Common functional alterations identified in blood transcriptome of autoimmune cholestatic liver and inflammatory bowel diseases

Primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and inflammatory bowel diseases (IBDs), including Crohn’s disease (CD) and ulcerative colitis (UC), are heterogeneous chronic autoimmune diseases that may share underlying pathogenic mechanisms. Herein, we compared simultaneously analyzed blood transcriptomes from patients with PBC, PSC, and IBD. Microarray-based measurements were conducted using RNA isolated from whole blood samples from 90, 45, 95 and 93 patients with PBC, PSC, CD, and UC, respectively, and 47 healthy controls. Expression levels of selected transcripts were analyzed by quantitative reverse-transcribed PCR using an independent cohort of 292, 71 and 727 patients with PBC, PSC, and IBD, respectively. Of 4026, 2650 and 4967 probe sets differentially expressed (adjusted p-value < 0.05) in samples from patients with PBC, PSC, and IBD, respectively, compared with healthy controls, 1946 were common to all three comparisons. Functional analyses indicated that most terms enriched for genes differentially expressed in PBC, PSC, and IBD patients compared with healthy controls were related to mitochondrial function, the vesicle endomembrane system, and GTPase-mediated processes. This study indicates that microarray-based profiling of blood gene expression supports research into the molecular mechanisms underlying disease, rather than being useful for selection of diagnostic biomarkers for use in clinical practice.

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

Global-scale profiling of differential expressed lysine acetylated proteins in colorectal cancer tumors and paired liver metastases

Lysine acetylated modification was indicated to impact colorectal cancer (CRC)'s distant metastasis. However, the global acetylated proteins in CRC and the differential expressed acetylated proteins and acetylated sites between CRC primary and distant metastatic tumor remains unclear. Our aim was to construct a complete atlas of acetylome in CRC and paired liver metastases. Combining high affinity enrichment of acetylated peptides with high sensitive mass spectrometry, we identified 603 acetylation sites from 316 proteins, among which 462 acetylation sites corresponding to 243 proteins were quantified. We further classified them into groups according to cell component, molecular function and biological process and analyzed the metabolic pathways, domain structures and protein interaction networks. Finally, we evaluated the differentially expressed lysine acetylation sites and revealed that 31 acetylated sites of 22 proteins were downregulated in CRC liver metastases compared to that in primary CRC while 40 acetylated sites of 32 proteins were upregulated, of which HIST2H3AK19Ac and H2BLK121Ac were the acetylated histones most changed, while TPM2 K152Ac and ADH1B K331Ac were the acetylated non-histones most altered. These results provide an expanded understanding of acetylome in CRC and its distant metastasis, and might prove applicable in the molecular targeted therapy of metastatic CRC.

BIOLOGICAL SIGNIFICANCE

This study described provides, for the first time, that full-scale profiling of lysine acetylated proteins were identified and quantified in colorectal cancer (CRC) and paired liver metastases. The novelty of the study is that we constructed a complete atlas of acetylome in CRC and paired liver metastases. Moreover, we analyzed these differentially expressed acetylated proteins in cell component, molecular function and biological process. In addition, metabolic pathways, domain structures and protein interaction networks of acetylated proteins were also investigated. Our approaches shows that of the differentially expressed proteins, HIST2H3AK19Ac and H2BLK121Ac were the acetylated histones most changed, while TPM2 K152Ac and ADH1B K331Ac were the acetylated non-histones most altered. Our findings provide an expanded understanding of acetylome in CRC and its distant metastasis, and might prove applicable in the molecular targeted therapy of metastatic CRC.

Proteomics in paediatric cardiac surgery: is a personalised approach feasible?

The incidence of congenital cardiac abnormalities remains high. Paediatric patients with congenital cardiac defects often require surgery at a young age. The surgeries are often long and complex, rendering this population particularly vulnerable to the deleterious effects of cardiopulmonary bypass and cardiac surgery. The search for cardioprotective strategies is ongoing in an attempt to reduce the morbidity in this population. In the post-genomic era, it is apparent that simply determining the genomic sequences holds little diagnostic potential and means to determine progression of disease and response to treatment. The field of proteomics is expanding and application of proteomic techniques in the clinical setting holds great potential to advance our understanding of the proteomic changes involved in specific disease stages. This review will assess the application of proteomic techniques in the setting of paediatric cardiac surgery and highlight the need to obtain a clear understanding of the role of various proteins in children with cardiac conditions. The success and challenges of the available proteomic technology will be discussed as well as the future potential of proteomic methods for advancing our understanding of protein changes in children requiring cardiac surgery.

