p-Value Histograms: Inference and Diagnostics

Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain

Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.

Parkinson's disease-associated shifts between DNA methylation and DNA hydroxymethylation in human brain in PD-related genes, including PARK19 (DNAJC6) and PTPRN2 (IA-2β)

Background

The majority of Parkinson's disease (PD) cases are due to a complex interaction between aging, genetics, and environmental factors; epigenetic mechanisms are thought to act as important mediators of these risk factors. While multiple studies to date have explored the role of DNA modifications in PD, few focus on 5-hydroxymethylcytosine (5hmC). Because 5hmC occurs at its highest levels in the brain and is thought to be particularly important in the central nervous system, particularly in the response to neurotoxicants, it is important to explore the potential role of 5hmC in PD. This study expands on our previously published epigenome-wide association study (EWAS) performed on DNA isolated from neuron-enriched nuclei from human postmortem parietal cortex from the Banner Sun Health Research Institute Brain Bank. The study aimed to identify paired changes in 5hmC and 5mC in PD in enriched neuronal nuclei isolated from PD post-mortem parietal cortex and age- and sex-matched controls. We performed oxidative bisulfite (oxBS) conversion and paired it with our previously published bisulfite (BS)-based EWAS on the same samples to identify cytosines with significant shifts between these two related epigenetic marks. Interaction differentially modified cytosines (iDMCs) were identified using our recently published mixed-effects model for co-analyzing βmC and βhmC data.

Results

We identified 1,030 iDMCs with paired changes in 5mC and 5hmC (FDR < 0.05) that map to 695 genes, including PARK19 (DNAJC6), a familial PD gene, and PTPRN2 (IA-2), which has been previously implicated in PD in both epigenetic and mechanistic studies. The majority of iDMC-containing genes have not previously been implicated in PD and were not identified in our previous BS-based EWAS.

Conclusions

These data potentially link epigenetic regulation of the PARK19 and PTPRN2 loci in the pathogenesis of idiopathic PD. In addition, iDMC-containing genes have known functions in synaptic formation and function, cell cycle and senescence, neuroinflammation, and epigenetic regulation. These data suggest that there are significant shifts between 5mC and 5hmC associated with PD in genes relevant to PD pathogenesis that are not captured by analyzing BS-based data alone or by analyzing each mark as a distinct dataset.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

Functional connectivity of cognition-related brain networks in adults with fetal alcohol syndrome

BACKGROUND

Fetal alcohol syndrome (FAS) can result in cognitive dysfunction. Cognitive functions affected are subserved by few functional brain networks. Functional connectivity (FC) in these networks can be assessed with resting-state functional MRI (rs-fMRI). Alterations of FC have been reported in children and adolescents prenatally exposed to alcohol. Previous reports varied substantially regarding the exact nature of findings. The purpose of this study was to assess FC of cognition-related networks in young adults with FAS.

METHODS

Cross-sectional rs-fMRI study in participants with FAS (n = 39, age: 20.9 ± 3.4 years) and healthy participants without prenatal alcohol exposure (n = 44, age: 22.2 ± 3.4 years). FC was calculated as correlation between cortical regions in ten cognition-related sub-networks. Subsequent modelling of overall FC was based on linear models comparing FC between FAS and controls. Results were subjected to a hierarchical statistical testing approach, first determining whether there is any alteration of FC in FAS in the full cognitive connectome, subsequently resolving these findings to the level of either FC within each network or between networks based on the Higher Criticism (HC) approach for detecting rare and weak effects in high-dimensional data. Finally, group differences in single connections were assessed using conventional multiple-comparison correction. In an additional exploratory analysis, dynamic FC states were assessed.

RESULTS

Comparing FAS participants with controls, we observed altered FC of cognition-related brain regions globally, within 7 out of 10 networks, and between networks employing the HC statistic. This was most obvious in attention-related network components. Findings also spanned across subcomponents of the fronto-parietal control and default mode networks. None of the single FC alterations within these networks yielded statistical significance in the conventional high-resolution analysis. The exploratory time-resolved FC analysis did not show significant group differences of dynamic FC states.

CONCLUSIONS

FC in cognition-related networks was altered in adults with FAS. Effects were widely distributed across networks, potentially reflecting the diversity of cognitive deficits in FAS. However, no altered single connections could be determined in the most detailed analysis level. Findings were pronounced in networks in line with attentional deficits previously reported.

