A semi-parametric Bayesian model for unsupervised differential co-expression analysis

Patient-specific analysis of co-expression to measure biological network rewiring in individuals

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

Limitation of permutation-based differential correlation analysis

The comparison of biological systems, through the analysis of molecular changes under different conditions, has played a crucial role in the progress of modern biological science. Specifically, differential correlation analysis (DCA) has been employed to determine whether relationships between genomic features differ across conditions or outcomes. Because ascertaining the null distribution of test statistics to capture variations in correlation is challenging, several DCA methods utilize permutation which can loosen parametric (e.g., normality) assumptions. However, permutation is often problematic for DCA due to violating the assumption that samples are exchangeable under the null. Here, we examine the limitations of permutation-based DCA and investigate instances where the permutation-based DCA exhibits poor performance. Experimental results show that the permutation-based DCA often fails to control the type I error under the null hypothesis of equal correlation structures.

Small extravesicular microRNA in head and neck squamous cell carcinoma and its potential as a liquid biopsy for early detection

BACKGROUND

The objective was to assess secretion of small extracellular vesicular microRNA (exo-miRNA) in head and neck squamous cell carcinoma (HNSCC) according to human papillomavirus (HPV) status, and determine the translational potential as a liquid biopsy for early detection.

METHODS

This study employed a combination of cell culture and case-control study design using archival pretreatment serum. Small extracellular vesicles (sEV) were isolated from conditioned culture media and human serum samples via differential ultracentrifugation. miRNA-sequencing was performed on each sEV isolate.

RESULTS

There were clear exo-miRNA profiles that distinguished HNSCC cell lines from nonpathologic oral epithelial control cells. While there was some overlap among profiles across all samples, there were apparent differences in exo-miRNA profiles according to HPV-status. Importantly, differential exo-miRNA profiles were also apparent in serum from early-stage HNSCC cases relative to cancer-free controls.

CONCLUSIONS

Our findings indicate that exo-miRNA are highly dysregulated in HNSCC and support the potential of exo-miRNA as biomarkers for HNSCC.

Determining biomarkers for evaluation and diagnosis of hereditary angioedema

Rationale

Kallikrein-bradykinin-forming cascade is known to cause hereditary angioedema (HAE) acute angioedema (AE) attacks. Further research of HAE attacks is needed to explain disease heterogeneity, predict treatment response and identify biomarkers for monitoring HAE attacks. Differential expression of the microvascular endothelial cell-surface receptors for example, g-C1qR, cytokeratin-1, and plasminogen-activator-urokinase-receptor (PLAUR) were hypothesized as biomarkers of AE attacks.

Method

To understand HAE attacks, the differentially expressed genes (DEGs) in RNAseq and mi-RNAseq data of total RNA extracted from skin biopsies of lesional versus non-lesional skin collected during and between attacks in Type-1 HAE patients (n = 11; F:M = 8:3) were compared. To understand the HAE variants, DEGs in skin biopsies from HAE with normal C1 inhibitor (n = 5, F:M = 5:0), and non-HAE (n = 7; F:M = 3:4) patients were compared. Gene-set enrichment analyses and regulator effects analysis of these DEGs identified biological pathways in HAE attacks and their regulators.

Results

PLAUR gene, encoding urokinase-type plasminogen activator (u-PAR), was constitutively over-expressed in HAE-Type-1 versus non-HAE controls suggestive of overactive u-PAR-mediated signaling via binding to Factor-XII. Baseline PLAUR expression was associated with severe AE (p = 0.05). The 18 significant DEGs investigated between baseline and AE attack samples in Type1-HAE were enriched in beta1/beta3-integrin cell surface interactions and IL-6-mediated signaling. Regulator effects analysis suggests a role for IL-1b in HAE flares. AKT2, the mRNA regulated by the differentially-expressed miR-184A, was also associated with HAE attacks.

Conclusion

Angiopoetin-activated β1-integrin signaling pathways causing endothelial destabilization, and avid binding of factor XII to u-PAR are possible novel mechanisms for progression of the endothelial kinin-bradykinin-forming cascade in HAE attacks.

