A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis

The quest for environmental analytical microbiology: absolute quantitative microbiome using cellular internal standards

BACKGROUND

High-throughput sequencing has revolutionized environmental microbiome research, providing both quantitative and qualitative insights into nucleic acid targets in the environment. The resulting microbial composition (community structure) data are essential for environmental analytical microbiology, enabling characterization of community dynamics and assessing microbial pollutants for the development of intervention strategies. However, the relative abundances derived from sequencing impede comparisons across samples and studies.

RESULTS

This review systematically summarizes various absolute quantification (AQ) methods and their applications to obtain the absolute abundance of microbial cells and genetic elements. By critically comparing the strengths and limitations of AQ methods, we advocate the use of cellular internal standard-based high-throughput sequencing as an appropriate AQ approach for studying environmental microbiome originated from samples of complex matrices and high heterogeneity. To minimize ambiguity and facilitate cross-study comparisons, we outline essential reporting elements for technical considerations, and provide a checklist as a reference for environmental microbiome research.

CONCLUSIONS

In summary, we propose absolute microbiome quantification using cellular internal standards for environmental analytical microbiology, and we anticipate that this approach will greatly benefit future studies. Video Abstract.

Genetic Prοpensity for Different Aspects of Dementia Pathology and Cognitive Decline in a Community Elderly Population

In the present study, we investigated the association of genetic predisposition with specific dimensions of dementia pathophysiology for global and domain-specific cognitive decline in older adults. The sample was drawn from the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) study, comprising 512 cognitively normal individuals over 64 years of age, with a mean follow-up of 2.9 years. Cognitive function was evaluated through a neuropsychological test battery, while genetic predisposition was assessed based on two distinct Polygenic Risk Scores (PRS) for amyloid-beta 42 (Aβ42) and white matter hyperintensities (WMH). The association of each PRS with the cognitive decline rate was examined using generalized estimating equation models. In the whole sample, higher PRSs Aβ42 (β = -0.042) and WMH (β =-0.029) were associated with a higher rate of global cognitive decline per year, an association which remained significant in age, sex, and education subgroups. Moreover, higher PRSs Aβ42 and WMH were related to significant memory decline only in females, older, and highly educated participants. Thus, while the association of both PRSs with global cognitive decline over time was independent of age, sex, or education, the relationship of the specific PRSs with the memory decline rate appeared to vary depending on these factors.

Walking time and genetic predisposition for Alzheimer's disease: Results from the HELIAD study

Objective: Our study aimed to explore whether physical condition might affect the association between genetic predisposition for Alzheimer's Disease (AD) and AD incidence. Methods: The sample of participants consisted of 561 community-dwelling adults over 64 years old, without baseline dementia (508 cognitively normal and 53 with mild cognitive impairment), deriving from the HELIAD, an ongoing longitudinal study with follow-up evaluations every 3 years. Physical condition was assessed at baseline through walking time (WT), while a Polygenic Risk Score for late onset AD (PRS-AD) was used to estimate genetic predisposition. The association between WT and PRS-AD with AD incidence was evaluated with Cox proportional hazard models adjusted for age, sex, education years, global cognition score and APOE ε-4 genotype. Then, the association between WT and AD incidence was investigated after stratifying participants by low and high PRS-AD. Finally, we examined the association between PRS-AD and AD incidence after stratifying participants by WT. Results: Both WT and PRS-AD were connected with increased AD incidence (p < 0.05), after adjustments. In stratified analyses, in the slow WT group participants with a greater genetic risk had a 2.5-fold higher risk of developing AD compared to participants with lower genetic risk (p = 0.047). No association was observed in the fast WT group or when participants were stratified based on PRS-AD. Conclusions: Genetic predisposition for AD is more closely related to AD incidence in the group of older adults with slow WT. Hence, physical condition might be a modifier in the relationship of genetic predisposition with AD incidence.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Variability of 7K and 11K SomaScan Plasma Proteomics Assays

SomaScan is an aptamer-based proteomics assay designed for the simultaneous measurement of thousands of human proteins with a broad range of endogenous concentrations. The 7K SomaScan assay has recently been expanded into the new 11K version. Following up on our previous assessment of the 7K assay, here, we expand our work on technical replicates from donors enrolled in the Baltimore Longitudinal Study of Aging. By generating SomaScan data from a second batch of technical replicates in the 7K version as well as additional intra- and interplate replicate measurements in the new 11K version using the same donor samples, this work provides useful precision benchmarks for the SomaScan user community. Beyond updating our previous technical assessment of the 7K assay with increased statistics, here, we estimate interbatch variability, assess inter- and intraplate variability in the new 11K assay, compare the observed variability between the 7K and 11K assays (leveraging the use of overlapping pairs of technical replicates), and explore the potential effects of sample storage time (ranging from 2 to 30 years) in the assays' precision.

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.

Circulating microRNAs associated with bronchodilator response in childhood asthma

BACKGROUND

Bronchodilator response (BDR) is a measure of improvement in airway smooth muscle tone, inhibition of liquid accumulation and mucus section into the lumen in response to short-acting beta-2 agonists that varies among asthmatic patients. MicroRNAs (miRNAs) are well-known post-translational regulators. Identifying miRNAs associated with BDR could lead to a better understanding of the underlying complex pathophysiology.

OBJECTIVE

The purpose of this study is to identify circulating miRNAs associated with bronchodilator response in asthma and decipher possible mechanism of bronchodilator response variation.

METHODS

We used available small RNA sequencing on blood serum from 1,134 asthmatic children aged 6 to 14 years who participated in the Genetics of Asthma in Costa Rica Study (GACRS). We filtered the participants into the highest and lowest bronchodilator response (BDR) quartiles and used DeSeq2 to identify miRNAs with differential expression (DE) in high (N = 277) vs. low (N = 278) BDR group. Replication was carried out in the Leukotriene modifier Or Corticosteroids or Corticosteroid-Salmeterol trial (LOCCS), an adult asthma cohort. The putative target genes of DE miRNAs were identified, and pathway enrichment analysis was performed.

RESULTS

We identified 10 down-regulated miRNAs having odds ratios (OR) between 0.37 and 0.76 for a doubling of miRNA counts and one up-regulated miRNA (OR = 2.26) between high and low BDR group. These were assessed for replication in the LOCCS cohort, where two miRNAs (miR-200b-3p and miR-1246) were associated. Further, functional annotation of 11 DE miRNAs were performed as well as of two replicated miRs. Target genes of these miRs were enriched in regulation of cholesterol biosynthesis by SREBPs, ESR-mediated signaling, G1/S transition, RHO GTPase cycle, and signaling by TGFB family pathways.

