Quick calculation for sample size while controlling false discovery rate with application to microarray analysis

Mineralocorticoid Receptor Antagonism Prevents Aortic Plaque Progression and Reduces Left Ventricular Mass and Fibrosis in Patients With Type 2 Diabetes and Chronic Kidney Disease: The MAGMA Trial

BACKGROUND

Persistent mineralocorticoid receptor activation is a pathologic response in type 2 diabetes and chronic kidney disease. Whereas mineralocorticoid receptor antagonists are beneficial in reducing cardiovascular complications, direct mechanistic pathways for these effects in humans are lacking.

METHODS

The MAGMA trial (Mineralocorticoid Receptor Antagonism Clinical Evaluation in Atherosclerosis) was a randomized, double-blind, placebo-controlled trial in patients with high-risk type 2 diabetes with chronic kidney disease (not receiving dialysis) on maximum tolerated renin-angiotensin system blockade. The primary end point was change in thoracic aortic wall volume, expressed as absolute or percent value (ΔTWV or ΔPWV), using 3T magnetic resonance imaging at 12 months. Secondary end points were changes in left ventricle (LV) mass; LV fibrosis, measured as a change in myocardial native T1; and 24-hour ambulatory and central aortic blood pressures. Tertiary end points included plasma proteomic changes in 7596 plasma proteins using an aptamer-based assay.

RESULTS

A total of 79 patients were randomized to placebo (n=42) or 25 mg of spironolactone daily (n=37). After a modified intent-to-treat, including available baseline data of study end points, patients who completed the trial protocol were included in the final analyses. At the 12-month follow-up, the average change in PWV was 7.1±10.7% in the placebo group and 0.87±10.0% in the spironolactone group (P=0.028), and ΔTWV was 1.2±1.7 cm3 in the placebo group and 0.037±1.9 cm3 in the spironolactone group (P=0.022). Change in LV mass was 3.1±8.4 g in the placebo group and -5.8±8.4 g in the spironolactone group (P=0.001). Changes in LV T1 values were significantly different between the placebo and spironolactone groups (26.0±41.9 ms in the placebo group versus a decrease of -10.1±36.3 ms in the spironolactone group; P=6.33×10-4). Mediation analysis revealed that the spironolactone effect on thoracic aortic wall volume and myocardial mass remained significant after adjustment for ambulatory and central blood pressures. Proteomic analysis revealed a dominant effect of spironolactone on pathways involving oxidative stress, inflammation, and leukocyte activation.

CONCLUSIONS

Among patients with diabetes with moderate to severe chronic kidney disease at elevated cardiovascular risk, treatment with spironolactone prevented progression of aortic wall volume and resulted in regression of LV mass and favorable alterations in native T1, suggesting amelioration of left-ventricular fibrosis.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02169089.

Untargeted blood serum proteomics identifies novel proteins related to neurological recovery after human spinal cord injury

BACKGROUND

The discovery of new prognostic biomarkers following spinal cord injury (SCI) is a rapidly growing field that could help uncover the underlying pathological mechanisms of SCI and aid in the development of new therapies. To date, this search has largely focused on the initial days after the lesion. However, during the subacute stage of SCI (weeks to months after the injury), there remains potential for sensorimotor recovery, and numerous secondary events develop in various organs. Additionally, the confounding effects of early interventions after the injury are less likely to interfere with the results.

METHODS

In this study, we conducted an untargeted proteomics analysis to identify biomarkers of recovery in blood serum samples during the subacute phase of SCI patients, comparing those with strong recovery to those with no recovery between 30 and 120 days. We analyzed the fraction of serum that is depleted of the most abundant proteins to unmask proteins that would otherwise go undetected. Linear models were used to identify peptides and proteins related to neurological recovery and we validated changes in some of these proteins using Enzyme-linked Immunosorbent Assay (ELISA).

RESULTS

Our findings reveal that differences in subacute recovery after SCI (from 30 to 120 days) are associated with an enrichment in proteins involved in inflammation, coagulation, and lipid metabolism. Technical validation using commercial ELISAs further confirms that high levels of SERPINE1 and ARHGAP35 are associated with strong neurological recovery, while high levels of CD300a and DEFA1 are associated with a lack of recovery.

CONCLUSIONS

Our study identifies new candidates for biomarkers of neurological recovery and for novel therapeutic targets after SCI.

DNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism

Cancer patients are commonly affected by fatigue. Herein, we sought to examine epigenetic modifications (i.e., DNA methylation) related to fatigue in peripheral blood among patients during and after treatment for head and neck cancer (HNC). Further, we determined whether these modifications were associated with gene expression and inflammatory protein markers, which we have previously linked to fatigue in HNC. This prospective, longitudinal study enrolled eligible patients with data collected at pre-radiotherapy, end of radiotherapy, and six months and one-year post-radiotherapy. Fatigue data were reported by patients using the Multidimensional Fatigue Inventory (MFI)-20. DNA methylation (Illumina MethylationEPIC) and gene expression (Applied Biosystems Clariom S) arrays and assays for seven inflammatory markers (R&D Systems multiplex) were performed. Mixed models and enrichment analyses were applied to establish the associations. A total of 386 methylation loci were associated with fatigue among 145 patients (False Discovery Rate [FDR] < 0.05). Enrichment analyses showed the involvement of genes related to immune and inflammatory responses, insulin and lipid metabolism, neuropsychological disorders, and tumors. We further identified 16 methylation-gene expression pairs (FDR < 0.05), which were linked to immune and inflammatory responses and lipid metabolism. Ninety-one percent (351) of the 386 methylation loci were also significantly associated with inflammatory markers (e.g., interleukin 6, c-reactive protein; FDR < 0.05), which further mediated the association between methylation and fatigue (FDR < 0.05). These data suggest that epigenetic modifications associated with inflammation and immunometabolism, in conjunction with relevant gene expression and protein markers, are potential targets for treating fatigue in HNC patients. The findings also merit future prospective studies in other cancer populations as well as interventional investigations.

The effects of exposure to and timing of a choline-deficient diet during pregnancy and early postnatal life on the skeletal muscle transcriptome of the offspring

BACKGROUND & AIMS

Nonalcoholic fatty liver disease (NAFLD) is related to muscle loss, but the precise mechanism underlying this association remains unclear. The aim of the present study was thus to determine the influence of maternal fatty liver and dietary choline deficiency during pregnancy and/or lactation periods on the skeletal muscle gene expression profile among 24-day-old male rat offspring.

METHODS

Histological examination of skeletal muscle tissue specimens obtained from offspring of dams suffering from fatty liver, provided with proper choline intake during pregnancy and lactation (NN), fed a choline-deficient diet during both periods (DD), deprived of choline only during pregnancy (DN), or only during lactation (ND), was performed. The global transcriptome pattern was assessed using a microarray approach (Affymetrix® Rat Gene 2.1 ST Array Strip). The relative expression of selected genes was validated by real-time PCR (qPCR).

RESULTS

Morphological differences in fat accumulation in skeletal muscle related to choline supply were observed. The global gene expression profile was consistent with abnormal morphological changes. Mettl21c gene was overexpressed in all choline-deficient groups compared to the NN group, while two genes, Cdkn1a and S100a4, were downregulated. Processes of protein biosynthesis were upregulated, and processes related to cell proliferation and lipid metabolism were inhibited in DD, DN, and ND groups compared to the NN group.

CONCLUSIONS

Prenatal and early postnatal exposure to fatty liver and dietary choline deficiency leads to changes in the transcriptome profile in skeletal muscle of 24-day old male rat offspring and is associated with muscle damage, but the mechanism of it seems to be different at different developmental stages of life. Adequate choline intake during pregnancy and lactation can prevent severe muscle disturbance in the progeny of females suffering from fatty liver.

Role of Gut Microbe Composition in Psychosocial Symptom Response to Exercise Training in Breast Cancer Survivors (ROME) study: protocol for a randomised controlled trial

INTRODUCTION

Breast cancer survivors have an increased risk for chronic fatigue and altered gut microbiota composition, both with negative health and quality of life affects. Exercise modestly improves fatigue and is linked to gut microbial diversity and production of beneficial metabolites. Studies suggest that gut microbiota composition is a potential mechanism underlying fatigue response to exercise. Randomised controlled trials testing the effects of exercise on the gut microbiome are limited and there is a scarcity of findings specific to breast cancer survivors. The objective of this study is to determine if fitness-related modifications to gut microbiota occur and, if so, mediate the effects of aerobic exercise on fatigue response.