Differences in peripheral immune system gene expression in frontotemporal degeneration

ABSTRACT

The peripheral immune system has a key pathophysiologic role in Frontotemporal degeneration (FTD). We sought a comprehensive transcriptome-wide evaluation of gene expression alterations unique to the peripheral immune system in FTD compared to healthy controls and amyotrophic lateral sclerosis.Nineteen subjects with FTD with 19 matched healthy controls and 9 subjects with amyotrophic lateral sclerosis underwent isolation of peripheral blood mononuclear cells (PBMCs) which then underwent bulk ribonucleic acid sequencing.There was increased expression in genes associated with CD19+ B-cells, CD4+ T-cells, and CD8+ T-cells in FTD participants compared to healthy controls. In contrast, there was decreased expression in CD33+ myeloid cells, CD14+ monocytes, BDCA4+ dendritic cells, and CD56+ natural killer cells in FTD and healthy controls. Additionally, there was decreased expression is seen in associated with 2 molecular processes: autophagy with phagosomes and lysosomes, and protein processing/export. Significantly downregulated in PBMCs of FTD subjects were genes involved in antigen processing and presentation as well as lysosomal lumen formation compared to healthy control PBMCs.Our findings that the immune signature based on gene expression in PBMCs of FTD participants favors adaptive immune cells compared to innate immune cells. And decreased expression in genes associated with phagosomes and lysosomes in PBMCs of FTD participants compared to healthy controls.

Axl regulated survival/proliferation network and its therapeutic intervention in mouse models of glomerulonephritis

BACKGROUND

Lupus nephritis (LN) is the most common and serious complication of systemic lupus erythematosus (SLE). LN pathogenesis is not fully understood. Axl receptor tyrosine kinase is upregulated and contributes to the pathogenic progress in LN. We have reported that Axl disruption attenuates nephritis development in mice.

METHODS

In this study, we analyzed the gene expression profiles with RNA-seq using renal cortical samples from nephritic mice. Axl-KO mice were bred onto a B6.lpr spontaneous lupus background, and renal disease development was followed and compared to the Axl-sufficient B6.lpr mice. Finally, anti-glomerular basement membrane (anti-GBM) Ab-induced nephritic mice were treated with Axl small molecule inhibitor, R428, at different stages of nephritis development. Blood urine nitrogen levels and renal pathologies were evaluated.

RESULTS

Transcriptome analysis revealed that renal Axl activation contributed to cell proliferation, survival, and motility through regulation of the Akt, c-Jun, and actin pathways. Spontaneous lupus-prone B6.lpr mice with Axl deficiency showed significantly reduced kidney damages and decreased T cell infiltration compared to the renal damage and T cell infiltration in Axl-sufficient B6.lpr mice. The improved kidney function was independent of autoAb production. Moreover, R428 significantly reduced anti-GBM glomerulonephritis at different stages of GN development compared to the untreated nephritic control mice. R428 administration reduced inflammatory cytokine (IL-6) production, T cell infiltration, and nephritis disease activity.

CONCLUSIONS

Results from this study emphasize the important role of Axl signaling in LN and highlight Axl as an attractive target in LN.

Tobacco smoking induces metabolic reprogramming of renal cell carcinoma

BACKGROUNDClear cell renal cell carcinoma (ccRCC) is the most common histologically defined renal cancer. However, it is not a uniform disease and includes several genetic subtypes with different prognoses. ccRCC is also characterized by distinctive metabolic reprogramming. Tobacco smoking (TS) is an established risk factor for ccRCC, with unknown effects on tumor pathobiology.METHODSWe investigated the landscape of ccRCCs and paired normal kidney tissues using integrated transcriptomic, metabolomic, and metallomic approaches in a cohort of white males who were long-term current smokers (LTS) or were never smokers (NS).RESULTSAll 3 Omics domains consistently identified a distinct metabolic subtype of ccRCCs in LTS, characterized by activation of oxidative phosphorylation (OXPHOS) coupled with reprogramming of the malate-aspartate shuttle and metabolism of aspartate, glutamate, glutamine, and histidine. Cadmium, copper, and inorganic arsenic accumulated in LTS tumors, showing redistribution among intracellular pools, including relocation of copper into the cytochrome c oxidase complex. A gene expression signature based on the LTS metabolic subtype provided prognostic stratification of The Cancer Genome Atlas ccRCC tumors that was independent of genomic alterations.CONCLUSIONThe work identified the TS-related metabolic subtype of ccRCC with vulnerabilities that can be exploited for precision medicine approaches targeting metabolic pathways. The results provided rationale for the development of metabolic biomarkers with diagnostic and prognostic applications using evaluation of OXPHOS status. The metallomic analysis revealed the role of disrupted metal homeostasis in ccRCC, highlighting the importance of studying effects of metals from e-cigarettes and environmental exposures.FUNDINGDepartment of Defense, Veteran Administration, NIH, ACS, and University of Cincinnati Cancer Institute.

Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

Low-Dose Bisphenol A in a Rat Model of Endometrial Cancer: A CLARITY-BPA Study

BACKGROUND

Bisphenol A (BPA) is known to be biologically active in experimental models even at low levels of exposure. However, its impact on endometrial cancer remains unclear.

OBJECTIVES

This study aimed to investigate whether lifelong exposure to different doses of BPA induced uterine abnormalities and molecular changes in a rat model.

