wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

Metataxonomic and metabolomic profiling revealed Pinus koraiensis cone essential oil reduced methane emission through affecting ruminal microbial interactions and host-microbial metabolism

BACKGROUND

Pinus koraiensis cone essential oil (PEO) contains functional compounds such as monoterpene hydrocarbons, and the administration of PEO reduced methane (CH4) emissions during growing phase of goats. However, the mode of action of PEO driven CH4 reduction is not known, especially how the administration of PEO can affect rumen microbiota and host metabolism in goats during the fattening phase. This study aimed to elucidate the potential microbial and host responses PEO supplementation in goats using metataxonomics (prokaryotes and protozoa) and metabolomics (rumen fluid and serum).

RESULTS

Ten fattening Korean native goats were divided into two dietary groups: control (CON; basal diet without additives) and PEO (basal diet + 1.5 g/d of PEO) with a 2 × 2 crossover design and the treatment lasted for 11 weeks. Administration of PEO reduced CH4 concentrations in the exhaled gas from eructation by 12.0-13.6% (P < 0.05). Although the microbial composition of prokaryotes (bacteria and archaea) and protozoa in the rumen was not altered after PEO administration. MaAsLin2 analysis revealed that the abundance of Selenomonas, Christensenellaceae R-7 group, and Anaerovibrio were enriched in the rumen of PEO supplemented goats (Q < 0.1). Co-occurrence network analysis revealed that Lachnospiraceae AC2044 group and Anaerovibrio were the keystone taxa in the CON and PEO groups, respectively. Methane metabolism (P < 0.05) was enriched in the CON group, whereas metabolism of sulfur (P < 0.001) and propionate (P < 0.1) were enriched in the PEO group based on microbial predicted functions. After PEO administration, the abundance of 11 rumen and 4 serum metabolites increased, whereas that of 25 rumen and 14 serum metabolites decreased (P < 0.1). Random forest analysis identified eight ruminal metabolites that were altered after PEO administration, among which four were associated with propionate production, with predictive accuracy ranging from 0.75 to 0.88. Additionally, we found that serum sarcosine (serum metabolite) was positively correlated with CH4 emission parameters and abundance of Methanobrevibacter in the rumen (|r|≥ 0.5, P < 0.05).

CONCLUSIONS

This study revealed that PEO administration reduced CH4 emission from of fattening goats with altered microbial interactions and metabolites in the rumen and host. Importantly, PEO administration affected utilizes various mechanisms such as formate, sulfur, methylated amines metabolism, and propionate production, collectively leading to CH4 reduction. The knowledge is important for future management strategies to maintain animal production and health while mitigate CH4 emission.

PDL1 targeting by miR-138-5p amplifies anti-tumor immunity and Jurkat cells survival in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) has constituted over 80% of the lung cancer population with a poor prognosis. Over the past decade, immunotherapy has been constructed in the enlargement of immune checkpoint inhibitors as a promising approach for NSCLC treatment. Evading the immune system using the PD-1/PD-L1 axis is an intelligent way for cancers, and T cells cannot respond fully and confront cancer. Recently, the miR-138 was reported as a PD-L1 regulator in NSCLC. However, its inhibitory impact on T-cell exhaustion has not been characterized. The present study aims to impair PD-L1 (B7-H1) expression in Adenocarcinoma cell lines using miR-138-5p and determines how it prevents Jurak cell exhaustion. To gain the purpose, first, 18 highly significant dysregulated miRNAs containing hsa-miR-138 and CD274-mRNA network were detected in NSCLC based on bioinformatics analysis. Moreover, our study revealed a high level of miR-138-5p could make significant changes like PDL1 downregulation, proliferation, and mortality rate in A549/Calu6 cells. We also simulate cancer environmental conditions by culturing Jurak cells and NSCLC cell lines under the influence of stimulator cytokines to show how miR-138-5p survives Jurak cells by targeting PD-L1/PD-1pathway.

Enhancing Gene Co-Expression Network Inference for the Malaria Parasite Plasmodium falciparum

BACKGROUND

Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance.

RESULTS

Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks' edges (gene co-expression relationships), as well as predicted functional knowledge. The networks' edges are overall complementary: 47-85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community.

CONCLUSIONS

The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.

SUPPLEMENTARY DATA

Attached.

