Genome-wide Transcriptome Data Research Articles

MMP-9-dependent proteolysis of the histone H3 N-terminal tail: a critical epigenetic step in driving oncogenic transcription and colon tumorigenesis.

Matrix metalloproteinase 9 (MMP-9) is a member of the MMP family and has been recently identified as a nuclear protease capable of clipping histone H3 N-terminal tails (H3NT). This MMP-9-dependent H3NT proteolysis is critical for establishing an active state of gene transcription during osteoclast differentiation and melanoma development. However, whether H3NT cleavage by MMP-9 plays a similar role in other cellular events has not been explored. Here, we dissect the functional contribution of MMP-9-dependent H3NT clipping to colonic tumorigenesis by using a combination of genome-wide transcriptome data, ChIP/ChIPac-qPCR, CRISPR/dCas9 gene-targeting system, and in vivo xenograft models. We show that MMP-9 is overexpressed in colon cancer cells and catalyzes H3NT proteolysis to drive transcriptional activation of growth stimulatory genes. Our studies using knockdown and inhibition approaches clearly indicate that MMP-9 mediates transcriptional activation and promotes colonic tumorigenesis in a manner dependent on its protease activity toward H3NT. Remarkably, artificial H3NT proteolysis at target gene promoters with dCAS9-MMP-9 is sufficient for establishing their transcriptional competence in colon cancer cells, underscoring the importance of MMP-9-dependent H3NT proteolysis per se in the transactivation process. Our data establish new functions and mechanisms for MMP-9 in driving the oncogenic transcription program in colon cancer through H3NT proteolysis, and demonstrate how this epigenetic pathway can be exploited as a potential therapeutic target for cancer treatment.

The succinct title: Genome-wide methylation, transcriptome and characteristic metabolites reveal the balance between diosgenin and brassinosteroids in Dioscorea zingiberensis

Abstract Diosgenin (DG) is a bioactive metabolite isolated from Dioscorea species, renowned for its medicinal properties. Brassinosteroids (BRs) are a class of crucial plant steroidal hormones. Cholesterol and campesterol are important intermediates of DG and BRs biosynthesis, respectively. DG and BRs are structurally similar components, however, the regulatory network and metabolic interplays have not been fully elucidated. In an effort to decode these complex networks, we conducted a comprehensive study integrating genome-wide methylation, transcriptome and characteristic metabolites data from Dioscorea zingiberensis. Leveraging these data, we were able to construct a comprehensive regulatory network linking DG and BRs. Mass spectrometry results enabled us to clarify the alterations in cholesterol, campesterol, diosgenin and castasterone (one of the major active BRs). The DG content decreased by 27.72% at 6 h following brassinolide treatment, whereas the content increased by 85.34% at 6 h after brassinazole treatment. Moreover, we pinpointed DG/BR-related genes, such as CASs, CYP90s and B3-ARFs, implicated in the metabolic pathways of DG and BRs. Moreover, CASs and CYP90s exhibit hypomethylation, which is closely related to their high transcription. These findings provide robust evidence for the homeostasis between DG and BRs. In conclusion, our research revealed the existence of a balance between DG and BRs in D. zingiberensis. Furthermore, our work not only provides new insights into the relationship between the two pathways but also offers a fresh perspective on the functions of secondary metabolites.

Synergistic induction of tertiary lymphoid structures by chemoimmunotherapy in bladder cancer

BackgroundA substantial number of patients with bladder cancer fail to benefit from immune checkpoint inhibitors (ICIs). We aim to investigate whether the addition of other therapeutic modalities into immunotherapy may augment the immune reactivity, thereby improving the overall response rate.MethodsWe conducted a comprehensive assessment of the immunological changes following immunotherapy and chemotherapy, employing both single-cell RNA sequencing and bulk RNA sequencing analyses.ResultsThe bladder cancer patient treated with ICIs exhibited a higher abundance of B cells and T follicular helper cells compared to the treatment-naïve patient. Analysis of public datasets and the in-house RJBLC-I2N003 cohort revealed the induction of tertiary lymphoid structure (TLS) neogenesis and maturation by immunotherapy. The IMvigor 210 study suggested that TLS could serve as a predictor of immunotherapy response and patient prognosis. In addition, genome-wide transcriptome data unveiled a shift towards the immune-enriched subtype over the desert subtype in patients receiving neoadjuvant chemotherapy. Notably, the proportions of CD20 + B cells, T follicular helper cells, and TLSs were significantly increased. In patients treated with a combination of neoadjuvant chemotherapy and ICIs, TLS positivity and maturity were improved compared to the baseline. Furthermore, neoadjuvant chemoimmunotherapy resulted in a higher rate of pathological complete response compared to monotherapies.ConclusionsThis work pinpointed the individual effect of immunotherapy and chemotherapy in fostering TLS development, and underscored the superior effectiveness of combined modalities in enhancing TLS maturation and response rates.

