LMO3 interacts with p53 and inhibits its transcriptional activity

PI3K/AKT pathway as a pivotal regulator of epithelial-mesenchymal transition in lung tumor cells

Lung cancer, as the leading cause of cancer related deaths, is one of the main global health challenges. Despite various progresses in diagnostic and therapeutic methods, there is still a high rate of mortality among lung cancer patients, which can be related to the lack of clinical symptoms to differentiate lung cancer from the other chronic respiratory disorders in the early tumor stages. Most lung cancer patients are identified in advanced and metastatic tumor stages, which is associated with a poor prognosis. Therefore, it is necessary to investigate the molecular mechanisms involved in lung tumor progression and metastasis in order to introduce early diagnostic markers as well as therapeutic targets. Epithelial-mesenchymal transition (EMT) is considered as one of the main cellular mechanisms involved in lung tumor metastasis, during which tumor cells gain the metastatic ability by acquiring mesenchymal characteristics. Since, majority of the oncogenic signaling pathways exert their role in tumor cell invasion by inducing the EMT process, in the present review we discussed the role of PI3K/AKT signaling pathway in regulation of EMT process during lung tumor metastasis. It has been reported that the PI3K/AKT acts as an inducer of EMT process through the activation of EMT-specific transcription factors in lung tumor cells. MicroRNAs also exerted their inhibitory effects during EMT process by inhibition of PI3K/AKT pathway. This review can be an effective step towards introducing the PI3K/AKT pathway as a suitable therapeutic target to inhibit the EMT process and tumor metastasis in lung cancer patients.

Spatiotemporal transcriptome atlas reveals the regional specification of the developing human brain

Different functional regions of brain are fundamental for basic neurophysiological activities. However, the regional specification remains largely unexplored during human brain development. Here, by combining spatial transcriptomics (scStereo-seq) and scRNA-seq, we built a spatiotemporal developmental atlas of multiple human brain regions from 6-23 gestational weeks (GWs). We discovered that, around GW8, radial glia (RG) cells have displayed regional heterogeneity and specific spatial distribution. Interestingly, we found that the regional heterogeneity of RG subtypes contributed to the subsequent neuronal specification. Specifically, two diencephalon-specific subtypes gave rise to glutamatergic and GABAergic neurons, whereas subtypes in ventral midbrain were associated with the dopaminergic neurons. Similar GABAergic neuronal subtypes were shared between neocortex and diencephalon. Additionally, we revealed that cell-cell interactions between oligodendrocyte precursor cells and GABAergic neurons influenced and promoted neuronal development coupled with regional specification. Altogether, this study provides comprehensive insights into the regional specification in the developing human brain.

Mutational Landscape and In-Silico Analysis of TP53, PIK3CA, and PTEN in Patients with Breast Cancer from Khyber Pakhtunkhwa

Herein, we report the mutational spectrum of three breast cancer candidate genes (TP53, PIK3CA, and PTEN) using WES for identifying potential biomarkers. The WES data were thoroughly analyzed using SAMtools for variant calling and identification of the mutations. Various bioinformatic tools (SIFT, PolyPhen-2, Mutation Taster, ISPRED-SEQ, SAAFEQ-SEQ, ConSurf, PROCHECK etc.) were used to determine the pathogenicity and nature of the SNVs. Selected interaction site (IS) mutations were visualized in PyMOL after building 3D structures in Swiss-Model. Ramachandran plots were generated by using the PROCHECK server. The selected IS mutations were subjected to molecular dynamic simulation (MDS) studies using Gromacs 4.5. STRING and GeneMANIA were used for the prediction of gene-gene interactions and pathways. Our results revealed that the luminal A molecular subtype of the breast cancer was most common, whereas a high percentage of was Her2 negatives. Moreover, the somatic mutations were more common as compared to the germline mutations in TP53, PIK3CA, and PTEN. 20% of the identified mutations are reported for the first time from Khyber Pakhtunkhwa. In the enrolled cohort, 23 mutations were nonsynonymous SNVs. The frequency of mutations was the highest in PIK3CA, followed by TP53 and PTEN. A total of 13 mutations were found to be highly pathogenic. Four novel mutations were identified on PIK3CA and one each on PTEN and TP53. SAAFEQ-SEQ predicted the destabilizing effect for all mutations. ISPRED-SEQ predicted 9 IS mutations (6 on TP53 and 3 on PIK3CA), whereas no IS mutation was predicted on PTEN. The TP53 IS mutations were TP53R43H, TP53Y73X, TP53K93Q, TP53K93R, TP53D149E, and TP53Q199X; whereas for PIK3CA, the IS mutations were PIK3CAL156V, PIK3CAM610K, and PIK3CAH1047R. Analysis from the ConSurf Web server revealed five SNVs with a highly conserved status (conservation score 9) across TP53 and PTEN. TP53P33R was found predominant in the grade 3 tumors, whereas PTENp.C65S was distributed on ER+, ER-, PR+, PR-, Her2+, and Her2- patients. TP53p.P33R mutation was found to be recurring in the 14/19 (73.6%) patients and, therefore, can be considered as a potential biomarker. Finally, these mutations were studied in the context of their potential association with different hormonal and social factors.

Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models (LDA, RF, CART, k-NN, SVM, XGBOOST, GBM). Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs.

The significance of co-mutations in EGFR-mutated non-small cell lung cancer: Optimizing the efficacy of targeted therapies?

Non-small cell lung cancer (NSCLC) is the most common cause of cancer death worldwide. In non-squamous NSCLC, the identification of oncogenic drivers and the development of target-specific molecules led to remarkable progress in therapeutic strategies and overall survival over the last decade. Nevertheless, responses are limited by systematically acquired mechanisms of resistance early on after starting a targeted therapy. Moreover, mounting evidence has demonstrated that each oncogenic-driven cluster is actually heterogeneous in terms of molecular features, clinical behaviour, and sensitivity to targeted therapy. In this review, we aimed to examine the prognostic and predictive significance of oncogene-driven co-mutations, focusing mainly on EGFR and TP53. A narrative review was performed by searching MEDLINE databases for English articles published over the last decade (from January 2012 until November 2022). The bibliographies of key references were manually reviewed to select those eligible for the topic. The genetic landscape of EGFR-mutated NSCLC is more complicated than what is known so far. In particular, the occurrence of TP53 co-mutations stratify patients carrying EGFR mutations in terms of treatment response. The study provides a deeper understanding of the mechanisms underlying the variability of the genetic landscape of EGFR-mutated NSCLC and summarizes notably the clinical importance of TP53 co-mutations for an open avenue to more properly addressing the clinical decision-making in the near future.

Expression of decitabine-targeted oncogenes in meningiomas in vivo

Treatment of meningiomas refractory to surgery and irradiation is challenging and effective chemotherapies are still lacking. Recently, in vitro analyses revealed decitabine (DCT, 5-aza-2’–deoxycytidine) to be effective in high-grade meningiomas and, moreover, to induce hypomethylation of distinct oncogenes only sparsely described in meningiomas in vivo yet.

Expression of the corresponding onco- and tumor suppressor genes TRIM58, FAM84B, ELOVL2, MAL2, LMO3, and DIO3 were analyzed and scored by immunohistochemical staining and RT-PCR in samples of 111 meningioma patients. Correlations with clinical and histological variables and prognosis were analyzed in uni- and multivariate analyses.

All analyzed oncogenes were highly expressed in meningiomas. Expression scores of TRIM58 tended to be higher in benign than in high-grade tumors 20 vs 16 (p = .002) and all 9 samples lacking TRIM58 expression displayed WHO grade II/III histology. In contrast, median expression scores for both FAM84B (6 vs 4, p ≤ .001) and ELOVL2 (9 vs 6, p < .001) were increased in high-grade as compared to benign meningiomas. DIO3 expression was distinctly higher in all analyzed samples as compared to the reference decitabine-resistant Ben-Men 1 cell line. Increased ELOVL2 expression (score ≥ 8) correlated with tumor relapse in both uni- (HR: 2.42, 95%CI 1.18–4.94; p = .015) and multivariate (HR: 2.09, 95%CI 1.01–4.44; p = .046) analyses.

All oncogenes involved in DCT efficacy in vitro are also widely expressed in vivo, and expression is partially associated with histology and prognosis. These results strongly encourage further analyses of DCT efficiency in meningiomas in vitro and in situ.

LMO3 promotes proliferation and metastasis of papillary thyroid carcinoma cells by regulating LIMK1-mediated cofilin and the β-catenin pathway

LIM domain only 3 (LMO3) interacts with transcription factors to regulate target genes involved in embryonic development. The oncogenic role of LMO3 in hepatocellular carcinoma, gastric cancer, and neuroblastoma has been reported recently. However, little is known about the biological function of LMO3 in papillary thyroid carcinoma (PTC). First, expression of LMO3 was dramatically enhanced in the PTC tissues and cell lines. Second, knockdown of LMO3 in PTC cells repressed cell proliferation and promoted cell apoptosis with downregulated Bcl-2 and upregulated cleaved caspase-3/PARP. In vitro cell migration and invasion of PTC were also retarded by siRNA-mediated silence of LMO3. Third, protein expression of LIM kinase (LIMK) 1-mediated phosphorylation of cofilin and nuclear translocation of β-catenin were reduced by the knockdown of LMO3. pcDNA-mediated overexpression of LIMK1 promoted cofilin phosphorylation and attenuated LMO3 silence-induced decrease of cofilin phosphorylation. Last, enhanced LIMK1 expression promoted PTC cell proliferation and metastasis and counteracted the suppressive effects of LMO3 silence on PTC cell proliferation and metastasis. In conclusion, LMO3 promoted PTC cell proliferation and metastasis by regulating LIMK1-mediated cofilin and the β-catenin pathway.

NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis

Background

Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues.

Results

We developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma–Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods.

