# cache

engine: gpt-3.5-turbo
prompt:
From the text below, extract the following entities in the following format:

genes: <semicolon-separated list of genes>
organisms: <semicolon-separated list of organism taxons>
gene_organisms: <semicolon-separated list of asterisk separated gene to organism relationships>
activities: <semicolon-separated list of molecular activities>
gene_functions: <semicolon-separated list of gene to molecular activity relationships>
cellular_processes: <semicolon-separated list of cellular processes>
pathways: <semicolon-separated list of pathways>
gene_gene_interactions: <semicolon-separated list of gene to gene interactions>
gene_localizations: <semicolon-separated list of genes plus their location in the cell; for example, "gene1 / cytoplasm; gene2 / mitochondrion">


Text:
Title: β-Catenin Is Required for the cGAS/STING Signaling Pathway but Antagonized by the Herpes Simplex Virus 1 US3 Protein
Text:
The cGAS/STING-mediated DNA-sensing signaling pathway is crucial
for interferon (IFN) production and host antiviral
responses. Herpes simplex virus I (HSV-1) is a DNA virus that has
evolved multiple strategies to evade host immune responses. Here,
we demonstrate that the highly conserved β-catenin protein in the
Wnt signaling pathway is an important factor to enhance the
transcription of type I interferon (IFN-I) in the cGAS/STING
signaling pathway, and the production of IFN-I mediated by
β-catenin was antagonized by HSV-1 US3 protein via its kinase
activity. Infection by US3-deficienct HSV-1 and its kinase-dead
variants failed to downregulate IFN-I and IFN-stimulated
gene (ISG) production induced by β-catenin. Consistent with this,
absence of β-catenin enhanced the replication of US3-deficienct
HSV-1, but not wild-type HSV-1. The underlying mechanism was the
interaction of US3 with β-catenin and its hyperphosphorylation of
β-catenin at Thr556 to block its nuclear translocation. For the
first time, HSV-1 US3 has been shown to inhibit IFN-I production
through hyperphosphorylation of β-catenin and to subvert host
antiviral innate immunity.IMPORTANCE Although increasing evidence
has demonstrated that HSV-1 subverts host immune responses and
establishes lifelong latent infection, the molecular mechanisms
by which HSV-1 interrupts antiviral innate immunity, especially
the cGAS/STING-mediated cellular DNA-sensing signaling pathway,
have not been fully explored. Here, we show that β-catenin
promotes cGAS/STING-mediated activation of the IFN pathway, which
is important for cellular innate immune responses and intrinsic
resistance to DNA virus infection. The protein kinase US3
antagonizes the production of IFN by targeting β-catenin via its
kinase activity. The findings in this study reveal a novel
mechanism for HSV-1 to evade host antiviral immunity and add new
knowledge to help in understanding the interaction between the
host and HSV-1 infection.

Keywords: HSV-1; US3; type I IFN; β-catenin.

===

payload:
genes: β-catenin; cGAS; STING; IFN; ISG; US3; Wnt signaling pathway
organisms: Herpes simplex virus 1 (HSV-1)
gene_organisms: β-catenin * Wnt signaling pathway; β-catenin * cGAS/STING; HSV-1 US3 protein * β-catenin
activities: transcription; kinase activity; replication; hyperphosphorylation; nuclear translocation; inhibit IFN-I production
gene_functions: β-catenin * enhance transcription of IFN-I; US3 protein * antagonize production of IFN-I
cellular_processes: interferon (IFN) production; host antiviral responses; IFN-stimulated gene (ISG) production; innate immunity; DNA virus infection
pathways: cGAS/STING signaling pathway; Wnt signaling pathway; IFN pathway; antiviral immunity
gene_gene_interactions: No gene to gene interactions mentioned
gene_localizations: No gene localizations mentioned


engine: gpt-3.5-turbo
payload: gene: β-catenin organism: Wnt signaling pathway
prompt:
Split the following piece of text into fields in the following format:

gene: <the value for gene>
organism: <the value for organism>


Text:
β-catenin * Wnt signaling pathway

===


engine: gpt-3.5-turbo
payload: gene: β-catenin organism: cGAS/STING
prompt:
Split the following piece of text into fields in the following format:

gene: <the value for gene>
organism: <the value for organism>


Text:
β-catenin * cGAS/STING

===


engine: gpt-3.5-turbo
payload: gene: US3 protein organism: HSV-1
prompt:
Split the following piece of text into fields in the following format:

gene: <the value for gene>
organism: <the value for organism>


Text:
HSV-1 US3 protein * β-catenin

===


engine: gpt-3.5-turbo
payload: gene: β-catenin molecular_activity: enhance transcription of IFN-I
prompt:
Split the following piece of text into fields in the following format:

gene: <the name of the gene in the pair. This comes first.>
molecular_activity: <the name of the molecular function in the pair. This comes second. May be a GO term.>


Text:
β-catenin * enhance transcription of IFN-I

===


engine: gpt-3.5-turbo
payload: gene: US3 protein molecular_activity: antagonize production of IFN-I
prompt:
Split the following piece of text into fields in the following format:

gene: <the name of the gene in the pair. This comes first.>
molecular_activity: <the name of the molecular function in the pair. This comes second. May be a GO term.>