Quantitative transcriptomic and epigenomic data analysis: a primer.

The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.

Establishment of comorbidity target pools and prediction of drugs candidate for multiple sclerosis and autoimmune thyroid diseases based on GWAS and transcriptome data.

Clinical observation has revealed that multiple sclerosis (MS) and autoimmune thyroid disease (AITD) are strongly correlated. The aim of this study was to explore the shared molecular causes of MS and AITD, and to conduct drug rearrangement on this basis, search for comorbidity drugs and feasible drugs for mutual reference between the two diseases. Based on genome-wide association study (GWAS) data and transcriptome data, susceptibility genes and differentially expressed genes related to MS and AITD were identified by bioinformatics analysis. Pathway enrichment, gene ontology (GO), protein-protein interaction analysis, and gene-pathway network analysis of the above genes were performed to identify a common target pool, including common genes, common hub genes, and common pathways, and to explore the specific pathogenesis of the two diseases, respectively. Drugs that target the common pathways/genes were identified through the Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. Common hub genes were compared with the target genes of drugs approved for treating MS/AITD and drugs under investigation identified by DrugBank and ClinicalTrials, respectively. We identified a pool of shared targets containing genes and pathways, including 46 common genetic susceptibility pathways and 9 common differentially expressed pathways, including JAK-STAT signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. In addition, a total of 29 hub genes, including TYK2, JAK1, STAT3, IL2RA, HLA-DRB1, and TLR3, were identified. Drugs approved for treating MS or AITD, such as methylprednisolone, cyclophosphamide, glatiramer, natalizumab, and methimazole, can target the shared genes and pathways, among which methylprednisolone and cyclophosphamide have been shown to be beneficial for the treatment of the two diseases, indicating that these drugs have the potential to become a priority in the treatment of comorbidities. Moreover, drugs targeting multiple common genes and pathways, including tacrolimus, deucravacitinib, and nivolumab, were identified as potential drugs for the treatment of MS, AITD, and their comorbidities. We observed that T-cell activation-related genes and pathways play a major role in the pathogenesis of both MS and AITD, which may be the molecular basis of the comorbidity. Moreover, we identified a variety of drugs which may be used as priority or potential treatments for comorbidities.

Data-driven transcriptomics analysis identifies PCSK9 as a novel key regulator in liver aging.

The liver, as a crucial metabolic organ, undergoes significant pathological changes during the aging process, which can have a profound impact on overall health. To gain a comprehensive understanding of these alterations, we employed data-driven approaches, along with biochemical methods, histology, and immunohistochemistry techniques, to systematically investigate the effects of aging on the liver. Our study utilized a well-established rat aging model provided by the National Institute of Aging. Systems biology approaches were used to analyze genome-wide transcriptomics data from liver samples obtained from young (4-5months old) and aging (20-21months old) Fischer 344 rats. Our findings revealed pathological changes occurring in various essential biological processes in aging livers. These included mitochondrial dysfunction, increased oxidative/nitrative stress, decreased NAD + content, impaired amino acid and protein synthesis, heightened inflammation, disrupted lipid metabolism, enhanced apoptosis, senescence, and fibrosis. These results were validated using independent datasets from both human and rat aging studies. Furthermore, by employing co-expression network analysis, we identified novel driver genes responsible for liver aging, confirmed our findings in human aging subjects, and pointed out the cellular localization of the driver genes using single-cell RNA-sequencing human data. Our study led to the discovery and validation of a liver-specific gene, proprotein convertase subtilisin/kexin type 9 (PCSK9), as a potential therapeutic target for mitigating the pathological processes associated with aging in the liver. This finding envisions new possibilities for developing interventions aimed to improve liver health during the aging process.

