The Pathway Coexpression Network: Revealing pathway relationships

Durable response to CAR T is associated with elevated activation and clonotypic expansion of the cytotoxic native T cell repertoire

While Chimeric Antigen Receptor (CAR) T cell therapy may result in durable remissions in recurrent large B cell lymphoma, persistence is limited and the mechanisms underlying long-term response are not fully elucidated. Using longitudinal single-cell immunoprofiling, here we compare the immune landscape in durable remission versus early relapse patients following CD19 CAR T cell infusion in the NCT02348216 (ZUMA-1) trial. Four weeks post-infusion, both cohorts demonstrate low circulating CAR T cells. We observe that long-term remission is associated with elevated native cytotoxic and proinflammatory effector cells, and post-infusion clonotypic expansion of effector memory T cells. Conversely, early relapse is associated with impaired NK cell cytotoxicity and elevated immunoregulatory cells, potentially dampening native T cell activation. Thus, we suggest that durable remission to CAR T is associated with a distinct T cell signature and pattern of clonotypic expansion within the native T cell compartment post-therapy, consistent with their contribution to the maintenance of response.

A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients

BACKGROUND

The molecular underpinnings of organ dysfunction in severe COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we perform single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents.

RESULTS

We identify hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells, and a central role in a pro-fibrotic TGFβ signaling cell-cell communications network. Integrated analysis and comparisons with healthy controls reveal extensive changes in the cellular composition and expression states in COVID-19 liver, providing the underpinning of hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis characteristic of COVID-19 cholangiopathy. We also observe Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition is dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells.

CONCLUSIONS

Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

Integrative pathway analysis across humans and 3D cellular models identifies the p38 MAPK-MK2 axis as a therapeutic target for Alzheimer's disease

Alzheimer's disease (AD) presents a complex pathological landscape, posing challenges to current therapeutic strategies that primarily target amyloid-β (Aβ). Using a novel integrative pathway activity analysis (IPAA), we identified 83 dysregulated pathways common between both post-mortem AD brains and three-dimensional AD cellular models showing robust Aβ42 accumulation. p38 mitogen-activated protein kinase (MAPK) was the most upregulated common pathway. Active p38 MAPK levels increased in the cellular models, human brains, and 5XFAD mice and selectively localized to presynaptic dystrophic neurites. Unbiased phosphoproteomics confirmed increased phosphorylation of p38 MAPK substrates. Downstream activation of MAPK-activated protein kinase 2 (MK2) plays a crucial role in Aβ42-p38 MAPK-mediated tau pathology. Therapeutic targeting of the p38 MAPK-MK2 axis with selective inhibitors significantly reduced Aβ42-driven tau pathology and neuronal loss. IPAA prioritizes the best models to derisk target-drug discovery by integrating human tissue gene expression with functional readouts from cellular models, enabling the identification and validation of high-confidence AD therapeutic targets.

Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer's disease

Background

A significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals.

Methods

We analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on β-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution.

Results

Transcriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience.

Conclusions

We identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies

This paper tackles the challenge of estimating correlations between higher-level biological variables (e.g. proteins and gene pathways) when only lower-level measurements are directly observed (e.g. peptides and individual genes). Existing methods typically aggregate lower-level data into higher-level variables and then estimate correlations based on the aggregated data. However, different data aggregation methods can yield varying correlation estimates as they target different higher-level quantities. Our solution is a latent factor model that directly estimates these higher-level correlations from lower-level data without the need for data aggregation. We further introduce a shrinkage estimator to ensure the positive definiteness and improve the accuracy of the estimated correlation matrix. Furthermore, we establish the asymptotic normality of our estimator, enabling efficient computation of P-values for the identification of significant correlations. The effectiveness of our approach is demonstrated through comprehensive simulations and the analysis of proteomics and gene expression datasets. We develop the R package highcor for implementing our method.