METHODS AND ANALYSIS

The research is a randomised controlled trial among breast cancer survivors aged 18-74 with fatigue. The primary aim is to determine the effects of aerobic exercise training compared with an attention control on gut microbiota composition. The secondary study aims are to test if exercise training (1) affects the gut microbiota composition directly and/or indirectly through inflammation (serum cytokines), autonomic nervous system (heart rate variability) or hypothalamic-pituitary-adrenal axis mediators (hair cortisol assays), and (2) effects on fatigue are direct and/or indirect through changes in the gut microbiota composition. All participants receive a standardised controlled diet. Assessments occur at baseline, 5 weeks, 10 weeks and 15 weeks (5 weeks post intervention completion). Faecal samples collect the gut microbiome and 16S gene sequencing will identify the microbiome. Fatigue is measured by a 13-item multidimensional fatigue scale.

ETHICS AND DISSEMINATION

The University of Alabama at Birmingham Institutional Review Board (IRB) approved this study on 15 May 2019, UAB IRB#30000320. A Data and Safety Monitoring Board convenes annually or more often if indicated. Findings will be disseminated in peer-reviewed journals and conference presentations.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov, NCT04088708.

Machine learning reveals serum myristic acid, palmitic acid and heptanoylcarnitine as biomarkers of coronary artery disease risk in patients with type 2 diabetes mellitus

BACKGROUND

Coronary heart disease (CHD) is the most important complication of type 2 diabetes mellitus (T2DM) and the leading cause of death. Identifying the risk of CHD in T2DM patients is important for early clinical intervention.

METHODS

A total of 213 participants, including 81 healthy controls (HCs), 69 T2DM patients and 63 T2DM patients complicated with CHD were recruited in this study. Serum metabolomics were conducted by using ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). Demographic information and clinical laboratory test results were also collected.

RESULTS

Metabolic phenotypes were significantly altered among HC, T2DM and T2DM-CHD. Acylcarnitines were the most disturbed metabolites between T2DM patients and HCs. Lower levels of bile acids and higher levels of fatty acids in serum were closely associated with CHD risk in T2DM patients. Artificial neural network model was constructed for the discrimination of T2DM and T2DM complicated with CHD based on myristic acid, palmitic acid and heptanoylcarnitine, with accuracy larger than 0.95 in both training set and testing set.

CONCLUSION

Altogether, these findings suggest that myristic acid, palmitic acid and heptanoylcarnitine have a good prospect for the warning of CHD complications in T2DM patients, and are superior to traditional lipid, blood glucose and blood pressure indicators.

Computing Power and Sample Size for the False Discovery Rate in Multiple Applications

The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a p-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package FDRsamplesize2, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The FDRsamplesize2 package is available on CRAN.

Humoral immunoprofiling identifies novel biomarkers and an immune suppressive autoantibody phenotype at the site of disease in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous cancer, with minimal response to therapeutic intervention and with 85% of cases diagnosed at an advanced stage due to lack of early symptoms, highlighting the importance of understanding PDAC immunology in greater detail. Here, we applied an immunoproteomic approach to investigate autoantibody responses against cancer-testis and tumor-associated antigens in PDAC using a high-throughput multiplexed protein microarray platform, comparing humoral immune responses in serum and at the site of disease in order to shed new light on immune responses in the tumor microenvironment. We simultaneously quantified serum or tissue IgG and IgA antibody isotypes and subclasses in a cohort of PDAC, disease control and healthy patients, observing inter alia that subclass utilization in tumor tissue samples was predominantly immune suppressive IgG4 and inflammatory IgA2, contrasting with predominant IgG3 and IgA1 subclass utilization in matched sera and implying local autoantibody production at the site of disease in an immune-tolerant environment. By comparison, serum autoantibody subclass profiling for the disease controls identified IgG4, IgG1, and IgA1 as the abundant subclasses. Combinatorial analysis of serum autoantibody responses identified panels of candidate biomarkers. The top IgG panel included ACVR2B, GAGE1, LEMD1, MAGEB1 and PAGE1 (sensitivity, specificity and AUC values of 0.933, 0.767 and 0.906). Conversely, the top IgA panel included AURKA, GAGE1, MAGEA10, PLEKHA5 and XAGE3aV1 (sensitivity, specificity, and AUC values of 1.000, 0.800, and 0.954). Assessment of antigen-specific serum autoantibody glycoforms revealed abundant sialylation on IgA in PDAC, consistent with an immune suppressive IgA response to disease.

Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes

Background: The relationship between genotype and phenotype is governed by numerous genetic interactions (GIs), and the mapping of GI networks is of interest for two main reasons: 1) By modelling biological robustness, GIs provide a powerful opportunity to infer compensatory biological mechanisms via the identification of functional relationships between genes, which is of interest for biological discovery and translational research. Biological systems have evolved to compensate for genetic (i.e., variations and mutations) and environmental (i.e., drug efficacy) perturbations by exploiting compensatory relationships between genes, pathways and biological processes; 2) GI facilitates the identification of the direction (alleviating or aggravating interactions) and magnitude of epistatic interactions that influence the phenotypic outcome. The generation of GIs for human diseases is impossible using experimental biology approaches such as systematic deletion analysis. Moreover, the generation of disease-specific GIs has never been undertaken in humans. Methods: We used our Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement the workflow. Standard GWAS sample quality control procedure was followed. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. Using the odds ratio (OR), we identified the GIs that increase or decrease the risk of a disease phenotype. The SNP-based epistatic results were transformed into gene-based epistatic results. Results: We have developed a novel approach by conducting gene-based statistical epistatic analysis using an Indian schizophrenia case-control genetic dataset and transforming these results to infer GIs that increase the risk of schizophrenia. There were ∼9.5 million GIs with a p-value ≤ 1 × 10-5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR <1.0) for schizophrenia. Conclusion: Unlike model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. The study exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models, potentially surpassing the limitations of conventional GWAS.

Plasma cell-free RNA profiling of Vietnamese Alzheimer's patients reveals a linkage with chronic inflammation and apoptosis: a pilot study

Introduction

Circulating cell-free RNA (cfRNA) is a potential hallmark for early diagnosis of Alzheimer's Disease (AD) as it construes the genetic expression level, giving insights into the pathological progress from the outset. Profiles of cfRNA in Caucasian AD patients have been investigated thoroughly, yet there was no report exploring cfRNAs in the ASEAN groups. This study examined the gap, expecting to support the development of point-of-care AD diagnosis.

Methods

cfRNA profiles were characterized from 20 Vietnamese plasma samples (10 probable AD and 10 age-matched controls). RNA reads were subjected to differential expression (DE) analysis. Weighted gene correlation network analysis (WGCNA) was performed to identify gene modules that were significantly co-expressed. These modules' expression profiles were then correlated with AD status to identify relevant modules. Genes with the highest intramodular connectivity (module membership) were selected as hub genes. Transcript counts of differentially expressed genes were correlated with key AD measures-MMSE and MTA scores-to identify potential biomarkers.

Results

136 genes were identified as significant AD hallmarks (p < 0.05), with 52 downregulated and 84 upregulated in the AD cohort. 45.6% of these genes are highly expressed in the hippocampus, cerebellum, and cerebral cortex. Notably, all markers related to chronic inflammation were upregulated, and there was a significant shift in all apoptotic markers. Three co-expressed modules were found to be significantly correlated with Alzheimer's status (p < 0.05; R2> 0.5). Functional enrichment analysis on these modules reveals an association with focal adhesion, nucleocytoplasmic transport, and metal ion response leading to apoptosis, suggesting the potential participation of these pathways in AD pathology. 47 significant hub genes were found to be differentially expressed genes with the highest connectivity. Six significant hub genes (CREB1, YTHDC1, IL1RL1, PHACTR2, ANKRD36B, RNF213) were found to be significantly correlated with MTA and MMSE scores. Other significant transcripts (XRN1, UBB, CHP1, THBS1, S100A9) were found to be involved in inflammation and neuronal death. Overall, we have identified candidate transcripts in plasma cf-RNA that are differentially expressed and are implicated in inflammation and apoptosis, which can jumpstart further investigations into applying cf-RNA as an AD biomarker in Vietnam and ASEAN countries.

Subjective cognitive decline exhibits alterations of resting-state phase-amplitude coupling in precuneus

OBJECTIVE

Subjective cognitive decline (SCD) is associated with increased risks for progressing to Alzheimer's disease (AD). This study aimed to investigate phase-amplitude coupling (PAC) in individuals with SCD and healthy controls (HCs) in the baseline year and determined the predictability of cognitive changes in the clinical follow-up.

METHODS

Resting-state magnetoencephalographic signals in 29 HCs and 23 SCD subjects were recorded in the baseline year. The parahippocampal gyrus, posterior cingulate cortex and precuneus were selected as regions of interest (ROIs). Based on the grand-averaged comodulograms, delta-beta, delta-gamma and theta-gamma PAC values were extracted from each ROI.