Conclusion

NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

Sex-Specific Genetic Associations for Barrett's Esophagus and Esophageal Adenocarcinoma

BACKGROUND & AIMS

Esophageal adenocarcinoma (EA) and its premalignant lesion, Barrett's esophagus (BE), are characterized by a strong and yet unexplained male predominance (with a male-to-female ratio in EA incidence of up to 6:1). Genome-wide association studies (GWAS) have identified more than 20 susceptibility loci for these conditions. However, potential sex differences in genetic associations with BE/EA remain largely unexplored.

METHODS

Given strong genetic overlap, BE and EA cases were combined into a single case group for analysis. These were compared with population-based controls. We performed sex-specific GWAS of BE/EA in 3 separate studies and then used fixed-effects meta-analysis to provide summary estimates for >9 million variants for male and female individuals. A series of downstream analyses were conducted separately in male and female individuals to identify genes associated with BE/EA and the genetic correlations between BE/EA and other traits.

RESULTS

We included 6758 male BE/EA cases, 7489 male controls, 1670 female BE/EA cases, and 6174 female controls. After Bonferroni correction, our meta-analysis of sex-specific GWAS identified 1 variant at chromosome 6q11.1 (rs112894788, KHDRBS2-MTRNR2L9, PBONF = .039) that was statistically significantly associated with BE/EA risk in male individuals only, and 1 variant at chromosome 8p23.1 (rs13259457, PRSS55-RP1L1, PBONF = 0.057) associated, at borderline significance, with BE/EA risk in female individuals only. We also observed strong genetic correlations of BE/EA with gastroesophageal reflux disease in male individuals and obesity in female individuals.

CONCLUSIONS

The identified novel sex-specific variants associated with BE/EA could improve the understanding of the genetic architecture of the disease and the reasons for the male predominance.

Structural And Computational Perspectives of Selectively Targeting Mutant Proteins

Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be "master regulators". For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues. One of the most widely researched proteins in cancer biology is p53, a tumor suppressor. Here, p53 is discussed as a specific example of a challenging target, with contemporary drugs and methodologies used as examples of burgeoning successes. The oncogene KRAS, which has been described as "undruggable", is another extensively investigated protein in cancer biology. This review also examines KRAS to exemplify progress made towards selective targeting of diseasecausing mutant proteins. Finally, possible future directions relevant to the topic are discussed.

Oncogenic Network and Hub Genes for Natural Killer/T-Cell Lymphoma Utilizing WGCNA

Natural killer (NK)/T-cell lymphoma (NKTCL) is a subtype of non-Hodgkin lymphoma with aggressive progression and poor prognosis. The molecular mechanisms of NKTCL have not been well-studied. Herein, we revealed the lymphoma-associated dysregulated genes and signaling pathways or biological processes in NKTCL. We characterized that the extracellular matrix (ECM) receptor interaction pathway and T-cell receptor signaling pathway were the main dysregulated pathways in NKTCL by Gene Ontology (GO) analysis and pathway enrichment analysis. By using weighted gene co-expression network analysis (WGCNA), the gene co-expression network of NKTCL (SRP049695) was constructed, and hub genes (LMO3, GRB14) were identified. In addition, another Gene Expression Omnibus (GEO) dataset (GSE69406) was used to validate these hub genes. Furthermore, these hub genes were identified and validated by survival analysis (GSE90597). These results provided novel insights into the pathogenesis of NKTCL. Of particular interest, LMO3 and GRB14 might be potential oncoproteins and biomarkers for the diagnosis and treatment of NKTCL.

Newly identified LMO3-BORCS5 fusion oncogene in Ewing sarcoma at relapse is a driver of tumor progression

Recently, we detected a new fusion transcript LMO3-BORCS5 in a patient with Ewing sarcoma within a cohort of relapsed pediatric cancers. LMO3-BORCS5 was as highly expressed as the characteristic fusion oncogene EWS/FLI1. However, the expression level of LMO3-BORCS5 at diagnosis was very low. Sanger sequencing depicted two LMO3-BORCS5 variants leading to loss of the functional domain LIM2 in LMO3 gene, and disruption of BORCS5. In vitro studies showed that LMO3-BORCS5 (i) increases proliferation, (ii) decreases expression of apoptosis-related genes and treatment sensitivity, and (iii) downregulates genes involved in differentiation and upregulates proliferative and extracellular matrix-related pathways. Remarkably, in vivo LMO3-BORCS5 demonstrated its high oncogenic potential by inducing tumors in mouse fibroblastic NIH-3T3 cell line. Moreover, BORCS5 probably acts, in vivo, as a tumor-suppressor gene. In conclusion, functional studies of fusion oncogenes at relapse are of great importance to define mechanisms involved in tumor progression and resistance to conventional treatments.

