Deeper Biological Insight Research Articles

A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer

BackgroundLactylation, a novel contributor to post-translational protein modifications, exhibits dysregulation across various tumors. Nevertheless, its intricate involvement in colorectal carcinoma, particularly for non-histone lactylation and its intersection with metabolism and immune evasion, remains enigmatic.MethodsEmploying immunohistochemistry on tissue microarray with clinical information and immunofluorescence on colorectal cell lines, we investigated the presence of global lactylation and its association with development and progression in colorectal cancer as well as its functional location. Leveraging the AUCell algorithm alongside correlation analysis in single-cell RNA sequencing data, as well as cox-regression and lasso-regression analysis in TCGA dataset and confirmed in GEO dataset, we identified a 23-gene signature predicting colorectal cancer prognosis. Subsequently, we analyzed the associations between the lactylation related gene risk and clinical characteristics, mutation landscapes, biological functions, immune cell infiltration, immunotherapy responses, and drug sensitivity. Core genes were further explored for deep biological insights through bioinformatics and in vitro experiments.ResultsOur study innovatively reveals a significant elevation of global lactylation in colorectal cancer, particularly in malignant tumors, confirming it as an independent prognostic factor for CRC. Through a comprehensive analysis integrating tumor tissue arrays, TCGA dataset, GEO dataset, combining in silico investigations and in vitro experiments, we identified a 23-gene Lactylation-Related Gene risk model capable of predicting the prognosis of colorectal cancer patients. Noteworthy variations were observed in clinical characteristics, biological functions, immune cell infiltration, immune checkpoint expression, immunotherapy responses and drug sensitivity among distinct risk groups.ConclusionsThe Lactylation-Related Gene risk model exhibits significant potential for improving the management of colorectal cancer patients and enhancing therapeutic outcomes, particularly at the intersection of metabolism and immune evasion. This finding underscores the clinical relevance of global lactylation in CRC and lays the groundwork for mechanism investigation and targeted therapeutic strategies given the high lactate concentration in CRC.

EPCO-53. MULTI-SCALE REGULATION OF SIGNALING CASCADES AND TUMOR EVOLUTION IN HIGH GRADE ASTROCYTOMAS

Abstract The National Cancer Institute Clinical Proteomic Atlas Consortium (CPTAC) herein reports our deep characterization of 228 grade IV IDH1 WT and mutant astrocytomas (including 28 matched primary and recurrent GBMs) using 15 proteogenomic and metabolomic platforms. Major advances over our first CPTAC GBM report (Wang et al., 2021, Cancer Cell), are the inclusion of many more samples including paired primary and recurrent tumors, application of new platforms, including glycoproteomics and targeted mass spectrometry methods, development and application of new computational techniques, and the integration of an atlas of experimentally determined, functional, kinase substrate interactions from Kinase Library. Paired primary-recurrent GBM analyses showed increased clonal diversity in recurrent tumors as a function of time, and treatment-induced mutation signatures. Proteomic and metabolomic analyses showed that different drivers can cause similar downstream effects. Only EGFR altered tumors were associated with dual EGFR glycosylation (N352 and N603) and EGFR phosphorylation (Y316) events. IDH1 mutation was associated with activated RTK signaling and decreased hypoxia pathway activities, concordant with epigenetic and metabolic profiles. Protein-protein interaction and kinase/phosphatase-substrate analyses uncovered detailed signaling events from different upstream drivers (e.g., EGFR, PDGFRA, and IDH1) converged through a PTPN11 hub to downstream effectors, including GAB1, IRS1, MAP3K5, and PTK2B. In summary, this multiscale resource presents new and deeper biological insights regarding treatment impact on tumor evolution, shared downstream consequences of independent drivers, and the potential importance of PTPN11 signaling circuitry across high-grade gliomas. We hope that reporting this new international resource to the SNO community will advance therapeutic development, including targeted therapies that may avoid known mechanisms of resistance.

