Identification Of Transcription Factors Research Articles

Transcriptional modules and hormonal metabolic pathways reveal the critical role of TgHB12-like in the regulation of flower opening and petal senescence in Tulipa gesneriana

Tulips (Tulipa gesneriana) are one of the most widely cultivated bulbous plants with substantial ornamental value. However, the lack of well-documented reference genomes has limited the research progress and molecular breeding of tulips. In the present study, a full-length transcriptome of a commercial tulip cultivar was obtained using single-molecule long-read sequencing (PacBio Iso-Seq). In total, 244,357 full-length transcripts were identified, which had an average length of 2,044 bp and an N50 value of 3,861; 67,350 of these were annotated to databases. An inaugural integrated analysis of the transcriptome and phytohormone profiles during flower opening and petal senescence was performed using Illumina RNA-seq, coupled with Mfuzz (an R pakage, http://mfuzz.sysbiolab.eu) and weighted gene coexpression network analysis (WGCNA). A total of 16 gene coexpression and six transcription factor (TF) modules were constructed. Additionally, 26 hormone analogs were comprehensively profiled. Finally, a prominently novel gene, Tulipa gesneriana Homeobox12-like (TgHB12-like), which encodes an homeodomain–leucine zipper (HD-zip) TF, was identified as a pivotal regulator of petal senescence. Overall, this work facilitates the identification of hormones and TFs in plants related to flower opening and senescence in tulips. It also provides an important and valuable genetic basis for further research in them.

Regulatory microRNAs and phasiRNAs of paclitaxel biosynthesis in Taxus chinensis.

Paclitaxel (trade name Taxol) is a rare diterpenoid with anticancer activity isolated from Taxus. At present, paclitaxel is mainly produced by the semi-synthetic method using extract of Taxus tissues as raw materials. The studies of regulatory mechanisms in paclitaxel biosynthesis would promote the production of paclitaxel through tissue/cell culture approaches. Here, we systematically identified 990 transcription factors (TFs), 460 microRNAs (miRNAs), and 160 phased small interfering RNAs (phasiRNAs) in Taxus chinensis to explore their interactions and potential roles in regulation of paclitaxel synthesis. The expression levels of enzyme genes in cone and root were higher than those in leaf and bark. Nearly all enzyme genes in the paclitaxel synthesis pathway were significantly up-regulated after jasmonate treatment, except for GGPPS and CoA Ligase. The expression level of enzyme genes located in the latter steps of the synthesis pathway was significantly higher in female barks than in male. Regulatory TFs were inferred through co-expression network analysis, resulting in the identification of TFs from diverse families including MYB and AP2. Genes with ADP binding and copper ion binding functions were overrepresented in targets of miRNA genes. The miRNA targets were mainly enriched with genes in plant hormone signal transduction, mRNA surveillance pathway, cell cycle and DNA replication. Genes in oxidoreductase activity, protein-disulfide reductase activity were enriched in targets of phasiRNAs. Regulatory networks were further constructed including components of enzyme genes, TFs, miRNAs, and phasiRNAs. The hierarchical regulation of paclitaxel production by miRNAs and phasiRNAs indicates a robust regulation at post-transcriptional level. Our study on transcriptional and posttranscriptional regulation of paclitaxel synthesis provides clues for enhancing paclitaxel production using synthetic biology technology.

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only ~ 700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality (1) to isolate FPs without binding motifs, (2) to perform de novo motif generation and (3) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

‘Mother(Nature) knows best’ – hijacking nature-designed transcriptional programs for enhancing stress resistance and protein production in Yarrowia lipolytica; presentation of YaliFunTome database