Regulation of anxiety-like behavior and Crhr1 expression in the basolateral amygdala by LMO3

The LIM domain only protein LMO3 is a transcriptional regulator that has been shown to regulate several behavioral responses to alcohol. Specifically, Lmo3 null (Lmo3^Z) mice consume more ethanol in a binge-drinking test and show enhanced ethanol-induced sedation. Due to the high comorbidity of alcohol use and anxiety, we investigated anxiety-like behavior in Lmo3^Z mice. Lmo3^Z mice spent more time in the open arms of the elevated plus maze compared with their wild-type littermates, but the effect was confounded by reduced locomotor activity. To verify the anxiety phenotype in the Lmo3^Z mice, we tested them for novelty-induced hypophagia and found that they also showed reduced anxiety in this test. We next explored the mechanism by which LMO3 might regulate anxiety by measuring mRNA and protein levels of corticotropin releasing factor (encoded by the Crh gene) and its receptor type 1 (Crhr1) in Lmo3^Z mice. Reduced Crhr1 mRNA and protein was evident in the basolateral amygdala (BLA) of Lmo3^Z mice. To examine whether Lmo3 in the amygdala is important for anxiety-like behavior, we locally reduced Lmo3 expression in the BLA of wild type mice using a lentiviral vector expressing a short hairpin RNA targeting the Lmo3 transcript. Mice with Lmo3 knockdown in the BLA exhibited decreased anxiety-like behavior relative to control mice. These results suggest that Lmo3 promotes anxiety-like behavior specifically in the BLA, possibly by altering Crhr1 expression. This study is the first to support a role for Lmo3 in anxiety-like behavior.

Enrichment of megabase-sized DNA molecules for single-molecule optical mapping and next-generation sequencing

Next-generation sequencing (NGS) has caused a revolution, yet left a gap: long-range genetic information from native, non-amplified DNA fragments is unavailable. It might be obtained by optical mapping of megabase-sized DNA molecules. Frequently only a specific genomic region is of interest, so here we introduce a method for selection and enrichment of megabase-sized DNA molecules intended for single-molecule optical mapping: DNA from a human cell line is digested by the NotI rare-cutting enzyme and size-selected by pulsed-field gel electrophoresis. For demonstration, more than 600 sub-megabase- to megabase-sized DNA molecules were recovered from the gel and analysed by denaturation-renaturation optical mapping. Size-selected molecules from the same gel were sequenced by NGS. The optically mapped molecules and the NGS reads showed enrichment from regions defined by NotI restriction sites. We demonstrate that the unannotated genome can be characterized in a locus-specific manner via molecules partially overlapping with the annotated genome. The method is a promising tool for investigation of structural variants in enriched human genomic regions for both research and diagnostic purposes. Our enrichment method could potentially work with other genomes or target specified regions by applying other genomic editing tools, such as the CRISPR/Cas9 system.

Roles of Thyroid Transcription Factor 1 in Lung Cancer Biology

Thyroid transcription factor 1 (TTF-1 or NKX2-1) is a transcription factor of fundamental importance in driving lung maturation and morphogenesis. In the last decade, scientists began to appreciate the functional roles of TTF-1 in lung tumorigenesis. This movement was triggered by the discoveries of genetic alterations of TTF-1 in the form of gene amplification in lung cancer. Many downstream target genes of TTF-1 relevant to the lung cancer biology of TTF-1 have been documented. One of the most surprising findings was that TTF-1 may exhibit either pro- or antitumorigenic activities, an outcome with the complexity exceeding the original anticipation purely based on the fact that TTF-1 undergoes gene amplification in lung cancer. In the coming decade, we believe, we will witness additional surprises as the research exploring the cancer roles of TTF-1 progresses.

An Exploration of Gene-Gene Interactions and Their Effects on Hypertension

Hypertension tends to perpetuate in families and the heritability of hypertension is estimated to be around 20-60%. So far, the main proportion of this heritability has not been found by single-locus genome-wide association studies. Therefore, the current study explored gene-gene interactions that have the potential to partially fill in the missing heritability. A two-stage discovery-confirmatory analysis was carried out in the Framingham Heart Study cohorts. The first stage was an exhaustive pairwise search performed in 2320 early-onset hypertensive cases with matched normotensive controls from the offspring cohort. Then, identified gene-gene interactions were assessed in an independent set of 694 subjects from the original cohort. Four unique gene-gene interactions were found to be related to hypertension. Three detected genes were recognized by previous studies, and the other 5 loci/genes (MAN1A1, LMO3, NPAP1/SNRPN, DNAL4, and RNA5SP455/KRT8P5) were novel findings, which had no strong main effect on hypertension and could not be easily identified by single-locus genome-wide studies. Also, by including the identified gene-gene interactions, more variance was explained in hypertension. Overall, our study provides evidence that the genome-wide gene-gene interaction analysis has the possibility to identify new susceptibility genes, which can provide more insights into the genetic background of blood pressure regulation.

Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net

Different types of genomic aberration may simultaneously contribute to tumorigenesis. To obtain a more accurate prognostic assessment to guide therapeutic regimen choice for cancer patients, the heterogeneous multi-omics data should be integrated harmoniously, which can often be difficult. For this purpose, we propose a Gene Interaction Regularized Elastic Net (GIREN) model that predicts clinical outcome by integrating multiple data types. GIREN conveniently embraces both gene measurements and gene-gene interaction information under an elastic net formulation, enforcing structure sparsity, and the "grouping effect" in solution to select the discriminate features with prognostic value. An iterative gradient descent algorithm is also developed to solve the model with regularized optimization. GIREN was applied to human ovarian cancer and breast cancer datasets obtained from The Cancer Genome Atlas, respectively. Result shows that, the proposed GIREN algorithm obtained more accurate and robust performance over competing algorithms (LASSO, Elastic Net, and Semi-supervised PCA, with or without average pathway expression features) in predicting cancer progression on both two datasets in terms of median area under curve (AUC) and interquartile range (IQR), suggesting a promising direction for more effective integration of gene measurement and gene interaction information.

Functional networks inference from rule-based machine learning models

BACKGROUND

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods.

RESULTS

We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process.

AVAILABILITY

The implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html.

Construction of synergy networks from gene expression data related to disease

A few methods have been developed to determine whether genes collaborate with each other in relation to a particular disease using an information theoretic measure of synergy. Here, we propose an alternative definition of synergy and justify that our definition improves upon the existing measures of synergy in the context of gene interactions. We use this definition on a prostate cancer data set consisting of gene expression levels in both cancerous and non-cancerous samples and identify pairs of genes which are unable to discriminate between cancerous and non-cancerous samples individually but can do so jointly when we take their synergistic property into account. We also propose a very simple yet effective technique for computation of conditional entropy at a very low cost. The worst case complexity of our method is O(n) while the best case complexity of a state-of-the-art method is O(n(2)). Furthermore, our method can also be extended to find synergistic relation among triplets or even among a larger number of genes. Finally, we validate our results by demonstrating that these findings cannot be due to pure chance and provide the relevance of the synergistic pairs in cancer biology.

The hematopoietic regulator, ELF-1, enhances the transcriptional response to Interferon-β of the OAS1 anti-viral gene

Interferon (IFN) therapy is effective in treating cancers, haematological and virus induced diseases. The classical Jak/Stat pathway of IFN signal transduction leading to changes in transcriptional activity is well established but alone does not explain the whole spectrum of cellular responses to IFN. Gene promoters contain cis-acting sequences that allow precise and contextual binding of transcription factors, which control gene expression. Using the transcriptional response to IFN as a starting point we report a high frequency of tandem GGAA motifs in the proximal promoters of Interferon stimulated genes, suggesting a key regulatory action. Utilizing the well-characterized anti-viral gene, OAS1, as an example Interferon stimulated gene promoter containing such a duplicated GGAA motif, we have demonstrated a regulatory role of this promoter in response to IFN by mutation analysis. Furthermore, we identified ELF-1 as a direct binding factor at this motif. Additionally, recruitment of RB1 and SP1 factors to the promoter following IFN stimulation is shown. ELF-1 overexpression enhanced and knockdown of ELF-1 inhibited full activation of OAS1 by IFN stimulation. Collectively, ELF-1 binds an important duplicated GGAA cis-acting element at the OAS1 promoter and in cooperation with RB1 and SP1 recruitment contributes to regulation in response to IFN stimulation.

Discovery of synergistic genetic network: A minimum spanning tree-based approach

Identification of gene interactions is one of the very well-known and important problems in the field of genetics. However, discovering synergistic gene interactions is a relatively new problem which has been proven to be as significant as the former in genetics. Several approaches have been proposed in this regard and most of them depend upon information theoretic measures. These approaches quantize the gene expression levels, explicitly or implicitly and therefore, may lose information. Here, we have proposed a novel approach for identifying synergistic gene interactions directly from the continuous expression levels, using a minimum spanning tree (MST)-based algorithm. We have used this approach to find pairs of synergistically interacting genes in prostate cancer. The advantages of our method are that it does not need any discretization and it can be extended straightway to find synergistically interacting sets of genes having three or more elements as per the requirement of the situation. We have demonstrated the relevance of the synergistic genes in cancer biology using KEGG pathway analysis and otherwise.

MiR-101 reverses the hypomethylation of the LMO3 promoter in glioma cells

LIM-only protein 3 (LMO3), a member of the LIM-only protein group, is a new DNA methylation gene that was identified in gliomas via the MeDIP-Chip in our previous study. In this study, we found that LIM-only protein 3 (LMO3) is hypomethylated and overexpressed in glioma cells and tissues. The overexpression of LMO3 was correlated with a poor prognosis in glioma patients, and LMO3 was indirectly inhibited by the tumor suppressor miR-101, which is a potential prognosis marker of gliomas. MiR-101 decreased the expression of LMO3 by reversing the methylation status of the LMO3 promoter and by inhibiting the presence of the methylation-related histones H3K4me2 and H3K27me3 and increasing the presence of H3K9me3 and H4K20me3 on the promoter. It was determined that miR-101 decreases the occupancy of H3K27me3 by inhibiting EZH2, DNMT3A and EED and decreases the H3K9me3 occupancy on the LMO3 promoter via SUV39H1, SUV39H2, G9a and PHF8. Furthermore, miR-101 suppresses the expression of LMO3 by decreasing USF and MZF1.

