Multivariable model of postoperative delirium in cardiac surgery patients: proteomic and demographic contributions

Background

Delirium following cardiac surgery is common, morbid, and costly, but may be prevented with risk stratification and targeted intervention. Preoperative protein signatures may identify patients at increased risk for worse postoperative outcomes, including delirium. In this study, we aimed to identify plasma protein biomarkers and develop a predictive model for postoperative delirium in older patients undergoing cardiac surgery, while also uncovering possible pathophysiological mechanisms.

Methods

SOMAscan analysis of 1,305 proteins in the plasma from 57 older adults undergoing cardiac surgery requiring cardiopulmonary bypass was conducted to define delirium-specific protein signatures at baseline (PREOP) and postoperative day 2 (POD2). Selected proteins were validated in 115 patients using the ELLA multiplex immunoassay platform. Proteins were combined with clinical and demographic variables to build multivariable models that estimate the risk of postoperative delirium and bring light to the underlying pathophysiology.

Results

A total of 115 and 85 proteins from SOMAscan analyses were found altered in delirious patients at PREOP and POD2, respectively (p<0.05). Using four criteria including associations with surgery, delirium, and biological plausibility, 12 biomarker candidates (Tukey's fold change (|tFC|)>1.4, Benjamini-Hochberg (BH)-p<0.01) were selected for ELLA multiplex validation. Eight proteins were significantly altered at PREOP, and seven proteins at POD2 (p<0.05), in patients who developed postoperative delirium compared to non-delirious patients. Statistical analyses of model fit resulted in the selection of a combination of age, sex, and three proteins (angiopoietin-2 (ANGPT2); C-C motif chemokine 5 (CCL5); and metalloproteinase inhibitor 1 (TIMP1); AUC=0.829) as the best performing predictive model for delirium at PREOP. The delirium-associated proteins identified as biomarker candidates are involved with inflammation, glial dysfunction, vascularization, and hemostasis, highlighting the multifactorial pathophysiology of delirium.

Conclusion

Our study proposes a model of postoperative delirium that includes a combination of older age, female sex, and altered levels of three proteins. Our results support the identification of patients at higher risk of developing postoperative delirium after cardiac surgery and provide insights on the underlying pathophysiology. ClinicalTrials.gov ( NCT02546765 ).

Detection of Cancer-Associated Gene Mutations in Urinary Cell-Free DNA among Prostate Cancer Patients in South Africa

Prostate cancer (PCa) is the most common cause of cancer death among African men. The presence of tumor-specific variations in cell-free DNA (cfDNA), such as mutations, microsatellite instability, and DNA methylation, has been explored as a source of biomarkers for cancer diagnosis. In this study, we investigated the diagnostic role of cfDNA among South African PCa patients. We performed whole exome sequencing (WES) of urinary cfDNA. We identified a novel panel of 31 significantly deregulated somatic mutated genes between PCa and benign prostatic hyperplasia (BPH). Additionally, we performed whole-genome sequencing (WGS) on matching PCa and normal prostate tissue in an independent PCa cohort from South Africa. Our results suggest that the mutations are of germline origin as they were also found in the normal prostate tissue. In conclusion, our study contributes to the knowledge of cfDNA as a biomarker for diagnosing PCa in the South African population.

Aptamer-Based Proteomics Measuring Preoperative Cerebrospinal Fluid Protein Alterations Associated with Postoperative Delirium

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case-control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort, and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p < 0.05). Due to the limited sample size, these proteins did not remain significant by multiple hypothesis testing using the Benjamini-Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case-control samples correctly, and principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80-0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identified 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology.

Potential of miRNAs in Plasma Extracellular Vesicle for the Stratification of Prostate Cancer in a South African Population

Prostate cancer (PCa) is the most common cause of cancer death among African men. The analysis of microRNAs (miRNAs) in plasma extracellular vesicles (EVs) can be utilized as a non-invasive tool for the diagnosis of PCa. In this study, we used small RNA sequencing to profile miRNAs cargo in plasma EVs from South African PCa patients. We evaluated the differential expression of miRNAs between low and high Gleason scores in the plasma EVs of South African patients and in the prostatic tissue from data available in the Cancer Genome Atlas (TCGA) Data Portal. We identified 7 miRNAs differently expressed in both EVs and prostatic tissues. We evaluated their expression using qPCR in a larger cohort of 10 patients with benign prostatic hyperplasia (BPH) and 24 patients with PCa. Here, we reported that the ratio between two of these miRNAs (i.e., miR-194-5p/miR-16-5p) showed a higher concentration in PCa compared to BPH and in metastatic PCa compared to localized PCa. We explored for the first time the profiling of miRNAs cargo in plasma EVs as a tool for the identification of putative markers in the South African population. Our finding indicated the ratio miR-194-5p/miR-16-5p as a non-invasive marker for the evaluation of PCa aggressiveness in this population.

Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge

Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.

Patterns and Persistence of Perioperative Plasma and Cerebrospinal Fluid Neuroinflammatory Protein Biomarkers After Elective Orthopedic Surgery Using SOMAscan

BACKGROUND

The neuroinflammatory response to surgery can be characterized by peripheral acute plasma protein changes in blood, but corresponding, persisting alterations in cerebrospinal fluid (CSF) proteins remain mostly unknown. Using the SOMAscan assay, we define acute and longer-term proteome changes associated with surgery in plasma and CSF. We hypothesized that biological pathways identified by these proteins would be in the categories of neuroinflammation and neuronal function and define neuroinflammatory proteome changes associated with surgery in older patients.

METHODS

SOMAscan analyzed 1305 proteins in blood plasma (n = 14) and CSF (n = 15) samples from older patients enrolled in the Role of Inflammation after Surgery for Elders (RISE) study undergoing elective hip and knee replacement surgery with spinal anesthesia. Systems biology analysis identified biological pathways enriched among the surgery-associated differentially expressed proteins in plasma and CSF.

RESULTS

Comparison of postoperative day 1 (POD1) to preoperative (PREOP) plasma protein levels identified 343 proteins with postsurgical changes ( P < .05; absolute value of the fold change [|FC|] > 1.2). Comparing postoperative 1-month (PO1MO) plasma and CSF with PREOP identified 67 proteins in plasma and 79 proteins in CSF with altered levels ( P < .05; |FC| > 1.2). In plasma, 21 proteins, primarily linked to immune response and inflammation, were similarly changed at POD1 and PO1MO. Comparison of plasma to CSF at PO1MO identified 8 shared proteins. Comparison of plasma at POD1 to CSF at PO1MO identified a larger number, 15 proteins in common, most of which are regulated by interleukin-6 (IL-6) or transforming growth factor beta-1 (TGFB1) and linked to the inflammatory response. Of the 79 CSF PO1MO-specific proteins, many are involved in neuronal function and neuroinflammation.

CONCLUSIONS

SOMAscan can characterize both short- and long-term surgery-induced protein alterations in plasma and CSF. Acute plasma protein changes at POD1 parallel changes in PO1MO CSF and suggest 15 potential biomarkers for longer-term neuroinflammation that warrant further investigation.

