The functional false discovery rate with applications to genomics

Polygenic Risk and Nutrient Intake Interactions on Obesity Outcomes: A Systematic Review and Meta-Analysis of Observational Studies

BACKGROUND

Diet is an important determinant of body weight and may modulate genetic susceptibility to obesity.

OBJECTIVE

This systematic review and meta-analysis aimed to synthesize evidence related to interactions between polygenic risk and nutrient intakes on obesity outcomes.

METHODS

MEDLINE, EMBASE, Web of Science, and Cochrane Library were systematically searched to identify observational studies that assessed interactions between polygenic risk and nutrient intakes on obesity-related outcomes. Random effects meta-analyses were performed for pooled polygenic risk score (PRS)-total fat intake and PRS-protein intake interaction coefficients on body mass index (BMI).

RESULTS

Twenty-six publications were retrieved with studies conducted among European, Asian, and African samples. Dietary fats (saturated fat, omega-3, and trans fat) and energy intake were most frequently reported to interact with PRS on obesity outcomes, but the total number of studies available was low. No significant interactions were identified in meta-analyses of PRS interactions with total fat intake and protein intake on BMI. Several studies were rated as low quality, heterogeneity was high, and although study samples were racially diverse, PRSs tended to be based on samples of European ancestry.

CONCLUSION

Evidence of interactions between polygenic risk and nutrient intakes on obesity outcomes is limited and inconsistent. Further research addressing limitations related to study quality and polygenic risk characterization is needed.

Deciphering the Regulatory Potential of Antioxidant and Electron-Shuttling Bioactive Compounds in Oolong Tea

OT has gained attention for its high polyphenol content and therapeutic potential. To elucidate this further, this study investigated the electron-shuttling bioactive compounds of OT and evaluated their effect on dysregulated breast cancer (BC) genes. OT extracts were obtained via solvent extraction (SE) and supercritical fluid extraction (SFE), followed by in vitro assays. Phytochemical analysis revealed that ethanol-extracted OT (OTL-E) had the highest polyphenol, flavonoid, and tannin contents, correlating with strong antioxidant activity, while water-extracted OT (OTL-W) exhibited greater bioelectricity-stimulating properties in microbial fuel cells (MFC), confirmed by cyclic voltammetry (CV). Based on phytochemical analyses, SE displayed a better extraction technique for isolating OT bioactive compounds compared to SFE. In silico approaches through network pharmacology, molecular docking and dynamics simulations revealed that polyphenols with ortho- or para-dihydroxyl groups targeted dysregulated BC proteins involved in kinase signaling, apoptosis, and hormone receptor pathways. Luteolin exhibited the highest binding affinities to MAPK1 and PIK3CA with free energy (ΔG) of -9.1 and -8.4 kcal/mol, respectively. Trajectory-based analyses confirmed enthalpy-favored ligand-induced conformational changes to these oncoproteins, altering their function in BC development. These findings suggest the potential of OT as a bioelectricity-stimulating and chemopreventive agent, warranting further in vitro and in vivo validation.

New insights into causal relationship between serum lipids, obesity, and asthma: a Mendelian randomization study

OBJECTIVE

To identify causal risk factors for asthma using a Mendelian randomization (MR) approach.

METHODS

Genetic variants associated with the exposures at the genome-wide significance level (p < 5 × 10 - 8) were obtained from corresponding genome-wide association studies. Summary-level statistical data for asthma were obtained from the UK Biobank (UKB) and the FinnGen Consortia. Univariate and multivariate MR analyses were performed to clarify causal relationships among obesity, serum lipids, and asthma. Meta-analyses were performed to combine UKB and FinnGen results using a fixed-effects model.

RESULTS

In FinnGen, the odds for asthma increased for every 1-SD increase in body mass index (BMI; odds ratio [OR] 1.292, p = 1.34 × 10-7), together with body fat percentage (BF%; OR 1.449, p = 4.90×10-3), and total cholesterol level (OR = 0.949, p = 0.027). However, higher BMI and BF% were found to increase the risk for asthma in the multivariate MR analysis. In the UKB, the BMI results were replicated. Meta-analysis revealed that high-density lipoprotein cholesterol could also increase the risk for asthma, although there were no associations with other risk factors included in this study.

CONCLUSION

This MR study found that genetically predicted higher BF% and BMI could increase the risk for asthma and corroborated some risk factors for asthma from previous MR studies. Moreover, the results suggest that higher BMI and BF% could serve as independent risk factors for asthma.