An integrative genomic toolkit for studying the genetic, evolutionary, and molecular underpinnings of eusociality in insects

While genomic resources for social insects have vastly increased over the past two decades, we are still far from understanding the genetic and molecular basis of eusociality. Here, we briefly review three scientific advancements that, when integrated, can be highly synergistic for advancing our knowledge of the genetics and evolution of eusocial traits. Population genomics provides a natural way to quantify the strength of natural selection on coding and regulatory sequences, highlighting genes that have undergone adaptive evolution during the evolution or maintenance of eusociality. Genome-wide association studies (GWAS) can be used to characterize the complex genetic architecture underlying eusocial traits and identify candidate causal variants. Concurrently, CRISPR/Cas9 enables the precise manipulation of gene function to both validate genotype-phenotype associations and study the molecular biology underlying interesting traits. While each approach has its own advantages and disadvantages, which we discuss herein, we argue that their combination will ultimately help us better understand the genetics and evolution of eusocial behavior. Specifically, by triangulating across these three different approaches, researchers can directly identify and study loci that have a causal association with key phenotypes and have evidence of positive selection over the relevant timescales associated with the evolution and maintenance of eusociality in insects.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs

Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.

PathIN: an integrated tool for the visualization of pathway interaction networks

PathIN is a web-service that provides an easy and flexible way for rapidly creating pathway-based networks at several functional biological levels: genes, compounds and reactions. The tool is supported by a database repository of reference pathway networks across a large set of species, developed through the freely available information included in the KEGG, Reactome and Wiki Pathways database repositories. PathIN provides networks by means of five diverse methodologies: (a) direct connections between pathways of interest, (b) direct connections as well as the first neighbours of the given pathways, (c) direct connections, the first neighbours and the connections in between them, and (d) two additional methodologies for creating complementary pathway-to-pathway networks that involve additional (missing) pathways that interfere in-between pathways of interest. PathIN is expected to be used as a simple yet informative reference tool for understanding networks of molecular mechanisms related to specific diseases.

A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients

The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients

The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

Insights into H2O2-induced signaling in Jurkat cells from analysis of gene expression

Hydrogen peroxide (H₂O₂) is a ubiquitous oxidant produced in a regulated manner by various enzymes in mammalian cells. H₂O₂ reversibly oxidizes thiol groups of cysteine residues to mediate intracellular signaling. While examples of H₂O_2-dependent signaling have been reported, the exact molecular mechanism(s) of signaling and the pathways affected are not well understood. Here, the transcriptomic response of Jurkat T cells to H₂O₂ was investigated to determine global effects on gene expression. With a low H₂O₂ concentration (10 µM) that did not induce an oxidative stress response or cell death, extensive changes in gene expression occurred after 4 h (6803 differentially expressed genes). Of the genes with a greater then 2-fold change in expression, 85% were upregulated suggesting that in a physiological setting H₂O₂ predominantly activates gene expression. Pathway analysis identified gene expression signatures associated with FOXO and NTRK signaling. These signatures were associated with an overlapping set of transcriptional regulators. Overall, our results provide a snapshot of gene expression changes in response to H₂O_2, which, along with further studies, will lead to new insights into the specific pathways that are activated in response to endogenous production of H₂O₂, and the molecular mechanisms of H₂O₂ signaling.

Most Pathways Can Be Related to the Pathogenesis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a complex neurodegenerative disorder. The relative contribution of the numerous underlying functional mechanisms is poorly understood. To comprehensively understand the context and distribution of pathways that contribute to AD, we performed text-mining to generate an exhaustive, systematic assessment of the breadth and diversity of biological pathways within a corpus of 206,324 dementia publication abstracts. A total of 91% (325/335) of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways have publications containing an association via at least 5 studies, while 63% of pathway terms have at least 50 studies providing a clear association with AD. Despite major technological advances, the same set of top-ranked pathways have been consistently related to AD for 30 years, including AD, immune system, metabolic pathways, cholinergic synapse, long-term depression, proteasome, diabetes, cancer, and chemokine signaling. AD pathways studied appear biased: animal model and human subject studies prioritize different AD pathways. Surprisingly, human genetic discoveries and drug targeting are not enriched in the most frequently studied pathways. Our findings suggest that not only is this disorder incredibly complex, but that its functional reach is also nearly global. As a consequence of our study, research results can now be assessed in the context of the wider AD literature, supporting the design of drug therapies that target a broader range of mechanisms. The results of this study can be explored at www.adpathways.org.

Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment

Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.