Proteome-Wide Analysis Using SOMAscan Identifies and Validates Chitinase-3-Like Protein 1 as a Risk and Disease Marker of Delirium Among Older Adults Undergoing Major Elective Surgery

BACKGROUND

Delirium (an acute change in cognition) is a common, morbid, and costly syndrome seen primarily in aging adults. Despite increasing knowledge of its epidemiology, delirium remains a clinical diagnosis with no established biomarkers to guide diagnosis or management. Advances in proteomics now provide opportunities to identify novel markers of risk and disease progression for postoperative delirium and its associated long-term consequences (eg, long-term cognitive decline and Alzheimer's disease [AD]).

METHODS

In a nested matched case-control study (18 delirium/no-delirium pairs) within the Successful Aging after Elective Surgery study (N = 556), we evaluated the association of 1305 plasma proteins preoperatively [PREOP] and on postoperative day 2 [POD2]) with delirium using SOMAscan. Generalized linear models were applied to enzyme-linked immunosorbant assay (ELISA) validation data of one protein across the full cohort. Multi-protein modeling included delirium biomarkers identified in prior work (C-reactive protein, interleukin-6 [IL6]).

RESULTS

We identified chitinase-3-like-protein-1 (CHI3L1/YKL-40) as the sole delirium-associated protein in both a PREOP and a POD2 predictor model, a finding confirmed by ELISA. Multi-protein modeling found high PREOP CHI3L1/YKL-40 and POD2 IL6 increased the risk of delirium (relative risk [95% confidence interval] Quartile [Q]4 vs Q1: 2.4[1.2-5.0] and 2.1[1.1-4.1], respectively).

CONCLUSIONS

Our identification of CHI3L1/YKL-40 in postoperative delirium parallels reports of CHI3L1/YKL-40 and its association with aging, mortality, and age-related conditions including AD onset and progression. This highlights the type 2 innate immune response, involving CHI3L1/YKL-40, as an underlying mechanism of postoperative delirium, a common, morbid, and costly syndrome that threatens the independence of older adults.

Integration of Multi-Omics Data Using Probabilistic Graph Models and External Knowledge

Background:

High-throughput sequencing technologies have revolutionized the ability to perform systems-level biology and elucidate molecular mechanisms of disease through the comprehensive characterization of different layers of biological information. Integration of these heterogeneous layers can provide insight into the underlying biology but is challenged by modeling complex interactions.

Objective:

We introduce OBaNK: omics integration using Bayesian networks and external knowledge, an algorithm to model interactions between heterogeneous high-dimensional biological data to elucidate complex functional clusters and emergent relationships associated with an observed phenotype.

Method:

Using Bayesian network learning, we modeled the statistical dependencies and interactions between lipidomics, proteomics, and metabolomics data. The strength of a learned interaction between molecules was altered based on external knowledge.

Results :

Networks learned from synthetic datasets based on real pathways achieved an average area under the curve score of ~0.85, an improvement of ~0.23 from baseline methods. When applied to real multi-omics data collected during pregnancy, five distinct functional networks of heterogeneous biological data were identified, and the results were compared to other multi-omics integration approaches.

Conclusion:

OBaNK successfully improved the accuracy of learning interaction networks from data integrating external knowledge, identified heterogeneous functional networks from real data, and suggested potential novel interactions associated with the phenotype. These findings can guide future hypothesis generation. OBaNK source code is available at: https://github.com/bridgettripp/OBaNK.git, and a graphical user interface is available at: http://otulab.unl.edu/OBaNK.

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

Background:

Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation is shown to be contributed by local DNA sequence features.

Objective:

To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI.

Methods:

We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific.

Results:

We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs.

Conclusion:

Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB® and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

KEGG2Net: Deducing gene interaction networks and acyclic graphs from KEGG pathways

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database provides a manual curation of biological pathways that involve genes (or gene products), metabolites, chemical compounds, maps, and other entries. However, most applications and datasets involved in omics are gene or protein-centric requiring pathway representations that include direct and indirect interactions only between genes. Furthermore, special methodologies, such as Bayesian networks require acyclic representations of graphs. We developed KEGG2Net, a web resource that generates a network involving only the genes represented on a KEGG pathway with all of the direct and indirect gene-gene interactions deduced from the pathway. KEGG2Net offers four different methods to remove cycles from the resulting gene interaction network, converting them into directed acyclic graphs (DAGs). We generated synthetic gene expression data using the gene interaction networks deduced from the KEGG pathways and performed a comparative analysis of different cycle removal methods by testing the fitness of their DAGs to the data and by the number of edges they eliminate. Our results indicate that an ensemble method for cycle removal performs as the best approach to convert the gene interaction networks into DAGs. Resulting gene interaction networks and DAGs are represented in multiple user-friendly formats that can be used in other applications, and as images for quick and easy visualisation. The KEGG2Net web portal converts KEGG maps for any organism into gene-gene interaction networks and corresponding DAGS representing all of the direct and indirect interactions among the genes.

Parenteral lipid emulsions induce unique ileal fatty acid and metabolomic profiles but do not increase the risk of necrotizing enterocolitis in preterm pigs

Necrotizing enterocolitis (NEC) is a manifestation of maladaptive intestinal responses in preterm infants centrally medicated by unattenuated inflammation. Early in the postnatal period, preterm infants develop a deficit in arachidonic and docosahexaenoic acid, both potent regulators of inflammation. We hypothesized that the fatty acid composition of parenteral lipid emulsions uniquely induces blood and intestinal fatty acid profiles which, in turn, modifies the risk of NEC development. Forty-two preterm pigs were randomized to receive one of three lipid emulsions containing 100% soybean oil (SO), 15% fish oil (MO15), or 100% fish oil (FO100) with enteral feedings over an 8-day protocol. Blood and distal ileum tissue were collected for fatty acid analysis. The distal ileum underwent histologic, proteomic, and metabolomic analyses. Eight pigs [3/14 SO (21%), 3/14 MO15 (21%), and 2/14 FO100 (14%)] developed NEC. No differences in NEC risk were evident between groups despite differences in induced fatty acid profiles in blood and ileal tissue. Metabolomic analysis of NEC versus no NEC tissue revealed differences in tryptophan metabolism and arachidonic acid-containing glycerophospholipids. Proteomic analysis demonstrated no differences by lipid group; however, 15 proteins differentiated NEC versus no NEC in the domains of tissue injury, glucose uptake, and chemokine signaling. Exposure to parenteral lipid emulsions induces unique intestinal fatty acid and metabolomic profiles; however, these profiles are not linked to a difference in NEC development. Metabolomic and proteomic analyses of NEC versus no NEC intestinal tissue provide mechanistic insights into the pathogenesis of NEC in preterm infants.NEW & NOTEWORTHY Exposure to parenteral lipid emulsions induces unique intestinal fatty acid and metabolomic profiles; however, these profiles are not linked to a difference in NEC risk in preterm pigs. Metabolomic and proteomic analyses provide mechanistic insights into NEC pathogenesis. Compared with healthy ileal tissue, metabolites in tryptophan metabolism and arachidonic acid-containing glycerophospholipids are increased in NEC tissue. Proteomic analysis differentiates NEC versus no NEC in the domains of tissue injury, glucose uptake, and chemokine signaling.