METHODS

Sprague-Dawley rats were exposed to 5 doses of BPA [0, 25, 250, 2,500, or 25,000 μ g / kg body weight (BW)/d] or 2 doses of 17 α - ethynylestradiol (EE2) (0.05 and 0.5 μ g / kg BW/d) starting from gestational day 6 up to 1 y old according to the CLARITY-BPA consortium protocol. The BW, uterus weight, and histopathology end points of the uteri were analyzed at postnatal (PND) day 21, 90, and 365. Estrous cycling status was evaluated in PND90 and PND365 rats. Transcriptomic analyses of estrus stage uteri were conducted on PND365 rats.

RESULTS

Based on the analysis of the combined effects of all testing outcomes (including immunohistological, morphological, and estrous cycle data) in a semiblinded fashion, using statistical models, 25 μ g / kg BW/d BPA [BPA(25)], or 250 μ g / kg BW/d BPA [BPA(250)] exerted effects similar to that of EE2 at 0.5 μ g / kg BW/d in 1-y-old rats. Transcriptome analyses of estrus stage uteri revealed a set of 710 genes shared only between the BPA(25) and BPA(250) groups, with 115 of them predicted to be regulated by estradiol and 57 associated with female cancers. An interesting finding is that the expression of 476 human orthologous genes in this rat BPA signature robustly predicted the overall survival (p = 1.68 × 10 - 5 , hazard ratio = 2.62 ) of endometrial cancer patients.

DISCUSSION

Lifelong exposure of rats to low-dose BPA at 25 and 250 μ g / kg BW/d altered the estrous cycle and uterine pathology with similarity to EE2. The exposure also disrupted a unique low-dose BPA-gene signature with predictive value for survival outcomes in patients with endometrial cancer. https://doi.org/10.1289/EHP6875.

Fibrocyte accumulation in the lungs of cystic fibrosis patients

BACKGROUND

Cystic fibrosis (CF) patients develop severe lung disease including chronic airway infections, neutrophilic inflammation, and progressive fibrotic remodeling in airways. However, cellular and molecular processes that regulate excessive collagen deposition in airways in these patients remain unclear. Fibrocytes are bone marrow (BM)-derived mesenchymal cells that express the hematopoietic cell marker CD45, and mesenchymal cell markers and implicated in collagen deposition in several fibrotic diseases. It is unknown whether fibrocytes accumulate in the lungs of CF patients, so the current study evaluates the presence of fibrocytes in the fibrotic lesions of airways in explanted CF lungs compared to non-CF unused donor lungs (control).

METHODS

We used immunofluorescence staining to determine if fibrocytes accumulate in explanted CF lungs compared to healthy donor lungs. Simultaneously, we evaluated cells collected by bronchoalveolar lavage (BAL) in CF patients using multi-color flow cytometry. Finally, we analyzed transcripts differentially expressed in fibrocytes isolated from the explanted CF lungs compared to control to assess fibrocyte-specific pro-fibrotic gene networks.

RESULTS

Our findings demonstrate fibrocyte accumulation in CF lungs compared to non-CF lungs. Additionally, fibrocytes were detected in the BAL of all CF children. Transcriptomic analysis of fibrocytes identified dysregulated genes associated with fibrotic remodeling in CF lungs.

CONCLUSIONS

With significantly increased fibrocytes that show increased expression of pro-fibrotic gene transcripts compared to control, our findings suggest an intervention for fibrotic remodeling as a potential therapeutic target in CF.

Comparability of the small RNA secretome across human biofluids concomitantly collected from healthy adults

Small extracellular vesicles (sEV) are nano-sized (40–150 nm), membrane-encapsulated vesicles that are released by essentially all cells into the extracellular space and function as intercellular signaling vectors through the horizontal transfer of biologic molecules, including microRNA (miRNA) and other small non-coding RNA (ncRNA), that can alter the phenotype of recipient cells. sEV are present in essentially all extracellular biofluids, including serum, urine and saliva, and offer a new avenue for discovery and development of novel biomarkers of various disease states and exposures. The objective of this study was to systematically interrogate similarities and differences between sEV ncRNA derived from saliva, serum and urine, as well as cell-free small ncRNA (cf-ncRNA) from serum. Saliva, urine and serum were concomitantly collected from 4 healthy donors to mitigate potential bias that can stem from interpersonal and temporal variability. sEV were isolated from each respective biofluid, along with cf-RNA from serum. sEV were isolated from the respective biofluids via differential ultracentrifugation with a 30% sucrose cushion to minimize protein contamination. Small RNA-sequencing was performed on each sample, and cluster analysis was performed based on ncRNA profiles. While some similarities existed in terms of sEV ncRNA cargo across biofluids, there are also notable differences in ncRNA class and ncRNA secretion, with sEV in each biofluid bearing a unique ncRNA profile, including major differences in composition by ncRNA class. We conclude that sEV ncRNA cargo varies according to biofluid, so thus should be carefully selected and interpreted when designing or contrasting translational or epidemiological studies.

Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer

BACKGROUND

Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions.

RESULTS

In this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of "true" networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package.

CONCLUSIONS

Our analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.

Balancing yield, purity and practicality: a modified differential ultracentrifugation protocol for efficient isolation of small extracellular vesicles from human serum

Ultracentrifugation remains the gold standard for isolation of small extracellular vesicles (sEV), particularly for cancer applications. The objective of this study was to determine if a widely used ultracentrifugation protocol for isolation of serum sEV could be modified to reduce the number of ultracentrifugation cycles and increase efficiency, while maintaining equal or better sample purity and yield. Serum was obtained from two healthy subjects. sEVs were isolated from 1 mL aliquots using three different ultracentrifugation protocols. Co-isolation of RNA carrier protein was assessed by performing Western blots for ApoA-I, ApoB, and Ago2. Small RNA-sequencing was performed on the sEV isolates, and differential detection of small ncRNA was compared across isolation protocols. Reduction from three- to two-ultracentrifuge cycles with no sucrose cushion resulted in a much higher sEV yield but also had the highest levels of lipoprotein and Ago2 contamination. However, the two-ultracentrifugation cycle protocol that incorporated a 30% sucrose cushion into the first cycle resulted in slightly higher sEV yields with lower levels of protein contamination compared to the lengthier three-ultracentrifugation cycle approach, therefore presenting a more efficient alternative approach for isolation of serum sEVs. It was also notable that there were some differences in sEV ncRNA cargo according to protocol, although it was less than expected given the differences in co-isolated RNA carrier proteins. Our results suggest that use of the modified serum sEV isolation protocol with two ultracentrifugation cycles and incorporating a 30% sucrose cushion offers a more efficient approach in terms of efficiency and purity.

Prenatal exposure to PCBs in Cyp1a2 knock-out mice interferes with F1 fertility, impairs long-term potentiation, reduces acoustic startle and impairs conditioned freezing contextual memory with minimal transgenerational effects

Polychlorinated biphenyls (PCBs) are toxic environmental pollutants. Humans are exposed to PCB mixtures via contaminated food or water. PCB exposure causes adverse effects in adults and after exposure in utero. PCB toxicity depends on the congener mixture and CYP1A2 gene activity. For coplanar PCBs, toxicity depends on ligand affinity for the aryl hydrocarbon receptor (AHR). Previously, we found that perinatal exposure of mice to a three-coplanar/five-noncoplanar PCB mixture induced deficits in novel object recognition and trial failures in the Morris water maze in Cyp1a2^-/- ::Ahr^b1 C57BL6/J mice compared with wild-type mice (Ahr^b1  = high AHR affinity). Here we exposed gravid Cyp1a2^-/- ::Ahr^b1 mice to a PCB mixture on embryonic day 10.5 by gavage and examined the F₁ and F₃ offspring (not F₂ ). PCB-exposed F₁ mice exhibited increased open-field central time, reduced acoustic startle, greater conditioned contextual freezing and reduced CA1 hippocampal long-term potentiation with no change in spatial learning or memory. F₁ mice also had inhibited growth, decreased heart rate and cardiac output, and impaired fertility. F₃ mice showed few effects. Gene expression changes were primarily in F₁ PCB males compared with wild-type males. There were minimal RNA and DNA methylation changes in the hippocampus from F₁ to F₃ with no clear relevance to the functional effects. F₀ PCB exposure during a period of rapid DNA de-/remethylation in a susceptible genotype produced clear F₁ effects with little evidence of transgenerational effects in the F₃ generation. While PCBs show clear developmental neurotoxicity, their effects do not persist across generations for effects assessed herein.

Intranasal carnosine attenuates transcriptomic alterations and improves mitochondrial function in the Thy1-aSyn mouse model of Parkinson's disease

Mitochondrial dysfunction plays a central role in the pathogenesis of neurodegenerative diseases such as Parkinson's disease (PD). This study was designed to determine whether the dipeptide carnosine, which has been shown to protect against oxidative stress and mitochondrial dysfunction, would provide a beneficial effect on mitochondrial function in the Thy1-aSyn mouse model of PD. Thy1-aSyn mice, which overexpress wild-type human alpha-synuclein (aSyn), exhibit progressive non-motor and motor deficits as early as 2 months of age. Two-month old Thy1-aSyn mice and wild-type littermates were randomly assigned to treatment groups with intranasal (IN) and drinking water carnosine, with controls receiving 10 μl of sterile waster intranasally or carnosine-free drinking water, respectively. After two months of treatment, mice were euthanized, and the midbrain was dissected for the evaluation of the gene expression and mitochondrial function. Transcriptional deficiencies associated with the aSyn overexpression in Thy1-aSyn mice were related to ribosomal and mitochondrial function. These deficiencies were attenuated by IN carnosine administration, which increased the expression of mitochondrial genes and enhanced mitochondrial function. These results suggest a potential neuroprotective role for IN-carnosine in PD patients.

