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Cystic echinococcosis (CE) is a zoonotic disease caused by the tapeworm Echinococcus granulosus sensu lato (s.l). In the intermediate host, this disease is characterized by the growth of cysts in viscera such as liver and lungs, inside of which the parasite develops to the next infective stage known as protoscoleces. There are records that the infected viscera affect the development and morphology of E. granulosus s.l. protoscolex in hosts such as buffalo or humans. However, the molecular mechanisms that drive these differences remains unknown. Weighted gene co-expression network analysis (WGCNA) using a set of RNAseq data obtained from E. granulosus sensu stricto (s.s.) protoscoleces found in liver and lung cysts reveals 34 modules in protoscoleces of liver origin, of which 12 have differential co-expression from protoscoleces of lung origin. Three of these twelve modules contain hub genes related to immune evasion: tegument antigen, tegumental protein, ubiquitin hydrolase isozyme L3, COP9 signalosome complex subunit 3, tetraspanin CD9 antigen, and the methyl-CpG-binding protein Mbd2. Also, two of the twelve modules contain only hypothetical proteins with unknown orthology, which means that there are a group of unknown function proteins co-expressed inside the protoscolex of liver CE cyst origin. This is the first evidence of gene expression differences in protoscoleces from CE cysts found in different viscera, with co-expression networks that are exclusive to protoscoleces from liver CE cyst samples. This should be considered in the control strategies of CE, as intermediate hosts can harbor CE cysts in liver, lungs, or both organs simultaneously.

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Osteoporosis is a systemic bone disease characterized by low bone mineral density and bone microstructure damage, resulting in increased bone fragility and fracture risk. The present study aimed to identify key genes and functionally enriched pathways in osteoporotic patients. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to microarray datasets of blood samples of osteoporotic patients from the Sao Paulo Ageing & Health [SPAH] study (26 osteoporotic samples and 31 normal samples) to construct co-expression networks and identify hub gene. The results showed that HDGF, AP2M1, DNAJC6, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, IGKV3-7, IGKV3D-11, and IGKV1D-42 are genes which were associated with the disease status of osteoporosis. Differentially expressed genes are enriched in proteasomal protein catabolic process, ubiquitin ligase complex, and ubiquitin-like protein transferase activity. Functional enrichment analysis demonstrated that genes in the tan module were enriched in immune-related functions, indicating that the immune system plays a critical role in osteoporosis. Validation assay demonstrated that the HDGF, AP2M1, TMEM183B, and MFSD2B levels were decreased in osteoporosis samples compared with healthy controls, while the levels of IGKV1-5, IGKV1-8, and IGKV1D-42 were increased in osteoporosis samples compared with healthy controls. In conclusion, our data identified and validated the association of HDGF, AP2M1, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, and IGKV1D-42 with osteoporosis in elderly women. These results suggest that these transcripts have potential clinical significance and may help to explain the molecular mechanisms and biological functions of osteoporosis.

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Fungal pathogens can have devastating effects on global crop production, leading to annual economic losses ranging from 10% to 23%. In light of climate change-related challenges, researchers anticipate an increase in fungal infections as a result of shifting environmental conditions. However, plants have developed intricate molecular mechanisms for effective defense against fungal attacks. Understanding these mechanisms is essential to the development of new strategies for protecting crops from multiple fungi threats. Public omics databases provide valuable resources for research on plant-pathogen interactions; however, integrating data from different studies can be challenging due to experimental variation. In this study, we aimed to identify the core genes that defend against the pathogenic fungi Colletotrichum higginsianum and Botrytis cinerea in Arabidopsis thaliana. Using a custom framework to control batch effects and construct Gene Co-expression Networks in publicly available RNA-seq dataset from infected A. thaliana plants, we successfully identified a gene module that was responsive to both pathogens. We also performed gene annotation to reveal the roles of previously unknown protein-coding genes in plant defenses against fungal infections. This research demonstrates the potential of publicly available RNA-seq data for identifying the core genes involved in defending against multiple fungal pathogens.