Clinical Consequences of Rare Germline Variation in Genes Associated with Coagulation across 451,958 Individuals

Introduction Despite deep biological insights over the preceding decades, our ability to assess for inherited hypercoagulable states remains limited. In light of these challenges and the increasing relevance of precision medicine and next-generation sequencing (NGS) to clinical practice, the identification of rare germline variants in coagulation-focused NGS panels is increasingly becoming accepted practice. However, the discovery of variants of unknown significance (VUS) remains a common clinical conundrum in molecular testing, particularly because evidence for the thrombosis risk associated with many genes derives from small, uncontrolled studies, case reports, and pedigree analysis. Large-scale evaluation of the clinical consequences associated with loss of function in individual coagulation-related genes would contribute to the clinical assessment of patients presenting with heterozygous VUS in poorly characterized genes. Objectives Determine the risk of four thrombosis phenotypes associated with rare, germline loss-of-function variants in a major clinical coagulation-focused NGS panel. Methods The UK Biobank (UKBB) contains paired clinical and whole exome sequencing data for over 450,000 participants. We identified rare (minor allele frequency <0.1%) germline variants that were predicted in silico to alter protein activity (functional impact score ≥0.8) within the 54 evaluable genes of the Yale NGS coagulation panel. For each gene, parallel multivariable Firth's logistic regression models were generated to assess the association between rare qualifying variants and four thrombotic phenotypes as well as coagulopathic bleeding in a gene-level collapsing analysis (N=270 independent models). We adjusted for age, sex, ancestry, and sequencing batch. Phenotypes of interest included venous thromboembolism (VTE), myocardial infarction (MI), stroke (CVA), peripheral artery disease (PAD), and coagulopathic bleeding. The Bonferroni corrected p-value for each phenotype was 0.001. We validated functional impact score predictions in seven secreted proteins measured in a subset of 47,923 UKBB participants who underwent plasma proteomic assessment (Olink Explore 1536 panel). Results A total of 451,958 individuals in the UKBB with whole exome sequencing data were evaluated. Among the 54 genes in the Yale NGS coagulation panel, there were 18,975 qualifying variants (98.4% heterozygous). Function-altering variants in 4 of 54 genes were associated with a statistically significant increased risk of thrombosis after adjusting for multiple comparisons: PROC (OR = 5.7, 95% CI: 3.2 - 9.6, p = 1.6x10 -7), SERPINC1 (OR = 5.0, 95% CI: 2.7 - 8.7, p = 4.0x10 -6), PROS1 (OR = 3.2, 95% CI: 2.0 - 4.8, p = 6.0x10 -6), and STAB2 (OR = 1.4, 95% CI: 1.2 - 1.7, p = 5.3x10 -4) (Figure 1). Nominal significance was achieved for 7 genes in the VTE phenotype, 1 gene in CVA, 3 genes in MI, 5 genes in PAD, and 0 genes in coagulopathic bleeding. Variants in a total of 37 genes did not appear to be significantly associated with any of the evaluated phenotypes. To assess the reliability of variant functional impact scores derived in silico, plasma levels for 13 of the 53 coagulation genes were analyzed. Of these, 8 were identified as secreted proteins amenable to assessment by plasma levels, including ADAMTS13 ( ADAMTS13), factor IX ( F9), factor VII ( F7), tissue plasminogen activator ( PLAT), urokinase ( PLAU), protein C ( PROC), plasminogen activator inhibitor 1 ( SERPINE1), and von Willebrand factor ( VWF). For these proteins, average plasma levels consistently demonstrated a negative correlation with functional impact score (Pearson r = -0.98, P=0.0009), indicating that our in silico assessments in this context are broadly reliable (Figure 2). Conclusion Within the commonly encountered clinical context of heterozygous loss of function, most genes contained in coagulation-focused NGS panels likely have fairly weak effects on disease risk that are limited to venous thrombosis. However, our findings do not rule out the possibility of more profound effects from homozygous or compound heterozygous loss of function in these same genes. By defining the clinical consequence of heterozygous loss of function in specific coagulation-related genes across a large population, our findings may help inform the interpretation of molecular testing results in which patients are found to have a VUS.