Unpacking the overlap between Autism and ADHD in adults: A multi-method approach

The overlap between Autism and Attention-Deficit Hyperactivity Disorder (ADHD) is widely observed in clinical settings, with growing interest in their co-occurrence in neurodiversity research. Until relatively recently, however, concurrent diagnoses of Autism and ADHD were not possible. This has limited the scope for large-scale research on their cross-condition associations, further stymied by a dearth of open science practices in the neurodiversity field. Additionally, almost all previous research linking Autism and ADHD has focused on children and adolescents, despite them being lifelong conditions. Tackling these limitations in previous research, 5504 adults - including a nationally representative sample of the UK (Study 1; n = 504) and a large pre-registered study (Study 2; n = 5000) - completed well-established self-report measures of Autism and ADHD traits. A series of network analyses unpacked the associations between Autism and ADHD at the individual trait level. Low inter-item connectivity was consistently found between conditions, supporting the distinction between Autism and ADHD as separable constructs. Subjective social enjoyment and hyperactivity-impulsivity traits were most condition-specific to Autism and ADHD, respectively. Traits related to attention control showed the greatest Bridge Expected Influence across conditions, revealing a potential transdiagnostic process underlying the overlap between Autism and ADHD. To investigate this further at the cognitive level, participants completed a large, well-powered, and pre-registered study measuring the relative contributions of Autism and ADHD traits to attention control (Study 3; n = 500). We detected age- and sex-related effects, however, attention control did not account for the covariance between Autism and ADHD traits. We situate our findings and discuss future directions in the cognitive science of Autism, ADHD, and neurodiversity, noting how our open datasets may be used in future research.

Noncoding RNAs improve the predictive power of network medicine

Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.

Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence

The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locally dependent pairs of variables using network modeling and a graph theory measure called weighted topological overlap (wTO). Using simulation, this approach is compared to contemporary local dependence detection methods such as exploratory structural equation modeling with standardized expected parameter change and a recently developed approach using partial correlations and a resampling procedure. Different approaches to determine local dependence using statistical significance and cutoff values are also compared. Continuous, polytomous (5-point Likert scale), and dichotomous (binary) data were generated with skew across a variety of conditions. Our results indicate that cutoff values work better than significance approaches. Overall, the network psychometrics approaches using wTO with graphical least absolute shrinkage and selector operator with extended Bayesian information criterion and wTO with Bayesian Gaussian graphical model were the best performing local dependence detection methods overall.

Identification of genes associated with abiotic stress tolerance in sweetpotato using weighted gene co-expression network analysis

Sweetpotato, Ipomoea batatas (L.), a key food security crop, is negatively impacted by heat, drought, and salinity stress. The orange-fleshed sweetpotato cultivar "Beauregard" was exposed to heat, salt, and drought treatments for 24 and 48 h to identify genes responding to each stress condition in leaves. Analysis revealed both common (35 up regulated, 259 down regulated genes in the three stress conditions) and unique sets of up regulated (1337 genes by drought, 516 genes by heat, and 97 genes by salt stress) and down regulated (2445 genes by drought, 678 genes by heat, and 204 genes by salt stress) differentially expressed genes (DEGs) suggesting common, yet stress-specific transcriptional responses to these three abiotic stressors. Gene Ontology analysis of down regulated DEGs common to both heat and salt stress revealed enrichment of terms associated with "cell population proliferation" suggestive of an impact on the cell cycle by the two stress conditions. To identify shared and unique gene co-expression networks under multiple abiotic stress conditions, weighted gene co-expression network analysis was performed using gene expression profiles from heat, salt, and drought stress treated 'Beauregard' leaves yielding 18 co-expression modules. One module was enriched for "response to water deprivation," "response to abscisic acid," and "nitrate transport" indicating synergetic crosstalk between nitrogen, water, and phytohormones with genes encoding osmotin, cell expansion, and cell wall modification proteins present as key hub genes in this drought-associated module. This research lays the groundwork for exploring to a further degree, mechanisms for abiotic stress tolerance in sweetpotato.

CD8 + T-cell marker genes reveal different immune subtypes of oral lichen planus by integrating single-cell RNA-seq and bulk RNA-sequencing

BACKGROUND

Oral lichen planus (OLP) is a local autoimmune disease induced by T-cell dysfunction that frequently affects middle-aged or elderly people, with a higher prevalence in women. CD8 + T cells, also known as killer T cells, play an important role in the progression and persistence of OLP. In order to identify different OLP subtypes associated with CD8 + T cell pathogenesis, consensus clustering was used.

METHODS

In this study, we preprocessed and downscaled the OLP single-cell dataset GSE211630 cohort downloaded from Gene Expression Omnibus (GEO) to finally obtain the marker genes of CD8 + T cells. Based on the expression of marker genes, we classified OLP patients into CMGs subtypes using unsupervised clustering analysis. The gene expression profiles were analyzed by WGCNA using the "WGCNA" R package based on the clinical disease traits and typing results, and 108 CD8 + T-cell related OLP pathogenicity-related genes were obtained from the intersection. Patients were once again classified into gene subtypes based on intersection gene expression using unsupervised clustering analysis.

RESULTS

After obtaining the intersecting genes of CD8 + T cells related to pathogenesis, OLP patients can be precisely classified into two different subtypes based on unsupervised clustering analysis, and subtype B has better immune infiltration results, providing clinicians with a reference for personalized treatment.

CONCLUSIONS

Classification of OLP into different subtypes improve our current understanding of the underlying pathogenesis of OLP and provides new insights for future studies.

