Background: Porcine respiratory and reproductive syndrome virus (PRRSV) is a single-stranded RNA virus member that infects pigs and causes losses to the commercial industry reaching upward of a billion dollars annually in combined direct and indirect costs. The virus can be separated into etiologies that contain multiple heterologous low and highly pathogenic strains. Recently, the United States has begun to see an increase in heterologous type 2 PRRSV strains of higher virulence (HP-PRRSV). The high pathogenicity of these strains can drastically alter host immune responses and the ability of the animal to maintain homeostasis. Because the loss of host homeostasis can denote underlying changes in gene and regulatory element expression profiles, the study aimed to examine the effect PRRSV infections has on miRNA and tRNA expression and the roles they play in host tolerance or susceptibility.Results: Using transcriptomic analysis of whole blood taken from control and infected pigs at several time points , the analysis returned a total of 149 statistically significant (FDR ⫹ 0.15) miRNAs (n = 89) and tRNAs (n = 60) that were evaluated for possible pro- and anti-viral effects. The tRNA differential expression increased in both magnitude and count as dpi increased, with no statistically significant expression at 1 dpi, but increases at 3 and 8 dpi. The most abundant tRNA amino acid at 3 dpi was alanine, while glycine was the most abundant at 8 dpi. For the miRNAs, focus was put on upregulation that can inhibit gene expression. These results yielded candidates with potential anti- and pro-viral actions such as Ssc-miR-125b, which is predicted to limit PRRSV viral levels, and Ssc-miR-145-5p shown to cause alternative macrophage priming. The results also showed that both the tRNAs and miRNAs displayed expression patterns.Conclusions: The results indicated that the HP-PRRSV infection affects host homeostasis through changes in miRNA and tRNA expression and their subsequent gene interactions that target and influence the function of host immune, metabolic, and structural pathways. Porcine respiratory and reproductive syndrome virus (PRRSV) is a single-stranded RNA virus member of the Nidovirales order that infects pigs and causes losses to the commercial swine industry that reach upward of a billion dollars annually in combined  direct aThe health of an animal, prior to and after illnesses, is, in many ways, a measure of the ability of that individual to maintain or return to a homeostatic state in which the interplay between immunologic and metabolic responses are in balance . In liveIt has been established that susceptibility to PRRSV infection and persistence involves a host genetic component , in whic4 TCID50/ml, 2 ml/pig). Whole blood sample collection occurred over several time points consisting of 1, 3, and 8 days post infection (dpi) using PAXgene® tubes. Blood samples were stored at -20 C prior to total RNA isolation and NGS library creation. At the conclusion of the study, the pigs were euthanized in the following manner: the animal was physically restrained for the intravenous administration of a barbiturate  following the manufacturer label dose (1 ml/4.54 kg). Only 24 samples were used due to four animals succumbing prior to 8 dpi.PRRSV-free 3-week-old crossbreed pigs (Landrace × Yorkshire × Duroc) were purchased from a USDA-approved vendor . Sample collection consisted of whole blood samples (∼2.5 ml/pig) collected from twenty-eight 9-week-old anesthetized pigs by jugular venipuncture. The piglets were inoculated with either a sham inoculation (prepared from MARC-145 cell culture used to propagate the virus) for the controls (N = 14) (2 ml/pig) or challenged (N = 14) with an infectious cDNA clone of a Chinese highly pathogenic (HP) PRRSV strain isolate rJXwn06 . Optimization of these protocols was done to increase small RNA recovery for downstream library creation. All RNA was globin-depleted to account for high levels of globin transcripts using porcine-specific hemoglobin A and B (HBA and HBB) oligonucleotides based on the procedure from ® with a starting RNA amount of ∼220 ng to ∼1.1 µg. Step one of the protocol, the 3’ SR adaptor ligation, was modified from the manufacturer’s protocol to incubate the samples for 18 h at 16°C to increase ligation efficiency of methylated RNAs. Next, the 5’ SR adaptor was ligated, and hybridization of the reverse transcription primer was performed. Samples were then barcoded using the NEBnext indexes (1–24) to allow for multiplexing prior to PCR amplification of cDNA. Samples were then put through a cleanup step using the Qiagen QIAquick PCR purification kit®, then checked for quality using the Agilent Bioanalyzer. Small RNA libraries were not size-selected to allow for the capture of multiple sncRNAs between 18–200 nt. Sequencing was carried out on the Illumina Hiseq 3000™ at the Iowa State University genomic sequencing center in Ames, IA to produce a total of one 100-bp single-end read for each of the 24 samples.Total RNA was extracted from 2.5-ml cryopreserved whole blood samples using the protocol from in silico resources within the Galaxy web interface . The 10.2 reference genome was used in lieu of the newer 11.1 version due to a current lack of updates to the miRNAs and tRNAs examined within the study. Data has been deposited in a public repository under GEO accession: GSE121980.The transcriptomic analysis was accomplished using nterface . Qualitynterface  to remove Hisat2  package e Hisat2  coupled Downstream analysis of miRNA gene and biological pathway targets was carried out using miRBase  and the The noncoding RNA (ncRNA)–protein interaction analysis was based on conversion of tRNAs to their human homologs. Swine tRNAs were matched using BLASTN to find matching human sequences and gene names prior to using the RAIN module of the RTH database site to predict possible interactions of other ncRNAs and/or mRNAs . PredictClinical evaluation of the infected animal was examined through an analysis of viral titers. The analysis showed that within the treated samples, viral titers were present at 1 dpi and increased at every time point indicating the success of the HP-PRRSV to replicate. The viral titers for the control samples were considered to be zero, as no replication was detectable Figure 1The outcome of our study provided differentially expressed (DE) miRNA and tRNA totals for each dpi that were statistically significant at fdr of q ≤ 0.15 except the 1-dpi tRNA results. For the miRNAs, we observed 41 in total at 1 dpi, 14 in total at 3 dpi, and 33 in total at 8 dpi. For the tRNAs, we observed no statistically significant differential expression for 1 dpi and a total of 20 and 40 for 3 and 8 dpi, respectively.2FC = 2.07) was the most upregulated, and trna1503_ProAGG (log2FC = −1.78) was the most downregulated. From 3 dpi, trna1668_ValAAC is predicted to form interaction networks with Surfactant protein B (SFTPB), which has a homeostatic effect, as it can support alveolar functions by fostering stability within peripheral air spaces (TRPV1) that is involved in the intercession of inflammatory stressors such as pain (TLR2 and TLR4), involved in the monocytic cell ability to recognize antigens and signal pro-inflammatory cytokines and the gene beta-1,4-N-acetyl-galactosaminyl transferase 2 (B4GALNT2), also known as B4GALT, which is a member of the Beta 4-glycosyltransferase gene family related to the gene beta-1, 4 Galactosyltransferase V (B4GALT5) that is shown to also be a target of the multiple miRNAs differentially expressed during this study  being the highest upregulated tRNA and trna552_AlaAGC (log2FC = −1.83) being the most downregulated (LEPROTL1), a growth hormone receptor highly expressed in porcine lung tissue (TIPARP), a host defensive gene able to detect mitochondrial damage and bind viral RNA (RAB1A), which is involved in the biological processes of autophagy, IL-8 secretion, and post-translational protein modification (2FC = −1.05) (MX1) and Calpain small subunit 1 (CAPNS1), which is involved in the biological processes of autophagy, apoptosis, cell adhesion, and extracellular matrix degradation  was the most abundant tRNA amino acid , which is shown to harbor a variant that confers some resistance to lowly pathogenic PRRSV strains (in vivo. In upregulated miRNAs such as ssc-miR-664-5p (1 dpi), which has previously shown to become upregulated in bacterially infected lung samples , predicted to target the gene guanylate binding protein 5 , a pro-inflammatory pathway, which was targeted on all dpi by the up- and downregulated miRNAs. The second most common pathway was the proteoglycans in cancer pathway (hsa05205). The proteoglycans in cancer pathway consisted of four total pathways: hyaluronan (HA), chondroitin sulfate/dermatan sulfate (CSPG/DSPG), keratan sulfate (KSPG), and heparan sulfate (HSPG). This composite of proteoglycan pathways is involved in structural integrity, extracellular matrix, and receptor-ligand binding and appeared as one of the most statistically significant pathways for each dpi as either a common (3 and 8 dpi) or unique (1 dpi) pathway. Other pathways shared across multiple dpi by both up- and downregulated miRNAs included endocytosis (hsa04144), bacterial invasion of epithelial cells (hsa05100), adherens junction (hsa04520), and mTOR signaling pathway (hsa04150).The pathway and G.O. analysis were examined for results that were unique to either upregulated miRNAs or downregulated miRNAs Table 2 