Plasma levels of amino acids and osteoporosis: a cross-sectional study

The role of amino acids (AAs) with bone health is still controversial. We examined the association between AAs and osteoporosis in a cross-sectional study of 135 participants aged 45 years or older from the Second Hospital of Jilin University. Plasma AAs were measured with targeted quantitative methodology. We measured bone mineral density (BMD) with dual energy x-ray absorptiometry, and osteoporosis was defined as a T-score ≤ -2.5. We estimated odds ratios (OR) and corresponding 95% confidence intervals (CIs) for the associations between AAs (per 1 standard deviation increase) with osteoporosis. Approximately 18.5% of participants (n = 25) had osteoporosis. Total (adjusted β = 0.052; P = 0.002) and non-essential AA (adjusted β = 0.064; P = 0.002) levels were associated with femoral neck BMD T-scores. Greater levels of total (adjusted OR: 0.734; 95% CI: 0.655-0.821), essential (adjusted OR: 0.763; 95% CI: 0.623-0.934) and non-essential AAs (adjusted OR: 0.721; 95% CI: 0.629-0.826) were associated with lower odds of osteoporosis. Higher tryptophan (adjusted OR: 0.498; 95% CI: 0.281-0.882), cysteine (adjusted OR: 0.561; 95% CI: 0.321-0.983), glycine (adjusted OR: 0.513; 95% CI: 0.285-0.922), and ornithine levels (adjusted OR: 0.581; 95% CI: 0.345-0.978) were associated with reduced osteoporosis risk. Higher AA levels were associated with higher femoral neck BMD, and lower odds of osteoporosis.

The Significant Effects of Threshold Selection for Advancing Nitrogen Use Efficiency in Whole Genome of Bread Wheat

Currently in wheat breeding, genome wide association studies (GWAS) have successfully revealed the genetic basis of complex traits such as nitrogen use efficiency (NUE) and its biological processes. In the GWAS model, thresholding is common strategy to indicate deviation of expected range of p-value(s), and it can be used to find the distribution of true positive associations under or over of test statistics. Therefore, the threshold plays a critical role to identify reliable and significant associations in wide genome, while the proportion of false positive results is relatively low. The problem of multiple comparisons arises when a statistical analysis involves multiple simultaneous statistical tests, each of them has the potential to be a discovery. There are several ways to address this problem, including the family-wise error rate and false discovery rate (FDR), raw and adjusted p-value(s), consideration of threshold coherence and consonance, and the properties of proportional hypothesis tests in the threshold definition. We encountered some limitations in the definition of FDR threshold, particularly in the upper bounds of linear and nonlinear approaches. We emphasize that empirical null distributions based on permutation test can be useful when the assumption of linear or parametric FDR approaches do not hold. Nevertheless, we believe that it is necessary to utilize modern statistical optimization techniques to evaluate the stability and performance of our results and to select significant FDR threshold. By incorporating the neural network algorithm, it is possible to improve the reliability of FDR threshold and increase the probability of identifying true genetic associations while minimizing the risk of false positives in GWAS results.

Theoretical Studies of DNA Microarray Present Potential Molecular and Cellular Interconnectivity of Signaling Pathways in Immune System Dysregulation

Autoimmunity is defined as the inability to regulate immunological activities in the body, especially in response to external triggers, leading to the attack of the tissues and organs of the host. Outcomes include the onset of autoimmune diseases whose effects are primarily due to dysregulated immune responses. In past years, there have been cases that show an increased susceptibility to other autoimmune disorders in patients who are already experiencing the same type of disease. Research in this field has started analyzing the potential molecular and cellular causes of this interconnectedness, bearing in mind the possibility of advancing drugs and therapies for the treatment of autoimmunity. With that, this study aimed to determine the correlation of four autoimmune diseases, which are type 1 diabetes (T1D), psoriasis (PSR), systemic sclerosis (SSc), and systemic lupus erythematosus (SLE), by identifying highly preserved co-expressed genes among datasets using WGCNA. Functional annotation was then employed to characterize these sets of genes based on their systemic relationship as a whole to elucidate the biological processes, cellular components, and molecular functions of the pathways they are involved in. Lastly, drug repurposing analysis was performed to screen candidate drugs for repositioning that could regulate the abnormal expression of genes among the diseases. A total of thirteen modules were obtained from the analysis, the majority of which were associated with transcriptional, post-transcriptional, and post-translational modification processes. Also, the evaluation based on KEGG suggested the possible role of TH17 differentiation in the simultaneous onset of the four diseases. Furthermore, clomiphene was the top drug candidate for regulating overexpressed hub genes; meanwhile, prilocaine was the top drug for regulating under-expressed hub genes. This study was geared towards utilizing transcriptomics approaches for the assessment of microarray data, which is different from the use of traditional genomic analyses. Such a research design for investigating correlations among autoimmune diseases may be the first of its kind.