An Organoid-Based Preclinical Model of Human Gastric Cancer

BACKGROUND & AIMS

Our goal was to develop an initial study for the proof of concept whereby gastric cancer organoids are used as an approach to predict the tumor response in individual patients.

METHODS

Organoids were derived from resected gastric cancer tumors (huTGOs) or normal stomach tissue collected from sleeve gastrectomies (huFGOs). Organoid cultures were treated with standard-of-care chemotherapeutic drugs corresponding to patient treatment: epirubicin, oxaliplatin, and 5-fluorouracil. Organoid response to chemotherapeutic treatment was correlated with the tumor response in each patient from whom the huTGOs were derived. HuTGOs were orthotopically transplanted into the gastric mucosa of NOD scid gamma mice.

RESULTS

Whereas huFGOs exhibited a half maximal inhibitory concentration that was similar among organoid lines, divergent responses and varying half maximal inhibitory concentration values among the huTGO lines were observed in response to chemotherapeutic drugs. HuTGOs that were sensitive to treatment were derived from a patient with a near complete tumor response to chemotherapy. However, organoids resistant to treatment were derived from patients who exhibited no response to chemotherapy. Orthotropic transplantation of organoids resulted in the engraftment and development of human adenocarcinoma. RNA sequencing revealed that huTGOs closely resembled the patient's native tumor tissue and not commonly used gastric cancer cell lines and cell lines derived from the organoid cultures.

CONCLUSIONS

The treatment of patient-derived organoids alongside patients from whom cultures were derived will ultimately test their usefulness to predict individual therapy response and patient outcome.

Lineage-specific plasmid acquisition and the evolution of specialized pathogens in Bacillus thuringiensis and the Bacillus cereus group

Bacterial plasmids can vary from small selfish genetic elements to large autonomous replicons that constitute a significant proportion of total cellular DNA. By conferring novel function to the cell, plasmids may facilitate evolution but their mobility may be opposed by co-evolutionary relationships with chromosomes or encouraged via the infectious sharing of genes encoding public goods. Here, we explore these hypotheses through large-scale examination of the association between plasmids and chromosomal DNA in the phenotypically diverse Bacillus cereus group. This complex group is rich in plasmids, many of which encode essential virulence factors (Cry toxins) that are known public goods. We characterized population genomic structure, gene content and plasmid distribution to investigate the role of mobile elements in diversification. We analysed coding sequence within the core and accessory genome of 190 B. cereus group isolates, including 23 novel sequences and genes from 410 reference plasmid genomes. While cry genes were widely distributed, those with invertebrate toxicity were predominantly associated with one sequence cluster (clade 2) and phenotypically defined Bacillus thuringiensis. Cry toxin plasmids in clade 2 showed evidence of recent horizontal transfer and variable gene content, a pattern of plasmid segregation consistent with transfer during infectious cooperation. Nevertheless, comparison between clades suggests that co-evolutionary interactions may drive association between plasmids and chromosomes and limit wider transfer of key virulence traits. Proliferation of successful plasmid and chromosome combinations is a feature of specialized pathogens with characteristic niches (Bacillus anthracis, B. thuringiensis) and has occurred multiple times in the B. cereus group.

MicroRNA networks associated with active systemic juvenile idiopathic arthritis regulate CD163 expression and anti-inflammatory functions in macrophages through two distinct mechanisms

Systemic juvenile idiopathic arthritis (SJIA) is a severe childhood arthropathy with features of autoinflammation. Monocytes and macrophages in SJIA have a complex phenotype with both pro- and anti-inflammatory properties that combine features of several well characterized in vitro conditions used to activate macrophages. An important anti-inflammatory phenotype is expression of CD163, a scavenger receptor that sequesters toxic pro-inflammatory complexes that is highly expressed in both active SJIA and macrophage activation syndrome (MAS). CD163 is most strongly up-regulated by IL-10 (M(IL-10)), and not by other conditions that reflect features seen in SJIA monocytes such as M(LPS+IC). MicroRNA plays key roles in integrating cellular signals such as those in macrophage polarization, and as such we hypothesize microRNAs regulate macrophage functional responses in SJIA including CD163 expression. We find that 2 microRNAs previously found to be elevated in active SJIA, miR-125a-5p and miR-181c, significantly reduced macrophage CD163 expression through 2 distinct mechanisms. Neither microRNA was elevated in M(IL-10) with robust CD163 expression, but were instead induced in M(LPS+IC) where they restricted CD163 mRNA expression. Mir-181 species directly targeted CD163 mRNA for degradation. In contrast, miR-125a-5p functions indirectly, as transcriptome analysis of miR-125a-5p overexpression identified "cytokine-cytokine receptor interactions" as the most significantly repressed gene pathway, including decreased IL10RA, required for IL-10-mediated CD163 expression. Finally, overexpression of miR-181c inhibited CD163 anti-inflammatory responses to hemoglobin or high mobility group box 1 (HMGB1) complexes. Together, these data show that microRNA utilizes multiple mechanisms to integrate well-characterized polarization phenotypes and regulate macrophage functional properties seen in SJIA.