Oral administration of Pinus koraiensis cone essential oil reduces rumen methane emission by altering the rumen microbial composition and functions in Korean native goat (Capra hircus coreanae)

This study aimed to investigate Pinus koraiensis cone essential oil (PEO) as a methane (CH₄) inhibitor and determine its impact on the taxonomic and functional characteristics of the rumen microbiota in goats. A total of 10 growing Korean native goats (Capra hircus coreanae, 29.9 ± 1.58 kg, male) were assigned to different dietary treatments: control (CON; basal diet without additive) and PEO (basal diet +1 g/d of PEO) by a 2 × 2 crossover design. Methane measurements were conducted every 4 consecutive days for 17-20 days using a laser CH₄ detector. Samples of rumen fluid and feces were collected during each experimental period to evaluate the biological effects and dry matter (DM) digestibility after PEO oral administration. The rumen microbiota was analyzed via 16S rRNA gene amplicon sequencing. The PEO oral administration resulted in reduced CH₄ emission (eructation CH₄/body weight^0.75, p = 0.079) without affecting DM intake; however, it lowered the total volatile fatty acids (p = 0.041), molar proportion of propionate (p = 0.075), and ammonia nitrogen (p = 0.087) in the rumen. Blood metabolites (i.e., albumin, alanine transaminase/serum glutamic pyruvate transaminase, creatinine, and triglyceride) were significantly affected (p < 0.05) by PEO oral administration. The absolute fungal abundance (p = 0.009) was reduced by PEO oral administration, whereas ciliate protozoa, total bacteria, and methanogen abundance were not affected. The composition of rumen prokaryotic microbiota was altered by PEO oral administration with lower evenness (p = 0.054) observed for the PEO group than the CON group. Moreover, PICRUSt2 analysis revealed that the metabolic pathways of prokaryotic bacteria, such as pyruvate metabolism, were enriched in the PEO group. We also identified the Rikenellaceae RC9 gut group as the taxa potentially contributing to the enriched KEGG modules for histidine biosynthesis and pyruvate oxidation in the rumen of the PEO group using the FishTaco analysis. The entire co-occurrence networks showed that more nodes and edges were detected in the PEO group. Overall, these findings provide an understanding of how PEO oral administration affects CH₄ emission and rumen prokaryotic microbiota composition and function. This study may help develop potential manipulation strategies to find new essential oils to mitigate enteric CH₄ emissions from ruminants.

Decoding the role of transcriptomic clocks in the human prefrontal cortex

Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk.

Dietary Sodium Butyrate Improves Intestinal Health of Triploid Oncorhynchus mykiss Fed a Low Fish Meal Diet

This study aimed to determine the effects of dietary sodium butyrate (NaB) on the growth and gut health of triploid Oncorhynchus mykiss juveniles (8.86 ± 0.36 g) fed a low fish meal diet for 8 weeks, including the inflammatory response, histomorphology, and the composition and functional prediction of microbiota. Five isonitrogenous and isoenergetic practical diets (15.00% fish meal and 21.60% soybean meal) were supplemented with 0.00% (G1), 0.10% (G2), 0.20% (G3), 0.30% (G4), and 0.40% NaB (G5), respectively. After the feeding trial, the mortality for G3 challenged with Aeromonas salmonicida for 7 days was lower than that for G1 and G5. The optimal NaB requirement for triploid O. mykiss based on weight gain rate (WGR) and the specific growth rate (SGR) was estimated to be 0.22% and 0.20%, respectively. The activities of intestinal digestive enzymes increased in fish fed a NaB diet compared to G1 (p < 0.05). G1 also showed obvious signs of inflammation, but this inflammation was significantly alleviated with dietary NaB supplementation. In comparison, G3 exhibited a more complete intestinal mucosal morphology. Dietary 0.20% NaB may play an anti-inflammatory role by inhibiting the NF-κB-P65 inflammatory signaling pathway. Additionally, the relative abundance of probiotics was altered by dietary NaB. In conclusion, dietary 0.20% NaB improved the intestinal health of triploid O. mykiss fed a low fish meal diet.

Measuring subjective quality of life in autistic adults with the PROMIS global-10: Psychometric study and development of an autism-specific scoring method

LAY ABSTRACT

Quality of Life an outcome that both researchers and autistic advocates agree is extremely important to consider when implementing services, interventions, and supports for autistic people. However, there has been little research on the topic of how quality of life can best be measured in autistic people or whether existing quality of life questionnaires are appropriate for use in the autistic population. This study aimed to validate an established quality of life measure, the Patient-Reported Outcomes Measurement Information System Global-10, in a large sample of autistic adults recruited online. We created a new way to score the Patient-Reported Outcomes Measurement Information System Global-10 scale and generate a "General quality of life" score specific to autistic adults. This new score performed very well in this sample, showing very little measurement error and relating in expected ways to similar constructs, such as physical health and emotional distress. Exploratory analyses found that lower quality of life was associated with female sex and self-identification as a sexual or gender minority (i.e. LGBTQ + identity). These findings suggest that the new Patient-Reported Outcomes Measurement Information System Global-10 quality of life score is a reliable and valid measure of quality of life in autistic adults, although additional studies are necessary to further explore its measurement properties in other subsets of the autistic population, such as individuals with intellectual disabilities. This measure is freely available for use as an outcome in both research and clinical practice, and an online score calculator is available to support the use of this measure in real-world applications.

