Studies on tumor-secreted microRNAs point to a functional role of these in cellular communication and reprogramming of the tumor microenvironment. Uptake of tumor-secreted microRNAs by neighboring cells may result in the silencing of mRNA targets and, in turn, modulation of the transcriptome. Studying miRNAs externalized from tumors could improve cancer patient diagnosis and disease monitoring and help to pinpoint which miRNA-gene interactions are central for tumor properties such as invasiveness and metastasis.Using a bioinformatics approach, we analyzed the profiles of secreted tumor and normal interstitial fluid (IF) microRNAs, from women with breast cancer (BC). We carried out differential abundance analysis (DAA), to obtain miRNAs, which were enriched or depleted in IFs, from patients with different clinical traits. Subsequently, miRNA family enrichment analysis was performed to assess whether any families were over-represented in the specific sets. We identified dysregulated genes in tumor tissues from the same cohort of patients and constructed weighted gene co-expression networks, to extract sets of co-expressed genes and co-abundant miRNAs. Lastly, we integrated miRNAs and mRNAs to obtain interaction networks and supported our findings using prediction tools and cancer gene databases.BTLA, CXCL13, IL7R, LAMP3, and LTB, was linked to the presence of tertiary lymphoid structures and high endothelial venules within tumors. Co-abundant tumor interstitial fluid miRNAs within this network, including miR-146a and miR-494, were annotated as negative regulators of immune-stimulatory responses. One co-expression network encompassed differences between BC subtypes. Genes differentially co-expressed between luminal B and triple-negative breast cancer (TNBC) were connected with sphingolipid metabolism and predicted to be co-regulated by miR-23a. Co-expressed genes and TIF miRNAs associated with tumor grade were BTRC, CHST1, miR-10a/b, miR-107, miR-301a, and miR-454.Network analysis showed co-expressed genes and miRNA regulators, associated with tumor lymphocyte infiltration. All of the genes were involved in immune system processes, and many had previously been associated with cancer immunity. A subset of these, Integration of IF miRNAs and mRNAs unveiled networks associated with patient clinicopathological traits, and underlined molecular mechanisms, specific to BC sub-groups. Our results highlight the benefits of an integrative approach to biomarker discovery, placing secreted miRNAs within a biological context. The TO dendrogram was used to define modules using the dynamic tree cut method function in WGCNA .(V2) terms; .General terms; [breast cancer], [miRNA/microRNA], [circulating/blood/serum/plasma].To investigate whether miRNA abundance patterns could partition NIF and TIF samples, as well as TIF samples from BC patients with different clinical and histological traits, we performed clustering analysis and dimensionality reduction for visualization purposes. Table Multidimensional scaling of tumor and normal interstitial miRNA abundance revealed a distinct clustering of the 51 NIF and 52 TIF samples. The first component (M1) highlighted the difference between normal and tumor, while the second component (M2) captured two clusters containing a mix of TIF and NIF samples, Additional file A look into sample IDs revealed that the clustering was most likely driven by patient heterogeneity, a result of a large fraction of samples being paired. As a control for this, we treated “patient” as a covariate and removed this effect, see Additional file As seen in Fig. K-means (k = 2) clustering of tumor mRNA data revealed a pattern comparable to that of the interstitial fluid miRNAs. Tumor mRNA cluster 1 encompassed high-tumor grade, high-TIL samples (89% of high TILs samples and 79% of high-grade samples), while cluster 2 contained samples with lower tumor grade and low-TIL statuses (81% of low TILs samples and 64% of low-grade samples), Fig. Subsequently, we examined whether mRNA expression patterns from paired tumor tissues yielded a similar clustering and partitioning of samples, as the one observed for interstitial fluid miRNAs. eans k =  clusteri+ vs ER−, (VI) PgR+ vs PgR−, and (VII) high grade vs low/medium grade. Figure We performed differential abundance|expression analysis to identify interstitial fluid miRNAs and solid tissue mRNAs, which were dysregulated in BC samples vs normal samples, and between different BC subgroups. Based on the clustering analysis Fig. a, b, we Approximately 1/3 of miRNAs from the TIF vs NIF set were unique to this comparison, i.e., these interstitial fluid miRNAs may have potentials as BC biomarkers. Another ~ 2/3 of miRNAs differentially abundant between TIF and NIF overlapped both with miRNAs from the TIF cluster 1 vs cluster 2 contrast and the BC subtype contrasts. It should be noted all of the miRNAs from the subtype contrasts were identified between TNBC and the other three subtypes . The 181 miRNAs identified in the TIF vs NIF comparison, in the subtype comparison and in the cluster 1 vs cluster 2 comparison, likely reflect both a general tumor-specific miRNA pattern, but also the aggressiveness of particular breast cancer subtypes. This observation came from the fact that samples in cluster 1 originated from more advanced tumors .