CONCLUSION

MiRNAs miR-1246 and miR-200b-3p are associated with both childhood and adult asthma BDR. Our findings add to the growing body of evidence that miRNAs play a significant role in the difference of asthma treatment response among patients as it points to genomic regulatory machinery underlying difference in bronchodilator response among patients.

TRIAL REGISTRATION

LOCCS cohort [ClinicalTrials.gov number NCT00156819, Registration date 20050912], GACRS cohort [ClinicalTrials.gov number NCT00021840].

Assessing and mitigating batch effects in large-scale omics studies

Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

Thinking points for effective batch correction on biomedical data

Batch effects introduce significant variability into high-dimensional data, complicating accurate analysis and leading to potentially misleading conclusions if not adequately addressed. Despite technological and algorithmic advancements in biomedical research, effectively managing batch effects remains a complex challenge requiring comprehensive considerations. This paper underscores the necessity of a flexible and holistic approach for selecting batch effect correction algorithms (BECAs), advocating for proper BECA evaluations and consideration of artificial intelligence-based strategies. We also discuss key challenges in batch effect correction, including the importance of uncovering hidden batch factors and understanding the impact of design imbalance, missing values, and aggressive correction. Our aim is to provide researchers with a robust framework for effective batch effects management and enhancing the reliability of high-dimensional data analyses.

Multivariate analyses and machine learning link sex and age with antibody responses to SARS-CoV-2 and vaccination

Prevention of negative COVID-19 infection outcomes is associated with the quality of antibody responses, whose variance by age and sex is poorly understood. Network approaches identified sex and age effects in antibody responses and neutralization potential of de novo infection and vaccination throughout the COVID-19 pandemic. Neutralization values followed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific receptor binding immunoglobulin G (RIgG), spike immunoglobulin G (SIgG) and spike and receptor immunoglobulin G (S, and RIgA) levels based on COVID-19 status. Serum immunoglobulin A (IgA) antibody titers correlated with neutralization only in females 40-60 years old (y.o.). Network analysis found males could improve IgA responses after vaccination dose 2. Complex correlation analyses found vaccination induced less antibody isotype switching and neutralization in older persons, especially in females. Sex-dependent antibody and neutralization decayed the fastest in older males. Shown sex and age characterization can direct studies integrating cell-mediated responses to define yet elusive correlates of protection and inform age and sex precision-focused vaccine design.

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.

Cerebral Amyloidosis in Individuals with Subjective Cognitive Decline: From Genetic Predisposition to Actual Cerebrospinal Fluid Measurements

The possible relationship between Subjective Cognitive Decline (SCD) and dementia needs further investigation. In the present study, we explored the association between specific biomarkers of Alzheimer's Disease (AD), amyloid-beta 42 (Aβ42) and Tau with the odds of SCD using data from two ongoing studies. In total, 849 cognitively normal (CN) individuals were included in our analyses. Among the participants, 107 had available results regarding cerebrospinal fluid (CSF) Aβ42 and Tau, while 742 had available genetic data to construct polygenic risk scores (PRSs) reflecting their genetic predisposition for CSF Aβ42 and plasma total Tau levels. The associations between AD biomarkers and SCD were tested using logistic regression models adjusted for possible confounders such as age, sex, education, depression, and baseline cognitive test scores. Abnormal values of CSF Aβ42 were related to 2.5-fold higher odds of SCD, while higher polygenic loading for Aβ42 was associated with 1.6-fold higher odds of SCD. CSF Tau, as well as polygenic loading for total Tau, were not associated with SCD. Thus, only cerebral amyloidosis appears to be related to SCD status, either in the form of polygenic risk or actual CSF measurements. The temporal sequence of amyloidosis being followed by tauopathy may partially explain our findings.

Principled and interpretable alignability testing and integration of single-cell data

Single-cell data integration can provide a comprehensive molecular view of cells, and many algorithms have been developed to remove unwanted technical or biological variations and integrate heterogeneous single-cell datasets. Despite their wide usage, existing methods suffer from several fundamental limitations. In particular, we lack a rigorous statistical test for whether two high-dimensional single-cell datasets are alignable (and therefore should even be aligned). Moreover, popular methods can substantially distort the data during alignment, making the aligned data and downstream analysis difficult to interpret. To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data with the same type of features. SMAI provides a statistical test to robustly assess the alignability between datasets to avoid misleading inference and is justified by high-dimensional statistical theory. On a diverse range of real and simulated benchmark datasets, it outperforms commonly used alignment methods. Moreover, we show that SMAI improves various downstream analyses such as identification of differentially expressed genes and imputation of single-cell spatial transcriptomics, providing further biological insights. SMAI's interpretability also enables quantification and a deeper understanding of the sources of technical confounders in single-cell data.

Genetic Predisposition for White Matter Hyperintensities and Risk of Mild Cognitive Impairment and Alzheimer's Disease: Results from the HELIAD Study

The present study investigated the association of genetic predisposition for white matter hyperintensities (WMHs) with incident amnestic mild cognitive impairment (aMCI) or Alzheimer's disease (AD), as well as whether such an association was influenced by age, sex, and cognitive reserve. Overall, 537 individuals without aMCI or dementia at baseline were included. Among them, 62 individuals developed aMCI/AD at follow up. Genetic propensity to WMH was estimated using a polygenic risk score for WMHs (PRS WMH). The association of PRS WMH with aMCI/AD incidence was examined using COX models. A higher PRS WMH was associated with a 47.2% higher aMCI/AD incidence (p = 0.015) in the fully adjusted model. Subgroup analyses showed significant results in the older age group, in which individuals with a higher genetic predisposition for WMHs had a 3.4-fold higher risk for developing aMCI/AD at follow up (p < 0.001), as well as in the lower cognitive reserve (CR, proxied by education years) group, in which individuals with a higher genetic predisposition for WMHs had an over 2-fold higher risk (p = 0.013). Genetic predisposition for WMHs was associated with aMCI/AD incidence, particularly in the group of participants with a low CR. Thus, CR might be a modifier in the relationship between genetic predisposition for WMHs and incident aMCI/AD.

MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects

Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Establishing reference material for the quest towards standardization in environmental microbial metagenomic studies

Breakthroughs in DNA-based technologies, especially in metagenomic sequencing, have drastically enhanced researchers' ability to explore environmental microbiome and the associated interplays within. However, as new methodologies are being actively developed for improvements in different aspects, metagenomic workflows become diversified and heterogeneous. Through a single-variable control approach, we quantified the microbial profiling variations arising from 6 common technical variables associated with metagenomic workflows for both simple and complex samples. The incurred variations were constantly the lowest in replicates of DNA isolation and DNA sequencing library construction. Different DNA extraction kits often caused the highest variation among all the tested variables. Additionally, sequencing run batch was an important source of variability for targeted platforms. As such, the development of an environmental reference material for complex environmental samples could be beneficial in benchmarking accrued non-biological variability within and between protocols and insuring reliable and reproducible sequencing outputs immediately upstream of bioinformatic analysis. To develop an environment reference material, sequencing of a well-homogenized environmental sample composed of activated sludge was performed using different pre-analytical assays in replications. In parallel, a certified mock community was processed and sequenced. Assays were ranked based on the reconstruction of the theoretical mock community profile. The reproducibility of the best-performing assay and the microbial profile of the reference material were further ascertained. We propose the adoption of our complex environmental reference material, which could reflect the degree of diversity in environmental microbiome studies, to facilitate accurate, reproducible, and comparable environmental metagenomics-based studies.

A primer on correlation-based dimension reduction methods for multi-omics analysis

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

Advances in methylation analysis of liquid biopsy in early cancer detection of colorectal and lung cancer

Methylation patterns in cell-free DNA (cfDNA) have emerged as a promising genomic feature for detecting the presence of cancer and determining its origin. The purpose of this study was to evaluate the diagnostic performance of methylation-sensitive restriction enzyme digestion followed by sequencing (MRE-Seq) using cfDNA, and to investigate the cancer signal origin (CSO) of the cancer using a deep neural network (DNN) analyses for liquid biopsy of colorectal and lung cancer. We developed a selective MRE-Seq method with DNN learning-based prediction model using demethylated-sequence-depth patterns from 63,266 CpG sites using SacII enzyme digestion. A total of 191 patients with stage I-IV cancers (95 lung cancers and 96 colorectal cancers) and 126 noncancer participants were enrolled in this study. Our study showed an area under the receiver operating characteristic curve (AUC) of 0.978 with a sensitivity of 78.1% for colorectal cancer, and an AUC of 0.956 with a sensitivity of 66.3% for lung cancer, both at a specificity of 99.2%. For colorectal cancer, sensitivities for stages I-IV ranged from 76.2 to 83.3% while for lung cancer, sensitivities for stages I-IV ranged from 44.4 to 78.9%, both again at a specificity of 99.2%. The CSO model's true-positive rates were 94.4% and 89.9% for colorectal and lung cancers, respectively. The MRE-Seq was found to be a useful method for detecting global hypomethylation patterns in liquid biopsy samples and accurately diagnosing colorectal and lung cancers, as well as determining CSO of the cancer using DNN analysis.Trial registration: This trial was registered at ClinicalTrials.gov (registration number: NCT04253509) for lung cancer on 5 February 2020, https://clinicaltrials.gov/ct2/show/NCT04253509 . Colorectal cancer samples were retrospectively registered at CRIS (Clinical Research Information Service, registration number: KCT0008037) on 23 December 2022, https://cris.nih.go.kr , https://who.init/ictrp . Healthy control samples were retrospectively registered.

Circulating MicroRNAs associated with Bronchodilator Response in Childhood Asthma

Rationale

Bronchodilator response (BDR) is a measure of improvement in airway smooth muscle tone, inhibition of liquid accumulation and mucus section into the lumen in response to short-acting beta-2 agonists that varies among asthmatic patients. MicroRNAs (miRNAs) are well-known post-translational regulators. Identifying miRNAs associated with BDR could lead to a better understanding of the underlying complex pathophysiology.

Objective

The purpose of this study is to identify circulating miRNAs associated with bronchodilator response in asthma and decipher possible mechanism of bronchodilator response variation.

Methods

We used available small RNA sequencing on blood serum from 1,134 asthmatic children aged 6 to 14 years who participated in the Genetics of Asthma in Costa Rica Study (GACRS). We filtered the participants into high and low bronchodilator response (BDR) quartiles and used DeSeq2 to identify miRNAs with differential expression (DE) in high (N= 277) vs low (N= 278) BDR group. Replication was carried out in the Leukotriene modifier Or Corticosteroids or Corticosteroid-Salmeterol trial (LOCCS), an adult asthma cohort. The putative target genes of DE miRNAs were identified, and pathway enrichment analysis was performed.

Results

We identified 10 down-regulated miRNAs having odds ratios (OR) between 0.37 and 0.76 for a doubling of miRNA counts and one up-regulated miRNA (OR=2.26) between high and low BDR group. These were assessed for replication in the LOCCS cohort, where two miRNAs (miR-200b-3p and miR-1246) were associated. Further, functional annotation of 11 DE miRNAs were performed as well as of two replicated miRs. Target genes of these miRs were enriched in regulation of cholesterol biosynthesis by SREBPs, ESR-mediated signaling, G1/S transition, RHO GTPase cycle, and signaling by TGFB family pathways.

Conclusion

MiRNAs miR-1246 and miR-200b-3p are associated with both childhood and adult asthma BDR. Our findings add to the growing body of evidence that miRNAs play a significant role in the difference of asthma treatment response among patients as it points to genomic regulatory machinery underlying difference in bronchodilator response among patients.

Trial registration

LOCCS cohort [ClinicalTrials.gov number: NCT00156819], GACRS cohort [ClinicalTrials.gov number: NCT00021840].

Systems Genomics Reveals microRNA Regulation of ICS Response in Childhood Asthma

BACKGROUND

Asthmatic patients' responses to inhaled corticosteroids (ICS) are variable and difficult to quantify. We have previously defined a Cross-sectional Asthma STEroid Response (CASTER) measure of ICS response. MicroRNAs (miRNAs) have shown strong effects on asthma and inflammatory processes.

OBJECTIVE

The purpose of this study was to identify key associations between circulating miRNAs and ICS response in childhood asthma.

METHODS

Small RNA sequencing in peripheral blood serum from 580 children with asthma on ICS treatment from The Genetics of Asthma in Costa Rica Study (GACRS) was used to identify miRNAs associated with ICS response using generalized linear models. Replication was conducted in children on ICS from the Childhood Asthma Management Program (CAMP) cohort. The association between replicated miRNAs and the transcriptome of lymphoblastoid cell lines in response to a glucocorticoid was assessed.