Clinical neutrophil-associated genes as reliable predictors of hepatocellular carcinoma

Background: Growing evidence suggests that infiltrating neutrophils are key players in hepatocellular carcinoma (HCC) tumor progression. However, a comprehensive analysis of the biological roles of neutrophil infiltration and related genes in clinical outcomes and immunotherapy is lacking.

Methods: HCC samples were obtained from the TCGA and GEO databases. The CIBERSORT algorithm was used to reveal the TIME landscape. Gene modules significantly associated with neutrophils were found using weighted gene co-expression network analysis (WGCNA), a “dynamic tree-cut” algorithm, and Pearson correlation analysis. Genes were screened using Cox regression analysis and LASSO and prognostic value validation was performed using Kaplan-Meier curves and receiver operating characteristic (ROC) curves. Risk scores (RS) were calculated and nomograms were constructed incorporating clinical variables. Gene set variation analysis (GSVA) was used to calculate signaling pathway activity. Immunophenoscore (IPS) was used to analyze differences in immunotherapy among samples with different risk scores. Finally, the relationship between RS and drug sensitivity was explored using the pRRophetic algorithm.

Results: 10530 genes in 424 samples (50 normal samples, 374 tumor samples) were obtained from the TCGA database. Using WGCNA, the “MEbrown” gene module was most associated with neutrophils. Nine genes with prognostic value in HCC (PDLIM3, KLF2, ROR2, PGF, EFNB1, PDZD4, PLN, PCDH17, DOK5) were finally screened. Prognostic nomograms based on RS, gender, tumor grade, clinical stage, T, N, and M stages were constructed. The nomogram performed well after calibration curve validation. There is an intrinsic link between risk score and TMB and TIME. Samples with different risk scores differed in different signaling pathway activity, immunopharmaceutical treatment and chemotherapy sensitivity.

Conclusion: In conclusion, a comprehensive analysis of neutrophil-related prognostic features will help in prognostic prediction and advance individualized treatment.

Mapping hormone-regulated cell-cell interaction networks in the human breast at single-cell resolution

The rise and fall of estrogen and progesterone across menstrual cycles and during pregnancy regulates breast development and modifies cancer risk. How these hormones impact each cell type in the breast remains poorly understood because they act indirectly through paracrine networks. Using single-cell analysis of premenopausal breast tissue, we reveal a network of coordinated transcriptional programs representing the tissue-level response to changing hormone levels. Our computational approach, DECIPHER-seq, leverages person-to-person variability in breast composition and cell state to uncover programs that co-vary across individuals. We use differences in cell-type proportions to infer a subset of programs that arise from direct cell-cell interactions regulated by hormones. Further, we demonstrate that prior pregnancy and obesity modify hormone responsiveness through distinct mechanisms: obesity reduces the proportion of hormone-responsive cells, whereas pregnancy dampens the direct response of these cells to hormones. Together, these results provide a comprehensive map of the cycling human breast.

The nosographic structure of posttraumatic stress symptoms across trauma types: An exploratory network analysis approach

The nosographic structure of posttraumatic stress disorder (PTSD) remains unclear, and attempts to determine its symptomatic organization have been unsatisfactory. Several explanations have been suggested, and the impact of trauma type is receiving increasing attention. As little is known about the differential impact trauma type in the nosographic structure of PTSD, we explored the nosology of PTSD and the effect of trauma type on its symptomatic organization. We reanalyzed five cross-sectional psychopathological networks involving different trauma types, encompassing a broad range of traumatic events in veterans, war-related trauma in veterans, sexual abuse, terrorist attacks, and various traumatic events in refugees. The weighted topological overlap was used to estimate the networks and attribute weights to their links. Coexpression differential network analysis was used to identify the common and specific network structures of the connections across different trauma types and to determine the importance of symptoms across the networks. We found a set of symptoms with more common connections with other symptoms, suggesting that these might constitute the prototypical nosographic structure of PTSD. We also found a set of symptoms that had a high number of specific connections with other symptoms; these connections varied according to trauma type. The importance of symptoms across the common and specific networks was ascertained. The present findings offer new insights into the symptomatic organization of PTSD and support previous research on the impact of trauma type on the nosology of this disorder.

Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma

Background

Numerous studies have found that infiltrating M2 macrophages play an important role in the tumor progression of lung adenocarcinoma (LUAD). However, the roles of M2 macrophage infiltration and M2 macrophage-related genes in immunotherapy and clinical outcomes remain obscure.

