Nonalcoholic fatty liver disease (NAFLD) is a risk factor for Hepatocellular carcinoma (HCC), but he transition from NAFLD to HCC is poorly understood Feature selection algorithms in human and genetically modified mice NAFLD and HCC microarray data were applied to generate signatures of NAFLD progression and HCC differential survival These signatures were used to study the pathogenesis of NAFLD derived HCC and explore which subtypes of cancers that can be investigated using mouse models Our findings show that: (I) HNF4 is a common potential transcription factor mediating the transcription of NAFLD progression genes (II) mice HCC derived from NAFLD co-cluster with a less aggressive human HCC subtype of differential prognosis and mixed etiology (III) the HCC survival signature is able to correctly classify 95% of the samples and gives Fgf20 and Tgfb1i1 as the most robust genes for prediction (IV) the expression values of genes composing the signature in an independent human HCC dataset revealed different HCC subtypes showing differences in survival time by a Logrank test In summary, we present marker signatures for NAFLD derived HCC molecular pathogenesis both at the gene and pathway level

/pdf/integrative-genomic-signatures-of-hepatocellular-carcinoma-3qid0kcxkd.pdf

Integrative Genomic Signatures Of Hepatocellular Carcinoma Derived from Nonalcoholic Fatty Liver Disease

The completion of human and mouse genome sequences and the increasing availability of gene annotations have made it possible for bioinformaticians to develop new approaches to analyze important biological problems. One such problem is the attempt to catalog the complete set of target genes for each of the ∼2000 transcription factors in the human genome. Numerous computational tools have been developed to facilitate the identification of transcription factor binding sites (TFBSs). One such current strategy is the application of ab initio motif discovery algorithms that search for recurring patterns in a given set of related sequences. Examples of this type of strategy include MEME (Bailey and Gribskov 1997), AlignACE (Roth et al. 1998), and Gibbs Motifs Sampler (Thompson et al. 2003). Another approach is to search for known binding sites based on a precompiled library of all previously characterized motifs or positional weight matrices (PWMs). MATCH (Kel et al. 2003) using the TRANSFAC database (Wingender et al. 2000) and MSCAN (Alkema et al. 2004) using JASPAR (Sandelin et al. 2004a) are two broadly used approaches. Unfortunately, using a strictly bioinformatics-based approach to identify target genes of transcription factors is still extremely challenging because most TFBSs are degenerate sequences that occur quite frequently in the mammalian genome.

Recently, computational strategies have been used in combination with data generated from high-throughput techniques such as gene expression and ChIP–chip. Although computational tools such as MDScan (Liu et al. 2002) and MarsMotifs (Smith et al. 2005) have aided experimental biologists in the discovery of regulatory information, a large false-positive prediction rate is still a major problem. One reason for the high false-positive rate is that some strategies fail to take into consideration other factors that might contribute to functional regulatory networks. Recently, several improvements have been designed to reduce spurious predictions (see Elnitski et al. 2006 for a review of several different computational approaches for identifying TFBS). One improvement applies a comparative genomics approach (phylogenetic footprinting) and is based on the assumption that orthologous genes will be subject to the same regulatory mechanisms in different species. The other improvement expands the analysis beyond the search for a single motif to the identification of cis-regulatory modules (CRMs) and is based on the concept that the biochemical specificity of transcription is generated by combinatorial interactions between transcription factors. To aid in identifying cis-regulatory modules for coexpressed genes, several bioinformatics tools, such as ModuleSearcher and ModuleScanner (Aerts et al. 2003), CREME (Sharan et al. 2003), oPOSSUM (Ho-Sui et al. 2005), CONFAC (Karanam and Moreno 2004), and ROVER (Haverty et al. 2004), have been developed. Also, researchers such as Jin et al. (2004) and Cheng et al. (2006) have combined phylogenetic footprinting and prior knowledge of interacting transcription factor partners to identify Estrogen Receptor alpha target genes from ChIP–chip experimental data and to generate predictions of substantially better specificity than analysis of isolated binding sites in promoter sequences.