Histone H3 lysine 27 acetylation is altered in colon cancer

Background

Histone post-translational modifications (PTMs) play an important role in the regulation of the expression of genes, including those involved in cancer development and progression. However, our knowledge of PTM patterns in human tumours is limited.

Methods

MS-based analyses were used to quantify global alterations of histone PTMs in colorectal cancer (CRC) samples. Histones isolated from 12 CRCs and their corresponding normal mucosa by acidic extraction were separated by SDS-PAGE and analysed by liquid chromatography-mass spectrometry.

Results

Among 96 modified peptides, 41 distinct PTM sites were identified, of which 7, 13, 11, and 10 were located within the H2A, H2B, H3, and H4 sequences, respectively, and distributed among the amino-terminal tails and the globular domain of the four histones. Modification intensities were quantified for 33 sites, of which 4 showed significant (p-value ≤ 0.05) differences between CRC tissues and healthy mucosa samples. We identified histone H3 lysine 27 acetylation (H3K27Ac) as a modification upregulated in CRC, which had not been shown previously.

Conclusions

The present results indicate the usefulness of a bottom-up proteomic approach for the detection of histone modifications at a global scale. The differential abundance of H3K27Ac mark in CRC, a PTM associated with active enhancers, suggests its role in regulating genes whose expression changes in CRC.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

Classification of time series gene expression in clinical studies via integration of biological network

The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of-the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.

Analysis of region-specific changes in gene expression upon treatment with citalopram and desipramine reveals temporal dynamics in response to antidepressant drugs at the transcriptome level

RATIONALE

The notion that the onset of action of antidepressant drugs (ADs) takes weeks is widely accepted; however, the sequence of events necessary for therapeutic effects still remains obscure.

OBJECTIVE

We aimed to evaluate a time-course of ADs-induced alterations in the expression of 95 selected genes in 4 regions of the rat brain: the prefrontal and cingulate cortices, the dentate gyrus of the hippocampus, and the amygdala.

METHODS

We employed RT-PCR array to evaluate changes during a time-course (1, 3, 7, 14, and 21 days) of treatments with desipramine (DMI) and citalopram (CIT). In addition to repeated treatment, we also conducted acute treatment (a single dose of drug followed by the same time intervals as the repeated doses).

RESULTS

Time-dependent and structure-specific changes in gene expression patterns allowed us to identify spatiotemporal differences in the molecular action of two ADs. Singular value decomposition analysis revealed differences in the global gene expression profiles between treatment types. The numbers of characteristic modes were generally smaller after CIT treatment than after DMI treatment. Analysis of the dynamics of gene expression revealed that the most significant changes concerned immediate early genes, whose expression was also visualized by in situ hybridization. Transcription factor binding site analysis revealed an over-representation of serum response factor binding sites in the promoters of genes that changed upon treatment with both ADs.

CONCLUSIONS

The observed gene expression patterns were highly dynamic, with oscillations and peaks at various time points of treatment. Our study also revealed novel potential targets of antidepressant action, i.e., Dbp and Id1 genes.

Perspectives on systems biology applications in diabetic kidney disease

Diabetic kidney disease (DKD) is a microvascular complication of type 1 and 2 diabetes with a devastating impact on individuals with the disease, their families, and society as a whole. DKD is the single most frequent cause of incident chronic kidney disease cases and accounts for over 40% of the population with end-stage renal disease. Contributing factors for the high prevalence are the increase in obesity and subsequent diabetes combined with an improved long-term survival with diabetes. Environment and genetic variations contribute to DKD susceptibility and progressive loss of kidney function. How the molecular mechanisms of genetic and environmental exposures interact during DKD initiation and progression is the focus of ongoing research efforts. The development of standardized, unbiased high-throughput profiling technologies of human DKD samples opens new avenues in capturing the multiple layers of DKD pathobiology. These techniques routinely interrogate analytes on a genome-wide scale generating comprehensive DKD-associated fingerprints. Linking the molecular fingerprints to deep clinical phenotypes may ultimately elucidate the intricate molecular interplay in a disease stage and subtype-specific manner. This insight will form the basis for accurate prognosis and facilitate targeted therapeutic interventions. In this review, we present ongoing efforts from large-scale data integration translating "-omics" research efforts into improved and individualized health care in DKD.