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This research elucidates the dynamic expression of expansin genes during the wheat grain (Triticum aestivum L.) development process using comprehensive meta-analysis and experimental validation. We leveraged RNA-seq data from multiple public databases, applying stringent criteria for selection, and identified 60,852 differentially expressed genes across developmental stages. From this pool, 28,558 DEGs were found to exhibit significant temporal regulation in at least two different datasets and were enriched for processes integral to grain development such as carbohydrate metabolism and cell wall organization. Notably, 30% of the 241 known expansin genes showed differential expression during grain growth. Hierarchical clustering and expression level analysis revealed temporal regulation and distinct contributions of expansin subfamilies during the early stages of grain development. Further analysis using co-expression networks underscored the significance of expansin genes, revealing their substantial co-expression with genes involved in cell wall modification. Finally, qPCR validation and grain morphological analysis under field conditions indicated a significant negative correlation between the expression of select expansin genes, and grain size and weight. This study illuminates the potential role of expansin genes in wheat grain development and provides new avenues for targeted genetic improvements in wheat.

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BACKGROUND: The solubilization of aluminum ions (Al3+) that results from soil acidity (pH < 5.5) is a limiting factor in oil palm yield. Al can be uptaken by the plant roots affecting DNA replication and cell division and triggering root morphological alterations, nutrient and water deprivation. In different oil palm-producing countries, oil palm is planted in acidic soils, representing a challenge for achieving high productivity. Several studies have reported the morphological, physiological, and biochemical oil palm mechanisms in response to Al-stress. However, the molecular mechanisms are just partially understood. RESULTS: Differential gene expression and network analysis of four contrasting oil palm genotypes (IRHO 7001, CTR 3-0-12, CR 10-0-2, and CD 19 - 12) exposed to Al-stress helped to identify a set of genes and modules involved in oil palm early response to the metal. Networks including the ABA-independent transcription factors DREB1F and NAC and the calcium sensor Calmodulin-like (CML) that could induce the expression of internal detoxifying enzymes GRXC1, PER15, ROMT, ZSS1, BBI, and HS1 against Al-stress were identified. Also, some gene networks pinpoint the role of secondary metabolites like polyphenols, sesquiterpenoids, and antimicrobial components in reducing oxidative stress in oil palm seedlings. STOP1 expression could be the first step of the induction of common Al-response genes as an external detoxification mechanism mediated by ABA-dependent pathways. CONCLUSIONS: Twelve hub genes were validated in this study, supporting the reliability of the experimental design and network analysis. Differential expression analysis and systems biology approaches provide a better understanding of the molecular network mechanisms of the response to aluminum stress in oil palm roots. These findings settled a basis for further functional characterization of candidate genes associated with Al-stress in oil palm.

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Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets.

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Biological systems respond to environmental perturbations and a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. Experimental information from transcriptomic studies has allowed the identification of gene networks that contribute to our understanding of microbial adaptations. In this study, we analyzed the gene co-expression networks of three Bifidobacterium species in response to different types of human milk oligosaccharides (HMO) using weighted gene co-expression analysis (WGCNA). RNA-seq data obtained from Geo Datasets were obtained for Bifidobacterium longum subsp. Infantis, Bifidobacterium bifidum and Bifidobacterium longum subsp. Longum. Between 10 and 20 co-expressing modules were obtained for each dataset. HMO-associated genes appeared in the modules with more genes for B. infantis and B. bifidum, in contrast with B. longum. Hub genes were identified in each module, and in general they participated in conserved essential processes. Certain modules were differentially enriched with LacI-like transcription factors, and others with certain metabolic pathways such as the biosynthesis of secondary metabolites. The three Bifidobacterium transcriptomes showed distinct regulation patterns for HMO utilization. HMO-associated genes in B. infantis co-expressed in two modules according to their participation in galactose or N-Acetylglucosamine utilization. Instead, B. bifidum showed a less structured co-expression of genes participating in HMO utilization. Finally, this category of genes in B. longum clustered in a small module, indicating a lack of co-expression with main cell processes and suggesting a recent acquisition. This study highlights distinct co-expression architectures in these bifidobacterial genomes during HMO consumption, and contributes to understanding gene regulation and co-expression in these species of the gut microbiome.