Targeted metabolomics analysis of postoperative delirium

Postoperative delirium is the most common complication among older adults undergoing major surgery. The pathophysiology of delirium is poorly understood, and no blood-based, predictive markers are available. We characterized the plasma metabolome of 52 delirium cases and 52 matched controls from the Successful Aging after Elective Surgery (SAGES) cohort (N = 560) of patients ≥ 70 years old without dementia undergoing scheduled major non-cardiac surgery. We applied targeted mass spectrometry with internal standards and pooled controls using a nested matched case-control study preoperatively (PREOP) and on postoperative day 2 (POD2) to identify potential delirium risk and disease markers. Univariate analyses identified 37 PREOP and 53 POD2 metabolites associated with delirium and multivariate analyses achieved significant separation between the two groups with an 11-metabolite prediction model at PREOP (AUC = 83.80%). Systems biology analysis using the metabolites with differential concentrations rendered "valine, leucine, and isoleucine biosynthesis" at PREOP and "citrate cycle" at POD2 as the most significantly enriched pathways (false discovery rate < 0.05). Perturbations in energy metabolism and amino acid synthesis pathways may be associated with postoperative delirium and suggest potential mechanisms for delirium pathogenesis. Our results could lead to the development of a metabolomic delirium predictor.

Placenta accreta spectrum: biomarker discovery using plasma proteomics

BACKGROUND

Many cases of placenta accreta spectrum are not diagnosed antenatally, despite identified risk factors and improved imaging methods. Identification of plasma protein biomarkers could further improve the antenatal diagnosis of placenta accreta spectrum .

OBJECTIVE

The purpose of this study was to determine if women with placenta accreta spectrum have a distinct plasma protein profile compared with control subjects.

STUDY DESIGN

We obtained plasma samples before delivery from 16 participants with placenta accreta spectrum and 10 control subjects with similar gestational ages (35.1 vs 35.5 weeks gestation, respectively). We analyzed plasma samples with an aptamer-based proteomics platform for alterations in 1305 unique proteins. Heat maps of the most differentially expressed proteins (T test, P<.01) were generated with matrix visualization and analysis software. Principal component analysis was performed with the use of all 1305 proteins and the top 21 dysregulated proteins. We then confirmed dysregulated proteins using enzyme-linked immunosorbent assay and report significant differences between placenta accreta spectrum and control cases (Wilcoxon-rank sum test, P<.05).

RESULTS

Many of the top 50 proteins that significantly dysregulated in participants with placenta accreta spectrum were inflammatory cytokines, factors that regulate vascular remodeling, and extracellular matrix proteins that regulate invasion. Placenta accreta spectrum, with the use of the top 21 proteins, distinctly separated the placenta accreta spectrum cases from control cases (P<.01). Using enzyme-linked immunosorbent assay, we confirmed 4 proteins that were dysregulated in placenta accreta spectrum compared with control cases: median antithrombin III concentrations (240.4 vs 150.3 mg/mL; P=.002), median plasminogen activator inhibitor 1 concentrations (4.1 vs 7.1 ng/mL; P<.001), soluble Tie2 (13.5 vs 10.4 ng/mL; P=.02), soluble vascular endothelial growth factor receptor 2 (9.0 vs 5.9 ng/mL; P=.003).

CONCLUSION

Participants with placenta accreta spectrum had a unique and distinct plasma protein signature.

Comparative analysis of single-cell transcriptomics in human and Zebrafish oocytes

Background

Zebrafish is a popular model organism, which is widely used in developmental biology research. Despite its general use, the direct comparison of the zebrafish and human oocyte transcriptomes has not been well studied. It is significant to see if the similarity observed between the two organisms at the gene sequence level is also observed at the expression level in key cell types such as the oocyte.

Results

We performed single-cell RNA-seq of the zebrafish oocyte and compared it with two studies that have performed single-cell RNA-seq of the human oocyte. We carried out a comparative analysis of genes expressed in the oocyte and genes highly expressed in the oocyte across the three studies. Overall, we found high consistency between the human studies and high concordance in expression for the orthologous genes in the two organisms. According to the Ensembl database, about 60% of the human protein coding genes are orthologous to the zebrafish genes. Our results showed that a higher percentage of the genes that are highly expressed in both organisms show orthology compared to the lower expressed genes. Systems biology analysis of the genes highly expressed in the three studies showed significant overlap of the enriched pathways and GO terms. Moreover, orthologous genes that are commonly overexpressed in both organisms were involved in biological mechanisms that are functionally essential to the oocyte.

Conclusions

Orthologous genes are concurrently highly expressed in the oocytes of the two organisms and these genes belong to similar functional categories. Our results provide evidence that zebrafish could serve as a valid model organism to study the oocyte with direct implications in human.

Cdc42-Dependent Transfer of mir301 from Breast Cancer-Derived Extracellular Vesicles Regulates the Matrix Modulating Ability of Astrocytes at the Blood-Brain Barrier

Breast cancer brain metastasis is a major clinical challenge and is associated with a dismal prognosis. Understanding the mechanisms underlying the early stages of brain metastasis can provide opportunities to develop efficient diagnostics and therapeutics for this significant clinical challenge. We have previously reported that breast cancer-derived extracellular vesicles (EVs) breach the blood-brain barrier (BBB) via transcytosis and can promote brain metastasis. Here, we elucidate the functional consequences of EV transport across the BBB. We demonstrate that brain metastasis-promoting EVs can be internalized by astrocytes and modulate the behavior of these cells to promote extracellular matrix remodeling in vivo. We have identified protein and miRNA signatures in these EVs that can lead to the interaction of EVs with astrocytes and, as such, have the potential to serve as targets for development of diagnostics and therapeutics for early detection and therapeutic intervention in breast cancer brain metastasis.

Unexpected effects of systemic steroids on the CRSwNP proteome: is protein upregulation more important than inhibition?

BACKGROUND

Oral steroids, traditionally thought of as immunosuppressive agents that are broad in their immunomodulatory effects, are a mainstay of treatment to reduce disease burden in chronic rhinosinusitis with nasal polyps (CRSwNP). The purpose of this study was to determine how differentially expressed proteins in CRSwNP are affected by oral steroid therapy.

METHODS

Matched exosomal proteomic arrays were quantified using aptamer-based methods in systemic steroid-naive CRSwNP patients before and after a standardized oral prednisone course (n = 12). Previously identified differentially expressed proteins in CRSwNP patients were compared to determine the effect of steroids on expression. Fisher's exact test and t test were applied to normalized protein expression profiles to determine significance.

RESULTS

Of 18 proteins previously identified to be highly underexpressed in CRSwNP, 16 (89%) had an average increase after systemic steroid treatment (p < 0.05). Lactoperoxidase, initially present at 9-fold lower concentrations in CRSwNP subjects, increased by 209% after steroid treatment. A similar trend was observed with other proteins of interest, including platelet factor 4 and C-C motif ligand 28. The converse of this steroid effect was not true; of the 53 proteins that are highly overexpressed in CRSwNP, only 22 (42%) decreased in quantity with steroid use.

CONCLUSION

Proteomic analysis of differentially expressed proteins in CRSwNP demonstrates that systemic steroids cause almost uniform upregulation of transcriptionally decreased proteins, whereas the effects of steroids on transcriptionally increased proteins are more heterogeneous. Thus, proteomic analysis may be an effective tool to understand specific therapeutic benefits of steroid use in polyp disease and to create more targeted treatments.