+ vs ER−, and PgR+ vs PgR−. MiRNAs, identified as differentially abundant in our analysis, were compared to those obtained in the study by Halvorsen et al. [Though there was a large overlap between the aforementioned sets, 41 miRNAs were specific to miRNA cluster 1 vs cluster 2 contrast. There was a large redundancy of miRNAs identified in the contrasts: high TILs vs low TILs, high grade vs lower grade, ERn et al.  in Addit+ and PgR−, perhaps due to an unbalanced number of samples in each group, in combination with the correction for multiple covariates . A total of 297 mRNAs were identified as differentially expressed between tumor cluster 1 vs cluster 2. Out of these, about half were unique to the cluster comparison, while the other half overlapped with DE mRNAs from the high TILs vs low TILs comparison, indicating that these mRNAs may drive the immune-related profile observed in Fig. The comparison of intra-tumor mRNA expression patterns from BC subtypes yielded ~ 450 differentially expressed (DE) genes, out of which 3/4 were unique to the contrast. This was expected, as subtype classification was based on the staining of hormone receptor expression at protein level (immunohistochemistry). The mRNAs not unique to the subtypes contrast nicely overlapped with those identified as DE in the comparisons of estrogen and Her2 receptor amplification (+|−) statuses. No mRNAs were identified as DE between PgR+ vs ER−, and (V) luminal A|B and Her2 vs TNBC.Collectively, the analysis resulted in sets of differentially abundant TIF miRNA, and comparison matched mRNA sets for (I) high TILs vs low TILs, (II) high grade vs medium/low grade, (III) cluster 1 vs cluster 2, (IV) ERmiRNAs with identical seed regions could potentially bind and silence the same gene targets , 78. AlsWe mapped miRNAs from each DA set to their respective families, highlighting any potential differential abundance of related miRNAs, see Fig. Common to the TIF-NIF, subtype, and cluster sets were miRNAs belonging to the let-7/miR-98 microRNA family, known to be aberrantly expressed in a range of cancer types  includinmir106a-363 and mir106b-25 clusters , paralogs to the polycistronic miRNA cluster mir-17-92, also designated oncomir-1 [While families miR-15, miR-17, and miR-130 were most prevalent in TIF cluster sets, miR-30 and miR-200bc were shared between this set and the TIF vs NIF set. More specific to the TIF vs NIF comparison were two miR families: miR-25/miR-92 and miR-548ay/559. The miR-25/miR-92 family members are situated in the ncomir-1 . miR-25 ncomir-1 .To further explore the interplay between circulating miRNAs and their potential intracellular gene targets, we constructed custom networks from the differential abundance|expression sets see the “” sectionFor the genes included in each interaction network, we extracted information about the frequency of mutation (SNPs predicted to be pathogenic) and copy number variations (CNVs) from the COSMIC database , as wellBTLA, ITK, ZAP70, SLAMF6/7) [cytokine-cytokine receptor interaction, chemokine signaling pathway, and NF-kappa B signaling pathway, both with and without genes which were DE in cluster 1 vs cluster 2 . The most interconnected gene was LCK, which is a well-known oncogene in T cell acute lymphoblastic leukemia supported by COSMIC and CancerMine, Additional file Figure LAMF6/7) . This obThere was a significant overlap of DE miRNA-mRNA pairs retained in the high TILs vs low TILs and the cluster 1 vs cluster 2 comparisons. This was not unexpected as cluster 1 contained high TILs tumors/TIFs, while cluster 2 contained low TILs tumors/TIFs. In accordance with this, the overlap mainly encompassed immune-related genes, including chemokines, immunoglobulins, and lymphocyte antigens –90, 45 iLAMP5 (lysosome-associated membrane protein), very recently shown to be an autophagy suppressor that protects leukemia fusion oncoproteins, helping these to evade degradation [cytokine-cytokine receptor interaction pathway, chemokine signaling pathway, and NF-kappa B signaling pathway. In addition to this, however, the genes from the cluster 1 vs cluster 2 comparison were also enriched within pathways in cancer, indicating that cluster comparison captured more than the tumor immune signature. The most interconnected gene in the cluster comparison was PARD6B, which had a high ranking based on the number of copy number gains; however, this gene was not annotated as an oncogene or TSG, neither the COSMIC census nor from text mining—Additional file Despite the overlap, some miRNAs and mRNAs were specific to each comparison, especially for the cluster set, which was large with many pairs. For the TILs set, only miR-543 was unique. miR-543 interacts with radation . A totalBTRC and CHST1 were downregulated in high-grade tumors and interacted with miRNAs miR-10(a/b)-3p, miR-107, and miR-301(a/b)-3p, miR-454-3p, respectively. miR-18a-5p was paired with genes: AMIGO1, KCND3, and SIK3 and was upregulated in grade 3 vs grade 1|2 tumors. The network is in Additional file The network of IF miRNAs and