An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index.

Investigating the cross talk of different omics layers is crucial to understand molecular pathomechanisms of metabolic diseases like obesity. Here, we present a large-scale association meta-analysis of genome-wide whole blood and peripheral blood mononuclear cell (PBMC) gene expressions profiled with Illumina HT12v4 microarrays and metabolite measurements from dried blood spots (DBS) characterized by targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) in three large German cohort studies with up to 7706 samples. We found 37,295 associations comprising 72 amino acids (AA) and acylcarnitine (AC) metabolites (including ratios) and 8579 transcripts. We applied this catalogue of associations to investigate the impact of associating transcript-metabolite pairs on body mass index (BMI) as an example metabolic trait. This is achieved by conducting a comprehensive mediation analysis considering metabolites as mediators of gene expression effects and vice versa. We discovered large mediation networks comprising 27,023 potential mediation effects within 20,507 transcript-metabolite pairs. Resulting networks of highly connected (hub) transcripts and metabolites were leveraged to gain mechanistic insights into metabolic signaling pathways. In conclusion, here, we present the largest available multi-omics integration of genome-wide transcriptome data and metabolite data of amino acid and fatty acid metabolism and further leverage these findings to characterize potential mediation effects towards BMI proposing candidate mechanisms of obesity and related metabolic diseases. KEY MESSAGES: Thousands of associations of 72 amino acid and acylcarnitine metabolites and 8579 genes expand the knowledge of metabolome-transcriptome associations. A mediation analysis of effects on body mass index revealed large mediation networks of thousands of obesity-related gene-metabolite pairs. Highly connected, potentially mediating hub genes and metabolites enabled insight into obesity and related metabolic disease pathomechanisms.

Integrating genomics and transcriptomics to identify candidate genes for high egg production in Wulong geese (Anser cygnoides orientalis)

BackgroundWulong geese (Anser cygnoides orientalis) are known for their excellent egg-laying performance. However, they show considerable population differences in egg-laying behavior. This study combined genome-wide selection signal analysis with transcriptome analysis (RNA-seq) to identify the genes related to high egg production in Wulong geese.ResultsA total of 132 selected genomic regions were screened using genome-wide selection signal analysis, and 130 genes related to high egg production were annotated in these regions. These selected genes were enriched in pathways related to egg production, including oocyte meiosis, the estrogen signaling pathway, the oxytocin signaling pathway, and progesterone-mediated oocyte maturation. Furthermore, a total of 890 differentially expressed genes (DEGs), including 340 up-regulated and 550 down-regulated genes, were identified by RNA-seq. Two genes — GCG and FAP — were common to the list of selected genes and DEGs. A non-synonymous single nucleotide polymorphism was identified in an exon of FAP.ConclusionsBased on genome-wide selection signal analysis and transcriptome data, GCG and FAP were identified as candidate genes associated with high egg production in Wulong geese. These findings could promote the breeding of Wulong geese with high egg production abilities and provide a theoretical basis for exploring the mechanisms of reproductive regulation in poultry.

Integrative analysis of GWAS and transcriptomics data reveal key genes for non-small lung cancer.

Lung cancer is one of the world's most common and deadly cancers. The two main types of lung cancer are non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). More than 85% of lung cancers are NSCLC. Genetic factors play a significant role in the risk of NSCLC. Growing studies focus on studying risk factors at the molecular level. The aim of the study is to build a pipeline to integrate Genome-wide association analysis (GWAS) and transcriptomics data with machine learning to effectively identify genetic risk factors of NSCLC. GWAS datasets and GWAS summary data were downloaded from GWAS catalog, which include lung carcinoma genetic variants among the European population. Then, with the GWAS summary, data functional analysis of significant SNPs was performed using a webserver called FUMAGWAS. The transcriptomics data of NSCLC and non-NSCLC people were used to build a machine learning model to identify the key genes that help predict the NSCLC. The top up-regulation and down-regulation genes were identified by the BART cancer webserver, and the mechanistic roles of the genes were validated by literature review. By performing integrative analysis of GWAS and transcriptomics analysis using machine learning, we identified multiple SNPs and genes that related to NSCLC. The computational pipeline may facilitate the biomarker discovery for NSCLC and other diseases.