Development of a Dynamic Multi-Protein Signature of Postoperative Delirium

Background

Delirium is common, morbid, and costly, yet its biology is poorly understood. We aimed to develop a multi-protein signature of delirium by identifying proteins associated with delirium from unbiased proteomics and combining them with delirium biomarkers identified in our prior work (interleukin [IL]-6 and IL-2).

Methods

We used the Successful Aging after Elective Surgery (SAGES) Study of adults age ≥70 undergoing major noncardiac surgery (N = 560; 24% delirium). Plasma was collected preoperatively (PREOP) and on postoperative day 2 (POD2). In a nested matched case-control study involving 12 pairs of delirium cases and no-delirium controls, isobaric tags for relative and absolute quantitation-based (iTRAQ) mass spectrometry proteomics was applied to identify the top set of delirium-related proteins. With these proteins, we then conducted enzyme-linked immunosorbent assay (ELISA) confirmation, and if confirmed, ELISA validation in 75 matched pairs. Multi-marker conditional logistic regression was used to select the "best" PREOP and POD2 models for delirium.

Results

We identified three proteins from iTRAQ: C-reactive protein (CRP), zinc alpha-2 glycoprotein (AZGP1), and alpha-1 antichymotrypsin (SERPINA3). The "best" multi-protein models of delirium included: PREOP: CRP and AZGP1 (Bayesian information criteria [BIC]: 93.82, c-statistic: 0.77); and POD2: IL-6, IL-2, and CRP (BIC: 87.11, c-statistic: 0.84).

Conclusion

The signature of postoperative delirium is dynamic, with some proteins important before surgery (risk markers) and others at the time of delirium (disease markers). Our dynamic, multi-protein signature for delirium improves our understanding of delirium pathophysiology and may identify patients at-risk of this devastating disorder that threatens independence of older adults.

Neutrophil activation in systemic capillary leak syndrome (Clarkson disease)

Systemic capillary leak syndrome (SCLS; Clarkson disease) is a rare orphan disorder characterized by transient yet recurrent episodes of hypotension and peripheral oedema due to diffuse vascular leakage of fluids and proteins into soft tissues. Humoral mediators, cellular responses and genetic features accounting for the clinical phenotype of SCLS are virtually unknown. Here, we searched for factors altered in acute SCLS plasma relative to matched convalescent samples using multiplexed aptamer‐based proteomic screening. Relative amounts of 612 proteins were changed greater than twofold and 81 proteins were changed at least threefold. Among the most enriched proteins in acute SCLS plasma were neutrophil granule components including bactericidal permeability inducing protein, myeloperoxidase and matrix metalloproteinase 8. Neutrophils isolated from blood of subjects with SCLS or healthy controls responded similarly to routine pro‐inflammatory mediators. However, acute SCLS sera activated neutrophils relative to remission sera. Activated neutrophil supernatants increased permeability of endothelial cells from both controls and SCLS subjects equivalently. Our results suggest systemic neutrophil degranulation during SCLS acute flares, which may contribute to the clinical manifestations of acute vascular leak.

Translating transcription: proteomics in chronic rhinosinusitis with nasal polyps reveals significant discordance with messenger RNA expression

BACKGROUND

Much of the literature examining chronic rhinosinusitis with nasal polyps (CRSwNP) immunopathology has been predicated on messenger RNA (mRNA) analysis with the assumption that transcriptional changes would reflect end-effector protein expression. The purpose of this study was to test this hypothesis using matched transcriptomic and proteomic data sets.

METHODS

Matched tissue proteomic and transcriptomic arrays were quantified in CRSwNP polyp tissue and control inferior turbinate tissue (n = 10/group). Mucus samples were additionally collected in 6 subjects from each group. Proteins were grouped into functional categories by bioinformatics and differential expression analyses. Log-log regression and Pearson correlations were performed to determine the level of agreement between data sets.

RESULTS

Of the 1310 proteins examined, 393 were significantly differentially expressed in CRSwNP. On regression analysis, differences in protein expression were poorly predicted by differences in mRNA expression (R² = 0.020, p < 0.05). Several genes canonically thought to be overexpressed in CRSwNP, including IL-5, IL-13, TSLP, CCL13, and CCL26, showed substantial increases in mRNA transcription, but had minimally or unchanged protein expression. Others, including IgE, periostin, CCL18, and CST1/2, were increased at both the transcriptomic and proteomic levels. Among differentially regulated proteins, tissue and mucus protein levels showed weak correlation (r = 0.26, p < 0.0001).

CONCLUSION

Proteomic analysis in CRSwNP has revealed novel disease-associated proteins and pathways, yet correlates poorly with transcriptomic data. The increasing availability of proteomic arrays opens the door to new potential explanatory mechanisms in CRSwNP and suggests that mRNA based studies should be validated with protein analysis.

Identification of Genes Differentially Expressed in Simvastatin-Induced Alveolar Bone Formation

Local delivery of simvastatin (SIM) has exhibited potential in preventing inflammation and limiting bone loss associated with experimental periodontitis. The primary aim of this study was to analyze transcriptome changes that may contribute to SIM's reduction of periodontal inflammation and bone loss. We evaluate the global genetic profile and signaling mechanisms induced by SIM on experimental periodontitis bone loss and inflammation. Twenty mature female Sprague Dawley rats were subjected to ligature-induced experimental periodontitis around maxillary second molars (M2) either unilaterally (one side untreated, n = 10) or bilaterally (n = 10). After the ligature removal at day 7, sites were injected with either carrier, pyrophosphate (PPi ×3), 1.5-mg SIM-dose equivalent SIM-pyrophosphate prodrug, or no injection. Three days after ligature removal, animals were euthanized; the M1-M2 interproximal was evaluated with μCT, histology, and protein expression. M2 palatal gingiva was harvested for RNA sequencing. Although ligature alone caused upregulation of proinflammatory and bone catabolic genes and proteins, seen in human periodontitis, SIM-PPi upregulated anti-inflammatory (IL-10, IL-1 receptor-like 1) and bone anabolic (insulin-like growth factor, osteocrin, fibroblast growth factor, and Wnt/ β-catenin) genes. The PPi carrier alone did not have these effects. Genetic profile and signaling mechanism data may help identify enhanced pharmacotherapeutic approaches to limit or regenerate periodontitis bone loss. © 2018 The Authors. JBMR Plus Published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.

Abstract 5083: Using proteomics profiling to elucidate the interactions of breast cancer-derived exosomes with the blood-brain barrier