Identifying Molecular Biomarkers for Diseases With Machine Learning Based on Integrative Omics

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball

MOTIVATION

Knowledge manipulation of Gene Ontology (GO) and Gene Ontology Annotation (GOA) can be done primarily by using vector representation of GO terms and genes. Previous studies have represented GO terms and genes or gene products in Euclidean space to measure their semantic similarity using an embedding method such as the Word2Vec-based method to represent entities as numeric vectors. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. On the other hand, hyperbolic spaces such as the Poincaré balls are more suitable for modeling hierarchies, as they have a geometric property in which the distance increases exponentially as it nears the boundary because of negative curvature.

RESULTS

In this article, we propose hierarchical representations of GO and genes (HiG2Vec) by applying Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge.

AVAILABILITYAND IMPLEMENTATION

https://github.com/JaesikKim/HiG2Vec.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Whole-genome sequencing reveals new Alzheimer's disease-associated rare variants in loci related to synaptic function and neuronal development

INTRODUCTION

Genome-wide association studies have led to numerous genetic loci associated with Alzheimer's disease (AD). Whole-genome sequencing (WGS) now permits genome-wide analyses to identify rare variants contributing to AD risk.

METHODS

We performed single-variant and spatial clustering-based testing on rare variants (minor allele frequency [MAF] ≤1%) in a family-based WGS-based association study of 2247 subjects from 605 multiplex AD families, followed by replication in 1669 unrelated individuals.

RESULTS

We identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, and CLSTN2.

DISCUSSION

Downstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity and amyloid processing. These loci have not been associated previously with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of the exome.

Linking genotype to phenotype in multi-omics data of small sample

BACKGROUND

Genome-wide association studies (GWAS) that link genotype to phenotype represent an effective means to associate an individual genetic background with a disease or trait. However, single-omics data only provide limited information on biological mechanisms, and it is necessary to improve the accuracy for predicting the biological association between genotype and phenotype by integrating multi-omics data. Typically, gene expression data are integrated to analyze the effect of single nucleotide polymorphisms (SNPs) on phenotype. Such multi-omics data integration mainly follows two approaches: multi-staged analysis and meta-dimensional analysis, which respectively ignore intra-omics and inter-omics associations. Moreover, both approaches require omics data from a single sample set, and the large feature set of SNPs necessitates a large sample size for model establishment, but it is difficult to obtain multi-omics data from a single, large sample set.

RESULTS

To address this problem, we propose a method of genotype-phenotype association based on multi-omics data from small samples. The workflow of this method includes clustering genes using a protein-protein interaction network and gene expression data, screening gene clusters with group lasso, obtaining SNP clusters corresponding to the selected gene clusters through expression quantitative trait locus data, integrating SNP clusters and corresponding gene clusters and phenotypes into three-layer network blocks, analyzing and predicting based on each block, and obtaining the final prediction by taking the average.

CONCLUSIONS

We compare this method to others using two datasets and find that our method shows better results in both cases. Our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.

KEA3: improved kinase enrichment analysis via data integration

Phosphoproteomics and proteomics experiments capture a global snapshot of the cellular signaling network, but these methods do not directly measure kinase state. Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrates are in a user-inputted list of proteins. KEA3 can be applied to analyze data from phosphoproteomics and proteomics studies to predict the upstream kinases responsible for observed differential phosphorylations. The KEA3 background database contains measured and predicted kinase-substrate interactions (KSI), kinase-protein interactions (KPI), and interactions supported by co-expression and co-occurrence data. To benchmark the performance of KEA3, we examined whether KEA3 can predict the perturbed kinase from single-kinase perturbation followed by gene expression experiments, and phosphoproteomics data collected from kinase-targeting small molecules. We show that integrating KSIs and KPIs across data sources to produce a composite ranking improves the recovery of the expected kinase. The KEA3 webserver is available at https://maayanlab.cloud/kea3.

Proteome of fluid from human ovarian small antral follicles reveals insights in folliculogenesis and oocyte maturation

STUDY QUESTION

Is it possible to identify by mass spectrometry a wider range of proteins and key proteins involved in folliculogenesis and oocyte growth and development by studying follicular fluid (FF) from human small antral follicles (hSAF)?