RESULTS

Compared with the HCs, the SCD group showed decreased theta-gamma PAC in the precuneus. Theta-gamma PAC of the left precuneus was associated with SCD severity and performance of immediate recall in the baseline year. The SCD group was followed for 3 years and divided into SCD-Stable and SCD-Decline groups based on scores of Mini-Mental State Examination. No significant differences in PAC of the baseline year were found between SCD-Stable and SCD-Decline groups.

CONCLUSIONS

The SCD group demonstrated reduced theta-gamma PAC in the precuneus.

SIGNIFICANCE

Subjective perception of cognitive decline is reflected by objective alterations of brain function.

Identification and characterization of sex-dependent gene expression profile in glioblastoma

Glioblastoma (GBM) is the most lethal primary tumor in the human brain and lacks favorable treatment options. Sex differences in the outcome of GBM are broadly acknowledged, but the underlying molecular mechanisms remain largely unknown. To identify the sex-dependent critical genes in the progression of GBM, raw data from several microarray datasets with the same array platform were downloaded from the Gene Expression Omnibus (GEO) database. These datasets included tumorous and normal tissue from patients with GBM and crucial sex features. Then, the differentially expressed genes (DEGs) in female and male tumors were identified via bioinformatics analysis, respectively. Functional signatures of the identified DEGs were further annotated by Gene Ontology (GO) and pathway enrichment analyses. Venn diagram and functional protein-protein interaction (PPI) network analyses were performed to screen out the sex-specific DEGs. Survival analysis of patients with differences in the expression level of selected genes was then carried out using the data from The Cancer Genome Atlas (TCGA). Here, we showed that ECT2, AURKA, TYMS, CDK1, NCAPH, CENPU, OIP5, KIF14, ASPM, FBXO5, SGOL2, CASC5, SHCBP1, FN1, LOX, IGFBP3, CSPG4, and CD44 were enriched in female tumor samples, whereas TNFSF13B, CXCL10, CXCL8, CXCR4, TLR2, CCL2, and FCGR2A were enriched in male tumor samples. Among these key genes, interestingly, ECT2 was associated with increased an survival rate for female patients, whileTNFSF13B could be regarded as a potential marker of poor prognosis in male patients. These results suggested that sex differences in patients may be attributed to the heterogeneous gene activity, which might influence the oncogenesis and the outcomes of GBM.

Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected Cells

Protein post-translational modifications (PTMs) are essential elements of cellular communication. Their variations in abundance can affect cellular pathways, leading to cellular disorders and diseases. A widely used method for revealing PTM-mediated regulatory networks is their label-free quantitation (LFQ) by high-resolution mass spectrometry. The raw data resulting from such experiments are generally interpreted using specific software, such as MaxQuant, MassChroQ, or Proline for instance. They provide data matrices containing quantified intensities for each modified peptide identified. Statistical analyses are then necessary (1) to ensure that the quantified data are of good enough quality and sufficiently reproducible, (2) to highlight the modified peptides that are differentially abundant between the biological conditions under study. The objective of this chapter is therefore to provide a complete data analysis pipeline for analyzing the quantified values of modified peptides in presence of two or more biological conditions using the R software. We illustrate our pipeline starting from MaxQuant outputs dealing with the analysis of A549-ACE2 cells infected by SARS-CoV-2 at different time stamps, freely available on PRIDE (PXD020019).

In situ spatial glycomic imaging of mouse and human Alzheimer's disease brains

N-linked protein glycosylation in the brain is an understudied facet of glucose utilization that impacts a myriad of cellular processes including resting membrane potential, axon firing, and synaptic vesicle trafficking. Currently, a spatial map of N-linked glycans within the normal and Alzheimer's disease (AD) human brain does not exist. A comprehensive analysis of the spatial N-linked glycome would improve our understanding of brain energy metabolism, linking metabolism to signaling events perturbed during AD progression, and could illuminate new therapeutic strategies. Herein we report an optimized in situ workflow for enzyme-assisted, matrix-assisted laser desorption and ionization (MALDI) mass spectrometry imaging (MSI) of brain N-linked glycans. Using this workflow, we spatially interrogated N-linked glycan heterogeneity in both mouse and human AD brains and their respective age-matched controls. We identified robust regional-specific N-linked glycan changes associated with AD in mice and humans. These data suggest that N-linked glycan dysregulation could be an underpinning of AD pathologies.

Placenta-derived proteins across gestation in healthy pregnancies-a novel approach to assess placental function?

BACKGROUND

Placenta-derived proteins in the systemic maternal circulation are suggested as potential biomarkers for placental function. However, the identity and longitudinal patterns of such proteins are largely unknown due to the inaccessibility of the human placenta and limitations in assay technologies. We aimed to identify proteins derived from and taken up by the placenta in the maternal circulation. Furthermore, we aimed to describe the longitudinal patterns across gestation of placenta-derived proteins as well as identify placenta-derived proteins that can serve as reference curves for placental function.

METHODS

We analyzed proteins in plasma samples collected in two cohorts using the Somalogic 5000-plex platform. Antecubital vein samples were collected at three time points (gestational weeks 14-16, 22-24, and 30-32) across gestation in 70 healthy pregnancies in the longitudinal STORK cohort. In the cross sectional 4-vessel cohort, blood samples were collected simultaneously from the maternal antecubital vein (AV), radial artery (RA), and uterine vein (UV) during cesarean section in 75 healthy pregnancies. Placenta-derived proteins and proteins taken up by the placenta were identified using venoarterial differences (UV-RA). Placenta-derived proteins were defined as placenta-specific by comparison to the venoarterial difference in the antecubital vein-radial artery (AV-RA). These proteins were described longitudinally based on the STORK cohort samples using a linear mixed effects model per protein. Using a machine learning algorithm, we identified placenta-derived proteins that could predict gestational age, meaning that they closely tracked gestation, and were potential read-outs of placental function.

RESULTS

Among the nearly 5000 measured proteins, we identified 256 placenta-derived proteins and 101 proteins taken up by the placenta (FDR < 0.05). Among the 256 placenta-derived proteins released to maternal circulation, 101 proteins were defined as placenta-specific. These proteins formed two clusters with distinct developmental patterns across gestation. We identified five placenta-derived proteins that closely tracked gestational age when measured in the systemic maternal circulation, termed a "placental proteomic clock."

CONCLUSIONS

Together, these data may serve as a first step towards a reference for the healthy placenta-derived proteome that can be measured in the systemic maternal circulation and potentially serve as biomarkers of placental function. The "placental proteomic clock" represents a novel concept that warrants further investigation. Deviations in the proteomic pattern across gestation of such proteomic clock proteins may serve as an indication of placental dysfunction.

A validated analysis pipeline for mass spectrometry-based vitreous proteomics: new insights into proliferative diabetic retinopathy

Background

Vitreous is an accessible, information-rich biofluid that has recently been studied as a source of retinal disease-related proteins and pathways. However, the number of samples required to confidently identify perturbed pathways remains unknown. In order to confidently identify these pathways, power analysis must be performed to determine the number of samples required, and sample preparation and analysis must be rigorously defined.

Methods

Control (n = 27) and proliferative diabetic retinopathy (n = 23) vitreous samples were treated as biologically distinct individuals or pooled together and aliquoted into technical replicates. Quantitative mass spectrometry with tandem mass tag labeling was used to identify proteins in individual or pooled control samples to determine technical and biological variability. To determine effect size and perform power analysis, control and proliferative diabetic retinopathy samples were analyzed across four 10-plexes. Pooled samples were used to normalize the data across plexes and generate a single data matrix for downstream analysis.

Results

The total number of unique proteins identified was 1152 in experiment 1, 989 of which were measured in all samples. In experiment 2, 1191 proteins were identified, 727 of which were measured across all samples in all plexes. Data are available via ProteomeXchange with identifier PXD025986. Spearman correlations of protein abundance estimations revealed minimal technical (0.99–1.00) and biological (0.94–0.98) variability. Each plex contained two unique pooled samples: one for normalizing across each 10-plex, and one to internally validate the normalization algorithm. Spearman correlation of the validation pool following normalization was 0.86–0.90. Principal component analysis revealed stratification of samples by disease and not by plex. Subsequent differential expression and pathway analyses demonstrated significant activation of metabolic pathways and inhibition of neuroprotective pathways in proliferative diabetic retinopathy samples relative to controls.