mRNAs from the high-grade (grade 3) vs the lower-grade (grade 1|2) biopsies was small with only five genes and six miRNAs. Two genes AR , CERS6, FOXA1 , GPR160, KIAA1244, KLK5, SPDEF , and XBP1 , all of which have been shown to be BC-related and differentially expressed between subtypes [Networks of differentially abundant IF miRNAs and mRNAs from the contrasts with Her2-enriched vs TNBC, luminal A vs TNBC, and luminal B vs TNBC shared eight gene transcripts. These were: subtypes –96. A toESR1 [While some miRNAs and mRNAs were shared by all three DE networks, some were specific, or partially so, to each comparison. As expected, the most interconnected gene in both luminal types compared to TNBC was ESR1  , and GREB1, all upregulated in ER+ samples, while ERBB2 which was downregulated. Unique to this comparison was miR-32-5p, which was over-expressed in ER+ vs ER− tumors and the most interconnected miRNA in the network.For the contrast of Her2-enriched vs TNBC subtypes, the gene with the most interactions was CPD, closely followed by genes ERBB2 , GRB7 , and LASP1 , all of which belong to the Her2 amplicon (chromosome region 17q-12-21) . These DNetworks may be seen in Additional file Network analysis resulted in multiple miRNA-mRNA pairs, where both TIF miRNA and intracellular mRNA profiles displayed meaningful directionality in accordance with previously published studies and in the context of a given comparison.Three databases of cancer-related miRNAs were downloaded and curated in order to compare and support the results obtained from the analysis of interstitial fluid miRNAs. These databases included (I) CMEP  (Circula+ vs PgR− contrast (see the section above), miRNAs from these sets were only supported by overlap with databases. Figure + vs ER− comparison were encompassed by the luminal/Her2-enriched vs TNBC. As seen from the heatmaps in Fig. Table Collectively, the best supported interstitial fluid miRNAs, from differential expression analysis, miRNA-mRNA interaction networks, and miRNA databases (circulating and solid tissue), could partition normal fluids and tumor fluids from BC patients with different TIL scores and tumor grade.We applied WGCNA to our set of mRNA in order to uncover genes with similar expression patterns. Sets of co-expressed genes could have similar functions and may belong to the same pathways and cascades involved in the development of breast cancer.WGCNA analysis of mRNA expression data revealed 31 modules encompassing a different number of genes. We explored the higher-level organization and the relationship between these modules by applying hierarchical clustering, using the correlation of the module eigengenes as input (distance metric). The resulting group of modules is sets of positively correlated eigengenes, which may serve as the input for future exploratory analyses, generating new interesting hypotheses , BC subtype, immune infiltration scores, tumor grade, and metastasis information, connecting co-expressed genes with the clinical metadata, see Additional file Out of the 31 modules identified, six were correlated with the clinical variables. The green and red modules showed a positive correlation with estrogen receptor status/luminal subtypes and a negative correlation with TIF clusters, respectively, characterized by tumor grades and TIL scores. Inversely, the yellow, green, and grey60 modules were positively correlated with TIF clusters, TIL status, and tumor grade, respectively, and negatively correlated with estrogen receptor status/luminal subtypes. The blue module was positively correlated with tumor grade (and metastasis) only. Based on these observations, we intersected differentially expressed mRNAs from each comparison with the respective modules of interest to see which of these, and how many, were retained in each module. Additional file K-means clusters and subtypes retained most pairs. Genes in these pairs belonged either to the green module, red module, or yellow module—see Fig. As we were specifically interested in TIF miRNAs, which could potentially regulate the expression of cancer-related genes through intracellular cross-talk, we extracted the DE miRNA-gene target interactions of those genes, both DE and co-expressed in the modules of interest. These miRNA-gene pairs were filtered to only retain pairs where the miRNA was supported by one or more databases (see the section above). Overall, this data “curation” resulted in a small handful of genes interacting with multiple miRNAs, many of which were redundant between comparisons. The comparisons with miRNA-gene pairs from ASB2, BCL11B, BTLA, CD3D, CD3G, CXCL13, CXCR5, FAM65B, IKZF3, IL7R, KCNA3, KLRC4, LAMP3, and LTB, mainly interacting with miR-146a-5p and miR-494-3p. We observed miR-146a and miR-494 to be downregulated in TIFs from high-grade tumors with high TIL scores. All miRNAs from the module, except miR-346, were annotated in the CMEP database as differentially expressed in the blood from BC patients. miR-103a, miR-494, and miR-369 were downregulated in TNBC compared to other subtypes, while miR-206, miR-299, and miR-874 were downregulated in relapse, metastasis, and stage 3 vs stage 2 cancers, respectively. As no