Comparison of microarray expression profiles between follicular variant of papillary thyroid carcinomas and follicular adenomas of the thyroid

Background

Follicular variant of papillary thyroid carcinoma (FVPTC) and follicular adenoma (FA) are histologically closely related tumors and differential diagnosis remains challenging. RNA expression profiling is an established method to unravel molecular mechanisms underlying the histopathology of diseases.

Methods

BRAF mutational status was established by direct sequencing the hotspot region of exon 15 in six FVPTCs and seven FAs. Whole-transcript arrays were employed to generate expression profiles in six FVPTCs, seven FAs and seven normal thyroid tissue samples. The threshold of significance for differential expression on the gene and exon level was a p-value with a false discovery rate (FDR) < 0.05 and a fold change cutoff > 2. Two dimensional average linkage hierarchical clustering was generated using differentially expressed genes. Network, pathway, and alternative splicing utilities were employed to interpret significance of expression data on the gene and exon level.

Results

Expression profiling in FVPTCs and FAs, all of which were negative for a BRAF mutation, revealed 55 transcripts that were significantly differentially expressed, 40 of which were upregulated and 15 downregulated in FVPTCs vs. FAs. Amongst the most significantly upregulated genes in FVPTCs were GABA B receptor, 2 (GABBR2), neuronal cell adhesion molecule (NRCAM), extracellular matrix protein 1 (ECM1), heparan sulfate 6-O-sulfotransferase 2 (HS6ST2), and retinoid X receptor, gamma (RXRG). The most significantly downregulated genes in FVPTCs included interaction protein for cytohesin exchange factors 1 (IPCEF1), G protein-coupled receptor 155 (GPR155), Purkinje cell protein 4 (PCP4), chondroitin sulfate N-acetylgalactosaminyltransferase 1 (CSGALNACT1), and glutamate receptor interacting protein 1 (GRIP1). Alternative splicing analysis detected 87 genes, 52 of which were also included in the list of 55 differentially expressed genes. Network analysis demonstrated multiple interactions for a number of differentially expressed molecules including vitamin D (1,25- dihydroxyvitamin D3) receptor (VDR), SMAD family member 9 (SMAD9), v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT), and RXRG.

Conclusions

This is one of the first studies using whole-transcript expression arrays to compare expression profiles between FVPTCs and FAs. A set of differentially expressed genes has been identified that contains valuable candidate genes to differentiate both histopathologically related tumor types on the molecular level.

Increased behavioral responses to ethanol in Lmo3 knockout mice

LIM-domain-only 3 (LMO3) is a transcriptional regulator involved in central nervous system development and neuroblastoma. Our previous studies implicated a potential role for LMO3 in regulating ethanol sensitivity and consumption. Here, we examined behavioral responses to ethanol in a line of Lmo3 null (Lmo3(Z) ) mice, utilizing the ethanol-induced loss-of-righting-reflex (LORR) test, two-bottle choice ethanol consumption and the drinking in the dark (DID) test, which models binge-like ethanol consumption. We found that Lmo3(Z) mice exhibited increased sedation time in response to ethanol in the LORR test and drank significantly more ethanol in the DID test compared with their wild-type counterparts, but showed no differences in two-bottle choice ethanol consumption. To explore where LMO3 may be acting in the brain to produce an ethanol phenotype, we also examined reporter gene (β-galactosidase) expression in heterozygous Lmo3(Z) mice and found strong expression in subcortical areas, particularly in those areas implicated in drug abuse, including the nucleus accumbens (Acb), cortex, hippocampus and amygdala. We also examined Lmo3 expression in the brains of wild-type mice who had undergone the DID test and found a negative correlation between Lmo3 expression in the Acb and the amount of ethanol consumed, consistent with the increased binge-like drinking observed in Lmo3(Z) mice. These results support a role for LMO3 in regulating behavioral responses to ethanol, potentially through its actions in the Acb.

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Michał Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.

Gene expression profiles of metabolic aggressiveness and tumor recurrence in benign meningioma

Around 20% of meningiomas histologically benign may be clinically aggressive and recur. This strongly affects management of meningioma patients. There is a need to evaluate the potential aggressiveness of an individual meningioma. Additional criteria for better classification of meningiomas will improve clinical decisions as well as patient follow up strategy after surgery. The aim of this study was to determine the relationship between gene expression profiles and new metabolic subgroups of benign meningioma with potential clinical relevance. Forty benign and fourteen atypical meningioma tissue samples were included in the study. We obtained metabolic profiles by NMR and recurrence after surgery information for all of them. We measured gene expression by oligonucleotide microarray measurements on 19 of them. To our knowledge, this is the first time that distinct gene expression profiles are reported for benign meningioma molecular subgroups with clinical correlation. Our results show that metabolic aggressiveness in otherwise histological benign meningioma proceeds mostly through alterations in the expression of genes involved in the regulation of transcription, mainly the LMO3 gene. Genes involved in tumor metabolism, like IGF1R, are also differentially expressed in those meningioma subgroups with higher rates of membrane turnover, higher energy demand and increased resistance to apoptosis. These new subgroups of benign meningiomas exhibit different rates of recurrence. This work shows that benign meningioma with metabolic aggressiveness constitute a subgroup of potentially recurrent tumors in which alterations in genes regulating critical features of aggressiveness, like increased angiogenesis or cell invasion, are still no predominant. The determination of these gene expression biosignatures may allow the early detection of clinically aggressive tumors.