Methods

Sample information was extracted from TCGA and GEO databases. The TIME landscape was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to find M2 macrophage-related gene modules. Through univariate Cox regression, lasso regression analysis, and multivariate Cox regression, the genes strongly associated with the prognosis of LUAD were screened out. Risk score (RS) was calculated, and all samples were divided into high-risk group (HRG) and low-risk group (LRG) according to the median RS. External validation of RS was performed using GSE68571 data information. Prognostic nomogram based on risk signatures and other clinical information were constructed and validated with calibration curves. Potential associations of tumor mutational burden (TMB) and risk signatures were analyzed. Finally, the potential association of risk signatures with chemotherapy efficacy was investigated using the pRRophetic algorithm.

Results

Based on 504 samples extracted from TCGA database, 183 core genes were identified using WGCNA. Through a series of screening, two M2 macrophage-related genes (GRIA1 and CLEC3B) strongly correlated with LUAD prognosis were finally selected. RS was calculated, and prognostic risk nomogram including gender, age, T, N, M stage, clinical stage, and RS were constructed. The calibration curve shows that our constructed model has good performance. HRG patients were suitable for new ICI immunotherapy, while LRG was more suitable for CTLA4-immunosuppressive therapy alone. The half-maximal inhibitory concentrations (IC50) of the four chemotherapeutic drugs (metformin, cisplatin, paclitaxel, and gemcitabine) showed significant differences in HRG/LRG.

Conclusions

In conclusion, a comprehensive analysis of the role of M2 macrophages in tumor progression will help predict prognosis and facilitate the advancement of therapeutic techniques.

The Dark Side of Humanity Scale: A reconstruction of the Dark Tetrad constructs

There has been an absence of consideration regarding measurement invariance across males and females in the widely available Dark Tetrad (DT) scales which measure psychopathy, Machiavellianism, narcissism and everyday sadism. This has resulted in criticisms of the measures, suggesting that the assessed constructs are not wholly relatable between the groups. This article documents the construction and validation of the Dark Side of Humanity Scale (DSHS), which measures dark personalities from an alternative viewpoint, determined by the constructs as they emerged from the male and female data, whilst aligning with theory and attaining invariance between sex. Across four samples (n = 2409), using a diverse range of statistical methods, including exploratory graph analysis, item response theory and confirmatory factor analysis, a divergence from the widely available DT measures emerged, whereby primary psychopathy and Machiavellianism were unified. This corroborated past research which had discussed the two constructs as being parallel. It further supported the DSHS with a shift away from the traditional DT conceptualisation. The resulting scale encompasses four factors which are sex invariant across samples and time. The first factor represents the successful psychopath, factor two addresses the grandiose form of entitlement, factor three taps into everyday sadism whilst the fourth factor pertains to narcissistic entitlement rage. Construct and external validity of the DSHS across two samples (n = 1338), as well as test-retest reliability (n = 413), was achieved. The DSHS provides an alternative approach to investigating the dark side of human nature, whilst also being sex invariant, thus making it highly suitable for use with mixed sex samples.

Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

BACKGROUND

Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression.

RESULTS

Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships.

CONCLUSIONS

Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

Gene Co-expression Network Analysis

Gene co-expression analysis is a data analysis technique that helps identify groups of genes with similar expression patterns across several different conditions. By means of these techniques, different groups have been able to assign putative metabolic pathways and functions to understudied genes and to identify novel metabolic regulation networks for different metabolites. Some groups have even used network comparative studies to understand the evolution of these networks from green algae to land plants. In this chapter, we will go over the basic definitions required to understand network topology and gene module identification. Additionally, we offer the reader a walk-through a standard analysis pipeline as implemented in the package WGCNA that takes as input raw fastq files and obtains co-expressed gene clusters and representative gene expression patterns from each module for downstream applications.

Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study

Multidimensional integrative data analysis of digital phenotyping is crucial for elucidating the pathologies of multifactorial and heterogeneous diseases, such as the dry eye (DE). This crowdsourced cross-sectional study explored a novel smartphone-based digital phenotyping strategy to stratify and visualize the heterogenous DE symptoms into distinct subgroups. Multidimensional integrative data were collected from 3,593 participants between November 2016 and September 2019. Dimension reduction via Uniform Manifold Approximation and Projection stratified the collected data into seven clusters of symptomatic DE. Symptom profiles and risk factors in each cluster were identified by hierarchical heatmaps and multivariate logistic regressions. Stratified DE subgroups were visualized by chord diagrams, co-occurrence networks, and Circos plot analyses to improve interpretability. Maximum blink interval was reduced in clusters 1, 2, and 5 compared to non-symptomatic DE. Clusters 1 and 5 had severe DE symptoms. A data-driven multidimensional analysis with digital phenotyping may establish predictive, preventive, personalized, and participatory medicine.

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases.