Association between amino acids and recent osteoporotic fracture: a matched incident case-control study

Context

Osteoporotic fracture is a major public health issue globally. Human research on the association between amino acids (AAs) and fracture is still lacking.

Objective

To examine the association between AAs and recent osteoporotic fractures.

Methods

This age and sex matched incident case-control study identified 44 recent x-ray confirmed fracture cases in the Second Hospital of Jilin University and 88 community-based healthy controls aged 50+ years. Plasma AAs were measured by high performance liquid chromatography coupled with mass spectrometry. After adjusting for covariates (i.e., body mass index, milk intake >1 time/week, falls and physical activity), we conducted conditional logistical regression models to test the association between AAs and fracture.

Results

Among cases there were 23 (52.3%) hip fractures and 21 (47.7%) non-hip fractures. Total, essential, and non-essential AAs were significantly lower in cases than in controls. In the multivariable conditional logistic regression models, after adjusting for covariates, each standard deviation increase in the total (odds ratio [OR]: 0.304; 95% confidence interval [CI]: 0.117-0.794), essential (OR: 0.408; 95% CI: 0.181-0.923) and non-essential AAs (OR: 0.290; 95%CI: 0.107-0.782) was negatively associated with recent fracture. These inverse associations were mainly found for hip fracture, rather than non-hip fractures. Among these AAs, lysine, alanine, arginine, glutamine, histidine and piperamide showed the significantly negative associations with fracture.

Conclusion

There was a negative relationship between AAs and recent osteoporotic fracture; such relationship appeared to be more obvious for hip fracture.

High Frequencies of Genetic Variants in Patients with Atypical Femoral Fractures

This study explores the genetic factors associated with atypical femoral fractures (AFF), rare fractures associated with prolonged anti-resorptive therapy. AFF are fragility fractures that typically appear in the subtrochanteric or diaphyseal regions of the femur. While some cases resemble fractures in rare genetic bone disorders, the exact cause remains unclear. This study investigates 457 genes related to skeletal homeostasis in 13 AFF patients by exome sequencing, comparing the results with osteoporotic patients (n = 27) and Iberian samples from the 1000 Genomes Project (n = 107). Only one AFF case carried a pathogenic variant in the gene set, specifically in the ALPL gene. The study then examined variant accumulation in the gene set, revealing significantly more variants in AFF patients than in osteoporotic patients without AFF (p = 3.7 × 10-5), particularly in ACAN, AKAP13, ARHGEF3, P4HB, PITX2, and SUCO genes, all of them related to osteogenesis. This suggests that variant accumulation in bone-related genes may contribute to AFF risk. The polygenic nature of AFF implies that a complex interplay of genetic factors determines the susceptibility to AFF, with ACAN, SUCO, AKAP13, ARHGEF3, PITX2, and P4HB as potential genetic risk factors. Larger studies are needed to confirm the utility of gene set analysis in identifying patients at high risk of AFF during anti-resorptive therapy.

Genetic causality and site-specific relationship between sarcopenia and osteoarthritis: a bidirectional Mendelian randomization study

