@Article{pagadala2023germline,
  title = {Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response},
  author = {Meghana Pagadala and Timothy J Sears and Victoria H Wu and Eva P{\'e}rez-Guijarro and Hyo Kim and Andrea Castro and James V Talwar and Cristian Gonzalez-Colin and Steven Cao and Benjamin J Schmiedel and Shervin Goudarzi and Divya Kirani and Jessica Au and Tongwu Zhang and Teresa Landi and Rany M Salem and Gerald P Morris and Olivier Harismendy and Sandip Pravin Patel and Ludmil B Alexandrov and Jill P Mesirov and Maurizio Zanetti and Chi-Ping Day and Chun Chieh Fan and Wesley K Thompson and Glenn Merlino and J Silvio Gutkind and Pandurangan Vijayanand and Hannah Carter},
  year = {2023},
  month = {5},
  journal = {Nature communications},
  volume = {14},
  number = {1},
  pages = {2744},
  eprint = {37173324},
  doi = {10.1038/s41467-023-38271-5},
  language = {eng},
  issn = {2041-1723},
  abstract = {With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.},
  eprinttype = {pubmed},
}

@Article{schmiedel2022single,
  title = {Single-cell eQTL analysis of activated T cell subsets reveals activation and cell type-dependent effects of disease-risk variants},
  author = {Benjamin J Schmiedel and Cristian Gonzalez-Colin and Vicente Fajardo and Job Rocha and Ariel Madrigal and Ciro Ram{\'\i}rez-Su{\'a}stegui and Sourya Bhattacharyya and Hayley Simon and Jason A Greenbaum and Bjoern Peters and Gr{\'e}gory Seumois and Ferhat Ay and Vivek Chandra and Pandurangan Vijayanand},
  year = {2022},
  month = {2},
  journal = {Science immunology},
  volume = {7},
  number = {68},
  pages = {eabm2508},
  eprint = {35213211},
  doi = {10.1126/sciimmunol.abm2508},
  language = {eng},
  issn = {2470-9468},
  abstract = {The impact of genetic variants on cells challenged in biologically relevant contexts has not been fully explored. Here, we activated CD4+ T cells from 89 healthy donors and performed a single-cell RNA sequencing assay with >1 million cells to examine cell type-specific and activation-dependent effects of genetic variants. Single-cell expression quantitative trait loci (sc-eQTL) analysis of 19 distinct CD4+ T cell subsets showed that the expression of over 4000 genes is significantly associated with common genetic polymorphisms and that most of these genes show their most prominent effects in specific cell types. These genes included many that encode for molecules important for activation, differentiation, and effector functions of T cells. We also found new gene associations for disease-risk variants identified from genome-wide association studies and highlighted the cell types in which their effects are most prominent. We found that biological sex has a major influence on activation-dependent gene expression in CD4+ T cell subsets. Sex-biased transcripts were significantly enriched in several pathways that are essential for the initiation and execution of effector functions by CD4+ T cells like TCR signaling, cytokines, cytokine receptors, costimulatory, apoptosis, and cell-cell adhesion pathways. Overall, this DICE (Database of Immune Cell Expression, eQTLs, and Epigenomics) subproject highlights the power of sc-eQTL studies for simultaneously exploring the activation and cell type-dependent effects of common genetic variants on gene expression (https://dice-database.org).},
  eprinttype = {pubmed},
}

@Article{schmiedel2021covid,
  title = {COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types},
  author = {Benjamin J Schmiedel and Job Rocha and Cristian Gonzalez-Colin and Sourya Bhattacharyya and Ariel Madrigal and Christian H Ottensmeier and Ferhat Ay and Vivek Chandra and Pandurangan Vijayanand},
  year = {2021},
  month = {11},
  journal = {Nature communications},
  volume = {12},
  number = {1},
  pages = {6760},
  eprint = {34799557},
  doi = {10.1038/s41467-021-26888-3},
  language = {eng},
  issn = {2041-1723},
  abstract = {Common genetic polymorphisms associated with COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Given the importance of immune cells in the pathogenesis of COVID-19 illness, here we assessed the effects of COVID-19-risk variants on gene expression in a wide range of immune cell types. Transcriptome-wide association study and colocalization analysis revealed putative causal genes and the specific immune cell types where gene expression is most influenced by COVID-19-risk variants. Notable examples include OAS1 in non-classical monocytes, DTX1 in B cells, IL10RB in NK cells, CXCR6 in follicular helper T cells, CCR9 in regulatory T cells and ARL17A in TH2 cells. By analysis of transposase accessible chromatin and H3K27ac-based chromatin-interaction maps of immune cell types, we prioritized potentially functional COVID-19-risk variants. Our study highlights the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations.},
  eprinttype = {pubmed},
}

