Weighted gene coexpression network analysis strategies applied to mouse weight

Transcriptomic responses in the nervous system and correlated behavioural changes of a cephalopod exposed to ocean acidification

BACKGROUND

The nervous system is central to coordinating behavioural responses to environmental change, likely including ocean acidification (OA). However, a clear understanding of neurobiological responses to OA is lacking, especially for marine invertebrates.

RESULTS

We evaluated the transcriptomic response of the central nervous system (CNS) and eyes of the two-toned pygmy squid (Idiosepius pygmaeus) to OA conditions, using a de novo transcriptome assembly created with long read PacBio ISO-sequencing data. We then correlated patterns of gene expression with CO2 treatment levels and OA-affected behaviours in the same individuals. OA induced transcriptomic responses within the nervous system related to various different types of neurotransmission, neuroplasticity, immune function and oxidative stress. These molecular changes may contribute to OA-induced behavioural changes, as suggested by correlations among gene expression profiles, CO2 treatment and OA-affected behaviours.

CONCLUSIONS

This study provides the first molecular insights into the neurobiological effects of OA on a cephalopod and correlates molecular changes with whole animal behavioural responses, helping to bridge the gaps in our knowledge between environmental change and animal responses.

Network analysis reveals age- and virus-specific circuits in nasal epithelial cells of extremely premature infants

BACKGROUND AND OBJECTIVES

Viral respiratory infections significantly affect young children, particularly extremely premature infants, resulting in high hospitalization rates and increased health-care burdens. Nasal epithelial cells, the primary defense against respiratory infections, are vital for understanding nasal immune responses and serve as a promising target for uncovering underlying molecular and cellular mechanisms.

METHODS

Using a trans-well pseudostratified nasal epithelial cell system, we examined age-dependent developmental differences and antiviral responses to influenza A and respiratory syncytial virus through systems biology approaches.

RESULTS

Our studies revealed differences in innate-receptor repertoires, distinct developmental pathways, and differentially connected antiviral network circuits between neonatal and adult nasal epithelial cells. Consensus network analysis identified unique and shared cellular-viral networks, emphasizing highly relevant virus-specific pathways, independent of viral replication kinetics.

CONCLUSION

This research highlights the importance of nasal epithelial cells in innate antiviral immune responses and offers crucial insights that allow for a deeper understanding of age-related differences in nasal epithelial cell immunity following respiratory virus infections.

Transcriptome meta-analysis reveals the hair genetic rules in six animal breeds and genes associated with wool fineness

Wool plays an irreplaceable role in the lives of livestock and the textile industry. The variety of hair quality and shape leads to the diversity of its functions and applications, and the finer wool has a higher economic value. In this study, 10 coarse and 10 fine ordos fine wool sheep skin samples were collected for RNA-seq, and coarse and fine skin/hair follicle RNA-seq datasets of other five animal breeds were obtained from NCBI. Weighted gene co-expression network analysis showed that the common genes were clustered into eight modules. Similar gene expression patterns in sheep and rabbits with the same wool types, different gene expression patterns in animal species with different hair types, and brown modules were significantly correlated with species and breeds. GO and KEGG enrichment analyses showed that, most genes in the brown module associated with hair follicle development. Hence, gene expression patterns in skin tissues may determine hair morphology in animal. The analysis of differentially expressed genes revealed that 32 highly expressed candidate genes associated with the wool fineness of Ordos fine wool sheep. Among them, KAZALD1 (grey module), MYOC (brown module), C1QTNF6 (brown module), FOS (tan module), ITGAM, MX2, MX1, and IFI6 genes have been reported to be involved in the regulation of the hair follicle cycle or hair loss. Additionally, 12 genes, including KAZALD1, MYOC, C1QTNF6, and FOS, are differentially expressed across various animal breeds and species. The above results suggest that different sheep breeds share a similar molecular regulatory basis of wool fineness. Finally, the study provides a theoretical reference for molecular breeding of sheep breeds as well as for the investigation of the origin and evolution of animal hair.

Multistage temporal transcriptomic atlas unveils major contributor to reproductive phase in upland cotton

Flowering is a major developmental transition in plants, but asynchronous flowering hinders the utilization of wild cotton relatives in breeding programs. We performed comparative transcriptomic profiling of early- and late-flowering Gossypium hirsutum genotypes to elucidate genetic factors influencing reproductive timing. Shoot apices were sampled from the photoperiod-sensitive landrace G. hirsutum purpurascens (GhP) and early-maturing variety ZhongMianSuo (ZMS) at five time points following the emergence of sympodial nodes. RNA-sequencing revealed extensive transcriptional differences during floral transition. Numerous flowering-associated genes exhibited genotype-specific expression, including FLOWERING LOCUS T (FT) homologs upregulated in ZMS. FT-interacting factors like SOC1 and CO-like also showed higher expression in ZMS, implicating florigen pathways in early flowering. Additionally, circadian clock and light signalling components were misregulated between varieties, suggesting altered photoperiod responses in GhP. Weighted co-expression network analysis specifically linked a module enriched for circadian-related genes to GhP's late flowering. Through an integrated transcriptome analysis, we defined a regulatory landscape of reproductive phase change in cotton. Differentially expressed genes related to photoperiod, circadian clock, and light signalling likely contribute to delayed flowering in wild cottons. Characterization of upstream flowering regulators will enable modifying photoperiod sensitivity and expand germplasm use for cotton improvement. This study provides candidate targets for elucidating interactive mechanisms that control cotton flowering time across diverse genotypes.

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

Impaired interferon response in plasmacytoid dendritic cells from children with persistent wheeze

BACKGROUND

Impaired interferon response and allergic sensitization may contribute to virus-induced wheeze and asthma development in young children. Plasmacytoid dendritic cells (pDCs) play a key role in antiviral immunity as critical producers of type I interferons. pDCs also express the high-affinity IgE receptor through which type I interferon production may be negatively regulated. Whether antiviral function of pDCs is associated with recurrent episodes of wheeze in young children is not well understood.

OBJECTIVE

We sought to evaluate the phenotype and function of circulating pDCs in children with a longitudinally defined wheezing phenotype.

METHODS

We performed multiparameter flow cytometry on PBMCs from 38 children presenting to the emergency department with an acute episode of respiratory wheeze and 19 controls. RNA sequencing on isolated pDCs from the same individuals was also performed. For each subject, their longitudinal exacerbation phenotype was determined using the Western Australia public hospital database.

RESULTS

We observed a significant depletion of circulating pDCs in young children with a persistent phenotype of wheeze. The same individuals also displayed upregulation of the FcεRI on their pDCs. Based on transcriptomic analysis, pDCs from these individuals did not mount a robust systemic antiviral response as observed in children who displayed a nonrecurrent wheezing phenotype.

CONCLUSIONS

Our data suggest that circulating pDC phenotype and function are altered in young children with a persistent longitudinal exacerbation phenotype. Expression of high-affinity IgE receptor is increased and their function as major interferon producers is impaired during acute exacerbations of wheeze.

Dissecting gene expression networks in the developing hippocampus through the lens of NEIL3 depletion

Gene regulation in the hippocampus is fundamental for its development, synaptic plasticity, memory formation, and adaptability. Comparisons of gene expression among different developmental stages, distinct cell types, and specific experimental conditions have identified differentially expressed genes contributing to the organization and functionality of hippocampal circuits. The NEIL3 DNA glycosylase, one of the DNA repair enzymes, plays an important role in hippocampal maturation and neuron functionality by shaping transcription. While differential gene expression (DGE) analysis has identified key genes involved, broader gene expression patterns crucial for high-order hippocampal functions remain uncharted. By utilizing the weighted gene co-expression network analysis (WGCNA), we mapped gene expression networks in immature (p8-neonatal) and mature (3 m-adult) hippocampal circuits in wild-type and NEIL3-deficient mice. Our study unveiled intricate gene network structures underlying hippocampal maturation, delineated modules of co-expressed genes, and pinpointed highly interconnected hub genes specific to the maturity of hippocampal subregions. We investigated variations within distinct gene network modules following NEIL3 depletion, uncovering NEIL3-targeted hub genes that influence module connectivity and specificity. By integrating WGCNA with DGE, we delve deeper into the NEIL3-dependent molecular intricacies of hippocampal maturation. This study provides a comprehensive systems-level analysis for assessing the potential correlation between gene connectivity and functional connectivity within the hippocampal network, thus shaping hippocampal function throughout development.