BackgroundIn the era of rationally designed synthetic biology, heterologous metabolites production, and other counter-nature engineering of cellular metabolism, we took a step back and recalled that ‘Mother(-Nature) knows best’. While still aiming at synthetic, non-natural outcomes of generating an ‘over-production phenotype’ we dug into the pre-designed transcriptional programs evolved in our host organism—Yarrowia lipolytica, hoping that some of these fine-tuned orchestrated programs could be hijacked and used. Having an interest in the practical outcomes of the research, we targeted industrially-relevant functionalities—stress resistance and enhanced synthesis of proteins, and gauged them over extensive experimental design’s completion.ResultsTechnically, the problem was addressed by screening a broad library of over 120 Y. lipolytica strains under 72 combinations of variables through a carefully pre-optimized high-throughput cultivation protocol, which enabled actual phenotype development. The abundance of the transcription program elicitors—transcription factors (TFs), was secured by their overexpression, while challenging the strains with the multitude of conditions was inflicted to impact their activation stratus. The data were subjected to mathematical modeling to increase their informativeness.The amount of the gathered data prompted us to present them in the form of a searchable catalog – the YaliFunTome database (https://sparrow.up.poznan.pl/tsdatabase/)—to facilitate the withdrawal of biological sense from numerical data. We succeeded in the identification of TFs that act as omni-boosters of protein synthesis, enhance resistance to limited oxygen availability, and improve protein synthesis capacity under inorganic nitrogen provision.ConclusionsAll potential users are invited to browse YaliFunTome in the search for homologous TFs and the TF-driven phenotypes of interest.

Genome-wide identification of the GRF family in sweet orange (Citrus sinensis) and functional analysis of the CsGRF04 in response to multiple abiotic stresses

BackgroundCitrus is one of the most valuable fruits worldwide and an economic pillar industry in southern China. Nevertheless, it frequently suffers from undesirable environmental stresses during the growth cycle, which severely restricts the growth, development and yield of citrus. In plants, the growth-regulating factor (GRF) family of transcription factors (TF) is extensively distributed and plays an vital part in plant growth and development, hormone response, as well as stress adaptation. However, the systematic identification and functional analysis of GRF TFs in citrus have not been reported.ResultsHere, a genome-wide identification of GRF TFs was performed in Citrus sinensis, 9 members of CsGRFs were systematically identified and discovered to be scattered throughout 5 chromosomes. Subsequently, physical and chemical properties, phylogenetic relationships, structural characteristics, gene duplication events, collinearity and cis-elements of promoter were elaborately analyzed. In particular, the expression patterns of the CsGRF genes in response to multiple phytohormone and abiotic stress treatments were investigated. Predicated on this result, CsGRF04, which exhibited the most differential expression pattern under multiple phytohormone and abiotic stress treatments was screened out. Virus-induced gene silencing (VIGS) technology was utilized to obtain gene silenced plants for CsGRF04 successfully. After the three stress treatments of high salinity, low temperature and drought, the CsGRF04-VIGS lines showed significantly reduced resistance to high salinity and low temperature stresses, but extremely increased resistance to drought stress.ConclusionsTaken together, our findings systematically analyzed the genomic characterization of GRF family in Citrus sinensis, and excavated a CsGRF04 with potential functions under multiple abiotic stresses. Our study lay a foundation for further study on the function of CsGRFs in abiotic stress and hormone signaling response.

Sea-ATI unravels novel vocabularies of plant active cistrome.

The cistrome consists of all cis-acting regulatory elements recognized by transcription factors (TFs). However, only a portion of the cistrome is active for TF binding in a specific tissue. Resolving the active cistrome in plants remains challenging. In this study, we report the assay sequential extraction assisted-active TF identification (sea-ATI), a low-input method that profiles the DNA sequences recognized by TFs in a target tissue. We applied sea-ATI to seven plant tissues to survey their active cistrome and generated 41 motif models, including 15 new models that represent previously unidentified cis-regulatory vocabularies. ATAC-seq and RNA-seq analyses confirmed the functionality of thecis-elements from the new models, in that they are actively bound in vivo, located near the transcription start site, and influence chromatin accessibility and transcription. Furthermore, comparing dimeric WRKY CREs between sea-ATI and DAP-seq libraries revealed that thermodynamics and genetic drifts cooperatively shaped their evolution. Notably, sea-ATI can identify not only positive but also negative regulatory cis-elements, thereby providing unique insights into the functional non-coding genome of plants.