RESULTS

The association study on the GACRS cohort identified 36 miRNAs associated with ICS response at 10% false discovery rate (FDR), three of which (miR-28-5p, miR-339-3p, and miR-432-5p) were in the same direction of effect and significant in the CAMP replication cohort. In addition, in vitro steroid response lymphoblastoid gene expression analysis revealed 22 dexamethasone responsive genes were significantly associated with three replicated miRNAs. Furthermore, Weighted Gene Co-expression Network Analysis (WGCNA) revealed a significant association between miR-339-3p and two modules (black and magenta) of genes associated with immune response and inflammation pathways.

CONCLUSION

This study highlighted significant association between circulating miRNAs miR-28-5p, miR-339-3p, and miR-432-5p and ICS response. miR-339-3p may be involved in immune dysregulation, which leads to a poor response to ICS treatment.

The importance of batch sensitization in missing value imputation

Data analysis is complex due to a myriad of technical problems. Amongst these, missing values and batch effects are endemic. Although many methods have been developed for missing value imputation (MVI) and batch correction respectively, no study has directly considered the confounding impact of MVI on downstream batch correction. This is surprising as missing values are imputed during early pre-processing while batch effects are mitigated during late pre-processing, prior to functional analysis. Unless actively managed, MVI approaches generally ignore the batch covariate, with unknown consequences. We examine this problem by modelling three simple imputation strategies: global (M1), self-batch (M2) and cross-batch (M3) first via simulations, and then corroborated on real proteomics and genomics data. We report that explicit consideration of batch covariates (M2) is important for good outcomes, resulting in enhanced batch correction and lower statistical errors. However, M1 and M3 are error-generating: global and cross-batch averaging may result in batch-effect dilution, with concomitant and irreversible increase in intra-sample noise. This noise is unremovable via batch correction algorithms and produces false positives and negatives. Hence, careless imputation in the presence of non-negligible covariates such as batch effects should be avoided.

Influence of RNA-Seq library construction, sampling methods, and tissue harvesting time on gene expression estimation

RNA sequencing (RNA-Seq) is popular for measuring gene expression in non-model organisms, including wild populations. While RNA-Seq can detect gene expression variation among wild-caught individuals and yield important insights into biological function, sampling methods can also affect gene expression estimates. We examined the influence of multiple technical variables on estimated gene expression in a non-model fish, the westslope cutthroat trout (Oncorhynchus clarkii lewisi), using two RNA-Seq library types: 3' RNA-Seq (QuantSeq) and whole mRNA-Seq (NEB). We evaluated effects of dip netting versus electrofishing, and of harvesting tissue immediately versus 5 min after euthanasia on estimated gene expression in blood, gill, and muscle. We found no significant differences in gene expression between sampling methods or tissue collection times with either library type. When library types were compared using the same blood samples, 58% of genes detected by both NEB and QuantSeq showed significantly different expression between library types, and NEB detected 31% more genes than QuantSeq. Although the two library types recovered different numbers of genes and expression levels, results with NEB and QuantSeq were consistent in that neither library type showed differences in gene expression between sampling methods and tissue harvesting times. Our study suggests that researchers can safely rely on different fish sampling strategies in the field. In addition, while QuantSeq is more cost effective, NEB detects more expressed genes. Therefore, when it is crucial to detect as many genes as possible (especially low expressed genes), when alternative splicing is of interest, or when working with an organism lacking good genomic resources, whole mRNA-Seq is more powerful.

Circulating metals, leukocyte microRNAs and microRNA networks: A profiling and functional analysis in Chinese adults

BACKGROUND

Metals in the human body represent both environmental exposure and nutritional status. Little is known about the miRNA signature in relation to circulating metals in humans.

OBJECTIVES

To characterize metal-associated miRNAs in leukocytes, individually and collectively as networks.

METHODS

In a panel of 160 Chinese adults, we measured 23 metals/metalloids in plasma, and sequenced miRNAs and mRNAs in leukocytes. We used linear regression to model the associations between ln-transformed metal concentrations and normalized miRNA levels adjusting for potential confounders. We inferred the enriched leukocyte subtypes for the identified miRNAs using an association approach. We utilized mRNA sequencing data to explore miRNA functions. We also constructed modules to identify metal-associated miRNA networks.

RESULTS

We identified 55 metal-associated miRNAs at false discovery rate-adjusted P < 0.05. In particular, we found that lead, nickel, and vanadium were positively associated with potentially lymphocyte-enriched miR-142-3p, miR-150-3p, miR-28-5p, miR-361-3p, and miR-769-5p, and were inversely associated with potentially granulocyte-enriched let-7a/c/d-5p and miR-1294. Interestingly, the five lymphocyte-enriched miRNAs inhibited, whereas miR-1294 activated, ROS and DNA repair pathways. We further confirmed the findings using oxidative damage biomarkers. Next, we clustered co-expressed miRNAs into modules, and identified four miRNA modules that were associated with different metals. The identified modules represented miRNAs enriched in different leukocyte subtypes, and were involved in biological processes including hematopoiesis and immune response, mitochondrial functions, and response to the stimulus.

CONCLUSIONS

At commonly exposed low levels, circulating metals were associated with distinct miRNA signatures in leukocytes. The identified miRNAs, individually or as regulatory networks, may provide a mechanistic link between metal exposure and pathophysiological changes in the immune system.

Genetic susceptibility loci for Chlamydia trachomatis endometrial infection influence expression of genes involved in T cell function, tryptophan metabolism and epithelial integrity

Objectives

Identify genetic loci of enhanced susceptibility to Chlamydial trachomatis (Ct) upper genital tract infection in women.

Methods

We performed an integrated analysis of DNA genotypes and blood-derived mRNA profiles from 200 Ct-exposed women to identify expression quantitative trait loci (eQTL) and determine their association with endometrial chlamydial infection using a mediation test. We further evaluated the effect of a lead eQTL on the expression of CD151 by immune cells from women with genotypes associated with low and high whole blood expression of CD151, respectively.

