Powerful p-value combination methods to detect incomplete association

Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer's disease using circMeta2

Alzheimer's disease (AD) is an age-related neurodegenerative disorder with regulatory RNAs playing significant roles in its etiology. Circular RNAs (CircRNA) are enriched in human brains and contribute to AD progression. Many circRNA isoforms derived from same gene loci share common back splicing sites, thus often form clusters and work as a group to additively regulate their downstream targets. Unfortunately, the coordinated role of clustered circRNAs is often overlooked in individual circRNA differential expression (DE) analysis. To address these challenges, we develop circMeta2, a computational tool designed to perform DE analysis focused on circRNA clusters, equipped with modules tailored for both a small sample of biological replicates and a large-scale population study. Using circMeta2, we identify brain region-specific circRNA clusters from six distinct brain regions in the ENCODE datasets, as well as brain region-specific alteration of circRNA clusters signatures associated with AD from Mount Sinai brain bank (MSBB) AD study. We also illustrate how AD-associated circRNA clusters within and across different brain regions work coordinately to contribute to AD etiology by impacting miRNA-mediated gene expression and identified key circRNA clusters that associated with AD progression and severity. Our study demonstrates circMeta2 as a highly accuracy and robust tool for analyzing circRNA clusters, offering valuable molecular insights into AD pathology.

Gene expression differences associated with alcohol use disorder in human brain

Excessive alcohol consumption is a leading cause of preventable death worldwide. To improve understanding of neurobiological mechanisms associated with alcohol use disorder (AUD) in humans, we compared gene expression data from deceased individuals with and without AUD across two addiction-relevant brain regions: the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC). Bulk RNA-seq data from NAc and DLPFC (N ≥50 with AUD, ≥46 non-AUD) were analyzed for differential gene expression using modified negative binomial regression adjusting for technical and biological covariates. The region-level results were meta-analyzed with those from an independent dataset (NNAc = 28 AUD, 29 non-AUD; NPFC = 66 AUD, 77 non-AUD). We further tested for heritability enrichment of AUD-related phenotypes, gene co-expression networks, gene ontology enrichment, and drug repurposing. We identified 176 differentially expressed genes (DEGs; 12 in both regions, 78 in NAc only, 86 in DLPFC only) for AUD in our new dataset. After meta-analyzing with published data, we identified 476 AUD DEGs (25 in both regions, 29 in NAc only, 422 in PFC only). Of these DEGs, 17 were significant when looked up in GWAS of problematic alcohol use or drinks per week. Gene co-expression analysis showed both concordant and unique gene networks across brain regions. We also identified 29 and 436 drug compounds that target DEGs from our meta-analysis in NAc and PFC, respectively. This study identified robust AUD-associated DEGs, contributing novel neurobiological insights into AUD and highlighting genes targeted by known drug compounds, generating opportunity for drug repurposing to treat AUD.

Tandem mass tag-based proteomic analysis of granulosa and theca interna cells of the porcine ovarian follicle following in vitro treatment with vitamin D3 and insulin alone or in combination

This study was performed to investigate the proteomic basis underlying the interaction between vitamin D3 (VD) and insulin (I) within ovarian follicle using the pig as a model. Porcine antral follicles were incubated in vitro for 12 h with VD alone and I alone or in combination (VD + I) or with no treatment as the control (C). In total, 7690 and 7467 proteins were identified in the granulosa and theca interna compartments, respectively. Comparative proteomic analysis revealed 97 differentially abundant proteins (DAPs) within the granulosa layer and 11 DAPs within the theca interna layer. In the granulosa compartment, VD affected proteome leading to the promotion of cell proliferation, whereas I influenced mainly proteins related to cellular adhesion. The VD + I treatment induced granulosa cell proliferation probably via the DAPs involved in DNA synthesis and the cell cycle regulation. In the theca interna layer, VD alone or in co-treatment with I affected DAPs associated with cholesterol transport and lipid and steroid metabolic processes that was further confirmed by diminished lipid droplet accumulation. SIGNIFICANCE: The application of quantitative proteomics demonstrated for the first time the complexity of VD and I interactions in porcine ovarian follicle, providing a framework for understanding the molecular mechanisms underlying their cross-talk. Although identified DAPs were related to crucial ovarian processes, including the granulosa cell proliferation and cholesterol transport in the theca interna layer, novel molecular pathways underlying these processes have been proposed. The identified unique proteins may serve as indicators of VD and I interactions in both follicle layers, and could be useful biomarkers of ovarian pathologies characterized by impaired VD and I levels, such as polycystic ovary syndrome.

Connecting genomic and proteomic signatures of amyloid burden in the brain

Background

Alzheimer's disease (AD) has a high heritable component characteristic of complex diseases, yet many of the genetic risk factors remain unknown. We combined genome-wide association studies (GWAS) on amyloid endophenotypes measured in cerebrospinal fluid (CSF) and positron emission tomography (PET) as surrogates of amyloid pathology, which may be helpful to understand the underlying biology of the disease.