The effect and mechanism of Qingre Huashi formula in the treatment of chronic hepatitis B with Gan-dan-shi-Re syndrome: An integrated transcriptomic and targeted metabolomic analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Qingre Huashi (QRHS) formula is an empirical prescription for the treatment of Gan-Dan-Shi-Re syndrome (GDSR) syndrome in traditional Chinese medicine (TCM). GDSR is one of the typical TCM syndromes in chronic hepatitis B (CHB). However, little is known about the mechanism of the QRHS formula in treating CHB patients with GDSR. The biological basis of GDSR also remains largely unknown.

AIM OF THE STUDY

GDSR mostly occurs in the acute and early stages of chronic liver disease. Effectively alleviating GDSR stalls disease development and benefits patients. The purpose of this study was to explore the molecular basis of GDSR in CHB and then study the mechanism of the QRHS formula treating GDSR using transcriptomics and metabolomics.

MATERIALS AND METHODS

The transcriptome and metabolome of CHB patients with GDSR syndrome were detected using RNA microarray combined with ultra-high performance liquid chromatography/mass spectrometry and information mining. The potential biomarkers were identified from differentially expressed genes and metabolites, and the metabolic pathway was analyzed. We also investigated the callback of metabolic biomarkers after treatment with the QRHS formula, an empirical prescription for the treatment of GDSR syndrome. RT-PCR analysis was carried out in an independent patient cohort of CHB for validation.

RESULTS

Four candidate genes-GPT2, HK2, DDIT3, and HIF1A-and 14 candidate metabolic biomarkers, including L-alpha-aminobutyric acid, selenomethionine, and fructose 1,6-bisphosphate, were identified and validated. All four transcripts of GPT2, HK2, DDIT3, and HIF1A were significantly differentially expressed between the GDSR and non-GDSR groups through independent microarray data and RT-PCR. After treatment with the QRHS formula, the clinical indexes and TCM syndrome were significantly improved, and the 14 disturbed biomarkers were obviously corrected. Three metabolic pathways were confirmed to be perturbed in CHB GDSR patients: alanine, aspartate, and glutamate metabolism, arginine biosynthesis, and aminoacyl-tRNA biosynthesis.

CONCLUSION

Using integrated transcriptomic and targeted metabolomic methods, we identified the potential biomarkers and dysregulated metabolic pathways in CHB patients with GDSR syndrome, which was alleviated by the QRHS formula treatment. These results may provide the mechanism of metabolic dysregulation in GDSR syndrome as well as that underlying the curative effect of the QRHS formula.

Transcriptomic meta-analysis reveals ERRα-mediated oxidative phosphorylation is downregulated in Fuchs' endothelial corneal dystrophy

BACKGROUND

Late-onset Fuchs' endothelial corneal dystrophy (FECD) is a degenerative disease of cornea and the leading indication for corneal transplantation. Genetically, FECD patients can be categorized as with (RE+) or without (RE-) the CTG trinucleotide repeat expansion in the transcription factor 4 gene. The molecular mechanisms underlying FECD remain unclear, though there are plausible pathogenic models proposed for RE+ FECD.

METHOD

In this study, we performed a meta-analysis on RNA sequencing datasets of FECD corneal endothelium including 3 RE+ datasets and 2 RE- datasets, aiming to compare the transcriptomic profiles of RE+ and RE- FECD. Gene differential expression analysis, co-expression networks analysis, and pathway analysis were conducted.

RESULTS

There was a striking similarity between RE+ and RE- transcriptomes. There were 1,184 genes significantly upregulated and 1,018 genes significantly downregulated in both RE+ and RE- cases. Pathway analysis identified multiple biological processes significantly enriched in both-mitochondrial functions, energy-related processes, ER-nucleus signaling pathway, demethylation, and RNA splicing were negatively enriched, whereas small GTPase mediated signaling, actin-filament processes, extracellular matrix organization, stem cell differentiation, and neutrophil mediated immunity were positively enriched. The translational initiation process was downregulated in the RE+ transcriptomes. Gene co-expression analysis identified modules with relatively distinct biological processes enriched including downregulation of mitochondrial respiratory chain complex assembly. The majority of oxidative phosphorylation (OXPHOS) subunit genes, as well as their upstream regulator gene estrogen-related receptor alpha (ESRRA), encoding ERRα, were downregulated in both RE+ and RE- cases, and the expression level of ESRRA was correlated with that of OXPHOS subunit genes.

CONCLUSION

Meta-analysis increased the power of detecting differentially expressed genes. Integrating differential expression analysis with co-expression analysis helped understand the underlying molecular mechanisms. FECD RE+ and RE- transcriptomic profiles are much alike with the hallmark of downregulation of genes in pathways related to ERRα-mediated OXPHOS.

Identification of core genes affecting IMF deposition in bovine

Intramuscular fat (IMF) content is an important economic factor in beef production. However, knowledge on the key factors controlling bovine IMF is limited. In this study, using weighted gene co-expression network analysis (WGCNA), nine modules were identified and the number of transcripts in these modules ranged from 36 to 3191. Two modules were found to be significantly associated with fat deposition and three genes (TCAP, MYH7, and TNNC1) were further identified by Protein-protein interaction (PPI), which may be the hub genes regulating bovine IMF deposition. In addition, considering the genetic variation, the PCK1 gene was found by functional enrichment analysis of overlapping genes, which was previously reported to be involved in IMF deposition. We noted that the core promoter region of buffalo PCK1 binds to transcription factors involved in lipid metabolism while cattle PCK1 binds transcription factors involved in muscle development. The results suggest that PCK1 participated in IMF deposition of buffalo and cattle in different ways. In summary, gene expression networks and new candidate genes associated with IMF deposition identified in this study. This would lay the foundation for further research into the molecular regulatory mechanisms underlying bovine IMF deposition.

Sex-dimorphic expression of extracellular matrix genes in mouse bone marrow neutrophils

The mammalian innate immune system is sex-dimorphic. Neutrophils are the most abundant leukocyte in humans and represent innate immunity's first line of defense. We previously found that primary mouse bone marrow neutrophils show widespread sex-dimorphism throughout life, including at the transcriptional level. Extracellular matrix [ECM]-related terms were observed among the top sex-dimorphic genes. Since the ECM is emerging as an important regulator of innate immune responses, we sought to further investigate the transcriptomic profile of primary mouse bone marrow neutrophils at both the bulk and single-cell level to understand how biological sex may influence ECM component expression in neutrophils throughout life. Here, using curated gene lists of ECM components and unbiased weighted gene co-expression network analysis [WGCNA], we find that multiple ECM-related gene sets show widespread female-bias in expression in primary mouse neutrophils. Since many immune-related diseases (e.g., rheumatoid arthritis) are more prevalent in females, our work may provide insights into the pathogenesis of sex-dimorphic inflammatory diseases.