Results

We identified cis-eQTLs modulating mRNA expression of 81 genes (eGenes) associated with altered risk of ascending infection. In women with endometrial infection, eGenes involved in proinflammatory signaling were upregulated. Downregulated eGenes included genes involved in T cell functions pivotal for chlamydial control. eGenes encoding molecules linked to metabolism of tryptophan, an essential chlamydial nutrient, and formation of epithelial tight junctions were also downregulated in women with endometrial infection. A lead eSNP rs10902226 was identified regulating CD151, a tetrospanin molecule important for immune cell adhesion and migration and T cell proliferation. Further in vitro experiments showed that women with a CC genotype at rs10902226 had reduced rates of endometrial infection with increased CD151 expression in whole blood and T cells when compared to women with a GG genotype.

Conclusions

We discovered genetic variants associated with altered risk for Ct ascension. A lead eSNP for CD151 is a candidate genetic marker for enhanced CD4 T cell function and reduced susceptibility.

Perspectives for better batch effect correction in mass-spectrometry-based proteomics

Mass-spectrometry-based proteomics presents some unique challenges for batch effect correction. Batch effects are technical sources of variation, can confound analysis and usually non-biological in nature. As proteomic analysis involves several stages of data transformation from spectra to protein, the decision on when and what to apply batch correction on is often unclear. Here, we explore several relevant issues pertinent to batch effect correct considerations. The first involves applications of batch effect correction requiring prior knowledge on batch factors and exploring data to uncover new/unknown batch factors. The second considers recent literature that suggests there is no single best batch effect correction algorithm---i.e., instead of a best approach, one may instead ask, what is a suitable approach. The third section considers issues of batch effect detection. And finally, we look at potential developments for proteomic-specific batch effect correction methods and how to do better functional evaluations on batch corrected data.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Efficient and specific DNA oligonucleotide rRNA probe-based rRNA removal in Talaromyces marneffei

Emerging evidence showed that lncRNAs play important roles in a wide range of biological processes of fungi such as Saccharomyces cerevisiae. However, systemic identification of lncRNAs in non-model fungi is a challenging task as the efficiency of rRNA removal has been proved to be affected by mismatches of universal rRNA-targeting probes of commercial kits, which forces deeper sequencing depth and increases costs. Here, we developed a low-cost and simple rRNA depletion method (rProbe) that could efficiently remove more than 99% rRNA in both yeast and mycelium samples of Talaromyces marneffei. The efficiency and robustness of rProbe were demonstrated to outperform the Illumina Ribo-Zero kit. Using rProbe RNA-seq, we identified 115 differentially expressed lncRNAs and constructed lncRNA-mRNA co-expression network related to dimorphic switch of T. marneffei. Our rRNA removal method has the potential to be a useful tool to explore non-coding transcriptomes of non-model fungi by adjusting rRNA probe sequences species specifically.

Blood miRNAs Are Linked to Frequent Asthma Exacerbations in Childhood Asthma and Adult COPD

MicroRNAs have been independently associated with asthma and COPD; however, it is unclear if microRNA associations will overlap when evaluating retrospective acute exacerbations. Objective: We hypothesized that peripheral blood microRNAs would be associated with retrospective acute asthma exacerbations in a pediatric asthma cohort and that such associations may also be relevant to acute COPD exacerbations. Methods: We conducted small-RNA sequencing on 374 whole-blood samples from children with asthma ages 6-14 years who participated in the Genetics of Asthma in Costa Rica Study (GACRS) and 450 current and former adult smokers with and without COPD who participated in the COPDGene study. Measurements and Main Results: After QC, we had 351 samples and 649 microRNAs for Differential Expression (DE) analysis between the frequent (n = 183) and no or infrequent exacerbation (n = 168) groups in GACRS. Fifteen upregulated miRs had odds ratios (OR) between 1.22 and 1.59 for a doubling of miR counts, while five downregulated miRs had ORs between 0.57 and 0.8. These were assessed for generalization in COPDGene, where three of the upregulated miRs (miR-532-3p, miR-296-5p, and miR-766-3p) and two of the downregulated miRs (miR-7-5p and miR-451b) replicated. Pathway enrichment analysis showed MAPK and PI3K-Akt signaling pathways were strongly enriched for target genes of DE miRNAs and miRNAs generalizing to COPD exacerbations, as well as infection response pathways to various pathogens. Conclusion: miRs (451b; 7-5p; 532-3p; 296-5p and 766-3p) associated with both childhood asthma and adult COPD exacerbations may play a vital role in airflow obstruction and exacerbations and point to shared genomic regulatory machinery underlying exacerbations in both diseases.

Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification approach

Considering human brain disorders, Major Depressive Disorder (MDD) is seen as a lethal disease in which a person goes to the extent of suicidal behavior. Physical detection of MDD patients is less precise but machine learning can aid in improved classification of disease. The present research included three RNA-seq data classes to classify DEGs and then train key gene data using a random forest machine learning method. The three classes in the sample are 29 CON (sudden death healthy control), 21 MDD-S (a Major Depressive Disorder Suicide) being included in the second group, and 9 MDD (non-suicides MDD) which are included in the third group. With PCA analysis, 99 key genes were obtained. 47.1% data variability is given by these 99 genes. The model training of 99 genes indicated improved classification. The RF classification model has an accuracy of 61.11% over test data and 97.56% over train data. It was also noticed that the RF method offered greater accuracy than the KNN method. 99 genes were annotated using DAVID and ClueGo packages. Some of the important pathways and function observed in the study were glutamatergic synapse, GABA receptor activation, long-term synaptic depression, and morphine addiction. Out Of 99 genes, four genes, namely DLGAP1, GNG2, GRIA1, and GRIA4, were found to be predominantly involved in the glutamatergic synapse pathway. Another substantial link was observed in the GABA receptor activation involving the following two genes, GABBR2 and GNG2. Also, the genes found responsible for long-term synaptic depression were GRIA1, MAPT, and PTEN. There was another finding of morphine addiction which comprises three genes, namely GABBR2, GNG2, and PDE4D. For massive datasets, this approach will act as the gold standard. The cases of CON, MDD, and MDD-S are physically distinct. There was dysregulation in the expression level of 12 genes. The 12 genes act as a possible biomarker for Major Depressive Disorder and open up a new path for depressed subjects to explore further.

Large-Scale Meta-Longitudinal Microbiome Data with a Known Batch Factor

Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts.