PSmad3+/Olig2- expression defines a subpopulation of gfap-GFP+/Sox9+ neural progenitors and radial glia-like cells in mouse dentate gyrus through embryonic and postnatal development.

In mouse dentate gyrus, radial glia-like cells (RGLs) persist throughout life and play a critical role in the generation of granule neurons. A large body of evidence has shown that the combinatorial expression of transcription factors (TFs) defines cell types in the developing central nervous system (CNS). As yet, the identification of specific TFs that exclusively define RGLs in the developing mouse dentate gyrus (DG) remains elusive. Here we show that phospho-Smad3 (PSmad3) is expressed in a subpopulation of neural progenitors in the DG. During embryonic stage (E14-15), PSmad3 was predominantly expressed in gfap-GFP-positive (GFP+)/Sox2+ progenitors located at the lower dentate notch (LDN). As the development proceeds (E16-17), the vast majority of PSmad3+ cells were GFP+/Sox2+/Prox1low+/Ki67+ proliferative progenitors that eventually differentiated into granule neurons. During postnatal stage (P1-P6) PSmad3 expression was observed in GFP+ progenitors and astrocytes. Subsequently, at P14-P60, PSmad3 expression was found both in GFP+ RGLs in the subgranular zone (SGZ) and astrocytes in the molecular layer (ML) and hilus. Notably, PSmad3+ SGZ cells did not express proliferation markers such as PCNA and phospho-vimentin, suggesting that they are predominantly quiescent from P14 onwards. Significantly PSmad3+/GFP+ astrocytes, but not SGZ cells, co-expressed Olig2 and S100β. Together, PSmad3+/Olig2- expression serves as an exclusive marker for a specific subpopulation of GFP+ neural progenitors and RGLs in the mouse DG during both embryonic and postnatal period.

Comparative genomics sheds light on transcription factor-mediated regulation in the extreme acidophilic Acidithiobacillia representatives

Extreme acidophiles thrive in acidic environments, confront a multitude of challenges, and demonstrate remarkable adaptability in their metabolism to cope with the ever-changing environmental fluctuations, which encompass variations in temperature, pH levels, and the availability of electron acceptors and donors. The survival and proliferation of members within the Acidithiobacillia class rely on the deployment of transcriptional regulatory systems linked to essential physiological traits. The study of these transcriptional regulatory systems provides valuable insights into critical processes, such as energy metabolism and nutrient assimilation, and how they integrate into major genetic-metabolic circuits. In this study, we examined the transcriptional regulatory repertoires and potential interactions of forty-three Acidithiobacillia complete and draft genomes, encompassing nine species. To investigate the function and diversity of Transcription Factors (TFs) and their DNA Binding Sites (DBSs), we conducted a genome-wide comparative analysis, which allowed us to identify these regulatory elements in representatives of Acidithiobacillia. We classified TFs into gene families and compared their occurrence among all representatives, revealing conservation patterns across the class. The results identified conserved regulators for several pathways, including iron and sulfur oxidation, the main pathways for energy acquisition, providing new evidence for viable regulatory interactions and branch-specific conservation in Acidithiobacillia. The identification of TFs and DBSs not only corroborates existing experimental information for selected species, but also introduces novel candidates for experimental validation. Moreover, these promising candidates have the potential for further extension to new representatives within the class.

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma cell states and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io.

Identification of Hub Genes and Potential Molecular Pathogenesis in Substantia Nigra in Parkinson's Disease via Bioinformatics Analysis.