Association Analysis of Transcriptome and Targeted Metabolites Identifies Key Genes Involved in Iris germanica Anthocyanin Biosynthesis

The anthocyanin biosynthetic pathway is the main pathway regulating floral coloration in Iris germanica, a well-known ornamental plant. We investigated the transcriptome profiles and targeted metabolites to elucidate the relationship between genes and metabolites in anthocyanin biosynthesis in the bitone flower cultivar 'Clarence', which has a deep blue outer perianth and nearly white inner perianth. In this study, delphinidin-, pelargonidin-, and cyanidin-based anthocyanins were detected in the flowers. The content of delphinidin-based anthocyanins increased with the development of the flower. At full bloom (stage 3), delphinidin-based anthocyanins accounted for most of the total anthocyanin metabolites, whereas the content of pelargonidin- and cyanidin-based anthocyanins was relatively low. Based on functional annotations, a number of novel genes in the anthocyanin pathway were identified, which included early biosynthetic genes IgCHS, IgCHI, and IgF3H and late biosynthetic genes Ig F3'5'H, IgANS, and IgDFR. The expression of key structural genes encoding enzymes, such as IgF3H, Ig F3'5'H, IgANS, and IgDFR, was significantly upregulated in the outer perianth compared to the inner perianth. In addition, most structural genes exhibited their highest expression at the half-color stage rather than at the full-bloom stage, which indicates that these genes function ahead of anthocyanins synthesis. Moreover, transcription factors (TFs) of plant R2R3-myeloblastosis (R2R3-MYB) related to the regulation of anthocyanin biosynthesis were identified. Among 56 R2R3-MYB genes, 2 members belonged to subgroup 4, with them regulating the expression of late biosynthetic genes in the anthocyanin biosynthetic pathway, and 4 members belonged to subgroup 7, with them regulating the expression of early biosynthetic genes in the anthocyanin biosynthetic pathway. Quantitative real-time PCR (qRT-PCR) analysis was used to validate the data of RNA sequencing (RNA-Seq). The relative expression profiles of most candidate genes were consistent with the FPKM of RNA-seq. This study identified the key structural genes encoding enzymes and TFs that affect anthocyanin biosynthesis, which provides a basis and reference for the regulation of plant anthocyanin biosynthesis in I. germanica.

Transcriptomic analysis reveals the beneficial effects of salt priming on enhancing defense responses in upland cotton under successive salt stress

Priming-mediated stress tolerance in plants stimulates defense mechanisms and enables plants to cope with future stresses. Seed priming has been proven effective for tolerance against abiotic stresses; however, underlying genetic mechanisms are still unknown. We aimed to assess upland cotton genotypes and their transcriptional behaviors under salt priming and successive induced salt stress. We pre-selected 16 genotypes based on previous studies and performed morpho-physiological characterization, from which we selected three genotypes, representing different tolerance levels, for transcriptomic analysis. We subjected these genotypes to four different treatments: salt priming (P0), salt priming with salinity dose at 3-true-leaf stage (PD), salinity dose at 3-true-leaf stage without salt priming (0D), and control (CK). Although the three genotypes displayed distinct expression patterns, we identified common differentially expressed genes (DEGs) under PD enriched in pathways related to transferase activity, terpene synthase activity, lipid biosynthesis, and regulation of acquired resistance, indicating the beneficial role of salt priming in enhancing salt stress resistance. Moreover, the number of unique DEGs associated with G. hirsutum purpurascens was significantly higher compared to other genotypes. Coexpression network analysis identified 16 hub genes involved in cell wall biogenesis, glucan metabolic processes, and ribosomal RNA binding. Functional characterization of XTH6 (XYLOGLUCAN ENDOTRANSGLUCOSYLASE/HYDROLASE) using virus-induced gene silencing revealed that suppressing its expression improves plant growth under salt stress. Overall, findings provide insights into the regulation of candidate genes in response to salt stress and the beneficial effects of salt priming on enhancing defense responses in upland cotton.

H19/Mir-130b-3p/Cyp4a14 potentiate the effect of praziquantel on liver in the treatment of Schistosoma japonicum infection

BACKGROUND

Schistosomiasis is a prevalent infectious disease caused by the parasitic trematodes of the genus Schistosoma. Praziquantel (PZQ), a safe and affordable drug, is the recommended oral treatment for schistosomiasis. The main pathologic manifestation of schistosomiasis is liver injury. However, the role and interactions of various RNA molecules in the effect of PZQ on the liver after S. japonicum infection have not been elucidated.

RESULTS

In this study, C57BL/6 mice were randomly divided into the control group, infection group, and PZQ treatment group. Total RNA was extracted from the livers of the mice. High-throughput whole transcriptome sequencing was performed to detect the RNA expression profiles in the three groups. A co-expression gene-interaction network was established based on the significant differentially expressed genes in the PZQ treatment group; messenger RNA (mRNA) Cyp4a14 was identified as a critical hub gene. Furthermore, competitive endogenous RNA networks were constructed by predicting the specific binding relations between mRNA and long noncoding (lnc) RNA and between lncRNA and microRNA (miRNA) of Cyp4a14, suggesting the involvement of the H19/miR-130b-3p/Cyp4a14 regulatory axis. Dual luciferase reporter assay result proved the specific binding of miR-130b-3p with Cyp4a14 3'UTR.

CONCLUSIONS

Our findings indicate the involvement of the H19/miR-130b-3p/Cyp4a14 axis in the effect of PZQ on the liver after S. japonicum infection. Moreover, the expression of mRNA Cyp4a14 could be regulated by the bonding of miR-130b-3p with 3'UTR of Cyp4a14. The findings of this study could provide a novel perspective to understand the host response to PZQ against S. japonicum in the future.

Different nutritional systems influence the tenderness and lipid oxidation of ewe lamb meat without altering gene expression

Feeding is a determining factor in the various characteristics of sheep meat and animal performance, the objectives were to evaluate the effect of supplementation of ewe lambs finished in different nutritional planes on the gene expression of CASP3, CAPN1, CAPN2 and CAST and its possible association with meat quality. Samples of the Longissimus lumborum muscle of 24 ewe lambs were used, distributed in 3 groups (n=8): P (pasture), PS (pasture and supplement) and F (feedlot). Physicochemical analyses were performed for centesimal analysis, pH, lipid oxidation, Warner-Bratzler shear force and RT-qPCR for the analysis of relative gene expression of the following genes: CASP3, CAPN1, CAPN2 and CAST. There is an increase in daily weight gain and ethereal extract values in the meat of confined animals, due to the greater energy intake in the nutrition of these animals. Animals kept only on pasture have lower lipid oxidation in meat than other treatments because of the lower percentage of lipids. The Warner-Bratzler shear force is considerably higher in the meat of animals kept only on pasture but is still considered tender. The different nutritional systems do not interfere with the gene expression of CASP3, CAPN1, CAPN2 and CAST in ewe lambs.

Hub genes and their key effects on prognosis of Burkitt lymphoma

BACKGROUND

Burkitt lymphoma (BL) is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells. Patients with BL often present with rapid tumor growth and require high-intensity multi-drug therapy combined with adequate intrathecal chemotherapy prophylaxis, however, a standard treatment program for BL has not yet been established. It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.

AIM

To identify hub genes and perform gene ontology (GO) and survival analysis in BL.

METHODS

Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression modules, and the cytoHubba tool was used to find the hub genes. Then, the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis. Additionally, a Protein-Protein Interaction network and a Genetic Interaction network were constructed. Prognostic candidate genes were identified through overall survival analysis. Finally, a nomogram was established to assess the predictive value of hub genes, and drug-gene interactions were also constructed.

RESULTS

In this study, we obtained 8 modules through WGCNA analysis, and there was a significant correlation between the yellow module and age. Then we identified 10 hub genes (SRC, TLR4, CD40, STAT3, SELL, CXCL10, IL2RA, IL10RA, CCR7 and FCGR2B) by cytoHubba tool. Within these hubs, two genes were found to be associated with OS (CXCL10, P = 0.029 and IL2RA, P = 0.0066) by survival analysis. Additionally, we combined these two hub genes and age to build a nomogram. Moreover, the drugs related to IL2RA and CXCL10 might have a potential therapeutic role in relapsed and refractory BL.

CONCLUSION

From WGCNA and survival analysis, we identified CXCL10 and IL2RA that might be prognostic markers for BL.

PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation

Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX.I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX.I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.I analysis identifies two known pathways i) that have already been implicated in the same context as the tamoxifen mechanism of action, and ii) that would have not have been identified using classical differential gene expression analysis.

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data

Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 2-3 fold higher accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from genomics data, empowering deeper insights into cellular mechanisms.

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

Gene co-expression network analysis for the selection of candidate early warning indicators of heat and nutrient stress in Posidonia oceanica

The continuous worldwide seagrasses decline calls for immediate actions in order to preserve this precious marine ecosystem. The main stressors that have been linked with decline in seagrasses are 1) the increasing ocean temperature due to climate change and 2) the continuous inputs of nutrients (eutrophication) associated with coastal human activities. To avoid the loss of seagrass populations, an "early warning" system is needed. We used Weighed Gene Co-expression Network Analysis (WGCNA), a systems biology approach, to identify potential candidate genes that can provide an early warning signal of stress in the Mediterranean iconic seagrass Posidonia oceanica, anticipating plant mortality. Plants were collected from both eutrophic (EU) and oligotrophic (OL) environments and were exposed to thermal and nutrient stress in a dedicated mesocosm. By correlating the whole-genome gene expression after 2-weeks exposure with the shoot survival percentage after 5-weeks exposure to stressors, we were able to identify several transcripts that indicated an early activation of several biological processes (BP) including: protein metabolic process, RNA metabolic process, organonitrogen compound biosynthetic process, catabolic process and response to stimulus, which were shared among OL and EU plants and among leaf and shoot apical meristem (SAM), in response to excessive heat and nutrients. Our results suggest a more dynamic and specific response of the SAM compared to the leaf, especially the SAM from plants coming from a stressful environment appeared more dynamic than the SAM from a pristine environment. A vast list of potential molecular markers is also provided that can be used as targets to assess field samples.

Metabolomic and transcriptomic analyses reveal the effects of grafting on blood orange quality

Introduction

Blood orange (Citrus sinensis L.) is a valuable source of nutrition because it is enriched in anthocyanins and has high organoleptic properties. Grafting is commonly used in citriculture and has crucial effects on various phenotypes of the blood orange, including its coloration, phenology, and biotic and abiotic resistance. Still, the underlying genetics and regulatory mechanisms are largely unexplored.

Methods

In this study, we investigated the phenotypic, metabolomic, and transcriptomic profiles at eight developmental stages of the lido blood orange cultivar (Citrus sinensis L. Osbeck cv. Lido) grafted onto two rootstocks.

Results and discussion

The Trifoliate orange rootstock provided the best fruit quality and flesh color for Lido blood orange. Comparative metabolomics suggested significant differences in accumulation patterns of metabolites and we identified 295 differentially accumulated metabolites. The major contributors were flavonoids, phenolic acids, lignans and coumarins, and terpenoids. Moreover, transcriptome profiling resulted in the identification of 4179 differentially expressed genes (DEGs), and 54 DEGs were associated with flavonoids and anthocyanins. Weighted gene co-expression network analysis identified major genes associated to 16 anthocyanins. Furthermore, seven transcription factors (C2H2, GANT, MYB-related, AP2/ERF, NAC, bZIP, and MYB) and five genes associated with anthocyanin synthesis pathway (CHS, F3H, UFGT, and ANS) were identified as key modulators of the anthocyanin content in lido blood orange. Overall, our results revealed the impact of rootstock on the global transcriptome and metabolome in relation to fruit quality in lido blood orange. The identified key genes and metabolites can be further utilized for the quality improvement of blood orange varieties.

Sex-dimorphic expression of extracellular matrix genes in mouse bone marrow neutrophils

The mammalian innate immune system is sex-dimorphic. Neutrophils are the most abundant leukocyte in humans and represent innate immunity's first line of defense. We previously found that primary mouse bone marrow neutrophils show widespread sex-dimorphism throughout life, including at the transcriptional level. Extracellular matrix [ECM]-related terms were observed among the top sex-dimorphic genes. Since the ECM is emerging as an important regulator of innate immune responses, we sought to further investigate the transcriptomic profile of primary mouse bone marrow neutrophils at both the bulk and single-cell level to understand how biological sex may influence ECM component expression in neutrophils throughout life. Here, using curated gene lists of ECM components and unbiased weighted gene co-expression network analysis [WGCNA], we find that multiple ECM-related gene sets show widespread female-bias in expression in primary mouse neutrophils. Since many immune-related diseases (e.g., rheumatoid arthritis) are more prevalent in females, our work may provide insights into the pathogenesis of sex-dimorphic inflammatory diseases.

Integrated transcriptomics contrasts fatty acid metabolism with hypoxia response in β-cell subpopulations associated with glycemic control

BACKGROUND

Understanding how heterogeneous β-cell function impacts diabetes is imperative for therapy development. Standard single-cell RNA sequencing analysis illuminates some factors driving heterogeneity, but new strategies are required to enhance information capture.

RESULTS

We integrate pancreatic islet single-cell and bulk RNA sequencing data to identify β-cell subpopulations based on gene expression and characterize genetic networks associated with β-cell function in obese SM/J mice. We identify β-cell subpopulations associated with basal insulin secretion, hypoxia response, cell polarity, and stress response. Network analysis associates fatty acid metabolism and basal insulin secretion with hyperglycemic-obesity, while expression of Pdyn and hypoxia response is associated with normoglycemic-obesity.

CONCLUSIONS

By integrating single-cell and bulk islet transcriptomes, our study explores β-cell heterogeneity and identifies novel subpopulations and genetic pathways associated with β-cell function in obesity.

Weighted gene co-expression network-based identification of genetic effect of mRNA vaccination and previous infection on SARS-CoV-2 infection

To investigate the effect conferred by vaccination and previous infection against SARS-CoV-2 infection in molecular level, weighted gene co-expression network analysis was applied to screen vaccination, prior infection and Omicron infection-related gene modules in 46 Omicron outpatients and 8 controls, and CIBERSORT algorithm was used to infer the proportions of 22 subsets of immune cells. 15 modules were identified, where the brown module showed positive correlations with Omicron infection (r = 0.35, P = 0.01) and vaccination (r = 0.62, P = 1 × 10^-6). Enrichment analysis revealed that LILRB2 was the unique gene shared by both phosphatase binding and MHC class I protein binding. Pathways including "B cell receptor signaling pathway" and "FcγR-mediated phagocytosis" were enriched in the vaccinated samples of the highly correlated LILRB2. LILRB2 was also identified as the second hub gene through PPI network, after LCP2. In conclusion, attenuated LILRB2 transcription in PBMC might highlight a novel target in overcoming immune evasion and improving vaccination strategies.

Gap-free genome assembly and comparative analysis reveal the evolution and anthocyanin accumulation mechanism of Rhodomyrtus tomentosa

Rhodomyrtus tomentosa is an important fleshy-fruited tree and a well-known medicinal plant of the Myrtaceae family that is widely cultivated in tropical and subtropical areas of the world. However, studies on the evolution and genomic breeding of R. tomentosa were hindered by the lack of a reference genome. Here, we presented a chromosome-level gap-free T2T genome assembly of R. tomentosa using PacBio and ONT long read sequencing. We assembled the genome with size of 470.35 Mb and contig N50 of ~43.80 Mb with 11 pseudochromosomes. A total of 33 382 genes and 239.31 Mb of repetitive sequences were annotated in this genome. Phylogenetic analysis elucidated the independent evolution of R. tomentosa starting from 14.37MYA and shared a recent WGD event with other Myrtaceae species. We identified four major compounds of anthocyanins and their synthetic pathways in R. tomentosa. Comparative genomic and gene expression analysis suggested the coloring and high anthocyanin accumulation in R. tomentosa tends to be determined by the activation of anthocyanin synthesis pathway. The positive selection and up-regulation of MYB transcription factors were the implicit factors in this process. The copy number increase of downstream anthocyanin transport-related OMT and GST gene were also detected in R. tomentosa. Expression analysis and pathway identification enriched the importance of starch degradation, response to stimuli, effect of hormones, and cell wall metabolism during the fleshy fruit development in Myrtaceae. Our genome assembly provided a foundation for investigating the origins and differentiation of Myrtaceae species and accelerated the genetic improvement of R. tomentosa.