serum set from CMEP was specifically related to immune status, we could not perform this check; however, the directionality of these miRNAs was somewhat consistent with our results, as high immune score tumors were mainly high-grade TNBC. A search through the STRING database [The analysis revealed that the yellow module Fig. d consistCCNI, CDKN2AIPNL, ELOVL6, EPB41L5, FLOT1, NEU1, PPM1H, and SERINC3, with miRNA partners almost equally distributed across these. miR-23a had the highest number of assigned gene partners, four in total: CDKN2AIPNL, ELOVL6, NEU1, and SERINC3. A look into these genes reveals that with the exception of CCNI, these genes were upregulated in luminal B vs TNBC comparison exclusively. Cross-reference of miR-23a with the CMEP database of circulating miRNAs showed this miRNA to be differentially expressed in the blood from patients with Her2-enriched cancers compared to TNBC. STRING enrichment analysis of the red co-expression module revealed genes ELOVL6, NEU1, and SERINC3 to belong to the sphingolipid metabolic process.The red module Fig. c containThe genes in this network were not well-annotated in terms of their role in breast cancer. However, EPB41L5 and SERINC3 had a high rank based on mutational burden and CNVs, ELOVL6 was annotated as an oncogene in prostate cancer, and PPM1H has been proposed to be an oncogene, due to its relation to PPM1D .C6orf211, CCND1, CERS6, ESR1, and THSD4. Enrichment and pathway analysis of the full set of genes related to BC subtypes revealed that four of these CERS6, ELOVL6, NEU1, and SERINC3 were involved in sphingolipid metabolism, whereas C6orf211 (ARMT1), CCND1, and ESR1 were annotated in the breast cancer KEGG pathway.Co-expressed genes from this module and the miRNAs that regulate them were confounded, on the one hand originating from the high-grade vs high-TIL set and from the comparison of TNBC vs other subtypes. This is supported by Fig. BTRC CGN, DOC2A, ESR1, FAM134B, GATA3, KCNE4, NEDD4L, NKAIN1, NPNT, PARD6B, RERG, RGS22, SIAH2, SLC39A6, SYT9, and TPRG1. This set of co-expressed genes were regulated by a range of TIF miRNAs, most of which paired with between 2 and 3 genes each, with miR-340-5p as the only miRNA interacting with four transcripts: NKAIN1, NPNT, RERG, and SIAH2. Other miRNAs of interest included members of the miR-30 family, regulating thee mRNAs: DOC2A, SIAH2, and TPRG1. Enrichment analysis and literature search using the STRING database [C6orf211, ESR1, GATA3, NPNT, RERG, SLC39A6, and TPRG1 have all been linked with estrogen-positive breast cancers and have been proposed to be part of a prognostic luminal signature [DOC2A, CGN (Cingulin), PARD6B, NEDD4L, and SYT9 which belonged to the KEGG pathway, tight junction (TJ) (hsa04530), and GEO term, cell junctions (GO:0030054). These genes were downregulated in high TILs|grade tumors (cluster 1). Another gene of interest was BTRC, encoding β-transducin repeat-containing E3 ubiquitin protein ligase (β-TrCP).Co-expressed DE gene transcripts from the green module associated with TIF miRNAs from the TILs/tumor-grade comparison were database  highlighignature . Some geNoteworthy was that a handful of genes from this network had been annotated as TSGs either specifically in relation to breast cancer  or in other types of cancer (DOC2A and RGS22). In accordance, genes from this network were all downregulated in high-grade, high-TIL, TNBC samples.To identify the best differentially co-abundant miRNA-mRNA pairs, we also applied weighted co-expression network analysis to the TIF miRNA dataset. The analysis returned three co-expression modules , each encompassing around 1/3 of the miRNAs—Additional file + vs PgR− tumor were almost exclusively found within this module. The main difference between these two modules was that the red module seemed to capture the miRNAs, which were only found to be DA in K-means clusters, not in direct comparisons of TIL status or tumor grade. The slightly smaller turquoise module contained miRNAs downregulated in high-grade, high-immune score tumors—Additional file The blue and red modules contained miRNAs upregulated in TIFs from high-grade, high-TIL, TNBC tumors . In accordance with this, miRNAs predicted to be downregulated in fluids from PgRDA miRNAs with predicted mRNA targets from interaction networks and WCGNA, supported by cancer miRNA databases, may be found in Additional file As only a few genes from the best miRNA-mRNA interaction pairs were annotated as oncogenes or TSGs, we explored which known (breast) cancer-related genes were co-expressed alongside these within modules. The intersection of co-expression modules with COSMIC census genes and CancerMine genes  were enriched for genes annotated as oncogenes, TSGs, or DRGs, while two modules (turquoise and brown) were depleted of these types of genes, Fig. Interestingly, some genes known to be key players in cancer, here among MAP 3K1|MAP 2K4, NOTCH1|NOTCH3, PIK3CA, SYK, FOXP1, and TP53, were not retained in any co-expression module, e.g., they were encompassed by the gray portion in Additional file Next, we looked at which genes from each module were most interconnected, and whether these had been annotated as oncogenes