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Introduction: Biological systems respond to environmental disturbances and a wide range of compounds through complex gene interaction networks. The enormous growth of experimental information obtained using large-scale genomic techniques such as microarrays and RNA sequencing led to the construction of a wide variety of gene co-expression networks in recent years. These networks allow the discovery of clusters of co-expressed genes that potentially work in the same process linking them to biological processes often of interest to industrial, medicinal, and academic research. Methods: In this study, we built the gene co-expression network of Ustilago maydis from the gene expression data of 168 samples belonging to 19 series, which correspond to the GPL3681 platform deposited in the NCBI using WGCNA software. This network was analyzed to identify clusters of co-expressed genes, gene hubs and Gene Ontology terms. Additionally, we identified relevant modules through a hypergeometric approach based on a predicted set of transcription factors and virulence genes. Results and Discussion: We identified 13 modules in the gene co-expression network of U. maydis. The TFs enriched in the modules of interest belong to the superfamilies of Nucleic acid-binding proteins, Winged helix DNA-binding, and Zn2/Cys6 DNA-binding. On the other hand, the modules enriched with virulence genes were classified into diseases related to corn smut, Invasive candidiasis, among others. Finally, a large number of hypothetical, a large number of hypothetical genes were identified as highly co-expressed with virulence genes, making them possible experimental targets.

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PURPOSE: Transcriptome analysis of pancreatic ductal adenocarcinoma (PDAC) has been useful to identify gene expression changes that sustain malignant phenotypes. Yet, most studies examined only tumor tissues and focused on protein-coding genes, leaving long non-coding RNAs (lncRNAs) largely underexplored. METHODS: We generated total RNA-Seq data from patient-matched tumor and nonmalignant pancreatic tissues and implemented a computational pipeline to survey known and novel lncRNAs. siRNA-mediated knockdown in tumor cell lines was performed to assess the contribution of PDAC-associated lncRNAs to malignant phenotypes. Gene co-expression network and functional enrichment analyses were used to assign deregulated lncRNAs to biological processes and molecular pathways. RESULTS: We detected 9,032 GENCODE lncRNAs as well as 523 unannotated lncRNAs, including transcripts significantly associated with patient outcome. Aberrant expression of a subset of novel and known lncRNAs was confirmed in patient samples and cell lines. siRNA-mediated knockdown of a subset of these lncRNAs (LINC01559, LINC01133, CCAT1, LINC00920 and UCA1) reduced cell proliferation, migration and invasion. Gene co-expression network analysis associated PDAC-deregulated lncRNAs with diverse biological processes, such as cell adhesion, protein glycosylation and DNA repair. Furthermore, UCA1 knockdown was shown to specifically deregulate co-expressed genes involved in DNA repair and to negatively impact DNA repair following damage induced by ionizing radiation. CONCLUSIONS: Our study expands the repertoire of lncRNAs deregulated in PDAC, thereby revealing novel candidate biomarkers for patient risk stratification. It also provides a roadmap for functional assays aimed to characterize novel mechanisms of action of lncRNAs in pancreatic cancer, which could be explored for therapeutic development.

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Multiple genes in sugarcane control sucrose accumulation and the biosynthesis of cell wall components; however, it is unclear how these genes are expressed in its apical culms. To better understand this process, we sequenced mRNA from +1 stem internodes collected from four genotypes with different concentrations of soluble solids. Culms were collected at four different time points, ranging from six to 12-month-old plants. Here we show differentially expressed genes related to sucrose metabolism and cell wall biosynthesis, including genes encoding invertases, sucrose synthase and cellulose synthase. Our results showed increased expression of invertases in IN84-58, the genotype with lower sugar and higher fiber content, as well as delayed expression of secondary cell wall-related cellulose synthase for the other genotypes. Interestingly, genes involved with hormone metabolism were differentially expressed across time points in the three genotypes with higher soluble solids content. A similar result was observed for genes controlling maturation and transition to reproductive stages, possibly a result of selection against flowering in sugarcane breeding programs. These results indicate that carbon partitioning in apical culms of contrasting genotypes is mainly associated with differential cell wall biosynthesis, and may include early modifications for subsequent sucrose accumulation. Co-expression network analysis identified transcription factors related to growth and development, showing a probable time shift for carbon partitioning occurred in 10-month-old plants.

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BACKGROUND: This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed approach is both a generalization and an extension of WGCNA. It aims to identify specific modules of overlapping communities underlying the co-expression network of genes. Module detection is achieved by using Hierarchical Link Clustering. The overlapping nature of the systems' regulatory domains that generate co-expression can be identified by such modules. LASSO regression is employed to analyze phenotypic responses of modules to treatment. RESULTS: The workflow is applied to rice (Oryza sativa), a major food source known to be highly sensitive to salt stress. The workflow identifies 19 rice genes that seem relevant in the response to salt stress. They are distributed across 6 modules: 3 modules, each grouping together 3 genes, are associated to shoot K content; 2 modules of 3 genes are associated to shoot biomass; and 1 module of 4 genes is associated to root biomass. These genes represent target genes for the improvement of salinity tolerance in rice. CONCLUSIONS: A more effective framework to reduce the search-space for target genes that respond to a specific treatment is introduced. It facilitates experimental validation by restraining efforts to a smaller subset of genes of high potential relevance.