Preterm labor with and without chorioamnionitis is associated with activation of myometrial inflammatory networks: a comprehensive transcriptomic analysis

BACKGROUND

The onset of preterm labor is associated with inflammation. Previous studies suggested that this is distinct from the inflammation observed during term labor. Our previous work on 44 genes differentially expressed in myometria in term labor demonstrated a different pattern of gene expression from that observed in preterm laboring and nonlaboring myometria. We found increased expression of inflammatory genes in preterm labor associated with chorioamnionitis, but in the absence of chorioamnionitis observed no difference in gene expression in preterm myometria regardless of laboring status, suggesting that preterm labor is associated with different myometrial genes or signals originating from outside the myometrium. Given that a small subset of genes were assessed, this study aimed to use RNA sequencing and bioinformatics to assess the myometrial transcriptome during preterm labor in the presence and absence of chorioamnionitis.

OBJECTIVE

This study aimed to comprehensively determine protein-coding transcriptomic differences between preterm nonlaboring and preterm laboring myometria with and without chorioamnionitis.

STUDY DESIGN

Myometria were collected at cesarean delivery from preterm patients not in labor (n=16) and preterm patients in labor with chorioamnionitis (n=8) or without chorioamnionitis (n=6). Extracted RNA from myometrial tissue was prepared and sequenced using Illumina NovaSeq. Gene expression was quantified by mapping the sequence reads to the human reference genome (hg38). Differential gene expression analysis, gene set enrichment analysis, and weighted gene coexpression network analysis were used to comprehensively interrogate transcriptomic differences and their associated biology.

RESULTS

Differential gene expression analysis comparing preterm patients in labor with chorioamnionitis with preterm patients not in labor identified 931 differentially expressed genes, whereas comparing preterm patients in labor without chorioamnionitis with preterm patients not in labor identified no statistically significant gene expression changes. In contrast, gene set enrichment analysis and weighted gene coexpression network analysis demonstrated that preterm labor with and without chorioamnionitis was associated with enrichment of pathways involved in activation of the innate immune system and inflammation, and activation of G protein-coupled receptors. Key genes identified included chemotactic CYP4F3, CXCL8, DOCK2, and IRF1 in preterm labor with chorioamnionitis and CYP4F3, FCAR, CHUK, and IL13RA2 in preterm labor without chorioamnionitis. There was marked overlap in the pathways enriched in both preterm labor subtypes.

CONCLUSION

Differential gene expression analysis demonstrated that myometria from preterm patients in labor without chorioamnionitis and preterm patients not in labor were transcriptionally similar, whereas the presence of chorioamnionitis was associated with marked gene changes. In contrast, comprehensive bioinformatic analysis indicated that preterm labor with or without chorioamnionitis was associated with innate immune activation. All causes of preterm labor were associated with activation of the innate immune system, but this was more marked in the presence of chorioamnionitis. These data suggest that anti-inflammatory therapy may be relevant in managing preterm labor of all etiologies.

mRNA Signatures in Peripheral White Blood Cells Predict Reproductive Potential in Beef Heifers at Weaning

Reproductive failure is a major contributor to inefficiency within the cow-calf industry. Particularly problematic is the inability to diagnose heifer reproductive issues prior to pregnancy diagnosis following their first breeding season. Therefore, we hypothesized that gene expression from the peripheral white blood cells at weaning could predict the future reproductive potential of beef heifers. To investigate this, the gene expression was measured using RNA-Seq in Angus-Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 8) or subfertile (SFH, n = 7) after pregnancy diagnosis. We identified 92 differentially expressed genes between the groups. Network co-expression analysis identified 14 and 52 hub targets. ENSBTAG00000052659, OLR1, TFF2, and NAIP were exclusive hubs to the FH group, while 42 hubs were exclusive to the SFH group. The differential connectivity between the networks of each group revealed a gain in connectivity due to the rewiring of major regulators in the SFH group. The exclusive hub targets from FH were over-represented for the CXCR chemokine receptor pathway and inflammasome complex, while for the SFH, they were over-represented for immune response and cytokine production pathways. These multiple interactions revealed novel targets and pathways predicting reproductive potential at an early stage of heifer development.

Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis

Background

Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.

Methods

To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.

Results

There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.

Conclusions

Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

Full-length transcriptome and metabolite analysis reveal reticuline epimerase-independent pathways for benzylisoquinoline alkaloids biosynthesis in Sinomenium acutum

Benzylisoquinoline alkaloids (BIAs) are a large family of plant natural products with important pharmaceutical applications. Sinomenium acutum is a medicinal plant from the Menispermaceae family and has been used to treat rheumatoid arthritis for hundreds of years. Sinomenium acutum contains more than 50 BIAs, and sinomenine is a representative BIA from this plant. Sinomenine was found to have preventive and curative effects on opioid dependence. Despite the broad applications of S. acutum, investigation on the biosynthetic pathways of BIAs from S. acutum is limited. In this study, we comprehensively analyzed the transcriptome data and BIAs in the root, stem, leaf, and seed of S. acutum. Metabolic analysis showed a noticeable difference in BIA contents in different tissues. Based on the study of the full-length transcriptome, differentially expressed genes, and weighted gene co-expression network, we proposed the biosynthetic pathways for a few BIAs from S. acutum, such as sinomenine, magnoflorine, and tetrahydropalmatine, and screened candidate genes involved in these biosynthesis processes. Notably, the reticuline epimerase (REPI/STORR), which converts (S)-reticuline to (R)-reticuline and plays an essential role in morphine and codeine biosynthesis, was not found in the transcriptome data of S. acutum. Our results shed light on the biogenesis of the BIAs in S. acutum and may pave the way for the future development of this important medicinal plant.

Soil Geochemical Properties Influencing the Diversity of Bacteria and Archaea in Soils of the Kitezh Lake Area, Antarctica

It is believed that polar regions are influenced by global warming more significantly, and because polar regions are less affected by human activities, they have certain reference values for future predictions. This study aimed to investigate the effects of climate warming on soil microbial communities in lake areas, taking Kitezh Lake, Antarctica as the research area. Below-peak soil, intertidal soil, and sediment were taken at the sampling sites, and we hypothesized that the diversity and composition of the bacterial and archaeal communities were different among the three sampling sites. Through 16S rDNA sequencing and analysis, bacteria and archaea with high abundance were obtained. Based on canonical correspondence analysis and redundancy analysis, pH and phosphate had a great influence on the bacterial community whereas pH and nitrite had a great influence on the archaeal community. Weighted gene coexpression network analysis was used to find the hub bacteria and archaea related to geochemical factors. The results showed that in addition to pH, phosphate, and nitrite, moisture content, ammonium, nitrate, and total carbon content also play important roles in microbial diversity and structure at different sites by changing the abundance of some key microbiota.

IRF2-ferroptosis related gene is associated with prognosis and EMT in gliomas

Ferroptosis is a new type of programmed cell death that has excellent anti-tumor potential in different tumors. However, the research on ferroptosis in glioma is still incomplete. In this study, we aimed to revealed the relationship between ferroptosis-related genes (FRGs) and glioma. We collected gene expression profiles of glioma patients from the TCGA and CGGA databases. All glioma samples were classified into five subtypes using the R software ConsensusClusterPlus. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between different subtypes and immune status and ferroptosis. Then co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA). A Gene Ontology (GO) analysis was conducted to analyze the potential biological functions of the genes in the modules. Finally, we identified 10 hub genes using the PPI network. The in vitro experiments were used to validate our predictions. We found that the expression level of IRF2 is positively correlated with the grade of glioma. The overexpression of IRF2 could protect glioma cells from ferroptosis and enhance the invasive and migratory abilities. Silence of IRF2 had the opposite effect. In conclusion, we demonstrated a novel ferroptosis-related signature for predicting prognosis, and IRF2 could be a potential biomarker for diagnosis and treatment in glioma.