Curation, inference, and assessment of a globally reconstructed gene regulatory network for Streptomyces coelicolor

Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. Even though S. coelicolor has an outstanding variety of regulators among bacteria, little effort to globally study its transcription has been made. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~ 41% of the total expected interactions). This provides the most extensive and up-to-date reconstruction available for the regulatory circuitry of this organism. Only ~ 6% (534/9707) are supported by experiments confirming the binding of the transcription factor to the upstream region of the target gene, the so-called “strong” evidence. While for the remaining interactions there is no confirmation of direct binding. To tackle network incompleteness, we performed network inference using several methods (including two proposed here) for motif identification in DNA sequences and GRN inference from transcriptomics. Further, we contrasted the structural properties and functional architecture of the networks to assess the reliability of the predictions, finding the inference from DNA sequence data to be the most trustworthy approach. Finally, we show two applications of the inferred and the curated networks. The inference allowed us to propose novel transcription factors for the key Streptomyces antibiotic regulatory proteins (SARPs). The curated network allowed us to study the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum. There we identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as Supplementary Material.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [18F]FDG PET/CT in Early Triple-Negative Breast Cancer

Simple Summary

The aim of this study was to evaluate PET/CT parameters to determine different prognostic groups in TNBC, in order to select patients with a high risk of relapse, for whom therapeutic escalation can be considered. We have demonstrated that the MTV, TLG and entropy of the primary breast lesion could be of interest to predict the prognostic outcome of TNBC patients.

Abstract

(1) Background: triple-negative breast cancer (TNBC) remains a clinical and therapeutic challenge primarily affecting young women with poor prognosis. TNBC is currently treated as a single entity but presents a very diverse profile in terms of prognosis and response to treatment. Positron emission tomography/computed tomography (PET/CT) with ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) is gaining importance for the staging of breast cancers. TNBCs often show high [¹⁸F]FDG uptake and some studies have suggested a prognostic value for metabolic and volumetric parameters, but no study to our knowledge has examined textural features in TNBC. The objective of this study was to evaluate the association between metabolic, volumetric and textural parameters measured at the initial [¹⁸F]FDG PET/CT and disease-free survival (DFS) and overall survival (OS) in patients with nonmetastatic TBNC. (2) Methods: all consecutive nonmetastatic TNBC patients who underwent a [¹⁸F]FDG PET/CT examination upon diagnosis between 2012 and 2018 were retrospectively included. The metabolic and volumetric parameters (SUV_max, SUV_mean, SUV_peak, MTV, and TLG) and the textural features (entropy, homogeneity, SRE, LRE, LGZE, and HGZE) of the primary tumor were collected. (3) Results: 111 patients were enrolled (median follow-up: 53.6 months). In the univariate analysis, high TLG, MTV and entropy values of the primary tumor were associated with lower DFS (p = 0.008, p = 0.006 and p = 0.025, respectively) and lower OS (p = 0.002, p = 0.001 and p = 0.046, respectively). The discriminating thresholds for two-year DFS were calculated as 7.5 for MTV, 55.8 for TLG and 2.6 for entropy. The discriminating thresholds for two-year OS were calculated as 9.3 for MTV, 57.4 for TLG and 2.67 for entropy. In the multivariate analysis, lymph node involvement in PET/CT was associated with lower DFS (p = 0.036), and the high MTV of the primary tumor was correlated with lower OS (p = 0.014). (4) Conclusions: textural features associated with metabolic and volumetric parameters of baseline [¹⁸F]FDG PET/CT have a prognostic value for identifying high-relapse-risk groups in early TNBC patients.

A transdisciplinary approach to understand the epigenetic basis of race/ethnicity health disparities

Health disparities correspond to differences in disease burden and mortality among socially defined population groups. Such disparities may emerge according to race/ethnicity, socioeconomic status and a variety of other social contexts, and are documented for a wide range of diseases. Here, we provide a transdisciplinary perspective on the contribution of epigenetics to the understanding of health disparities, with a special emphasis on disparities across socially defined racial/ethnic groups. Scientists in the fields of biological anthropology, bioinformatics and molecular epidemiology provide a summary of theoretical, statistical and practical considerations for conducting epigenetic health disparities research, and provide examples of successful applications from cancer research using this approach.

Overexpression of P4HA1 associates with poor prognosis and promotes cell proliferation and metastasis of lung adenocarcinoma

Prolyl 4-hydroxylase subunit alpha 1 (P4HA1) is the core active catalytic portion of prolyl 4-hydroxylase, and has contributed to tumorigenesis in several cancers. In this study, we identified that P4HA1 mRNA and protein are both up-regulated in non-small cell lung cancer (NSCLC). Besides, overexpressed P4HA1 is correlated with poor clinical outcomes and serve as an independent prognosis biomarker in lung adenocarcinoma (LUAD), but not lung squamous cell carcinoma (LUSC). In vitro studies, decreased P4HA1 significantly inhibits proliferation and cell cycle, by regulating cyclin-dependent kinases (CDKs), cyclins and CDK inhibitor (CKI). Moreover, via inhibiting epithelial-mesenchymal transition (EMT) and matrix metalloprotease (MMPs), dysregulation of P4HA1 could restrain the tumor cell invasion and metastasis of lung adenocarcinoma. In addition, we found that P4HA1 could enhance cell stemness and cisplatin-resistance in lung adenocarcinoma. In summary, P4HA1 plays a crucial role in the development of NSCLC and may provide a brand-new target for lung cancer treatment.

Extracellular miRNA biomarkers in neurologic disease: is cerebrospinal fluid helpful?

Aim: The aim of our work is to aggregate data from publications of cerebrospinal fluid extracellular miRNA to identify candidate diagnostic biomarkers, and those warranting further study. Materials & methods: Data were pooled from nine studies, encompassing 864 patients across 16 diseases. Unsupervised clustering grouped patients by a broad category of diseases. Results & conclusion: Compared with healthy controls, in patients with Alzheimer's disease, hsa-miR-767-5p was overexpressed (p < 0.001) and in patients with Huntington's disease, hsa-miR-361-3p was underexpressed (p < 10^-4). We also define a subset of extracellular miRNA as candidate biomarkers that are robustly detected across patients, studies and diseases; thereby, warranting further study.

A proteogenomic portrait of lung squamous cell carcinoma

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Seasonal Variation in miR-328-3p and let-7d-3p Are Associated With Seasonal Allergies and Asthma Symptoms in Children

OBJECTIVE

MicroRNAs (miRs) are small non-coding RNA molecules of around 18-22 nucleotides that are key regulators of many biologic processes, particularly inflammation. The purpose of this study was to determine the association of circulating miRs from asthmatic children with seasonal variation in allergic inflammation and asthma symptoms.