Methods

We performed a meta-analysis of GWAS of CSF Aβ42 and PET measures combining six independent cohorts (n=2,076). Due to the opposite effect direction of Aβ phenotypes in CSF and PET measures, only genetic signals in the opposite direction were considered for analysis (n=376,599). Polygenic risk scores (PRS) were calculated and evaluated for AD status and amyloid endophenotypes. We then searched the CSF proteome signature of brain amyloidosis using SOMAscan proteomic data (Ace cohort, n=1,008) and connected it with GWAS results of loci modulating amyloidosis. Finally, we compared our results with a large meta-analysis using publicly available datasets in CSF (n=13,409) and PET (n=13,116). This combined approach enabled the identification of overlapping genes and proteins associated with amyloid burden and the assessment of their biological significance using enrichment analyses.

Results

After filtering the meta-GWAS, we observed genome-wide significance in the rs429358-APOE locus and nine suggestive hits were annotated. We replicated the APOE loci using the large CSF-PET meta-GWAS and identified multiple AD-associated genes as well as the novel GADL1 locus. Additionally, we found a significant association between the AD PRS and amyloid levels, whereas no significant association was found between any Aβ PRS with AD risk. CSF SOMAscan analysis identified 1,387 FDR-significant proteins associated with CSF Aβ42 levels. The overlap among GWAS loci and proteins associated with amyloid burden was very poor (n=35). The enrichment analysis of overlapping hits strongly suggested several signalling pathways connecting amyloidosis with the anchored component of the plasma membrane, synapse physiology and mental disorders that were replicated in the large CSF-PET meta-analysis.

Conclusions

The strategy of combining CSF and PET amyloid endophenotypes GWAS with CSF proteome analyses might be effective for identifying signals associated with the AD pathological process and elucidate causative molecular mechanisms behind the amyloid mobilization in AD.

Meta-analysis of RNA interaction profiles of RNA-binding protein using the RBPInper tool

Motivation

Recent RNA-centric experimental methods have significantly expanded our knowledge of proteins with known RNA-binding functions. However, the complete regulatory network and pathways for many of these RNA-binding proteins (RBPs) in different cellular contexts remain unknown. Although critical to understanding the role of RBPs in health and disease, experimentally mapping the RBP-RNA interactomes in every single context is an impossible task due the cost and manpower required. Additionally, identifying relevant RNAs bound by RBPs is challenging due to their diverse binding modes and function.

Results

To address these challenges, we developed RBP interaction mapper RBPInper an integrative framework that discovers global RBP interactome using statistical data fusion. Experiments on splicing factor proline and glutamine rich (SFPQ) datasets revealed cogent global SFPQ interactome. Several biological processes associated with this interactome were previously linked with SFPQ function. Furthermore, we conducted tests using independent dataset to assess the transferability of the SFPQ interactome to another context. The results demonstrated robust utility in generating interactomes that transfers to unseen cellular context. Overall, RBPInper is a fast and user-friendly method that enables a systems-level understanding of RBP functions by integrating multiple molecular datasets. The tool is designed with a focus on simplicity, minimal dependencies, and straightforward input requirements. This intentional design aims to empower everyday biologists, making it easy for them to incorporate the tool into their research.

Availability and implementation

The source code, documentation, and installation instructions as well as results for use case are freely available at https://github.com/AneneLab/RBPInper. A user can easily compile similar datasets for a target RBP.

Evidence for ~12-h ultradian gene programs in humans

Mice and many marine organisms exhibit ~12-h ultradian rhythms, however, direct evidence of ~12-h ultradian rhythms in humans is lacking. Here, we performed prospective, temporal transcriptome profiling of peripheral white blood cells from three healthy humans. All three participants independently exhibited robust ~12-h transcriptional rhythms in molecular programs involved in RNA and protein metabolism, with strong homology to circatidal gene programs previously identified in Cnidarian marine species.

A meta-analysis approach for annotation and identification of lncRNAs controlling perirenal fat deposition in suckling lambs

Long non-coding RNAs (lncRNAs) are being studied in farm animals due to their association with traits of economic interest, such as fat deposition. Based on the analysis of perirenal fat transcriptomes, this research explored the relevance of these regulatory elements to fat deposition in suckling lambs. To that end, meta-analysis techniques have been implemented to efficiently characterize and detect differentially expressed transcripts from two different RNA-seq datasets, one including samples of two sheep breeds that differ in fat deposition features, Churra and Assaf (n = 14), and one generated from Assaf suckling lambs with different fat deposition levels (n = 8). The joint analysis of the 22 perirenal fat RNA-seq samples with the FEELnc software allowed the detection of 3953 novel lncRNAs. After the meta-analysis, 251 differentially expressed genes were identified, 21 of which were novel lncRNAs. Additionally, a co-expression analysis revealed that, in suckling lambs, lncRNAs may play a role in controlling angiogenesis and thermogenesis, processes highlighted in relation to high and low fat deposition levels, respectively. Overall, while providing information that could be applied for the improvement of suckling lamb carcass traits, this study offers insights into the biology of perirenal fat deposition regulation in mammals.