or TSGs , between subtypes in our analysis, with their known cardinality from literature. Only eight PAM50 genes were significantly DE between subtypes in our analysis. However, if we performed DEA without correction for covariates, such as the level of immune infiltration, this number rose to 33 PAM50 genes, indicating that the lack of PAM50 genes in the corrected DE sets, was mainly a result of low power, perhaps in combination with differences between the TNBC subtypes and basal-like cancers. Figure Finally, we looked into whether miRNA-gene pairs were supported, either by miRNA gene target prediction tools and databases, other than TargetScan , or by eIn this study, we integrated interstitial fluid miRNA abundances with expression levels of mRNA from paired tumor tissues. Our analysis allowed us to explore whether miRNAs secreted into the interstitium could be associated with differentially expressed gene targets and whether these targets were co-expressed and/or co-regulated. We partitioned the data based on sample molecular and clinical information to obtain sets of differentially expressed IF miRNA and their intracellular gene targets, hereby elucidating potential pathways and mechanisms underlying breast cancer.As expected, we observed a good separation of BC subtypes based on intracellular mRNA expression; however, this was not the case for the interstitial fluid miRNAs. Although we did see some clustering of TIF from TNBC samples, this could just as well be related to the common higher immune status and higher tumor grades of these samples . Other sChoice of statistical framework. limma [k. limma , which wk. limma . This isCorrection for batch effects and confounders. Clustering of datasets revealed significant confounding of covariates; as such, we incorporated information on confounders into the design matrix for generalized linear modeling with limma.Integrative analysis. As we performed an integrated analysis, including co-abundance analysis and collective analysis of both TIF miRNA data and paired intra-tumor mRNA data, we naturally curated our results based on miRNA abundances, as well as the relationship between miRNAs and predicted differentially expressed mRNA targets. As such, we obtained a very different set of miRNA top candidates for further analysis and validation.When comparing the results of our miRNA analysis to those obtained in the original study by Halvorsen et al. , resultsIn addition to the aforementioned, other differences may have contributed to varying results, (i) how comparisons were defined, (ii) cutoff for retaining a miRNA in the dataset, (iii) cutoff for significance , and (iv) missing value imputation.We believe that the solid bioinformatic framework and data integration implemented in our study have resulted in new and valuable biological insights while highlighting the major impact of data correction and choice of statistical set-up has for down-stream results.Overall, our analysis revealed a set of miRNAs which were upregulated in tumor interstitial fluids from mainly TNBC patients with high-grade and high immune infiltration score tumors. Subsets of these miRNAs were predicted to have target genes, which were also differentially expressed in tumors from the same cohort of patients. Table In the following sections, we will discuss some of these most interesting miRNAs and genes, in greater detail.phosphatase and tensin homolog), as well as key players involved in TGF-β (transforming growth factor beta) signaling [The most interconnected DE miRNAs from the comparisons of BC subtypes were miR-9, miR-15b, miR-17, miR-19a, and miR-30d. We found these to be depleted in tumor interstitial fluids from patients with luminal and Her2-enriched breast cancers, compared to samples from TNBC patients. Interestingly, all of these miRNAs have been shown to be highly abundant in the basal-like BC subtype, which is largely similar to TNBC , 116. Paignaling .Multiple studies on miR-17 have found an association between the over-expression of this family of miRNAs with poor patient prognosis  , 120 andOver-expression of miR-30d and miR-9 has been associated with an aggressive phenotype, shorter time to recurrence, and a poor prognosis in patients with breast cancer , 124. MoAR (androgen receptor), CERS6 (ceramide synthase 6), FOXA1 (forkhead box A1), GPR160 (G protein-coupled receptor 160), KIAA1244 (ARFGEF family member 3), KLK5 , SPDEF (SAM pointed domain-containing ETS transcription factor), and XBP1 (X-box binding protein 1), all of which were upregulated in luminal types and Her2-enriched TIF samples vs TNBC. Three of these genes belonged to the PAM50 set: AR, FOXA1, and GPR160 [MiR-9, miR-15b, miR-17, miR-19a, and miR-30d were predicted to interact with a set of differentially expressed genes, some of which were common to the three subtype comparisons. Common genes were d GPR160 –96.ESR1 (estrogen receptor 1), KIF3B (kinesin family member 3B), KRT4 (keratin 4), and NFIB (nuclear factor I B), which were associated with luminal types only. KIF3B was upregulated in the luminal samples, and in accordance with this, KIF3B has been shown to be over-expressed in ER-positive tumors, with estrogen directly