Transcriptome analysis reveals genes associated with stem cell activation by physical exercise in the dentate gyrus of aged p16Ink4a knockout mice

Throughout adulthood neural stem cells divide in neurogenic niches-the dentate gyrus of the hippocampus and the subventricular zone-producing progenitor cells and new neurons. Stem cells self-renew, thus preserving their pool. Furthermore, the number of stem/progenitor cells in the neurogenic niches decreases with age. We have previously demonstrated that the cyclin-dependent kinase inhibitor p16Ink4a maintains, in aged mice, the pool of dentate gyrus stem cells by preventing their activation after a neurogenic stimulus such as exercise (running). We showed that, although p16Ink4a ablation by itself does not activate stem/progenitor cells, exercise strongly induced stem cell proliferation in p16Ink4a knockout dentate gyrus, but not in wild-type. As p16Ink4a regulates stem cell self-renewal during aging, we sought to profile the dentate gyrus transcriptome from p16Ink4a wild-type and knockout aged mice, either sedentary or running for 12 days. By pairwise comparisons of differentially expressed genes and by correlative analyses through the DESeq2 software, we identified genes regulated by p16Ink4a deletion, either without stimulus (running) added, or following running. The p16Ink4a knockout basic gene signature, i.e., in sedentary mice, involves upregulation of apoptotic, neuroinflammation- and synaptic activity-associated genes, suggesting a reactive cellular state. Conversely, another set of 106 genes we identified, whose differential expression specifically reflects the pattern of proliferative response of p16 knockout stem cells to running, are involved in processes that regulate stem cell activation, such as synaptic function, neurotransmitter metabolism, stem cell proliferation control, and reactive oxygen species level regulation. Moreover, we analyzed the regulation of these stem cell-specific genes after a second running stimulus. Surprisingly, the second running neither activated stem cell proliferation in the p16Ink4a knockout dentate gyrus nor changed the expression of these genes, confirming that they are correlated to the stem cell reactivity to stimulus, a process where they may play a role regulating stem cell activation.

DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues

In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Drug-induced blockade of the human ether-à-go-go-related gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligand-based approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein–ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUC_MAX = 0.86 ± 0.01) and negative predictive values (NPV_MAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein–ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

Transcriptome Analysis in a Mouse Model of Premature Aging of Dentate Gyrus: Rescue of Alpha-Synuclein Deficit by Virus-Driven Expression or by Running Restores the Defective Neurogenesis

The dentate gyrus of the hippocampus and the subventricular zone are neurogenic niches where neural stem and progenitor cells replicate throughout life to generate new neurons. The Btg1 gene maintains the stem cells of the neurogenic niches in quiescence. The deletion of Btg1 leads to an early transient increase of stem/progenitor cells division, followed, however, by a decrease during adulthood of their proliferative capability, accompanied by apoptosis. Since a physiological decrease of neurogenesis occurs during aging, the Btg1 knockout mouse may represent a model of neural aging. We have previously observed that the defective neurogenesis of the Btg1 knockout model is rescued by the powerful neurogenic stimulus of physical exercise (running). To identify genes responsible for stem and progenitor cells maintenance, we sought here to find genes underlying this premature neural aging, and whose deregulated expression could be rescued by running. Through RNA sequencing we analyzed the transcriptomic profiles of the dentate gyrus isolated from Btg1 wild-type or Btg1 knockout adult (2-month-old) mice submitted to physical exercise or sedentary. In Btg1 knockout mice, 545 genes were deregulated, relative to wild-type, while 2081 genes were deregulated by running. We identified 42 genes whose expression was not only down-regulated in the dentate gyrus of Btg1 knockout, but was also counter-regulated to control levels by running in Btg1 knockout mice, vs. sedentary. Among these 42 counter-regulated genes, alpha-synuclein (Snca), Fos, Arc and Npas4 showed significantly greater differential regulation. These genes control neural proliferation, apoptosis, plasticity and memory and are involved in aging. In particular, Snca expression decreases during aging. We tested, therefore, whether an Snca-expressing lentivirus, by rescuing the defective Snca levels in the dentate gyrus of Btg1 knockout mice, could also reverse the aging phenotype, in particular the defective neurogenesis. We found that the exogenous expression of Snca reversed the Btg1 knockout-dependent decrease of stem cell proliferation as well as the increase of progenitor cell apoptosis. This indicates that Snca has a functional role in the process of neural aging observed in this model, and also suggests that Snca acts as a positive regulator of stem cell maintenance.

Cannabinoid Receptor Subtype 2 (CB2R) in a Multitarget Approach: Perspective of an Innovative Strategy in Cancer and Neurodegeneration

The cannabinoid receptor subtype 2 (CB2R) represents an interesting and new therapeutic target for its involvement in the first steps of neurodegeneration as well as in cancer onset and progression. Several studies, focused on different types of tumors, report a promising anticancer activity induced by CB2R agonists due to their ability to reduce inflammation and cell proliferation. Moreover, in neuroinflammation, the stimulation of CB2R, overexpressed in microglial cells, exerts beneficial effects in neurodegenerative disorders. With the aim to overcome current treatment limitations, new drugs can be developed by specifically modulating, together with CB2R, other targets involved in such multifactorial disorders. Building on successful case studies of already developed multitarget strategies involving CB2R, in this Perspective we aim at prompting the scientific community to consider new promising target associations involving HDACs (histone deacetylases) and σ receptors by employing modern approaches based on molecular hybridization, computational polypharmacology, and machine learning algorithms.