Apoptotic cell death in neuroblastoma

Neuroblastoma (NB) is one of the most common malignant solid tumors in childhood, which derives from the sympathoadrenal lineage of the neural crest and exhibits extremely heterogeneous biological and clinical behaviors. The infant patients frequently undergo spontaneous regression even with metastatic disease, whereas the patients of more than one year of age who suffer from disseminated disease have a poor outcome despite intensive multimodal treatment. Spontaneous regression in favorable NBs has been proposed to be triggered by nerve growth factor (NGF) deficiency in the tumor with NGF dependency for survival, while aggressive NBs have defective apoptotic machinery which enables the tumor cells to evade apoptosis and confers the resistance to treatment. This paper reviews the molecules and pathways that have been recently identified to be involved in apoptotic cell death in NB and discusses their potential prospects for developing more effective therapeutic strategies against aggressive NB.

ICAD deficiency in human colon cancer and predisposition to colon tumorigenesis: linkage to apoptosis resistance and genomic instability

We previously showed that DNA fragmentation factor, which comprises a caspase-3-activated DNase (CAD) and its inhibitor (ICAD), may influence the rate of cell death by generating PARP-1-activating DNA breaks. Here we tested the hypothesis that ICAD-deficient colon epithelial cells exhibiting resistance to death stimuli may accumulate additional genetic modifications, leading to a tumorigenic phenotype. We show that ICAD deficiency may be associated with colon malignancy in humans. Indeed, an examination of ICAD expression using immunohistochemistry in an array of both colon cancer and normal tissues revealed that ICAD expression levels were severely compromised in the cancerous tissues. Upon DNA damage caused by a low dose of irradiation, ICAD cells acquire a tumorigenic phenotype. Colon epithelial cells derived from ICAD mice showed a significant resistance to death induced by the colon carcinogen dimethylhydrazine in vitro and in mice. Such resistance was associated with a decrease in PARP-1 activation. In an animal model of dimethylhydrazine-induced colon tumorigenesis, ICAD^−/− mice developed significantly higher numbers of tumors with markedly larger sizes than the wild-type counterparts. Interestingly, the phenotype of the ICAD^−/− mice was not associated with a significant increase in the precancerous aberrant crypt foci suggesting a potential link to tumor progression rather than initiation. More importantly, ICAD deficiency was associated with severe genomic instability as assessed by array comparative genomic hybridization. Such genomic instability consisted most prominently of amplifications but with sizable deletions as compared to the wild-type counterparts affecting several cancer-related genes including RAF-1, GSN, LMO3, and Fzd6 independently of p53. Altogether, our results present a viable case for the involvement of ICAD deficiency in colon carcinogenesis and show that apoptosis and genomic instability may comprise the means by which such deficiency may contribute to the process of increasing susceptibility to carcinogen-induced tumorigenesis.

LMO4 inhibits p53-mediated proliferative inhibition of breast cancer cells through interacting p53

AIMS

The LIM domain only proteins (LMOs) which consist of four members (LMO1-LMO4) are a family of nuclear transcription coregulators that are characterized by the exclusive presence of two tandem LIM domains and no other functional domains. They regulate gene transcription by functioning as "linker" or "scaffolding" proteins by virtue of their LIM domains and are involved in the formation of multiprotein complexes with several DNA-binding factors and transcriptional regulatory proteins. In the present study, we tried to find the physical interaction between p53 and LMO4, and the effect of LMO4 on p53-mediated proliferative inhibition of breast cancer cells.

MAIN METHODS

FCM analysis was developed to detect the apoptosis of breast cancer cells after adriamycin (ADR) treatment. RT-PCR and Western blot analysis were performed to detect the expression of LMO4 and p53-related genes and proteins. Immunoprecipitation assay was used to detect the interaction between LMO4 and p53. Colony formation assay was developed to detect the proliferation of breast cancer cells.

KEY FINDINGS

We found that p53 was induced, but LMO4 was down-regulated in response to ADR. We also found that enforced expression of p53 inhibited the expression of LMO4, suggesting that LMO4 is a direct transcriptional target of p53. Furthermore, LMO4 can interact with p53 and inhibit p53-mediated inhibition of colony formation of breast cancer MDA-MB-453 cells.

SIGNIFICANCE

The present study showed that LMO4 is a direct target of p53 and inhibits p53-mediated proliferative inhibition of breast cancer cells through interacting p53.