Using Isoform Sequencing for De Novo Transcriptome Sequencing and the Identification of Genes Related to Drought Tolerance and Agronomic Traits in Tall Fescue (Festuca arundinacea Schreb.)

Plant height and tillering are the key traits of tall fescue (Festuca arundinacea Schreb.), a perennial turf grass widely used for forage and turf worldwide. It exhibits excellent abiotic stress tolerance. However, the investigation of these traits at the genetic level is limited by the lack of a completely sequenced genome of tall fescue. Here, we generated genome-wide transcriptome data using PacBio isoform sequencing (Iso-Seq) technology. We obtained 73,628 transcripts with an average length of 2410 bp. Among these transcripts, 42,265 (60.09%) were predicted as complete full-length open reading frames. The dataset contained 11,520 simple sequence repeats; 737 bp long non-coding RNAs; and 1599 alternative splicing sites in unigenes. Sixty-three unigenes involved in abscisic acid biosynthesis, catabolism, and signaling pathways were identified. The Illumina-sequencing analysis results, further verified using qRT-PCR, revealed the significant upregulation of nine unigenes under drought stress. Ninety-three unigenes involved in controlling plant height and tillering were also identified, of which FaMAX1 was functionally verified to regulate plant tillering. Our results provide a valuable genetic resource about the complete transcriptome of tall fescue; the identified candidate genes can aid in improving the drought tolerance and agronomic traits in tall fescue and other related plants.

Discovering pathway biomarkers of hepatocellular carcinoma occurrence and development by dynamic network entropy analysis

ObjectiveGene expression profiling techniques measure the transcription of thousands of genes in a parallel manner. With more and more hepatocellular carcinoma (HCC) transcriptomic data becoming available, the high-throughput data provides an unprecedented opportunity to discover HCC diagnostic biomarkers. In this work, we propose a bioinformatics method based on dynamic network entropy analysis, called DNEA, to identify potential pathway biomarkers for HCC occurrence and development by integrating transcriptome and interactome. MethodsWe firstly collect the pathways documented in different knowledge-bases and then impose the genome-wide human transcriptomic data of multistage cancerous tissues during the development and progression of HCC. After linking the gene sets of pathways into individual connected networks, we map the corresponding gene expression information onto these pathways. The dynamic network entropy of individual pathways is calculated to evaluate its activities and dysfunctionalities during the disease occurrence and development. We use the overall significant difference in the entropic dynamics during the time course to prioritize distinctive pathways during disease progression. Then machine learning classification methods are employed to screen out pathway biomarkers with the classification ability to distinguish different-stage samples of HCC progression. ResultsPathway biomarkers discovered based on DNEA demonstrate good classification performance in measuring HCC progression. The classification accuracy is as follows: DNA replication pathway (mean AUC = 0.82, 20 genes) from KEGG, FMLP pathway (mean AUC = 0.84, 14 genes) from BioCarta, and downstream signaling of activated FGFR pathway (mean AUC = 0.80, 15 genes) from Reactome. At the same time, previous studies have shown that these genes and pathways screened are closely related to the occurrence and development of HCC in terms of oncogenesis dysfunctions. ConclusionsOur method for cancer biomarker discovery based on dynamic network entropy analysis is effective and efficient in identifying pathway biomarkers related to the progression of complex diseases.

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; PHEIDI > 0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR < 0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including “cell aging”, “metal ion binding” and “oxidative damage”. Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

Discovering and prioritizing candidate resistance genes against soybean pests by integrating GWAS and gene coexpression networks

Soybean is one of the most important legume crops worldwide. Soybean pests have a considerable impact on crop yield. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate resistance genes against the insects Aphis glycines and Spodoptera litura, and the nematode Heterodera glycines. We identified 171, 7, and 228 high-confidence candidate resistance genes against A. glycines, S. litura, and H. glycines, respectively. We found some overlap of candidate genes between insect species, but not between insects and H. glycines. Although 15% of the prioritized candidate genes encode proteins of unknown function, the vast majority of the candidates are related to plant immunity processes, such as transcriptional regulation, signaling, oxidative stress, recognition, and physical defense. Based on the number of resistance alleles, we selected the ten most promising accessions against each pest species in the soybean USDA germplasm. The most resistant accessions do not reach the maximum theoretical resistance potential, indicating that they might be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression networks we inferred in this work are available in a user-friendly web application (https://soypestgcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyPestGCN) that serve as a public resource to explore soybean-pest interactions at the transcriptional level.

Применение метода Менделевской рандомизации при перепрофилировании лекарственных средств

The development of new drugs is a time consuming and costly process, so the use of approved drugs for new indications (repurposing) is a promising area of development for the pharmaceutical industry. There are two main approaches for drug development: experimental and computational. Currently, due to the availability of large data sets, computational methods, including those based on the use of artificial intelligence, are being actively developed. The widespread use of genetic data in drug development and repurposing has led to the development of such a field of science as pharmacogenetics. The availability of genome-wide association analyses and transcriptome data allow the Mendelian randomization method to be applied to determine the potential for drug repurposing. This article briefly describes the Mendelian randomization method and provides examples of its application to assess the effect of drugs on various diseases.

Neutrophil-derived interleukin-17A participates in neuroinflammation induced by traumatic brain injury.

After brain injury, infiltration and abnormal activation of neutrophils damages brain tissue and worsens inflammation, but the mediators that connect activated neutrophils with neuroinflammation have not yet been fully clarified. To identify regulators of neutrophil-mediated neuroinflammation after traumatic brain injury, a mouse model of traumatic brain injury was established by controlled cortical impact. At 7 days post-injury (sub-acute phase), genome-wide transcriptomic data showed that interleukin 17A-associated signaling pathways were markedly upregulated, suggesting that interleukin 17A may be involved in neuroinflammation. Double immunofluorescence staining showed that interleukin 17A was largely secreted by neutrophils rather than by glial cells and neurons. Furthermore, nuclear factor-kappaB and Stat3, both of which are important effectors in interleukin 17A-mediated proinflammatory responses, were significantly activated. Collectively, our findings suggest that neutrophil-derived interleukin 17A participates in neutrophil-mediated neuroinflammation during the subacute phase of traumatic brain injury. Therefore, interleukin 17A may be a promising therapeutic target for traumatic brain injury.

Ecological Speciation Promoted by Divergent Regulation of Functional Genes Within African Cichlid Fishes.

Rapid ecological speciation along depth gradients has taken place repeatedly in freshwater fishes, yet molecular mechanisms facilitating such diversification are typically unclear. In Lake Masoko, an African crater lake, the cichlid Astatotilapia calliptera has diverged into shallow-littoral and deep-benthic ecomorphs with strikingly different jaw structures within the last 1,000 years. Using genome-wide transcriptome data, we explore two major regulatory transcriptional mechanisms, expression and splicing-QTL variants, and examine their contributions to differential gene expression underpinning functional phenotypes. We identified 7,550 genes with significant differential expression between ecomorphs, of which 5.4% were regulated by cis-regulatory expression QTLs, and 9.2% were regulated by cis-regulatory splicing QTLs. We also found strong signals of divergent selection on differentially expressed genes associated with craniofacial development. These results suggest that large-scale transcriptome modification plays an important role during early-stage speciation. We conclude that regulatory variants are important targets of selection driving ecologically relevant divergence in gene expression during adaptive diversification.

Integrating multi-locus genome-wide association studies with transcriptomic data to identify genetic loci underlying adult root trait responses to drought stress in flax (Linum usitatissimum L.)