Abstract LB060: Integrated analysis of transcriptomics and kinase activity data for better characterization of cancer models

Abstract Introduction: Quantitative measurements of transcripts and proteins are key to investigate the basal state of a biological system, while functional proteomics inform about the active state of regulatory networks. Here we describe how the integration of transcriptomics and kinase activity data lead to a better characterization of various cancer models. Methods: We performed RNA sequencing (RNAseq) and kinase activity profiling of 63 Patient Derived Xenograft (PDX) models from six tumor types (Breast, Ovarian, Colon, Melanoma, Lung and Acute Myeloid Leukemia, AML). RNAseq was performed on an Illumina NovaSeq platform. The data was DESeq2-normalized and log2-transformed. Protein Tyrosine Kinase and Serine-Threonine Kinase activities were profiled on PamChip® peptide microarray. To identify the role of kinase signaling related genes we defined a set of signaling-specific genes (n=2932), based on the elements from the reactome signal transduction pathway database (n=2560) and additional kinases (n=372) represented on Pamchip, that was used for further analysis. Integrated analysis of transcriptomics and kinase activity data was performed using Multi Omics Factor Analysis (MOFA). Results: Principal Component Analysis (PCA) of RNAseq data using all included genes or 2932 kinase signaling-specific genes showed clustering of the data according to cancer type, with ovarian cancer showing most heterogeneity, which indicates the importance of kinase signaling in these malignancies. Interestingly, with integrated RNAseq-Kinase activity data all except ovarian cancer show clustering of cancer types on the MOFA Factor 1 - Factor 3. Pathway analysis on the highest ranking 100 genes from principal component 1 of RNAseq data (capturing variation between AML and the other tumor types) resulted in 60 KEGG pathways. Importantly, highest ranking 50 genes and 47 peptides comprising MOFA Factor1 identified 115 significant KEGG pathways, and the statistical score of pathways identified by RNAseq alone was further improved. Finally, significant correlation between gene expression and kinase activity was found for selected PDX model per malignancy. Furthermore, ranking PDX models based on correlation score provided suitable tool to select PDX models for disease or pathway specific research question. Conclusion: Integrating transcriptomics with kinase activity data can be used to confirm transcriptomics findings on a functional level and provides deeper biological insights than transcriptomics alone. We show that integrative analysis leads to more significant and a higher number of enriched pathways. High correlation between two datasets allows for selecting animal models addressing specific research questions. Integrated analysis of transcriptomics and kinase activity data has great potential in improving diagnosis, prognosis and prediction of response to treatment. Citation Format: Dóra Schuller, Rik de Wijn, Dirk Pijnenburg, Tobias Deigner, Julia Schueler, Simar Pal Singh. Integrated analysis of transcriptomics and kinase activity data for better characterization of cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB060.

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq.

The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.

Spatial transcriptomics characterization of hepatocellular carcinoma using Molecular Cartography.

e16110 Background: The microenvironment within hepatocellular carcinoma (HCC) tumors is composed of interacting cancer cells, endothelial cells and other stromal cells, and immune cells. The emerging field of spatial transcriptomics enables the measurement of gene expression in specific regions within a tissue sample. This technique retains the spatial context of intact tissue, can provide deep biological insights into cell-cell interactions, tumor microenvironment, and tumor progression. Methods: Resolve Biosciences’ Molecular Cartography platform was used on serial fresh frozen sections obtained from treatment naïve, HCC samples, and matched non-cancerous control liver tissues. Results: Given the high sensitivity, single-cell resolution, and specificity of the platform, the gene profiles of the hepatic endothelial, mesenchymal and parenchymal cell were studied in-depth to characterize the tumor microenvironment. The data obtained from the analyses revealed several local interactions between cancer cells, endothelial cells, and immune cells. These interactions include both known immune checkpoints and angiogenesis drivers and novel interactions. Conclusions: A deeper in situ understanding of the interacting cellular components of the HCC microenvironment may give insight into the natural history (prognosis) of the disease, predict response to therapies that target the immune system and vasculature, and define novel targets for the next generation of drug development.

SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes

Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.