Epigenome-wide association study of peripheral immune cell populations in Parkinson's disease

Understanding the contribution of immune mechanisms to Parkinson's disease pathogenesis is an important challenge, potentially of major therapeutic implications. To further elucidate the involvement of peripheral immune cells, we studied epigenome-wide DNA methylation in isolated populations of CD14+ monocytes, CD19+ B cells, CD4+ T cells, and CD8+ T cells from Parkinson's disease patients and healthy control participants. We included 25 patients with a maximum five years of disease duration and 25 controls, and isolated four immune cell populations from each fresh blood sample. Epigenome-wide DNA methylation profiles were generated from 186 samples using the Illumina MethylationEpic array and association with disease status was tested using linear regression models. We identified six differentially methylated CpGs in CD14+ monocytes and one in CD8 + T cells. Four differentially methylated regions were identified in monocytes, including a region upstream of RAB32, a gene that has been linked to LRRK2. Methylation upstream of RAB32 correlated negatively with mRNA expression, and RAB32 expression was upregulated in Parkinson's disease both in our samples and in summary statistics from a previous study. Our epigenome-wide association study of early Parkinson's disease provides evidence for methylation changes across different peripheral immune cell types, highlighting monocytes and the RAB32 locus. The findings were predominantly cell-type-specific, demonstrating the value of isolating purified cell populations for genomic studies.

Activity-regulated gene expression across cell types of the mouse hippocampus

Activity-regulated gene (ARG) expression patterns in the hippocampus (HPC) regulate synaptic plasticity, learning, and memory, and are linked to both risk and treatment responses for many neuropsychiatric disorders. The HPC contains discrete classes of neurons with specialized functions, but cell type-specific activity-regulated transcriptional programs are not well characterized. Here, we used single-nucleus RNA-sequencing (snRNA-seq) in a mouse model of acute electroconvulsive seizures (ECS) to identify cell type-specific molecular signatures associated with induced activity in HPC neurons. We used unsupervised clustering and a priori marker genes to computationally annotate 15,990 high-quality HPC neuronal nuclei from N = 4 mice across all major HPC subregions and neuron types. Activity-induced transcriptomic responses were divergent across neuron populations, with dentate granule cells being particularly responsive to activity. Differential expression analysis identified both upregulated and downregulated cell type-specific gene sets in neurons following ECS. Within these gene sets, we identified enrichment of pathways associated with varying biological processes such as synapse organization, cellular signaling, and transcriptional regulation. Finally, we used matrix factorization to reveal continuous gene expression patterns differentially associated with cell type, ECS, and biological processes. This work provides a rich resource for interrogating activity-regulated transcriptional responses in HPC neurons at single-nuclei resolution in the context of ECS, which can provide biological insight into the roles of defined neuronal subtypes in HPC function.

DNA Methylation Changes in Autologous Hematopoietic Stem Cell Transplant Patients

BACKGROUND

Blood cancers may be potentially cured with hematopoietic stem cell transplantation (HCT); however, standard pre-assessments for transplant eligibility do not capture all contributing factors for transplant outcomes. Epigenetic biomarkers predict outcomes in various diseases. This pilot study aims to explore epigenetic changes (epigenetic age and differentially methylated genes) in patients before and after autologous HCT, that can serve as potential biomarkers to better predict HCT outcomes.

METHODS

This study used a prospective longitudinal study design to compare genome wide DNA methylation changes in 36 autologous HCT eligible patients recruited from the Cellular Immunotherapies and Transplant clinic at a designated National Cancer Center.

RESULTS

Genome-wide DNA methylation, measured by the Illumina Infinium Human Methylation 850K BeadChip, showed a significant difference in DNA methylation patterns post-HCT compared to pre-HCT. Compared to baseline levels of DNA methylation pre-HCT, 3358 CpG sites were hypo-methylated and 3687 were hyper-methylated. Identified differentially methylated positions overlapped with genes involved in hematopoiesis, blood cancers, inflammation and immune responses. Enrichment analyses showed significant alterations in biological processes such as immune response and cell structure organization, however no significant pathways were noted. Though participants had an advanced epigenetic age compared to chronologic age before and after HCT, both epigenetic age and accelerated age decreased post-HCT.