Hippocampal transcriptome-wide association study and pathway analysis of mitochondrial solute carriers in Alzheimer's disease

The etiopathogenesis of late-onset Alzheimer's disease (AD) is increasingly recognized as the result of the combination of the aging process, toxic proteins, brain dysmetabolism, and genetic risks. Although the role of mitochondrial dysfunction in the pathogenesis of AD has been well-appreciated, the interaction between mitochondrial function and genetic variability in promoting dementia is still poorly understood. In this study, by tissue-specific transcriptome-wide association study (TWAS) and further meta-analysis, we examined the genetic association between mitochondrial solute carrier family (SLC25) genes and AD in three independent cohorts and identified three AD-susceptibility genes, including SLC25A10, SLC25A17, and SLC25A22. Integrative analysis using neuroimaging data and hippocampal TWAS-predicted gene expression of the three susceptibility genes showed an inverse correlation of SLC25A22 with hippocampal atrophy rate in AD patients, which outweighed the impacts of sex, age, and apolipoprotein E4 (ApoE4). Furthermore, SLC25A22 downregulation demonstrated an association with AD onset, as compared with the other two transcriptome-wide significant genes. Pathway and network analysis related hippocampal SLC25A22 downregulation to defects in neuronal function and development, echoing the enrichment of SLC25A22 expression in human glutamatergic neurons. The most parsimonious interpretation of the results is that we have identified AD-susceptibility genes in the SLC25 family through the prediction of hippocampal gene expression. Moreover, our findings mechanistically yield insight into the mitochondrial cascade hypothesis of AD and pave the way for the future development of diagnostic tools for the early prevention of AD from a perspective of precision medicine by targeting the mitochondria-related genes.

Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity

Despite the abundance of epidemiological evidence for the high comorbid rate between psoriasis and obesity, systematic approaches to common inflammatory mechanisms have not been adequately explored. We performed a meta-analysis of publicly available RNA-sequencing datasets to unveil putative mechanisms that are postulated to exacerbate both diseases, utilizing both late-stage, disease-specific meta-analyses and consensus gene co-expression network (cWGCNA). Single-gene meta-analyses reported several common inflammatory mechanisms fostered by the perturbed expression profile of inflammatory cells. Assessment of gene overlaps between both diseases revealed significant overlaps between up- (n = 170, P value = 6.07 × 10-65) and down-regulated (n = 49, P value = 7.1 × 10-7) genes, associated with increased T cell response and activated transcription factors. Our cWGCNA approach disentangled 48 consensus modules, associated with either the differentiation of leukocytes or metabolic pathways with similar correlation signals in both diseases. Notably, all our analyses confirmed the association of the perturbed T helper (Th)17 differentiation pathway in both diseases. Our novel findings through whole transcriptomic analyses characterize the inflammatory commonalities between psoriasis and obesity implying the assessment of several expression profiles that could serve as putative comorbid disease progression biomarkers and therapeutic interventions.

Identifying novel gene dysregulation associated with opioid overdose death: A meta-analysis of differential gene expression in human prefrontal cortex

Only recently have human postmortem brain studies of differential gene expression (DGE) associated with opioid overdose death (OOD) been published; sample sizes from these studies have been modest (N = 40-153). To increase statistical power to identify OOD-associated genes, we leveraged human prefrontal cortex RNAseq data from four independent OOD studies and conducted a transcriptome-wide DGE meta-analysis (N = 285). Using a unified gene expression data processing and analysis framework across studies, we meta-analyzed 20 098 genes and found 335 significant differentially expressed genes (DEGs) by OOD status (false discovery rate < 0.05). Of these, 66 DEGs were among the list of 303 genes reported as OOD-associated in prior prefrontal cortex molecular studies, including genes/gene families (e.g., OPRK1, NPAS4, DUSP, EGR). The remaining 269 DEGs were not previously reported (e.g., NR4A2, SYT1, HCRTR2, BDNF). There was little evidence of genetic drivers for the observed differences in gene expression between opioid addiction cases and controls. Enrichment analyses for the DEGs across molecular pathway and biological process databases highlight an interconnected set of genes and pathways from orexin and tyrosine kinase receptors through MEK/ERK/MAPK signaling to affect neuronal plasticity.

Empirically adjusted fixed-effects meta-analysis methods in genomic studies

In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to an individual genomic study. Meta analysis methods that combine statistical estimates across studies are known to be statistically more powerful than those combining statistical significance measures. An approach combining effect size estimates based on a fixed-effects model, called METAL, has gained extreme popularity to perform the former type of meta-analysis. In this article, we discuss the limitations of METAL due to its dependence on the theoretical null distribution, leading to incorrect significance testing results. Through various simulation studies and real genomic data application, we show how modifying the z-scores in METAL, using an empirical null distribution, can significantly improve the results, especially in presence of hidden confounders. For the estimation of the null distribution, we consider two different approaches, and we highlight the scenarios when one null estimation approach outperforms the other. This article will allow researchers to gain an insight into the importance of using an empirical null distribution in the fixed-effects meta-analysis as well as in choosing the appropriate empirical null distribution estimation approach.

Calibration and Application of PUF Disk Passive Air Samplers To Assess Chlorinated Paraffins in Ambient Air in Australia, China, and Vietnam

Ambient air samples were collected in Brisbane (Australia), Dalian (China), and Hanoi (Vietnam) during Mar 2013-Feb 2018 using polyurethane foam based passive air samplers. A sampling rate calibration experiment was conducted for chlorinated paraffins (CPs, i.e., short-chain, medium-chain, and long-chain CPs), where the sampling rates were 4.5 ± 0.7, 4.8 ± 0.3, and 4.8 ± 2.1 m3 day-1 for SCCPs, MCCPs, and LCCPs, respectively. The atmospheric concentration of CPs was then calculated and the medians of ∑CPs were 0.079, 1.0, and 0.89 ng m-3 in Brisbane, Dalian, and Hanoi, respectively. The concentration of CPs in Brisbane's air remained at low levels, with no significant differences observed between the city background site and the city center site, indicating limited usage and production of CPs in this city. The highest concentration of MCCPs was detected in Dalian, while the highest concentration of SCCPs was detected in Hanoi. A decrease of SCCP concentration and an increase of MCCPs' were found in Brisbane's air from 2016 to 2018, while increasing trends for both SCCPs and MCCPs were observed in Dalian. These results indicated impacts from different sources of CPs in the investigated cities.

Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ - 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.

Label-free multimodal electro-thermo-mechanical (ETM) phenotyping as a novel biomarker to differentiate between normal, benign, and cancerous breast biopsy tissues

BACKGROUND

Technologies for quick and label-free diagnosis of malignancies from breast tissues have the potential to be a significant adjunct to routine diagnostics. The biophysical phenotypes of breast tissues, such as its electrical, thermal, and mechanical properties (ETM), have the potential to serve as novel markers to differentiate between normal, benign, and malignant tissue.

RESULTS

We report a system-of-biochips (SoB) integrated into a semi-automated mechatronic system that can characterize breast biopsy tissues using electro-thermo-mechanical sensing. The SoB, fabricated on silicon using microfabrication techniques, can measure the electrical impedance (Z), thermal conductivity (K), mechanical stiffness (k), and viscoelastic stress relaxation (%R) of the samples. The key sensing elements of the biochips include interdigitated electrodes, resistance temperature detectors, microheaters, and a micromachined diaphragm with piezoresistive bridges. Multi-modal ETM measurements performed on formalin-fixed tumour and adjacent normal breast biopsy samples from N = 14 subjects were able to differentiate between invasive ductal carcinoma (malignant), fibroadenoma (benign), and adjacent normal (healthy) tissues with a root mean square error of 0.2419 using a Gaussian process classifier. Carcinoma tissues were observed to have the highest mean impedance (110018.8 ± 20293.8 Ω) and stiffness (0.076 ± 0.009 kNm-1) and the lowest thermal conductivity (0.189 ± 0.019 Wm-1 K-1) amongst the three groups, while the fibroadenoma samples had the highest percentage relaxation in normalized load (47.8 ± 5.12%).

CONCLUSIONS

The work presents a novel strategy to characterize the multi-modal biophysical phenotype of breast biopsy tissues to aid in cancer diagnosis from small-sized tumour samples. The methodology envisions to supplement the existing technology gap in the analysis of breast tissue samples in the pathology laboratories to aid the diagnostic workflow.

A molecular staging model for accurately dating the endometrial biopsy

Natural variability in menstrual cycle length, coupled with rapid changes in endometrial gene expression, makes it difficult to accurately define and compare different stages of the endometrial cycle. Here we develop and validate a method for precisely determining endometrial cycle stage based on global gene expression. Our 'molecular staging model' reveals significant and remarkably synchronised daily changes in expression for over 3400 endometrial genes throughout the cycle, with the most dramatic changes occurring during the secretory phase. Our study significantly extends existing data on the endometrial transcriptome, and for the first time enables identification of differentially expressed endometrial genes with increasing age and different ethnicities. It also allows reinterpretation of all endometrial RNA-seq and array data that has been published to date. Our molecular staging model will significantly advance understanding of endometrial-related disorders that affect nearly all women at some stage of their lives, such as heavy menstrual bleeding, endometriosis, adenomyosis, and recurrent implantation failure.

Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex

Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulate that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employ atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings are consistent across six complementary processing pipelines: 1) SFC and its temporal variance respectively decrease and increase across the unimodal-transmodal and granular-agranular gradients; 2) increased myelination and lower EI-ratio are associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 3) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurs when traversing from granular to agranular cortical regions. Collectively, our work delivers a framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.

A tool for rapid, automated characterization of population epigenomics in plants

Epigenetic variation in plant populations is an important factor in determining phenotype and adaptation to the environment. However, while advances have been made in the molecular and computational methods to analyze the methylation status of a given sample of DNA, tools to profile and compare the methylomes of multiple individual plants or groups of plants at high resolution and low cost are lacking. Here, we describe a computational approach and R package (sounDMR) that leverages the benefits of long read nanopore sequencing to enable robust identification of differential methylation from complex experimental designs, as well as assess the variability within treatment groups and identify individual plants of interest. We demonstrate the utility of this approach by profiling a population of Arabidopsis thaliana exposed to a demethylating agent and identify genomic regions of high epigenetic variability between individuals. Given the low cost of nanopore sequencing devices and the ease of sample preparation, these results show that high resolution epigenetic profiling of plant populations can be made more broadly accessible in plant breeding and biotechnology.

RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure

Many biologically important RNAs fold into specific 3D structures conserved through evolution. Knowing when an RNA sequence includes a conserved RNA structure that could lead to new biology is not trivial and depends on clues left behind by conservation in the form of covariation and variation. For that purpose, the R-scape statistical test was created to identify from alignments of RNA sequences, the base pairs that significantly covary above phylogenetic expectation. R-scape treats base pairs as independent units. However, RNA base pairs do not occur in isolation. The Watson-Crick (WC) base pairs stack together forming helices that constitute the scaffold that facilitates the formation of the non-WC base pairs, and ultimately the complete 3D structure. The helix-forming WC base pairs carry most of the covariation signal in an RNA structure. Here, I introduce a new measure of statistically significant covariation at helix-level by aggregation of the covariation significance and covariation power calculated at base-pair-level resolution. Performance benchmarks show that helix-level aggregated covariation increases sensitivity in the detection of evolutionarily conserved RNA structure without sacrificing specificity. This additional helix-level sensitivity reveals an artifact that results from using covariation to build an alignment for a hypothetical structure and then testing the alignment for whether its covariation significantly supports the structure. Helix-level reanalysis of the evolutionary evidence for a selection of long non-coding RNAs (lncRNAs) reinforces the evidence against these lncRNAs having a conserved secondary structure.