hsa04510), proteoglycans in cancer pathway (hsa05205), and the notch signaling pathway (hsa04330). In regards to the downregulated miRNAs expressed at 1 dpi, the unique pathways included key immune-related KEGG pathways related to inflammatory immune functions during PRRSV infection. This included traditional innate immune response pathways such as the TNF signaling pathway (hsa04668), NF-kappa B signaling pathway (hsa04064), the AMPK signaling pathway (hsa04152) capable of sensing metabolic stressors to host homeostasis, and the multifunction ECM–receptor interaction (hsa04512) pathway (The pathways that were targeted by the upregulated vs. downregulated miRNAs Table 2  pathway .hsa04064) and ECM–receptor interaction (hsa04512) that were activated on the previous dpi, and the PI3K-Akt signaling pathway (hsa04151) involved in metabolism and apoptosis. The theme of the pathways unique to the downregulated miRNAs also appeared to switch at 3 dpi. For the downregulated miRNAs, emphasis switched from mostly immune regulated to a higher quantity of structural-related pathways with roles in viral entry of respiratory tissue types such as focal adhesion (hsa04510), Mucin type O-Glycan biosynthesis (hsa00512), and Glycosaminoglycan biosynthesis—keratan sulfate (hsa00533) (hsa04210) and the Wnt signaling pathway (hsa04310)  . By 8 dpsa04310)   analysis was conducted for the upregulated miRNAs in order to facilitate a better understanding of what biological and molecular functions were being inhibited by the overexpression of the different miRNAs. A Venn diagram (not shown) was used to filter the G.O. terms and determine which were common or unique to each time point in an attempt to elucidate which biological functions of the host were the most attenuated by miRNA overexpression. The terms that were shared across all time points (n = 127) included a majority of host immune processes generally related to viral–host interactions during infections. The shared list contained the G.O. terms negative regulation of type I interferon production (GO:0032480), extracellular matrix disassembly (GO:0022617), and virus receptor activity (GO:0001618).via MHC class I (GO:0002474), ncRNA metabolic process (GO:0034660), negative regulation of transforming growth factor beta receptor signaling pathway (GO:0030512), and tRNA metabolic process. At 3 dpi, the counts of unique terms dropped considerably (n = 6) from 1 dpi and highlighted terms related to sensing of biotic and abiotic stressors and inhibition by small RNA functions with the terms cytoplasmic stress granule (GO:0010494) and negative regulation of translation (GO:0017148). The unique G.O. terms associated with the miRNA upregulation at 8 dpi (n = 41) were the largest grouping of terms of all time points and encompassed multiple host immune and metabolic function-related terms. Some of the key terms from 8 dpi included collagen catabolic process (GO:0030574), O-glycan processing (GO:0016266), and type I interferon signaling pathway (GO:0060337). The G.O. terms that were unique to the downregulated miRNAs at each dpi were also examined and showed groupings of terms that indicated targeting of various immune functions (not shown).At 1 dpi, the unique G.O. terms (n = 20) included antigen processing and presentation of peptide antigen Examination of the results indicated that the miRNAs and tRNAs displayed expression patterns that seem to suggest they behave in a manner that both promotes and fights HP-PRRSV infection. This battle for post-transcriptional control over the regulation of host gene expression during infection appeared to take place on several fronts. These fronts were highlighted by the differential expression of candidate miRNAs, tRNAs, and pathways related to several different host biological groupings. The groupings are best represented by the relationship of the pathways they contain, such as structural-related networks that consist of pathways that affect structural integrity and receptor binding. Additionally, there are the immune function-related pathways that spotlight the tug-of-war between host and pathogen. Lastly, there are the pathways that control metabolic activity, which collectively point out the perturbation the virus causes to host homeostasis. Even more striking is that these biological groupings are unique to either the overexpressed or under-expressed miRNAs and reinforced by the ncRNA:mRNA interactions predicted for some of the tRNA genes .Analysis of the miRNA results yielded a group of candidates from each dpi that appeared to be linked to the immunosuppressive effects of the HP-PRRSV infection. Many of these were upregulated miRNAs listed as residing in the extracellular space of cells and may possibly highlight the usurping of host extracellular miRNAs by HP-PRRSV to facilitate viral entry/replication. The upregulation was matched by downregulation of additional miRNAs, likely still under host control, which could help boost the activity and expression of immune-related pathways. The direction of expression for the miRNAs were closely tied to the dpi, as many were observed to be overexpressed at one time point, only to be under-expressed at another. Despite this, the results yielded candidate miRNA’s with potential anti- and pro-viral actions during HP-PRRSV infections of swine. From the upregulated miRNAs at 1, 3, and, 8 dpi were miRNAs Ssc-miR-145-5p, Ssc-miR-142-3p, and Ssc-miR-125b, respectively. The miRNA Ssc-miR-125b (upregulated at 1 and 8 dpi only) along with Ssc-miR-361-3p  were the only miRNAs to appear statistically significant at each dpi. The miRNA Ssc-miR-145-5p has been shown to be an inhibitor of inflammatory cytokines signaling with the ability to cause alternative macrophage priming that accents anti-inflammatory signaling within the host . This maGBP5) gene, which contains the WUR SNP, a low pathogenic PRRSV resistance variant  and lumican (LUM), have been shown to be heavily differentiated in HP-PRRSV infections  pathway is specifically targeted for activation by the downregulated miRNAs. In line with the viruses’ infection and proliferation at 1 dpi, the host was not only bolstering defense to viral entry with increased ECM–receptor activity but also innate immune responses for cytokine signaling possible through activity of the TNF signaling pathway (hsa04668) and the NF-kappa B signaling pathway (hsa04064). There is also some indication that the host metabolism is starting to become perturbed at 1 dpi due to activation of genes within the AMPK signaling pathway (hsa04152) that is involved in metabolic functions, especially in low energy states  and NF-kappa B signaling pathway (hsa04064) that were previously being activated. The switch from structural to immune pathway inhibition may serve as possible indicators of the dysregulation of monocytic cell immune functions experienced by infected macrophages to protect and prolong viral replication. Also supporting the idea that HP-PRRSV had switched to modulating host immune pathways to support survival was the observance of the PI3K-Akt signaling pathway (hsa04151) inhibition. This pathway is known to be involved in cell survival and can be usurped by viral pathogens to promote survival (hsa04068) and is a means for HP-PRRSV to lower the apoptotic functions for these pathways to promote self-survival against host immune functions. The unique pathways based on the downregulated miRNAs at 3 dpi had now begun to target more structural and metabolic pathways such as the Mucin type O-Glycan biosynthesis (hsa00512), AMPK signaling pathway (hsa04152), and the HIF-1 signaling pathway (hsa04066). It is uncertain whether this potential increase in structural and metabolic activity is anti- or pro-viral in nature; however, it does show evidence that host homeostasis is being dysregulated. If it is anti-viral in nature, the pathway activity may be supporting repair of components such as the ECM after viral entry. However, if it is pro-viral, it might hint at AMPK and HIF-1 signaling pathway involvement in metabolic processes that can detract from normal macrophage activation and may be connected to the 8 dpi upregulation of glycine tRNAs based on HIF1A’s role in glycolysis , Fc gamma R-mediated phagocytosis (hsa04666), and the MAPK signaling pathway (hsa04010). The 8-dpi pathway analysis also showed that the unique activated pathways also clustered around both immune and structural pathway indicating some importance of proteoglycans and pro-inflammatory cytokine signaling. It is also possible that the pathways targeted by the miRNA differential expression at 8 dpi is a reflection of the losing battle between host immunity and HP-PRRSV virulence related to impairment of normal macrophage functions due to PRRSV cell tropism.By 8 dpi, the affected unique pathways had flipped roles once again in regard to the biological clustering of inhibited and activated pathways. The 8-dpi inhibited pathways now incorporated a combination of structural and immunological pathways that included the ECM–receptor interaction pathway  expression during HP-PRRSV infection can affect the differential expression of miRNAs and tRNAs and can exist as an evasion to canonical host immune responses when expressed in patterns exhibited across the experiment’s time points. Highly pathogenic PRRSV appeared to have the ability to induce differential expression of both miRNAs and tRNAs as part of its pathogenic course that perturbed structural, metabolic, and immunogenic pathways. The action of these sncRNAs created post-transcriptional changes to the overall ability of the host to maintain cellular homeostasis in the presence of the pathogen.