Dopaminergic therapies modulate the T-CELL proteome of patients with Parkinson's disease

Dopamine receptor agonists and L-dihydroxyphenylalanine (L-DOPA) counteract dopamine loss in the striatum and are therefore used in the treatment of Parkinson's disease (PD). T-Lymphocytes express some features of the dopaminergic system, and their function or activation might be regulated by dopaminergic treatments. Two-dimensional electrophoresis of total protein extract from T-lymphocytes was performed to identify therapy-induced proteome changes in T-cells of 17 patients with PD. Specific protein level alterations were further validated by Western blotting. Of 17 enrolled patients, 11 were treated with different doses of L-DOPA; in this group, we found that the levels of two spots, corresponding to ATP synthase subunit β and proteasome subunit β type-2, correlated linearly with the L-DOPA daily dose. Moreover, we identified seven proteins (prolidase, actin-related protein 2, F-actin-capping protein subunit β, tropomyosin α-3 chain, proteasome activator complex subunit 1, peroxiredoxin 6, and a glyceraldehyde-3-phosphate dehydrogenase isoform) whose levels were significantly different in patients treated with dopamine agonists. These findings demonstrate that dopaminergic stimulation has important effects on T-cell proteome in patients under long-term treatment. Therefore, therapies acting on the dopaminergic system may have additional effects on the immune system.

From basic to clinical neuropharmacology: targetophilia or pharmacodynamics?

Historically, much drug discovery and development in psychopharmacology tended to be empirical. However, over the last 20 years it has primarily been target oriented, with synthesis and selection of compounds designed to act at a specific neurochemical site. Such compounds are then examined in functional animal models of disease. There is little evidence that this approach (which we call 'targetophilia') has enhanced the discovery process and some indications that it may have retarded it. A major problem is the weakness of many animal models in mimicking the disease and the lack of appropriate biochemical markers of drug action in animals and patients. In this review we argue that preclinical studies should be conducted as if they were clinical studies in design, analysis, and reporting, and that clinical pharmacologists should be involved at the earliest stages, to help ensure that animal models reflect as closely as possible the clinical disease. In addition, their familiarity with pharmacokinetic-pharmacodynamic integration (PK-PD) would help ensure that appropriate dosing and drug measurement techniques are applied to the discovery process, thereby producing results with relevance to therapeutics. Better integration of experimental and clinical pharmacologists early in the discovery process would allow observations in animals and patients to be quickly exchanged between the two disciplines. This non-linear approach to discovery used to be the way research proceeded, and it resulted in productivity that has never been bettered. It also follows that occasionally 'look-see' studies, a proven technique for drug discovery, deserve to be reintroduced.

Pooled sample-based GWAS: a cost-effective alternative for identifying colorectal and prostate cancer risk variants in the Polish population

BACKGROUND

Prostate cancer (PCa) and colorectal cancer (CRC) are the most commonly diagnosed cancers and cancer-related causes of death in Poland. To date, numerous single nucleotide polymorphisms (SNPs) associated with susceptibility to both cancer types have been identified, but their effect on disease risk may differ among populations.

METHODS

To identify new SNPs associated with PCa and CRC in the Polish population, a genome-wide association study (GWAS) was performed using DNA sample pools on Affymetrix Genome-Wide Human SNP 6.0 arrays. A total of 135 PCa patients and 270 healthy men (PCa sub-study) and 525 patients with adenoma (AD), 630 patients with CRC and 690 controls (AD/CRC sub-study) were included in the analysis. Allele frequency distributions were compared with t-tests and χ(2)-tests. Only those significantly associated SNPs with a proxy SNP (p<0.001; distance of 100 kb; r(2)>0.7) were selected. GWAS marker selection was conducted using PLINK. The study was replicated using extended cohorts of patients and controls. The association with previously reported PCa and CRC susceptibility variants was also examined. Individual patients were genotyped using TaqMan SNP Genotyping Assays.

RESULTS

The GWAS selected six and 24 new candidate SNPs associated with PCa and CRC susceptibility, respectively. In the replication study, 17 of these associations were confirmed as significant in additive model of inheritance. Seven of them remained significant after correction for multiple hypothesis testing. Additionally, 17 previously reported risk variants have been identified, five of which remained significant after correction.