METHODS

We used available small RNA sequencing on blood serum from 398 children with mild-to-moderate asthma from the Childhood Asthma Management Program. We used seasonal asthma symptom data at the study baseline and allergen affection status from baseline skin prick tests as primary outcomes. We identified differentially expressed (DE) miRs between pairs of seasons using DESeq2. Regression analysis was used to identify associations between allergy status to specific seasonal allergens and DE miRs in 4 seasons and between seasonal asthma symptom data and DE miRs. We performed pathway enrichment analysis for target genes of the DE miRs using DAVID.

RESULTS

After quality control, 398 samples underwent differential analysis between the 4 seasons. We found 52 unique miRs from a total of 81 DE miRs across seasons. Further investigation of the association between these miRs and sensitization to seasonal allergens using skin prick tests revealed that 26 unique miRs from a total of 38 miRs were significantly associated with a same-season allergen. Comparison between seasonal asthma symptom data revealed that 2 of these 26 miRs also had significant associations with asthma symptoms in the same seasons: miR-328-3p (P < 0.03) and let-7d-3p (P < 0.05). Enrichment analysis showed that the most enriched pathway clusters were Rap1, Ras, and MAPK signaling pathways.

CONCLUSION

Our results show seasonal variation in miR-328-3p and let-7d-3p are significantly associated with seasonal asthma symptoms and seasonal allergies. These indicate a potentially protective role for let-7d-3p and a deleterious role for miR-328-3p in asthmatics sensitized to mulberry. Further work will determine whether these miRs are drivers or results of the allergic response.

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.

COPD-associated miR-145-5p is downregulated in early-decline FEV1 trajectories in childhood asthma

BACKGROUND

Many microRNAs (miRNAs) have been associated with asthma and chronic obstructive pulmonary disease (COPD). Longitudinal lung function growth trajectories of children with asthma-normal growth, reduced growth (RG), early decline (ED), and RG with an ED (RGED)-have been observed, with RG and RGED associated with adverse outcomes, including COPD.

OBJECTIVE

Our aim was to determine whether circulating miRNAs from an early age in children with asthma would be prognostic of reduced lung function growth patterns over the next 16 years.

METHODS

We performed small RNA sequencing on sera from 492 children aged 5 to 12 years with mild-to-moderate asthma from the CAMP clinical trial, who were subsequently followed for 12 to 16 years. miRNAs were assessed for differential expression between previously assigned lung function growth patterns.

RESULTS

We had 448 samples and 259 miRNAs for differential analysis. In a comparison of the normal and the most severe group (ie, normal growth compared with RGED), we found 1 strongly dysregulated miRNA, hsa-miR-145-5p (P < 8.01E-05). This miR was downregulated in both ED groups (ie, ED and RGED). We verified that miR-145-5p was strongly associated with airway smooth muscle cell growth in vitro.

CONCLUSION

Our results showed that miR-145-5p is associated with the ED patterns of lung function growth leading to COPD in children with asthma and additionally increases airway smooth muscle cell proliferation. This represents a significant extension of our understanding of the role of miR-145-5p in COPD and suggests that reduced expression of miR-145-5p is a risk factor for ED of long-term lung function.

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY

Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and the quadratic penalty method with a Smoothly-Clipped Absolute Deviation (SCAD) penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership, even in the presence of batch effects. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

Microbiota Assessments for the Identification and Confirmation of Slit Defect-Causing Bacteria in Milk and Cheddar Cheese

Validated methods are needed to detect spoilage microbes present in low numbers in foods and ingredients prior to defect onset. We applied propidium monoazide combined with 16S rRNA gene sequencing, qPCR, isolate identification, and pilot-scale cheese making to identify the microorganisms that cause slit defects in industrially produced Cheddar cheese. To investigate milk as the source of spoilage microbes, bacterial composition in milk was measured immediately before and after high-temperature, short-time (HTST) pasteurization over 10-h periods on 10 days and in the resulting cheese blocks. Besides HTST pasteurization-induced changes to milk microbiota composition, a significant increase in numbers of viable bacteria was observed over the 10-h run times of the pasteurizer, including 68-fold-higher numbers of the genus Thermus However, Thermus was not associated with slit development. Milk used to make cheese which developed slits instead contained a lower number of total bacteria, higher alpha diversity, and higher proportions of Lactobacillus, Bacillus, Brevibacillus, and Clostridium Only Lactobacillus proportions were significantly increased during cheese aging, and Limosilactobacillus (Lactobacillus) fermentum, in particular, was enriched in slit-containing cheeses and the pre- and post-HTST-pasteurization milk used to make them. Pilot-scale cheeses developed slits when inoculated with strains of L. fermentum, other heterofermentative lactic acid bacteria, or uncultured bacterial consortia from slit-associated pasteurized milk, thereby confirming that low-abundance taxa in milk can negatively affect cheese quality. The likelihood that certain microorganisms in milk cause slit defects can be predicted based on comparisons of the bacteria present in the milk used for cheese manufacture.IMPORTANCE Food production involves numerous control points for microorganisms to ensure quality and safety. These control points (e.g., pasteurization) are difficult to develop for fermented foods wherein some microbial contaminants are also expected to provide positive contributions to the final product and spoilage microbes may constitute only a small proportion of all microorganisms present. We showed that microbial composition assessments with 16S rRNA marker gene DNA sequencing are sufficiently robust to detect very-low-abundance bacterial taxa responsible for a major but sporadic Cheddar cheese spoilage defect. Bacterial composition in the (pasteurized) milk and cheese was associated with slit defect development. The application of Koch's postulates showed that individual bacterial isolates as well as uncultured bacterial consortia were sufficient to cause slits, even when present in very low numbers. This approach may be useful for detection and control of low-abundance spoilage microorganisms present in other foods.

Mast Cell Activation in the Systemic Sclerosis Esophagus

INTRODUCTION

Previously, we discovered similar esophageal gene expression patterns in patients with systemic sclerosis (SSc) and eosinophilic esophagitis (EoE) where eosinophil/mast cell-targeted therapies are beneficial. Because SSc and EoE patients experience similar esophageal symptoms, we hypothesized that eosinophil/mast cell-directed therapy may potentially benefit SSc patients. Herein, we determine the association between esophageal mast cell quantities, gene expression and clinical parameters in order to identify SSc patients who may benefit from eosinophil/mast cell-directed therapy.