Distinct longevity mechanisms across and within species and their association with aging

Lifespan varies within and across species, but the general principles of its control remain unclear. Here, we conducted multi-tissue RNA-seq analyses across 41 mammalian species, identifying longevity signatures and examining their relationship with transcriptomic biomarkers of aging and established lifespan-extending interventions. An integrative analysis uncovered shared longevity mechanisms within and across species, including downregulated Igf1 and upregulated mitochondrial translation genes, and unique features, such as distinct regulation of the innate immune response and cellular respiration. Signatures of long-lived species were positively correlated with age-related changes and enriched for evolutionarily ancient essential genes, involved in proteolysis and PI3K-Akt signaling. Conversely, lifespan-extending interventions counteracted aging patterns and affected younger, mutable genes enriched for energy metabolism. The identified biomarkers revealed longevity interventions, including KU0063794, which extended mouse lifespan and healthspan. Overall, this study uncovers universal and distinct strategies of lifespan regulation within and across species and provides tools for discovering longevity interventions.

Single-gene resolution of diversity-driven overyielding in plant genotype mixtures

In plant communities, diversity often increases productivity and functioning, but the specific underlying drivers are difficult to identify. Most ecological theories attribute positive diversity effects to complementary niches occupied by different species or genotypes. However, the specific nature of niche complementarity often remains unclear, including how it is expressed in terms of trait differences between plants. Here, we use a gene-centred approach to study positive diversity effects in mixtures of natural Arabidopsis thaliana genotypes. Using two orthogonal genetic mapping approaches, we find that between-plant allelic differences at the AtSUC8 locus are strongly associated with mixture overyielding. AtSUC8 encodes a proton-sucrose symporter and is expressed in root tissues. Genetic variation in AtSUC8 affects the biochemical activities of protein variants and natural variation at this locus is associated with different sensitivities of root growth to changes in substrate pH. We thus speculate that - in the particular case studied here - evolutionary divergence along an edaphic gradient resulted in the niche complementarity between genotypes that now drives overyielding in mixtures. Identifying genes important for ecosystem functioning may ultimately allow linking ecological processes to evolutionary drivers, help identify traits underlying positive diversity effects, and facilitate the development of high-performance crop variety mixtures.

Molecular profiling of a real-world breast cancer cohort with genetically inferred ancestries reveals actionable tumor biology differences between European ancestry and African ancestry patient populations

BACKGROUND

Endocrine-resistant HR+/HER2- breast cancer (BC) and triple-negative BC (TNBC) are of interest for molecularly informed treatment due to their aggressive natures and limited treatment profiles. Patients of African Ancestry (AA) experience higher rates of TNBC and mortality than European Ancestry (EA) patients, despite lower overall BC incidence. Here, we compare the molecular landscapes of AA and EA patients with HR+/HER2- BC and TNBC in a real-world cohort to promote equity in precision oncology by illuminating the heterogeneity of potentially druggable genomic and transcriptomic pathways.

METHODS

De-identified records from patients with TNBC or HR+/HER2- BC in the Tempus Database were randomly selected (N = 5000), with most having stage IV disease. Mutations, gene expression, and transcriptional signatures were evaluated from next-generation sequencing data. Genetic ancestry was estimated from DNA-seq. Differences in mutational prevalence, gene expression, and transcriptional signatures between AA and EA were compared. EA patients were used as the reference population for log fold-changes (logFC) in expression.

RESULTS

After applying inclusion criteria, 3433 samples were evaluated (n = 623 AA and n = 2810 EA). Observed patterns of dysregulated pathways demonstrated significant heterogeneity among the two groups. Notably, PIK3CA mutations were significantly lower in AA HR+/HER2- tumors (AA = 34% vs. EA = 42%, P < 0.05) and the overall cohort (AA = 28% vs. EA = 37%, P = 2.08e-05). Conversely, KMT2C mutation was significantly more frequent in AA than EA TNBC (23% vs. 12%, P < 0.05) and HR+/HER2- (24% vs. 15%, P = 3e-03) tumors. Across all subtypes and stages, over 8000 genes were differentially expressed between the two ancestral groups including RPL10 (logFC = 2.26, P = 1.70e-162), HSPA1A (logFC = - 2.73, P = 2.43e-49), ATRX (logFC = - 1.93, P = 5.89e-83), and NUTM2F (logFC = 2.28, P = 3.22e-196). Ten differentially expressed gene sets were identified among stage IV HR+/HER2- tumors, of which four were considered relevant to BC treatment and were significantly enriched in EA: ERBB2_UP.V1_UP (P = 3.95e-06), LTE2_UP.V1_UP (P = 2.90e-05), HALLMARK_FATTY_ACID_METABOLISM (P = 0.0073), and HALLMARK_ANDROGEN_RESPONSE (P = 0.0074).