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121980.The datasets generated and/or analyzed for this study can be found in the GEO repository, GSE121980, The animal use protocol was reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of the National Animal Disease Center-USDA-Agricultural Research Service. Written informed consent to use the animals in the study was obtained from the Wilson Farms, WI.LM contributed to the study conception, data collection, research design, and manuscript writing. DF contributed to the data preparation, data analysis, research design, and manuscript writing.Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. USDA is an equal opportunity provider and employer. This work was mainly supported by the USDA NIFA AFRI 2013-67015-21236 and in part by the USDA NIFA AFRI 2015-67015-23216. DF was supported in part by an appointment to the Agricultural Research Service Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy (DOE) and the US Department of Agriculture. ORISE is managed by Oak Ridge Associated Universities under DOE contract no. DE-AC05-06OR23100.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Zea mays mays) oil is a rich source of polyunsaturated fatty acids (FAs) and energy, making it a valuable resource for human food, animal feed, and bio‐energy. Although this trait has been studied via conventional genome‐wide association study (GWAS), the single nucleotide polymorphism (SNP)‐trait associations generated by GWAS may miss the underlying associations when traits are based on many genes, each with small effects that can be overshadowed by genetic background and environmental variation. Detecting these SNPs statistically is also limited by the levels set for false discovery rate. A complementary pathways analysis that emphasizes the cumulative aspects of SNP‐trait associations, rather than just the significance of single SNPs, was performed to understand the balance of lipid metabolism, conversion, and catabolism in this study. This pathway analysis indicated that acyl‐lipid pathways, including biosynthesis of wax esters, sphingolipids, phospholipids and flavonoids, along with FA and triacylglycerol (TAG) biosynthesis, were important for increasing oil and FA content. The allelic variation found among the genes involved in many degradation pathways, and many biosynthesis pathways leading from FAs and carbon partitioning pathways, was critical for determining final FA content, changing FA ratios and, ultimately, to final oil content. The pathways and pathway networks identified in this study, and especially the acyl‐lipid associated pathways identified beyond what had been found with GWAS alone, provide a real opportunity to precisely and efficiently manipulate high‐oil maize genetic improvement.Maize ( This pathway analysis provided insights into the genetic basis of lipid metabolism, beyond what could be detected via GWAS alone, and indicated specific opportunities to precisely and efficiently manipulate high‐oil maize genetic improvement. Zea mays mays) kernel quality , which encodes an acyl‐acyl carrier protein (ACP) thioesterase. An 11‐bp insertion in the last exon of this gene decreases palmitic acid content and concentration, leading to an optimization of the ratio of saturated to unsaturated FAs, while having no effect on total oil content  insertion in the 3′‐untranslated region was associated with increased EER and kernel oil, and was a selection target during long‐term selection for high EER in a high‐oil population  for oil concentration and FA composition in maize have been identified from multiple studies of high‐oil maize lines  and map‐based cloning are highly accurate, but the process is time‐consuming and challenging. The genomic regions identified often span very large physical distances containing many repetitive sequences. Association mapping can shorten the research period while simultaneously analyzing greater allele numbers and greatly improving mapping resolution, often to the single gene level  pathway identified in this study. Linoleate is a polyunsaturated FA that accounts for more than 50% of the oil content in the maize grains of this panel  had also been identified via GWAS , stearic (C18:0), oleic (C18:1), linoleic C18:2), and linolenic (C18:3) acids, and the ratios of palmitic to stearic (C16:0/C18:0), stearic to oleic (C18:0/C18:1), oleic to linoleic (C18:1/C18:2), and linoleic to linolenic (C18:2/C18:3). Of the 313 MaizeCyc pathways analyzed, 28 that increased the concentration of one or more FA were identified, and had enrichment scores that were significant at , and lin6:0, steare Table . Two of P < 0.001, Li et al., P < 0.05  and GRMZM5G829544 (FATB.b), contributed to increasing the RES. These two genes were identified in previous GWAS results  functions as an acyl carrier and carbonyl‐activating group in numerous central biochemical transformations, including the tricarboxylic acid cycle  or Krebs cycle) and FA metabolism  for C18:1; sphingolipid biosynthesis PWY‐5129) and phospholipid biosynthesis II (PHOSLIPSYN2‐PWY) for C18:2; flavonoid biosynthesis (PWY1F‐FLAVSYN) for C18:2 and C18:3; and the superpathway of phosphatidylcholine biosynthesis (PWY4FS‐5) for C18:3. The wax esters biosynthesis pathway (PWY‐5885) is carried out by several unrelated acyltransferases, including wax ester synthase (WS)/DGAT. Wax esters biosynthesis uses very long‐chain fatty acids (VLCFA) , which are esterified to a short chain alcohol (King 9 and phoP < 0.05) associated pathways, 11 were related to lipid metabolism. These were mainly lipid degradation pathways, and 23 were related to degradation of primary metabolites in general  and C18:1 , which was found to be enriched in the analysis of C16:0 and C18:0  to create diacylglycerol (DAG), which is then available for acyltransferase reaction. DGAT transfers acyl‐CoAs to the sn‐3 position of DAG to produce TAG. Phosphatidylcholine (PC) is created via the CDP‐choline pathway using CDP‐choline:diacylglycerol cholinephosphotransferase (DAG‐CPT); and cardiolipin (CL) is created via the action of enzymes phosphatidylglycerol phosphate (PGP) synthase (PGPS), PGP phosphatase (PGPP), and CL synthase (CLS).By combining pathway analysis results with knowledge about lipid metabolism in the model species Arabidopsis . The resulting β‐ketoacyl‐CoA is reduced by a β–ketoacyl‐CoA reductase (KCR) and the resulting β‐hydroxyacyl‐CoA then undergoes dehydration by a β–hydroxyacyl‐CoA dehydratase (HCD). In the final step, the enoyl‐CoA is reduced to an acyl‐CoA by enoyl‐CoA reductase (ECR). This cycle results in an acyl chain extended by two carbons, and the cycle can be repeated. FA elongation is catalyzed by an ER‐associated, multienzyme complex known as fatty acid elongase (FAE). Therefore, the levels of specific FAs, and total oil, depend on the synthesis of these lipids from precursors, which are frequently other lipids, and the degradation of these lipids . There will be many more pathways associated with the amount of each FA, ratios of FAs, and total oil than simply the biosynthesis pathways that create them, and these pathways will be a key to achieve target levels of any specific FA or lipid.et al.  in the 368 lines.The GWAS analysis results of Li et al. , which w et al., . Overallet al. , correlation (R2 or proportion of the phenotypic variation accounted for), and effect values along with the calculated LD values for D′, and R2, and P between each marker SNP and its closest neighboring SNPs (50 upstream and 50 downstream). SNPs were then assigned to genes using a tagSNP approach outlined by Tang et al.  causing the SNP‐trait association was then assumed to be within 1 Kb of the SNP. The reason for using only 1 kb is because many cis‐acting elements that regulate gene expression reside within the gene or in immediate regions upstream and downstream of the gene coding region. If a gene was located, then the SNP‐trait association effect value was transferred to the gene and used for gene‐set enrichment analysis  using the QVALUE package in R .The resulting SNP‐trait association data generated by TASSEL .i et al. , and thoTable S2. Summary of significantly associated SNPs from the GWAS study of Li et al.  and name are drawn from the MaizeCyc database (https://www.maizegdb.org/metabolic_pathways/), and P and FDR were calculated in this study.Table S4. Number of genes which contributed to the enrichment score in significant pathways at P < 0.01, and which are shown as hatch marks at the top of the running enrichment score graphs. Pathway identifier (ID) and name are drawn from the MaizeCyc database (https://www.maizegdb.org/metabolic_pathways/).Table S5. Pathways associated with decreased oil and FA concentration with enrichment scores significant at P < 0.05. Pathway identifier (ID) and name are drawn from the MaizeCyc database (https://www.maizegdb.org/metabolic_pathways/), and P and FDR were calculated in this study.Table S6. Pathways associated with effects on the ratio of different FA traits with enrichment scores significant at P < 0.01. Pathway identifier (ID) and name are drawn from the MaizeCyc database (https://www.maizegdb.org/metabolic_pathways/), and P and FDR were calculated in this study. (https://www.maizegdb.org/metabolic_pathways/).Click here for additional data file. Click here for additional data file.