Parkinson's disease (PD) is the second most common neurodegenerative disease, with significant socioeconomic burdens. One of the crucial pathological features of PD is the loss of dopaminergic neurons in the substantia nigra (SN). However, the exact pathogenesis remains unknown. Moreover, therapies to prevent neurodegenerative progress are still being explored. We performed bioinformatics analysis to identify candidate genes and molecular pathogenesis in the SN of patients with PD. We analyzed the expression profiles, GSE49036 and GSE7621, which included 31 SN tissues in PD samples and 17 SN tissues in healthy control samples, and identified 86 common differentially expressed genes (DEGs). Then, GO and KEGG pathway analyses of the identified DEGs were performed to understand the biological processes and significant pathways of PD. Subsequently, a protein-protein interaction network was established, with 15 hub genes and four key modules which were screened in this network. The expression profiles, GSE8397 and GSE42966, were used to verify these hub genes. We demonstrated a decrease in the expression levels of 14 hub genes in the SN tissues of PD samples. Our results indicated that, among the 14 hub genes, DRD2, SLC18A2, and SLC6A3 may participate in the pathogenesis of PD by influencing the function of the dopaminergic synapse. CACNA1E, KCNJ6, and KCNB1 may affect the function of the dopaminergic synapse by regulating ion transmembrane transport. Moreover, we identified eight microRNAs (miRNAs) that can regulate the hub genes and 339 transcription factors (TFs) targeting these hub genes and miRNAs. Subsequently, we established an mTF-miRNA-gene-gTF regulatory network. Together, the identification of DEGs, hub genes, miRNAs, and TFs could provide better insights into the pathogenesis of PD and contribute to the diagnosis and therapies.

Genome-wide identification and comparative expression profiling of the WRKY transcription factor family in two Citrus species with different Candidatus Liberibacter asiaticus susceptibility

BackgroundSalicylic Acid (SA) is a pivotal phytohormone in plant innate immunity enhancement of triggered by various pathogens, such as Candidatus Liberibacter asiaticus (CLas), the causal agent of Huanglongbing (HLB). WRKY is a plant specific transcription factor (TF) family, which plays crucial roles in plant response to biotic stresses. So far, the evolutionary history, functions, and expression patterns under SA treatment and CLas infection of WRKY family are poorly understood in Citrus, despite the release of the genome of several Citrus species. A comprehensive genomic and expressional analysis is worth to conduct for this family.ResultsHere, a genome-wide identification of WRKY TFs was performed in two Citrus species: Citrus sinensis (HLB-sensitive) and Poncirus trifoliata (HLB-tolerant). In total, 52 CsWRKYs and 51 PtrWRKYs were identified, whose physical and chemical properties, chromosome locations, phylogenetic relationships and structural characteristics were comparatively analyzed. Especially, expression patterns of these WRKY genes before and after SA treatment and CLas infection were compared. Based on this result, seven pairs of orthologous WRKY genes showing opposite expression patterns in two Citrus species were screened out. Moreover, two pairs of orthologous WRKY genes with significant differences in the number or type of stress-responsive cis-elements in the promoter regions were discovered. Subcellular localization and transcriptional activation activity assays revealed that these two pairs of orthologous genes are classic WRKY TFs localize in the nucleus and could function as transcriptional activators.ConclusionIn this study, we systematically analyzed the genomic characterization of WRKY family in two Citrus species, together with the analyses of expression patterns under SA signaling and CLas infection. Our study laid a foundation for further study on the function of WRKY TFs in HLB response and SA signaling of Citrus.

Transcription factor E2F8 is a therapeutic target in the basal-like subtype of breast cancer.