Transcriptional profiles of JIA patient blood with subsequent poor response to methotrexate

Objective

The mechanisms that determine the efficacy or inefficacy of MTX in JIA are ill-defined. The objective of this study was to identify a gene expression transcriptional signature associated with poor response to MTX in patients with JIA.

Methods

RNA sequencing was used to measure gene expression in peripheral blood mononuclear cells collected from 47 patients with JIA prior to MTX treatment and 14 age-matched controls. Differentially expressed baseline genes between responders and non-responders were evaluated. Biological differences between all JIA patients and controls were explored by constructing a signature of differentially expressed genes. Unsupervised clustering and pathway analysis was performed.

Results

A signature of 99 differentially expressed genes (Bonferroni-corrected P < 0.05) capturing the biological differences between all JIA patients and controls was identified. Unsupervised clustering of samples based on this list of 99 genes produced subgroups enriched for MTX response status. Comparing this gene signature with reference signatures from sorted cell populations revealed high concordance between the expression signatures of monocytes and of MTX non-responders. CXCL8 (IL-8) was the most significantly differentially expressed gene transcript comparing all JIA patients with controls (Bonferroni-corrected P = 4.12 × 10-10).

Conclusion

Variability in clinical response to MTX in JIA patients is associated with differences in gene transcripts modulated in monocytes. These gene expression profiles may provide a basis for biomarkers predictive of treatment response.

CD44 variant isoform 9 emerges in response to injury and contributes to the regeneration of the gastric epithelium

The CD44 gene encodes several protein isoforms due to alternative splicing and post translational modifications. Given that CD44 variant isoform 9 (CD44v9) is expressed within Spasmolytic Polypeptide/TFF2-Expressing Metaplasia (SPEM) glands during repair, CD44v9 may be play a funcitonal role during the process of regeneration of the gastric epithelium. Here we hypothesize that CD44v9 marks a regenerative cell lineage responsive to infiltrating macrophages during regeneration of the gastric epithelium. Ulcers were induced in CD44-deficient (CD44KO) and C57BL/6 (BL6) mice by a localized application of acetic acid to the serosal surface of the stomach. Gastric organoids expressing CD44v9 were derived from mouse stomachs and transplanted at the ulcer site of CD44KO mice. Ulcers, CD44v9 expression, proliferation and histology were measured 1, 3, 5 and 7-days post-injury. Human-derived gastric organoids were generated from stomach tissue collected from elderly (>55 years) or young (14-20 years) patients. Organoids were transplanted into the stomachs of NOD scid gamma (NSG) mice at the site of injury. Gastric injury was induced in NRG-SGM3 (NRGS) mice harboring human-derived immune cells (hnNRGS) and the immune profile anlayzed by CyTOF. CD44v9 expression emerged within regenerating glands the ulcer margin in response to injury. While ulcers in BL6 mice healed within 7-days post-injury, CD44KO mice exhibited loss of repair and epithelial regeneration. Ulcer healing was promoted in CD44KO mice by transplanted CD55v9-expressing gastric organoids. NSG mice exhibited loss of CD44v9 expression and gastric repair. Transplantation of human-derived gastric organoids from young, but not aged stomachs promoted repair in NSG mouse stomachs in response to injury. Finally, compared to NRGS mice, huNRGS animals exhibited reduced ulcer sizes, an infiltration of human CD162+ macrophages and an emergence of CD44v9 expression in SPEM. Thus, during repair of the gastic epithelium CD44v9 emerges within a regenerative cell lineage that coincides with macrophage inflitration within the injured mucosa. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Comprehensive microRNA-sequencing of exosomes derived from head and neck carcinoma cells in vitro reveals common secretion profiles and potential utility as salivary biomarkers