Conclusions

This study demonstrates a feasible, rigorous, and scalable method that can be applied to future proteomic studies of vitreous and identifies previously unrecognized metabolic pathways that advance understanding of diabetic retinopathy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09328-8.

scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

An RNAi Screening of Clinically Relevant Transcription Factors Regulating Human Adipogenesis and Adipocyte Metabolism

CONTEXT

Healthy hyperplasic (many but smaller fat cells) white adipose tissue (WAT) expansion is mediated by recruitment, proliferation and/or differentiation of new fat cells. This process (adipogenesis) is controlled by transcriptional programs that have been mostly identified in rodents.

OBJECTIVE

A systemic investigation of adipogenic human transcription factors (TFs) that are relevant for metabolic conditions has not been revealed previously.

METHODS

TFs regulated in WAT by obesity, adipose morphology, cancer cachexia, and insulin resistance were selected from microarrays. Their role in differentiation of human adipose tissue-derived stem cells (hASC) was investigated by RNA interference (RNAi) screen. Lipid accumulation, cell number, and lipolysis were measured for all screened factors (148 TFs). RNA (RNAseq), protein (Western blot) expression, insulin, and catecholamine responsiveness were examined in hASC following siRNA treatment of selected target TFs.

RESULTS

Analysis of TFs regulated by metabolic conditions in human WAT revealed that many of them belong to adipogenesis-regulating pathways. The RNAi screen identified 39 genes that affected fat cell differentiation in vitro, where 11 genes were novel. Of the latter JARID2 stood out as being necessary for formation of healthy fat cell metabolic phenotype by regulating expression of multiple fat cell phenotype-specific genes.

CONCLUSION

This comprehensive RNAi screening in hASC suggests that a large proportion of WAT TFs that are impacted by metabolic conditions might be important for hyperplastic adipose tissue expansion. The screen also identified JARID2 as a novel TF essential for the development of functional adipocytes.

RNA Sequencing Reveals Interacting Key Determinants of Osteoarthritis Acting in Subchondral Bone and Articular Cartilage: Identification of IL11 and CHADL as Attractive Treatment Targets

OBJECTIVE

To identify key determinants of the interactive pathophysiologic processes in subchondral bone and cartilage in osteoarthritis (OA).

METHODS

We performed RNA sequencing on macroscopically preserved and lesional OA subchondral bone from patients in the Research Arthritis and Articular Cartilage study who underwent joint replacement surgery due to OA (n = 24 sample pairs: 6 hips and 18 knees). Unsupervised hierarchical clustering and differential expression analyses were conducted. Results were combined with data on previously identified differentially expressed genes in cartilage (partly overlapping samples) as well as data on recently identified OA risk genes.

RESULTS

We identified 1,569 genes that were significantly differentially expressed between lesional and preserved subchondral bone, including CNTNAP2 (fold change [FC] 2.4, false discovery rate [FDR] 3.36 × 10^-5 ) and STMN2 (FC 9.6, FDR 2.36 × 10^-3 ). Among these 1,569 genes, 305 were also differentially expressed, and with the same direction of effect, in cartilage, including the recently recognized OA susceptibility genes IL11 and CHADL. Upon differential expression analysis with stratification for joint site, we identified 509 genes that were exclusively differentially expressed in subchondral bone of the knee, including KLF11 and WNT4. These genes that were differentially expressed exclusively in the knee were enriched for involvement in epigenetic processes, characterized by, e.g., HIST1H3J and HIST1H3H.

CONCLUSION

IL11 and CHADL were among the most consistently differentially expressed genes OA pathophysiology-related genes in both bone and cartilage. As these genes were recently also identified as robust OA risk genes, they classify as attractive therapeutic targets acting on 2 OA-relevant tissues.

Long-Term Effects of Very Low Dose Particle Radiation on Gene Expression in the Heart: Degenerative Disease Risks

Compared to low doses of gamma irradiation (γ-IR), high-charge-and-energy (HZE) particle IR may have different biological response thresholds in cardiac tissue at lower doses, and these effects may be IR type and dose dependent. Three- to four-month-old female CB6F1/Hsd mice were exposed once to one of four different doses of the following types of radiation: γ-IR ¹³⁷Cs (40-160 cGy, 0.662 MeV), ¹⁴Si-IR (4-32 cGy, 260 MeV/n), or ²²Ti-IR (3-26 cGy, 1 GeV/n). At 16 months post-exposure, animals were sacrificed and hearts were harvested and archived as part of the NASA Space Radiation Tissue Sharing Forum. These heart tissue samples were used in our study for RNA isolation and microarray hybridization. Functional annotation of twofold up/down differentially expressed genes (DEGs) and bioinformatics analyses revealed the following: (i) there were no clear lower IR thresholds for HZE- or γ-IR; (ii) there were 12 common DEGs across all 3 IR types; (iii) these 12 overlapping genes predicted various degrees of cardiovascular, pulmonary, and metabolic diseases, cancer, and aging; and (iv) these 12 genes revealed an exclusive non-linear DEG pattern in ¹⁴Si- and ²²Ti-IR-exposed hearts, whereas two-thirds of γ-IR-exposed hearts revealed a linear pattern of DEGs. Thus, our study may provide experimental evidence of excess relative risk (ERR) quantification of low/very low doses of full-body space-type IR-associated degenerative disease development.

Statistical Methods for Analysis of Protein Microarray Data Using R

This chapter aims to provide statistical methods for analyzing protein microarray data. It uses a publicly available protein array dataset and emphasizes practical applications in statistics using R, a statistical software. A wide range of statistical methods will be demonstrated, including descriptive statistics, hypothesis testing, false discovery rate, receiver operating characteristic curve, correlation, visualization, and power analysis. The R code used to perform the statistical analyses will be provided.

Establishment of a Colorectal Cancer-Related MicroRNA-mRNA Regulatory Network by Microarray and Bioinformatics

Colorectal cancer (CRC) is one of the most malignant cancers with high morbidity and mortality. MicroRNAs (miRNAs) are small non-coding RNAs that affect biological processes by binding to mRNAs and regulating their expression, and epigenetic alterations including miRNA dysregulation are significantly involved in CRC development. Determining the effect of the miRNA-mRNA network on CRC could be helpful for developing novel therapeutic targets and prognostic biomarkers, and even improving survival. In this study, microarray assays were used to screen differentially expressed miRNAs (DE miRNAs) and mRNAs (DE mRNAs) in CRC and the adjacent normal tissues. Among the detected genes, 42 miRNAs and 142 mRNAs were significantly upregulated in CRC, while 23 miRNAs and 279 mRNAs were significantly downregulated. Through overlapping of predicted targets of DE miRNAs and anti-expressed DE mRNAs, networks of DE miRNAs and DE mRNAs in CRC were established. Additionally, the formation of a protein-protein interaction network of DE mRNAs possibly targeted by DE miRNAs, functional annotation and pathway analysis, stable subnetwork mining, and determination of hub genes provided the probable mechanism used by DE miRNAs and DE mRNAs to regulate CRC growth. Finally, validation of expression and prognostic potential of hub genes provided further support for the results above and indicated that CCL-28, GPR15, PNOC, NUSAP1, and their interacted miRNAs may be a potential signature for prognosis of CRC patients. In sum, we successfully established miRNA-mRNA regulatory networks based on microarray results targeting CRC, and these findings may elucidate the mechanisms used for CRC growth and identify miRNA-related signatures for prognosis and treatment of CRC.

Unique molecular signatures of microRNAs in ocular fluids and plasma in diabetic retinopathy

The main objective of this pilot study was to identify circulatory microRNAs in aqueous or plasma that were reflecting changes in vitreous of diabetic retinopathy patients. Aqueous, vitreous and plasma samples were collected from a total of 27 patients undergoing vitreoretinal surgery: 11 controls (macular pucker or macular hole patients) and 16 with diabetes mellitus(DM): DM-Type I with proliferative diabetic retinopathy(PDR) (DMI-PDR), DM Type II with PDR(DMII-PDR) and DM Type II with nonproliferative DR(DMII-NPDR). MicroRNAs were isolated using Qiagen microRNeasy kit, quantified on BioAnalyzer, and profiled on Affymetrix GeneChip miRNA 3.0 microarrays. Data were analyzed using Expression Console, Transcriptome Analysis Console, and Ingenuity Pathway Analysis. The comparison analysis of circulatory microRNAs showed that out of a total of 847 human microRNA probes on the microarrays, common microRNAs present both in aqueous and vitreous were identified, and a large number of unique microRNA, dependent on the DM type and severity of retinopathy. Most of the dysregulated microRNAs in aqueous and vitreous of DM patients were upregulated, while in plasma, they were downregulated. Dysregulation of miRNAs in aqueous did not appear to be a good representative of the miRNA abundance in vitreous, or plasma, although a few potential candidates for common biomarkers stood out: let-7b, miR-320b, miR-762 and miR-4488. Additionally, each of the DR subtypes showed miRNAs that were uniquely dysregulated in each fluid (i.e. aqueous: for DMII-NPDR was miR-455-3p; for DMII-PDR was miR-296, and for DMI-PDR it was miR-3202). Pathway analysis identified TGF-beta and VEGF pathways affected. The comparative profiling of circulatory miRNAs showed that a small number of them displayed differential presence in diabetic retinopathy vs. controls. A pattern is emerging of unique molecular microRNA signatures in bodily fluids of DR subtypes, offering promise for the use of ocular fluids and plasma for diagnostic and therapeutic purposes.