Our study showed that neurotrophic signaling pathway and some immune-associated pathways may be involved in the development of AS. Besides, our bioinformatics analysis revealed a total of 15 small molecules which may play a role in perturbing the development of AS. Our study proposes the use of an approach based on targeting sub-pathways to identify potential agents for AS. Candidate agents identified by our approach may provide the groundwork for a combination therapy approach for AS.The need for new therapeutics for Ankylosing Spondylitis (AS) is highlighted by the general lack of efficacy for most agents currently available for this disease. Many recent studies have detailed molecular pathways in AS, and several molecule-targeting agents are undergoing evaluation. We aimed to explore the mechanism of AS and identify biologically active small molecules capable of targeting the sub-pathways which were disregulated in the development of AS. By using the GSE25101 microarray data accessible from the Gene Expression Omnibus database, we first identified the differentially expressed genes (DEGs) between AS samples and healthy controls, followed by the sub-pathway enrichment analysis of the DEGs. In addition, we propose the use of an approach based on targeting sub-pathways to identify potential agents for AS. A total of 3,280 genes were identified as being significantly different between patients and controls with  Ankylosing Spondylitis (AS) is a chronic inflammatory arthritis involving primarily the sacroiliac joints and the axial skeleton that can lead to bone resorption and bone formation, ultimately resulting in ankylosis ,2. AS caThe therapeutic management of AS includes regular physical exercise together with the use of NSAIDs. Second-line treatments, such as sulfasalazine, are required in cases of NSAID-refractory AS . BesidesIn this present study, we sought to explore the mechanism of AS, and then identify biologically active small molecules capable of targeting the sub-pathways which were disregulated in the development of AS. Candidate agents identified by our approach may provide the groundwork for a combination therapy approach for AS. However, further evaluations for their potential use in the treatment of AS are still needed.p < 0.1.To identify the genes specifically differentially expressed between AS patients and controls, we obtained the publically available microarray dataset GSE25101 from the GEO database and carried out the classical t-test corrected for multiple comparisons. A total of 3,280 genes were considered significantly differentially expressed with k = 3). With the strict cutoff of p less than 0.01, our system identified 35 enriched sub-pathways corresponding to 18 entire metabolic pathways  involving 1,309 bioactive small molecules in the Connectivity Map, we performed DEGs analysis for these 1,309 small molecules. A total of 1,221 out of the 1,309 small molecules have DEGs. For the 1,221 small molecules, we then used SubpathwayMiner to annotate these DEGs to entire metabolic pathways and sub-pathways. With the strict cutoff of FDR less than 0.005, our system identified 233 enriched sub-pathways corresponding to 107 small molecules.By integrating the previous 35 sub-pathways and these 233 sub-pathways, we could select the significant overlapping sub-pathways, that is, these sub-pathways are related to both AS and small molecules. Thus, we could hypothesize that these small molecules may play a role in perturbing the development of AS. A total of 15 small molecules were identified .We then built a network between the small molecules and sub-pathways which were perturbed by the small molecules by integrating the relationships above . In thisGene expression profiling in disease reveals the underlying gene activity changes contributing to the disease and enables identification of targets for therapeutic intervention. In this study, we used the gene expression profile downloaded from GEO to explore the mechanism of AS development. Besides, we showed the utility of using bioinformatics analysis for the identification of new therapeutics for AS. A total of 3,280 genes were identified as being differentially expressed between AS patients and healthy controls. Sub-pathway mining result showed 35 sub-pathways corresponding to 18 entire pathways were disregulated in AS, and a total of 15 small molecules were identified which may play a role in perturbing the development of AS.Current approaches typically study entire pathways, whether by singular enrichment analysis or by gene set enrichment analysis . HoweverNeurotrophins are a family of trophic factors involved in differentiation and survival of neural cells . The neuThere are several important implications of this work. The identification of a group of small molecules with potential therapeutic efficacy for Ankylosing Spondylitis is an important observation. A total of 15 small molecules were identified have common sub-pathways with AS, including anisomycin (number of overlaps = 8), vorinostat (number of overlaps = 5), trichostatin A (number of overlaps = 5).Streptomyces griseolus which inhibits protein synthesis [Anisomycin is an antibiotic produced by ynthesis . Previouynthesis . Vorinosynthesis . Trichosynthesis . In agreIn conclusion, our study showed that neurotrophic signaling pathway and some immune-associated pathways may involve in the development of AS. Besides, our bioinformatics analysis revealed a total of 15 small molecules which may play a role in perturbing the development of AS. Our study proposed the use of an approach based on targeting sub-pathways to identify potential agents for AS. Further experimental verification is still needed to make sure that the use of these agents is effective without inhibiting important host defense mechanisms.The transcription profile of GSE25101 was obtained from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database  which waThe Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of online databases dealing with genomes, enzymatic pathways, and biological chemicals . The PATThe connectivity map (CMap) can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs . It is tp. The p-value less than 0.1 was chosen as the cutoff criterion for DEGs.The classical t-test was used to identify the differentially expressed genes (DEGs). We used the Affy package in R  to preprSubpathwayMiner is an R-based software  package The metabolic pathways downloaded from KEGG were converted to undirected graph with enzymes as nodes. Two nodes in an undirected graph are connected by an edge if there is a common compound in the enzymes corresponding reactions. To find the sub-pathways in which all enzymes have highly similar functions, we adopt the k-clique concept in social network analysis to define sub-pathways based on distance similarity among enzymes . In sociOur study proposes the use of an approach based on targeting sub-pathways to identify potential agents for AS. Candidate agents identified by our approach may provide the groundwork for a combination therapy approach for AS.


Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research.https://github.com/DataX-JieHao/PASNet.PASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at  Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine –3. AlongThe capabilities of high-level biological representation and interpretation of the prognosis are often more desired in biomedical research rather than merely improving predictive performance. Pathway-based analysis is an approach that a number of studies have been investigating to improve both predictive performance and biological interpretability –6. In paMoreover, pathway-based approaches have shown more reproducible analysis results than gene expression data analysis alone , 7–10. HHowever, reliable and accurate prognosis still remains poor in many diseases due to the following challenges: high-dimension, low-sample size data and complex nonlinear effects between biological components.Genomic data are highly dimensional relative to their sample sizes. High-dimension, low-sample size (HDLSS) data often make prediction models sensitive to noise and false positive associations, which consequently make predicting accurate prognoses difficult. LASSO-based approaches have been mainly considered to estimate the effects of a gene set that are associated with various types of clinical outcomes on HDLSS data. The LASSO-based approaches embed sparse coding schemes into linear or logistic regression models for selecting few but greatly informative features among the high-dimensional data. For instance, a logistic regression with sparse regularization was applied for the prognostic model of mortality after acute myocardial infarction . Random Pathway-based analysis also helps to reduce data dimensionality. The number of biological pathways is relatively smaller than the number of genes, and a set of genes in the same pathway can be represented by the pathway’s effect. Thus, pathways can be used as summary variables for the input of the predictive model instead of including all genes, which consequently reduces the model complexity.Most association studies between a gene set and various clinical outcomes have considered linear or logistic regression models for identifying prognostic factors as well as understanding a biological mechanism of the progression of disease. However, nonlinear effects of genes or pathways may fail to be identified by linear-based approaches. As a solution, kernel-based models have been proposed to capture nonlinear effects of complex pathways , 16. MulRecently, several attempts to capture hierarchical effects of genes and pathways have been made. Inferences of multilayered hierarchical gene regulatory networks have been considered to understand how pathways regulate each other hierarchically. A bottom-up graphic Gaussian model  and a reblack box nature of deep learning. Our new sparse deep learning architecture represents multiple molecular biological layers, such as a gene layer and a pathway layer, along with their hierarchical relationships, which use sparse regularization.In this paper, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) to achieve the goals: (1) to predict prognosis in patients accurately by incorporating biological pathways, (2) to provide a solution for hierarchical interpretation of nonlinear relationships between biological pathways of disease systematically, and (3) to handle computational problems on HDLSS data with unbalanced classes. An innovative aspect of our model is biological interpretability; we achieved this with sparse coding and by constructing hidden layers with biological pathways, which oppose the Pathway-Associated Sparse Deep Neural Network (PASNet) identifies a subset of genes and pathways involved in a disease as prognostic biomarkers, as well as their interactions. PASNet models a multilayered, hierarchical biological system of genes and pathways on a disease, while leveraging the strengths of deep learning for competitive predictive performance. The sparsity of PASNet allows one to interpret the model, which is what conventional fully-connected networks lack. The architecture of PASNet and the strategies for training a sparse neural network model with HDLSS and imbalanced data are described in “We conducted experiments to evaluate PASNet’s predictive performance for long-term survival prediction in Glioblastoma multiforme (GBM). The capability of the prediction was assessed by comparing our model with the classifiers that have been used for long-term survival prediction. Furthermore, we will describe how PASNet can represent the biological system of GBM in the following section.GBM is a primary brain cancer that shows poor prognosis performance due to the above challenges. Comprising more than half of all brain tumors, GBM is the most prevailing and aggressive malignant type of primary astrocytomas . Patienthttp://cancergenome.nih.gov). The dataset includes the gene expression data of 522 samples and 12,042 genes and provides survival time and status. We considered patients who survived past 24 months  as long-term survivals (LTS) and patients that deceased in less than 24 months as short-term survivals (non-LTS). Living patients with a survival time of less than 24 months were excluded in the experiments and considered censored data. Finally, we obtained 99 LTS and 376 non-LTS samples, where around 20% of the samples were LTS patients.We used the gene expression data of GBM patients, which is available at The Cancer Genome Atlas  . In MSigη) was set to 1e−4, and L2 regularization (λ) was set to 3e−4. Adaptive Moment Estimation (Adam) was performed as the stochastic optimizer [https://github.com/DataX-JieHao/PASNet.We followed a typical design of conventional deep neural networks for PASNet. A sigmoid function and cross-entropy were considered for the activation and the cost function, respectively. A softmax function was used in the output layer so that the probabilities of output nodes add up to one. For the optimal tuning of PASNet’s training, we empirically determined the hyper-parameters by random search before cross-validation experiments. The learning rate , Random LASSO , LASSO Lγ=2−16 and C=23.9 by two-step grid search [L1 regularization parameter was determined by 10-fold cross-validation. The LASSO parameter for LLR was also selected by 10-fold cross-validation. The fully-connected Dropout NN was designed with the same numbers of intermediate layers and neurons as the proposed PASNet as well as the dropout probabilities. The learning rate was 0.01 and the L2 regularization was 0.005. Note that PASNet has less number of weights to be trained in each epoch because of sparse coding, compared to Dropout NN. Hence, the optimal hyper-parameters of L2 regularization and learning rate should be different between PASNet and Dropout NN. We empirically searched the optimal hyper-parameters for PASNet and Dropout NN separately through multiple experiments. Dropout NN was implemented by PyTorch (https://pytorch.org/).Specifically, we used a SVM with a radial basis function (RBF) kernel (d search ). RandomThe experiments were carried out by stratified 5-fold cross-validation for maintaining the same proportions of the imbalanced samples in the classes. The cross-validation experiments were repeated ten times for performance reproducibility. Data preprocessing, such as data normalization, was separately applied on each fold. The testing data on each fold was scaled with the mean and standard deviation of the training data of the same fold.TPR=TP/(TP+FN)) and False Positive Rate (FPR=FP/(FP+TN)), where LTS was considered positive. An AUC was computed by the area under the ROC curve. An F1-score, an average of Positive Predicted Value (PPV=TP/(TP+FP)) and TPR, is calculated by 2(PPV×TPR)/(PPV+TPR). The F1-score was computed for the LTS class.The predictive performances of the five models were evaluated with two metrics: Area Under the Curve (AUC) and F1-scores. The Receiver Operating Characteristic (ROC) curve . Hence, the outperformance of PASNet was statistically significant compared to the benchmark classifiers.To statistically assess the performance of PASNet (AUC) as compared to others, we conducted the Wilcoxon signed-rank test: a non-parametric paired, two sided test for the null hypothesis that states the median difference in paired samples is zero. Specifically, the null hypothesis is that the benchmark classifier has equal or better performance than our proposed algorithm. Table SVM and Dropout NN showed a higher AUC than LASSO logistic regression and Random LASSO, probably because of their capability of capturing nonlinear effects of genes. Compared to Dropout NN, PASNet is a relatively thin network, where the connections between layers are very sparse. However, PASNet interestingly produced higher performance than Dropout NN. It shows that PASNet builds a robust network model, which is simplified to represent the biological processes for prognosis prediction by incorporating biological prior knowledge.Although PASNet yielded competitive predictive performance in the experiments, a more promising contribution of PASNet is in the model’s interpretability. In this section, we demonstrate a plausible biological mechanism inferred by PASNet for long-term survival prediction in GBM. The graphical representations of the PASNet model are illustrated in Figs. log2) of the LTS samples, while the remaining ones are non-LTS samples. The weight values of the connections from hidden nodes to the output nodes are depicted in Fig. First, Fig. The hidden node values of the samples are shown in Fig. The weights between the pathway nodes and the hidden nodes are exhibited in Fig. The genes of the pathways are illustrated by the weight values in Fig. Finally, we demonstrate a hierarchical representation of genes and pathways in PASNet. In Fig. In this paper, we proposed pathway-associated sparse deep neural network for prognosis predictions . PASNet builds a network model by leveraging prior biological knowledge of pathway databases and by taking hierarchical nonlinear relationships of biological processes into account. To improve the model interpretability, PASNet introduces sparse coding. Moreover, we developed a training strategy to avoid the