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.

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.

Identification of Plasma Proteome Signatures Associated With Surgery Using SOMAscan.

OBJECTIVES: To characterize the proteomic signature of surgery in older adults and association with postoperative outcomes. SUMMARY OF BACKGROUND DATA: Circulating plasma proteins can reflect the physiological response to and clinical outcomes after surgery. METHODS: Blood plasma from older adults undergoing elective surgery was analyzed for 1305 proteins using SOMAscan. Surgery-associated proteins underwent Ingenuity Pathways Analysis. Selected surgery-associated proteins were independently validated using Luminex or enzyme-linked immunosorbent assay methods. Generalized linear models estimated correlations with postoperative outcomes. RESULTS: Plasma from a subcohort (n = 36) of the Successful Aging after Elective Surgery (SAGES) study was used for SOMAscan. Systems biology analysis of 110 proteins with Benjamini-Hochberg (BH) corrected P value ≤0.01 and an absolute foldchange (|FC|) ≥1.5 between postoperative day 2 (POD2) and preoperative (PREOP) identified functional pathways with major effects on pro-inflammatory proteins. Chitinase-3-like protein 1 (CHI3L1), C-reactive protein (CRP), and interleukin-6 (IL-6) were independently validated in separate validation cohorts from SAGES (n = 150 for CRP, IL-6; n = 126 for CHI3L1). Foldchange CHI3L1 and IL-6 were associated with increased postoperative complications [relative risk (RR) 1.50, 95% confidence interval (95% CI) 1.21-1.85 and RR 1.63, 95% CI 1.18-2.26, respectively], length of stay (RR 1.35, 95% CI 0.77-1.92 and RR 0.98, 95% CI 0.52-1.45), and risk of discharge to postacute facility (RR 1.15, 95% CI 1.04-1.26 and RR 1.11, 95% CI 1.04-1.18); POD2 and PREOP CRP difference was associated with discharge to postacute facility (RR 1.14, 95% CI 1.04-1.25). CONCLUSION: SOMAscan can identify novel and clinically relevant surgery-induced protein changes. Ultimately, proteomics may provide insights about pathways by which surgical stress contributes to postoperative outcomes.

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.

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.

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.

FQStat: a parallel architecture for very high-speed assessment of sequencing quality metrics.

BACKGROUND: High throughput DNA/RNA sequencing has revolutionized biological and clinical research. Sequencing is widely used, and generates very large amounts of data, mainly due to reduced cost and advanced technologies. Quickly assessing the quality of giga-to-tera base levels of sequencing data has become a routine but important task. Identification and elimination of low-quality sequence data is crucial for reliability of downstream analysis results. There is a need for a high-speed tool that uses optimized parallel programming for batch processing and simply gauges the quality of sequencing data from multiple datasets independent of any other processing steps. RESULTS: FQStat is a stand-alone, platform-independent software tool that assesses the quality of FASTQ files using parallel programming. Based on the machine architecture and input data, FQStat automatically determines the number of cores and the amount of memory to be allocated per file for optimum performance. Our results indicate that in a core-limited case, core assignment overhead exceeds the benefit of additional cores. In a core-unlimited case, there is a saturation point reached in performance by increasingly assigning additional cores per file. We also show that memory allocation per file has a lower priority in performance when compared to the allocation of cores. FQStat's output is summarized in HTML web page, tab-delimited text file, and high-resolution image formats. FQStat calculates and plots read count, read length, quality score, and high-quality base statistics. FQStat identifies and marks low-quality sequencing data to suggest removal from downstream analysis. We applied FQStat on real sequencing data to optimize performance and to demonstrate its capabilities. We also compared FQStat's performance to similar quality control (QC) tools that utilize parallel programming and attained improvements in run time. CONCLUSIONS: FQStat is a user-friendly tool with a graphical interface that employs a parallel programming architecture and automatically optimizes its performance to generate quality control statistics for sequencing data. Unlike existing tools, these statistics are calculated for multiple datasets and separately at the "lane," "sample," and "experiment" level to identify subsets of the samples with low quality, thereby preventing the loss of complete samples when reliable data can still be obtained.

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.

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.

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.

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.

Essential role of Jun family transcription factors in PU.1 knockdown-induced leukemic stem cells.

Knockdown of the transcription factor PU.1 (encoded by Sfpi1) leads to acute myeloid leukemia (AML) in mice. We examined the transcriptome of preleukemic hematopoietic stem cells (HSCs) in which PU.1 was knocked down (referred to as 'PU.1-knockdown HSCs') to identify transcriptional changes preceding malignant transformation. Transcription factors c-Jun and JunB were among the top-downregulated targets. Restoration of c-Jun expression in preleukemic cells rescued the PU.1 knockdown-initiated myelomonocytic differentiation block. Lentiviral restoration of JunB at the leukemic stage led to loss of leukemic self-renewal capacity and prevented leukemia in NOD-SCID mice into which leukemic PU.1-knockdown cells were transplanted. Examination of human individuals with AML confirmed the correlation between PU.1 and JunB downregulation. These results delineate a transcriptional pattern that precedes leukemic transformation in PU.1-knockdown HSCs and demonstrate that decreased levels of c-Jun and JunB contribute to the development of PU.1 knockdown-induced AML by blocking differentiation and increasing self-renewal. Therefore, examination of disturbed gene expression in HSCs can identify genes whose dysregulation is essential for leukemic stem cell function and that are targets for therapeutic interventions.

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.

Gene expression profiling of granulosa cells from PCOS patients following varying doses of human chorionic gonadotropin.

PURPOSE: Human chorionic gonadotrophin (hCG) has been used to induce ovulation and oocyte maturation. Although the most common dose of hCG used in IVF is 10,000 IU, there are reports that suggest 5,000 IU is sufficient to yield similar results. The objective of this study is to evaluate the dose dependent differences in gene expression of granulosa cells following various doses of hCG treatment. METHODS: Patients with polycystic ovarian syndrome (PCOS) were stimulated for IVF treatment. The hCG injection was either withheld or given at 5,000 or 10,000 IU. Granulosa cells from the follicular fluids have been collected for RNA isolation and analyzed using Affymetrix genechip arrays. RESULTS: Unsupervised hierarchical clustering based on whole gene expression revealed two distinct groups of patients in this experiment. All untreated patients were clustered together whereas hCG-treated patients separated to a different group regardless of the dose. A large number of the transcripts were similarly up- or down-regulated across both hCG doses (2229 and 1945 transcripts, respectively). However, we observed dose-dependent statistically significant differences in gene expression in only 15 transcripts. CONCLUSIONS: Although hCG injection caused a major change in the gene expression profile of granulosa cells, 10,000 IU hCG resulted in minimal changes in the gene expression profiles of granulosa cells as compared with 5,000 IU. Thus, based on our results, we suggest the use of 10,000 IU hCG should be reconsidered in PCOS patients.

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.

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.

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.

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.