Investigating the structure of trait rumination in autistic adults: A network analysis

LAY ABSTRACT

Autistic adults are substantially more likely to develop depression than individuals in the general population, and recent research has indicated that certain differences in thinking styles associated with autism may play a role in this association. Rumination, the act of thinking about the same thing over and over without a functional outcome, is a significant risk factor for depression in both autistic and non-autistic adults. However, little is known about how different kinds of rumination relate to each other and to depressive symptoms in the autistic population specifically. To fill this gap in knowledge, we recruited a large online sample of autistic adults, who completed questionnaire measures of both the tendency to ruminate and symptoms of depression. By examining the interacting network of rumination and depression symptoms, this study was able to identify particular aspects of rumination-such as thinking repetitively about one's guilty feelings or criticizing oneself-that may be particularly important in maintaining these harmful thought patterns in autistic adults. Although further study is needed, it is possible that the symptoms identified as most "influential" in the network may be particularly good targets for future interventions for mood and anxiety disorders in the autistic population.

Microbial transfers from permanent grassland ecosystems to milk in dairy farms in the Comté cheese area

The specificity of dairy Protected Designation of Origin (PDO) products is related to their “terroir” of production. This relationship needs better understanding for efficient and sustainable productions preserving the agroecological equilibrium of agroecosystems, especially grasslands. Specificity of PDO Comté cheese was related to the diversity of natural raw milk bacterial communities, but their sources need to be determined. It is hypothesized that raw milk indigenous microbial communities may originate from permanent grazed grasslands by the intermediate of dairy cows according to the sequence soil–phyllosphere–teat–milk. This hypothesis was evaluated on a 44 dairy farms network across PDO Comté cheese area by characterizing prokaryotic and fungal communities of these compartments by metabarcoding analysis (16S rRNA gene: V3–V4 region, 18S rRNA gene: V7–V8 region). Strong and significant links were highlighted between the four compartments through a network analysis (0.34 < r < 0.58), and were modulated by soil pH, plant diversity and elevation; but also by farming practices: organic fertilization levels, cattle intensity and cow-teat care. This causal relationship suggests that microbial diversity of agroecosystems is a key player in relating a PDO product to its “terroir”; this under the dependency of farming practices. Altogether, this makes the “terroir” even more local and needs to be considered for production sustainability.

Longitudinal variations in the gastrointestinal microbiome of the white shrimp, Litopenaeus vannamei

The shrimp gut is a long digestive structure that includes the Foregut (stomach), Midgut (hepatopancreas) and Hindgut (intestine). Each component has different structural, immunity and digestion roles. Given these three gut digestive tract components' significance, we examined the bacterial compositions of the Foregut, Hindgut, and Midgut digestive fractions. Those bacterial communities' structures were evaluated by sequencing the V3 hypervariable region of the 16S rRNA gene, while the functions were predicted by PICRUSt2 bioinformatics workflow. Also, to avoid contamination with environmental bacteria, shrimp were maintained under strictly controlled conditions. The pairwise differential abundance analysis revealed differences among digestive tract fractions. The families Rhodobacteraceae and Rubritalaceae registered higher abundances in the Foregut fraction, while in the Midgut, the families with a higher proportion were Aeromonadaceae, Beijerinckiaceae and Propionibacteriaceae. Finally, the Cellulomonadaceae family resulted in a higher proportion in the Hindgut. Regarding the predicted functions, amino acid and carbohydrate metabolism pathways were the primary functions registered for Foregut microbiota; conversely, pathways associated with the metabolism of lipids, terpenoids and polyketides, were detected in the Midgut fraction. In the Hindgut, pathways like the metabolism of cofactors and vitamins along with energy metabolism were enriched. Structural changes were followed by significant alterations in functional capabilities, suggesting that each fraction's bacteria communities may carry out specific metabolic functions. Results indicate that white shrimp's gut microbiota is widely related to the fraction analyzed across the digestive tract. Overall, our results suggest a role for the dominant bacteria in each digestive tract fraction, contributing with a novel insight into the bacterial community.

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

BACKGROUND

Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.

RESULTS

Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions.

CONCLUSION

GWENA is an R package available through Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GWENA.html ) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

Accelerated Evolution of Tissue-Specific Genes Mediates Divergence Amidst Gene Flow in European Green Lizards

The European green lizards of the Lacerta viridis complex consist of two closely related species, L. viridis and Lacerta bilineata that split less than 7 million years ago in the presence of gene flow. Recently, a third lineage, referred to as the “Adriatic” was described within the L. viridis complex distributed from Slovenia to Greece. However, whether gene flow between the Adriatic lineage and L. viridis or L. bilineata has occurred and the evolutionary processes involved in their diversification are currently unknown. We hypothesized that divergence occurred in the presence of gene flow between multiple lineages and involved tissue-specific gene evolution. In this study, we sequenced the whole genome of an individual of the Adriatic lineage and tested for the presence of gene flow amongst L. viridis, L. bilineata, and Adriatic. Additionally, we sequenced transcriptomes from multiple tissues to understand tissue-specific effects. The species tree supports that the Adriatic lineage is a sister taxon to L. bilineata. We detected gene flow between the Adriatic lineage and L. viridis suggesting that the evolutionary history of the L. viridis complex is likely shaped by gene flow. Interestingly, we observed topological differences between the autosomal and Z-chromosome phylogenies with a few fast evolving genes on the Z-chromosome. Genes highly expressed in the ovaries and strongly co-expressed in the brain experienced accelerated evolution presumably contributing to establishing reproductive isolation in the L. viridis complex.