overfitting problem with HDLSS data and the imbalanced problem.To investigate the performance of PASNet, we used gene expression data of GBM patients in TCGA. PASNet was assessed by comparing the predictive performance with support vector machine, random LASSO, LASSO logistic Regression, and neural network with dropout that have been widely used for prognosis prediction. PASNet outperformed them with respect to both AUC and F1-score in the multiple stratified 5-fold cross-validation experiments. Furthermore, we discussed how PASNet can describe the biological system of GBM.PASNet is the first deep neural network-based model that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning.PASNet incorporates biological pathways and the concept of sparse modeling based on Deep Neural Network (DNN). The neural network architecture of PASNet consists of a gene layer (an input layer), a pathway layer that represents the biological pathways linked with input genes, a hidden layer that represents hierarchical relationships among biological pathways, and an output layer that corresponds with clinical outcomes, e.g. a binary class that has long-term survival and short-term survival, stages of cancer see Fig. .Fig. 6AIn PASNet, sparse coding is considered on the connections between layers for model interpretability. Sparse coding provides a solution to capture significant components of a biological mechanism in the model, since biological processes may involve only a few biological components. On the other hand, conventional fully-connected networks lack to represent biological mechanisms.m gene expressions is formed as a column vector, which is denoted by x={x1,x2,...,xm}. Each input node represents one gene feature.The gene layer (as an input layer) corresponds to gene expression data. A patient sample of The pathway layer represents biological pathways, where each node indicates an individual pathway. The connections between the gene layer and the pathway layer are established by well-known pathway databases . Pathway databases contain associations between pathways and genes; each of which provides a set of gene components. Therefore, the pathway layer makes it possible to interpret the model as a pathway-based analysis.A) from biological pathway databases. The biadjacency matrix can be defined as n is number of pathways and m is number of genes. Then, an element of A, i.e., aij, is set to one if gene j belongs to pathway i; otherwise, zero. Sparse coding is applied based on the matrix A to represent the relationships between genes and pathways in the model.To begin with initializing the connections between the gene layer and the pathway layer, we consider a binary biadjacency matrix  to predict clinical outcomes. In PASNet, more than two clinical outcomes can be easily represented with multiple nodes in the output layer.generative model-based approach would be often useful to predict prognosis accurately with complex data of HDLSS. When data is highly complex and only small sample sizes are available, model optimization may be easily biased to the training data rather than providing a general solution. On the other hand, the integration of the biological structures and prior knowledge to the model would produce a robust solution.Consequently, PASNet can dissect distinguishable biological processes of hierarchical nonlinear relationships and associations of genes and pathways to predict clinical outcomes. Furthermore, this The main challenge in training PASNet is to reduce both risk of overfitting and computational complexity of training on HDLSS data. The related works that have handled the HDLSS data problem are discussed in “A. The weights of active connections and biases are randomly initialized from standard normal distribution, while the weights of inactive connections are set to zero. The sparsity of the connections between the gene layer and the pathway layer is invariant over the entire training. The remaining layers are fully interconnected as the initial.First, we initialize the connections between the gene layer and the pathway layer with prior biological knowledge of pathways see Fig. a. ActiveIn the training phase, we repeat training sub-networks and applying sparse coding on the sub-networks until convergence Fig. b–c. A suM on each layer as: Once a small sub-network is completed to train with the HDLSS data, the sub-network is imposed to be sparse for the model interpretation. The sparsity of the sub-network is determined by the mask matrix a(·) is an activation function. hℓ)( and bℓ) that indicates the proportion of weights to be dropped in a single layer. S is a value between 0 to 100, where zero creates a fully-connected layer while 100 causes no connection. The optimal S∗ is approximated on each layer individually in the sub-network, while most related methods consider a single hyper-parameter for the sparsity of all layers [The mask matrix l layers , 37. TheS∗ that minimizes the cost score. For efficient computation, the cost scores are computed with a small number of finite sparsity levels. Then, the optimal sparsity level is estimated by applying a cubic-spline interpolation to the cost scores with the assumption that the cost function, with respect to the sparsity level, is continuous.We obtain the optimal sparsity level M is set to one if the absolute value of the corresponding weight is greater than threshold Q; otherwise, the element is zero, where Q is an S-th percentile of absolute values of W. Note that the mask between the gene layer and the pathway layer, i.e. M(0), is determined by the biadjacency matrix A of biological pathways. Thus, the mask matrices are formulated as In particular, an element of Qℓ)( is the S-th percentile of |Wℓ) method , which pK be the number of clinical outcomes. The normalized cost is computed separately for each class by: Let k, and nk is the number of samples in the class k. yi is a vectorized ground truth class label of the i-th sample, and c(·) denotes a cost function , and W∥2 denotes a L2-norm of W, and λ>0 is a regularization hyperparameter.where ℓ-th layer are updated by: In the backpropagation phrase, the gradient is also computed separately for each class. Hence, the weights and biases on the η is a learning rate. The algorithm of PASNet is briefly described in Algorithm 1.where In recent years, deep learning has been spotlighted as the most active research field in various machine learning communities, such as image analysis, speech recognition, and natural language processing as its promising potential is being actively discussed in bioinformatics and biomedicine . Most deAlthough only a small subset of deep learning research has been reported in bioinformatics due to the difficulty of structure definition and interpretation, the future of deep learning in biology and medicine is promising . First, However, training deep neural networks with HDLSS data poses a computational problem. A large number of parameters are involved in deep neural networks, and it often makes the training infeasible or causes a model overfit on HDLSS data. Particularly, backpropagation gradients in neural networks are of high variance on HDLSS data, which consequently causes the model overfit . In ordeFor HDLSS data, feature selection is one of the conventional approaches. Deep Feature Selection (DFS) was developed to select a discriminative feature subset in a deep learning model . Althoug


Chlamydia trachomatis is the most commonly diagnosed bacterial sexually transmitted infection and can lead to tubal factor infertility, a disease characterised by fibrosis of the fallopian tubes. Genetic polymorphisms in molecular pathways involving G protein-coupled receptor signalling, the Akt/PI3K cascade, the mitotic cell cycle, and immune response have been identified in association with the development of trachomatous scarring, an ocular form of chlamydia-related fibrotic pathology. In this case-control study, we performed genome-wide association and pathways-based analysis in a sample of 71 Dutch women who attended an STI clinic who were seropositive for Chlamydia trachomatis antibodies and 169 high-risk Dutch women who sought similar health services but who were seronegative. We identified two regions of within-gene SNP association with Chlamydia trachomatis serological response and found that GPCR signalling and cell cycle pathways were also associated with the trait. These pathway-level associations appear to be common to immunological sequelae of chlamydial infections in both ocular and urogenital tropisms. These pathways may be central mediators of human refractoriness to chlamydial diseases. Chlamydia trachomatis (Ct) is diagnosed as the cause of around 106 million sexually transmitted infections (STIs) per annum , T = 1 : 100 [n = 19], T = 1 : 200 [n = 24], T = 1 : 400 [n = 9], T = 1 : 800 [n = 7], and T = 1 : 1600 [n = 3]. In the negative population, all specimens had a titre of T = 1 : 1 [n = 169]. Tests for infection were performed on endocervical swabs using the COBAS AMPLICOR CT/NG Test .Peripheral venous blood was collected for the analysis of IgG antibodies against  and MIF . A Ct Ig and MIF , 31. Then = 240) in the analysis. All 71 seropositive cases were additionally infection-positive, whilst all 169 seronegative cases were infection-negative. Cases were significantly younger than controls in this sample (p = 0.007). The median age of cases was 23 . The median age of controls was 24 . The genome-wide inflation statistic was =1.04 before correcting for age and =1.02 after correction , regions B (In the genome-wide association study (GWAS), 46 single-nucleotide polymorphisms (SNPs) with egions B  and C (Fegions B  containeNPSR1-AS1) and immediately upstream of the G protein-coupled receptor coding neuropeptide S receptor 1 (NPSR1) (PRKG1) (The 33 SNPs in region B lay within the nonprotein-coding NPSR Antisense RNA1 ( (NPSR1) . The 12  (PRKG1) , a mediapperm < 0.05) in chlamydial seropositivity. These pathways (Supplementary p < 0.01), 9998 (p < 0.001), and 1168 (p < 0.0001). The numbers of SNPs mapping to genes at each threshold were 64,397 (p < 0.01), 6.274 (p < 0.001), and 451 (p < 0.0001). The number of significant pathways at each cut-off were 17 (p < 0.01), 20 (p < 0.001), and 