CONCLUSION

Epigenetic changes, both in epigenetic age and differentially methylated genes were observed in autologous HCT recipients, and should be explored as biomarkers to predict transplant outcomes after autologous HCT in larger, longitudinal studies.

Evaluation of differentially expressed genes during replication using gene expression landscape of monkeypox-infected MK2 cells: A bioinformatics and systems biology approach to understanding the genomic pattern of viral replication

PURPOSE

The current outbreak of monkeypox (MPX) has created colossal concerns. However, immense research gaps have been noted in our understanding of the replication process, machinery, and genomic landscape during host cell infection. To fill this gap, differentially expressed genes (DEGs) were comprehensively analyzed during viral replication in host (MK2) cells.

METHODS

We used a microarray GEO dataset which was divided into three groups: control, MPXV-infected MK2 cells at 3 h, and MPXV-infected MK2 cells at 7 h. Using the dataset, DEG analysis, PPI network analysis, co-expression, and pathway analysis were conducted using bioinformatics, systems biology, and statistical approaches.

RESULTS

We identified 250 DEGs and 24 top-ranked genes. During the DEG analysis, we identified eight up-regulated genes (LOC695323, TMEM107, LOC695427, HIST1H2AD, LOC705469, PMAIP1, HIST1H2BJ, and HIST1H3D) and 16 down-regulated genes (HOXA9, BAMBI, LMO4, PAX6, AJUBA, CREBRF, CD24, JADE1, SLC7A11, EID2, SOX4, B4GALT5, PPARGC1A, BUB3, SOS2, and CDK19). We also developed PPI networks and performed co-expression analyses using the top-ranked genes. Furthermore, five genes were listed for co-expression pattern analysis.

CONCLUSIONS

This study will help in better understanding the replication process, machinery, and genomic landscape of the virus. This will further aid the discovery and development of therapeutics against viruses.

A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias

We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.

Genetic Variants Associated with Opioid Use Disorder

Genetics are presumed to contribute 30-40% to opioid use disorder (OUD), allowing for the possibility that genetic markers could be used to identify personal risk for developing OUD. We aimed to test the potential association among 180 candidate single nucleotide polymorphisms (SNPs), 120 of which were related to the dopamine reward pathway and 60 related to pharmacokinetics. Participants were randomly recruited in 2020-2021 in a cross-sectional genetic association study. Self-reported health history including Diagnostic and Statistical Manual of Mental Disorders (DSM-5) OUD criteria and buccal swabs were collected. A total of 1,301 participants were included in the analyses for this study. Of included participants, 250 met the DSM-5 criteria for ever having OUD. Logistic regression, adjusting for age and biologic sex, was used to characterize the association between each SNP and DSM-5 criteria consistent with OUD. Six SNPs found in 4 genes were associated with OUD: increased odds with CYP3A5 (rs15524 and rs776746) and DRD3 (rs324029 and rs2654754), and decreased odds with CYP3A4 (rs2740574) and CYP1A2 (rs2069514). Homozygotic CYP3A5 (rs15524 and rs776746) had the highest adjusted odds ratio of 2.812 (95% confidence interval (CI) 1.737, 4.798) and 2.495 (95% CI 1.670, 3.835), respectively. Variants within the dopamine reward and opioid metabolism pathways have significant positive (DRD3 and CYP3A5) and negative (CYP3A4 and CYP1A2) associations with OUD. Identification of these variants provides promising possibilities for genetic prognostic and therapeutic targets for future investigation.

Systematic benchmarking of statistical methods to assess differential expression of circular RNAs

Circular RNAs (circRNAs) are covalently closed transcripts involved in critical regulatory axes, cancer pathways and disease mechanisms. CircRNA expression measured with RNA-seq has particular characteristics that might hamper the performance of standard biostatistical differential expression assessment methods (DEMs). We compared 38 DEM pipelines configured to fit circRNA expression data's statistical properties, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq) and metagenomics DEMs. The DEMs performed poorly on data sets of typical size. Widely used DEMs, such as DESeq2, edgeR and Limma-Voom, gave scarce results, unreliable predictions or even contravened the expected behaviour with some parameter configurations. Limma-Voom achieved the most consistent performance throughout different benchmark data sets and, as well as SAMseq, reasonably balanced false discovery rate (FDR) and recall rate. Interestingly, a few scRNA-seq DEMs obtained results comparable with the best-performing bulk RNA-seq tools. Almost all DEMs' performance improved when increasing the number of replicates. CircRNA expression studies require careful design, choice of DEM and DEM configuration. This analysis can guide scientists in selecting the appropriate tools to investigate circRNA differential expression with RNA-seq experiments.