@Article{chandra2021promoter,
  title = {Promoter-interacting expression quantitative trait loci are enriched for functional genetic variants},
  author = {Vivek Chandra and Sourya Bhattacharyya and Benjamin J Schmiedel and Ariel Madrigal and Cristian Gonzalez-Colin and Stephanie Fotsing and Austin Crinklaw and Gregory Seumois and Pejman Mohammadi and Mitchell Kronenberg and Bjoern Peters and Ferhat Ay and Pandurangan Vijayanand},
  year = {2021},
  month = {1},
  journal = {Nature genetics},
  volume = {53},
  number = {1},
  pages = {110-119},
  eprint = {33349701},
  doi = {10.1038/s41588-020-00745-3},
  language = {eng},
  issn = {1546-1718},
  abstract = {Expression quantitative trait loci (eQTLs) studies provide associations of genetic variants with gene expression but fall short of pinpointing functionally important eQTLs. Here, using H3K27ac HiChIP assays, we mapped eQTLs overlapping active cis-regulatory elements that interact with their target gene promoters (promoter-interacting eQTLs, pieQTLs) in five common immune cell types (Database of Immune Cell Expression, Expression quantitative trait loci and Epigenomics (DICE) cis-interactome project). This approach allowed us to identify functionally important eQTLs and show mechanisms that explain their cell-type restriction. We also devised an approach to eQTL discovery that relies on HiChIP-based promoter interaction maps as a structural framework for deciding which SNPs to test for association with gene expression, and observe ultra-long-distance pieQTLs (>1 megabase away), including several disease-risk variants. We validated the functional role of pieQTLs using reporter assays, CRISPRi, dCas9-tiling guides and Cas9-mediated base-pair editing. In this article we present a method for functional eQTL discovery and provide insights into relevance of noncoding variants for cell-specific gene regulation and for disease association beyond conventional eQTL mapping.},
  eprinttype = {pubmed},
}

@Article{mndezcruz2020knowledge,
  title = {Knowledge extraction for assisted curation of summaries of bacterial transcription factor properties},
  author = {Carlos-Francisco M{\'e}ndez-Cruz and Antonio Blanchet and Alan God{\'\i}nez and Ignacio Arroyo-Fern{\'a}ndez and Socorro Gama-Castro and Sara Berenice Mart{\'\i}nez-Luna and Cristian Gonzalez-Colin and Julio Collado-Vides},
  year = {2020},
  month = {12},
  journal = {Database : the journal of biological databases and curation},
  volume = {2020},
  eprint = {33306798},
  doi = {10.1093/database/baaa109},
  language = {eng},
  issn = {1758-0463},
  abstract = {Transcription factors (TFs) play a main role in transcriptional regulation of bacteria, as they regulate transcription of the genetic information encoded in DNA. Thus, the curation of the properties of these regulatory proteins is essential for a better understanding of transcriptional regulation. However, traditional manual curation of article collections to compile descriptions of TF properties takes significant time and effort due to the overwhelming amount of biomedical literature, which increases every day. The development of automatic approaches for knowledge extraction to assist curation is therefore critical. Here, we show an effective approach for knowledge extraction to assist curation of summaries describing bacterial TF properties based on an automatic text summarization strategy. We were able to recover automatically a median 77\% of the knowledge contained in manual summaries describing properties of 177 TFs of Escherichia coli K-12 by processing 5961 scientific articles. For 71\% of the TFs, our approach extracted new knowledge that can be used to expand manual descriptions. Furthermore, as we trained our predictive model with manual summaries of E. coli, we also generated summaries for 185 TFs of Salmonella enterica serovar Typhimurium from 3498 articles. According to the manual curation of 10 of these Salmonella typhimurium summaries, 96\% of their sentences contained relevant knowledge. Our results demonstrate the feasibility to assist manual curation to expand manual summaries with new knowledge automatically extracted and to create new summaries of bacteria for which these curation efforts do not exist. Database URL: The automatic summaries of the TFs of E. coli and Salmonella and the automatic summarizer are available in GitHub (https://github.com/laigen-unam/tf-properties-summarizer.git).},
  eprinttype = {pubmed},
}