inducing the expression of KIF3B [KRT4 and NFIB have both been shown to be over-expressed in basal/TNBC tumors [ESR1 and CERS6 (see the section above) were co-expressed in the green module, along with C6orf211 , CCND1 (cyclin D1), and THSD4 (thrombospondin type 1 domain containing 4). This set of genes has been suggested as markers for a prognostic luminal signature [FOXA1/ESR1-interacting pathway [While some genes were common to the three contrasts, others were subtype-specific, such as of KIF3B . KRT4 anC tumors , 129, suC tumors . Of partignature  and has  pathway , highligCCND1 was upregulated and highly interconnected in the luminal A vs TNBC comparison. CCND1 is a well-studied breast cancer driver gene [CCND1 amplification is also associated with a more aggressive phenotype within both luminal subtypes, as well as in familial and sporadic tumors [CCND1 gene and its predicted miRNA regulators.The gene ver gene , the ampver gene . Amplifiver gene . Howeverc tumors , this miELOVL6 (ELOVL fatty acid elongase 6) gene was found to be upregulated and interact with both a larger number of genes and miRNAs. A high level of ELOVL6 (oncogene in prostate cancer) has been proposed to be a marker of poor prognosis in BC [ELOVL6 gene, have been proposed to characterize cell proliferation and differentiation state, and many of these have been linked to BC patient survival [ELOVL6), returned the gene ontology term sphingolipid metabolism. Genes assigned to this term were ELOVL6, NEU1 (neuraminidase 1), and SERINC3 (serine incorporator 3). A literature search revealed that another gene from this module, FLOT1 (flotillin), had recently been linked to the sphingolipid pathway, proposed to be a regulator of cellular sphingolipid distribution and signaling [CCN1, were specifically upregulated in luminal B vs TNBC, but not in luminal A type, indicating that over-expression of sphingolipid-related genes might be specific to luminal B tumors. Interestingly, ELOVL6, NEU1, and SERINC3 were the predicted targets of miR-23a, which was also highly interconnected and downregulated in the luminal B vs TNBC comparison. A literature search for miR-23a revealed this miRNA to be a well-known oncogenic miRNA, and a recent study by Ma et al. [For the contrast of luminal B vs TNBC, the is in BC , which iis in BC . Dysregusurvival . STRING ignaling . All gena et al.  found tha et al. . miR-23ba et al. , many tia et al. .+ vs ER− tumors and interstitial fluids showed ESR1, GATA3 (GATA binding protein 3), and GREB1 (growth-regulating estrogen receptor binding 1) to all be upregulated, while ERBB2 (Erb-B2 receptor tyrosine kinase 2) was downregulated. Both GATA3 and GREB1 have been linked to estrogen receptor-positive breast tumors and have been proposed as markers for patient response to hormone treatment [The DE expression network generated for ERreatment –143.+ tumors vs ER− tumors, and the most interconnected miRNA in the network. Not much is known about this miRNA in connection with breast cancer; interestingly, however, miR-32-5p interacts with genes NFIB, SOX11 (SRY-box 11), and DSC2 (desmocollin 2) (downregulated in ER+ vs ER−), all three of which are known to be over-expressed in basal-like/TNBC/ER− tumors and associated with poor survival [The most interesting miRNA from this network was miR-32-5p, which was over-expressed in ERsurvival , 145.growth factor receptor bound protein 7) and LASP1 (LIM and SH3 protein 1), all of which were upregulated. These genes are well-supported central players in Her2-enriched cancers and belong to the Her2 amplicon (chromosome region 17q-12-21) [carboxypeptidase D) is another gene known to be amplified in patients with Her2-enriched tumors on chromosome 17, right upstream of ERBB2 (chromosome region 17q-11-2) [Specific to the contrast Her2-enriched vs TNBC, were genes ERBB2, GRB7 (q-12-21) . ERBB2 a7q-11-2) .NEDD4L (NEDD4 like E3 ubiquitin protein ligase) gene, to be paired with the highest number of miRNAs. NEDD4L, which was downregulated in samples with high tumor-infiltrating lymphocytes scores, has been shown to be a negative regulator of Wnt-signaling [NEDD4L inhibitory effects on the Wnt signaling, it was noted that NEDD4L is often found to be downregulated in cancers, while its Wnt-target Dvl (disheveled), which is modified by NEDD4L for proteasomal degradation, is often upregulated in the same cancers [NEDD4L has been implicated in the initiation of breast tumor development, and this gene has been proposed as a prognostic lung cancer marker linked to histological grade, tumor stage, and lymph node metastasis [Par-6 family cell polarity regulator beta) and CGN (cingulin). These two genes were co-expressed in the green module and were downregulated in cluster 1  vs cluster 2 . PARD6B, CGN (cingulin), and NEDD4L belong to the KEGG pathway, tight junction (TJ) (hsa04530). Aberrant levels of tight junction proteins result in incorrect formation and maintenance of cellular polarity, contact inhibition, and proliferation, contributing to epithelial-mesenchymal-transition (EMT) [PARD6B expression is critical for TJ assembly, and decreased expression of this gene has been proposed to result in epithelial cell changes and