SUMMARY ANSWER

The largest number of proteins currently reported in human FF was identified in this study analysing hSAF where several proteins showed a strong relationship with follicular developmental processes.

WHAT IS KNOWN ALREADY

Protein composition of human ovarian FF constitutes the microenvironment for oocyte development. Previous proteomics studies have analysed fluids from pre-ovulatory follicles, where large numbers of plasma constituents are transferred through the follicular basal membrane. This attenuates the detection of low abundant proteins, however, the basal membrane of small antral follicles is less permeable, making it possible to detect a large number of proteins, and thereby offering further insights in folliculogenesis.

STUDY DESIGN, SIZE, DURATION

Proteins in FF from unstimulated hSAF (size 6.1 ± 0.4 mm) were characterised by mass spectrometry, supported by high-throughput and targeted proteomics and bioinformatics. The FF protein profiles from hSAF containing oocytes, capable or not of maturing to metaphase II of the second meiotic division during an IVM (n = 13, from 6 women), were also analysed.

PARTICIPANTS/MATERIALS, SETTING, METHODS

We collected FF from hSAF of ovaries that had been surgically removed from 31 women (∼28.5 years old) undergoing unilateral ovariectomy for fertility preservation.

MAIN RESULTS AND THE ROLE OF CHANCE

In total, 2461 proteins were identified, of which 1108 identified for the first time in FF. Of the identified proteins, 24 were related to follicular regulatory processes. A total of 35 and 65 proteins were down- and up-regulated, respectively, in fluid from hSAF surrounding oocytes capable of maturing (to MII). We found that changes at the protein level occur already in FF from small antral follicles related to subsequent oocyte maturation.

LIMITATIONS, REASONS FOR CAUTION

A possible limitation of our study is the uncertainty of the proportion of the sampled follicles that are undergoing atresia. Although the FF samples were carefully aspirated and processed to remove possible contaminants, we cannot ensure the absence of some proteins derived from cellular lysis provoked by technical reasons.

WIDER IMPLICATIONS OF THE FINDINGS

This study is, to our knowledge, the first proteomics characterisation of FF from hSAF obtained from women in their natural menstrual cycle. We demonstrated that the analysis by mass spectrometry of FF from hSAF allows the identification of a greater number of proteins compared to the results obtained from previous analyses of larger follicles. Significant differences found at the protein level in hSAF fluid could predict the ability of the enclosed oocyte to sustain meiotic resumption. If this can be confirmed in further studies, it demonstrates that the viability of the oocyte is determined early on in follicular development and this may open up new pathways for augmenting or attenuating subsequent oocyte viability in the pre-ovulatory follicle ready to undergo ovulation.

STUDY FUNDING/COMPETING INTEREST(S)

The authors thank the financial support from ReproUnion, which is funded by the Interreg V EU programme. No conflict of interest was reported by the authors.

TRIAL REGISTRATION NUMBER

N/A.

SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays

Hit selection from high-throughput assays remains a critical bottleneck in realizing the potential of omic-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs. The specific limitations of these individual approaches and the lack of a systematic way by which to integrate their rankings have contributed to limited overlap in the reported results from comparable genome-wide studies and costly inefficiencies in secondary validation efforts. Using comparative analysis of parallel independent studies as a benchmark, we characterize the specific complementary contributions of each approach and demonstrate an optimal framework to integrate these methods. We describe selection by iterative pathway group and network analysis looping (SIGNAL), an integrated, iterative approach that uses both pathway and network methods to optimize gene prioritization. SIGNAL is accessible as a rapid user-friendly web-based application (https://signal.niaid.nih.gov). A record of this paper's transparent peer review is included in the Supplemental information.

Whole-genome sequencing reveals new Alzheimer's disease-associated rare variants in loci related to synaptic function and neuronal development

INTRODUCTION

Genome-wide association studies have led to numerous genetic loci associated with Alzheimer's disease (AD). Whole-genome sequencing (WGS) now permit genome-wide analyses to identify rare variants contributing to AD risk.

METHODS

We performed single-variant and spatial clustering-based testing on rare variants (minor allele frequency ≤1%) in a family-based WGS-based association study of 2,247 subjects from 605 multiplex AD families, followed by replication in 1,669 unrelated individuals.