Effect of different light-emitting diode (LED) irradiation on the shelf life and phytonutrient content of broccoli (Brassica oleracea L. var. italica)

Broccoli (Brassica oleracea L. var. italica) is largely cultivated in southern Italy. It is an important source of phytonutrients, which are partially lost during postharvest storage. The aim of this work was to evaluate the overall effect of five different low-intensity light-emitting diodes (LEDs) on the quality parameters of broccoli florets over 20 d of cold storage. The level of ascorbic acid, chlorophylls, carotenoids, phenolic compounds and soluble proteins, as well as colour analysis, were evaluated. Green LED increased the chlorophyll and ascorbic acid content; white, red and yellow LEDs had a positive effect on the redox status of broccoli. Globally, only green LED had a statistically significant positive effect when considering all analysed parameters and could be proposed to prolong the shelf life of broccoli during cold storage.

Combined microRNA and mRNA expression analysis in pediatric multiple sclerosis: an integrated approach to uncover novel pathogenic mechanisms of the disease

Multiple sclerosis (MS) is a complex disease of the CNS that usually affects young adults, although 3-5% of cases are diagnosed in childhood and adolescence (hence called pediatric MS, PedMS). Genetic predisposition, among other factors, seems to contribute to the risk of the onset, in pediatric as in adult ages, but few studies have investigated the genetic 'environmentally naïve' load of PedMS. The main goal of this study was to identify circulating markers (miRNAs), target genes (mRNAs) and functional pathways associated with PedMS; we also verified the impact of miRNAs on clinical features, i.e. disability and cognitive performances. The investigation was performed in 19 PedMS and 20 pediatric controls (PCs) using a High-Throughput Next-generation Sequencing (HT-NGS) approach followed by an integrated bioinformatics/biostatistics analysis. Twelve miRNAs were significantly upregulated (let-7a-5p, let-7b-5p, miR-25-3p, miR-125a-5p, miR-942-5p, miR-221-3p, miR-652-3p, miR-182-5p, miR-185-5p, miR-181a-5p, miR-320a, miR-99b-5p) and 1 miRNA was downregulated (miR-148b-3p) in PedMS compared with PCs. The interactions between the significant miRNAs and their targets uncovered predicted genes (i.e. TNFSF13B, TLR2, BACH2, KLF4) related to immunological functions, as well as genes involved in autophagy-related processes (i.e. ATG16L1, SORT1, LAMP2) and ATPase activity (i.e. ABCA1, GPX3). No significant molecular profiles were associated with any PedMS demographic/clinical features. Both miRNAs and mRNA expressions predicted the phenotypes (PedMS-PC) with an accuracy of 92% and 91%, respectively. In our view, this original strategy of contemporary miRNA/mRNA analysis may help to shed light in the genetic background of the disease, suggesting further molecular investigations in novel pathogenic mechanisms.

Proteome Response of Staphylococcus xylosus DSM 20266T to Anaerobiosis and Nitrite Exposure

The viability and competitiveness of Staphylococcus xylosus in meat mostly depend on the ability to adapt itself to rapid oxygen and nutrients depletion during meat fermentation. The utilization of nitrite instead of oxygen becomes a successful strategy for this strain to improve its performance in anaerobiosis; however, metabolic pathways of this strain underlying this adaptation, are partially known. The aim of this study was to provide an overview on proteomic changes of S. xylosus DSM 20266T cultured under anaerobiosis and nitrite exposure. Thus, two different cultures of this strain, supplemented or not with nitrite, were in vitro incubated in aerobiosis and anaerobiosis monitoring cell viability, pH, oxidation reduction potential and nitrite content. Protein extracts, obtained from cells, collected as nitrite content was depleted, were analyzed by 2DE/MALDI-TOF/TOF-MS. Results showed that DSM 20266T growth was significantly sustained by nitrite in anaerobiosis, whereas no differences were found in aerobiosis. Accordingly, nitrite content was depleted after 13 h only in anaerobiosis. At this time of sampling, a comparative proteomic analysis showed 45 differentially expressed proteins. Most differences were found between aerobic and anaerobic cultures without nitrite; the induction of glycolytic enzymes and glyoxylate cycle, the reduction of TCA enzymes, and acetate fermentation were found in anaerobiosis to produce ATP and maintain the cell redox balance. In anaerobic cultures the nitrite supplementation partially restored TCA cycle, and reduced the amount of glycolytic enzymes. These results were confirmed by phenotypic microarray that, for the first time, was carried out on cell previously adapted at the different growth conditions. Overall, metabolic changes were similar between aerobiosis and anaerobiosis NO₂-adapted cells, whilst cells grown under anaerobiosis showed different assimilation profiles by confirming proteomic data; indeed, these latter extensively assimilated substrates addressed at both supplying glucose for glycolysis or fueling alternative pathways to TCA cycle. In conclusion, metabolic pathways underlying the ability of S. xylosus to adapt itself to oxygen starvation were revealed; the addition of nitrite allowed S. xylosus to take advantage of nitrite to this condition, restoring some metabolic pathway underlying aerobic behavior of the strain.