Conserved paradoxical relationships among the evolutionary, structural and expressional features of KRAB zinc-finger proteins reveal their special functional characteristics

Background

One striking feature of the large KRAB domain-containing zinc finger protein (KZFP) family is its rapid evolution, leading to hundreds of member genes with various origination time in a certain mammalian genome. However, a comprehensive genome-wide and across-taxa analysis of the structural and expressional features of KZFPs with different origination time is lacking. This type of analysis will provide valuable clues about the functional characteristics of this special family.

Results

In this study, we found several conserved paradoxical phenomena about this issue. 1) Ordinary young domains/proteins tend to be disordered, but most of KRAB domains are completely structured in 64 representative species across the superclass of Sarcopterygii and most of KZFPs are also highly structured, indicating their rigid and unique structural and functional characteristics; as exceptions, old-zinc-finger-containing KZFPs have relatively disordered KRAB domains and linker regions, contributing to diverse interacting partners and functions. 2) In general, young or highly structured proteins tend to be spatiotemporal specific and have low abundance. However, by integrated analysis of 29 RNA-seq datasets, including 725 samples across early embryonic development, embryonic stem cell differentiation, embryonic and adult organs, tissues in 7 mammals, we found that KZFPs tend to express ubiquitously with medium abundance regardless of evolutionary age and structural disorder degree, indicating the wide functional requirements of KZFPs in various states. 3) Clustering and correlation analysis reveal that there are differential expression patterns across different spatiotemporal states, suggesting the specific-high-expression KZFPs may play important roles in the corresponding states. In particular, part of young-zinc-finger-containing KZFPs are highly expressed in early embryonic development and ESCs differentiation into endoderm or mesoderm. Co-expression analysis revealed that young-zinc-finger-containing KZFPs are significantly enriched in five co-expression modules. Among them, one module, including 13 young-zinc-finger-containing KZFPs, showed an ‘early-high and late-low’ expression pattern. Further functional analysis revealed that they may function in early embryonic development and ESC differentiation via participating in cell cycle related processes.

Conclusions

This study shows the conserved and special structural, expressional features of KZFPs, providing new clues about their functional characteristics and potential causes of their rapid evolution.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12860-021-00346-w.

Identification of key proteins involved in stickleback environmental adaptation with system-level analysis

Using abundance measurements of 1,490 proteins from four separate populations of three-spined sticklebacks, we implemented a system-level approach to correlate proteome dynamics with environmental salinity and temperature and the fish's population and morphotype. We identified robust and accurate fingerprints that classify environmental salinity, temperature, morphotype, and the population sample origin, observing that proteins with specific functions are enriched in these fingerprints. Highly apparent functions represented in all fingerprints include ion transport, proteostasis, growth, and immunity, suggesting that these functions are most diversified in populations inhabiting different environments. Applying a differential network approach, we analyzed the network of protein interactions that differs between populations. Looking at specific population combinations of differential interaction, we identify sets of connected proteins. We find that these sets and their corresponding enriched functions reflect key processes that have diverged between the four populations. Moreover, the extent of divergence, i.e., the number of enriched functions that differ between populations, is highest when all three environmental parameters are different between two populations. Key nodes in the differential interaction network signify functions that are also inherent in the fingerprints, most prominently proteostasis-related functions. However, the differential interaction network also reveals additional functions that have diverged between populations, notably cytoskeletal organization and morphogenesis. The strength of these analyses is that the results are purely data driven. With such an unbiased approach applied on a large proteomic data set, we find the strongest signals given by the data, making it possible to develop more discriminatory and complex biomarkers for specific contexts of interest.

Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and—to best of our knowledge—no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E Δ H S C compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E Δ H S C mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E Δ H S C mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Seasonal Dynamics of Pelagic Mycoplanktonic Communities: Interplay of Taxon Abundance, Temporal Occurrence, and Biotic Interactions