Harmonization of quality metrics and power calculation in multi-omic studies

Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.

Early-onset preeclampsia, plasma microRNAs, and endothelial cell function

BACKGROUND

Preeclampsia is a hypertensive pregnancy disorder in which generalized systemic inflammation and maternal endothelial dysfunction are involved in the pathophysiology. MiRNAs are small noncoding RNAs responsible for post-transcriptional regulation of gene expression and involved in many physiological processes. They mainly downregulate translation of their target genes.

OBJECTIVE

We aimed to compare the plasma miRNA concentrations in preeclampsia, healthy pregnant women, and nonpregnant women. Furthermore, we aimed to evaluate the effect of 3 highly increased plasma miRNAs in preeclampsia on endothelial cell function in vitro.

STUDY DESIGN

We compared 3391 (precursor) miRNA concentrations in plasma samples from early-onset preeclamptic women, gestational age-matched healthy pregnant women, and nonpregnant women using miRNA 3.1. arrays (Affymetrix) and validated our findings by real-time quantitative polymerase chain reaction. Subsequently, endothelial cells (human umbilical vein endothelial cells) were transfected with microRNA mimics (we choose the 3 miRNAs with the greatest fold change and lowest false-discovery rate in preeclampsia vs healthy pregnancy). After transfection, functional assays were performed to evaluate whether overexpression of the microRNAs in endothelial cells affected endothelial cell function in vitro. Functional assays were the wound-healing assay (which measures cell migration and proliferation), the proliferation assay, and the tube-formation assay (which assesses formation of endothelial cell tubes during the angiogenic process). To determine whether the miRNAs are able to decrease gene expression of certain genes, RNA was isolated from transfected endothelial cells and gene expression (by measuring RNA expression) was evaluated by gene expression microarray (Genechip Human Gene 2.1 ST arrays; Life Technologies). For the microarray, we used pooled samples, but the differently expressed genes in the microarray were validated by real-time quantitative polymerase chain reaction in individual samples.

RESULTS

No significant differences (fold change <-1.2 or >1.2 with a false-discovery rate <0.05) were found in miRNA plasma concentrations between healthy pregnant and nonpregnant women. The plasma concentrations of 26 (precursor) miRNAs were different between preeclampsia and healthy pregnancy. The 3 miRNAs that were increased with the greatest fold change and lowest false-discovery rate in preeclampsia vs healthy pregnancy were miR-574-5p, miR-1972, and miR-4793-3p. Transfection of endothelial cells with these miRNAs in showed that miR-574-5p decreased (P<.05) the wound-healing capacity (ie, decreased endothelial cell migration and/or proliferation) and tended (P<.1) to decrease proliferation, miR-1972 decreased tube formation (P<.05), and also tended (P<.1) to decrease proliferation, and miR-4793-3p tended (P<.1) to decrease both the wound-healing capacity and tube formation in vitro. Gene expression analysis of transfected endothelial cells revealed that miR-574-5p tended (P<.1) to decrease the expression of the proliferation marker MKI67.

CONCLUSION

We conclude that in the early-onset preeclampsia group in our study different concentrations of plasma miRNAs are present as compared with healthy pregnancy. Our results suggest that miR-574-5p and miR-1972 decrease the proliferation (probably via decreasing MKI67) and/or migration as well as the tube-formation capacity of endothelial cells. Therefore, these miRNAs may be antiangiogenic factors affecting endothelial cells in preeclampsia.

A Non-Coding RNA Landscape of Bronchial Epitheliums of Lung Cancer Patients

We propose to systematically identify a non-coding RNA (ncRNA) profile of exfoliated bronchial epitheliums of sputum from lung cancer patients. Bronchial epithelial cells enriched from sputum of 32 lung cancer patients and 33 cancer-free smokers were analyzed by next-generation sequencing to comprehensively characterize the ncRNA profiles. In addition, 108 miRNAs, 88 small nucleolar RNAs, 13 piwi-interacting RNAs, 6 transfer RNAs, 4 ribosomal RNAs, 19 small nuclear RNAs, and 25 long-noncoding (lnc) RNAs displayed a significantly different level in bronchial epitheliums of sputum of lung cancer patients versus cancer-free smokers (all <0.001). PCR analysis confirmed their different expression levels in the sputum specimens. A high expression of SNHG9, an lncRNA, was validated in 78 lung tumor tissues, and the expression was inversely associated with overall survival of lung cancer patients (p = 0.002). Knockdown of SNHG9 in cancer cells reduced the cell growth, proliferation, and invasion in vitro and tumorigenesis in vivo. The multiple differentially expressed ncRNAs in bronchial epitheliums may contribute to the development and progression of lung cancer and provide potential biomarkers and therapeutic targets for the disease.

Circulating microRNA Profiles in Acetaminophen Toxicity

INTRODUCTION

Acetaminophen toxicity has been associated with elevation of microRNAs. The present study was to evaluate overall microRNA profiles and previously identified microRNAs to differentiate acetaminophen (APAP) toxicity from other causes of transaminase elevation.

METHODS

This was an observational study of adults with presumed acetaminophen toxicity at presentation. Serum samples were collected every 12 hours during hospitalization. Total miRNAs were extracted from plasma and levels of 327 microRNAs were quantified using real-time polymerase chain reaction. A standard measure of miRNA expression (delta-delta cycle threshold) was calculated for each microRNAs. A two-level cluster analysis was performed using a random k-means algorithm. Demographic and clinical characteristics of each cluster were compared using ANOVA, Wilcoxon rank sum, Kruskal-Wallis, and chi-square tests. Performance of specific miRNAs of interest was also evaluated.

RESULTS

Twenty-seven subjects were enrolled (21 with a final diagnosis of acetaminophen toxicity), and a total of 61 samples were analyzed. Five clusters were identified, two of which demonstrated clear clinical patterns and included specific elevated miRNAs previously reported to be elevated in APAP toxicity patients. Features associated with clusters 1 and 5 included confirmed acetaminophen toxicity, high peak alanine aminotransferase, and late presentation. Clusters 2-4 contained lower peak microRNAs, lower peak alanine aminotransferase, and heterogeneous clinical characteristics.

CONCLUSIONS

Severe cases of acetaminophen toxicity showed two distinct patterns of microRNA elevation which were similar to previous work, while less severe cases were difficult to distinguish from non-acetaminophen-associated cases. Further work is needed to incorporate microRNA profiles into the diagnostic algorithm of acetaminophen toxicity.

Mycobacterium tuberculosis-Induced Bronchoalveolar Lavage Gene Expression Signature in Latent Tuberculosis Infection Is Dominated by Pleiotropic Effects of CD4+ T Cell-Dependent IFN-γ Production despite the Presence of Polyfunctional T Cells within the Airways

Tuberculosis (TB) remains a worldwide public health threat. Development of a more effective vaccination strategy to prevent pulmonary TB, the most common and contagious form of the disease, is a research priority for international TB control. A key to reaching this goal is improved understanding of the mechanisms of local immunity to Mycobacterium tuberculosis, the causative organism of TB. In this study, we evaluated global M. tuberculosis-induced gene expression in airway immune cells obtained by bronchoalveolar lavage (BAL) of individuals with latent TB infection (LTBI) and M. tuberculosis-naive controls. In prior studies, we demonstrated that BAL cells from LTBI individuals display substantial enrichment for M. tuberculosis-responsive CD4⁺ T cells compared with matched peripheral blood samples. We therefore specifically assessed the impact of the depletion of CD4⁺ and CD8⁺ T cells on M. tuberculosis-induced BAL cell gene expression in LTBI. Our studies identified 12 canonical pathways and a 47-gene signature that was both sensitive and specific for the contribution of CD4⁺ T cells to local recall responses to M. tuberculosis In contrast, depletion of CD8⁺ cells did not identify any genes that fit our strict criteria for inclusion in this signature. Although BAL CD4⁺ T cells in LTBI displayed polyfunctionality, the observed gene signature predominantly reflected the impact of IFN-γ production on a wide range of host immune responses. These findings provide a standard for comparison of the efficacy of standard bacillus Calmette-Guérin vaccination as well as novel TB vaccines now in development at impacting the initial response to re-exposure to M. tuberculosis in the human lung.