CONCLUSIONS

We observed significant differences in mutational spectra, gene expression, and relevant transcriptional signatures between patients with genetically determined African and European ancestries, particularly within the HR+/HER2- BC and TNBC subtypes. These findings could guide future development of treatment strategies by providing opportunities for biomarker-informed research and, ultimately, clinical decisions for precision oncology care in diverse populations.

RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure

Many biologically important RNAs fold into specific 3D structures conserved through evolution. Knowing when an RNA sequence includes a conserved RNA structure that could lead to new biology is not trivial and depends on clues left behind by conservation in the form of covariation and variation. For that purpose, the R-scape statistical test was created to identify from alignments of RNA sequences, the base pairs that significantly covary above phylogenetic expectation. R-scape treats base pairs as independent units. However, RNA base pairs do not occur in isolation. The Watson-Crick (WC) base pairs stack together forming helices that constitute the scaffold that facilitates the formation of the non-WC base pairs, and ultimately the complete 3D structure. The helix-forming WC base pairs carry most of the covariation signal in an RNA structure. Here, I introduce a new measure of statistically significant covariation at helix-level by aggregation of the covariation significance and covariation power calculated at base-pair-level resolution. Performance benchmarks show that helix-level aggregated covariation increases sensitivity in the detection of evolutionarily conserved RNA structure without sacrificing specificity. This additional helix-level sensitivity reveals an artifact that results from using covariation to build an alignment for a hypothetical structure and then testing the alignment for whether its covariation significantly supports the structure. Helix-level reanalysis of the evolutionary evidence for a selection of long non-coding RNAs (lncRNAs) reinforces the evidence against these lncRNAs having a conserved secondary structure.

Availability

Helix aggregated E-values are integrated in the R-scape software package (version 2.0.0.p and higher). The R-scape web server eddylab.org/R-scape includes a link to download the source code.

Contact

elenarivas@fas.harvard.edu.

Supplementary information

Supplementary data and code are provided with this manuscript at rivaslab.org .

Benchmarking integration of single-cell differential expression

Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes.

ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs

Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD .

Combining p-values from various statistical methods for microbiome data

MOTIVATION

In the field of microbiome analysis, there exist various statistical methods that have been developed for identifying differentially expressed features, that account for the overdispersion and the high sparsity of microbiome data. However, due to the differences in statistical models or test formulations, it is quite often to have inconsistent significance results across statistical methods, that makes it difficult to determine the importance of microbiome taxa. Thus, it is practically important to have the integration of the result from all statistical methods to determine the importance of microbiome taxa. A standard meta-analysis is a powerful tool for integrative analysis and it provides a summary measure by combining p-values from various statistical methods. While there are many meta-analyses available, it is not easy to choose the best meta-analysis that is the most suitable for microbiome data.

RESULTS

In this study, we investigated which meta-analysis method most adequately represents the importance of microbiome taxa. We considered Fisher's method, minimum value of p method, Simes method, Stouffer's method, Kost method, and Cauchy combination test. Through simulation studies, we showed that Cauchy combination test provides the best combined value of p in the sense that it performed the best among the examined methods while controlling the type 1 error rates. Furthermore, it produced high rank similarity with the true ranks. Through the real data application of colorectal cancer microbiome data, we demonstrated that the most highly ranked microbiome taxa by Cauchy combination test have been reported to be associated with colorectal cancer.

Integrated analysis of cervical squamous cell carcinoma cohorts from three continents reveals conserved subtypes of prognostic significance

Human papillomavirus (HPV)-associated cervical cancer is a leading cause of cancer deaths in women. Here we present an integrated multi-omic analysis of 643 cervical squamous cell carcinomas (CSCC, the most common histological variant of cervical cancer), representing patient populations from the USA, Europe and Sub-Saharan Africa and identify two CSCC subtypes (C1 and C2) with differing prognosis. C1 and C2 tumours can be driven by either of the two most common HPV types in cervical cancer (16 and 18) and while HPV16 and HPV18 are overrepresented among C1 and C2 tumours respectively, the prognostic difference between groups is not due to HPV type. C2 tumours, which comprise approximately 20% of CSCCs across these cohorts, display distinct genomic alterations, including loss or mutation of the STK11 tumour suppressor gene, increased expression of several immune checkpoint genes and differences in the tumour immune microenvironment that may explain the shorter survival associated with this group. In conclusion, we identify two therapy-relevant CSCC subtypes that share the same defining characteristics across three geographically diverse cohorts.

Human papillomavirus (HPV) is a known cause of cervical cancer. Here, the authors perform a multi-omic analysis using published cervical squamous cell carcinoma cohorts from the USA, Europe, and SubSaharan Africa and identify two cervical squamous cell carcinoma subtypes that display prognostic differences.