tumor metastatic behavior [PARD6B has been shown to be amplified in breast cancer [PARD6B is generally considered to be an oncogene, it has also been linked to suppression of cell proliferation in breast cancer, indicating that the role of this gene may be complex [PARD6B, CGN (cingulin), and NEDD4L were all predicted targets of the OncomiR-1 (13q31.3) cluster, or one of its paralogues 106a/363 (Xq26.2) and 106b/25 (7q22/1), miRNA included miR-17, miR-19a/b, miR-20a/a, and miR-106a/b. In accordance with this, NEDD4L has experimentally been shown to be the gene target of the miR-106-25 cluster miRNAs [Analysis of the miRNA-mRNA pairs differentially expressed in high TILs  vs low TILs  revealed the ignaling —a pathwaignaling . Wnt sigignaling . In a stignaling  on NEDD4 cancers . This cotastasis , 150. Thon (EMT) . PARD6B behavior . PARD6B t cancer ; howevert cancer . This ob complex . PARD6B,r miRNAs .BTRC (beta-transducin repeat containing E3 ubiquitin protein ligase) predicted to interact with miR-10a/b and miR-107. Interestingly, we found these three miRNAs to be co-abundant  in cancer development and progression is convoluted. BTRC has been proposed to be a DRG, having oncogenic properties in one context and anti-tumor functions in another [Network analysis of miRNA-mRNA DE pairs high-grade tumors (grade 3) vs medium/low-grade tumors  revealed two genes of interest. One of these genes,  another . More re another . Another another . Our res another . Althoug another  found th another .CHST1 (carbohydrate sulfotransferase 1), which was paired with miRNAs miR-301a/b and miR-454. Analysis revealed miR-301a/b and miRNA-454 to be co-abundant in the same module as miR-10a/b and miR-107 , supporting the notion that these miRNAs might be associated with tumor grade and progression. The literature on CHST1 and cancer is very limited; however, studies on other members of the carbohydrate sulfotransferase (CS) family show that while some CS members may be oncogenic, others could have tumor suppressor functions. Overexpression of CHST3 and CHST11 have been linked to BC aggressiveness, relapse, and development of metastasis [CHST10 and CHST14 has been linked to invasive melanoma and to late stages of colon cancer progression, respectively [CHST1 to be downregulated in grade 3 vs grade 1|2 tumors. The miRNAs predicted to interact with CHST1 are more well-studied then their target. MiR-301 is thought to be a breast cancer oncomiR, which promotes tumor invasion and nodal or distant relapses via direct interaction with FOXF2, PTEN, BBC3iso-2, and COL2A1 [Another gene of interest in relation to tumor grade was tastasis ; in contectively , 166. Ind COL2A1 . This mid COL2A1 . In accod COL2A1 . These fd COL2A1 , althougd COL2A1 .ASB2, BCL11B, BTLA, CD3D, CD3G, CXCL13, CXCR5, FAM65B, IKZF3, IL7R, KCNA3, KLRC4, LAMP3, and LTB. This set of genes, which were all upregulated in high-TIL vs low-TIL samples, has all been linked to immune system processes [CD3D and CD3G encode T cell surface glycoproteins and are well-known players in anti-tumor immunity [BCL11B, IKZF3, and KLRC4, which have very recently been linked to a prognostic immunogenic signature of triple-negative breast cancers [Analyses revealed a set of TIF miRNAs and co-expression gene targets, which were associated with tumor-infiltrating lymphocyte scores. This network included miRNAs; miR-103a, miR-136, miR-146a, miR-299, miR-301, miR-346, miR-369, and miR-494 predicted to interact with gene transcripts: rocesses –173. Genimmunity . High leimmunity , 176. Th cancers . Liu et  cancers  found thBTLA, CXCR5, CXCL13, IL7R, LAMP3, and LTB. The protein products encoded by genes have been linked to the presence or absence of high endothelial venules and tertiary lymphoid structures in multiple cancer types [CXCL13 has been linked to both the presence of TLS and HEV in breast cancer stroma [BTLA, IL7R, and LAMP3 in relation to these, we refer to the original publications [Of particular interest was the co-expression of genes: er types –181. TLSer types , and theer types , 180. Ther types . The abur stroma . One stur stroma  found thr stroma . The resications –180, 184The set of co-expressed immune genes discussed above was mainly predicted to be the targets of miR-146a and miR-494. We found these miRNAs, along with miR-206, miR-369, and miR-376a, to be co-abundant  and high endothelial venules (HEV) within tumors. TIF miR-146a and miR-494, the most interconnected and co-abundant miRNAs in this cluster, were both previously annotated as negative regulators of immune-stimulatory genes and were DE in the plasma from patients with BC [We identified genes that were differentially co-expressed between tumors with high and low infiltrating lymphocyte scores—most of these had already been associated with cancer immunity through other studies –173. Of  with BC , 192. AsAnother co-expression module encompassed genes, which were DE between luminal B tumors and TNBC. A subset of these was related to sphingolipid metabolism and predicted to be co-regulated by miR-23a. miR-23a has been found to be differentially abundant in the serum of healthy individuals and breast cancer, as well as between BC patients with