An integrative gene expression signature analysis identifies CMS4 KRAS-mutated colorectal cancers sensitive to combined MEK and SRC targeted therapy

BackgroundOver half of colorectal cancers (CRCs) are hard-wired to RAS/RAF/MEK/ERK pathway oncogenic signaling. However, the promise of targeted therapeutic inhibitors, has been tempered by disappointing clinical activity, likely due to complex resistance mechanisms that are not well understood. This study aims to investigate MEK inhibitor-associated resistance signaling and identify subpopulation(s) of CRC patients who may be sensitive to biomarker-driven drug combination(s).MethodsWe classified 2250 primary and metastatic human CRC tumors by consensus molecular subtypes (CMS). For each tumor, we generated multiple gene expression signature scores measuring MEK pathway activation, MEKi “bypass” resistance, SRC activation, dasatinib sensitivity, EMT, PC1, Hu-Lgr5-ISC, Hu-EphB2-ISC, Hu-Late TA, Hu-Proliferation, and WNT activity. We carried out correlation, survival and other bioinformatic analyses. Validation analyses were performed in two independent publicly available CRC tumor datasets (n = 585 and n = 677) and a CRC cell line dataset (n = 154).ResultsHere we report a central role of SRC in mediating “bypass”-resistance to MEK inhibition (MEKi), primarily in cancer stem cells (CSCs). Our integrated and comprehensive gene expression signature analyses in 2250 CRC tumors reveal that MEKi-resistance is strikingly-correlated with SRC activation (Spearman P < 10–320), which is similarly associated with EMT (epithelial to mesenchymal transition), regional metastasis and disease recurrence with poor prognosis. Deeper analysis shows that both MEKi-resistance and SRC activation are preferentially associated with a mesenchymal CSC phenotype. This association is validated in additional independent CRC tumor and cell lines datasets. The CMS classification analysis demonstrates the strikingly-distinct associations of CMS1-4 subtypes with the MEKi-resistance and SRC activation. Importantly, MEKi + SRCi sensitivities are predicted to occur predominantly in the KRAS mutant, mesenchymal CSC-like CMS4 CRCs.ConclusionsLarge human tumor gene expression datasets representing CRC heterogeneity can provide deep biological insights heretofore not possible with cell line models, suggesting novel repurposed drug combinations. We identified SRC as a common targetable node–-an Achilles’ heel–-in MEKi-targeted therapy-associated resistance in mesenchymal stem-like CRCs, which may help development of a biomarker-driven drug combination (MEKi + SRCi) to treat problematic subpopulations of CRC.

Getting more out of FLAG-Tag co-immunoprecipitation mass spectrometry experiments using FAIMS

Co-immunoprecipitation of proteins coupled to mass spectrometry is critical for the understanding of protein interaction networks. In instances where a suitable antibody is not available, it is common to graft synthetic tags onto a target protein sequence thereby allowing the use of commercially available antibodies for affinity purification. A common approach is through FLAG-Tag co-immunoprecipitation. To allow the selective elution of protein complexes, competitive displacement using a large molar excess of the tag peptides is often carried out. Yet, this creates downstream challenges for the mass spectrometry analysis due to the presence of large quantities of these peptides. Here, we demonstrate that Field Asymmetric Ion Mobility Spectrometry (FAIMS), a gas phase ion separation device prior to mass spectrometry analysis can be applied to FLAG-Tag co-immunoprecipitation experiments to increase the depth of protein coverage. By excluding these abundant tag peptides, we were able to observe deeper coverage of interacting proteins and as a result, deeper biological insights, without the need for additional sample handling or altering sample preparation protocols. SignificanceWe have shown that application of FAIMS separation in the gas phase can increase the proteome coverage of Flag-Tagged co-immunoprecipitation mass spectrometry experiments versus one without FAIMS. We were able to observe deeper coverage of interacting proteins and as a result, deeper biological insights, without additional sample handling, fractionation, machine run time or modifying the sample preparation protocol.

The Calcitriol/Vitamin D Receptor System Regulates Key Immune Signaling Pathways in Chronic Lymphocytic Leukemia.