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The genus Coffea (Rubiaceae) encompasses a group of perennial plant species, including a commodity crop from which seeds are roasted, ground, and infused to make one of the most appreciated beverages in the world. As an important tropical crop restricted to specific regions of the world, coffee production is highly susceptible to the effects of environmental instabilities (i.e., local year-to-year weather fluctuations and global climate change) and threatening pest pressures, not to mention an increasing quality rigor by consumers in industrialized countries. Specialized metabolites are substances that largely affect plant-environment interactions as well as how consumers experience agricultural products. Membrane transporters are key targets, albeit understudied, for understanding and tailoring the spatiotemporal distribution of specialized metabolites as they mediate and control molecular trafficking and substance accumulation. Therefore, we analyzed the transportome of C. canephora encoded within the 25,574 protein-coding genes annotated in the genome of this species and identified 1847 putative membrane transporters. Following, we mined 152 transcriptional profiles of C. canephora and C. arabica and performed a comprehensive co-expression analysis to identify transporters potentially involved in the accumulation of specialized metabolites associated with beverage quality and bioactivity attributes. In toto, this report points to an avenue of possibilities on Coffea genomic and transcriptomic data mining for genetic breeding strategies, which can lead to the development of new, resilient varieties for more sustainable coffee production systems.

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Once considered nonfunctional, pseudogene transcripts are now known to provide valuable information for cancer susceptibility, including head and neck cancer (HNC), a serious health problem worldwide, with about 50% unimproved overall survival over the last decades. The present review focuses on the role of pseudogene transcripts involved in HNC risk and prognosis. We combined current literature and in silico analyses from The Cancer Genome Atlas (TCGA) database to identify the most deregulated pseudogene transcripts in HNC and their genetic variations. We then built a co-expression network and performed gene ontology enrichment analysis to better understand the pseudogenes' interactions and pathways in HNC. In the literature, few pseudogenes have been studied in HNC. Our in silico analysis identified 370 pseudogene transcripts associated with HNC, where SPATA31D5P, HERC2P3, SPATA31C2, MAGEB6P1, SLC25A51P1, BAGE2, DNM1P47, SPATA31C1, ZNF733P and OR2W5 were found to be the most deregulated and presented several genetic alterations. NBPF25P, HSP90AB2P, ZNF658B and DPY19L2P3 pseudogenes were predicted to interact with 12 genes known to participate in HNC, DNM1P47 was predicted to interact with the TP53 gene, and HLA-H pseudogene was predicted to interact with HLA-A and HLA-B genes. The identified pseudogenes were associated with cancer biology pathways involving cell communication, response to stress, cell death, regulation of the immune system, regulation of gene expression, and Wnt signaling. Finally, we assessed the prognostic values of the pseudogenes with the Kaplan-Meier Plotter database, and found that expression of SPATA31D5P, SPATA31C2, BAGE2, SPATA31C1, ZNF733P and OR2W5 pseudogenes were associated with patients' survival. Due to pseudogene transcripts' potential for cancer diagnosis, progression, and as therapeutic targets, our study can guide new research to HNC understanding and development of new target therapies.

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The goal of this study was to identify potential transcriptomic markers in pediatric septic shock prognosis by an integrative analysis of multiple public microarray datasets. Using the R software and bioconductor packages, we performed a statistical analysis to identify differentially expressed (DE) genes in pediatric septic shock non-survivors, and further performed functional interpretation (enrichment analysis and co-expression network construction) and classification quality evaluation of the DE genes identified. Four microarray datasets (3 training datasets and 1 testing dataset, 252 pediatric patients with septic shock in total) were collected for the integrative analysis. A total of 32 DE genes (18 upregulated genes; 14 downregulated genes) were identified in pediatric septic shock non-survivors. Enrichment analysis revealed that those DE genes were strongly associated with acute inflammatory response to antigenic stimulus, response to yeast, and defense response to bacterium. A support vector machine classifier (non-survivors vs survivors) was also trained based on DE genes. In conclusion, the DE genes identified in this study are suggested as candidate transcriptomic markers for pediatric septic shock prognosis and provide novel insights into the progression of pediatric septic shock.