Background: Previous studies demonstrated a controversial relationship between sarcopenia (SP) and osteoarthritis (OA) and their genetic causality is unclear. Thus, we conducted a Mendelian randomization (MR) analysis to evaluate the possible causal association between sarcopenia-related traits (appendicular lean mass (ALM), grip strength, usual walking pace) and OA. Method: We used pooled genetic data from the UK Biobank for ALM(n = 450,243), left-hand grip strength (n = 461,026), right-hand grip strength (n = 461,089) and usual walking pace (n = 459,915). Moreover, summary statistics for OA were obtained from the latest study conducted by the Genetics of Osteoarthritis Consortium, including all OA (n = 826,690), hand OA (n = 303,7782), hip OA (n = 353,388) and knee OA (n = 396,054). The primary method for estimating causal effects was the inverse-variance weighted (IVW) method, with the utilizing of false discovery rate adjusted p values (P FDR). Additional MR methods such as MR-Egger regression, MR pleiotropy residual sum and outlier (MR-PRESSO), weighted median were employed as supplementary analyses. Results: We discovered ALM (odds ratio (OR) = 1.103, 95% confidence interval (CI) = 1.052-1.156, P FDR = 2.87E-04), hand grip strength (left, IVW OR = 0.823, 95% CI = 0.712 to 0.952, P FDR = 0.020; right, OR = 0.826, 95% CI = 0.718 to 0.950, P FDR = 0.020), and usual walking pace (OR = 0.339, 95% CI = 0.204 to 0.564, P FDR = 2.38E-04) were causally associated with OA risk. In the reverse MR analysis, we identified a causal effect of OA on ALM (β = -0.258, 95% CI = -0.369 to 0.146, P FDR = 0.6.07E-06), grip strength (left, β = -0.064, 95% CI = -0.104 to 0.024, P FDR = 0.002; right, β = -0.055, 95% CI = -0.095 to 0.014, P FDR = 0.008), and usual walking pace (β = -0.104, 95% CI = -0.147 to 0.061, P FDR = 1.61E-05). Conclusion: This present study suggests an obvious causality of SP on OA, with condition exhibiting site-specific effects, while evidence was also provided for the causal effect of OA on SP.

Genetic dissection of root architectural plasticity and identification of candidate loci in response to drought stress in bread wheat

BACKGROUND

The frequency of droughts has dramatically increased over the last 50 years, causing yield declines in cereals, including wheat. Crop varieties with efficient root systems show great potential for plant adaptation to drought stress, however; genetic control of root systems in wheat under field conditions is not yet well understood.

RESULTS

Natural variation in root architecture plasticity (phenotypic alteration due to changing environments) was dissected under field-based control (well-irrigated) and drought (rain-out shelter) conditions by a genome-wide association study using 200 diverse wheat cultivars. Our results revealed root architecture and plasticity traits were differentially responded to drought stress. A total of 25 marker-trait associations (MTAs) underlying natural variations in root architectural plasticity were identified in response to drought stress. They were abundantly distributed on chromosomes 1 A, 1B, 2 A, 2B, 3 A, 3B, 4B, 5 A, 5D, 7 A and 7B of the wheat genome. Gene ontology annotation showed that many candidate genes associated with plasticity were involved in water-transport and water channel activity, cellular response to water deprivation, scavenging reactive oxygen species, root growth and development and hormone-activated signaling pathway-transmembrane transport, indicating their response to drought stress. Further, in silico transcript abundance analysis demonstrated that root plasticity-associated candidate genes were highly expressed in roots across different root growth stages and under drought treatments.

CONCLUSION

Our results suggest that root phenotypic plasticity is highly quantitative, and the corresponding loci are associated with drought stress that may provide novel ways to enable root trait breeding.

A beginner's guide into curated analyses of open access datasets for biomarker discovery in neurodegeneration

The discovery of surrogate biomarkers reflecting neuronal dysfunction in neurodegenerative diseases (NDDs) remains an active area of research. To boost these efforts, we demonstrate the utility of publicly available datasets for probing the pathogenic relevance of candidate markers in NDDs. As a starting point, we introduce the readers to several open access resources, which contain gene expression profiles and proteomics datasets from patient studies in common NDDs, including proteomics analyses of cerebrospinal fluid (CSF). Then, we illustrate the method for curated gene expression analyses across select brain regions from four cohorts of Parkinson disease patients (and from one study in common NDDs), probing glutathione biogenesis, calcium signaling and autophagy. These data are complemented by findings of select markers in CSF-based studies in NDDs. Additionally, we enclose several annotated microarray studies, and summarize reports on CSF proteomics across the NDDs, which the readers can utilize for translational purposes. We anticipate that this "beginner's guide" will benefit the research community in NDDs, and would serve as a useful educational tool.

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

Identification of potential molecular mechanisms and candidate drugs for radiotherapy- and chemotherapy-induced mucositis

BACKGROUND

Radiotherapy-induced oral mucositis (RIOM) and chemotherapy-induced oral mucositis (CIOM) are common complications in cancer patients, leading to negative clinical manifestations, reduced quality of life, and unsatisfactory treatment outcomes.