Functional Implications of MicroRNAs in Crohn's Disease Revealed by Integrating MicroRNA and Messenger RNA Expression Profiling

Crohn's disease (CD) is a debilitating inflammatory bowel disease (IBD) that emerges due to the influence of genetic and environmental factors. microRNAs (miRNAs) have been identified in the tissue and sera of IBD patients and may play an important role in the induction of IBD. Our study aimed to identify differentially expressed miRNAs and miRNAs with the ability to alter transcriptome activity by comparing inflamed tissue samples with their non-inflamed counterparts. We studied changes in miRNA-mRNA interactions associated with CD by examining their differential co-expression relative to normal mucosa from the same patients. Correlation changes between the two conditions were incorporated into scores of predefined gene sets to identify biological processes with altered miRNA-mediated control. Our study identified 28 miRNAs differentially expressed (p-values < 0.01), of which 14 are up-regulated. Notably, our differential co-expression analysis highlights microRNAs (i.e., miR-4284, miR-3194 and miR-21) that have known functional interactions with key mechanisms implicated in IBD. Most of these miRNAs cannot be detected by differential expression analysis that do not take into account miRNA-mRNA interactions. The identification of differential miRNA-mRNA co-expression patterns will facilitate the investigation of the miRNA-mediated molecular mechanisms underlying CD pathogenesis and could suggest novel drug targets for validation.

Multiple rare genetic variants co-segregating with familial IgA nephropathy all act within a single immune-related network

BACKGROUND

IgA nephropathy (IgAN) is a common complex disease with a strong genetic involvement. We aimed to identify novel, rare, highly penetrant risk variants combining family-based linkage analysis with whole-exome sequencing (WES).

METHODS

Linkage analysis of 16 kindreds of South Italian ancestry was performed using an 'affected-only' strategy. Eight most informative trios composed of two familial cases and an intrafamilial control were selected for WES. High-priority variants in linked regions were identified and validated using Sanger sequencing. Custom TaqMan assays were designed and carried out in the 16 kindreds and an independent cohort of 240 IgAN patients and 113 control subjects.

RESULTS

We found suggestive linkage signals in 12 loci. After sequential filtering and validation of WES data, we identified 24 private or extremely rare (MAF <0.0003) linked variants segregating with IgAN status. These were present within coding or regulatory regions of 23 genes that merged into a common functional network. The genes were interconnected by AKT, CTNNB1, NFKB, MYC and UBC, key modulators of WNT/β-catenin and PI3K/Akt pathways, which are implicated in IgAN pathogenesis. Overlaying publicly available expression data, genes/proteins with expression notably altered in IgAN were included in this immune-related network. In particular, the network included the glucocorticoid receptor gene, NR3C1, which is the target of corticosteroid therapy routinely used in the treatment of IgAN.

CONCLUSION

Our findings suggest that disease susceptibility could be influenced by multiple rare variants acting in a common network that could provide the starting point for the identification of potential drug targets for personalized therapy.

The Immune Landscapes of Polypoid and Nonpolypoid Precancerous Colorectal Lesions

Little is known about the immunoediting process in precancerous lesions. We explored this aspect of benign colorectal adenomas with a descriptive analysis of the immune pathways and immune cells whose regulation is linked to the morphology and size of these lesions. Two series of polypoid and nonpolypoid colorectal adenomas were used in this study: 1) 84 samples (42 lesions, each with matched samples of normal mucosa) whose gene expression data were used to quantify the tumor morphology- and size-related dysregulation of immune pathways collected in the Molecular Signature Database, using Gene Set Enrichment Analysis; 2) 40 other lesions examined with immunohistochemistry to quantify the presence of immune cells in the stromal compartment. In the analysis of transcriptomic data, 429 immune pathways displayed significant differential regulation in neoplasms of different morphology and size. Most pathways were significantly upregulated or downregulated in polypoid lesions versus nonpolypoid lesions (regardless of size). Differential pathway regulation associated with lesion size was observed only in polypoid neoplasms. These findings were mirrored by tissue immunostaining with CD4, CD8, FOXP3, MHC-I, CD68, and CD163 antibodies: stromal immune cell counts (mainly T lymphocytes and macrophages) were significantly higher in polypoid lesions. Certain markers displayed significant size-related differences regardless of lesion morphology. Multivariate analysis of variance showed that the marker panel clearly discriminated between precancerous lesions of different morphologies and sizes. Statistical analysis of immunostained cell counts fully support the results of the transcriptomic data analysis: the density of infiltration of most immune cells in the stroma of polypoid precancerous lesions was significantly higher than that observed in nonpolypoid lesions. Large neoplasms also have more immune cells in their stroma than small lesions. Immunoediting in precancerous colorectal tumors may vary with lesion morphology and stage of development, and this variability could influence a given lesion's trajectory to cancer.

Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment.