Genome-wide analysis of DNA methylation and the gene expression change in lung cancer

INTRODUCTION

The recent DNA methylation studies on cancers have revealed the necessity of profiling an entire human genome and not to restrict the profiling to specific regions of the human genome. It has been suggested that genome-wide DNA methylation analysis enables us to identify the genes that are regulated by DNA methylation in carcinogenesis.

METHODS

So, we performed whole-genome DNA methylation analysis for human lung squamous cell carcinoma (SCC), which is strongly related with smoking. We also performed microarrays using 21 pairs of normal lung tissues and tumors from patients with SCC. By combining these data, 30 hypermethylated and down-regulated genes, and 22 hypomethylated and up-regulated genes were selected. The gene expression level and DNA methylation pattern were confirmed by semiquantitative reverse-transcriptase polymerase chain reaction and pyrosequencing, respectively.

RESULTS

By these validations, we selected five hypermethylated and down-regulated genes and one hypomethylated and up-regulated gene. Moreover, these six genes were proven to be actually regulated by DNA methylation by confirming the recovery of their DNA methylation pattern and gene expression level using a demethylating agent. The DNA methylation pattern of the CYTL1 promoter region was significantly different between early and advanced stages of SCC.

CONCLUSION

In conclusion, by combining the whole-genome DNA methylation pattern and the gene expression profile, we identified the six genes (CCDC37, CYTL1, CDO1, SLIT2, LMO3, and SERPINB5) that are regulated by DNA methylation, and we suggest their value as target molecules for further study of SCC.

Lmo genes regulate behavioral responses to ethanol in Drosophila melanogaster and the mouse

BACKGROUND

Previous work from our laboratory demonstrated a role for the Drosophila Lim-only (dLmo) gene in regulating behavioral responses to cocaine. Herein, we examined whether dLmo influences the flies' sensitivity to ethanol's sedating effects. We also investigated whether 1 of the mammalian homologs of dLmo, Lmo3, is involved in behavioral responses to ethanol in mice.

METHODS

To examine dLmo function in ethanol-induced sedation, mutant flies with reduced or increased dLmo expression were tested using the loss of righting (LOR) assay. To determine whether mouse Lmo3 regulates behavioral responses to ethanol, we generated transgenic mice expressing a short-hairpin RNA targeting Lmo3 for RNA interference-mediated knockdown by lentiviral infection of single cell embryos. Adult founder mice, expressing varying amounts of Lmo3 in the brain, were tested using ethanol loss-of-righting-reflex (LORR) and 2-bottle choice ethanol consumption assays.

RESULTS

We found that in flies, reduced dLmo activity increased sensitivity to ethanol-induced sedation, whereas increased expression of dLmo led to increased resistance to ethanol-induced sedation. In mice, reduced levels of Lmo3 were correlated with increased sedation time in the LORR test and decreased ethanol consumption in the 2-bottle choice protocol.

CONCLUSIONS

These data describe a novel and conserved role for Lmo genes in flies and mice in behavioral responses to ethanol. These studies also demonstrate the feasibility of rapidly translating findings from invertebrate systems to mammalian models of alcohol abuse by combining RNA interference in transgenic mice and behavioral testing.

A 6-gene signature identifies four molecular subgroups of neuroblastoma

Background

There are currently three postulated genomic subtypes of the childhood tumour neuroblastoma (NB); Type 1, Type 2A, and Type 2B. The most aggressive forms of NB are characterized by amplification of the oncogene MYCN (MNA) and low expression of the favourable marker NTRK1. Recently, mutations or high expression of the familial predisposition gene Anaplastic Lymphoma Kinase (ALK) was associated to unfavourable biology of sporadic NB. Also, various other genes have been linked to NB pathogenesis.

Results

The present study explores subgroup discrimination by gene expression profiling using three published microarray studies on NB (47 samples). Four distinct clusters were identified by Principal Components Analysis (PCA) in two separate data sets, which could be verified by an unsupervised hierarchical clustering in a third independent data set (101 NB samples) using a set of 74 discriminative genes. The expression signature of six NB-associated genes ALK, BIRC5, CCND1, MYCN, NTRK1, and PHOX2B, significantly discriminated the four clusters (p < 0.05, one-way ANOVA test). PCA clusters p1, p2, and p3 were found to correspond well to the postulated subtypes 1, 2A, and 2B, respectively. Remarkably, a fourth novel cluster was detected in all three independent data sets. This cluster comprised mainly 11q-deleted MNA-negative tumours with low expression of ALK, BIRC5, and PHOX2B, and was significantly associated with higher tumour stage, poor outcome and poor survival compared to the Type 1-corresponding favourable group (INSS stage 4 and/or dead of disease, p < 0.05, Fisher's exact test).

Conclusions

Based on expression profiling we have identified four molecular subgroups of neuroblastoma, which can be distinguished by a 6-gene signature. The fourth subgroup has not been described elsewhere, and efforts are currently made to further investigate this group's specific characteristics.