Tear Film Proteomics Reveal Important Differences Between Patients With and Without Ocular GvHD After Allogeneic Hematopoietic Cell Transplantation

Purpose

To date, no biomarkers for ocular graft versus host disease (GvHD), a frequent complication following allogeneic hematopoietic cell transplantation (HCT), exist. In this prospective study, we evaluated the potential of human tear proteins as biomarkers for ocular GvHD.

Methods

Tears from 10 patients with moderate-to-severe ocular GvHD were compared to 10 patients without ocular GvHD. After a full ocular surface clinical examination, tears were collected onto Schirmer strips and protein composition was analyzed by liquid chromatography tandem mass spectrometry. Statistical evaluation was performed using the Mann-Whitney U test to compare means and the false discovery rate method to adjust for multiple comparisons. Functional annotation of differentially expressed proteins was done with the PANTHER classification system.

Results

We identified 282 proteins in tryptic digests of Schirmer strips; 79 proteins were significantly differentially expressed between the two groups, from which 54 were up- and 25 downregulated. The most upregulated proteins were classified as nucleic acid binding and cytoskeletal proteins, while the most extensively downregulated proteins belong to an array of classes including transfer and receptor proteins, enzyme modulators, and hydrolases. In addition to proteins already confirmed as differentially expressed in dry eye disease, we report changes in 36 novel proteins.

Conclusions

This study reports the proteomic profile of tears in ocular GvHD for the first time and identifies a number of unique differentially expressed proteins. Further studies with a higher number of participants are necessary to confirm these results and to evaluate the reliability of these expression patterns in longitudinal studies.

Dynamic molecular changes during the first week of human life follow a robust developmental trajectory

Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.

The first week of life impacts health for all of life, but the mechanisms are little-understood. Here the authors extract multi-omic data from small volumes of blood to study the dynamic molecular changes during the first week of life, revealing a robust developmental trajectory common to different populations.

Feasibility of sample size calculation for RNA-seq studies

Sample size calculation is a crucial step in study design but is not yet fully established for RNA sequencing (RNA-seq) analyses. To evaluate feasibility and provide guidance, we evaluated RNA-seq sample size tools identified from a systematic search. The focus was on whether real pilot data would be needed for reliable results and on identifying tools that would perform well in scenarios with different levels of biological heterogeneity and fold changes (FCs) between conditions. We used simulations based on real data for tool evaluation. In all settings, the six evaluated tools provided widely different answers, which were strongly affected by FC. Although all tools failed for small FCs, some tools can at least be recommended when closely matching pilot data are available and relatively large FCs are anticipated.

The Feasibility of Examining the Effects of Gastric Bypass Surgery on Intestinal Metabolism: Prospective, Longitudinal Mechanistic Clinical Trial

Background

Bariatric surgery, especially Roux-en-Y gastric bypass (RYGB), is the best treatment for severe obesity and its complications including type 2 diabetes mellitus (T2DM). Understanding the mechanisms responsible for the beneficial metabolic effects will help to engineer ways to improve the procedure or produce these effects without surgery.

Objective

The aim is to present data on recruitment and feasibility of a translational study designed to collect intestinal samples before and after bariatric surgery. The goal of biobanking is to allow future studies to test the hypothesis that the mechanism of action of RYGB involves specific changes in the postsurgical short- and long-term metabolism and morphology of the jejunum (Roux limb). Specifically, to test whether the intestine enhances its metabolism and activity after RYGB and increases its fuel utilization, we designed a prospective, longitudinal study, which involved the recruitment of candidates for RYGB with and without T2DM. We describe the tissue bank that we have generated, and our experience, hoping to further facilitate the performance of longitudinal mechanistic studies in human patients undergoing bariatric surgery and especially those involving post-RYGB intestinal biology.

Methods

We conducted a trial to characterize the effects of RYGB on intestinal metabolism. Intestinal tissue samples were collected from the jejunum at surgery, 1, 6, and 12 months postoperatively for the analysis of intestinal gene expression and metabolomic and morphologic changes. The target number of patients who completed at least the 6-month follow-up was 26, and we included a 20% attrition rate, increasing the total number to 32.

Results

To enroll 26 patients, we had to approach 79 potential participants. A total of 37 agreed to participate and started the study; 33, 30, and 26 active participants completed their 1-month, 6-month, and 12-month studies, respectively. Three participants withdrew, and 30 participants are still active. Altruism and interest in research were the most common reasons for participation. Important factors for feasibility and successful retention included (1) large volume case flow, (2) inclusion and exclusion criteria broad enough to capture a large segment of the patient population but narrow enough to ensure the completion of study aims and protection of safety concerns, (3) accurate assessment of willingness and motivation to participate in a study, (4) seamless integration of the recruitment process into normal clinical flow, (5) financial reimbursement and nonfinancial rewards and gestures of appreciation, and (6) nonburdensome follow-up visits and measures and reasonable time allotted.

Conclusions

Human translational studies of the intestinal mechanisms of metabolic and weight changes after bariatric surgery are important and feasible. A tissue bank with unique samples has been established that could be used by investigators in many research fields, further enabling mechanistic studies on the effects of bariatric surgery.

Trial Registration

ClinicalTrials.gov NCT02710370; https://clinicaltrials.gov/ct2/show/NCT02710370 (Archived by WebCite at http://www.webcitation.org/75HrQT8Dl)

Gene expression changes related to immune processes associate with cognitive endophenotypes of schizophrenia

Schizophrenia is a heterogeneous disorder characterized by a spectrum of symptoms and many different underlying causes. Thus, instead of using the broad diagnosis, intermediate phenotypes can be used to possibly decrease the underlying complexity of the disorder. Alongside the classical symptoms of delusions and hallucinations, cognitive deficits are a core feature of schizophrenia. To increase our understanding of the biological processes related to these cognitive deficits, we performed a genome-wide gene expression analysis. A battery of 14 neuropsychological tests was administered to 844 individuals from a Finnish familial schizophrenia cohort. We grouped the applied neuropsychological tests into five factors for further analysis. Cognitive endophenotypes, whole blood mRNA, genotype, and medication use data were studied from 47 individuals. Expression level of several RNA probes were significantly associated with cognitive performance. The factor representing Verbal Working Memory was associated with altered expression levels of 11 probes, of which one probe was also associated with a specific sub-measure of this factor (WMS-R Digit span backward). While, the factor Processing speed was related to one probe, which additionally associated among 55 probes with a specific sub-measure of this factor (WAIS-R Digit symbol). Two probes were associated with the measure recognition memory performance. Enrichment analysis of these differentially expressed probes highlighted immunological processes. Our findings are in line with genome-wide genetic discoveries made in schizophrenia, suggesting that immunological processes may be of biological interest for future drug design towards schizophrenia and the cognitive dysfunctions that underlie it.

RNASeqDesign: A framework for RNA-Seq genome-wide power calculation and study design issues

Massively parallel sequencing (a.k.a. next-generation sequencing, NGS) technology has emerged as a powerful tool in characterizing genomic profiles. Among many NGS applications, RNA sequencing (RNA-Seq) has gradually become a standard tool for global transcriptomic monitoring. Although the cost of NGS experiments has dropped constantly, the high sequencing cost and bioinformatic complexity are still obstacles for many biomedical projects. Unlike earlier fluorescence-based technologies such as microarray, modelling of NGS data should consider discrete count data. In addition to sample size, sequencing depth also directly relates to the experimental cost. Consequently, given total budget and pre-specified unit experimental cost, the study design issue in RNA-Seq is conceptually a more complex multi-dimensional constrained optimization problem rather than one-dimensional sample size calculation in traditional hypothesis setting. In this paper, we propose a statistical framework, namely "RNASeqDesign", to utilize pilot data for power calculation and study design of RNA-Seq experiments. The approach is based on mixture model fitting of p-value distribution from pilot data and a parametric bootstrap procedure based on approximated Wald test statistics to infer genome-wide power for optimal sample size and sequencing depth. We further illustrate five practical study design tasks for practitioners. We perform simulations and three real applications to evaluate the performance and compare to existing methods.

Epigenetic meta-analysis across three civilian cohorts identifies NRG1 and HGS as blood-based biomarkers for post-traumatic stress disorder

AIM

Trauma exposure is a necessary, but not deterministic, contributor to post-traumatic stress disorder (PTSD). Epigenetic factors may distinguish between trauma-exposed individuals with versus without PTSD.