Breast cancer metastasis to brain is associated with a dismal prognosis commonly attributed to a limited understating of the mechanisms driving this pathological process. Elucidation of the early events leading to brain metastasis is essential to the development of more effective therapeutic and diagnostic approaches. With an interest in the role of breast cancer-derived exosomes in brain metastasis, our group has previously shown that exosomes derived from a brain-seeking variant of the breast cancer cell line MDA-MB-231 (Br-Ex) can facilitate brain metastasis by inducing alterations in the protein expression profile of astrocytes, one of the components of the blood brain barrier (BBB). This observation led us to hypothesize that the interaction(s) between exosomes and astrocytes is more efficient compared to brain endothelial cells (ECs) or pericytes, the two additional major components of the BBB, resulting in more prominent alterations in the protein expression profile of astrocytes. To test this hypothesis, we first quantified and compared the uptake of exosomes by brain ECs, astrocytes, and pericytes in vitro. The uptake of Br-Ex by astrocytes was significantly greater than that of brain ECs (P&lt;4e-3) and pericytes (P&lt;1e-3). In contrast, exosomes derived from parental or bone-seeking MDA-MB-231 cells (P-Ex and Bo-Ex, respectively) did not show a preferential uptake by astrocytes. We have also demonstrated the uptake of Br-Ex by astrocytes in vivo. The uptake of exosomes by different cell types predominantly relies on the interaction of exosomal proteins with different receptors on the recipient cells. To determine the exosomal proteins potentially involved in the preferential uptake of Br-Ex by astrocytes, we performed quantitative mass spectrometry on the P-, Bo-, and Br-Ex via Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) analysis. Database searches were performed against human proteins in the SwissProt database and a total of 126 proteins were detected with over 95% confidence. Pairwise comparisons identified a total of 27 and 21 proteins with statistically significant differential expression (P &lt; 0.05) in the Br-Ex compared to the P- and Bo-Ex, respectively. Functional enrichment analysis of the detected proteins demonstrated that proteins belonging to the cell migration and focal adhesion categories were over-represented in the Br-Ex. Among these proteins, a number of integrins and annexins were highly enriched in the Br-Ex and can potentially be involved in the preferential uptake of these exosomes by astrocytes.

These findings indicate that exosomes derived from brain-seeking breast cancer cells can preferentially interact with astrocytes and these interactions can be driven by exosomal integrins and annexins. (The authors are grateful for the support of the Breast Cancer Research Foundation and the Advanced Medical Research Foundation.)

Citation Format: Golnaz Morad, Hasan H. Otu, Simon T. Dillon, Marsha A. Moses. Using proteomics profiling to elucidate the interactions of breast cancer-derived exosomes with the blood-brain barrier [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5083.

Higher C-Reactive Protein Levels Predict Postoperative Delirium in Older Patients Undergoing Major Elective Surgery: A Longitudinal Nested Case-Control Study

BACKGROUND

Delirium is a common, morbid, and costly postoperative complication. We aimed to identify blood-based postoperative delirium markers in a nested case-control study of older surgical patients using a proteomics approach followed by enzyme-linked immunosorbent assay (ELISA) validation.

METHODS

The Successful Aging after Elective Surgery study enrolled dementia-free adults ≥70 years old undergoing major scheduled noncardiac surgery (N = 566; 24% delirium). Plasma was collected at four time points: preoperative, postanesthesia care unit, postoperative day 2, and 1 month postoperative. Matched pairs were selected for the independent discovery (39 pairs) and replication cohorts (36 pairs), which were subsequently combined into the pooled cohort (75 pairs). Isobaric tags for relative and absolute quantitation-based relative quantitation mass spectrometry proteomics were performed to identify the strongest delirium-related protein, which was selected for ELISA validation. Using the ELISA results, statistical analyses using nonparametric signed rank tests were performed in all cohorts examining the association between the identified protein and delirium.

RESULTS

C-reactive protein emerged from the proteomics analysis as the strongest delirium-related protein. Validation by ELISA confirmed that compared with controls, cases had significantly higher C-reactive protein levels in the discovery, replication, and pooled cohorts at the preoperative (median paired difference [MPD] 1.97 mg/L [p < .05], 0.29 mg/L, 1.56 mg/L [p < .01]), postanesthesia care unit (MPD 2.83 mg/L, 2.22 mg/L [p < .05], 2.53 mg/L [p < .01]) and postoperative day 2 (MPD 71.97 mg/L [p < .01], 35.18 mg/L [p < .05], 63.76 mg/L [p < .01]) time points, but not 1 month postoperative (MPD 2.72 mg/L, -0.66 mg/L, 1.10 mg/L).

CONCLUSIONS

Elevated preoperative and postoperative plasma levels of C-reactive protein were associated with delirium, suggesting that a preinflammatory state and heightened inflammatory response to surgery are potential pathophysiologic mechanisms of delirium.

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification.

EXPERIMENTAL DESIGN

Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells.

RESULTS

A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion.

CONCLUSIONS

This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets.

Detecting Microbial Dysbiosis Associated with Pediatric Crohn Disease Despite the High Variability of the Gut Microbiota

The relationship between the host and its microbiota is challenging to understand because both microbial communities and their environments are highly variable. We have developed a set of techniques based on population dynamics and information theory to address this challenge. These methods identify additional bacterial taxa associated with pediatric Crohn disease and can detect significant changes in microbial communities with fewer samples than previous statistical approaches required. We have also substantially improved the accuracy of the diagnosis based on the microbiota from stool samples, and we found that the ecological niche of a microbe predicts its role in Crohn disease. Bacteria typically residing in the lumen of healthy individuals decrease in disease, whereas bacteria typically residing on the mucosa of healthy individuals increase in disease. Our results also show that the associations with Crohn disease are evolutionarily conserved and provide a mutual information-based method to depict dysbiosis.

Nicotine Exposure During Pregnancy Results in Persistent Midline Epithelial Seam With Improper Palatal Fusion

INTRODUCTION

Nonsyndromic cleft palate is a common birth defect (1:700) with a complex etiology involving both genetic and environmental risk factors. Nicotine, a major teratogen present in tobacco products, was shown to cause alterations and delays in the developing fetus.

METHODS

To demonstrate the postpartum effects of nicotine on palatal development, we delivered three different doses of nicotine (1.5, 3.0, and 4.5mg/kg/d) and sterile saline (control) into pregnant BALB/c mice throughout their entire pregnancy using subcutaneous micro-osmotic pump. Dams were allowed to deliver (~day 21 of pregnancy) and neonatal assessments (weight, length, nicotine levels) were conducted, and palatal tissues were harvested for morphological and molecular analyses, as well as transcriptional profiling using microarrays.

RESULTS

Consistent administration of nicotine caused developmental retardation, still birth, low birth weight, and significant palatal size and shape abnormality and persistent midline epithelial seam in the pups. Through microarray analysis, we detected that 6232 genes were up-regulated and 6310 genes were down-regulated in nicotine-treated groups compared to the control. Moreover, 46% of the cleft palate-causing genes were found to be affected by nicotine exposure. Alterations of a subset of differentially expressed genes were illustrated with hierarchal clustering and a series of formal pathway analyses were performed using the bioinformatics tools.

CONCLUSIONS

We concluded that nicotine exposure during pregnancy interferes with normal growth and development of the fetus, as well results in persistent midline epithelial seam with type B and C patterns of palatal fusion.

IMPLICATIONS

Although there are several studies analyzing the genetic and environmental causes of palatal deformities, this study primarily shows the morphological and large-scale genomic outcomes of gestational nicotine exposure in neonatal mice palate.The previous version was incorrect. New authors Ali Nawshad, Hasan Otu, Janki Sharma, and Elizabeth Sheldon have been included in this version; the funding and acknowledgement sections have been updated accordingly; the article title, some text, and one supplementary data file have been edited; and the corresponding author has been changed. The original corresponding author regrets these earlier errors.