Systematic analysis of circadian genes using genome-wide cDNA microarrays in the inflammatory bowel disease transcriptome

Simultaneous analysis of the transcripts of thousands of genes by cDNA microarrays allows the identification of genetic regulatory mechanisms involved in disease pathophysiology. The circadian clock circuitry controls essential cell processes and the functioning of organ systems, which are characterized by rhythmic variations with 24-hour periodicity. The derangement of these processes is involved in the basic mechanisms of inflammatory, metabolic, degenerative and neoplastic diseases. We evaluated by genome-wide cDNA microarray analysis the transcriptome of endoscopic mucosal biopsies of patients with inflammatory bowel diseases (IBD) focusing on the expression of circadian genes in Crohn's disease (CD) and ulcerative colitis (UC). Twenty-nine IBD patients (15 with CD and 14 with UC) were enrolled and mucosal biopsies were sampled at either inflamed or adjacent non-inflamed areas of the colon. A total of 150 circadian genes involved in pathways controlling crucial cell processes and tissue functions were investigated. In CD specimens 50 genes were differentially expressed, and 21 genes resulted up-regulated when compared to healthy colonic mucosa. In UC specimens 50 genes were differentially expressed, and 27 genes resulted up-regulated when compared to healthy colonic mucosa. Among the core clock genes ARNTL2 and RORA were up-regulated, while CSNK2B, NPAS2, PER1 and PER3 were down-regulated in CD specimens. Conversely, ARNTL2, CRY1, CSNK1E, RORA and TIPIN were up-regulated, while NR1D2 and PER3 were down-regulated in UC. In conclusion, in CD and UC patients there are differences in the expression of circadian genes between normal and diseased intestinal mucosa. The deregulated genes evidenced by transcriptome analysis in the major IBDs may play a crucial role in the pathophysiological mechanisms and may suggest novel therapeutic approaches.

Karyopherins: potential biological elements involved in the delayed graft function in renal transplant recipients

Background

Immediately after renal transplantation, patients experience rapid and significant improvement of their clinical conditions and undergo considerable systemic and cellular modifications. However, some patients present a slow recovery of the renal function commonly defined as delayed graft function (DGF). Although clinically well characterized, the molecular mechanisms underlying this condition are not totally defined, thus, we are currently missing specific clinical markers to predict and to make early diagnosis of this event.

Methods

We investigated, using a pathway analysis approach, the transcriptomic profile of peripheral blood mononuclear cells (PBMC) from renal transplant recipients with DGF and with early graft function (EGF), before (T0) and 24 hours (T24) after transplantation.

Results

Bioinformatics/statistical analysis showed that 15 pathways (8 up-regulated and 7 down-regulated) and 11 pathways (5 up-regulated and 6 down-regulated) were able to identify DGF patients at T0 and T24, respectively. Interestingly, the most up-regulated pathway at both time points was NLS-bearing substrate import into nucleus, which includes genes encoding for several subtypes of karyopherins, a group of proteins involved in nucleocytoplasmic transport. Signal transducers and activators of transcription (STAT) utilize karyopherins-alpha (KPNA) for their passage from cytoplasm into the nucleus. In vitro functional analysis demonstrated that in PBMCs of DGF patients, there was a significant KPNA-mediated nuclear translocation of the phosphorylated form of STAT3 (pSTAT3) after short-time stimulation (2 and 5 minutes) with interleukin-6.

Conclusions

Our study suggests the involvement, immediately before transplantation, of karyopherin-mediated nuclear transport in the onset and development of DGF. Additionally, it reveals that karyopherins could be good candidates as potential DGF predictive clinical biomarkers and targets for pharmacological interventions in renal transplantation. However, because of the low number of patients analyzed and some methodological limitations, additional studies are needed to validate and to better address these points.

Molecular pathways undergoing dramatic transcriptomic changes during tumor development in the human colon

BACKGROUND

The malignant transformation of precancerous colorectal lesions involves progressive alterations at both the molecular and morphologic levels, the latter consisting of increases in size and in the degree of cellular atypia. Analyzing preinvasive tumors of different sizes can therefore shed light on the sequence of these alterations.

METHODS

We used a molecular pathway-based approach to analyze transcriptomic profiles of 59 colorectal tumors representing early and late preinvasive stages and the invasive stage of tumorigenesis. Random set analysis was used to identify biological pathways enriched for genes differentially regulated in tumors (compared with 59 samples of normal mucosa).

RESULTS

Of the 880 canonical pathways we investigated, 112 displayed significant tumor-related upregulation or downregulation at one or more stages of tumorigenesis. This allowed us to distinguish between pathways whose dysregulation is probably necessary throughout tumorigenesis and those whose involvement specifically drives progression from one stage to the next. We were also able to pinpoint specific changes within each gene set that seem to play key roles at each transition. The early preinvasive stage was characterized by cell-cycle checkpoint activation triggered by DNA replication stress and dramatic downregulation of basic transmembrane signaling processes that maintain epithelial/stromal homeostasis in the normal mucosa. In late preinvasive lesions, there was also downregulation of signal transduction pathways (e.g., those mediated by G proteins and nuclear hormone receptors) involved in cell differentiation and upregulation of pathways governing nuclear envelope dynamics and the G2>M transition in the cell cycle. The main features of the invasive stage were activation of the G1>S transition in the cell cycle, upregulated expression of tumor-promoting microenvironmental factors, and profound dysregulation of metabolic pathways (e.g., increased aerobic glycolysis, downregulation of pathways that metabolize drugs and xenobiotics).

CONCLUSIONS

Our analysis revealed specific pathways whose dysregulation might play a role in each transition of the transformation process. This is the first study in which such an approach has been used to gain further insights into colorectal tumorigenesis. Therefore, these data provide a launchpad for further exploration of the molecular characterization of colorectal tumorigenesis using systems biology approaches.