CONCLUSION

Pooled-DNA GWAS enabled the identification of new susceptibility loci for CRC in the Polish population. Previously reported CRC and PCa predisposition variants were also identified, validating the global nature of their associations. Further independent replication studies are required to confirm significance of the newly uncovered candidate susceptibility loci.

Phosphoproteomic analysis: an emerging role in deciphering cellular signaling in human embryonic stem cells and their differentiated derivatives

Cellular signaling is largely controlled by protein phosphorylation. This post-translational modification (PTM) has been extensively analyzed when examining one or a few protein phosphorylation events that effect cell signaling. However, protein kinase-driven signaling networks, comprising total (phospho)proteomes, largely control cell fate. Therefore, large-scale analysis of differentially regulated protein phosphorylation is central to elucidating complex cellular events, including maintenance of pluripotency and differentiation of embryonic stem cells (ESCs). The current technology of choice for total phosphoproteome and combined total proteome plus total phosphoproteome (termed (phospho)proteome) analyses is multidimensional liquid chromatography-(MDLC) tandem mass spectrometry (MS/MS). Advances in the use of MDLC for separation of peptides comprising total (phospho)proteomes, phosphopeptide enrichment, separation of enriched fractions, and quantitative peptide identification by MS/MS have been rapid in recent years, as have improvements in the sensitivity, speed, and accuracy of mass spectrometers. Increasingly deep coverage of (phospho)proteomes is allowing an improved understanding of changes in protein phosphorylation networks as cells respond to stimuli and progress from one undifferentiated or differentiated state to another. Although MDLC-MS/MS studies are powerful, understanding the interpretation of the data is important, and targeted experimental pursuit of biological predictions provided by total (phospho)proteome analyses is needed. (Phospho)proteomic analyses of pluripotent stem cells are in their infancy at this time. However, such studies have already begun to contribute to an improved and accelerated understanding of basic pluripotent stem cell signaling and fate control, especially at the systems-biology level.

Computational oncology

Oncology research has traditionally been conducted using techniques from the biological sciences. The new field of computational oncology has forged a new relationship between the physical sciences and oncology to further advance research. By applying physics and mathematics to oncologic problems, new insights will emerge into the pathogenesis and treatment of malignancies. One major area of investigation in computational oncology centers around the acquisition and analysis of data, using improved computing hardware and software. Large databases of cellular pathways are being analyzed to understand the interrelationship among complex biological processes. Computer-aided detection is being applied to the analysis of routine imaging data including mammography and chest imaging to improve the accuracy and detection rate for population screening. The second major area of investigation uses computers to construct sophisticated mathematical models of individual cancer cells as well as larger systems using partial differential equations. These models are further refined with clinically available information to more accurately reflect living systems. One of the major obstacles in the partnership between physical scientists and the oncology community is communications. Standard ways to convey information must be developed. Future progress in computational oncology will depend on close collaboration between clinicians and investigators to further the understanding of cancer using these new approaches.

Integrating proteomic and transcriptomic high-throughput surveys for search of new biomarkers of colon tumors

To the search of new colon tumor biomarkers in the transition from normal colon (NC) mucosa to adenoma (AD) and adenocarcinoma (AC), we integrated microarray data with the results of a high-throughput proteomic workflow. In proteomic study, we used a modified isoelectric focusing protocol on strips with an immobilized pH gradient to separate peptides labeled with iTRAQ (isobaric tags for relative and absolute quantitation) tags followed by liquid chromatography-tandem mass spectrometry analysis. Gene expression measurements were done using Affymetrix GeneChip HG-U133plus2 microarrays and quantitative reverse transcriptase PCR (q-RT-PCR). We identified 3,886 proteins with at least two peptides. Of them, 1,061 proteins were differentially expressed [FC ≥ 1.5; FDR ≤ 0.01] in two pair-wise comparisons: AD vs. NC and AC vs. AD while 15 and 23 proteins were progressively up-regulated and down-regulated in the NC/AD/AC sequence, respectively. The quantitative proteomic information was subsequently correlated with microarray data. For a collection of genes with the same direction of changes of both mRNA and protein levels, we obtained 785/853/795 genes in AD vs. NC/AC vs. NC/AC vs. AD comparison, respectively. Further evaluation of sequentially altered gene expression by q-RT-PCR on individual samples of 24 NCs, 42 ADs, and 26 ACs confirmed progressive expression of six genes: biglycan, calumenin, collagen type XII, alpha 1 (COL12A1), monoamine oxidase A (MAOA), ectonucleoside triphosphate diphosphohydrolase 5 (ENTPD5), and MOCO sulphurase C-terminal domain-containing 2 (MOSC2). Among them, three continuously down-regulated (MAOA, ENTPD5, and MOSC2) and one continuously overexpressed (COL12A1) are reported, to our best knowledge, for the first time in a connection to colon cancer onset.