33 (p < 0.0001). In total, these pathways contained 2556 unique genes. Clustering the Reactome pathways by genic intersection demonstrated a large gene overlap . Using the Gene Ontology (GO) database, over 10% of genes were assigned to the GO biological processes (GO-BP): “Cell surface receptor linked signal transduction,” “G-protein coupled receptor signalling pathway,” and “Sensory perception of smell.” This final process shows a large genic overlap with the “Signalling by GPCR” Reactome pathway . The “PlpEMMAX < 5 × 10−6) in Dutch women attending sexual health clinics for which there was at least one supporting SNP in LD with R2 > 0.6. One of these genomic regions of association was within the protein kinase, CGMP-dependent, type I gene (PRKG1) whilst another was within NPSR1 antisense RNA 1 (NPSR1-AS1), which presumably has a role in posttranscriptional control of the nearby gene neuropeptide S receptor 1 (NPSR1).In this study, we have identified two candidate regions of genetic association with anti-Ct serological status  is partoma GWAS . They wePRKG1 and NPSR1-AS1 region are interesting enough to warrant further study, we recognize that the false-negative rate is very high. To leverage additional power from the small sample, we focussed on pathway-wide analysis that was similar to the one used previously [p < 0.01) lead to a more sensitive analysis that identifies more pathways but lacks the more granular specificity of more conservative thresholds . Collating the results of three different runs of the ALIGATOR analysis allowed us to average across the various sources of error  whilst identifying pathways. When we assessed the data with functional analysis using DAVID [This study has some significant limitations. The sample used in this study was very small, and the SNP-based analyses presented in this paper are therefore critically underpowered. Whilst we are confident that the positive signals from the eviously . We deteng DAVID , the resChlamydia muridarum infection in the BXD recombinant inbred mouse [We identified potential roles for pathways relating to PI3K/Akt signalling, FGFRs, and control of the cell cycle. G-PCRs and their signalling pathways were previously highlighted in trachoma cases , 35 and ed mouse . Neural ed mouse  previousC. pneumoniae infections and subsequent fibrillogenic Alzheimer's like plaque formation in the olfactory centres of mouse brains [Chlamydiaceae can utilise a variety of different, but related, cell surface receptors to gain access to the host cell at different body sites. These diverse receptors may well share a common downstream signalling process via G proteins and particularly via the PI3K pathway. Collectively, these data appear to confirm that G protein-coupled receptor signalling pathways and the cell cycle are associated with chlamydial infections in a way that transcends tissue tropisms and which might be utilised by many or all chlamydial species in their animal hosts. In the absence of an appropriate replication panel for the more granular analyses of the SNP-based association tests, we infer more meaning from the functional overlap of the pathways identified in this study and the previous GWAS in trachoma. We interpret our data in the broadest sense, which is that the key pathways identified between the studies have substantial functional and gene content overlap. A cross-study comparison of the ‘p values' for any specific pathway is not however an appropriate way to interpret this overlap, largely because the high false negative risk leads to a certain instability in the association test results. With this small study, we are unfortunately not able to quantify the convergence of the results between the studies.We also detected a significant role for olfactory receptor signalling cascades in this study .Another limitation of the study that we highlight is that trait of sero-positivity was measured using a single antigen (MOMP) test. It was not possible to prove unequivocally that all the women who were included in the study as controls had been exposed to We have selected statistical analysis methods for the pathway analysis that allow for a high multiplicity of testing, whilst not imposing the same burden of signal loss that are associated with more rudimentary corrections such as Bonferroni's method. They also collapse highly dimensional data, leading to a better powered ‘burden' test. In the light of the robust permutation-based analysis, we used the remarkable similarities between our findings and those of the previous trachoma GWAS; it is extremely unlikely that these associations have been identified in both the trachoma and STI tropisms either by chance or because of systematic errors in our analysis approach.By focussing on a trait that is generally accepted to indicate lifetime responses to infectious challenge, we believe that we have identified pathway-wide genotypes that appear to associate with protection from infection in some women. This protection could take the form of a partial or complete refractoriness to primary cellular infection, or a rapid, intense, and highly effective innate immune response.Chlamydiaceae, even though the specific receptors that mediate introgression into the cell might be highly variable.Between the tissue tropisms of the eye and the genital tract, the pathway-wide genetic risk and protection patterns overlap to a substantial degree. This supports a model under which common pathways of intracellular signalling might be utilised by all


The pathogenesis Graves' Orbitopathy (GO) is not yet fully understood. Here, we conducted a pathway analysis based on genome-wide DNA methylation data of Chinese GO patients to explore GO-related pathways and potential feature genes. Six GO patients and 6 age-matched control individuals were recruited, and a genome-scale screen of DNA methylation was measured using their peripheral blood sample. After extracting the differentially methylated regions (DMRs), we classified DMRs into three clusters with respect to median absolute deviation (MAD) for GO and control group, respectively. Then the extract tests were performed to identify significant pathways by comparing the counts of genes in each cluster between GO and control group in a pathway. For each significant pathway, we calculated the Methylation-based Inference of Regulatory Activity (MIRA) score to infer the regulatory activity of genes involved in the pathway. Furthermore, we took the significant pathways as the subsets and applied Random forests (RF) method to extract GO-related feature genes. We identified four potential significant pathways associated with the occurrence and development of GO disease. There were Toxoplasmosis, Axon guidance, Focal adhesion, and Proteoglycans in cancer (p<0.001 or p=0.007). The identified genes involved in the significant pathways, such as LDLR (p=0.019), CDK5 (p=0.036), and PIK3CB (p=0.020), were found to be correlated with GO phenotype. Our study suggested pathway analyses can help understand the potential relationships between the DNA methylation level of some certain genes and their regulation in Chinese GO patients. Graves' Orbitopathy (GO), an autoimmune disease that is associated with a wide spectrum of ocular changes, is a difficult challenge in endocrinology and ophthalmology. GO often occurs in patients with abnormal thyroid function, such as hyperthyroidism. The clinical manifestations include extraocular muscle enlargement and orbital fat expansion . BecauseDNA methylation (DNAm) affects gene expression, cellular differentiation, and molecular response to environmental factors. DNAm patterns changes may explain the increased risk of some diseases. In our previous study, we found that DNAm differences were associated with GO patients from a genome-wide DNA methylation analysis in peripheral blood. Several genomic loci were identified with significant differences in methylation patterns that were associated with GO incidence . ClusterThe disease heterogeneity and complexity indicate that GO is not caused by any single gene but caused by the complex regulation among multiple genes . CurrentIn our previous study, the general enrichment analysis based on hypergeometric distribution test did not find any significant pathway, which might be due to the following two reasons: (1) the multiple tests corrections eliminate the positive results; (2) the analysis is based on gene lists defined by a cut-off and does not take into account the expression distribution of genes in the pathway. Indeed, methods for investigating how changes in gene expression variability in the context of pathways can help find pathways and the gene function sets. Here, we reanalyzed our previous data and adopted a novel pathway analysis to extract significant pathways. This method can identify pathways associated with different patterns of expression variability which may highlight those pathways that contribute to group-specific differences . In the DNA was obtained from 6 Chinese patients with GO as well as 6 age-matched controls who had normal thyroid function and no clinical manifestations of GO. The diagnosis of GO was based on the EUGOGO consensus . The sevIn the present study, RRBS (Reduced Representation Bisulfite Sequencing) assay was performed . DNA washttp://www.r-project.org) to implement this analysis.Firstly, we used methylKit  and eDMRThe concept of the Methylation-based Inference of Regulatory Activity (MIRA) score  relies ohttp://www.r-project.org) to implement the analysis. In addition, for each significant pathway, we applied the independent two sample t test to compare MIRA scores between GO and control group.Here, for each of significant pathways, we calculated the MIRA score of each sample to infer the regulatory activity of genes involved in this pathway. We used MIRA package of R software (http://www.r-project.org). The flowchart of our work was shown in Random forests (RF) method is an ensemble classifier that consists of many decision trees and each tree depends on the values of a random vector sampled independently . As a brAccording to the criterion of p < 0.05, 1583 differentially DNA methylation regions were identified. After removing those regions obtained from missing samples, 841 differentially