The expression of leucine-rich repeat gene family members in colorectal cancer

This study was conducted to evaluate the association of the leucine-rich repeat (LRR) gene family with colorectal cancer (CRC). The expression of members of the LRR gene family were analyzed in 17 CRC specimens and in 59 healthy colorectal tissues by using Human Exon1.0ST microarray, and in 25 CRC specimens and 32 healthy colorectal tissues by U133Plus2.0 microarray. An association was found for 25 genes belonging to the plant-specific (PS) class of LRR genes (P = 0.05 for Exon1.0 ST and P = 0.04 for U133Plus2.0). In both data-sets, in CRC, we found down-regulation of SHOC2 (P < 0.00003) and LRRC28 (P < 0.01) and up-regulation of LRSAM1 (P < 0.000001), while up-regulation of MFHAS1 (P = 0.0005) and down-regulation of WDFY3 (P = 0.026) were found only in the Exon1.0 ST data-set. The PS LLR gene class encodes proteins that activate immune cells and might play a key role in programmed cell death and autophagy. SHOC2 and LRRC28 genes involved in RAS-mediated signaling, which hinders nutrient deprivation-induced autophagy, might be a possible link between the negative control of autophagy and tumorigenesis.

Activated innate immunity and the involvement of CX3CR1-fractalkine in promoting hematuria in patients with IgA nephropathy

A hallmark of immunoglobulin A nephropathy (IgAN) is episodes of gross hematuria coinciding with mucosal infections that can represent the disease-triggering event. Here we performed a whole genomic screen of IgAN patients during gross hematuria to clarify the link between mucosal antigens and glomerular hematuria. Modulated genes showed a clear involvement of the intracellular interferon signaling, antigen-presenting pathway, and the immunoproteasome. The mRNA and protein level of the chemokine receptor characterizing cytotoxic effector lymphocytes, CX3CR1, was upregulated. In vitro antigenic stimulation of peripheral blood mononuclear cells from IgAN patients, healthy blood donors, and other nephropathies with microscopic hematuria showed that only in IgAN patients was CX3CR1 enhanced in a dose-dependent manner. A significantly higher amount of glomerular and urinary fractalkine, the only ligand of CX3CR1, was also found in IgAN patients with recurrent episodes of gross hematuria compared with other patients with microscopic or no hematuria. This suggests a predisposition for cytotoxic cell extravasation only in patients with recurrent gross hematuria. Thus, we found a defect in antigen handling in peripheral blood mononuclear cells of IgAN patients with a specific increase of CX3CR1. This constitutive upregulation of glomerular and urinary fractalkine suggests an involvement of the CX3CR1-fractalkine axis in the exacerbation of gross hematuria.

Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets

Single variant or single gene analyses generally account for only a small proportion of the phenotypic variation in complex traits. Alternatively, gene set or pathway association analyses are playing an increasingly important role in uncovering genetic architectures of complex traits through the identification of systematic genetic interactions. Two dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those based on gene expression profiles. However, gene-disease association can manifest in many ways, such as alterations of gene expression, genotype, and copy number; thus, an integrative approach combining multiple forms of evidence can more accurately and comprehensively capture pathway associations. We have developed a single statistical framework, Gene Set Association Analysis (GSAA), that simultaneously measures genome-wide patterns of genetic variation and gene expression variation to identify sets of genes enriched for differential expression and/or trait-associated genetic markers. Simulation studies illustrate that joint analyses of genomic data increase the power to detect real associations when compared with gene set methods that use only one genomic data type. The analysis of two human diseases, glioblastoma and Crohn's disease, detected abnormalities in previously identified disease-associated pathways, such as pathways related to PI3K signaling, DNA damage response, and the activation of NFKB. In addition, GSAA predicted novel pathway associations, for example, differential genetic and expression characteristics in genes from the ABC transporter family in glioblastoma and from the HLA system in Crohn's disease. These demonstrate that GSAA can help uncover biological pathways underlying human diseases and complex traits.

Discovering genetic variants in Crohn's disease by exploring genomic regions enriched of weak association signals

BACKGROUND

A meta-analysis has re-analysed previous genome-wide association scanning definitively confirming eleven genes and further identifying 21 new loci. However, the identified genes/loci still explain only the minority of genetic predisposition of Crohn's disease.

AIMS

To identify genes weakly involved in disease predisposition by analysing chromosomal regions enriched of single nucleotide polymorphisms with modest statistical association.

METHODS

We utilized the WTCCC data set evaluating 1748 CD and 2938 controls. The identification of candidate genes/loci was performed by a two-step procedure: first of all chromosomal regions enriched of weak association signals were localized; subsequently, weak signals clustered in gene regions were identified. The statistical significance was assessed by non parametric permutation tests.

RESULTS

The cytoband enrichment analysis highlighted 44 regions (P≤0.05) enriched with single nucleotide polymorphisms significantly associated with the trait including 23 out of 31 previously confirmed and replicated genes. Importantly, we highlight further 20 novel chromosomal regions carrying approximately one hundred genes/loci with modest association. Amongst these we find compelling functional candidate genes such as MAPT, GRB2 and CREM, LCT, and IL12RB2.

CONCLUSION

Our study suggests a different statistical perspective to discover genes weakly associated with a given trait, although further confirmatory functional studies are needed.