MATERIALS & METHODS

We conducted a meta-analysis of PTSD epigenome-wide association studies in trauma-exposed cohorts drawn from civilian contexts. Whole blood-derived DNA methylation levels were analyzed in 545 study participants, drawn from the three civilian cohorts participating in the PTSD working group of the Psychiatric Genomics Consortium.

RESULTS

Two CpG sites significantly associated with current PTSD in NRG1 (cg23637605) and in HGS (cg19577098).

CONCLUSION

PTSD is associated with differential methylation, measured in blood, within HGS and NRG1 across three civilian cohorts.

Sigmoid colon mucosal gene expression supports alterations of neuronal signaling in irritable bowel syndrome with constipation

Peripheral factors likely play a role in at least a subset of irritable bowel syndrome (IBS) patients. Few studies have investigated mucosal gene expression using an unbiased approach. Here, we performed mucosal gene profiling in a sex-balanced sample to identify relevant signaling pathways and gene networks and compare with publicly available profiling data from additional cohorts. Twenty Rome III+ IBS patients [10 IBS with constipation (IBS-C), 10 IBS with diarrhea (IBS-D), 5 men/women each), and 10 age-/sex-matched healthy controls (HCs)] underwent sigmoidoscopy with biopsy for gene microarray analysis, including differential expression, weighted gene coexpression network analysis (WGCNA), gene set enrichment analysis, and comparison with publicly available data. Expression levels of 67 genes were validated in an expanded cohort, including the above samples and 18 additional participants (6 each of IBS-C, IBS-D, HCs) using NanoString nCounter technology. There were 1,270 differentially expressed genes (FDR < 0.05) in IBS-C vs. HCs but none in IBS or IBS-D vs. HCs. WGNCA analysis identified activation of the cAMP/protein kinase A signaling pathway. Nine of 67 genes were validated by the NanoString nCounter technology (FDR < 0.05) in the expanded sample. Comparison with publicly available microarray data from the Mayo Clinic and University of Nottingham supports the reproducibility of 17 genes from the microarray analysis and three of nine genes validated by nCounter in IBS-C vs. HCs. This study supports the involvement of peripheral mechanisms in IBS-C, particularly pathways mediating neuronal signaling. NEW & NOTEWORTHY Peripheral factors play a role in the pathophysiology of irritable bowel syndrome (IBS), which, to date, has been mostly evident in IBS with diarrhea. Here, we show that sigmoid colon mucosal gene expression profiles differentiate IBS with constipation from healthy controls. These profiling data and analysis of additional cohorts also support the concept that peripheral neuronal pathways contribute to IBS pathophysiology.

The NDE1 genomic locus can affect treatment of psychiatric illness through gene expression changes related to microRNA-484

Genetic studies of familial schizophrenia in Finland have observed significant associations with a group of biologically related genes, DISC1, NDE1, NDEL1, PDE4B and PDE4D, the ‘DISC1 network’. Here, we use gene expression and psychoactive medication use data to study their biological consequences and potential treatment implications. Gene expression levels were determined in 64 individuals from 18 families, while prescription medication information has been collected over a 10-year period for 931 affected individuals. We demonstrate that the NDE1 SNP rs2242549 associates with significant changes in gene expression for 2908 probes (2542 genes), of which 794 probes (719 genes) were replicable. A significant number of the genes altered were predicted targets of microRNA-484 (p = 3.0 × 10⁻⁸), located on a non-coding exon of NDE1. Variants within the NDE1 locus also displayed significant genotype by gender interaction to early cessation of psychoactive medications metabolized by CYP2C19. Furthermore, we demonstrate that miR-484 can affect the expression of CYP2C19 in a cell culture system. Thus, variation at the NDE1 locus may alter risk of mental illness, in part through modification of miR-484, and such modification alters treatment response to specific psychoactive medications, leading to the potential for use of this locus in targeting treatment.

Study Design and qPCR Data Analysis Guidelines for Reliable Circulating miRNA Biomarker Experiments: A Review

BACKGROUND

In the past decade, the search for circulating microRNA (miRNA) biomarkers has yielded numerous associations between miRNAs and different types of disease. However, many of these relations could not be replicated in subsequent studies under similar experimental conditions. Although this lack of replicability may be explained by the variation in experimental design and analysis methods, guidelines on the most appropriate design and analysis methods to study circulating miRNAs are scarce.

CONTENT

miRNA biomarker experiments generally consist of a discovery phase and a validation phase. In the discovery phase, typically hundreds of miRNAs are measured in parallel to identify candidate biomarkers. Because of the costs of such high-throughput experiments, the number of individuals included in those studies is often too small, which can easily lead to false positives and false negatives. In the validation phase, a small number of identified biomarker candidates are measured in a large cohort of cases and controls, generally by quantitative PCR (qPCR). Although qPCR is a sensitive method to measure miRNAs in the circulation, experimental design and qPCR data analysis remain challenging. Omitting some crucial steps in the design and analysis of the qPCR experiment or performing them incorrectly can cause serious biases, ultimately leading to false conclusions.

SUMMARY

In this review, we aim to expose and discuss the most common sources of interstudy variation in miRNA research from a methodological point of view and to provide guidelines on how to perform these steps correctly to increase replicability of studies on circulating miRNAs.

Protein Multiplexed Immunoassay Analysis with R

Plasma samples from 177 control and type 2 diabetes patients collected at three Australian hospitals are screened for 14 analytes using six custom-made multiplex kits across 60 96-well plates. In total 354 samples were collected from the patients, representing one baseline and one end point sample from each patient. R methods and source code for analyzing the analyte fluorescence response obtained from these samples by Luminex Bio-Plex^® xMap multiplexed immunoassay technology are disclosed. Techniques and R procedures for reading Bio-Plex^® result files for statistical analysis and data visualization are also presented. The need for technical replicates and the number of technical replicates are addressed as well as plate layout design strategies. Multinomial regression is used to determine plate to sample covariate balance. Methods for matching clinical covariate information to Bio-Plex^® results and vice versa are given. As well as methods for measuring and inspecting the quality of the fluorescence responses are presented. Both fixed and mixed-effect approaches for immunoassay statistical differential analysis are presented and discussed. A random effect approach to outlier analysis and detection is also shown. The bioinformatics R methodology present here provides a foundation for rigorous and reproducible analysis of the fluorescence response obtained from multiplexed immunoassays.

Small sample sizes in high-throughput miRNA screens: A common pitfall for the identification of miRNA biomarkers

Since the discovery of microRNAs (miRNAs), circulating miRNAs have been proposed as biomarkers for disease. Consequently, many groups have tried to identify circulating miRNA biomarkers for various types of diseases including cardiovascular disease and cancer. However, the replicability of these experiments has been disappointingly low. In order to identify circulating miRNA candidate biomarkers, in general, first an unbiased high-throughput screen is performed in which a large number of miRNAs is detected and quantified in the circulation. Because these are costly experiments, many of such studies have been performed using a low number of study subjects (small sample size). Due to lack of power in small sample size experiments, true effects are often missed and many of the detected effects are wrong. Therefore, it is important to have a good estimate of the appropriate sample size for a miRNA high-throughput screen. In this review, we discuss the effects of small sample sizes in high-throughput screens for circulating miRNAs. Using data from a miRNA high-throughput experiment on isolated monocytes, we illustrate that the implementation of power calculations in a high-throughput miRNA discovery experiment will avoid unnecessarily large and expensive experiments, while still having enough power to be able to detect clinically important differences.

Advancing cancer drug development through precision medicine and innovative designs

Precision medicine has been a hot topic in drug development over the last decade. Biomarkers have been proven useful for understanding the disease progression and treatment response in precision medicine development. Advancement of high-throughput omics technologies has enabled fast identification of molecular biomarkers with low cost. Although biomarkers have brought many promises to drug development, steep challenges arise due to a large amount of data, complexity of technology, and lack of full understanding of biology. In this article, we discuss the technologies and statistical issues that are related to omics biomarker discovery. We also provide an overview of the current development of biomarker-enabled cancer clinical trial designs.