Modeling oncogenic signaling in colon tumors by multidirectional analyses of microarray data directed for maximization of analytical reliability

Background

Clinical progression of colorectal cancers (CRC) may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence.

Methodology/Principal Findings

Studies were performed on normal mucosa, adenoma, and carcinoma samples obtained during surgery or colonoscopy. Collections of cryostat sections prepared from the tissue samples were evaluated by a pathologist to control the relative cell type content. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data was generated using two normalization algorithms: MAS5.0 and GCRMA with least-variant set (LVS). The data was evaluated using pair-wise comparisons and data decomposition into singular value decomposition (SVD) modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Expressional profiles obtained in 105 samples of whole tissue sections were used to establish oncogenic signaling alterations in progression of CRC, while those representing 40 microdissected specimens were used to select differences in KEGG pathways between epithelium and mucosa. Based on a consensus of the results obtained by two normalization algorithms, and two probe set sorting criteria, we identified 14 and 17 KEGG signaling and metabolic pathways that are significantly altered between normal and tumor samples and between benign and malignant tumors, respectively. Several of them were also selected from the raw microarray data of 2 recently published studies (GSE4183 and GSE8671).

Conclusion/Significance

Although the proposed strategy is computationally complex and labor–intensive, it may reduce the number of false results.

Proteomic profiling of cancer--opportunities, challenges, and context

The article by Roesch-Ely and colleagues in a recent issue of The Journal of Pathology describes the use of proteomic techniques to examine mucosal biopsies in patients with head and neck squamous cell cancer (HNSCC) and in corresponding control samples. The authors were able to determine the anatomical site of origin of the biopsies based on modelling of multiplex protein datasets, and to use the information to analyse field cancerization as a means of predicting tumour recurrence. Although the study included only a relatively small number of cases, and will require future validation in a larger patient cohort, the results point to the potential of proteomics to increase our understanding of cancer biology, and in this instance to offer clinical value.

Proteomics and the search for biomarkers of female reproductive diseases

Over the past decade, high-throughput proteomics technologies have evolved considerably and have become increasingly more commonly applied to the investigation of female reproductive diseases. Proteomic approaches facilitate the identification of new disease biomarkers by comparing the abundance of hundreds of proteins simultaneously to find those specific to a particular clinical condition. Some of the best studied areas of female reproductive biology applying proteomics include gynaecological cancers, endometriosis and endometrial infertility. This review will discuss the progress that has been made in these areas and will highlight some of the emerging technologies that promise to contribute to better understanding of the female reproductive disease.

A manifesto for clinical pharmacology from principles to practice

1. This is a manifesto for UK clinical pharmacology. 2. A clinical pharmacologist is a medically qualified practitioner who teaches, does research, frames policy, and gives information and advice about the actions and proper uses of medicines in humans and implements that knowledge in clinical practice. Those without medical qualifications who practise some aspect of clinical pharmacology could be described as, say, 'applied pharmacologists'. 3. Clinical pharmacology is operationally defined as a translational discipline in terms of the basic tools of human pharmacology (e.g. receptor pharmacology) and applied pharmacology (e.g. pharmacokinetics) and how they are used in drug discovery and development and in solving practical therapeutic problems in individuals and populations. 4. Clinical pharmacologists are employed by universities, health-care services, private organizations (such as drug companies), and regulatory agencies. They are mentors and teachers, teaching laboratory science, clinical science, and all aspects of practical drug therapy as underpinned by the science of pharmacology; they write and edit didactic and reference texts; researchers, covering research described by the operational definition; clinicians, practising general medicine, clinical toxicology, other medical specialties, and general practice; policy makers, framing local, national, and international medicines policy, including formularies, licensing of medicines and prescribing policies. 5. The future of clinical pharmacology depends on the expansion and maintenance of a central core of practitioners (employed by universities or health-care services), training clinical pharmacologists to practise in universities, health-care services, private organizations, and regulatory agencies, and training other clinicians in the principles and practice of clinical pharmacology.