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

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.

Obese dogs exhibit different fecal microbiome and specific microbial networks compared with normal weight dogs

Canine obesity is a major health concern that predisposes dogs to various disorders. The fecal microbiome has been attracting attention because of their impact on energy efficiency and metabolic disorders of host. However, little is known about specific microbial interactions, and how these may be affected by obesity in dogs. The objective of this study was to investigate the differences in fecal microbiome and specific microbial networks between obese and normal dogs. A total of 20 beagle dogs (males = 12, body weight [BW]: 10.5 ± 1.08 kg; females = 8, BW: 11.3 ± 1.71 kg; all 2-year-old) were fed to meet the maintenance energy requirements for 18 weeks. Then, 12 beagle dogs were selected based on body condition score (BCS) and divided into two groups: high BCS group (HBCS; BCS range: 7-9, males = 4, females = 2) and normal BCS group (NBCS; BCS range: 4-6, males = 4, females = 2). In the final week of the experiment, fecal samples were collected directly from the rectum, before breakfast, for analyzing the fecal microbiome using 16S rRNA gene amplicon sequencing. The HBCS group had a significantly higher final BW than the NBCS group (P < 0.01). The relative abundances of Faecalibacterium, Phascolarctobacterium, Megamonas, Bacteroides, Mucispirillum, and an unclassified genus within Ruminococcaceae were significantly higher in the HBCS group than those in the NBCS group (P < 0.05). Furthermore, some Kyoto Encyclopedia of Genes and Genomes (KEGG) modules related to amino acid biosynthesis and B vitamins biosynthesis were enriched in the HBCS group (P < 0.10), whereas those related to carbohydrate metabolism were enriched in the NBCS group (P < 0.10). Microbial network analysis revealed distinct co-occurrence and mutually exclusive interactions between the HBCS and NBCS groups. In conclusion, several genera related to short-chain fatty acid production were enriched in the HBCS group. The enriched KEGG modules in the HBCS group enhanced energy efficiency through cross-feeding between auxotrophs and prototrophs. However, further studies are needed to investigate how specific networks can be interpreted in the context of fermentation characteristics in the lower gut and obesity in dogs.

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.

scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene–gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.

Distinct immune and transcriptomic profiles in dominant versus subordinate males in mouse social hierarchies

Social status is a critical factor determining health outcomes in human and nonhuman social species. In social hierarchies with reproductive skew, individuals compete to monopolize resources and increase mating opportunities. This can come at a significant energetic cost leading to trade-offs between different physiological systems. In particular, changes in energetic investment in the immune system can have significant short and long-term effects on fitness and health. We have previously found that dominant alpha male mice living in social hierarchies have increased metabolic demands related to territorial defense. In this study, we tested the hypothesis that high-ranking male mice favor adaptive immunity, while subordinate mice show higher investment in innate immunity. We housed 12 groups of 10 outbred CD-1 male mice in a social housing system. All formed linear social hierarchies and subordinate mice had higher concentrations of plasma corticosterone (CORT) than alpha males. This difference was heightened in highly despotic hierarchies. Using flow cytometry, we found that dominant status was associated with a significant shift in immunophenotypes towards favoring adaptive versus innate immunity. Using Tag-Seq to profile hepatic and splenic transcriptomes of alpha and subordinate males, we identified genes that regulate metabolic and immune defense pathways that are associated with status and/or CORT concentration. In the liver, dominant animals showed a relatively higher expression of specific genes involved in major urinary production and catabolic processes, whereas subordinate animals showed relatively higher expression of genes promoting biosynthetic processes, wound healing, and proinflammatory responses. In spleen, subordinate mice showed relatively higher expression of genes facilitating oxidative phosphorylation and DNA repair and CORT was negatively associated with genes involved in lymphocyte proliferation and activation. Together, our findings suggest that dominant and subordinate animals adaptively shift immune profiles and peripheral gene expression to match their contextual needs.

csdR, an R package for differential co-expression analysis

Background

Differential co-expression network analysis has become an important tool to gain understanding of biological phenotypes and diseases. The CSD algorithm is a method to generate differential co-expression networks by comparing gene co-expressions from two different conditions. Each of the gene pairs is assigned conserved (C), specific (S) and differentiated (D) scores based on the co-expression of the gene pair between the two conditions. The result of the procedure is a network where the nodes are genes and the links are the gene pairs with the highest C-, S-, and D-scores. However, the existing CSD-implementations suffer from poor computational performance, difficult user procedures and lack of documentation.

Results

We created the R-package aimed at reaching good performance together with ease of use, sufficient documentation, and with the ability to play well with other tools for data analysis. was benchmarked on a realistic dataset with 20,645 genes. After verifying that the chosen number of iterations gave sufficient robustness, we tested the performance against the two existing CSD implementations. was superior in performance to one of the implementations, whereas the other did not run. Our implementation can utilize multiple processing cores. However, we were unable to achieve more than $$\sim$$ ∼ 2.7 parallel speedup with saturation reached at about 10 cores.

Conclusion

The results suggest that is a useful tool for differential co-expression analysis and is able to generate robust results within a workday on datasets of realistic sizes when run on a workstation or compute server.

Re-wiring and gene expression changes of AC025034.1 and ATP2B1 play complex roles in early-to-late breast cancer progression

Background

Elucidating the dynamic topological changes across different stages of breast cancer, called stage re-wiring, could lead to identifying key latent regulatory signatures involved in cancer progression. Such dynamic regulators and their functions are mostly unknown. Here, we reconstructed differential co-expression networks for four stages of breast cancer to assess the dynamic patterns of cancer progression. A new computational approach was applied to identify stage-specific subnetworks for each stage. Next, prognostic traits of genes and the efficiency of stage-related groups were evaluated and validated, using the Log-Rank test, SVM classifier, and sample clustering. Furthermore, by conducting the stepwise VIF-feature selection method, a Cox-PH model was developed to predict patients’ risk. Finally, the re-wiring network for prognostic signatures was reconstructed and assessed across stages to detect gain/loss, positive/negative interactions as well as rewired-hub nodes contributing to dynamic cancer progression.

Results

After having implemented our new approach, we could identify four stage-specific core biological pathways. We could also detect an essential non-coding RNA, AC025034.1, which is not the only antisense to ATP2B1 (cell proliferation regulator), but also revealed a statistically significant stage-descending pattern; Moreover, AC025034.1 revealed both a dynamic topological pattern across stages and prognostic trait. We also identified a high-performance Overall-Survival-Risk model, including 12 re-wired genes to predict patients’ risk (c-index = 0.89). Finally, breast cancer-specific prognostic biomarkers of LINC01612, AC092142.1, and AC008969.1 were identified.

Conclusions

In summary new scoring method highlighted stage-specific core pathways for early-to-late progressions. Moreover, detecting the significant re-wired hub nodes indicated stage-associated traits, which reflects the importance of such regulators from different perspectives.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01015-9.

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.