Microarray meta-analysis reveals IL6 and p38β/MAPK11 as potential targets of hsa-miR-124 in endothelial progenitor cells: Implications for stent re-endothelization in diabetic patients

Circulating endothelial progenitor cells (EPCs) play an important role in the repair processes of damaged vessels, favoring re-endothelization of stented vessels to minimize restenosis. EPCs number and function is diminished in patients with type 2 diabetes, a known risk factor for restenosis. Considering the impact of EPCs in vascular injury repair, we conducted a meta-analysis of microarray to assess the transcriptomic profile and determine target genes during the differentiation process of EPCs into mature ECs. Five microarray datasets, including 13 EPC and 12 EC samples were analyzed, using the online tool ExpressAnalyst. Differentially expressed genes (DEGs) analysis was done by Limma method, with an | log2FC| &gt; 1 and FDR &lt; 0.05. Combined p-value by Fisher exact method was computed for the intersection of datasets. There were 3,267 DEGs, 1,539 up-regulated and 1,728 down-regulated in EPCs, with 407 common DEGs in at least four datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed enrichment for terms related to “AGE-RAGE signaling pathway in diabetic complications.” Intersection of common DEGs, KEGG pathways genes and genes in protein-protein interaction network (PPI) identified four key genes, two up-regulated (IL1B and STAT5A) and two down-regulated (IL6 and MAPK11). MicroRNA enrichment analysis of common DEGs depicted five hub microRNA targeting 175 DEGs, including STAT5A, IL6 and MAPK11, with hsa-miR-124 as common regulator. This group of genes and microRNAs could serve as biomarkers of EPCs differentiation during coronary stenting as well as potential therapeutic targets to improve stent re-endothelization, especially in diabetic patients.

Meta-Analysis of qPCR for Bovine Respiratory Disease Based on MIQE Guidelines

Qualitative and quantitative PCR-based tests are widely used in both diagnostics and research to assess the prevalence of disease-causing pathogens in veterinary medicine. The efficacy of these tests, usually measured in terms of sensitivity and specificity, is critical in confirming or excluding a clinical diagnosis. We undertook a meta-analysis to assess the inherent value of published PCR diagnostic approaches used to confirm and quantify bacteria and viruses associated with bovine respiratory disease (BRD) in cattle. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough search of nine electronic databases (Web of Science, EBSCOhost, Cambridge journals online, ProQuest, PubMed, Sage journals online, ScienceDirect, Wiley online library and MEDLINE) was undertaken to find studies that had reported on the use of PCR and/or qPCR for the detection and/or quantification of BRD associated organisms. All studies meeting the inclusion criteria for reporting quantitative PCR for identification of BRD associated microorganisms were included in the analysis. Studies were then assessed on the applications of the Minimum Information for Publication of Quantitative Real-Time PCR Experiment (MIQE) and PCR primer/probe sequences were extracted and tested for in silico specificity using a high level of stringency. Fourteen full-text articles were included in this study. Of these, 79% of the analysed articles did not report the application of the MIQE guidelines in their study. High stringency in silico testing of 144 previously published PCR primer/probe sequences found many to have questionable specificity. This review identified a high occurrence of primer/probe sequences with a variable in silico specificity such that this may have implications for the accuracy of reporting. Although this analysis was only applied to one specific disease state, identification of animals suspected to be suffering from bovine respiratory disease, there appears to be more broadly a need for veterinary diagnostic studies to adopt international best practice for reporting of quantitative PCR diagnostic data to be both accurate and comparable between studies and methodologies.

Genetic variants of PKLR are associated with acute pain in sickle cell disease

Acute pain, the most prominent complication of sickle cell disease (SCD), results from vaso-occlusion triggered by sickling of deoxygenated red blood cells (RBCs). Concentration of 2,3-diphosphoglycerate (2,3-DPG) in RBCs promotes deoxygenation by preferentially binding to the low-affinity T conformation of HbS. 2,3-DPG is an intermediate substrate in the glycolytic pathway in which pyruvate kinase (gene PKLR, protein PKR) is a rate-limiting enzyme; variants in PKLR may affect PKR activity, 2,3-DPG levels in RBCs, RBC sickling, and acute pain episodes (APEs). We performed a candidate gene association study using 2 cohorts: 242 adult SCD-HbSS patients and 977 children with SCD-HbSS or SCD-HbSβ0 thalassemia. Seven of 47 PKLR variants evaluated in the adult cohort were associated with hospitalization: intron 4, rs2071053; intron 2, rs8177970, rs116244351, rs114455416, rs12741350, rs3020781, and rs8177964. All 7 variants showed consistent effect directions in both cohorts and remained significant in weighted Fisher's meta-analyses of the adult and pediatric cohorts using P < .0071 as threshold to correct for multiple testing. Allele-specific expression analyses in an independent cohort of 52 SCD adults showed that the intronic variants are likely to influence APE by affecting expression of PKLR, although the causal variant and mechanism are not defined.

A Robust Immuno-Prognostic Model of Non-Muscle-Invasive Bladder Cancer Indicates Dynamic Interaction in Tumor Immune Microenvironment Contributes to Cancer Progression

Non-muscle-invasive bladder cancer (NMIBC) accounts for more than 70% of urothelial cancer. More than half of NMIBC patients experience recurrence, progression, or metastasis, which essentially reduces life quality and survival time. Identifying the high-risk patients prone to progression remains the primary concern of risk management of NMIBC. In this study, we included 1370 NMIBC transcripts data from nine public datasets, identified nine tumor-infiltrating marker cells highly related to the survival of NMIBC, quantified the cells’ proportion by self-defined differentially expressed signature genes, and established a robust immuno-prognostic model dividing NMIBC patients into low-risk versus high-risk progression groups. Our model implies that the loss of crosstalk between tumor cells and adjacent normal epithelium, along with enriched cell proliferation signals, may facilitate tumor progression. Thus, evaluating tumor progression should consider various components in the tumor immune microenvironment instead of the single marker in a single dimension. Moreover, we also appeal to the necessity of using appropriate meta-analysis methods to integrate the evidence from multiple sources in the feature selection step from large-scale heterogeneous omics data such as our study.