Simple SummaryCalcitriol, the biologically active form of vitamin D, modulates a plethora of cellular processes following its receptor ligation, namely the vitamin D receptor (VDR). Epidemiological studies have linked low blood levels of vitamin D to adverse disease outcome in several B cell malignancies, including chronic lymphocytic leukemia (CLL), for as yet undetermined reasons. In this study, we sought to obtain deeper biological insight into the role of vitamin D in the pathophysiology of CLL. To this end, we investigated whether the calcitriol/VDR system is functional in CLL and analyzed key signaling pathways that are regulated by calcitriol supplementation, while also exploring the role of microenvironmental signals in the regulation of calcitriol/VDR system. Overall, we provide evidence that the calcitriol/VDR system is functional in CLL, regulating signaling pathways critical for cell survival/proliferation. Although microenvironmental triggers can modulate VDR expression and function, calcitriol appears to act independently, alluding to a potential clinical utility of vitamin D supplementation in CLL.It has been proposed that vitamin D may play a role in prevention and treatment of cancer while epidemiological studies have linked vitamin D insufficiency to adverse disease outcomes in various B cell malignancies, including chronic lymphocytic leukemia (CLL). In this study, we sought to obtain deeper biological insight into the role of vitamin D and its receptor (VDR) in the pathophysiology of CLL. To this end, we performed expression analysis of the vitamin D pathway molecules; complemented by RNA-Sequencing analysis in primary CLL cells that were treated in vitro with calcitriol, the biologically active form of vitamin D. In addition, we examined calcitriol effects ex vivo in CLL cells cultured in the presence of microenvironmental signals, namely anti-IgM/CD40L, or co-cultured with the supportive HS-5 cells; and, CLL cells from patients under ibrutinib treatment. Our study reports that the calcitriol/VDR system is functional in CLL regulating signaling pathways critical for cell survival and proliferation, including the TLR and PI3K/AKT pathways. Moreover, calcitriol action is likely independent of the microenvironmental signals in CLL, since it was not significantly affected when combined with anti-IgM/CD40L or in the context of the co-culture system. This finding was also supported by our finding of preserved calcitriol signaling capacity in CLL patients under ibrutinib treatment. Overall, our results indicate a relevant biological role for vitamin D in CLL pathophysiology and allude to the potential clinical utility of vitamin D supplementation in patients with CLL.

Analysis of Pig Vomeronasal Receptor Type 1 (V1R) Promoter Region Reveals a Common Promoter Motif but Poor CpG Islands

ABSTRACTPromoters are, generally, located immediately upstream of a transcription start site (TSS) and have a variety of regulatory motifs, such as transcription factors (TFs) and CpG islands (CGIs), that participate in the regulation of gene expression. Here analysis of the promoter region for pig vomeronasal receptor type 1 (V1R) was described. In the analysis, TSSs for pig V1R genes was first identified and five motifs (MV1, MV2, MV3, MV4, and MV5) were found that are shared by at least 50% of the pig V1R promoter input sequences from both strands. Among the five motifs, MV2 was identified as a common promoter motif shared by all (100%) pig V1R promoters. For further analysis, to better characterize and get deeper biological insight associated with MV2, TOMTOM web application was used. MV2 was compared to the known motif databases (such as JASPAR) to see if they are similar to a known regulatory motif (transcription factor). Hence, it was revealed that MV2 serves as the binding site mainly for the BetaBetaAlpha-zinc finger (BTB-ZF) transcription factor gene family to regulate expression of pig V1R genes. Moreover, it was shown that pig V1R promoters are CpG poor, suggesting that their gene expression regulation pattern is in tissue specific manner.

Advances in Genomic Profiling and Analysis of 3D Chromatin Structure and Interaction.

Recent sequence-based profiling technologies such as high-throughput sequencing to detect fragment nucleotide sequence (Hi-C) and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) have revolutionized the field of three-dimensional (3D) chromatin architecture. It is now recognized that human genome functions as folded 3D chromatin units and looping paradigm is the basic principle of gene regulation. To better interpret the 3D data dramatically accumulating in past five years and to gain deep biological insights, huge efforts have been made in developing novel quantitative analysis methods. However, the full understanding of genome regulation requires thorough knowledge in both genomic technologies and their related data analyses. We summarize the recent advances in genomic technologies in identifying the 3D chromatin structure and interaction, and illustrate the quantitative analysis methods to infer functional domains and chromatin interactions, and further elucidate the emerging single-cell Hi-C technique and its computational analysis, and finally discuss the future directions such as advances of 3D chromatin techniques in diseases.