In this study, we have developed a computational genomics approach called ChIPModules (summarized in Figure 1 and Supplemental Figure S5) to identify cis-regulatory modules for human transcription factors. Of critical importance, we began with a set of experimentally identified binding sites for the factor of interest, which in this study was composed of E2F1 binding sites identified by ChIP–chip in 30 Mb of the human genome. We employed PWMs and evolutionary conservation to refine the set of E2F1 sites and then searched for sites for other factors that occur within a short distance of these E2F1 sites. The predicted ChIPModules were then confirmed experimentally using ChIP–chip assays and arrays that contained tens of thousands of human promoters. Finally, we compiled a database that includes both experimentally and computationally identified E2F1 target promoters. The strength of our approach is that it is sequenced-based and unbiased, and can be applied to any set of ChIP–chip experimental data.



Figure 1.

Flow chart showing the ChIPModules approach. Shown is a schematic indicating the steps needed to develop a database of target promoters for a particular site-specific human transcription factor. The approach begins with a set of experimentally defined ...

/pdf/a-computational-genomics-approach-to-identify-cis-regulatory-15u2bnhj6s.pdf

A computational genomics approach to identify cis-regulatory modules from chromatin immunoprecipitation microarray data—A case study using E2F1

In vitro oocyte maturation (IVM) holds great promise as a tool for enhancing clinical treatment of infertility, enhancing availability of nonhuman primates for development of disease models, and fa

Effects of in vitro maturation on gene expression in Rhesus Monkey oocytes.

Gene expression profiles were determined for 12 cutaneous squamous cell carcinomas (SCC) removed from sun-exposed sites on non-immunosuppressed patients. Gene expression in each SCC was compared to that in sun-exposed skin from the same patient using the Affymetrix HGU133 2.0 PlusGeneChip. We identified 440 genes with increased expression in SCC and 738 with decreased expression; overall we identified a large number of small changes in gene expression rather than a few marked changes that distinguished SCC from sun-exposed skin. Analyzing this robust data set according to biofunctional pathways using DAVID, transcriptional control elements using oPOSSUM, and chromosomal location using GSEA suggested genetic and epigenetic mechanisms of gene expression regulation in SCC. Some altered patterns of gene expression in SCC were consistent with regulation of spatially separated genes by a number of developmentally important transcription factors (forkhead, HMG, and homeo factors) that negatively regulated gene expression and to a few factors that positively regulated expression (Creb-1, NFκB, RelA, and Sp-1). Enhanced expression of keratin 13 We also found that coordinately enhanced expression of epidermal differentiation complex genes on chromosome 1q21 was a hallmark of SCC. A novel finding in our study was enhanced expression of keratin 13 in SCC, a result validated by immunohistochemical staining of an SCC tumor tissue array.

/pdf/microarray-analysis-of-cutaneous-squamous-cell-carcinomas-3jp2umjvnu.pdf

Microarray analysis of cutaneous squamous cell carcinomas reveals enhanced expression of epidermal differentiation complex genes

Impact of acute psychosocial stress on peripheral blood gene expression pathways in healthy men.

Transcription regulation is mediated by combinatorial interactions between diverse trans-acting proteins and arrays of cis-regulatory sequences. Revealing this complex interplay between transcription factors and binding sites remains a fundamental problem for understanding the flow of genetic information. The oPOSSUM analysis system facilitates the interpretation of gene expression data through the analysis of transcription factor binding sites shared by sets of co-expressed genes. The system is based on cross-species sequence comparisons for phylogenetic footprinting and motif models for binding site prediction. We introduce a new set of analysis algorithms for the study of the combinatorial properties of transcription factor binding sites shared by sets of co-expressed genes. The new methods circumvent computational challenges through an applied focus on families of transcription factors with similar binding properties. The algorithm accurately identifies combinations of binding sites over-represented in reference collections and clarifies the results obtained by existing methods for the study of isolated binding sites.

/pdf/identification-of-over-represented-combinations-of-2ksl8qtsns.pdf

Identification of over-represented combinations of transcription factor binding sites in sets of co-expressed genes

https://www.nature.com/articles/s41592-023-01768-4.pdf

Author Correction: CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping

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Identification of over-represented combinations of transcription factor binding sites in sets of co-expressed genes

Author Correction: CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping