Editing Reporter gene (section)

==Further Applications==
A more complex use of reporter genes on a large scale is in [[two-hybrid screening]], which aims to identify proteins that natively interact with one another ''[[in vivo]]''.The yeast [[Two-hybrid screening|two-hybrid (Y2H) system]], developed in the late 1980s and early 1990s, was an immense advancement in the use of reporter genes to study [[Protein–protein interaction|protein-protein interactions]] ''[[in vivo]]''.<ref>{{Cite journal |last1=Brückner |first1=Anna |last2=Polge |first2=Cécile |last3=Lentze |first3=Nicolas |last4=Auerbach |first4=Daniel |last5=Schlattner |first5=Uwe |date=2009-06-18 |title=Yeast Two-Hybrid, a Powerful Tool for Systems Biology |journal=International Journal of Molecular Sciences |language=en |volume=10 |issue=6 |pages=2763–2788 |doi=10.3390/ijms10062763 |doi-access=free |issn=1422-0067 |pmc=2705515 |pmid=19582228}}</ref>  This technique takes advantage of transcription factors' modular nature, which often consists of separate [[DNA-binding domain|DNA-binding]] and [[Transactivation domain|activation domains]]. By genetically fusing two proteins of interest to these domains, researchers can detect physical interactions between them through the activation of a downstream reporter gene. Due to the simple genetic nature of the Y2H system, this technique significantly increased the accessibility of [[Protein–protein interaction|protein-protein interaction]] studies without the requirement of [[protein purification]] or complex biochemical [[assay]]s. Experimental Y2H data have played a pivotal role in building large-scale synthetic human [[interactome]]s and in dissecting mechanisms in human disease.<ref>{{Cite journal |last1=Gandhi |first1=T. K. B. |last2=Zhong |first2=Jun |last3=Mathivanan |first3=Suresh |last4=Karthick |first4=L. |last5=Chandrika |first5=K. N. |last6=Mohan |first6=S. Sujatha |last7=Sharma |first7=Salil |last8=Pinkert |first8=Stefan |last9=Nagaraju |first9=Shilpa |last10=Periaswamy |first10=Balamurugan |last11=Mishra |first11=Goparani |last12=Nandakumar |first12=Kannabiran |last13=Shen |first13=Beiyi |last14=Deshpande |first14=Nandan |last15=Nayak |first15=Rashmi |date=March 2006 |title=Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets |url=https://www.nature.com/articles/ng1747 |journal=Nature Genetics |language=en |volume=38 |issue=3 |pages=285–293 |doi=10.1038/ng1747 |pmid=16501559 |issn=1546-1718|url-access=subscription }}</ref><ref>{{Cite journal |last1=Lim |first1=Janghoo |last2=Hao |first2=Tong |last3=Shaw |first3=Chad |last4=Patel |first4=Akash J. |last5=Szabó |first5=Gábor |last6=Rual |first6=Jean-François |last7=Fisk |first7=C. Joseph |last8=Li |first8=Ning |last9=Smolyar |first9=Alex |last10=Hill |first10=David E. |last11=Barabási |first11=Albert-László |last12=Vidal |first12=Marc |last13=Zoghbi |first13=Huda Y. |date=2006-05-19 |title=A Protein–Protein Interaction Network for Human Inherited Ataxias and Disorders of Purkinje Cell Degeneration |url=https://linkinghub.elsevier.com/retrieve/pii/S0092867406004399 |journal=Cell |language=English |volume=125 |issue=4 |pages=801–814 |doi=10.1016/j.cell.2006.03.032 |issn=0092-8674 |pmid=16713569}}</ref>