Integration of differential expression and network structure for 'omics data analysis

Differential expression (DE) analysis has been routinely used to identify molecular features that are statistically significantly different between distinct biological groups. In recent years, differential network (DN) analysis has emerged as a powerful approach to uncover molecular network structure changes from one biological condition to the other where the molecular features with larger topological changes are selected as biomarkers. Although a large number of DE and a few DN-based methods are available, they have been usually implemented independently. DE analysis ignores the relationship among molecular features while DN analysis does not account for the expression changes at individual level. Therefore, an integrative analysis approach that accounts for both DE and DN is required to identify disease associated key features. Although, a handful of methods have been proposed, there is no method that optimizes the combination of DE and DN. We propose a novel integrative analysis method, DNrank, to identify disease-associated molecular features that leverages the strengths of both DE and DN by calculating a weight using resampling based cross validation scheme within the algorithm. First, differential expression analysis of individual molecular features is carried out. Second, a differential network structure is constructed using the differential partial correlation analysis. Third, the molecular features are ranked in the order of their significances by integrating their DE measures and DN structure using the modified Google's PageRank algorithm. In the algorithm, the optimum combination of DE and DN analyses is achieved by evaluating the prediction performance of top-ranked features utilizing support vector machine classifier with Monte Carlo cross validation. The proposed method is illustrated using both simulated data and three real data sets. The results show that the proposed method has a better performance in identifying important molecular features with respect to predictive discrimination. Also, as compared to existing feature selection methods, the top-ranked features selected by our method had a higher stability in selection. DNrank allows the researchers to identify the disease-associated features by utilizing both expression and network topology changes between two groups.

Differential network analysis of oral microbiome metatranscriptomes identifies community scale metabolic restructuring in dental caries

Dental caries is a microbial disease and the most common chronic health condition, affecting nearly 3.5 billion people worldwide. In this study, we used a multiomics approach to characterize the supragingival plaque microbiome of 91 Australian children, generating 658 bacterial and 189 viral metagenome-assembled genomes with transcriptional profiling and gene-expression network analysis. We developed a reproducible pipeline for clustering sample-specific genomes to integrate metagenomics and metatranscriptomics analyses regardless of biosample overlap. We introduce novel feature engineering and compositionally-aware ensemble network frameworks while demonstrating their utility for investigating regime shifts associated with caries dysbiosis. These methods can be applied when differential abundance modeling does not capture statistical enrichments or the results from such analysis are not adequate for providing deeper insight into disease. We identified which organisms and metabolic pathways were central in a coexpression network as well as how these networks were rewired between caries and caries-free phenotypes. Our findings provide evidence of a core bacterial microbiome that was transcriptionally active in the supragingival plaque of all participants regardless of phenotype, but also show highly diagnostic changes in the ways that organisms interact. Specifically, many organisms exhibit high connectedness with central carbon metabolism to Cardiobacterium and this shift serves a bridge between phenotypes. Our evidence supports the hypothesis that caries is a multifactorial ecological disease.

An Integrated Bioinformatics Approach to Identify Network-Derived Hub Genes in Starving Zebrafish

The present study was aimed at identifying causative hub genes within modules formed by co-expression and protein-protein interaction (PPI) networks, followed by Bayesian network (BN) construction in the liver transcriptome of starved zebrafish. To this end, the GSE11107 and GSE112272 datasets from the GEO databases were downloaded and meta-analyzed using the MetaDE package, an add-on R package. Differentially expressed genes (DEGs) were identified based upon expression intensity N(µ = 0.2, σ² = 0.4). Reconstruction of BNs was performed by the bnlearn R package on genes within modules using STRINGdb and CEMiTool. ndufs5 (shared among PPI, BN and COEX), rps26, rpl10, sdhc (shared between PPI and BN), ndufa6, ndufa10, ndufb8 (shared between PPI and COEX), skp1, atp5h, ndufb10, rpl5b, zgc:193613, zgc:123327, zgc:123178, wu:fc58f10, zgc:111986, wu:fc37b12, taldo1, wu:fb62f08, zgc:64133 and acp5a (shared between COEX and BN) were identified as causative hub genes affecting gene expression in the liver of starving zebrafish. Future work will shed light on using integrative analyses of miRNA and DNA microarrays simultaneously, and performing in silico and experimental validation of these hub-causative (CST) genes affecting starvation in zebrafish.

Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows

Simple Summary

Female reproductive failure is still a challenge for the beef industry. Several biological processes that underlie fertility-related traits, such as the establishment of pregnancy and embryo survival, are still unclear. Increased availability of transcriptomic data has allowed a deep investigation of the potential mechanisms involved in fertility. This study investigated candidate gene biomarkers predictive of pregnancy status and underlying fertility-related networks. To this end, we integrated gene expression profiles through supervised machine learning and gene network modeling. We identified nine biologically relevant endometrial gene biomarkers that could discriminate against pregnancy status in cows. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. This study outlined key pathways involved with pregnancy success and provided predictive candidate biomarkers for pregnancy outcome in cows.

Abstract

Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified SERPINE3, PDCD1, FNDC1, MRTFA, ARHGEF7, MEF2B, NAA16, ENSBTAG00000019474, and ENSBTAG00000054585 as candidate biomarker predictors of pregnancy status. Then, based on co-expression networks, we identified seven genes significantly rewired (gaining or losing connections) between the P and NP networks. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. We provided insights into the regulatory networks of fertility-related processes and demonstrated the potential of combining different analytical tools to prioritize candidate genes.

Integrative analysis of drug response and clinical outcome in acute myeloid leukemia

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

Co-Expression Network and Integrative Analysis of Metabolome and Transcriptome Uncovers Biological Pathways for Fertility in Beef Heifers

Reproductive failure remains a significant challenge to the beef industry. The omics technologies have provided opportunities to improve reproductive efficiency. We used a multistaged analysis from blood profiles to integrate metabolome (plasma) and transcriptome (peripheral white blood cells) in beef heifers. We used untargeted metabolomics and RNA-Seq paired data from six AI-pregnant (AI-P) and six nonpregnant (NP) Angus-Simmental crossbred heifers at artificial insemination (AI). Based on network co-expression analysis, we identified 17 and 37 hub genes in the AI-P and NP groups, respectively. Further, we identified TGM2, TMEM51, TAC3, NDRG4, and PDGFB as more connected in the NP heifers’ network. The NP gene network showed a connectivity gain due to the rewiring of major regulators. The metabolomic analysis identified 18 and 15 hub metabolites in the AI-P and NP networks. Tryptophan and allantoic acid exhibited a connectivity gain in the NP and AI-P networks, respectively. The gene–metabolite integration identified tocopherol-a as positively correlated with ENSBTAG00000009943 in the AI-P group. Conversely, tocopherol-a was negatively correlated in the NP group with EXOSC2, TRNAUIAP, and SNX12. In the NP group, α-ketoglutarate-SMG8 and putrescine-HSD17B13 were positively correlated, whereas a-ketoglutarate-ALAS2 and tryptophan-MTMR1 were negatively correlated. These multiple interactions identified novel targets and pathways underlying fertility in bovines.