The anti-fibrotic effect of mycophenolic acid-induced neutral endopeptidase

Mycophenolic acid (MPA) appears to have anti-fibrotic effects, but the molecular mechanisms underlying this are unknown. We prospectively studied 35 stable kidney transplant recipients maintained on cyclosporine and azathioprine. We converted 20 patients from azathioprine to enteric-coated mycophenolate sodium (EC-MPS) and continued the remaining 15 patients on azathioprine. Exploratory mRNA expression profiling, performed on five randomly selected EC-MPS patients, revealed significant upregulation of neutral endopeptidase (NEP), which is an enzyme that degrades angiotensin II. We confirmed these microarray data by measuring levels of NEP expression in all subjects; in addition, we found that NEP gene expression correlated inversely with proteinuria. In an additional 33 patients, glomerular and tubular NEP protein levels from renal graft biopsies were significantly higher among the 13 patients receiving cyclosporine + EC-MPS than among the 12 patients receiving cyclosporine + azathioprine or 8 patients receiving cyclosporine alone. Glomerular NEP expression inversely correlated with glomerulosclerosis and proteinuria, and tubular NEP expression inversely correlated with interstitial fibrosis. Incubation of human proximal tubular cells with MPA increased NEP gene expression in a dose- and time-dependent manner. Moreover, MPA reduced angiotensin II-induced expression of the profibrotic factor plasminogen activator inhibitor-1, and a specific NEP inhibitor completely reversed this effect. Taken together, our data suggest that MPA directly induces expression of neutral endopeptidase, which may reduce proteinuria and slow the progression of renal damage in kidney transplant recipients.

On the reproducibility of results of pathway analysis in genome-wide expression studies of colorectal cancers

One of the major problems in genomics and medicine is the identification of gene networks and pathways deregulated in complex and polygenic diseases, like cancer. In this paper, we address the problem of assessing the variability of results of pathways analysis identified in different and independent genome wide expression studies, in which the same phenotypic conditions are assayed. To this end, we assessed the deregulation of 1891 curated gene sets in four independent gene expression data sets of subjects affected by colorectal cancer (CRC). In this comparison we used two well-founded statistical models for evaluating deregulation of gene networks. We found that the results of pathway analysis in expression studies are highly reproducible. Our study revealed 53 pathways identified by the two methods in all the four data sets analyzed with high statistical significance and strong biological relevance with the pathology examined. This set of pathways associated to single markers as well as to whole biological processes altered constitutes a signature of the disease which sheds light on the genetics bases of CRC.

Comparative study of gene set enrichment methods

Background

The analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited.

Results

The simulation study highlights that none of the three method outperforms all others consistently. GSEA and RS are able to detect weak signals of deregulation and they perform differently when genes in a gene set are both differentially up and down regulated. GLAPA is more conservative and large differences between the two phenotypes are required to allow the method to detect differential deregulation in gene sets. This is due to the fact that the enrichment statistic in GLAPA is prediction error which is a stronger criteria than classical two sample statistic as used in RS and GSEA. This was reflected in the analysis on real data sets as GSEA and RS were seen to be significant for particular gene sets while GLAPA was not, suggesting a small effect size. We find that the rank of gene set enrichment induced by GLAPA is more similar to RS than GSEA. More importantly, the rankings of the three methods share significant overlap.

Conclusion

The three methods considered in our study recover relevant gene sets known to be deregulated in the experimental conditions and pathologies analyzed. There are differences between the three methods and GSEA seems to be more consistent in finding enriched gene sets, although no method uniformly dominates over all data sets. Our analysis highlights the deep difference existing between associative and predictive methods for detecting enrichment and the use of both to better interpret results of pathway analysis. We close with suggestions for users of gene set methods.

Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements

Background

The identification of protein coding elements in sets of mammalian conserved elements is one of the major challenges in the current molecular biology research. Many features have been proposed for automatically distinguishing coding and non coding conserved sequences, making so necessary a systematic statistical assessment of their differences. A comprehensive study should be composed of an association study, i.e. a comparison of the distributions of the features in the two classes, and a prediction study in which the prediction accuracies of classifiers trained on single and groups of features are analyzed, conditionally to the compared species and to the sequence lengths.

Results

In this paper we compared distributions of a set of comparative and non comparative features and evaluated the prediction accuracy of classifiers trained for discriminating sequence elements conserved among human, mouse and rat species. The association study showed that the analyzed features are statistically different in the two classes. In order to study the influence of the sequence lengths on the feature performances, a predictive study was performed on different data sets composed of coding and non coding alignments in equal number and equally long with an ascending average length. We found that the most discriminant feature was a comparative measure indicating the proportion of synonymous nucleotide substitutions per synonymous sites. Moreover, linear discriminant classifiers trained by using comparative features in general outperformed classifiers based on intrinsic ones. Finally, the prediction accuracy of classifiers trained on comparative features increased significantly by adding intrinsic features to the set of input variables, independently on sequence length (Kolmogorov-Smirnov P-value ≤ 0.05).

Conclusion

We observed distinct and consistent patterns for individual and combined use of comparative and intrinsic classifiers, both with respect to different lengths of sequences/alignments and with respect to error rates in the classification of coding and non-coding elements. In particular, we noted that comparative features tend to be more accurate in the classification of coding sequences – this is likely related to the fact that such features capture deviations from strictly neutral evolution expected as a consequence of the characteristics of the genetic code.