Parkinson's disease-associated, sex-specific changes in DNA methylation at PARK7 (DJ-1), SLC17A6 (VGLUT2), PTPRN2 (IA-2β), and NR4A2 (NURR1) in cortical neurons

Evidence for epigenetic regulation playing a role in Parkinson's disease (PD) is growing, particularly for DNA methylation. Approximately 90% of PD cases are due to a complex interaction between age, genes, and environmental factors, and epigenetic marks are thought to mediate the relationship between aging, genetics, the environment, and disease risk. To date, there are a small number of published genome-wide studies of DNA methylation in PD, but none accounted for cell type or sex in their analyses. Given the heterogeneity of bulk brain tissue samples and known sex differences in PD risk, progression, and severity, these are critical variables to account for. In this genome-wide analysis of DNA methylation in an enriched neuronal population from PD postmortem parietal cortex, we report sex-specific PD-associated methylation changes in PARK7 (DJ-1), SLC17A6 (VGLUT2), PTPRN2 (IA-2β), NR4A2 (NURR1), and other genes involved in developmental pathways, neurotransmitter packaging and release, and axon and neuron projection guidance.

Intellectual freedom and alternative priorities in library and information science research: A longitudinal study

This article presents a bibliometric analysis of the library and information science literature to trace the emphasis that intellectual freedom and neutrality have received relative to an index of alternative and possibly competing topics. Emphasis is captured longitudinally by recording the number of results for various search terms associated with intellectual freedom, neutrality, diversity, equity, and inclusion in Web of Science from 1993 through 2020 and Library, Information Science and Technology Abstracts from 1970 through 2020. The results show that the number of works mentioning intellectual freedom and neutrality has increased only slightly over the study period, in sharp contrast to many entries on the diversity, equity, and inclusion index. With research interests being partially indicative of personal beliefs and professional activity, the impact of this relative change in emphasis on professional practice is discussed. Public controversies regarding library neutrality, intellectual freedom, and freedom of expression in libraries are summarized.

Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis

Brassinosteroids (BRs) are plant steroid hormones that regulate cell division and stress response. Here we use a systems biology approach to integrate multi-omic datasets and unravel the molecular signaling events of BR response in Arabidopsis. We profile the levels of 26,669 transcripts, 9,533 protein groups, and 26,617 phosphorylation sites from Arabidopsis seedlings treated with brassinolide (BL) for six different lengths of time. We then construct a network inference pipeline called Spatiotemporal Clustering and Inference of Omics Networks (SC-ION) to integrate these data. We use our network predictions to identify putative phosphorylation sites on BES1 and experimentally validate their importance. Additionally, we identify BRONTOSAURUS (BRON) as a transcription factor that regulates cell division, and we show that BRON expression is modulated by BR-responsive kinases and transcription factors. This work demonstrates the power of integrative network analysis applied to multi-omic data and provides fundamental insights into the molecular signaling events occurring during BR response.

An approach for normalization and quality control for NanoString RNA expression data

The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString's commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.

Randomized Controlled Trial of the Gastrin/CCK2 Receptor Antagonist Netazepide in Patients with Barrett's Esophagus