Metabolomic and transcriptomic analyses reveal the effects of self- and hetero-grafting on anthocyanin biosynthesis in grapevine

Grafting, which joins a scion from a cultivar with the stem of a rootstock from a grapevine wild relative, is commonly used in viticulture. Grafting has crucial effects on various phenotypes of the cultivar, including its phenology, biotic and abiotic resistance, berry metabolome, and coloration, but the underlying genetics and regulatory mechanisms are largely unexplored. In this study, we investigated the phenotypic, metabolomic, and transcriptomic profiles at three developmental stages (45, 75, and 105 days after flowering) of the Crimson Seedless cultivar (Vitis vinifera) grafted onto four rootstocks (three heterografts, CS/101-14, CS/SO4, and CS/110R and one self-graft, CS/CS) with own-rooted graft-free Crimson Seedless (CS) as the control. All the heterografts had a significant effect on berry reddening as early as ~45 days after flowering. The grafting of rootstocks promoted anthocyanin biosynthesis and accumulation in grape berries. The metabolomic features showed that cyanidin 3-O-glucoside, delphinidin 3-O-glucoside, malvidin 3-O-glucoside, peonidin 3-O-glucoside, and petunidin 3-O-glucoside were the pigments responsible for the purplish-red peel color. Transcriptomic analyses revealed that anthocyanin biosynthesis-related genes, from upstream (phenylalanine ammonia-lyase) to downstream (anthocyanidin 3-O-glucosyltransferase and anthocyanidin synthase), were upregulated with the accumulation of anthocyanins in the heterografted plants. At the same time, all these genes were also highly expressed and more anthocyanin was accumulated in self-grafted CS/CS samples compared with own-rooted graft-free CS samples, suggesting that self-grafting may also have promoted berry reddening in grapevine. Our results reveal global transcriptomic and metabolomic features in berry color regulation under different grafting conditions that may be useful for improving berry quality in viticulture.

A novel lncRNA-miRNA-mRNA triple network identifies lncRNA XIST as a biomarker for acute myocardial infarction

Despite the well-established role of long non-coding RNAs (lncRNAs) across various biological processes, their mechanisms in acute myocardial infarction (AMI) are not fully elucidated. The GSE34198 dataset from the Gene Expression Omnibus (GEO) database, which comprised 49 specimens from individuals with AMI and 47 specimens from controls, was extracted and analysed using the weighted gene co-expression network analysis (WGCNA) package. Twenty-seven key genes were identified through a combination of the degree and gene significance (GS) values, and the CDC42 (degree = 64), JAK2 (degree = 41), and CHUK (degree = 30) genes were identified as having the top three-degree values among the 27 genes. Potential interactions between lncRNA, miRNAs and mRNAs were predicted using the starBase V3.0 database, and a lncRNA-miRNA-mRNA triple network containing the lncRNA XIST, twenty-one miRNAs and three hub genes (CDC42, JAK2 and CHUK) was identified. RT-qPCR validation showed that the expression of the JAK2 and CDC42 genes and the lncRNA XIST was noticeably increased in samples from patients with AMI compared to normal samples. Pearson's correlation analysis also proved that JAK2 and CDC42 expression levels correlated positively with lncRNA XIST expression levels. The area under ROC curve (AUC) of lncRNA XIST was 0.886, and the diagnostic efficacy of the lncRNA XIST was significantly better than that of JAK2 and CDC42. The results suggested that the lncRNA XIST appears to be a risk factor for AMI likely through its ability to regulate JAK2 and CDC42 gene expressions, and it is expected to be a novel and reliable biomarker for the diagnosis of AMI.

Environmental induced transgenerational inheritance impacts systems epigenetics in disease etiology

Environmental toxicants have been shown to promote the epigenetic transgenerational inheritance of disease through exposure specific epigenetic alterations in the germline. The current study examines the actions of hydrocarbon jet fuel, dioxin, pesticides (permethrin and methoxychlor), plastics, and herbicides (glyphosate and atrazine) in the promotion of transgenerational disease in the great grand-offspring rats that correlates with specific disease associated differential DNA methylation regions (DMRs). The transgenerational disease observed was similar for all exposures and includes pathologies of the kidney, prostate, and testis, pubertal abnormalities, and obesity. The disease specific DMRs in sperm were exposure specific for each pathology with negligible overlap. Therefore, for each disease the DMRs and associated genes were distinct for each exposure generational lineage. Observations suggest a large number of DMRs and associated genes are involved in a specific pathology, and various environmental exposures influence unique subsets of DMRs and genes to promote the transgenerational developmental origins of disease susceptibility later in life. A novel multiscale systems biology basis of disease etiology is proposed involving an integration of environmental epigenetics, genetics and generational toxicology.

WGCNA-Based DNA Methylation Profiling Analysis on Allopurinol-Induced Severe Cutaneous Adverse Reactions: A DNA Methylation Signature for Predisposing Drug Hypersensitivity

Background: The role of aberrant DNA methylation in allopurinol-induced severe cutaneous adverse reactions (SCARs) is incompletely understood. To fill the gap, we analyze the DNA methylation profiling in allopurinol-induced Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) patients and identify the DNA methylation signature for predisposing allopurinol hypersensitivity. Methods: Genome-scale methylation analysis was conducted using the Illumina® HumanMethylation450 BeadChip. Weighted Gene Co-Expression Network Analysis (WGCNA) was utilized to analyze the data. Results: A total of 21,497 annotated promoter regions were analyzed. Ten modules were identified between allopurinol hypersensitivity and tolerance, with turquoise and yellow modules being the most significant correlation. ATG13, EPM2AIP1, and SRSF11 were the top three hub genes in the turquoise module. MIR412, MIR369, and MIR409 were the top three hub genes in the yellow module. Gene Ontology (GO) analysis revealed that the turquoise module was related to the metabolic process in intracellular organelles and the binding of various compounds, proteins, or nucleotides. The yellow module, however, was related to stimulus sensory perception in cytoskeletal elements and the activity of the receptor or transducer. Conclusion: DNA methylation plays a vital role in allopurinol-induced severe cutaneous adverse reactions (SCARs). DNA methylation profiling of SJS/TEN is significantly related to autophagy and microRNAs (miRNAs).

Identification of Potential Candidate Genes From Co-Expression Module Analysis During Preadipocyte Differentiation in Landrace Pig

Preadipocyte differentiation plays an important role in lipid deposition and affects fattening efficiency in pigs. In the present study, preadipocytes isolated from the subcutaneous adipose tissue of three Landrace piglets were induced into mature adipocytes in vitro. Gene clusters associated with fat deposition were investigated using RNA sequencing data at four time points during preadipocyte differentiation. Twenty-seven co-expression modules were subsequently constructed using weighted gene co-expression network analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed three modules (blue, magenta, and brown) as being the most critical during preadipocyte differentiation. Based on these data and our previous differentially expressed gene analysis, angiopoietin-like 4 (ANGPTL4) was identified as a key regulator of preadipocyte differentiation and lipid metabolism. After inhibition of ANGPTL4, the expression of adipogenesis-related genes was reduced, except for that of lipoprotein lipase (LPL), which was negatively regulated by ANGPTL4 during preadipocyte differentiation. Our findings provide a new perspective to understand the mechanism of fat deposition.