RESULTS

We identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, CLSTN2.

DISCUSSION

Downstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity. These loci have not been previously associated with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of coding regions.

TMT-based proteomics analysis to screen potential biomarkers of acute-phase TBI in rats

AIMS

Traumatic brain injury (TBI) is a common nervous system injury. However, the detailed mechanisms about functional dysregulation and dignostic biomarkers post-TBI are still unclear. So we aimed to identify potential differentially expressed proteins and genes in TBI for clinical diagnosis and therapeutic purposes.

MAIN METHODS

Rat TBI model was established by the weight-drop method. First, through TMT-proteomics, we screened for the change in the proteins expression profile acute phase post-TBI. The DAVID and Reactome databases were used to analyze and visualize the dysregulation proteins. Then, using publicly available microarray datasets GSE45997, differentially expressed genes (DGEs) were identified for the 24 h post-TBI stage. Also, the proteomic data were compared with microarray data to analyze the similarity.

KEY FINDINGS

We found significant proteomics and transcriptomic changes in post-TBI samples. 989, 881, 832, 1057 proteins were quantitated at 1 h, 6 h, 24 h, and 3 d post-injury correspondingly. Concerning proteomics findings, oxygen transport, acute-phase response, and negative regulation of endopeptidase activity were influenced throughout the acute phrase of TBI. Also, pathways related to scavenging of heme from plasma, binding, and uptake of ligands by scavenger receptors were highly enriched in all time-points of TBI samples.

SIGNIFICANCE

We noticed that the interaction-networks trend to get complicated with more node connections following the progression of TBI. We inferred that Hk-1, PRKAR2A, and MBP could be novel candidate biomarkers related to time-injury in acute-phase TBI. Also, Ceruloplasmin and Complement C3 were found to be important proteins and genes are involved in the TBI.

Cognitive function: holarchy or holacracy?

Cognition is the most complex function of the brain. When exploring the inner workings of cognitive processes, it is crucial to understand the complexity of the brain's dynamics. This paper aims to describe the integrated framework of the cognitive function, seen as the result of organization and interactions between several systems and subsystems. We briefly describe several organizational concepts, spanning from the reductionist hierarchical approach, up to the more dynamic theory of open complex systems. The homeostatic regulation of the mechanisms responsible for cognitive processes is showcased as a dynamic interplay between several anticorrelated mechanisms, which can be found at every level of the brain's organization, from molecular and cellular level to large-scale networks (e.g., excitation-inhibition, long-term plasticity-long-term depression, synchronization-desynchronization, segregation-integration, order-chaos). We support the hypothesis that cognitive function is the consequence of multiple network interactions, integrating intricate relationships between several systems, in addition to neural circuits.

Perturbations of pathway co-expression network identify a core network in metastatic breast cancer

Metastases are the main cause of death in advanced breast cancer (BC) patients. Although chemotherapy and hormone therapy are current treatment strategies, drug resistance is frequent and still not completely understood. In this study, a bioinformatics analysis was performed on BC patients to explore the molecular mechanisms associated with BC metastasis. Microarray gene expression profiles of metastatic and non metastatic BC patients were downloaded from Gene Expression Omnibus (GEO) dataset. Raw data were normalized and merged using the Combat tool. Pathways enriched with differently expressed genes were identified and a pathway co-expression network was generated using Pearson's correlation. We identified from this network, which includes 17 pathways and 128 interactions, the pathways that most influence the network efficiency. Moreover, protein interaction network was investigated to identify hub genes of the pathway network. The prognostic role of the network was evaluated with a survival analysis using an independent dataset. In conclusion, the pathway co-expression network could contribute to understanding the mechanism and development of BC metastases.

