MEK) in certain cell types, including human endometrial stromal cells, THP-1 monocytic cells, and human smooth muscle cells .

The experimental data indicates that RLN2 confers chemoresistance in part through the AKT/NF-κB pathway, making this pathway an important therapeutic target for overcoming chemoresistance in osteosarcoma cells .

How does RLN2 affect chemosensitivity to anticancer drugs?

Research indicates that RLN2 influences the sensitivity of cancer cells to chemotherapeutic agents, particularly cisplatin:

• RLN2 silencing has been shown to increase the chemosensitivity to cisplatin in MG-63 osteosarcoma cells

• Conversely, RLN2 overexpression increased chemoresistance to cisplatin in U2-OS cells

• The AKT/NF-κB signaling pathway appears to be involved in mediating this chemoresistance effect

As stated in the research: "In the present study, we provide evidence to support the hypothesis that RLN2 plays a role in invasiveness and chemosensitivity to cisplatin of human OS through AKT/NF-κB" . This finding offers potential for combined therapeutic approaches targeting both RLN2 and conventional chemotherapy.

What are the current methods for detecting and quantifying RLN2?

Several advanced methodologies are available for detecting and quantifying RLN2 in biological samples:

• Immunoassays: Traditional methods that may have limitations in distinguishing between RLN1 and RLN2 due to structural similarities and potential cross-reactivity

• Immunoaffinity-selected reaction monitoring (IA-SRM): This highly selective and sensitive technique has emerged as a superior method for identifying and quantifying RLN2. It combines the specificity of immunoaffinity enrichment with the sensitivity of mass spectrometry

• RT-PCR: For detecting RLN2 at the transcript level, though this does not necessarily correlate with protein levels

When implementing these methods, researchers should consider the limit of detection for their assay (e.g., <9.4 pg/mL for some IA-SRM assays of RLN2) and potential cross-reactivity issues, especially with conventional immunoassays .

How can researchers effectively manipulate RLN2 expression in experimental models?

Several approaches are available for manipulating RLN2 expression in experimental models:

• RNA interference (RNAi): siRNA targeting RLN2 has been successfully used to silence RLN2 expression in cancer cell lines. When designing siRNA experiments, researchers should test multiple siRNA sequences to identify the most effective ones (as demonstrated by the RLN2 siRNA1, siRNA2, and siRNA3 comparison in osteosarcoma cells), and validate knockdown at both mRNA and protein levels .

• Recombinant RLN2 treatment: To study the effects of RLN2 overexpression, cells can be treated with recombinant human RLN2 (e.g., 100 μM of B-29/A-24 for 24 hours has been shown to significantly increase RLN2 protein levels in U-2OS cells) .

The efficacy of these approaches should be confirmed through western blot assays, which can demonstrate significant reduction or increase in RLN2 protein levels compared to control conditions.

What explains the discrepancy between RLN2 transcript and protein detection?

Research has revealed interesting discrepancies between transcript and protein detection for RLN2:

These findings highlight important methodological considerations:

• Transcript presence does not always correlate with protein expression

• Highly sensitive and specific methods like IA-SRM are necessary to accurately quantify RLN2 protein

• IA-SRM assays have uncovered potential cross-reactivity and nonspecific binding issues with relaxin immunoassays

How can big data analytics enhance RLN2 research?

Big data analytics offers new approaches to understanding RLN2's functions and clinical implications:

• Prediction vs. explanation: While prediction models using big data can be valuable, particularly in healthcare contexts where delaying action could have serious consequences, researchers should also pursue explanatory models to understand the underlying mechanisms of RLN2 action

• Biomedical applications: The biomedical community has begun to acknowledge that big data provides a critical complement to randomized controlled trials by supporting massive observational studies that were not feasible before

• Evaluation criteria: When designing big data studies for RLN2 research, investigators should consider:

  • The novelty and interestingness of the research question

  • The rigor of empirical methods

  • Attention to threats to inferential validity and causal claims

  • Economic significance of findings

What are the challenges in applying big data approaches to RLN2 research?

Researchers applying big data approaches to RLN2 studies face several challenges:

• Heterogeneous and fragmented data: The increasing volume of unstructured data being generated requires more powerful algorithms and better knowledge representation schemes

• Network effects: When studying RLN2 in biological networks, researchers must consider how treatments of individuals can affect neighboring individuals along the structure of the underlying network, requiring newer algorithms that support valid sampling and estimation of treatment effects

• Tension between correlation and causation: While predictive models based on correlations can be valuable, establishing causal relationships remains important for developing targeted interventions

• Data sharing and transparency: To advance RLN2 research, data sharing practices that enable replication and validation of findings are essential but often overlooked in favor of producing novel results with data sets that are not shared

What methodological approaches should be prioritized for future RLN2 research?

Future RLN2 research would benefit from prioritizing several methodological approaches:

• Development of more specific detection methods: Given the challenges in distinguishing between RLN1 and RLN2, continued refinement of specific detection methods like IA-SRM is crucial

• Integration of multi-omics data: Combining genomic, transcriptomic, and proteomic data can provide a more comprehensive understanding of RLN2's functions and regulatory networks

• Standardized experimental protocols: To enable comparison across studies, standardized protocols for RLN2 manipulation and measurement should be established

• Clinical correlation studies: Investigating the relationship between RLN2 levels and clinical outcomes in larger patient cohorts could help establish its utility as a biomarker

• Cross-disciplinary research approaches: As noted in research methodology studies, effective human research requires a combination of epistemological frameworks, comparative methods, and both quantitative and qualitative approaches