Exosomes are nano-scale, membrane encapsulated vesicles that are released by cells into the extracellular space and function as intercellular signaling vectors through horizontal transfer of biologic molecules, including microRNA (miRNA). There is evidence that cancer-derived exosomes enable the tumor to manipulate its microenvironment, thus contributing to the capacity of the tumor for immune evasion, growth, invasion, and metastatic spread. The objective of this study was to characterize differential secretion of exosomal miRNA by head and neck squamous cell carcinoma (HNSCC) and identify a set of candidate biomarkers that could be detected in non-invasive saliva samples. We isolated exosomes from conditioned media from 4 HNSCC cell lines and oral epithelial control cells and applied miRNA-sequencing to comprehensively characterize their miRNA cargo and compare transcript levels of each HNSCC cell line to that of oral epithelial control cells. A candidate set of miRNA differentially secreted by all 4 HNSCC cell lines was further evaluated in saliva collected from HNSCC patients and healthy controls. We observed extensive differences in exosomal miRNA content between HNSCC cells when compared to normal oral epithelial control cells, with a high degree of overlap in exosomal miRNA profiles between the 4 distinct HNSCC cell lines. Importantly, several of the exosomal miRNA secreted solely by cancer cells in culture were detected at substantially elevated levels in saliva from HNSCC patients relative to saliva from healthy controls. These findings provide important insight into tumor biology and yields a promising set of candidate HNSCC biomarkers for use with non-invasive saliva samples.

GSAR: Bioconductor package for Gene Set analysis in R

BACKGROUND

Gene set analysis (in a form of functionally related genes or pathways) has become the method of choice for analyzing omics data in general and gene expression data in particular. There are many statistical methods that either summarize gene-level statistics for a gene set or apply a multivariate statistic that accounts for intergene correlations. Most available methods detect complex departures from the null hypothesis but lack the ability to identify the specific alternative hypothesis that rejects the null.

RESULTS

GSAR (Gene Set Analysis in R) is an open-source R/Bioconductor software package for gene set analysis (GSA). It implements self-contained multivariate non-parametric statistical methods testing a complex null hypothesis against specific alternatives, such as differences in mean (shift), variance (scale), or net correlation structure. The package also provides a graphical visualization tool, based on the union of two minimum spanning trees, for correlation networks to examine the change in the correlation structures of a gene set between two conditions and highlight influential genes (hubs).

CONCLUSIONS

Package GSAR provides a set of multivariate non-parametric statistical methods that test a complex null hypothesis against specific alternatives. The methods in package GSAR are applicable to any type of omics data that can be represented in a matrix format. The package, with detailed instructions and examples, is freely available under the GPL (> = 2) license from the Bioconductor web site.

Module Based Differential Coexpression Analysis Method for Type 2 Diabetes

More and more studies have shown that many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional biological pathway or network and are highly correlated. Differential coexpression analysis, as a more comprehensive technique to the differential expression analysis, was raised to research gene regulatory networks and biological pathways of phenotypic changes through measuring gene correlation changes between disease and normal conditions. In this paper, we propose a gene differential coexpression analysis algorithm in the level of gene sets and apply the algorithm to a publicly available type 2 diabetes (T2D) expression dataset. Firstly, we calculate coexpression biweight midcorrelation coefficients between all gene pairs. Then, we select informative correlation pairs using the “differential coexpression threshold” strategy. Finally, we identify the differential coexpression gene modules using maximum clique concept and k-clique algorithm. We apply the proposed differential coexpression analysis method on simulated data and T2D data. Two differential coexpression gene modules about T2D were detected, which should be useful for exploring the biological function of the related genes.

Gene differential coexpression analysis based on biweight correlation and maximum clique

Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure 'similarity' between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.

Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

MOTIVATION

To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.

RESULTS

In GSNCA, weight factors are assigned to genes in proportion to the genes' cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA captures changes in the structure of genes' cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses.

AVAILABILITY AND IMPLEMENTATION

Implementation of the GSNCA test in R is available upon request from the authors.

New network topology approaches reveal differential correlation patterns in breast cancer

Background

Analysis of genome-wide data is often carried out using standard methods such as differential expression analysis, clustering analysis and heatmaps. Beyond that, differential correlation analysis was suggested to identify changes in the correlation patterns between disease states. The detection of differential correlation is a demanding task, as the number of entries in the gene-by-gene correlation matrix is large. Currently, there is no gold standard for the detection of differential correlation and statistical validation.

Results

We developed two untargeted algorithms (DCloc and DCglob) that identify differential correlation patterns by comparing the local or global topology of correlation networks. Construction of networks from correlation structures requires fixing of a correlation threshold. Instead of a single cutoff, the algorithms systematically investigate a series of correlation thresholds and permit to detect different kinds of correlation changes at the same level of significance: strong changes of a few genes and moderate changes of many genes. Comparing the correlation structure of 208 ER- breast carcinomas and 208 ER+ breast carcinomas, DCloc detected 770 differentially correlated genes with a FDR of 12.8%, while DCglob detected 630 differentially correlated genes with a FDR of 12.1%. In two-fold cross-validation, the reproducibility of the list of the top 5% differentially correlated genes in 140 ER- tumors and in 140 ER+ tumors was 49% for DCloc and 33% for DCglob.