Text:
US3 protein * antagonize production of IFN-I

===


engine: gpt-3.5-turbo
payload: No gene to gene interactions mentioned
prompt:
Split the following piece of text into fields in the following format:

gene1: <the value for gene1>
gene2: <the value for gene2>


Text:
No gene to gene interactions mentioned

===


engine: gpt-3.5-turbo
payload: gene: None location: None
prompt:
Split the following piece of text into fields in the following format:

gene: <the value for gene>
location: <the value for location>


Text:
No gene localizations mentioned

===


engine: gpt-3.5-turbo
prompt:
From the text below, extract the following entities in the following format:

human_specimen_identifier: <What is the identifier for the human specimen?>
human_specimen_collection_site: <Where was the human specimen collected from?>
human_specimen_specimen_type: <What type of specimen is the human specimen?>
name: <What is the name of the patient?>
contact_info: <What is the patient's contact information?>
birth_date: <What is the patient's birth date?>
gender: <What is the patient's gender?>
zip_code: <What is the patient's zip code?>
us_core_race: <What is the patient's recorded race (US Core standard)?>
us_core_birth_sex: <What is the patient's birth sex (US Core standard)?>
us_core_ethnicity: <What is the patient's ethnicity (US Core standard)?>
death_date: <What is the patient's date of death?>
disease_status_evidence_type: <What type of evidence indicates the disease status?>
tumor_identifier: <What is the identifier for the tumor?>
tumor_body_location: <Where is the tumor located in the body?>
tumor_size_longest_dimension: <What is the tumor's longest dimension?>
cancer_stage_stage_type: <What type of cancer stage is identified?>
cancer_asserted_date: <What date was the cancer asserted?>
cancer_body_site: <What body site is affected by the cancer?>
tumor_marker_test_result_value: <What is the result value of the tumor marker test?>
people_human_specimen_identifier_interaction: <list of what people has Human Specimen Identifier, separated by semi-colons>
people_human_specimen_collection_site_interaction: <list of what people has Human Specimen Collection Site, separated by semi-colons>
people_human_specimen_specimen_type_interaction: <list of what people has Human Specimen Specimen Type, separated by semi-colons>
people_name_interaction: <list of what people has Name, separated by semi-colons>
people_contact_info_interaction: <list of what people has Contact Info, separated by semi-colons>
people_birth_date_interaction: <list of what people has Birth Date, separated by semi-colons>
people_gender_interaction: <list of what people has Gender, separated by semi-colons>
people_zip_code_interaction: <list of what people has Zip Code, separated by semi-colons>
people_us_core_race_interaction: <list of what people has US Core Race, separated by semi-colons>
people_us_core_birth_sex_interaction: <list of what people has US Core Birth Sex, separated by semi-colons>
people_us_core_ethnicity_interaction: <list of what people has US Core Ethnicity, separated by semi-colons>
people_death_date_interaction: <list of what people has Death Date, separated by semi-colons>
people_disease_status_evidence_type_interaction: <list of what people has Disease Status Evidence Type, separated by semi-colons>
people_tumor_identifier_interaction: <list of what people has Tumor Identifier, separated by semi-colons>
people_tumor_body_location_interaction: <list of what people has Tumor Body Location, separated by semi-colons>
people_tumor_size_longest_dimension_interaction: <list of what people has Tumor Size Longest Dimension, separated by semi-colons>
people_cancer_stage_stage_type_interaction: <list of what people has Cancer Stage Stage Type, separated by semi-colons>
people_cancer_asserted_date_interaction: <list of what people has Cancer Asserted Date, separated by semi-colons>
people_cancer_body_site_interaction: <list of what people has Cancer Body Site, separated by semi-colons>
people_tumor_marker_test_result_value_interaction: <list of what people has Tumor Marker Test Result Value, separated by semi-colons>


Text:
Patient John Smith (HSI-5421) underwent a tissue biopsy at the bladder collection site. The histopathology report confirmed the presence of cancer in the bladder. The tumor, identified as T-BC5421, measures 4 cm in its longest dimension. The cancer has been staged as T2, indicating its progression beyond the bladder wall. The cancer was asserted on January 10, 2022. The tumor marker test revealed elevated levels of urinary bladder tumor antigen (UBTA). John Smith, a Caucasian male born on April 15, 1975, is non-Hispanic and resides in zip code 90210. He is still alive, with no reported death date at this time. For any further information, please contact John Smith at jsmith@email.com or (555) 123-4567.