Epigenome-wide association of PTSD from heterogeneous cohorts with a common multi-site analysis pipeline

Compelling evidence suggests that epigenetic mechanisms such as DNA methylation play a role in stress regulation and in the etiologic basis of stress related disorders such as Post traumatic Stress Disorder (PTSD). Here we describe the purpose and methods of an international consortium that was developed to study the role of epigenetics in PTSD. Inspired by the approach used in the Psychiatric Genomics Consortium, we brought together investigators representing seven cohorts with a collective sample size of N = 1147 that included detailed information on trauma exposure, PTSD symptoms, and genome-wide DNA methylation data. The objective of this consortium is to increase the analytical sample size by pooling data and combining expertise so that DNA methylation patterns associated with PTSD can be identified. Several quality control and analytical pipelines were evaluated for their control of genomic inflation and technical artifacts with a joint analysis procedure established to derive comparable data over the cohorts for meta-analysis. We propose methods to deal with ancestry population stratification and type I error inflation and discuss the advantages and disadvantages of applying robust error estimates. To evaluate our pipeline, we report results from an epigenome-wide association study (EWAS) of age, which is a well-characterized phenotype with known epigenetic associations. Overall, while EWAS are highly complex and subject to similar challenges as genome-wide association studies (GWAS), we demonstrate that an epigenetic meta-analysis with a relatively modest sample size can be well-powered to identify epigenetic associations. Our pipeline can be used as a framework for consortium efforts for EWAS.

Designing Successful Proteomics Experiments

Because proteomics experiments are so complex they can readily fail, and do so without clear cause. Using standard experimental design techniques and incorporating quality control can greatly increase the chances of success. This chapter introduces the relevant concepts and provides examples specific to proteomic workflows. Applying these notions to design successful proteomics experiments is straightforward. It can help identify failure causes and greatly increase the likelihood of inter-laboratory reproducibility.

Transcriptomics, proteomics and metabolomics driven biomarker discovery in COPD: an update

INTRODUCTION

Diagnosis of chronic obstructive pulmonary disease (COPD), characterized by progressive irreversible airflow limitation, remains a challenge. Lack of sensitive diagnostic markers and alternative treatments have limited patients' survival rate. Herein, we provide for clinicians and scientists a comprehensive review on the various omics platforms used to investigate COPD.

AREAS COVERED

This review consists of articles from PubMed (2009-2016) as well as views of the contributing authors. The review highlights the need for COPD biomarker identification and also provides an update on promising candidate markers identified in various biological fluids using omics technologies. Expert commentary: The multi-omics approach holds promise for the development of robust early stage COPD diagnostic markers, screening of high-risk population, and also improved prognosis which could lead to personalized medicine in future. Various factors regulating an omics study including sample size, control selection, disease phenotyping, usage of complementary techniques and result replication in omics-based research are outlined.

Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments

Background

RNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In addition, false discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis. So far, there are very few proposals on sample size calculation for RNA-seq experiments.

Results

In this paper, we propose a procedure for sample size calculation while controlling FDR for RNA-seq experimental design. Our procedure is based on the weighted linear model analysis facilitated by the voom method which has been shown to have competitive performance in terms of power and FDR control for RNA-seq differential expression analysis. We derive a method that approximates the average power across the differentially expressed genes, and then calculate the sample size to achieve a desired average power while controlling FDR. Simulation results demonstrate that the actual power of several popularly applied tests for differential expression is achieved and is close to the desired power for RNA-seq data with sample size calculated based on our method.

Conclusions

Our proposed method provides an efficient algorithm to calculate sample size while controlling FDR for RNA-seq experimental design. We also provide an R package ssizeRNA that implements our proposed method and can be downloaded from the Comprehensive R Archive Network (http://cran.r-project.org).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0994-9) contains supplementary material, which is available to authorized users.

Endometrial transcriptome analysis indicates superiority of natural over artificial cycles in recurrent implantation failure patients undergoing frozen embryo transfer

Little consensus has been reached on the best protocol for endometrial preparation for frozen embryo transfer (FET). It is not known how, and to what extent, hormone supplementation in artificial cycles influences endometrial preparation for embryo implantation at a molecular level, especially in patients who have experienced recurrent implantation failure. Transcriptome analysis of 15 endometrial biopsy samples at the time of embryo implantation was used to compare two different endometrial preparation protocols, natural versus artificial cycles, for FET in women who have experienced recurrent implantation failure compared with fertile women. IPA and DAVID were used for functional analyses of differentially expressed genes. The TRANSFAC database was used to identify oestrogen and progesterone response elements upstream of differentially expressed genes. Cluster analysis demonstrated that natural cycles are associated with a better endometrial receptivity transcriptome than artificial cycles. Artificial cycles seemed to have a stronger negative effect on expression of genes and pathways crucial for endometrial receptivity, including ESR2, FSHR, LEP, and several interleukins and matrix metalloproteinases. Significant overrepresentation of oestrogen response elements among the genes with deteriorated expression in artificial cycles (P < 0.001) was found; progesterone response elements predominated in genes with amended expression with artificial cycles (P = 0.0052).

Altered expression of mRNA profiles in blood of early-onset schizophrenia

To identify gene expression abnormalities in schizophrenia (SZ), we generated whole-genome gene expression profiles using microarrays on peripheral blood mononuclear cells (PBMCs) from 18 early-onset SZ cases and 12 controls. We detected 84 transcripts differentially expressed by diagnostic status, with 82 genes being upregulated and 2 downregulated. We identified two SZ associated gene coexpression modules (green and red), including 446 genes . The green module is positively correlated with SZ, encompassing predominantly up-regulated genes in SZ; while the red module was negatively correlated with disease status, involving mostly nominally down-regulated genes in SZ. The olfactory transduction pathway was the most enriched pathways for the genes within the two modules. The expression levels of several hub genes, including AKT1, BRCA1, CCDC134, UBD, and ZIC2 were validated using real-time quantitative PCR. Our findings indicate that mRNA coexpression abnormalities may serve as a promising mechanism underlying the development of SZ.

CCL20 and IL22 Messenger RNA Expression After Adalimumab vs Methotrexate Treatment of Psoriasis: A Randomized Clinical Trial

IMPORTANCE

Methotrexate is a first-line systemic agent for treating of psoriasis, although its onset of effects is slower and overall it is less effective than tumor necrosis factor blockers.

OBJECTIVE

To differentiate the response of psoriatic disease to adalimumab and methotrexate sodium.

DESIGN, SETTING, AND PARTICIPANTS

Single-center, randomized, assessor-blind, 2-arm clinical trial of 30 patients from the outpatient dermatology center of Tufts Medical Center, enrolled from August 18, 2009, to October 11, 2011. Patients aged 18 to 85 years with chronic plaque-type psoriasis, a minimum Physician Global Assessment score of 3 (higher scores indicate more severe disease), and a psoriatic plaque of at least 2 cm were randomized in a 1:1 fashion to receive subcutaneous adalimumab or oral methotrexate. Skin biopsy specimens obtained at baseline and weeks 1, 2, 4, and 16 were given a histologic grade by blinded assessors to evaluate treatment response. Analyses were conducted from April 16, 2013, to January 5, 2015.

INTERVENTIONS

A 16-week course of subcutaneous adalimumab (40 mg every 2 weeks after a loading dose) or low-dosage oral methotrexate sodium (7.5-25 mg/wk).

MAIN OUTCOMES AND MEASURES

Changes in genomic, immunohistochemical, and messenger RNA (mRNA) profiles.

RESULTS

Methotrexate responders experienced significant downregulation of helper T-cell-related (T(H)1, T(H)17, and T(H)22) mRNA expression compared with methotrexate nonresponders. Comparisons among adalimumab-treated patients were limited by the number of nonresponders (n = 1). Between adalimumab and methotrexate responders, we found no significant differences in gene expression at any study point or in the expression of T-cell-related mRNA at week 16. Adalimumab responders demonstrated early downregulation of chemokine (C-C motif) ligand 20 (CCL20) mRNA (mean [SE] at week 2, -1.83 [0.52], P < .001; week 16, -3.55 [0.54], P < .001) compared with late downregulation for methotrexate responders (week 2, 0.02 [0.51], P = .96; week 16, -2.96 [0.51], P < .001). Similar differences were observed with interleukin 22 (IL22) mRNA showing early downregulation for adalimumab responders (week 2, -3.17 [1.00], P < .001; week 16, -3.58 [1.00], P < .001) compared with late downregulation for methotrexate responders (week 2, -0.44 [0.68], P = .64; week 16, -5.14 [0.68], P < .001). Analysis of variance findings for key mRNA and immunohistochemical marker expression over the study course were significant only for CCL20 (P = .03) and IL22 (P = .006) mRNA comparing adalimumab and methotrexate responders.

CONCLUSIONS AND RELEVANCE

Methotrexate is an immunomodulator with effects on helper T-cell signaling in psoriasis. Similar genomic and immunohistochemical response signatures and levels of mRNA downregulation at study completion among adalimumab and methotrexate responders suggest a disease-driven instead of therapeutic-driven pathway regulation. Adalimumab and methotrexate responses are differentiated by patterns of normalization of CCL20 and IL22 mRNA expression and may explain the varied onset and degree of clinical responses by each treatment.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT00932113.