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The ability to expand crop plantations without irrigation is a major goal to increase agriculture sustainability. To achieve this end, we need to understand the mechanisms that govern plant growth responses under drought conditions. In this study, we combined physiological, transcriptomic, and genomic data to provide a comprehensive picture of drought and recovery responses in the leaves and roots of sugarcane. Transcriptomic profiling using oligoarrays and RNA-seq identified 2898 (out of 21,902) and 46,062 (out of 373,869) transcripts as differentially expressed, respectively. Co-expression analysis revealed modules enriched in photosynthesis, small molecule metabolism, alpha-amino acid metabolism, trehalose biosynthesis, serine family amino acid metabolism, and carbohydrate transport. Together, our findings reveal that carbohydrate metabolism is coordinated with the degradation of amino acids to provide carbon skeletons to the tricarboxylic acid cycle. This coordination may help to maintain energetic balance during drought stress adaptation, facilitating recovery after the stress is alleviated. Our results shed light on candidate regulatory elements and pave the way to biotechnology strategies towards the development of drought-tolerant sugarcane plants.

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Long non-coding RNA (lncRNA) can regulate several aspects of gene expression, being associated with complex phenotypes in humans and livestock species. In taurine beef cattle, recent evidence points to the involvement of lncRNA in feed efficiency (FE), a proxy for increased productivity and sustainability. Here, we hypothesized specific regulatory roles of lncRNA in FE of indicine cattle. Using RNA-Seq data from the liver, muscle, hypothalamus, pituitary gland and adrenal gland from Nellore bulls with divergent FE, we submitted new transcripts to a series of filters to confidently predict lncRNA. Then, we identified lncRNA that were differentially expressed (DE) and/or key regulators of FE. Finally, we explored lncRNA genomic location and interactions with miRNA and mRNA to infer potential function. We were able to identify 126 relevant lncRNA for FE in Bos indicus, some with high homology to previously identified lncRNA in Bos taurus and some possible specific regulators of FE in indicine cattle. Moreover, lncRNA identified here were linked to previously described mechanisms related to FE in hypothalamus-pituitary-adrenal axis and are expected to help elucidate this complex phenotype. This study contributes to expanding the catalogue of lncRNA, particularly in indicine cattle, and identifies candidates for further studies in animal selection and management.

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BACKGROUND: We have previously developed a murine cellular system that models the transformation from melanocytes to metastatic melanoma cells. This model was established by cycles of anchorage impediment of melanocytes and consists of four cell lines: differentiated melanocytes (melan-a), pre-malignant melanocytes (4C), malignant (4C11-), and metastasis-prone (4C11+) melanoma cells. Here, we searched for transcriptional and epigenetic signatures associated with melanoma progression and metastasis by performing a gene co-expression analysis of transcriptome data and a mass-spectrometry-based profiling of histone modifications in this model. RESULTS: Eighteen modules of co-expressed genes were identified, and some of them were associated with melanoma progression, epithelial-to-mesenchymal transition (EMT), and metastasis. The genes in these modules participate in biological processes like focal adhesion, cell migration, extracellular matrix organization, endocytosis, cell cycle, DNA repair, protein ubiquitination, and autophagy. Modules and hub signatures related to EMT and metastasis (turquoise, green yellow, and yellow) were significantly enriched in genes associated to patient survival in two independent melanoma cohorts (TCGA and Leeds), suggesting they could be sources of novel prognostic biomarkers. Clusters of histone modifications were also linked to melanoma progression, EMT, and metastasis. Reduced levels of H4K5ac and H4K8ac marks were seen in the pre-malignant and tumorigenic cell lines, whereas the methylation patterns of H3K4, H3K56, and H4K20 were related to EMT. Moreover, the metastatic 4C11+ cell line showed higher H3K9me2 and H3K36me3 methylation, lower H3K18me1, H3K23me1, H3K79me2, and H3K36me2 marks and, in agreement, downregulation of the H3K36me2 methyltransferase Nsd1. CONCLUSIONS: We uncovered transcriptional and histone modification signatures that may be molecular events driving melanoma progression and metastasis, which can aid in the identification of novel prognostic genes and drug targets for treating the disease.