Tumorigenesis in breast cancers usually accompanied by the dysregulation of transcription factors (TFs). Abnormal amplification of TFs leads aberrant expression of its downstream target genes. However, breast cancers are heterogeneous disease with different subtypes that have distinguished clinical behaviours, and the identification of prognostic TFs may enable to provide diagnosis and treatment of breast cancer based on subtypes, especially in Basal-like breast cancer. The RNA-sequencing was performed to screen differential TFs in breast cancer subtypes. The GEPIA dataset analysis was used to analyze the genes expression in invasive breast carcinoma. The expression of MYBL2, HOXC13, and E2F8 was verified by qRT-PCR assay in breast cancers. The depiction analysis of co-expressed proteins was revealed using the STRING datasets. The cellular infiltration level analysis by the TISIDB and TIMER databases. The transwell assay was performed to analyze cellular migration and invasion. CCK-8 assay was used to evaluate cellular drug susceptibility for docetaxel treatment. Predicted targeted drugs in breast cancers by GSCA Lite database online. Kaplan-Meier plotter suggested that high expression of both E2F8 and MYBL2 in Basal-like subtype had a poor relapse-free survival. Functional enrichment results identified that apoptosis, cell cycle, and hormone ER pathway were represented the crucial regulation pathways by both E2F8 and MYBL2. In the meantime, database analysis indicated that high expression of E2F8 responded to chemotherapy, while those patients of high expression of MYBL2 responded to endocrinotherapy, and a positive correlation between the expression of E2F8 and PD-L1/CTLA4. Our cell line experiments confirmed the importance of E2F8 and MYBL2 in proliferation and chemotherapy sensitivity, possibly, the relationship with PD-L1. Additionally, we also observed that the up-regulation of E2F8 was accompanied with higher enrichments of CD4+ T cells and CD8+ T cells in breast cancers. Taken together, our findings elucidated a prospective target in Basal-like breast cancer, providing underlying molecular biomarkers for the development of breast cancer treatment.

CREPE: a Shiny app for transcription factor cataloguing.

Transcription factors (TFs) are proteins that directly interpret the genome to regulate gene expression and determine cellular phenotypes. TF identification is a common first step in unraveling gene regulatory networks. We present CREPE, an R Shiny app to catalogue and annotate TFs. CREPE was benchmarked against curated human TF datasets. Next, we use CREPE to explore the TF repertoires of Heliconius erato and Heliconius melpomene butterflies. CREPE is available as a Shiny app package available at GitHub (github.com/dirostri/CREPE). Supplementary data are available at Bioinformatics Advances online.

DsDBF1, a Type A-5 DREB Gene, Identified and Characterized in the Moss Dicranum scoparium.

Plant dehydration-responsive element binding (DREB) transcription factors (TFs) play important roles during stress tolerance by regulating the expression of numerous genes involved in stresses. DREB TFs have been extensively studied in a variety of angiosperms and bryophytes. To date, no information on the identification and characterization of DREB TFs in Dicranum scoparium has been reported. In this study, a new DBF1 gene from D. scoparium was identified by cloning and sequencing. Analysis of the conserved domain and physicochemical properties revealed that DsDBF1 protein has a classic AP2 domain encoding a 238 amino acid polypeptide with a molecular mass of 26 kDa and a pI of 5.98. Subcellular prediction suggested that DsDBF1 is a nuclear and cytoplasmic protein. Phylogenetic analysis showed that DsDBF1 belongs to group A-5 DREBs. Expression analysis by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) revealed that DsDBF1 was significantly upregulated in response to abiotic stresses such as desiccation/rehydration, exposure to paraquat, CdCl2, high and freezing temperatures. Taken together, our data suggest that DsDBF1 could be a promising gene candidate to improve stress tolerance in crop plants, and the characterization of TFs of a stress tolerant moss such as D. scoparium provides a better understanding of plant adaptation mechanisms.

Identifying Transcription Factors That Prefer Binding to Methylated DNA Using Reduced G-Gap Dipeptide Composition.

Transcription factors (TFs) play an important role in gene expression and regulation of 3D genome conformation. TFs have ability to bind to specific DNA fragments called enhancers and promoters. Some TFs bind to promoter DNA fragments which are near the transcription initiation site and form complexes that allow polymerase enzymes to bind to initiate transcription. Previous studies showed that methylated DNAs had ability to inhibit and prevent TFs from binding to DNA fragments. However, recent studies have found that there were TFs that could bind to methylated DNA fragments. The identification of these TFs is an important steppingstone to a better understanding of cellular gene expression mechanisms. However, as experimental methods are often time-consuming and labor-intensive, developing computational methods is essential. In this study, we propose two machine learning methods for two problems: (1) identifying TFs and (2) identifying TFs that prefer binding to methylated DNA targets (TFPMs). For the TF identification problem, the proposed method uses the position-specific scoring matrix for data representation and a deep convolutional neural network for modeling. This method achieved 90.56% sensitivity, 83.96% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.9596 on an independent test set. For the TFPM identification problem, we propose to use the reduced g-gap dipeptide composition for data representation and the support vector machine algorithm for modeling. This method achieved 82.61% sensitivity, 64.86% specificity, and an AUC of 0.8486 on another independent test set. These results are higher than those of other studies on the same problems.

Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding

BackgroundWhile methylation of CpG dinucleotides is traditionally considered antagonistic to the DNA-binding activity of most transcription factors (TFs), recent in vitro studies have revealed a more complex picture, suggesting that over a third of TFs may preferentially bind to methylated sequences. Expanding these in vitro observations to in vivo TF binding preferences is challenging since the effect of methylation of individual CpG sites cannot be easily isolated from the confounding effects of DNA accessibility and regional DNA methylation. Thus, in vivo methylation preferences of most TFs remain uncharacterized.ResultsWe introduce joint accessibility-methylation-sequence (JAMS) models, which connect the strength of the binding signal observed in ChIP-seq to the DNA accessibility of the binding site, regional methylation level, DNA sequence, and base-resolution cytosine methylation. We show that JAMS models quantitatively explain TF occupancy, recapitulate cell type-specific TF binding, and have high positive predictive value for identification of TFs affected by intra-motif methylation. Analysis of 2209 ChIP-seq experiments results in high-confidence JAMS models for 260 TFs, revealing a negative association between in vivo TF occupancy and intra-motif methylation for 45% of studied TFs, as well as 16 TFs that are predicted to bind to methylated sites, including 11 novel methyl-binding TFs mostly from the multi-zinc finger family.ConclusionsOur study substantially expands the repertoire of in vivo methyl-binding TFs, but also suggests that most TFs that prefer methylated CpGs in vitro present themselves as methylation agnostic in vivo, potentially due to the balancing effect of competition with other methyl-binding proteins.

R2R3-MYBs in Durum Wheat: Genome-Wide Identification, Poaceae-Specific Clusters, Expression, and Regulatory Dynamics Under Abiotic Stresses.

MYB transcription factors (TFs) represent one of the biggest TF families in plants, being involved in various specific plant processes, such as responses to biotic and abiotic stresses. The implication of MYB TFs in the tolerance mechanisms to abiotic stress is particularly interesting for crop breeding, since environmental conditions can negatively affect growth and productivity. Wheat is a worldwide-cultivated cereal, and is a major source of plant-based proteins in human food. In particular, durum wheat plays an important role in global food security improvement, since its adaptation to hot and dry conditions constitutes the base for the success of wheat breeding programs in future. In the present study, a genome-wide identification of R2R3-MYB TFs in durum wheat was performed. MYB profile search and phylogenetic analyses based on homology with Arabidopsis and rice MYB TFs led to the identification of 233 R2R3-TdMYB (Triticum durum MYB). Three Poaceae-specific MYB clusters were detected, one of which had never been described before. The expression of eight selected genes under different abiotic stress conditions, revealed that most of them responded especially to salt and drought stress. Finally, gene regulatory network analyses led to the identification of 41 gene targets for three TdR2R3-MYBs that represent novel candidates for functional analyses. This study provides a detailed description of durum wheat R2R3-MYB genes and contributes to a deeper understanding of the molecular response of durum wheat to unfavorable climate conditions.

Abstract 5020: Identification of transcriptional regulatory networks associated with papillary thyroid carcinoma