Bioinformatics approaches to single-cell analysis in developmental biology

Individual cells within the same population show various degrees of heterogeneity, which may be better handled with single-cell analysis to address biological and clinical questions. Single-cell analysis is especially important in developmental biology as subtle spatial and temporal differences in cells have significant associations with cell fate decisions during differentiation and with the description of a particular state of a cell exhibiting an aberrant phenotype. Biotechnological advances, especially in the area of microfluidics, have led to a robust, massively parallel and multi-dimensional capturing, sorting, and lysis of single-cells and amplification of related macromolecules, which have enabled the use of imaging and omics techniques on single cells. There have been improvements in computational single-cell image analysis in developmental biology regarding feature extraction, segmentation, image enhancement and machine learning, handling limitations of optical resolution to gain new perspectives from the raw microscopy images. Omics approaches, such as transcriptomics, genomics and epigenomics, targeting gene and small RNA expression, single nucleotide and structural variations and methylation and histone modifications, rely heavily on high-throughput sequencing technologies. Although there are well-established bioinformatics methods for analysis of sequence data, there are limited bioinformatics approaches which address experimental design, sample size considerations, amplification bias, normalization, differential expression, coverage, clustering and classification issues, specifically applied at the single-cell level. In this review, we summarize biological and technological advancements, discuss challenges faced in the aforementioned data acquisition and analysis issues and present future prospects for application of single-cell analyses to developmental biology.

Cell reprogramming. Histone chaperone ASF1A is required for maintenance of pluripotency and cellular reprogramming

Unfertilized oocytes have the intrinsic capacity to remodel sperm and the nuclei of somatic cells. The discoveries that cells can change their phenotype from differentiated to embryonic state using oocytes or specific transcription factors have been recognized as two major breakthroughs in the biomedical field. Here, we show that ASF1A, a histone-remodeling chaperone specifically enriched in the metaphase II human oocyte, is necessary for reprogramming of human adult dermal fibroblasts (hADFs) into undifferentiated induced pluripotent stem cell. We also show that overexpression of just ASF1A and OCT4 in hADFs exposed to the oocyte-specific paracrine growth factor GDF9 can reprogram hADFs into pluripotent cells. Our Report underscores the importance of studying the unfertilized MII oocyte as a means to understand the molecular pathways governing somatic cell reprogramming.

Whole genome sequence of a Turkish individual

Although whole human genome sequencing can be done with readily available technical and financial resources, the need for detailed analyses of genomes of certain populations still exists. Here we present, for the first time, sequencing and analysis of a Turkish human genome. We have performed 35x coverage using paired-end sequencing, where over 95% of sequencing reads are mapped to the reference genome covering more than 99% of the bases. The assembly of unmapped reads rendered 11,654 contigs, 2,168 of which did not reveal any homology to known sequences, resulting in ∼1 Mbp of unmapped sequence. Single nucleotide polymorphism (SNP) discovery resulted in 3,537,794 SNP calls with 29,184 SNPs identified in coding regions, where 106 were nonsense and 259 were categorized as having a high-impact effect. The homo/hetero zygosity (1,415,123∶2,122,671 or 1∶1.5) and transition/transversion ratios (2,383,204∶1,154,590 or 2.06∶1) were within expected limits. Of the identified SNPs, 480,396 were potentially novel with 2,925 in coding regions, including 48 nonsense and 95 high-impact SNPs. Functional analysis of novel high-impact SNPs revealed various interaction networks, notably involving hereditary and neurological disorders or diseases. Assembly results indicated 713,640 indels (1∶1.09 insertion/deletion ratio), ranging from -52 bp to 34 bp in length and causing about 180 codon insertion/deletions and 246 frame shifts. Using paired-end- and read-depth-based methods, we discovered 9,109 structural variants and compared our variant findings with other populations. Our results suggest that whole genome sequencing is a valuable tool for understanding variations in the human genome across different populations. Detailed analyses of genomes of diverse origins greatly benefits research in genetics and medicine and should be conducted on a larger scale.

Objective functions

Multiple sequence alignment involves alignment of more than two sequences and is an NP-complete problem. Therefore, heuristic algorithms that use different criteria to find an approximation to the optimal solution are employed. At the heart of these approaches lie the scoring and objective functions that a given algorithm uses to compare competing solutions in constructing a multiple sequence alignment. These objective functions are often motivated by the biological paradigms that govern functional similarities and evolutionary relations. Most existing approaches utilize a progressive process where the final alignment is constructed sequentially by adding new sequences into an existing multiple sequence alignment matrix, which is dynamically updated. In doing this, the core scoring function to assess accuracies of pairwise alignments generally remains the same, while the objective functions used in intermediary steps differ. Nevertheless, the overall assessment of the final multiple sequence alignment is generally calculated by an extension of pairwise scorings. In this chapter, we explore different scoring and objective functions used in calculating the accuracy and optimization of a multiple sequence alignment and provide utilization of these criteria in popularly used multiple sequence alignment algorithms.

Bayesian network prior: network analysis of biological data using external knowledge

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process.

Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods.

Availability: Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP.

Contact:hasan.otu@bilgi.edu.tr

Supplementary Information:Supplementary data are available at Bioinformatics online.

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

BACKGROUND

MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently.

RESULTS

The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II.

CONCLUSIONS

Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths.

Testing robustness of relative complexity measure method constructing robust phylogenetic trees for Galanthus L. using the relative complexity measure

BACKGROUND

Most phylogeny analysis methods based on molecular sequences use multiple alignment where the quality of the alignment, which is dependent on the alignment parameters, determines the accuracy of the resulting trees. Different parameter combinations chosen for the multiple alignment may result in different phylogenies. A new non-alignment based approach, Relative Complexity Measure (RCM), has been introduced to tackle this problem and proven to work in fungi and mitochondrial DNA.

RESULT

In this work, we present an application of the RCM method to reconstruct robust phylogenetic trees using sequence data for genus Galanthus obtained from different regions in Turkey. Phylogenies have been analyzed using nuclear and chloroplast DNA sequences. Results showed that, the tree obtained from nuclear ribosomal RNA gene sequences was more robust, while the tree obtained from the chloroplast DNA showed a higher degree of variation.

CONCLUSIONS

Phylogenies generated by Relative Complexity Measure were found to be robust and results of RCM were more reliable than the compared techniques. Particularly, to overcome MSA-based problems, RCM seems to be a reasonable way and a good alternative to MSA-based phylogenetic analysis. We believe our method will become a mainstream phylogeny construction method especially for the highly variable sequence families where the accuracy of the MSA heavily depends on the alignment parameters.

Reprogrammed transcriptome in rhesus-bovine interspecies somatic cell nuclear transfer embryos

Background

Global activation of the embryonic genome (EGA), one of the most critical steps in early mammalian embryo development, is recognized as the time when interspecies somatic cell nuclear transfer (iSCNT) embryos fail to thrive.

Methodology/Principal Findings

In this study, we analyzed the EGA-related transcriptome of rhesus-bovine iSCNT 8- to 16-cell embryos and dissected the reprogramming process in terms of embryonic gene activation, somatic gene silencing, and maternal RNA degradation. Compared with fibroblast donor cells, two thousand and seven genes were activated in iSCNT embryos, one quarter of them reaching expression levels comparable to those found in in vitro fertilized (IVF) rhesus embryos. This suggested that EGA in iSCNT embryos had partially recapitulated rhesus embryonic development. Eight hundred and sixty somatic genes were not silenced properly and continued to be expressed in iSCNT embryos, which indicated incomplete nuclear reprogramming. We compared maternal RNA degradation in bovine oocytes between bovine-bovine SCNT and iSCNT embryos. While maternal RNA degradation occurred in both SCNT and iSCNT embryos, we saw more limited overall degradation of maternal RNA in iSCNT embryos than in SCNT embryos. Several important maternal RNAs, like GPF9, were not properly processed in SCNT embryos.