Water stress is a major constraint on crop production and, root systems are one of the key features of plants determining drought tolerance and yield. In this study, five root morphological traits and their stability indices were evaluated at the reproductive stage on an association panel consisting of 120 diverse Linum usitatissimum accessions grown under well-watered and water-limiting conditions. To identify quantitative trait loci (QTL) and candidate genes positively influencing root traits under drought, combined multi-locus genome-wide association studies (ML-GWAS) and transcriptomic data analysis were carried out. A total of 112 QTL were identified accounting for 0.45–24.28 % of the phenotypic variation for root-related traits. Fifty QTL were detected by at least two ML-GWAS models, and 26 were co-located with previously reported QTL associated with yield-related traits under drought and early root and shoot development. Phenotypic differences between two contrasting subsets of accessions for the traits assessed were attributable to the accumulation of positive QTL alleles (PQTL), where the subset with the largest number of PQTL registered 154 % and 69 % greater total root length and yield under drought stress, respectively, than the subset with the fewest PQTL. Transcriptomic data analysis identified 766 differentially expressed genes (DEGs) responsive to drought stress in 108 QTL. Annotation of the 766 DEGs yielded functional genes involved in root development, stomatal closure, ROS (reactive oxygen species) scavenging, and hormone responses, among others. Taken together, the combined use of GWAS and transcriptomic data analyses provide the foundation for our enhanced understanding of root trait morphological changes in response to drought stress, and to achieve superior yield resilience/stability under water shortages in flax through enhanced molecular breeding. All data supporting the findings of this study are available within the paper and within its Supplementary data • Large genetic variation observed for adult root traits under drought and irrigated conditions. • Root traits explained 25–32 % of yield variation under drought stress. • A total of 112 QTL were identified for root traits and their stability indices in flax. • Twenty-six QTL co-located with previously reported QTL for yield traits under drought and root and shoot development. • Transcriptomic data analysis identified 766 drought responsive differentially expressed genes within the LD blocks of 108 significant QTL.

Analysis on the desert adaptability of indigenous sheep in the southern edge of Taklimakan Desert

The southern margin of the Taklimakan Desert is characterized by low rainfall, heavy sandstorms, sparse vegetation and harsh ecological environment. The indigenous sheep in this area are rich in resources, with the advantages of perennial estrus and good resistance to stress in most sheep. Exploring the molecular markers of livestock adaptability in this environment will provide the molecular basis for breeding research to cope with extreme future changes in the desert environment. In this study, we analyzed the population genetic structure and linkage imbalance of five sheep breeds with three different agricultural geographic characteristics using four complementary genomic selection signals: fixation index (FST), cross-population extended haplotype homozygosity (xp-EHH), Rsb (extended haplotype homozygosity between-populations) and iHS (integrated haplotype homozygosity score). We used Illumina Ovine SNP 50K Genotyping BeadChip Array, and gene annotation and enrichment analysis were performed on selected regions of the obtained genome. The ovary of Qira Black sheep (Follicular phase, Luteal phase, 30th day of pregnancy, 45th day of pregnancy) was collected, and the differentially expressed genes were screened by transcriptomic sequencing. Genome-wide selective sweep results and transcriptome data were combined for association analysis to obtain candidate genes associated with perennial estrus and stable reproduction. In order to verify the significance of the results, 15 resulting genes were randomly selected for fluorescence quantitative analysis. The results showed that Dolang sheep and Qira Black sheep evolved from Kazak sheep. Linkage disequilibrium analysis showed that the decay rate of sheep breeds in the Taklimakan Desert was higher than that in Yili grassland. The signals of FST, xp-EHH, Rsb and iHS detected 526, 332, 308 and 408 genes, respectively, under the threshold of 1% and 17 overlapping genes under the threshold of 5%. A total of 29 genes were detected in association analysis of whole-genome and transcriptome data. This study reveals the genetic mechanism of perennial estrus and environmental adaptability of indigenous sheep breeds in the Taklimakan Desert. It provides a theoretical basis for the conservation and exploitation of genetic resources of indigenous sheep breeds in extreme desert environment. This provides a new perspective for the quick adaptation of sheep and other mammals to extreme environments and future climate changes.

Transcriptome analysis revealed that jasmonic acid biosynthesis/signaling is involved in plant response to Strontium stress

Strontium (Sr) has become an increasing global threat for both environment and human health due to its radioactive isotope, Sr-90 which can be found in the nuclear-contaminated soils and water. Although excessive Sr has been known to be toxic to plant growth and development, the molecular mechanisms underlying plant response to Sr stress, especially on the transcription level, remains largely unknown. To date, there is no published genome-wide transcriptome data available for the plant responses to Sr toxicity. Therefore, we aimed to gain insight on the molecular events occurring in plants in Sr toxicity condition by comparing the genome-wide gene expression profiles between control and Sr-treated plants using RNA-seq analysis. A total of 842 differentially expressed genes (DEGs) were identified in response to Sr stress compared to the control. Based on the analysis of DEGs using Gene Ontology (GO), DEGs were significantly enriched in the GO terms of response to salicylic acid (SA), response to jasmonic acid (JA), and defense response to bacterium. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that DEGs were mainly involved in metabolic processes including phenylpropanoid biosynthesis and alpha-linolenic acid metabolism, which is known as a precursor of JA biosynthesis. Furthermore, MapMan analysis revealed that a number of genes related to the biotic stress such as pathogenesis-related protein (PR) genes were highly up-regulated under Sr stress. Taken together, this study revealed that JA biosynthesis and/or signaling might be associated with plant response to Sr stress, and play important roles to maintain proper growth and development under Sr stress.