Promoter methylation correlates with reduced NDRG2 expression in advanced colon tumour

Background

Aberrant DNA methylation of CpG islands of cancer-related genes is among the earliest and most frequent alterations in cancerogenesis and might be of value for either diagnosing cancer or evaluating recurrent disease. This mechanism usually leads to inactivation of tumour-suppressor genes. We have designed the current study to validate our previous microarray data and to identify novel hypermethylated gene promoters.

Methods

The validation assay was performed in a different set of 8 patients with colorectal cancer (CRC) by means quantitative reverse-transcriptase polymerase chain reaction analysis. The differential RNA expression profiles of three CRC cell lines before and after 5-aza-2'-deoxycytidine treatment were compared to identify the hypermethylated genes. The DNA methylation status of these genes was evaluated by means of bisulphite genomic sequencing and methylation-specific polymerase chain reaction (MSP) in the 3 cell lines and in tumour tissues from 30 patients with CRC.

Results

Data from our previous genome search have received confirmation in the new set of 8 patients with CRC. In this validation set six genes showed a high induction after drug treatment in at least two of three CRC cell lines. Among them, the N-myc downstream-regulated gene 2 (NDRG2) promoter was found methylated in all CRC cell lines. NDRG2 hypermethylation was also detected in 8 out of 30 (27%) primary CRC tissues and was significantly associated with advanced AJCC stage IV. Normal colon tissues were not methylated.

Conclusion

The findings highlight the usefulness of combining gene expression patterns and epigenetic data to identify tumour biomarkers, and suggest that NDRG2 silencing might bear influence on tumour invasiveness, being associated with a more advanced stage.

Biological and functional analysis of statistically significant pathways deregulated in colon cancer by using gene expression profiles

Gene expression profiling offers a great opportunity for studying multi-factor diseases and for understanding the key role of genes in mechanisms which drive a normal cell to a cancer state. Single gene analysis is insufficient to describe the complex perturbations responsible for cancer onset, progression and invasion. A deeper understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways rather than on individual genes. We apply two known and statistically well founded methods for finding pathways and biological processes deregulated in pathological conditions by analyzing gene expression profiles. In particular, we measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to a curated collection (Molecular Signature Database) in a colon cancer data set. We find that pathways strongly involved in different tumors are strictly connected with colon cancer. Moreover, our experimental results show that the study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. Our study shows the importance of using gene sets rather than single genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis evidences that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis. In this new perspective, the focus shifts from finding differentially expressed genes to identifying biological processes, cellular functions and pathways perturbed in the phenotypic conditions by analyzing genes co-expressed in a given pathway as a whole, taking into account the possible interactions among them and, more importantly, the correlation of their expression with the phenotypical conditions.

Statistical assessment of functional categories of genes deregulated in pathological conditions by using microarray data

MOTIVATION

A major challenge in current biomedical research is the identification of cellular processes deregulated in a given pathology through the analysis of gene expression profiles. To this end, predefined lists of genes, coding specific functions, are compared with a list of genes ordered according to their values of differential expression measured by suitable univariate statistics.

RESULTS

We propose a statistically well-founded method for measuring the relevance of predefined lists of genes and for assessing their statistical significance starting from their raw expression levels as recorded on the microarray. We use prediction accuracy as a measure of relevance of the list. The rationale is that a functional category, coded through a list of genes, is perturbed in a given pathology if it is possible to correctly predict the occurrence of the disease in new subjects on the basis of the expression levels of the genes belonging to the list only. The accuracy is estimated with multiple random validation strategy and its statistical significance is assessed against a couple of null hypothesis, by using two independent permutation tests. The utility of the proposed methodology is illustrated by analyzing the relevance of Gene Ontology terms belonging to biological process category in colon and prostate cancer, by using three different microarray data sets and by comparing it with current approaches.

AVAILABILITY

Source code for the algorithms is available from author upon request.

SUPPLEMENTARY INFORMATION

Colon cancer data set and a complete description of experimental results are available at: ftp://bioftp:76bioftpxxx@marx.ba.issia.cnr.it/supp-info.htm.

Regularized least squares classifiers may predict Crohn's disease from profiles of single nucleotide polymorphisms

In this paper we focus on the prediction of Crohn's disease (CD) susceptibility by analyzing SNP profiles for a number of defined or suggested gene polymorphisms. We assess the correlation between genetic markers and the phenotype by using well-founded methods and procedures developed in the field of statistical learning theory. To this end, we use a sample generated by a case-control study composed of 178 CD patients and 127 healthy controls. The genetic profile of each subject is composed of 16 genetic variants distributed over 11 genes. We find that regularized least squares (RLS) classifiers predict Crohn's disease with a statistically significant accuracy of 62%(p= 0.018), significantly increasing the diagnostic accuracy by at least 10% compared to that obtained with the more largely confirmed gene involved in CD predisposition, namely CARD15. This also demonstrates that our sample size is adequate for accurate and significant prediction estimates. The strength of this methodology, in contrast to classical statistical methods, is that it accounts simultaneously for the effect of several genetics markers and their possible interactions. The findings of this study show that RLS methodology is able to increase the diagnostic accuracy of CD prediction by contemporary evaluation of a large number of gene polymorphisms. This approach may be particularly useful in large-scale population screening programs, and when evaluating large datasets of gene polymorphisms (i.e. chips, microarrays). Moreover, it could shed more light on possible candidate genes with a weak genetic contribution, and for evaluating gene-gene and gene-phenotype interactions by analyzing populations with a reasonably small sample size.