Conclusions/Significance

Our data suggested that iSCNT embryos are capable of triggering EGA, while a portion of somatic cell-associated genes maintain their expression. Maternal RNA degradation seems to be impaired in iSCNT embryos. Further understanding of the biological roles of these genes, networks, and pathways revealed by iSCNT may expand our knowledge about cell reprogramming, pluripotency, and differentiation.

Pathway analysis of high-throughput biological data within a Bayesian network framework

MOTIVATION

Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network.

RESULTS

Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC.

AVAILABILITY

Accompanying BPA software (BPAS) package is freely available for academic use at http://bumil.boun.edu.tr/bpa.

Clustering of protein families into functional subtypes using Relative Complexity Measure with reduced amino acid alphabets

Background

Phylogenetic analysis can be used to divide a protein family into subfamilies in the absence of experimental information. Most phylogenetic analysis methods utilize multiple alignment of sequences and are based on an evolutionary model. However, multiple alignment is not an automated procedure and requires human intervention to maintain alignment integrity and to produce phylogenies consistent with the functional splits in underlying sequences. To address this problem, we propose to use the alignment-free Relative Complexity Measure (RCM) combined with reduced amino acid alphabets to cluster protein families into functional subtypes purely on sequence criteria. Comparison with an alignment-based approach was also carried out to test the quality of the clustering.

Results

We demonstrate the robustness of RCM with reduced alphabets in clustering of protein sequences into families in a simulated dataset and seven well-characterized protein datasets. On protein datasets, crotonases, mandelate racemases, nucleotidyl cyclases and glycoside hydrolase family 2 were clustered into subfamilies with 100% accuracy whereas acyl transferase domains, haloacid dehalogenases, and vicinal oxygen chelates could be assigned to subfamilies with 97.2%, 96.9% and 92.2% accuracies, respectively.

Conclusions

The overall combination of methods in this paper is useful for clustering protein families into subtypes based on solely protein sequence information. The method is also flexible and computationally fast because it does not require multiple alignment of sequences.

Tob1 is a constitutively expressed repressor of liver regeneration

How proliferative and inhibitory signals integrate to control liver regeneration remains poorly understood. A screen for antiproliferative factors repressed after liver injury identified transducer of ErbB2.1 (Tob1), a member of the PC3/BTG1 family of mito-inhibitory molecules as a target for further evaluation. Tob1 protein decreases after 2/3 hepatectomy in mice secondary to posttranscriptional mechanisms. Deletion of Tob1 increases hepatocyte proliferation and accelerates restoration of liver mass after hepatectomy. Down-regulation of Tob1 is required for normal liver regeneration, and Tob1 controls hepatocyte proliferation in a dose-dependent fashion. Tob1 associates directly with both Caf1 and cyclin-dependent kinase (Cdk) 1 and modulates Cdk1 kinase activity. In addition, Tob1 has significant effects on the transcription of critical cell cycle components, including E2F target genes and genes involved in p53 signaling. We provide direct evidence that levels of an inhibitory factor control the rate of liver regeneration, and we identify Tob1 as a crucial check point molecule that modulates the expression and activity of cell cycle proteins.

Bioinformatic identification and characterization of human endothelial cell-restricted genes

Background

In this study, we used a systematic bioinformatics analysis approach to elucidate genes that exhibit an endothelial cell (EC) restricted expression pattern, and began to define their regulation, tissue distribution, and potential biological role.

Results

Using a high throughput microarray platform, a primary set of 1,191 transcripts that are enriched in different primary ECs compared to non-ECs was identified (LCB >3, FDR <2%). Further refinement of this initial subset of transcripts, using published data, yielded 152 transcripts (representing 109 genes) with different degrees of EC-specificity. Several interesting patterns emerged among these genes: some were expressed in all ECs and several were restricted to microvascular ECs. Pathway analysis and gene ontology demonstrated that several of the identified genes are known to be involved in vasculature development, angiogenesis, and endothelial function (P < 0.01). These genes are enriched in cardiovascular diseases, hemorrhage and ischemia gene sets (P < 0.001). Most of the identified genes are ubiquitously expressed in many different tissues. Analysis of the proximal promoter revealed the enrichment of conserved binding sites for 26 different transcription factors and analysis of the untranslated regions suggests that a subset of the EC-restricted genes are targets of 15 microRNAs. While many of the identified genes are known for their regulatory role in ECs, we have also identified several novel EC-restricted genes, the function of which have yet to be fully defined.

Conclusion

The study provides an initial catalogue of EC-restricted genes most of which are ubiquitously expressed in different endothelial cells.

Gene expression profile of mouse prostate tumors reveals dysregulations in major biological processes and identifies potential murine targets for preclinical development of human prostate cancer therapy

BACKGROUND

Translation of preclinical studies into effective human cancer therapy is hampered by the lack of defined molecular expression patterns in mouse models that correspond to the human counterpart. We sought to generate an open source TRAMP mouse microarray dataset and to use this array to identify differentially expressed genes from human prostate cancer (PCa) that have concordant expression in TRAMP tumors, and thereby represent lead targets for preclinical therapy development.

METHODS

We performed microarrays on total RNA extracted and amplified from eight TRAMP tumors and nine normal prostates. A subset of differentially expressed genes was validated by QRT-PCR. Differentially expressed TRAMP genes were analyzed for concordant expression in publicly available human prostate array datasets and a subset of resulting genes was analyzed by QRT-PCR.

RESULTS

Cross-referencing differentially expressed TRAMP genes to public human prostate array datasets revealed 66 genes with concordant expression in mouse and human PCa; 56 between metastases and normal and 10 between primary tumor and normal tissues. Of these 10 genes, two, Sox4 and Tubb2a, were validated by QRT-PCR. Our analysis also revealed various dysregulations in major biologic pathways in the TRAMP prostates.

CONCLUSIONS

We report a TRAMP microarray dataset of which a gene subset was validated by QRT-PCR with expression patterns consistent with previous gene-specific TRAMP studies. Concordance analysis between TRAMP and human PCa associated genes supports the utility of the model and suggests several novel molecular targets for preclinical therapy.

Differential expression of GADD45beta in normal and osteoarthritic cartilage: potential role in homeostasis of articular chondrocytes

OBJECTIVE

Our previous study suggested that growth arrest and DNA damage-inducible protein 45beta (GADD45beta) prolonged the survival of hypertrophic chondrocytes in the developing mouse embryo. This study was undertaken, therefore, to investigate whether GADD45beta plays a role in adult articular cartilage.

METHODS

Gene expression profiles of cartilage from patients with late-stage osteoarthritis (OA) were compared with those from patients with early OA and normal controls in 2 separate microarray analyses. Histologic features of cartilage were graded using the Mankin scale, and GADD45beta was localized by immunohistochemistry. Human chondrocytes were transduced with small interfering RNA (siRNA)-GADD45beta or GADD45beta-FLAG. GADD45beta and COL2A1 messenger RNA (mRNA) levels were analyzed by real-time reverse transcriptase-polymerase chain reaction, and promoter activities were analyzed by transient transfection. Cell death was detected by Hoechst 33342 staining of condensed chromatin.