Single-neuron identification of chemical constituents, physiological changes, and metabolism using mass spectrometry

The use of single-cell assays has emerged as a cutting-edge technique during the past decade. Although single-cell mass spectrometry (MS) has recently achieved remarkable results, deep biological insights have not yet been obtained, probably because of various technical issues, including the unavoidable use of matrices, the inability to maintain cell viability, low throughput because of sample pretreatment, and the lack of recordings of cell physiological activities from the same cell. In this study, we describe a patch clamp/MS-based platform that enables the sensitive, rapid, and in situ chemical profiling of single living neurons. This approach integrates modified patch clamp technique and modified MS measurements to directly collect and detect nanoliter-scale samples from the cytoplasm of single neurons in mice brain slices. Abundant possible cytoplasmic constituents were detected in a single neuron at a relatively fast rate, and over 50 metabolites were identified in this study. The advantages of direct, rapid, and in situ sampling and analysis enabled us to measure the biological activities of the cytoplasmic constituents in a single neuron, including comparing neuron types by cytoplasmic chemical constituents; observing changes in constituent concentrations as the physiological conditions, such as age, vary; and identifying the metabolic pathways of small molecules.

Genomics Study of Gastric Cancer and Its Molecular Subtypes.

Gastric cancer is a heterogeneous disease encompassing diverse morphological (intestinal versus diffuse) and molecular subtypes (MSI, EBV, TP53 mutation). Recent advances in genomic technology have led to an improved understanding of the driver gene mutational profile, gene expression, and epigenetic alterations that underlie each of the subgroups, with therapeutic implications in some of these alterations. There have been attempts to classify gastric cancers based on these genomic features, with an aim to improve prognostication and predict responsiveness to specific drug therapy. The eventual aims of these genomic studies are to develop deep biological insights into the carcinogenic pathway in each of these subtypes. Future large-scale drug screening strategies may then be able to link these genomic features to drug responsiveness, eventually leading to genome-guided personalized medicine with improved cure rates.

Cautions about the reliability of pairwise gene correlations based on expression data.

Background: Rapid growth in the availability of genome-wide transcript abundance levels through gene expression microarrays and RNAseq promises to provide deep biological insights into the complex, genome-wide transcriptional behavior of single-celled organisms. However, this promise has not yet been fully realized.Results: We find that computation of pairwise gene associations (correlation; mutual information) across a set of 2782 total genome-wide expression samples from six diverse bacteria produces unexpectedly large variation in estimates of pairwise gene association—regardless of the metric used, the organism under study, or the number and source of the samples. We pinpoint the cause to sampling bias. In particular, in repositories of expression data (e.g., Gene Expression Omnibus, GEO), many individual genes show small differences in absolute gene expression levels across the set of samples. We demonstrate that these small differences are due mainly to “noise” instead of “signal” attributable to environmental or genetic perturbations. We show that downstream analysis using gene expression levels of genes with small differences yields biased estimates of pairwise association.Conclusions: We propose flagging genes with small differences in absolute, RMA-normalized, expression levels (e.g., standard deviation less than 0.5), as potentially yielding biased pairwise association metrics. This strategy has the potential to substantially improve the confidence in genome-wide conclusions about transcriptional behavior in bacterial organisms. Further work is needed to further refine strategies to identify genes with small difference in expression levels prior to computing gene-gene association metrics.

Genomic tumor evolution of breast cancer.