However, there are still some limitations. Y2H sometimes detects interactions that don't occur naturally or fails to detect weak or transient interactions, and because it occurs in an artificial setting, Y2H is missing key factors like [[post-translational modification]]s and [[cellular compartment|compartmentalization]]. For example, Y2H has been shown to generate false positives due to indirect interactions mediated by host proteins, as demonstrated in studies of cyanobacterial PipX interactions where the self-interaction of PipX was found to be dependent on PII homologues from the host organism rather than a direct interaction.<ref>{{Cite journal |last1=Feuer |first1=Erez |last2=Zimran |first2=Gil |last3=Shpilman |first3=Michal |last4=Mosquna |first4=Assaf |date=2022-08-19 |title=A Modified Yeast Two-Hybrid Platform Enables Dynamic Control of Expression Intensities to Unmask Properties of Protein–Protein Interactions |journal=ACS Synthetic Biology |volume=11 |issue=8 |pages=2589–2598 |doi=10.1021/acssynbio.2c00192 |pmc=9442787 |pmid=35895499}}</ref>

[[Massive parallel sequencing|Massively parallel reporter assays]] (MPRAs) and [[machine learning]] are newer ways we study gene regulation which utilize reporter genes. One major use is in synthetic biology and gene therapy, where researchers can design better regulatory elements to control gene expression.<ref name=":4">{{Cite journal |last1=Fleur |first1=Alyssa La |last2=Shi |first2=Yongsheng |last3=Seelig |first3=Georg |date=2024-09-01 |title=Decoding biology with massively parallel reporter assays and machine learning |url=https://genesdev.cshlp.org/content/38/17-20/843 |journal=Genes & Development |language=en |volume=38 |issue=17–20 |pages=843–865 |doi=10.1101/gad.351800.124 |issn=0890-9369 |pmc=11535156 |pmid=39362779}}</ref> For example, deep learning models trained on MPRA data have been used to optimize 5' untranslated regions (UTRs) for mRNA translation, enabling tailored designs that enhance gene-editing efficiency in the therapeutic context. This could make mRNA-based treatments more effective, as MPRAs also help identify how genetic variants affect gene expression, which is used in precision medicine and developing personalized treatments.<ref name=":4" />

Machine learning models trained on MPRA data can predict how different sequences impact gene activity, making it easier to design reporter genes that respond in specific ways. Combining MPRAs with next-gen sequencing also makes reporter gene experiments faster and more scalable. These advances could even improve mRNA-based vaccines and therapeutics by optimizing [[untranslated region]]s (UTRs) to boost stability and translation. For instance, modular MPRAs have uncovered context-specific regulatory sequences linked to type 2 diabetes, revealing enhancer-promoter interactions dependent on cell-specific transcription factors like HNF1.<ref>{{Citation |last1=Tovar |first1=Adelaide |title=Using a modular massively parallel reporter assay to discover context-specific regulatory grammars in type 2 diabetes |date=2023-10-10 |url=https://www.biorxiv.org/content/10.1101/2023.10.08.561391v1 |access-date=2025-04-04 |publisher=bioRxiv |language=en |doi=10.1101/2023.10.08.561391 |pmc=10592691 |pmid=37873175 |last2=Kyono |first2=Yasuhiro |last3=Nishino |first3=Kirsten |last4=Bose |first4=Maya |last5=Varshney |first5=Arushi |last6=Parker |first6=Stephen C. J. |last7=Kitzman |first7=Jacob O.|journal=bioRxiv: The Preprint Server for Biology }}</ref> Similarly, MPRA screens of cardiac enhancer variants have pinpointed functional noncoding sequences influencing QT interval variability, directly linking genetic variation to disease-associated gene dysregulation.<ref>{{Citation |last1=Lee |first1=Dongwon |title=Massively parallel reporter assays identify functional enhancer variants at QT interval GWAS loci |date=2025-03-12 |url=https://www.biorxiv.org/content/10.1101/2025.03.11.642686v1 |access-date=2025-04-04 |publisher=bioRxiv |language=en |doi=10.1101/2025.03.11.642686 |pmc=11952420 |pmid=40161821 |last2=Gunamalai |first2=Lavanya |last3=Kannan |first3=Jeerthi |last4=Vickery |first4=Kyla |last5=Yaacov |first5=Or |last6=Onuchic-Whitford |first6=Ana C. |last7=Chakravarti |first7=Aravinda |last8=Kapoor |first8=Ashish|journal=bioRxiv: The Preprint Server for Biology }}</ref>