===

payload:
human_specimen_identifier: HSI-5421
human_specimen_collection_site: bladder collection site
human_specimen_specimen_type: tissue biopsy
name: John Smith
contact_info: jsmith@email.com or (555) 123-4567
birth_date: April 15, 1975
gender: male
zip_code: 90210
us_core_race: Caucasian
us_core_birth_sex: male
us_core_ethnicity: non-Hispanic
death_date: N/A
disease_status_evidence_type: histopathology report
tumor_identifier: T-BC5421
tumor_body_location: bladder
tumor_size_longest_dimension: 4 cm
cancer_stage_stage_type: T2
cancer_asserted_date: January 10, 2022
cancer_body_site: bladder
tumor_marker_test_result_value: elevated levels of urinary bladder tumor antigen (UBTA)
people_human_specimen_identifier_interaction: John Smith
people_human_specimen_collection_site_interaction: bladder collection site
people_human_specimen_specimen_type_interaction: tissue biopsy
people_name_interaction: John Smith
people_contact_info_interaction: jsmith@email.com or (555) 123-4567
people_birth_date_interaction: April 15, 1975
people_gender_interaction: male
people_zip_code_interaction: 90210
people_us_core_race_interaction: Caucasian
people_us_core_birth_sex_interaction: male
people_us_core_ethnicity_interaction: non-Hispanic
people_death_date_interaction: N/A
people_disease_status_evidence_type_interaction: histopathology report
people_tumor_identifier_interaction: T-BC5421
people_tumor_body_location_interaction: bladder
people_tumor_size_longest_dimension_interaction: 4 cm
people_cancer_stage_stage_type_interaction: T2
people_cancer_asserted_date_interaction: January 10, 2022
people_cancer_body_site_interaction: bladder
people_tumor_marker_test_result_value_interaction: elevated levels of urinary bladder tumor antigen (UBTA)


engine: gpt-3.5-turbo
payload: people: John Smith human_specimen_identifier: undefined
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
human_specimen_identifier: <What is the identifier for the human specimen?>


Text:
John Smith

===


engine: gpt-3.5-turbo
payload: people: human_specimen_collection_site: bladder
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
human_specimen_collection_site: <Where was the human specimen collected from?>


Text:
bladder collection site

===


engine: gpt-3.5-turbo
payload: people: human_specimen_specimen_type: tissue biopsy
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
human_specimen_specimen_type: <What type of specimen is the human specimen?>


Text:
tissue biopsy

===


engine: gpt-3.5-turbo
payload: people: John Smith name: What is the name of the patient?
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
name: <What is the name of the patient?>


Text:
John Smith

===


engine: gpt-3.5-turbo
payload: people: None contact_info: jsmith@email.com or (555) 123-4567
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
contact_info: <What is the patient's contact information?>


Text:
jsmith@email.com or (555) 123-4567

===


engine: gpt-3.5-turbo
payload: people: [empty] birth_date: April 15, 1975
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
birth_date: <What is the patient's birth date?>


Text:
April 15, 1975

===


engine: gpt-3.5-turbo
payload: people: gender: male
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
gender: <What is the patient's gender?>


Text:
male

===


engine: gpt-3.5-turbo
payload: people: zip_code: 90210
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
zip_code: <What is the patient's zip code?>


Text:
90210

===


engine: gpt-3.5-turbo
payload: people: us_core_race: Caucasian
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
us_core_race: <What is the patient's recorded race (US Core standard)?>


Text:
Caucasian

===


engine: gpt-3.5-turbo
payload: people: male us_core_birth_sex: male
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
us_core_birth_sex: <What is the patient's birth sex (US Core standard)?>


Text:
male

===


engine: gpt-3.5-turbo
payload: people: patient us_core_ethnicity: non-Hispanic
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
us_core_ethnicity: <What is the patient's ethnicity (US Core standard)?>


Text:
non-Hispanic

===


engine: gpt-3.5-turbo
payload: people: N/A death_date: N/A
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
death_date: <What is the patient's date of death?>


Text:
N/A

===


engine: gpt-3.5-turbo
payload: people: disease_status_evidence_type: histopathology report
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
disease_status_evidence_type: <What type of evidence indicates the disease status?>


Text:
histopathology report

===


engine: gpt-3.5-turbo
payload: people: tumor_identifier: T-BC5421
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
tumor_identifier: <What is the identifier for the tumor?>


Text:
T-BC5421

===


engine: gpt-3.5-turbo
payload: people: tumor_body_location: bladder
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
tumor_body_location: <Where is the tumor located in the body?>


Text:
bladder

===


engine: gpt-3.5-turbo
payload: people: tumor_size_longest_dimension: 4 cm
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
tumor_size_longest_dimension: <What is the tumor's longest dimension?>


Text:
4 cm

===


engine: gpt-3.5-turbo
payload: people: cancer_stage_stage_type: T2
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
cancer_stage_stage_type: <What type of cancer stage is identified?>


Text:
T2

===


engine: gpt-3.5-turbo
payload: people: cancer_asserted_date: January 10, 2022
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
cancer_asserted_date: <What date was the cancer asserted?>


Text:
January 10, 2022

===


engine: gpt-3.5-turbo
payload: people: cancer_body_site: bladder
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
cancer_body_site: <What body site is affected by the cancer?>


Text:
bladder

===


engine: gpt-3.5-turbo
payload: people: tumor_marker_test_result_value: elevated levels of urinary bladder tumor antigen (UBTA)
prompt:
Split the following piece of text into fields in the following format:

people: <the name of the person>
tumor_marker_test_result_value: <What is the result value of the tumor marker test?>


Text:
elevated levels of urinary bladder tumor antigen (UBTA)

===