Novel functional proteins coded by the human genome discovered in metastases of melanoma patients

In the advanced stages, malignant melanoma (MM) has a very poor prognosis. Due to tremendous efforts in cancer research over the last 10 years, and the introduction of novel therapies such as targeted therapies and immunomodulators, the rather dark horizon of the median survival has dramatically changed from under 1 year to several years. With the advent of proteomics, deep-mining studies can reach low-abundant expression levels. The complexity of the proteome, however, still surpasses the dynamic range capabilities of current analytical techniques. Consequently, many predicted protein products with potential biological functions have not yet been verified in experimental proteomic data. This category of 'missing proteins' (MP) is comprised of all proteins that have been predicted but are currently unverified. As part of the initiative launched in 2016 in the USA, the European Cancer Moonshot Center has performed numerous deep proteomics analyses on samples from MM patients. In this study, nine MPs were clearly identified by mass spectrometry in MM metastases. Some MPs significantly correlated with proteins that possess identical PFAM structural domains; and other MPs were significantly associated with cancer-related proteins. This is the first study to our knowledge, where unknown and novel proteins have been annotated in metastatic melanoma tumour tissue.

Cell-cycle dependence of transcriptome gene modules: comparison of regression lines

The transcriptome consists of various gene modules that can be mutually dependent, and ignoring these dependencies may lead to misinterpretation. The most important problem is module dependence on cell-cycle activity. Using meta-analysis of over 30 000 single-cell transcriptomes, we show gene module dependencies on cell-cycle signature, which can be consistently observed in various normal and cancer cells. Transcript levels of receptors, plasma membrane, and differentiation-related genes are negatively regressed on cell-cycle signature. Pluripotency, stress response, DNA repair, chromatin remodeling, proteasomal protein degradation, protein network connectivity, and unicellular evolutionary origin are regressed positively. These effects cannot be explained by partial overlap of corresponding gene sets because they remain if the overlapped genes were removed. We propose a visual analysis of gene module-specific regression lines as complement to an uncurated enrichment analysis. The different lines for a same gene module indicate different cell conditions. The approach is tested on several problems (polyploidy, pluripotency, cancer, phylostratigraphy). Intriguingly, we found variation in cell-cycle activity, which is independent of cell progression through the cycle. The upregulation of G2/M checkpoint genes with downregulation of G2/M transition and cytokinesis is revealed in polyploid cells. A temporal increase in cell-cycle activity at transition from pluripotent to more differentiated state is found in human embryonic stem cells. The upregulation of unicellular interactome cluster in human cancers is shown in single cells with control for cell-cycle activity. The greater scatter around regression line in cancer cells suggests greater heterogeneity caused by deviation from a line of normal cells.

Mutation Enrichment and Transcriptomic Activation Signatures of 419 Molecular Pathways in Cancer

Carcinogenesis is linked with massive changes in regulation of gene networks. We used high throughput mutation and gene expression data to interrogate involvement of 278 signaling, 72 metabolic, 48 DNA repair and 47 cytoskeleton molecular pathways in cancer. Totally, we analyzed 4910 primary tumor samples with individual cancer RNA sequencing and whole exome sequencing profiles including ~1.3 million DNA mutations and representing thirteen cancer types. Gene expression in cancers was compared with the corresponding 655 normal tissue profiles. For the first time, we calculated mutation enrichment values and activation levels for these pathways. We found that pathway activation profiles were largely congruent among the different cancer types. However, we observed no correlation between mutation enrichment and expression changes both at the gene and at the pathway levels. Overall, positive median cancer-specific activation levels were seen in the DNA repair, versus similar slightly negative values in the other types of pathways. The DNA repair pathways also demonstrated the highest values of mutation enrichment. However, the signaling and cytoskeleton pathways had the biggest proportions of representatives among the outstandingly frequently mutated genes thus suggesting their initiator roles in carcinogenesis and the auxiliary/supporting roles for the other groups of molecular pathways.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Benefits of the Mediterranean diet: Epidemiological and molecular aspects