OBJECTIVE

The present study aimed to identify potential molecular mechanisms and candidate drugs by data mining.

METHODS

We obtained a preliminary list of genes associated with RIOM and CIOM. In-depth information on these genes was explored by functional and enrichment analyses. Then, the drug-gene interaction database was used to determine the interaction of the final enriched gene list with known drugs and analyze the drug candidates.

RESULTS AND CONCLUSION

This study identified 21 hub genes that may play an important role in RIOM and CIOM, respectively. Through our data mining, bioinformatics survey, and candidate drug selection, TNF, IL-6, and TLR9 could play an important role in disease progression and treatment. In addition, eight candidate drugs (olokizumab, chloroquine, hydroxychloroquine, adalimumab, etanercept, golimumab, infliximab, and thalidomide) were selected by the drug-gene interaction literature search additionally, as candidates for treating RIOM and CIOM.

RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals

Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram's (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model's efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques.

Identification of hub genes related to CD4+ memory T cell infiltration with gene co-expression network predicts prognosis and immunotherapy effect in colon adenocarcinoma

Background: CD4+ memory T cells (CD4+ MTCs), as an important part of the microenvironment affecting tumorigenesis and progression, have rarely been systematically analyzed. Our purpose was to comprehensively analyze the effect of CD4+ MTC infiltration on the prognosis of colon adenocarcinoma (COAD). Methods: Based on RNA-Seq data, weighted gene co-expression network analysis (WGCNA) was used to screen the CD4+ MTC infiltration genes most associated with colon cancer and then identify hub genes and construct a prognostic model using the least absolute shrinkage and selection operator algorithm (LASSO). Finally, survival analysis, immune efficacy analysis, and drug sensitivity analysis were performed to evaluate the role of the prognostic model in COAD. Results: We identified 929 differentially expressed genes (DEGs) associated with CD4+ MTCs and constructed a prognosis model based on five hub genes (F2RL2, TGFB2, DTNA, S1PR5, and MPP2) to predict overall survival (OS) in COAD. Kaplan-Meier analysis showed poor prognosis in the high-risk group, and the analysis of the hub gene showed that overexpression of TGFB2, DTNA, S1PR5, or MPP2 was associated with poor prognosis. Clinical prediction nomograms combining CD4+ MTC-related DEGs and clinical features were constructed to accurately predict OS and had high clinical application value. Immune efficacy and drug sensitivity analysis provide new insights for individualized treatment. Conclusion: We constructed a prognostic risk model to predict OS in COAD and analyzed the effects of risk score on immunotherapy efficacy or drug sensitivity. These studies have important clinical significance for individualized targeted therapy and prognosis.

mRNA expression analysis of the hippocampus in a vervet monkey model of fetal alcohol spectrum disorder

Background

Fetal alcohol spectrum disorders (FASD) are common, yet preventable developmental disorders that stem from prenatal exposure to alcohol. This exposure leads to a wide array of behavioural and physical problems with a complex and poorly defined biological basis.

Molecular investigations to date predominantly use rodent animal models, but because of genetic, developmental and social behavioral similarity, primate models are more relevant. We previously reported reduced cortical and hippocampal neuron levels in an Old World monkey (Chlorocebus sabaeus) model with ethanol exposure targeted to the period of rapid synaptogenesis and report here an initial molecular study of this model. The goal of this study was to evaluate mRNA expression of the hippocampus at two different behavioural stages (5 months, 2 years) corresponding to human infancy and early childhood.

Methods

Offspring of alcohol-preferring or control dams drank a maximum of 3.5 g ethanol per kg body weight or calorically matched sucrose solution 4 days per week during the last 2 months of gestation. Total mRNA expression was measured with the Affymetrix GeneChip Rhesus Macaque Genome Array in a 2 × 2 study design that interrogated two independent variables, age at sacrifice, and alcohol consumption during gestation.

Results and discussion

Statistical analysis identified a preferential downregulation of expression when interrogating the factor ‘alcohol’ with a balanced effect of upregulation vs. downregulation for the independent variable ‘age’. Functional exploration of both independent variables shows that the alcohol consumption factor generates broad functional annotation clusters that likely implicate a role for epigenetics in the observed differential expression, while the variable age reliably produced functional annotation clusters predominantly related to development. Furthermore, our data reveals a novel connection between EFNB1 and the FASDs; this is highly plausible both due to the role of EFNB1 in neuronal development as well as its central role in craniofrontal nasal syndrome (CFNS). Fold changes for key genes were subsequently confirmed via qRT-PCR.