Conclusions

We developed two correlation network topology based algorithms for the detection of differential correlations in different disease states. Clusters of differentially correlated genes could be interpreted biologically and included the marker genes hydroxyprostaglandin dehydrogenase (PGDH) and acyl-CoA synthetase medium chain 1 (ACSM1) of invasive apocrine carcinomas that were differentially correlated, but not differentially expressed. Using random subsampling and cross-validation, DCloc and DCglob were shown to identify specific and reproducible lists of differentially correlated genes.

Scalable high-throughput identification of genetic targets by network filtering

BACKGROUND

Discovering the molecular targets of compounds or the cause of physiological conditions, among the multitude of known genes, is one of the major challenges of bioinformatics. One of the most common approaches to this problem is finding sets of differentially expressed, and more recently differentially co-expressed, genes. Other approaches require libraries of genetic mutants or require to perform a large number of assays. Another elegant approach is the filtering of mRNA expression profiles using reverse-engineered gene network models of the target cell. This approach has the advantage of not needing control samples, libraries or numerous assays. Nevertheless, the impementations of this strategy proposed so far are computationally demanding. Moreover the user has to arbitrarily choose a threshold on the number of potentially relevant genes from the algorithm output.

RESULTS

Our solution, while performing comparably to state of the art algorithms in terms of discovered targets, is more efficient in terms of memory and time consumption. The proposed algorithm computes the likelihood associated to each gene and outputs to the user only the list of likely perturbed genes.

CONCLUSIONS

The proposed algorithm is a valid alternative to existing algorithms and is particularly suited to contemporary gene expression microarrays, given the number of probe sets in each chip, also when executed on common desktop computers.

A Nonparametric Bayesian Model for Local Clustering with Application to Proteomics

We propose a nonparametric Bayesian local clustering (NoB-LoC) approach for heterogeneous data. NoB-LoC implements inference for nested clusters as posterior inference under a Bayesian model. Using protein expression data as an example, the NoB-LoC model defines a protein (column) cluster as a set of proteins that give rise to the same partition of the samples (rows). In other words, the sample partitions are nested within protein clusters. The common clustering of the samples gives meaning to the protein clusters. Any pair of samples might belong to the same cluster for one protein set but to different clusters for another protein set. These local features are different from features obtained by global clustering approaches such as hierarchical clustering, which create only one partition of samples that applies for all the proteins in the data set. In addition, the NoB-LoC model is different from most other local or nested clustering methods, which define clusters based on common parameters in the sampling model. As an added and important feature, the NoB-LoC method probabilistically excludes sets of irrelevant proteins and samples that do not meaningfully co-cluster with other proteins and samples, thus improving the inference on the clustering of the remaining proteins and samples. Inference is guided by a joint probability model for all the random elements. We provide a simulation study and a motivating example to demonstrate the unique features of the NoB-LoC model.

Pathway correlation profile of gene-gene co-expression for identifying pathway perturbation

Identifying perturbed or dysregulated pathways is critical to understanding the biological processes that change within an experiment. Previous methods identified important pathways that are significantly enriched among differentially expressed genes; however, these methods cannot account for small, coordinated changes in gene expression that amass across a whole pathway. In order to overcome this limitation, we use microarray gene expression data to identify pathway perturbation based on pathway correlation profiles. By identifying the distribution of gene-gene pair correlations within a pathway, we can rank the pathways based on the level of perturbation and dysregulation. We have shown this successfully for differences between two experimental conditions in Escherichia coli and changes within time series data in Saccharomyces cerevisiae, as well as two estrogen receptor response classes of breast cancer. Overall, our method made significant predictions as to the pathway perturbations that are involved in the experimental conditions.

WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results

Cluster analysis methods have been extensively researched, but the adoption of new methods is often hindered by technical barriers in their implementation and use. WebGimm is a free cluster analysis web-service, and an open source general purpose clustering web-server infrastructure designed to facilitate easy deployment of integrated cluster analysis servers based on clustering and functional annotation algorithms implemented in R. Integrated functional analyses and interactive browsing of both, clustering structure and functional annotations provides a complete analytical environment for cluster analysis and interpretation of results. The Java Web Start client-based interface is modeled after the familiar cluster/treeview packages making its use intuitive to a wide array of biomedical researchers. For biomedical researchers, WebGimm provides an avenue to access state of the art clustering procedures. For Bioinformatics methods developers, WebGimm offers a convenient avenue to deploy their newly developed clustering methods. WebGimm server, software and manuals can be freely accessed at http://ClusterAnalysis.org/.