More than 50 years after the Seven Countries Study, a large number of epidemiological studies have explored the relationship between the Mediterranean diet (MD) and health, through observational, case-control, some longitudinal and a few experimental studies. The overall results show strong evidence suggesting a protective effect of the MD mainly on the risk of cardiovascular disease (CVD) and certain types of cancer. The beneficial effects have been attributed to the types of food consumed, total dietary pattern, components in the food, cooking techniques, eating behaviors and lifestyle behaviors, among others. The aim of this article is to review and summarize the knowledge derived from the literature focusing on the benefits of the MD on health, including those that have been extensively investigated (CVD, cancer) along with more recent issues such as mental health, immunity, quality of life, etc. The review begins with a brief description of the MD and its components. Then we present a review of studies evaluating metabolic biomarkers and genotypes in relation to the MD. Other sections are dedicated to observation and intervention studies for various pathologies. Finally, some insights into the relationship between the MD and sustainability are explored. In conclusion, the research undertaken on metabolomics approaches has identified potential markers for certain MD components and patterns, but more investigation is needed to obtain valid measures. Further evaluation of gene-MD interactions are also required to better understand the mechanisms by which the MD diet exerts its beneficial effects on health. Observation and intervention studies, particularly PREDIMED, have provided invaluable data on the benefits of the MD for a wide range of chronic diseases. However further research is needed to explore the effects of other lifestyle components associated with Mediterranean populations, its environmental impact, as well as the MD extrapolation to non-Mediterranean contexts.

Pathway crosstalk perturbation network modeling for identification of connectivity changes induced by diabetic neuropathy and pioglitazone

BACKGROUND

Aggregation of high-throughput biological data using pathway-based approaches is useful to associate molecular results to functional features related to the studied phenomenon. Biological pathways communicate with one another through the crosstalk phenomenon, forming large networks of interacting processes.

RESULTS

In this work, we present the pathway crosstalk perturbation network (PXPN) model, a novel model used to analyze and integrate pathway perturbation data based on graph theory. With this model, the changes in activity and communication between pathways observed in transitions between physiological states are represented as networks. The model presented here is agnostic to the type of biological data and pathway definition used and can be implemented to analyze any type of high-throughput perturbation experiments. We present a case study in which we use our proposed model to analyze a gene expression dataset derived from experiments in a BKS-db/db mouse model of type 2 diabetes mellitus-associated neuropathy (DN) and the effects of the drug pioglitazone in this condition. The networks generated describe the profile of pathway perturbation involved in the transitions between the healthy and the pathological state and the pharmacologically treated pathology. We identify changes in the connectivity of perturbed pathways associated to each biological transition, such as rewiring between extracellular matrix, neuronal system, and G-protein coupled receptor signaling pathways.

CONCLUSION

The PXPN model is a novel, flexible method used to integrate high-throughput data derived from perturbation experiments; it is agnostic to the type of data and enrichment function used, and it is applicable to a wide range of biological phenomena of interest.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases

Although pathways are widely used for the analysis and representation of biological systems, their lack of clear boundaries, their dispersion across numerous databases, and the lack of interoperability impedes the evaluation of the coverage, agreements, and discrepancies between them. Here, we present ComPath, an ecosystem that supports curation of pathway mappings between databases and fosters the exploration of pathway knowledge through several novel visualizations. We have curated mappings between three of the major pathway databases and present a case study focusing on Parkinson’s disease that illustrates how ComPath can generate new biological insights by identifying pathway modules, clusters, and cross-talks with these mappings. The ComPath source code and resources are available at https://github.com/ComPath and the web application can be accessed at https://compath.scai.fraunhofer.de/.

PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia

Background

Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies.

Results

We developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data.

Conclusions

The PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0175-7) contains supplementary material, which is available to authorized users.

The relative resistance of children to sepsis mortality: from pathways to drug candidates

Attempts to develop drugs that address sepsis based on leads developed in animal models have failed. We sought to identify leads based on human data by exploiting a natural experiment: the relative resistance of children to mortality from severe infections and sepsis. Using public datasets, we identified key differences in pathway activity (Pathprint) in blood transcriptome profiles of septic adults and children. To find drugs that could promote beneficial (child) pathways or inhibit harmful (adult) ones, we built an in silico pathway drug network (PDN) using expression correlation between drug, disease, and pathway gene signatures across 58,475 microarrays. Specific pathway clusters from children or adults were assessed for correlation with drug‐based signatures. Validation by literature curation and by direct testing in an endotoxemia model of murine sepsis of the most correlated drug candidates demonstrated that the Pathprint‐PDN methodology is more effective at generating positive drug leads than gene‐level methods (e.g., CMap). Pathway‐centric Pathprint‐PDN is a powerful new way to identify drug candidates for intervention against sepsis and provides direct insight into pathways that may determine survival.