methylated regions were extracted [In order to extract significant pathways, we classified DMRs into three clusters with respect to MAD for GO and control group, respectively. Then the extract tests were performed to extract significant pathways based on the comparison of the counts of genes in each cluster in a pathway between GO and control group. According to p<0.05, four significant pathways were extracted . There wTo explore the level of regulatory activity of genes involved in significant pathways, we calculated the MIRA score of each sample. For each significant pathway, we compared MIRA scores between GO patients and normal controls and found that the MIRA score of genes involved in focal adhesion pathway displayed the significant difference between GO and control (p=0.015), which was shown in Specially, in focal adhesion pathway, we observed 63.6% genes showed lower methylation level in GO patients than in normal controls, which is in accordance with the assumption that the DNA methylation will generally be lower in active regions .For each significant pathway, we took the genes involved in pathway as the subset and applied RF method to classify samples and to extract GO-related feature genes. We adopt leave-one-out (LOO) to implement this analysis and to evaluate the classification performance. The classification accuracy rates were 91.7% for toxoplasmosis pathway, 75.0% for Axon guidance pathway, 91.7% for focal adhesion pathway, and 66.7% for Proteoglycans in cancer pathway, respectively. For each significant pathway, we extracted those genes whose MDG ranked the top 5 in all of 12 models as GO-related feature genes. In toxoplasmosis pathway, LAMA5, HSPA2, PIK3CB, and LDLR were extracted. In Axon guidance pathway, NFACT2, PAK1, CDK5, and PLXNB2 were extracted. In focal adhesion pathway, LAMA, ACTB, PAK1, and PIK3CB were extracted. In Proteoglycans in cancer pathway, FZD9, GPC1, PAK1, FZD1, and ACTB were extracted. In addition, PIK3CB (p=0.020) and FZD9 (p=0.031) were found to be associated with CAS; LDLR (p=0.019) and CDK5 (p=0.036) were found to be associated with TRAb .GO is an orbit autoimmune disease that often occurs in patients with abnormal thyroid function, usually hyperthyroidism. Due to the fact that the pathogenesis mechanism of this disease is not fully understood, the identification of the genes associated with this disease has become an urgent task for clinical treatment. In this study, we performed a genome-scale screen of DNA methylation in peripheral blood samples of Chinese patients and applied pathway analyses to extract potential GO-related feature genes. In previous study, the general enrichment analysis did not find any significant pathway . In pres+ T cells, which is observed in autoimmune disease of rheumatoid arthritis [In the practice, some evidences supporting these pathways might be related to GO directly or indirectly. For example, the focal adhesion pathway was found to be significant. The focal adhesion function is both mechanical and responsive. Focal adhesion complex (FAC) is one of the candidate biomarkers of cellular signal transduction pathways. FAC can be involved in regulating inflammatory gene expression via signal transduction pathways such as interleukin 1 (IL-1) signaling , 17 or rrthritis . In addirthritis . It is arthritis .κB activation and suppresses proinflammatory gene expression [There are some identified genes correlated with GO phenotype involved in the significant pathway. Graves' disease (GD) and GO are typical autoimmune diseases. Meanwhile, oxidative stress (OS) is found to be associated with GD and GO . Once thpression . We alsopression .Although our findings might provide new insights into understand GO disease, it should point out the limitations of the small sample size in the present study. To minimize the impact of sample size on the analysis results, we used strict criterion to select GO patients and their matched controls. By this strict definition, we hope to ensure this explore analysis can have a certain quality. In addition, we chose the statistical methods which are suitable for analyzing high dimensional data with a small sample size. Our future study will enlarge sample size to validate these findings and integrate different data type, including DNA methylation data, gene microarray data, imaging data, and the network information into the prognostic biomarker discovery of GO.We performed a Genome-wide DNAm screen in peripheral blood and applied pathway analyses to extract potential GO-related feature genes in Chinese patients with GO. As a result, we identified significant pathways which might be associated with GO and genes involved in the significant pathways, such as LDLR, CDK5, and PIK3CB correlated with GO phenotype. Our study suggested pathway analyses can help understand the potential relationships between the DNA methylation level of some certain genes and their regulation in Chinese GO patients.


Small cell lung cancer (SCLC) is a highly malignant cancer, and over 70% of patients with SCLC present with the metastatic disease. We aimed to explore some novel differentially expressed genes (DEGs) or microRNAs (miRNAs) associated with the lymph node metastasis of SCLC.The DEGs between the metastasis and cancer groups were identified, and GO functional and KEGG pathway enrichment analyses for these DEGs were implemented. Subsequently, the protein–protein interaction network and subnetwork of module were constructed. Then the regulatory networks based on miRNAs, transcription factors (TFs) and target DEGs were constructed. Ultimately, the survival analysis for DEGs was performed to obtain the DEGs related to the survival of SCLC.Here, 186 upregulated  and 144 downregulated DEGs  were identified between the SCLC patients with lymph node metastasis and without lymph node metastasis. GRM8 was attracted to the G-protein coupled receptor signaling pathway. Besides, miR-126 was identified in the miRNAs-TFs-target regulatory network. GRM8 and DACH1 were all regulated by miR-126. In particular, GSR and HCP5 were correlated with survival of SCLC patients.MiR-126, DACH1, GRM8, MET, GSR, and HCP5 were implicated in the lymph node metastasis process of SCLC. Lung cancer (LC) is a malignant lung tumor characterized by unbounded cell growth in the lung tissues . It is eLymph nodes, the central trafficking hubs for recirculating immune cells, are widely present throughout the body . ConceivThe bioinformatics analysis methods were carried out for screening DEGs correlated with the lymph node metastasis process of SCLC. Firstly, the DEGs between the metastasis and cancer groups were screened. Afterwards, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the DEGs were implemented to obtain the potential functions of DEGs. Afterwards, the protein–protein interaction (PPI) network and subnetwork of module were established. Then the regulatory networks based on microRNA (miRNAs), TFs and target DEGs were constructed. Ultimately, the survival analysis for DEGs was performed to obtain the DEGs related to the survival of SCLC.http://www.ncbi.nlm.nih.gov/geo/) [The gene expression profiling GSE40275 was obtained from the Gene Expression Omnibus (GEO) database (ov/geo/) , which ihttp://bioconductor.org/help/search/index.html?q=oligo/) [http://www.bioconductor.org/packages/2.9/bioc/html/limma.html) [We downloaded the raw CEL data and used the Oligo package (ver.1.38.0) (=oligo/)  in R lanma.html) . The sethttps://david-d.ncifcrf.gov/) [GO  and KEGGrf.gov/)  tool. Thhttp://www.string-db.org/) [http://www.cytoscape.org/) software was applied to construct the PPI network. The significant clustering modules were analyzed using Molecular Complex Detection (MCODE)  [The Search Tool for the Retrieval of Interacting Genes (STRING)   databases/MCODE)  plugin. http://apps.cytoscape.org/apps/iRegulon) [http://www.webgestalt.org/option.php) using the Overrepresentation Enrichment Analysis (ORA) method. The setting of threshold was count number ≥ 2 and p value < 0.05. Ultimately, the miRNAs-TFs-target regulatory network was constructed utilizing the Cytoscape software.The iRegulon   plugin ihttp://www.ncbi.nlm.nih.gov/geo/). The common samples between the SCLC samples and clinical data were screened and removed the samples with survival time less than 1 month. Here, a total of 14 samples were enrolled in the present study.GSE29016 gene expression profiling data including 20 SCLC samples and clinical data were obtained from the GEO database  in R language and median grouping method were used to conduct the survival analysis. Finally, DEGs under p value < 0.05 were selected to generate the survival curve.We obtained 330 DEGs between the metastasis and cancer groups, of these, 186 were upregulated and 144 were downregulated. The volcano map and heatmap of DEGs were presented in Fig. −4), extracellular region , and calcium ion binding . The enriched functions for downregulated DEGs were presented in Table −6), integral component of plasma membrane , and calcium ion binding . Here, the upregulated DEGs were not enriched in any pathway. However, downregulated DEGs were attracted to 5 pathways (Table −3), axon guidance , and alcoholism . Moreover, a total 6 DEGs were attracted to the pathway of transcriptional misregulation in cancer, such as MET.The enriched functions for upregulated DEGs were listed in Table −5), regulation of phosphorylation , and regulation of phosphate metabolic process . Meanwhile, the enriched functions for module a DEGs were listed in Table −3), cell surface receptor linked signal transduction , and regulation of inflammatory response . Here, our results showed that GRM8 was attracted to the G-protein coupled receptor signaling pathway.There were 178 nodes and 237 interaction pairs in the PPI network  to the sulfhydryl form glutathione (GSH). Whereas, GSH played complex roles in cancer, including the protective and pathogenic roles . In a prIn addition, HCP5 was significantly down-regulated in patients with ovarian cancer . SimilarIn summary, our results suggested that miR-126 and its target gene DACH1 may implicate in the lymph node metastasis process of SCLC. Additionally, GRM8, MET, RSD and HCP5 were implicated in the lymph node metastasis process of SCLC.