RESULTS

GADD45beta was expressed at higher levels in cartilage from normal donors and patients with early OA than in cartilage from patients with late-stage OA. All chondrocyte nuclei in normal cartilage immunostained for GADD45beta. In early OA cartilage, GADD45beta was distributed variably in chondrocyte clusters, in middle and deep zone cells, and in osteophytes. In contrast, COL2A1, other collagen genes, and factors associated with skeletal development were up-regulated in late OA, compared with early OA or normal cartilage. In overexpression and knockdown experiments, GADD45beta down-regulated COL2A1 mRNA and promoter activity. NF-kappaB overexpression increased GADD45beta promoter activity, and siRNA-GADD45beta decreased cell survival per se and enhanced tumor necrosis factor alpha-induced cell death in human articular chondrocytes.

CONCLUSION

These observations suggest that GADD45beta might play an important role in regulating chondrocyte homeostasis by modulating collagen gene expression and promoting cell survival in normal adult cartilage and in early OA.

Grammar-based distance in progressive multiple sequence alignment

BACKGROUND

We propose a multiple sequence alignment (MSA) algorithm and compare the alignment-quality and execution-time of the proposed algorithm with that of existing algorithms. The proposed progressive alignment algorithm uses a grammar-based distance metric to determine the order in which biological sequences are to be pairwise aligned. The progressive alignment occurs via pairwise aligning new sequences with an ensemble of the sequences previously aligned.

RESULTS

The performance of the proposed algorithm is validated via comparison to popular progressive multiple alignment approaches, ClustalW and T-Coffee, and to the more recently developed algorithms MAFFT, MUSCLE, Kalign, and PSAlign using the BAliBASE 3.0 database of amino acid alignment files and a set of longer sequences generated by Rose software. The proposed algorithm has successfully built multiple alignments comparable to other programs with significant improvements in running time. The results are especially striking for large datasets.

CONCLUSION

We introduce a computationally efficient progressive alignment algorithm using a grammar based sequence distance particularly useful in aligning large datasets.

Genomic counter-stress changes induced by the relaxation response

Background

Mind-body practices that elicit the relaxation response (RR) have been used worldwide for millennia to prevent and treat disease. The RR is characterized by decreased oxygen consumption, increased exhaled nitric oxide, and reduced psychological distress. It is believed to be the counterpart of the stress response that exhibits a distinct pattern of physiology and transcriptional profile. We hypothesized that RR elicitation results in characteristic gene expression changes that can be used to measure physiological responses elicited by the RR in an unbiased fashion.

Methods/Principal Findings

We assessed whole blood transcriptional profiles in 19 healthy, long-term practitioners of daily RR practice (group M), 19 healthy controls (group N₁), and 20 N₁ individuals who completed 8 weeks of RR training (group N₂). 2209 genes were differentially expressed in group M relative to group N₁ (p<0.05) and 1561 genes in group N₂ compared to group N₁ (p<0.05). Importantly, 433 (p<10₋₁₀) of 2209 and 1561 differentially expressed genes were shared among long-term (M) and short-term practitioners (N₂). Gene ontology and gene set enrichment analyses revealed significant alterations in cellular metabolism, oxidative phosphorylation, generation of reactive oxygen species and response to oxidative stress in long-term and short-term practitioners of daily RR practice that may counteract cellular damage related to chronic psychological stress. A significant number of genes and pathways were confirmed in an independent validation set containing 5 N₁ controls, 5 N₂ short-term and 6 M long-term practitioners.

Conclusions/Significance

This study provides the first compelling evidence that the RR elicits specific gene expression changes in short-term and long-term practitioners. Our results suggest consistent and constitutive changes in gene expression resulting from RR may relate to long term physiological effects. Our study may stimulate new investigations into applying transcriptional profiling for accurately measuring RR and stress related responses in multiple disease settings.

Gene expression analysis of embryonic stem cells expressing VE-cadherin (CD144) during endothelial differentiation

Background

Endothelial differentiation occurs during normal vascular development in the developing embryo. This process is recapitulated in the adult when endothelial progenitor cells are generated in the bone marrow and can contribute to vascular repair or angiogenesis at sites of vascular injury or ischemia. The molecular mechanisms of endothelial differentiation remain incompletely understood. Novel approaches are needed to identify the factors that regulate endothelial differentiation.

Methods

Mouse embryonic stem (ES) cells were used to further define the molecular mechanisms of endothelial differentiation. By flow cytometry a population of VEGF-R2 positive cells was identified as early as 2.5 days after differentiation of ES cells, and a subset of VEGF-R2⁺ cells, that were CD41 positive at 3.5 days. A separate population of VEGF-R2⁺ stem cells expressing the endothelial-specific marker CD144 (VE-cadherin) was also identified at this same time point. Channels lined by VE-cadherin positive cells developed within the embryoid bodies (EBs) formed by differentiating ES cells. VE-cadherin and CD41 expressing cells differentiate in close proximity to each other within the EBs, supporting the concept of a common origin for cells of hematopoietic and endothelial lineages.

Results

Microarray analysis of >45,000 transcripts was performed on RNA obtained from cells expressing VEGF-R2⁺, CD41⁺, and CD144⁺ and VEGF-R2^-, CD41^-, and CD144^-. All microarray experiments were performed in duplicate using RNA obtained from independent experiments, for each subset of cells. Expression profiling confirmed the role of several genes involved in hematopoiesis, and identified several putative genes involved in endothelial differentiation.

Conclusion

The isolation of CD144⁺ cells during ES cell differentiation from embryoid bodies provides an excellent model system and method for identifying genes that are expressed during endothelial differentiation and that are distinct from hematopoiesis.

Gene expression of purified beta-cell tissue obtained from human pancreas with laser capture microdissection

CONTEXT

Human beta-cell gene profiling is a powerful tool for understanding beta-cell biology in normal and pathological conditions. Assessment is complicated when isolated islets are studied because of contamination by non-beta-cells and the trauma of the isolation procedure.

OBJECTIVE

The objective was to use laser capture microdissection (LCM) of human beta-cells from pancreases of cadaver donors and compare their gene expression with that of handpicked isolated islets.

DESIGN

Endogenous autofluorescence of beta-cells facilitated procurement of purified beta-cell tissue from frozen pancreatic sections with LCM. Gene expression profiles of three microdissected beta-cell samples and three isolated islet preparations were obtained. The array data were normalized using DNA-Chip Analyzer software (Harvard School of Public Health, Boston, MA), and the lower confidence bound evaluated differentially expressed genes. Real-time PCR was performed on selected acinar genes and on the duct cell markers, carbonic anhydrase II and keratin 19.

RESULTS

Endogenous autofluorescence facilitates the microdissection of beta-cell rich tissue in human pancreas. When compared with array profiles of purified beta-cell tissue, with lower confidence bound set at 1.2, there were 4560 genes up-regulated and 1226 genes down-regulated in the isolated islets. Among the genes up-regulated in isolated islets were pancreatic acinar and duct genes, chemokine genes, and genes associated with hypoxia, apoptosis, and stress. Quantitative RT-PCR confirmed the differential expression of acinar gene transcripts and the duct marker carbonic anhydrase II in isolated islets.

CONCLUSION

LCM makes it possible to obtain beta-cell enriched tissue from human pancreas sections without the trauma and ischemia of islet isolation.