Hypergastrinemia has been associated with high-grade dysplasia and adenocarcinoma in patients with Barrett's esophagus, and experimental studies suggest proinflammatory and proneoplastic effects of gastrin on Barrett's esophagus. This is of potential concern, as patients with Barrett's esophagus are treated with medications that suppress gastric acid production, resulting in increased physiologic levels of gastrin. We aimed to determine whether treatment with the novel gastrin/CCK₂ receptor antagonist netazepide reduces expression of markers associated with inflammation and neoplasia in Barrett's esophagus. This was a randomized, double-blind, placebo-controlled trial of netazepide in patients with Barrett's esophagus without dysplasia. Subjects were treated for 12 weeks, with endoscopic assessment at baseline and at end of treatment. The primary outcome was within-individual change in cellular proliferation as assessed by Ki67. Secondary analyses included changes in gene expression, assessed by RNA-sequencing, and safety and tolerability. A total of 20 subjects completed the study and were included in the analyses. There was no difference between arms in mean change in cellular proliferation (netazepide: +35.6 Ki67+ cells/mm², SD 620.7; placebo: +307.8 Ki67+ cells/mm², SD 640.3; P = 0.35). Netazepide treatment resulted in increased expression of genes related to gastric phenotype (TFF2, MUC5B) and certain cancer-associated markers (REG3A, PAX9, MUC1), and decreased expression of intestinal markers MUC2, FABP1, FABP2, and CDX1 No serious adverse events related to study drug occurred. The gastrin/CCK₂ receptor antagonist netazepide did not reduce cellular proliferation in patients with nondysplastic Barrett's esophagus. Further research should focus on the biological effects of gastrin in Barrett's esophagus.Prevention Relevance: Treatment of patients with Barrett's esophagus with a gastrin/CCK₂ receptor antagonist did not have obvious chemopreventive effects.

On the structural connectivity of large-scale models of brain networks at cellular level

The brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

Dynamic time warping outperforms Pearson correlation in detecting atypical functional connectivity in autism spectrum disorders

Resting state fMRI (rsfMRI) is frequently used to study brain function, including in clinical populations. Similarity of blood-oxygen-level-dependent (BOLD) fluctuations during rsfMRI between brain regions is thought to reflect intrinsic functional connectivity (FC), potentially due to history of coactivation. To quantify similarity, studies have almost exclusively relied on Pearson correlation, which assumes linearity and can therefore underestimate FC if the hemodynamic response function differs regionally or there is BOLD signal lag between timeseries. Here we show in three cohorts of children, adolescents and adults, with and without autism spectrum disorders (ASDs), that measuring the similarity of BOLD signal fluctuations using non-linear dynamic time warping (DTW) is more robust to global signal regression (GSR), has higher test-retest reliability and is more sensitive to task-related changes in FC. Additionally, when comparing FC between individuals with ASDs and typical controls, more group differences are detected using DTW. DTW estimates are also more related to ASD symptom severity and executive function, while Pearson correlation estimates of FC are more strongly associated with respiration during rsfMRI. Together these findings suggest that non-linear methods such as DTW improve estimation of resting state FC, particularly when studying clinical populations whose hemodynamics or neurovascular coupling may be altered compared to typical controls.

Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes

Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.

Phylotranscriptomic Analyses Reveal Asymmetrical Gene Duplication Dynamics and Signatures of Ancient Polyploidy in Mints

Ancient duplication events and retained gene duplicates have contributed to the evolution of many novel plant traits and, consequently, to the diversity and complexity within and across plant lineages. Although mounting evidence highlights the importance of whole-genome duplication (WGD; polyploidy) and its key role as an evolutionary driver, gene duplication dynamics and mechanisms, both of which are fundamental to our understanding of evolutionary process and patterns of plant diversity, remain poorly characterized in many clades. We use newly available transcriptomic data and a robust phylogeny to investigate the prevalence, occurrence, and timing of gene duplications in Lamiaceae (mints), a species-rich and chemically diverse clade with many ecologically, economically, and culturally important species. We also infer putative WGDs—an extreme mechanism of gene duplication—using large-scale data sets from synonymous divergence (K_S), phylotranscriptomic, and divergence time analyses. We find evidence for widespread but asymmetrical levels of gene duplication and ancient polyploidy in Lamiaceae that correlate with species richness, including pronounced levels of gene duplication and putative ancient WGDs (7–18 events) within the large subclade Nepetoideae and up to 10 additional WGD events in other subclades. Our results help disentangle WGD-derived gene duplicates from those produced by other mechanisms and illustrate the nonuniformity of duplication dynamics in mints, setting the stage for future investigations that explore their impacts on trait diversity and species diversification. Our results also provide a practical context for evaluating the benefits and limitations of transcriptome-based approaches to inferring WGD, and we offer recommendations for researchers interested in investigating ancient WGDs in other plant groups.

"notame": Workflow for Non-Targeted LC-MS Metabolic Profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.