Marine fungi are an important component of pelagic planktonic communities. However, it is not yet clear how individual fungal taxa are integrated in marine processes of the microbial loop and food webs. Most likely, biotic interactions play a major role in shaping the fungal community structure. Thus, the aim of our work was to identify possible biotic interactions of mycoplankton with phytoplankton and zooplankton groups and among fungi, and to investigate whether there is coherence between interactions and the dynamics, abundance and temporal occurrence of individual fungal OTUs. Marine surface water was sampled weekly over the course of 1 year, in the vicinity of the island of Helgoland in the German Bight (North Sea). The mycoplankton community was analyzed using 18S rRNA gene tag-sequencing and the identified dynamics were correlated to environmental data including phytoplankton, zooplankton, and abiotic factors. Finally, co-occurrence patterns of fungal taxa were detected with network analyses based on weighted topological overlaps (wTO). Of all abundant and persistent OTUs, 77% showed no biotic relations suggesting a saprotrophic lifestyle. Of all other fungal OTUs, nearly the half (44%) had at least one significant negative relationship, especially with zooplankton and other fungi, or to a lesser extent with phytoplankton. These findings suggest that mycoplankton OTUs are embedded into marine food web chains via highly complex and manifold relationships such as parasitism, predation, grazing, or allelopathy. Furthermore, about one third of all rare OTUs were part of a dense fungal co-occurrence network probably stabilizing the fungal community against environmental changes and acting as functional guilds or being involved in fungal cross-feeding. Placed in an ecological context, strong antagonistic relationships of the mycoplankton community with other components of the plankton suggest that: (i) there is a top-down control by fungi on zooplankton and phytoplankton; (ii) fungi serve as a food source for zooplankton and thereby transfer nutrients and organic material; (iii) the dynamics of fungi harmful to other plankton groups are controlled by antagonistic fungal taxa.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Challenges in microRNAs' targetome prediction and validation

MicroRNAs (miRNAs) are small RNA molecules with important roles in post-transcriptional regulation of gene expression. In recent years, the predicted number of miRNAs has skyrocketed, largely as a consequence of high-throughput sequencing technologies becoming ubiquitous. This dramatic increase in miRNA candidates poses multiple challenges in terms of data deposition, curation, and validation. Although multiple databases containing miRNA annotations and targets have been developed, ensuring data quality by validating miRNA-target interactions requires the efforts of the research community. In order to generate databases containing biologically active miRNAs, it is imperative to overcome a multitude of hurdles, including restricted miRNA expression patterns, distinct miRNA biogenesis machineries, and divergent miRNA-mRNA interaction dynamics. In the present review, we discuss recent advances and limitations in miRNA prediction, identification, and validation. Lastly, we focus on the most enriched neuronal miRNA, miR-124, and its gene regulatory network in human neurons, which has been revealed using a combined computational and experimental approach.

Exposure to environmental radionuclides is associated with altered metabolic and immunity pathways in a wild rodent

Wildlife inhabiting environments contaminated by radionuclides face putative detrimental effects of exposure to ionizing radiation, with biomarkers such as an increase in DNA damage and/or oxidative stress commonly associated with radiation exposure. To examine the effects of exposure to radiation on gene expression in wildlife, we conducted a de novo RNA sequencing study of liver and spleen tissues from a rodent, the bank vole Myodes glareolus. Bank voles were collected from the Chernobyl Exclusion Zone (CEZ), where animals were exposed to elevated levels of radionuclides, and from uncontaminated areas near Kyiv, Ukraine. Counter to expectations, we did not observe a strong DNA damage response in animals exposed to radionuclides, although some signs of oxidative stress were identified. Rather, exposure to environmental radionuclides was associated with upregulation of genes involved in lipid metabolism and fatty acid oxidation in the livers - an apparent shift in energy metabolism. Moreover, using stable isotope analysis, we identified that fur from bank voles inhabiting the CEZ had enriched isotope values of nitrogen: such an increase is consistent with increased fatty acid metabolism, but also could arise from a difference in diet or habitat between the CEZ and elsewhere. In livers and spleens, voles inhabiting the CEZ were characterized by immunosuppression, such as impaired antigen processing, and activation of leucocytes involved in inflammatory responses. In conclusion, exposure to low dose environmental radiation impacts pathways associated with immunity and lipid metabolism, potentially as a stress-induced coping mechanism.

Combined Experimental and System-Level Analyses Reveal the Complex Regulatory Network of miR-124 during Human Neurogenesis

Non-coding RNAs regulate many biological processes including neurogenesis. The brain-enriched miR-124 has been assigned as a key player of neuronal differentiation via its complex but little understood regulation of thousands of annotated targets. To systematically chart its regulatory functions, we used CRISPR/Cas9 gene editing to disrupt all six miR-124 alleles in human induced pluripotent stem cells. Upon neuronal induction, miR-124-deleted cells underwent neurogenesis and became functional neurons, albeit with altered morphology and neurotransmitter specification. Using RNA-induced-silencing-complex precipitation, we identified 98 high-confidence miR-124 targets, of which some directly led to decreased viability. By performing advanced transcription-factor-network analysis, we identified indirect miR-124 effects on apoptosis, neuronal subtype differentiation, and the regulation of previously uncharacterized zinc finger transcription factors. Our data emphasize the need for combined experimental- and system-level analyses to comprehensively disentangle and reveal miRNA functions, including their involvement in the neurogenesis of diverse neuronal cell types found in the human brain.

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miR-124 is not essential for neurogenesis from human iPSCs

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miR-124 regulation mediates neuroprotection and refines neuronal cell fates

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miRNA knockout characterization by experimental and advanced computational analyses

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Identification of 98 targets including the neuronal feature repressor ZNF787