P229 Integrated analysis of dermal blister fluid proteomics and skin biopsy transcriptomics gives new insight into pathogenesis of systemic sclerosis

Abstract Background/Aims Suction blister fluid (BF) provides a unique opportunity to analyse the dermal microenvironment of SSc. We utilised an integrated approach to analyse proteomic data from dermal interstitial fluid and genome-wide transcriptomic data from skin biopsies from early dcSSc and healthy controls (HC). Interpreting differential protein expression in the context of RNA sequencing (RNAseq) provides unique insight into potential mediators and pathways relevant to SSc. Methods The BIOPSY cohort recruited 21 early dcSSc patients with mean skin score (MRSS) of 21 (sd 11) and 16 HCs. Forearm skin BF was obtained using the dermal suction blister method and assayed using the Olink platform (www.olink.com) (1192 proteins). Simultaneous 4mm punch biopsies were taken from all subjects, for genome-wide transcriptomic profiling by RNAseq. Integrated analysis of clinical characteristics, gene and protein expression data was conducted using WGCNA and clusterProfiler in R. This method clusters individual analytes into modules sharing similar expression patterns. Each module has been arbitrarily assigned as a colour. Results WGCNA identified 6 modules in BF, and 30 modules from skin. Two BF modules and three in the skin positively correlated with each other, and all of them correlated with a diagnosis of early dcSSc. The magenta module in the skin (385 genes) and blue module (450 proteins) in BF had the strongest correlation (r = 0.74, p &amp;lt; 0.001 and r = 0.6, p &amp;lt; 0.001 respectively). Interrogating each module, key biological processes consistent with current concepts of SSc pathogenesis were identified, and skin and blister modules largely seemed to complement each other. Magenta module processes focused on angiogenesis, extracellular matrix (ECM) structure, and endothelial cell proliferation, with the highest fold change in COMP (FC = 5.06, p&amp;lt;0.001), COL4A4 (FC = 3.86,p&amp;lt;0.001), COL11A1 (FC = 3.46, p&amp;lt;0.001) having the highest fold change compared to HC. The red module from the skin transcriptomics consisted of genes relating to immune activation, whereas the interferon signature was predominantly seen in the genes of the pink module. Within BF proteomics, the blue module consisted of proteins centred around ECM organisation, cell adhesion, and response to TNF, with upregulation of SFRP1 (FC = 4.88, p = 0.013), TNC(FC = 4.81, p=0.0018) and KLK4 (FC = 7.17, p&amp;lt;0.001) in early dcSSc. Whereas metabolic processes were the centre of the turquoise module. Significant Hallmark pathways were compared between all five modules in each tissue modality. Notably, some pathways were upregulated in both skin gene expression and blister fluid modules including NFKB signalling, allograft rejection, and interferon gamma response. Significantly activated pathways in skin gene but not the blister protein modules include G2M checkpoint, p53 pathway, and xenobiotic metabolism. Conversely, protein secretion, unfolded protein response and mitotic spindle pathways were significantly increased only in the blister modules. Conclusion Simultaneous high dimensional analysis of skin gene and protein expression gives new insight into the molecular pathology of SSc. This provides a potential platform for developing a novel composite biomarker for skin. Disclosure K.E.N. Clark: None. E. Csomor: Corporate appointments; employee of GSK. C. Campochiaro: None. Y. Teo: Corporate appointments; employee of GSK. J. Freudenberg: Corporate appointments; employee of GSK. K. Nevin: Corporate appointments; employee of GSK. M.A. Morse: Corporate appointments; employee of GSK. V.H. Ong: None. E. Derrett-Smith: None. N. Wisniacki: Corporate appointments; employee of GSK. S.M. Flint: Corporate appointments; employee of GSK. C.P. Denton: None.