Selection of relevant genes in cancer diagnosis based on their prediction accuracy

MOTIVATIONS

One of the main problems in cancer diagnosis by using DNA microarray data is selecting genes relevant for the pathology by analyzing their expression profiles in tissues in two different phenotypical conditions. The question we pose is the following: how do we measure the relevance of a single gene in a given pathology?

METHODS

A gene is relevant for a particular disease if we are able to correctly predict the occurrence of the pathology in new patients on the basis of its expression level only. In other words, a gene is informative for the disease if its expression levels are useful for training a classifier able to generalize, that is, able to correctly predict the status of new patients. In this paper we present a selection bias free, statistically well founded method for finding relevant genes on the basis of their classification ability.

RESULTS

We applied the method on a colon cancer data set and produced a list of relevant genes, ranked on the basis of their prediction accuracy. We found, out of more than 6500 available genes, 54 overexpressed in normal tissues and 77 overexpressed in tumor tissues having prediction accuracy greater than 70% with p-value <or=0.05.

CONCLUSIONS

The relevance of the selected genes was assessed (a) statistically, evaluating the p-value of the estimate prediction accuracy of each gene; (b) biologically, confirming the involvement of many genes in generic carcinogenic processes and in particular for the colon; (c) comparatively, verifying the presence of these genes in other studies on the same data-set.

On the statistical assessment of classifiers using DNA microarray data

Background

In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia – Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data.

Results

We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed.

Conclusions

The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required.

An invariance property of predictors in kernel-induced hypothesis spaces

We consider kernel-based learning methods for regression and analyze what happens to the risk minimizer when new variables, statistically independent of input and target variables, are added to the set of input variables. This problem arises, for example, in the detection of causality relations between two time series. We find that the risk minimizer remains unchanged if we constrain the risk minimization to hypothesis spaces induced by suitable kernel functions. We show that not all kernel-induced hypothesis spaces enjoy this property. We present sufficient conditions ensuring that the risk minimizer does not change and show that they hold for inhomogeneous polynomial and gaussian radial basis function kernels. We also provide examples of kernel-induced hypothesis spaces whose risk minimizer changes if independent variables are added as input.

An Invariance Property of Predictors in Kernel-Induced Hypothesis Spaces

We consider kernel-based learning methods for regression and analyze what happens to the risk minimizer when new variables, statistically independent of input and target variables, are added to the set of input variables. This problem arises, for example, in the detection of causality relations between two time series. We find that the risk minimizer remains unchanged if we constrain the risk minimization to hypothesis spaces induced by suitable kernel functions. We show that not all kernel-induced hypothesis spaces enjoy this property. We present sufficient conditions ensuring that the risk minimizer does not change and show that they hold for inhomogeneous polynomial and gaussian radial basis function kernels. We also provide examples of kernel-induced hypothesis spaces whose risk minimizer changes if independent variables are added as input.

Regularized Least Squares Cancer classifiers from DNA microarray data

BACKGROUND

The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information provided by DNA microarrays allows an approach to the problem of cancer analysis from a quantitative rather than qualitative point of view. Cancer classification requires well founded mathematical methods which are able to predict the status of new specimens with high significance levels starting from a limited number of data. In this paper we assess the performances of Regularized Least Squares (RLS) classifiers, originally proposed in regularization theory, by comparing them with Support Vector Machines (SVM), the state-of-the-art supervised learning technique for cancer classification by DNA microarray data. The performances of both approaches have been also investigated with respect to the number of selected genes and different gene selection strategies.

RESULTS

We show that RLS classifiers have performances comparable to those of SVM classifiers as the Leave-One-Out (LOO) error evaluated on three different data sets shows. The main advantage of RLS machines is that for solving a classification problem they use a linear system of order equal to either the number of features or the number of training examples. Moreover, RLS machines allow to get an exact measure of the LOO error with just one training.

CONCLUSION

RLS classifiers are a valuable alternative to SVM classifiers for the problem of cancer classification by gene expression data, due to their simplicity and low computational complexity. Moreover, RLS classifiers show generalization ability comparable to the ones of SVM classifiers also in the case the classification of new specimens involves very few gene expression levels.

Leave-one-out prediction error of systolic arterial pressure time series under paced breathing

In this paper, we consider systolic arterial pressure time series from healthy subjects and chronic heart failure patients, undergoing paced respiration, and show that different physiological states and pathological conditions may be characterized in terms of predictability of time series signals from the underlying biological system. We model time series by the regularized least-squares approach and quantify predictability by the leave-one-out error. We find that the entrainment mechanism connected to paced breath, that renders the arterial blood pressure signal more regular and thus more predictable, is less effective in patients, and this effect correlates with the seriousness of the heart failure. Using a Gaussian kernel, so that all orders of nonlinearity are taken into account, the leave-one-out error separates controls from patients (probability less than 10(-7)), and alive patients from patients for whom cardiac death occurred (probability less than 0.01).

Radial basis function approach to nonlinear Granger causality of time series

We consider an extension of Granger causality to nonlinear bivariate time series. In this frame, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. Not all the nonlinear prediction schemes are suitable to evaluate causality; indeed, not all of them allow one to quantify how much knowledge of the other time series counts to improve prediction error. We present an approach with bivariate time series modeled by a generalization of radial basis functions and show its application to a pair of unidirectionally coupled chaotic maps and to physiological examples.