Dynamic Control Balancing Cell Proliferation and Inflammation is Crucial for an Effective Immune Response to Malaria

Malaria has a complex pathology with varying manifestations and symptoms, effects on host tissues, and different degrees of severity and ultimate outcome, depending on the causative Plasmodium pathogen and host species. Previously, we compared the peripheral blood transcriptomes of two macaque species (Macaca mulatta and Macaca fascicularis) in response to acute primary infection by Plasmodium knowlesi. Although these two species are very closely related, the infection in M. mulatta is fatal, unless aggressively treated, whereas M. fascicularis develops a chronic, but tolerable infection in the blood. As a reason for this stark difference, our analysis suggests delayed pathogen detection in M. mulatta followed by extended inflammation that eventually overwhelms this monkey’s immune response. By contrast, the natural host M. fascicularis detects the pathogen earlier and controls the inflammation. Additionally, M. fascicularis limits cell proliferation pathways during the log phase of infection, presumably in an attempt to control inflammation. Subsequent cell proliferation suggests a cell-mediated adaptive immune response. Here, we focus on molecular mechanisms underlying the key differences in the host and parasite responses and their coordination. SICAvar Type 1 surface antigens are highly correlated with pattern recognition receptor signaling and important inflammatory genes for both hosts. Analysis of pathogen detection pathways reveals a similar signaling mechanism, but with important differences in the glutamate G-protein coupled receptor (GPCR) signaling pathway. Furthermore, differences in inflammasome assembly processes suggests an important role of S100 proteins in balancing inflammation and cell proliferation. Both differences point to the importance of Ca²⁺ homeostasis in inflammation. Additionally, the kynurenine-to-tryptophan ratio, a known inflammatory biomarker, emphasizes higher inflammation in M. mulatta during log phase. Transcriptomics-aided metabolic modeling provides a functional method for evaluating these changes and understanding downstream changes in NAD metabolism and aryl hydrocarbon receptor (AhR) signaling, with enhanced NAD metabolism in M. fascicularis and stronger AhR signaling in M. mulatta. AhR signaling controls important immune genes like IL6, IFNγ and IDO1. However, direct changes due to AhR signaling could not be established due to complicated regulatory feedback mechanisms associated with the AhR repressor (AhRR). A complete understanding of the exact dynamics of the immune response is difficult to achieve. Nonetheless, our comparative analysis provides clear suggestions of processes that underlie an effective immune response. Thus, our study identifies multiple points of intervention that are apparently responsible for a balanced and effective immune response and thereby paves the way toward future immune strategies for treating malaria.

Spatial and Temporal Transcriptomic Heredity and Asymmetry in an Artificially Constructed Allotetraploid Wheat (AADD)

Polyploidy, or whole-genome duplication (WGD), often induces dramatic changes in gene expression due to "transcriptome shock. " However, questions remain about how allopolyploidy (the merging of multiple nuclear genomes in the same nucleus) affects gene expression within and across multiple tissues and developmental stages during the initial foundation of allopolyploid plants. Here, we systematically investigated the immediate effect of allopolyploidy on gene expression variation in an artificial allopolyploidy system consisting of a constructed allotetraploid wheat (AADD genome, accession AT2) and its diploid progenitors Triticum urartu and Aegilops tauschii. We performed comprehensive RNA sequencing of 81 samples from different genotypes, tissues, and developmental stages. First, we found that intrinsic interspecific differences between the diploid parents played a major role in establishing the expression architecture of the allopolyploid. Nonetheless, allopolyploidy per se also induced dramatic and asymmetric patterns of differential gene expression between the subgenomes, and genes from the D subgenome exhibited a more drastic response. Second, analysis of homoeolog expression bias (HEB) revealed that the D subgenome exhibited significant expression bias and that de novo-generated HEB was attributed mainly to asymmetrical differential gene expression. Homoeolog-specific expression (HSE) analyses showed that the cis-only regulatory pattern was predominant in AT2, reflecting significant divergence between the parents. Co-expression network analysis revealed that homoeolog expression connectivity (HEC) was significantly correlated with sequence divergence in cis elements between subgenomes. Interestingly, allopolyploidy-induced reconstruction of network modules was also associated with different HSE patterns. Finally, a transcriptome atlas of spike development demonstrated that the phenotypic similarity of AT2 to T. urartu may be attributed to the combination of relatively stable expression of A-subgenome genes and drastic downregulation of their D-subgenome homoeologs. These findings provide a broad, multidimensional characterization of allopolyploidy-induced transcriptomic responses and suggest that allopolyploidy can have immediate and complex regulatory effects on the expression of nuclear genes.

Network Analysis of Microarray Data

DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.

A joint transcriptional regulatory network and protein activity inference analysis identifies clinically associated master regulators for biliary atresia

Biliary atresia (BA) is a devastating cholangiopathy in neonate. Transcription factors (TFs), a type of master regulators in biological processes and diseases, have been implicated in pathogenesis of BA. However, a global view of TFs and how they link to clinical presentations remain explored. Here, we perform a joint transcriptional regulatory network and protein activity inference analysis in order to investigate transcription factor activity in BA. By integration of three independent human BA liver transcriptome datasets, we identify 22 common master regulators, with 14 activated- and 8 repressed TFs. Gene targets of activated TFs are enriched in biological processes of SMAD, NF-kappaB and TGF-beta, while those of repressed TFs are related to lipid metabolism. Mining the clinical association of TFs, we identify inflammation-, fibrosis- and survival associated TFs. In particular, ZNF14 is predictive of poor survival and advanced live fibrosis. Supporting this observation, ZNF14 is positively correlated with T helper cells, cholangiocytes and hepatic stellate cells. In sum, our analysis reveals key clinically associated master regulators for BA.

[Identification of potential hub genes of Alzheimer's disease by weighted gene co-expression network analysis]

OBJECTIVE

To investigate the differential expression gene modules and hub genes associated with Alzheimer's disease (AD) by weighted gene co-expression network analysis (WGCNA) and annotate the biological functions of these modules.

METHODS

We downloaded transcriptome sequencing data from the GEO database, and according to the correlation of the genes, a gene co-expression network was constructed with the parameter setting of β=8 and a correlation coefficient threshold of 0.85. Pearson correlation test was used to calculate the correlation between the module genes and clinical traits to screen the gene modules significantly associated with AD and identify the hub genes according to the connectivity within modules. GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules. A cell model of AD was established in SH-SY5Y cells by Aβ1-42 treatment, and the mRNA expression levels of the hub genes were compared between the Aβ1-42-treated cells and the control cells.

RESULTS

Ten gene co-expression modules were constructed based on the correlations of gene expression, in which the brown (r=0.66, P < 0.001) and turquoise modules (r=-0.68, P < 0.001) were significantly correlated with the AD group. Forty-eight genes were identified as the hub genes in the co-expression network. Function annotation revealed that the genes in both modules were mainly enriched in DNA damage and repair pathways and metabolism-related pathways. Differential expression analysis of the genes revealed that the genes DNASE1, TEKT2 and MTSS1L were highly expressed while ACP2, LANCL2 and GMPR2 were lowly expressed in AD group. The results of cell experiment confirmed the up-regulation of DNASE1, TEKT2 and MTSS1L genes and the down-regulation of ACP2, LANCL2, and GMPR2 in Aβ1-42-treated SH-SY5Y cells (P < 0.01).

CONCLUSION

The brown and turquoise modules are closely correlated with AD. The hub genes including MTSS1L, GMPR2, ACP2, ACTG1 and LANCL2 selected from the modules may participate in AD pathogenesis by regulating DNA damage and repair.

RYR2 mutation in non-small cell lung cancer prolongs survival via down-regulation of DKK1 and up-regulation of GS1-115G20.1: A weighted gene Co-expression network analysis and risk prognostic models

RYR2 mutation is clinically frequent in non-small cell lung cancer (NSCLC) with its function being elusive. We downloaded lung squamous cell carcinoma and lung adenocarcinoma samples from the TCGA database, split the samples into RYR2 mutant group (n = 337) and RYR2 wild group (n = 634), and established Kaplan-Meier curves. The results showed that RYR2 mutant group lived longer than the wild group (p = 0.027). Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) yielded prognosis-related genes. Five mRNAs and 10 lncRNAs were selected to build survival prognostic models with other clinical features. The AUCs of 2 models are 0.622 and 0.565 for predicting survival at 3 years. Among these genes, the AUCs of DKK1 and GS1-115G20.1 expression levels were 0.607 and 0.560, respectively, which predicted the 3-year survival rate of NSCLC sufferers. GSEA identified an association of high DKK1 expression with TP53, MTOR, and VEGF expression. Several target miRNAs interacting with GS1-115G20.1 were observed to show the relationship with the phenotype, treatment, and survival of NSCLC. NSCLC patients with RYR2 mutation may obtain better prognosis by down-regulating DKK1 and up-regulating GS1-115G20.1.