Impact of Maternal Obesity on Liver Disease in the Offspring: A Comprehensive Transcriptomic Analysis and Confirmation of Results in a Murine Model

The global obesity epidemic particularly affects women of reproductive age. Offspring of obese mothers suffer from an increased risk of liver disease but the molecular mechanisms involved remain unknown. We performed an integrative genomic analysis of datasets that investigated the impact of maternal obesity on the hepatic gene expression profile of the offspring in mice. Furthermore, we developed a murine model of maternal obesity and studied the development of liver disease and the gene expression profile of the top dysregulated genes by quantitative real-time polymerase chain reaction (qPCR). Our data are available for interactive exploration on our companion webpage. We identified five publicly available datasets relevant to our research question. Pathways involved in metabolism, the innate immune system, the clotting cascade, and the cell cycle were consistently dysregulated in the offspring of obese mothers. Concerning genes involved in the development of liver disease, Egfr, Vegfb, Wnt2,Pparg and six other genes were dysregulated in multiple independent datasets. In our own model, we observed a higher tendency towards the development of non-alcoholic liver disease (60 vs. 20%) and higher levels of alanine aminotransferase (41.0 vs. 12.5 IU/l, p = 0.008) in female offspring of obese mothers. Male offspring presented higher levels of liver fibrosis (2.4 vs. 0.6% relative surface area, p = 0.045). In a qPCR gene expression analysis of our own samples, we found Fgf21, Pparg, Ppard, and Casp6 to be dysregulated by maternal obesity. Maternal obesity represents a looming threat to the liver health of future generations. Our comprehensive transcriptomic analysis will help to better understand the mechanisms of the development of liver disease in the offspring of obese mothers and can give rise to further explorations.

Amanida: an R package for meta-analysis of metabolomics non-integral data

SUMMARY

The combination, analysis and evaluation of different studies which try to answer or solve the same scientific question, also known as a meta-analysis, plays a crucial role in answering relevant clinical relevant questions. Unfortunately, metabolomics studies rarely disclose all the statistical information needed to perform a meta-analysis. Here, we present a meta-analysis approach using only the most reported statistical parameters in this field: P-value and fold-change. The P-values are combined via Fisher's method and fold-changes by averaging, both weighted by the study size (n). The amanida package includes several visualization options: a volcano plot for quantitative results, a vote plot for total regulation behaviours (up/down regulations) for each compound, and a explore plot of the vote-counting results with the number of times a compound is found upregulated or downregulated. In this way, it is very easy to detect discrepancies between studies at a first glance.

AVAILABILITY AND IMPLEMENTATION

Amanida code and documentation are at CRAN and https://github.com/mariallr/amanida.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs

Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose a new drug recommendation system called: A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD defines a novel drug score predicting drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. We tested ASGARD on multiple diseases, including breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC of 0.92) compared to two other bulk-cell-based drug repurposing methods (AUC of 0.80 and 0.76). It is also considerably better (AUC of 0.82) than other cell cluster level predicting methods (AUC of 0.67 and 0.55). In addition, ASGARD is also validated by the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. Many top-ranked drugs are either approved by FDA or in clinical trials treating corresponding diseases. In silico cell-type specific drop-out experiments using triple-negative breast cancers show the importance of T cells in the tumor microenvironment in affecting drug predictions. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD.

Mutations in Epigenetic Regulation Genes in Gastric Cancer

Simple Summary

Epigenetic mechanisms, such as DNA methylation/demethylation, covalent modifications of histone proteins, and chromatin remodeling, create specific patterns of gene expression. Epigenetic deregulations are associated with oncogenesis, relapse of the disease and metastases, and can serve as a useful clinical marker. We assessed the clinical relevance of integrity of the genes coding for epigenetic regulator proteins by mutational profiling of 25 genes in 135 gastric cancer (GC) samples. Overall, mutations in the epigenetic regulation genes were found to be significantly associated with reduced overall survival of patients in the group with metastases and in the group with tumors with signet ring cells. We have also discovered mutual exclusivity of somatic mutations in the KMT2D, KMT2C, ARID1A, and CHD7 genes in our cohort. Our results suggest that mutations in epigenetic regulation genes may be valuable clinical markers and deserve further exploration in independent cohorts.

Abstract

We have performed mutational profiling of 25 genes involved in epigenetic processes on 135 gastric cancer (GC) samples. In total, we identified 79 somatic mutations in 49/135 (36%) samples. The minority (n = 8) of mutations was identified in DNA methylation/demethylation genes, while the majority (n = 41), in histone modifier genes, among which mutations were most commonly found in KMT2D and KMT2C. Somatic mutations in KMT2D, KMT2C, ARID1A and CHD7 were mutually exclusive (p = 0.038). Mutations in ARID1A were associated with distant metastases (p = 0.03). The overall survival of patients in the group with metastases and in the group with tumors with signet ring cells was significantly reduced in the presence of mutations in epigenetic regulation genes (p = 0.036 and p = 0.041, respectively). Separately, somatic mutations in chromatin remodeling genes correlate with low survival rate of patients without distant metastasis (p = 0.045) and in the presence of signet ring cells (p = 0.0014). Our results suggest that mutations in epigenetic regulation genes may be valuable clinical markers and deserve further exploration in independent cohorts.