Owing to recent technical development of comprehensive genome-wide analysis such as next generation sequencing, deep biological insights of breast cancer have been revealed. Information of genomic mutations and rearrangements in patients' tumors is indispensable to understand the mechanism in carcinogenesis, progression, metastasis, and resistance to systemic treatment of breast cancer. To date, comprehensive genomic analyses illustrate not only base substitution patterns and lists of driver mutations and key rearrangements, but also a manner of tumor evolution. Breast cancer genome is dynamically changing and evolving during cancer development course from non-invasive disease via invasive primary tumor to metastatic tumor, and during treatment exposure. The accumulation pattern of base substitution and genomic rearrangement looks gradual and punctuated, respectively, in analogy with contrasting theories for evolution manner of species, Darwin's phyletic gradualism, and Eldredge and Gould's "punctuated equilibrium". Liquid biopsy is a non-invasive method to detect the genomic evolution of breast cancer. Genomic mutation patterns in circulating tumor cells and circulating cell-free tumor DNA represent those of tumors existing in patient body. Liquid biopsy methods are now under development for future application to clinical practice of cancer treatment. In this article, latest knowledge regarding breast cancer genome, especially in terms of 'tumorevolution', is summarized.

Toward microRNA-mediated gene regulatory networks in plants

Current achievements in plant microRNA (miRNA) research area are inspiring. Molecular cloning and functional elucidation have greatly advanced our understanding of this small RNA species. As one of the ultimate goals, many research efforts devoted to draw a comprehensive view of miRNA-mediated gene regulatory networks in plants. Numerous bioinformatics tools competent for network analysis have been available. However, the most important point for network construction is to obtain reliable analytical results based on sufficient experimental data. Here, we introduced a general workflow to retrieve and analyze the desired data sets that serve as the cornerstones for network construction. For the upstream analyses of miRNA genes, the sequence feature of miRNA promoters should be characterized. And, regulatory relationships between transcription factors (TFs) and miRNA genes need to be investigated. For the downstream part, we emphasized that the high-throughput degradome sequencing data were especially useful for genuine miRNA-target pair identification. Functional characterization of the miRNA targets is essential to provide deep biological insights into certain miRNA-mediated pathways. For miRNAs themselves, studies on their organ- or tissue-specific expression patterns and the mechanism of self-regulation were discussed. Besides, exhaustive literature mining is required to further support or improve the established networks. It is desired that the introduced framework for miRNA-mediated network construction is timely and useful and could inspire more research efforts in the miRNA research area.

Identifying biologically relevant differences between metagenomic communities

Metagenomics is the study of genetic material recovered directly from environmental samples. Taxonomic and functional differences between metagenomic samples can highlight the influence of ecological factors on patterns of microbial life in a wide range of habitats. Statistical hypothesis tests can help us distinguish ecological influences from sampling artifacts, but knowledge of only the P-value from a statistical hypothesis test is insufficient to make inferences about biological relevance. Current reporting practices for pairwise comparative metagenomics are inadequate, and better tools are needed for comparative metagenomic analysis. We have developed a new software package, STAMP, for comparative metagenomics that supports best practices in analysis and reporting. Examination of a pair of iron mine metagenomes demonstrates that deeper biological insights can be gained using statistical techniques available in our software. An analysis of the functional potential of 'Candidatus Accumulibacter phosphatis' in two enhanced biological phosphorus removal metagenomes identified several subsystems that differ between the A.phosphatis stains in these related communities, including phosphate metabolism, secretion and metal transport. Python source code and binaries are freely available from our website at http://kiwi.cs.dal.ca/Software/STAMP CONTACT: beiko@cs.dal.ca Supplementary data are available at Bioinformatics online.

Molecular mapping of human hepatocellular carcinoma provides deeper biological insight from genomic data

DNA microarray analysis of human cancer has resulted in considerable accumulation of global gene profiles. However, extraction and understanding the underlying biology of cancer progression remains a significant challenge. This study applied a novel integrative computational and analytical approach to this challenge in human hepatocellular carcinoma (HCC) with the aim of identifying potential molecular markers or novel therapeutic targets. We analysed 100 HCC tissue samples by human 30K DNA microarray. The gene expression data were uploaded into the network analysis tool, and the biological networks were displayed graphically. We identified several activated ‘hotspot’ regions harbouring a concentration of upregulated genes. Several ‘hotspot’ regions revealed integrin and Akt/NF-κB signalling. We identified key members linked to these signalling pathways including osteopontin (SPP1), glypican-3 (GPC3), annexin 2 (ANXA2), S100A10 and vimentin (VIM). Our integrative approach should significantly enhance the power of microarray data in identifying novel potential targets in human cancer.