METHODS

Esophageal biopsies from SSc patients and healthy participants were stained for tryptase, a mast cell marker, and associations with relevant clinical parameters including 24h esophageal pH testing were assessed. Intra-epithelial mast cell density was quantified by semi-automated microscopy. Microarray data were utilized for functional and gene set enrichment analyses and to identify intrinsic subset (IS) assignment, an SSc molecular classification system that includes inflammatory, proliferative, limited and normal-like subsets.

RESULTS

Esophageal biopsies from 40 SSc patients (39 receiving proton pump inhibition) and eleven healthy participants were studied. Mast cell numbers in both the upper esophagus (rs = 0.638, p = 0.004) and the entire (upper + lower) esophagus (rs = 0.562, p = 0.019) significantly correlated with acid exposure time percentage. The inflammatory, fibroproliferative, and normal-like ISs originally defined in skin biopsies were identified in esophageal biopsies. Although esophageal mast cell numbers in SSc patients and healthy participants were similar, gene expression for mast cell-related pathways showed significant upregulation in the inflammatory IS of SSc patients compared to patients classified as proliferative or normal-like.

DISCUSSION

Esophageal mast cell numbers are heterogeneous in SSc patients and may correlate with acid exposure. Patients with inflammatory IS profiles in the esophagus demonstrate more tryptase staining. Mast cell targeted therapy may be a useful therapeutic approach in SSc patients belonging to the inflammatory IS, but additional studies are warranted.

Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches

Triple-negative breast cancer (TNBC) heterogeneity represents one of the main obstacles to precision medicine for this disease. Recent concordant transcriptomics studies have shown that TNBC could be divided into at least three subtypes with potential therapeutic implications. Although a few studies have been conducted to predict TNBC subtype using transcriptomics data, the subtyping was partially sensitive and limited by batch effect and dependence on a given dataset, which may penalize the switch to routine diagnostic testing. Therefore, we sought to build an absolute predictor (i.e., intra-patient diagnosis) based on machine learning algorithms with a limited number of probes. To that end, we started by introducing probe binary comparison for each patient (indicators). We based the predictive analysis on this transformed data. Probe selection was first involved combining both filter and wrapper methods for variable selection using cross-validation. We tested three prediction models (random forest, gradient boosting [GB], and extreme gradient boosting) using this optimal subset of indicators as inputs. Nested cross-validation consistently allowed us to choose the best model. The results showed that the fifty selected indicators highlighted the biological characteristics associated with each TNBC subtype. The GB based on this subset of indicators performs better than other models.

How to do quantile normalization correctly for gene expression data analyses

Quantile normalization is an important normalization technique commonly used in high-dimensional data analysis. However, it is susceptible to class-effect proportion effects (the proportion of class-correlated variables in a dataset) and batch effects (the presence of potentially confounding technical variation) when applied blindly on whole data sets, resulting in higher false-positive and false-negative rates. We evaluate five strategies for performing quantile normalization, and demonstrate that good performance in terms of batch-effect correction and statistical feature selection can be readily achieved by first splitting data by sample class-labels before performing quantile normalization independently on each split (“Class-specific”). Via simulations with both real and simulated batch effects, we demonstrate that the “Class-specific” strategy (and others relying on similar principles) readily outperform whole-data quantile normalization, and is robust-preserving useful signals even during the combined analysis of separately-normalized datasets. Quantile normalization is a commonly used procedure. But when carelessly applied on whole datasets without first considering class-effect proportion and batch effects, can result in poor performance. If quantile normalization must be used, then we recommend using the “Class-specific” strategy.

Identification and validation of core genes for serous ovarian adenocarcinoma via bioinformatics analysis

Ovarian cancer is a fatal gynaecological malignancy in women worldwide, and serous ovarian cancer (SOC) is considered the most common histological subtype of this malignancy. Thus, the present study aimed to identify the core genes for SOC via bioinformatics analysis. The GSE18520 and GSE14407 datasets were downloaded from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs) and perform gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed to identify the core genes, while The Cancer Genome Atlas (TCGA) database was used to screen for prognosis-associated DEGs. Furthermore, clinical samples were collected for further validation of kinesin family member 11 (KIF11) gene. In the GEO analysis, a total of 198 DEGs were identified, including 81 upregulated and 117 downregulated genes compared SOC to normal tissue. GSEA across the two datasets demonstrated that 16 gene sets, including those involved in the cell cycle and DNA replication, were notably associated with SOC. A PPI network of the DEGs was constructed with 130 nodes and 387 edges. Subsequently, 20 core genes involved in the same top-ranked module were filtered out by submodule analysis. Survival analysis identified three predictive genes for SOC prognosis, including KIF11, CLDN3 and FGF13. KIF11 was identified as a core and predictive gene and thus was further validated using clinical samples. The results demonstrated that KIF11 was upregulated in tumour tissues compared with adjacent normal tissues and was associated with aggressive factors, including tumour grade, TNM stage and lymph node invasion. In conclusions, the present study identified the core genes and gene sets for SOC, thus extending the understanding of SOC occurrence and progression. Furthermore, KIF11 was identified as a promising tumour-promoting gene and a potential target for the diagnosis and treatment of SOC.

A new risk model comprising genes highly correlated with CD133 identifies different tumor-immune microenvironment subtypes impacting prognosis in hepatocellular carcinoma

The existence of cancer stem cells (CSCs), marked by CD133, is the primary cause of death in hepatocellular carcinoma (HCC). Here, we generated a new risk model comprising the signatures of four genes highly correlated with CD133 (CD133_(hi)) that help improve survival in HCC. Three datasets were used to identify the differential CD133_(hi) genes by comparing sorted CD133⁺ liver CSCs and CD133^- differentiated counterparts. Univariate analysis was used to screen significantly differential CD133_(hi) genes associated with overall survival in the training dataset, which were used for risk model construction. High-risk patients were strongly associated with poor survival by Kaplan-Meier survival analysis in both the training and validation datasets. Clinical stratification analyses further demonstrated that the risk factors acted as independent factors and that high-risk patients were characterized by more aggressive cancer features. Functional enrichment analyses performed by gene set enrichment analysis (GSEA) and the Database for Annotation, Visualization and Integrated Discovery (DAVID) revealed that high-risk patients showed the disturbance of immune hepatic homeostasis involving aberrant immune cells, including macrophages and T and B cells, and an abnormal inflammatory response including the IL6/Jak/STAT3 pathway and TNF signaling pathway. In conclusion, our constructed CD133_(hi) gene risk model provides a resource for understanding the role of CD133⁺ CSCs in the progression of HCC in terms of tumor-immune interactions.