Abstract Background: Identification of critical transcription factors (TFs) required by cancer cells to sustain biological processes that support their growth and survival is urgently needed to develop strategies targeting them. Papillary thyroid carcinoma (PTC) is the commonest thyroid malignancy making about 80% of all thyroid cancers cases. Gene expression of a number of thyroid-specific TFs has been found deregulated in thyroid carcinomas. Therefore, molecular approaches targeting overexpressed oncogenes are desirable therapeutic methods to improve the treatment of cancer patients. In this study, we used Transcription Factor Enrichment Analysis (TFEA) to detect positional motif enrichment associated with changes in transcription observed in this cancer type. Methods: We previously identified differentially expressed genes between PTC and normal thyroid tissue using DNA microarrays. In the current study, the former overexpressed genes were subjected to TFEA using the web-based tool ChEA3. For comparison purposes, the Gene Expression Profiling Interactive Analysis (GEPIA2) tool was used to identify the most differentially overexpressed genes in thyroid carcinoma versus paired normal samples from patients in the TCGA database, and the differentially overexpressed gene set was also subjected to TFEA. Results: TFEA using the ChEA3 web-server identified several TFs associated with overexpressed genes observed in PTC. The top-ranked TFs found in both the gene set identified in PTC patient samples using microarrays and the gene set identified in the TCGA database using GEPIA2 were: EHF, POU2F3, KLF5, ELF3, HES2, GRHL3, and HNF1B. Several of these TFs have previously been identified in thyroid carcinomas, while others are newly identified. EHF and ELF3 are known to be overexpressed in thyroid carcinogenesis; KLF5 is a zinc-finger transcriptional factor recently found to be highly expressed in a subset of PTC patients with aggressive behavior. Bioinformatics analysis has previously shown that HNF1B is up-regulated in several cancers including thyroid carcinomas. The role of the remaining identified TFs in thyroid carcinogenesis has not been described. POU2F3 is a member of the POU domain family of transcription factors that bind to a specific octamer DNA motif and regulate cell type-specific differentiation pathways. Hes2 encodes a mammalian basic helix-loop-helix transcriptional repressor. GRHL3 (Grainyhead-like 3) is a transcription factor involved in epithelial morphogenesis. Conclusions: Bioinformatics analysis of differentially expressed genes in tumor versus normal paired thyroid tissue samples from PTC patients and in thyroid carcinoma versus paired normal samples from patients in the TCGA database allowed the identification of TFs that may play a role in PTC. The group of TFs identified in this study may represent potential therapeutic targets for this cancer type. Citation Format: Niradiz Reyes, Stephanie Figueroa, Christian Figueroa, Jan Geliebter. Identification of transcriptional regulatory networks associated with papillary thyroid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5020.

Type I interferon transcriptional network regulates expression of coinhibitory receptors in human T cells.

While inhibition of T cell co-inhibitory receptors has revolutionized cancer therapy, the mechanisms governing their expression on human T cells have not been elucidated. Here we show that type 1 interferon (IFN-I) regulates co-inhibitory receptor expression on human T cells, inducing PD-1/TIM-3/LAG-3 while inhibiting TIGIT expression. High-temporal-resolution mRNA profiling of IFN-I responses established the dynamic regulatory networks uncovering three temporal transcriptional waves. Perturbation of key transcription factors (TFs) and TF footprint analysis revealed two regulator modules with different temporal kinetics that control expression of co-inhibitory receptors and IFN-I response genes with SP140 highlighted as one of the key regulators that differentiates LAG-3 and TIGIT expression. Finally, we found that the dynamic IFN-I response in vitro closely mirrored T cell features in acute SARS-CoV-2 infection. The identification of unique TFs controlling co-inhibitory receptor expression under IFN-I response may provide targets for enhancement of immunotherapy in cancer, infectious diseases, and autoimmunity.

Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification.

SummaryElucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. Unfortunately, existing computational gene regulatory network (GRN) reconstruction methods are imprecise, computationally burdensome, and fail to reveal dynamic regulatory topologies. Here, we present Epoch, a reconstruction tool that uses single-cell transcriptomics to accurately infer dynamic networks. We apply Epoch to identify the dynamic networks underpinning directed differentiation of mouse embryonic stem cells (ESCs) guided by multiple signaling pathways, and we demonstrate that modulating these pathways drives topological changes that bias cell fate potential. We also find that Peg3 rewires the pluripotency network to favor mesoderm specification. By integrating signaling pathways with GRNs, we trace how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively. Finally, we identify regulatory circuits of patterning and axis formation that distinguish in vitro and in vivo mesoderm specification.