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BACKGROUND: Cestoda is a class of endoparasitic worms in the flatworm phylum (Platyhelminthes). During the course of their evolution cestodes have evolved some interesting aspects, such as their increased reproductive capacity. In this sense, they have serial repetition of their reproductive organs in the adult stage, which is often associated with external segmentation in a developmental process called strobilation. However, the molecular basis of strobilation is poorly understood. To assess this issue, an evolutionary comparative study among strobilated and non-strobilated flatworm species was conducted to identify genes and proteins related to the strobilation process. RESULTS: We compared the genomic content of 10 parasitic platyhelminth species; five from cestode species, representing strobilated parasitic platyhelminths, and five from trematode species, representing non-strobilated parasitic platyhelminths. This dataset was used to identify 1813 genes with orthologues that are present in all cestode (strobilated) species, but absent from at least one trematode (non-strobilated) species. Development-related genes, along with genes of unknown function (UF), were then selected based on their transcriptional profiles, resulting in a total of 34 genes that were differentially expressed between the larval (pre-strobilation) and adult (strobilated) stages in at least one cestode species. These 34 genes were then assumed to be strobilation related; they included 12 encoding proteins of known function, with 6 related to the Wnt, TGF-ß/BMP, or G-protein coupled receptor signaling pathways; and 22 encoding UF proteins. In order to assign function to at least some of the UF genes/proteins, a global gene co-expression analysis was performed for the cestode species Echinococcus multilocularis. This resulted in eight UF genes/proteins being predicted as related to developmental, reproductive, vesicle transport, or signaling processes. CONCLUSIONS: Overall, the described in silico data provided evidence of the involvement of 34 genes/proteins and at least 3 developmental pathways in the cestode strobilation process. These results highlight on the molecular mechanisms and evolution of the cestode strobilation process, and point to several interesting proteins as potential developmental markers and/or targets for the development of novel antihelminthic drugs.

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Schistosoma japonicum is a flatworm that causes schistosomiasis, a neglected tropical disease. S. japonicum RNA-Seq analyses has been previously reported in the literature on females and males obtained during sexual maturation from 14 to 28 days post-infection in mouse, resulting in the identification of protein-coding genes and pathways, whose expression levels were related to sexual development. However, this work did not include an analysis of long non-coding RNAs (lncRNAs). Here, we applied a pipeline to identify and annotate lncRNAs in 66 S. japonicum RNA-Seq publicly available libraries, from different life-cycle stages. We also performed co-expression analyses to find stage-specific lncRNAs possibly related to sexual maturation. We identified 12,291 S. japonicum expressed lncRNAs. Sequence similarity search and synteny conservation indicated that some 14% of S. japonicum intergenic lncRNAs have synteny conservation with S. mansoni intergenic lncRNAs. Co-expression analyses showed that lncRNAs and protein-coding genes in S. japonicum males and females have a dynamic co-expression throughout sexual maturation, showing differential expression between the sexes; the protein-coding genes were related to the nervous system development, lipid and drug metabolism, and overall parasite survival. Co-expression pattern suggests that lncRNAs possibly regulate these processes or are regulated by the same activation program as that of protein-coding genes.

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Plant lignocellulosic biomass, mostly composed of polysaccharide-rich secondary cell walls (SCWs), provides fermentable sugars that may be used to produce biofuels and biomaterials. However, the complex chemical composition and physical structure of SCWs hinder efficient processing of plant biomass. Understanding the molecular mechanisms underlying SCW deposition is, thus, essential to optimize bioenergy feedstocks. Here, we establish a xylogenic culture as a model system to study SCW deposition in sugarcane; the first of its kind in a C4 grass species. We used auxin and brassinolide to differentiate sugarcane suspension cells into tracheary elements, which showed metaxylem-like reticulate or pitted SCW patterning. The differentiation led to increased lignin levels, mainly caused by S-lignin units, and a rise in p-coumarate, leading to increased p-coumarate:ferulate ratios. RNAseq analysis revealed massive transcriptional reprogramming during differentiation, with upregulation of genes associated with cell wall biogenesis and phenylpropanoid metabolism and downregulation of genes related to cell division and primary metabolism. To better understand the differentiation process, we constructed regulatory networks of transcription factors and SCW-related genes based on co-expression analyses. Accordingly, we found multiple regulatory modules that may underpin SCW deposition in sugarcane. Our results provide important insights and resources to identify biotechnological strategies for sugarcane biomass optimization.