Conclusion

Prenatal alcohol exposure leads to global downregulation in mRNA expression. The cellular interference model of EFNB1 provides a potential clue regarding how genetically susceptible individuals may develop the phenotypic triad generally associated with classic fetal alcohol syndrome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-022-09427-z.

Leveraging the Mendelian disorders of the epigenetic machinery to systematically map functional epigenetic variation

Although each Mendelian Disorder of the Epigenetic Machinery (MDEM) has a different causative gene, there are shared disease manifestations. We hypothesize that this phenotypic convergence is a consequence of shared epigenetic alterations. To identify such shared alterations, we interrogate chromatin (ATAC-seq) and expression (RNA-seq) states in B cells from three MDEM mouse models (Kabuki [KS] type 1 and 2 and Rubinstein-Taybi type 1 [RT1] syndromes). We develop a new approach for the overlap analysis and find extensive overlap primarily localized in gene promoters. We show that disruption of chromatin accessibility at promoters often disrupts downstream gene expression, and identify 587 loci and 264 genes with shared disruption across all three MDEMs. Subtle expression alterations of multiple, IgA-relevant genes, collectively contribute to IgA deficiency in KS1 and RT1, but not in KS2. We propose that the joint study of MDEMs offers a principled approach for systematically mapping functional epigenetic variation in mammals.

Upregulated LINC00319 aggravates neuronal injury induced by oxygen-glucose deprivation via modulating miR-200a-3p

Ischemic stroke is one of the main causes of physical disability and mortality worldwide. Long non-coding RNAs (lncRNAs) are reported to be dysregulated in various biological progressions and serve important roles in pathological processes of cerebral ischemia. However, their biological actions and potential mechanisms in the progression of ischemic stroke remain unknown. The present study aimed to investigate the functions of LINC00319 on ischemic brain injury. It was identified that LINC00319 was significantly upregulated in the Gene Expression Omnibus profile of ischemic stroke. Furthermore, LINC00319 overexpression elevated caspase-3 activity and increased the apoptotic rate of neuronal cells, as well as decreased cell viability and glucose uptake. It was also demonstrated that LINC00319 participated in oxygen-glucose deprivation (OGD)-induced cerebral ischemic injury. LINC00319 could competitively bind with microRNA (miR)-200a-3p and decrease its expression. Moreover, miR-200a-3p could partly offset the negative effects of LINC00319 overexpression on neuronal injury caused by OGD. Collectively, the present results suggested that LINC00319 promoted apoptosis and aggravated neuronal injury induced by OGD by regulating miR-200a-3p, which may be important for ischemic stroke treatment.

Effect of Baicalin on Transcriptome Changes in Piglet Vascular Endothelial Cells Induced by a Combination of Glaesserella parasuis and Lipopolysaccharide

Glaesserella parasuis causes porcine Glässer's disease and lipopolysaccharide (LPS) induces acute inflammation and pathological damage. Baicalin has antioxidant, antimicrobial, and anti-inflammatory functions. Long noncoding RNAs (lncRNAs) play key regulatory functions during bacterial infection. However, the role of lncRNAs in the vascular dysfunction induced by a combination of G. parasuis and LPS during systemic inflammation and the effect of baicalin on lncRNA expression induced in porcine aortic vascular endothelial cells (PAVECs) by a combination of G. parasuis and LPS have not been investigated. In this study, we investigated the changes in lncRNA and mRNA expression induced in PAVECs by G. parasuis, LPS, or a combination of G. parasuis and LPS, and the action of baicalin on lncRNA expression induced in PAVECs by the combination of G. parasuis and LPS. Our results showed 133 lncRNAs and 602 genes were differentially expressed when PAVECs were stimulated with the combination of G. parasuis and LPS, whereas 107 lncRNAs and 936 genes were differentially expressed when PAVECs were stimulated with the combination of G. parasuis and LPS after pretreatment with baicalin. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed the dominant signaling pathways triggered by the combination of G. parasuis and LPS were the tumor necrosis factor signaling pathway, phosphatidylinositol signaling system, and inositol phosphate metabolism. Protein-protein interaction network analysis showed the differentially expressed target genes of the differentially expressed lncRNAs (DELs) were related to each other. A coexpression analysis indicated the expression levels of the DELs were co-regulated with those of their differentially expressed target genes. This is the first study to systematically compare the changes in lncRNAs and mRNAs in PAVECs stimulated with a combination of G. parasuis and LPS. Our data clarified the mechanisms underlying the vascular inflammation and damage triggered by G. parasuis and LPS, and it may provide novel targets for the treatment of LPS-induced systemic inflammation.

The optimal discovery procedure for significance analysis of general gene expression studies

Motivation

Analysis of biological data often involves the simultaneous testing of thousands of genes. This requires two key steps: the ranking of genes and the selection of important genes based on a significance threshold. One such testing procedure, called the optimal discovery procedure (ODP), leverages information across different tests to provide an optimal ranking of genes. This approach can lead to substantial improvements in statistical power compared to other methods. However, current applications of the ODP have only been established for simple study designs using microarray technology. Here, we extend this work to the analysis of complex study designs and RNA-sequencing studies.

Results

We apply our extended framework to a static RNA-sequencing study, a longitudinal study, an independent sampling time-series study,and an independent sampling dose–response study. Our method shows improved performance compared to other testing procedures, finding more differentially expressed genes and increasing power for enrichment analysis. Thus, the extended ODP enables a favorable significance analysis of genome-wide gene expression studies.

Availability and implementation

The algorithm is implemented in our freely available R package called edge and can be downloaded at https://www.bioconductor.org/packages/release/bioc/html/edge.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

lncRNA KTN1‑AS1 promotes glioma cell proliferation and invasion by negatively regulating miR‑505‑3p

Glioblastoma (GBM) is one of the most prevalent and aggressive central nervous tumors with high mobility and mortality. The prognosis of patients with GBM is poor. It is therefore essential to explore the therapeutic strategies for the treatment of GBM. Previous studies have demonstrated that the long non-coding RNA (lncRNA) Kinectin 1-Antisense RNA 1 (KTN1-AS1) can participate in the development of several types of cancer. However, the underlying mechanism of KTN1-AS1 in GBM remains unknown. The present study aimed to determine the potential role of KTN1-AS1 in GBM. In this study, reverse transcription quantitative PCR analysis was conducted and the results demonstrated that KTN1-AS1 was upregulated in GBM tissues and cell lines compared with normal tissues and astrocytes (NHA). Furthermore, KTN1-AS1 knockdown decreased the viability and invasive ability of glioma cells in vitro and in vivo. In addition, high level of KTN1-AS1 was correlated with poor prognosis in TCGA GBM database. Furthermore, microRNA-505-3p (miR-505-3p) was a promising target of KTN1-AS1, and the suppressing effects of miR-505-3p on cell proliferation and invasive ability was reversed by downregulating KTN1-AS1. Taken together, the results from the present provided novel insights into the roles of KTN1-AS1 in GBM, and suggested that the KTN1-AS1/miR-505-3p axis may be considered as a novel therapeutic target for the treatment of patients with GBM.

A weighted FDR procedure under discrete and heterogeneous null distributions

Multiple testing (MT) with false discovery rate (FDR) control has been widely conducted in the "discrete paradigm" where p-values have discrete and heterogeneous null distributions. However, in this scenario existing FDR procedures often lose some power and may yield unreliable inference, and for this scenario there does not seem to be an FDR procedure that partitions hypotheses into groups, employs data-adaptive weights and is nonasymptotically conservative. We propose a weighted p-value-based FDR procedure, "weighted FDR (wFDR) procedure" for short, for MT in the discrete paradigm that efficiently adapts to both heterogeneity and discreteness of p-value distributions. We theoretically justify the nonasymptotic conservativeness of the wFDR procedure under independence, and show via simulation studies that, for MT based on p-values of binomial test or Fisher's exact test, it is more powerful than six other procedures. The wFDR procedure is applied to two examples based on discrete data, a drug safety study, and a differential methylation study, where it makes more discoveries than two existing methods.

A practical guide to methods controlling false discoveries in computational biology

BACKGROUND

In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as informative covariates to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigate the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology.

RESULTS

Methods that incorporate informative covariates are modestly more powerful than classic approaches, and do not underperform classic approaches, even when the covariate is completely uninformative. The majority of methods are successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we find that the improvement of the modern FDR methods over the classic methods increases with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses.

CONCLUSIONS

Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.