different subtypes . As suchBTRC. The interaction of miR-10-BTRC has been experimentally validated [BTRC is proposed to have tumor-suppressive functions [A small set of genes and TIF miRNAs were more specifically associated with tumor grade, here among miR-10a/b and gene target alidated , and miRalidated . BTRC isunctions , 161, whCollectively, integration of expression data from interstitial fluid miRNAs and paired solid tissue mRNAs resulted in sets of miRNA-mRNA pairs, associated with underlying molecular mechanisms and clinical features of breast cancer.Whether TIF miRNAs highlighted in our study are indeed transferred between cells in the tumor microenvironment, or whether these merely reflect that level of miRNAs within the tumor donor cells themselves, is unknown. However, as the uptake of miRNAs from the extracellular space is a well-known phenomenon, communication and transcriptome regulation via interstitial fluid miRNAs are an attractive therapeutic angle for cancer treatment.Additional file 1: Figure S1. Multidimensional Scaling Plot. Plot depicts the relationship (squared euclidian distances) between breast cancer samples based on the abundance of interstitial fluid miRNA or expression of intra-tumor mRNA. S1A = clustering based on miRNA abundances in IFs before correction for patient-specific effects. Colors: grey = normal interstitial fluids, red = tumor interstitial fluids. S1B = clustering based on miRNA abundances in IFs after correction for patient specific effects (heterogeneity). Colors: grey = normal interstitial fluids, red = tumor interstitial fluids. S1C = clustering based on miRNA abundances in TIFs. Colors denote BC subtypes: dark green= luminal A, light green = luminal B, pink = luminal B Her2-enriched, orange = Her2-enriched and deep red = TNBC. S1D = clustering based on intra-tumor mRNA expression. Colors denote BC subtypes: dark green= luminal A, light green = luminal B, pink = luminal B Her2-enriched, orange = Her2-enriched and deep red = TNBC.Additional file 2: Table S1. K-means Clusters. Partitioning of samples into two K-means clusters , based on interstitial fluid miRNA abundances or intra-tumor mRNA expression levels. Dot represents an un-matched sample. The column “consensus” denotes whether the sample was assigned to the same cluster based both on miRNA and mRNA levels.Additional file 3: Figure S2. Comparison of Differentially Abundant miRNAs. Comparison of differentially abundant miRNAs from current analysis with original publication . S2A = Comparison of miRNAs DA in TIF vs NIF, and expressed in paired serum, set include (i) miRNAs DA between TIF vs NIF, from Halvorsen, et al. 2017, (ii) miRNAs DA between TIF vs NIF, also in serum, from Halvorsen, et al. 2017, and (iii) miRNAs DA between TIF vs NIF from current analysis. S2B = Comparison of miRNAs DA between BC subtypes. Sets include (i) miRNAs DA between subtypes, from Halvorsen, et al. 2017, (ii) miRNAs DA between subtypes significant after correction for multiple testing, from Halvorsen, et al. 2017, (iii) miRNAs DA between subtypes significant from current analysis and (iv) miRNAs DA between ER+ and ER- from current analysis. S2C = Comparison of miRNAs associated with the degree of tumor infiltrating lymphocytes. Sets include (i) miRNAs associated with TILs and tumor percentage, from Halvorsen, et al. 2017, (ii) miRNAs DA between high (+2|+3) vs low TILs (0/1) from current analysis. (iii) miRNAs DA between high (gr 3) vs low/medium tumor grade (gr 1|2) from current analysis, and (iv) miRNAs DA between TIF Cluster 1 vs Cluster 2 from current analysis.Additional file 4: Table S2. This table contains sets of genes (mRNAs) from miRNA-mRNA networks, with accompanying information on logFC, adjusted p-values, information from the COSMIC database about frequency of mutations (predicted to be pathogenic), copy number variations  and information about known role in cancer. Additionally the table(s) also contains information about genes from CancerMine text-mining tool, e.g. if a given gene has been referred to as an oncogene, driver gene or tumour suppressor in literature. Genes are ranked based on mutational burden and CNVs.Additional file 5: Figure S3. miRNA-Gene Interaction Networks. Networks of differentially expressed miRNAs and gene targets predicted by TargetScan. Colors refer to expression directionality, red = up-regulated, black = down-regulated. S3A = TIF Cluster 1 vs Cluster 2, S3B = High TILs (+2|+3) vs low TILs (0|+1), S3C = High-grade (gr 3) vs medium/low-grade (gr 1|2), S3D = Her2 vs TNBC, S3E = luminal A vs TNBC, S3F = luminal B vs TNBC, S3G = ER+ vs ER-.Additional file 6: Table S3. Set-wise Results of Differentially Abundance/Expression Analysis. List of differentially expressed intracellular mRNAs and interstitial fluid miRNAs from pairs which were either common across sets (Table S3.1) or unique to sets (Table S3.2).Additional file 7: Figure S4. Module Relationships. The hierarchical clustering and heatmap show how similar the modules are . The color assignment is reported as well on the X and Y axes.Additional file 8: Table S4. Intramodular Connectivity. The table contains results from the intramodular connectivity analysis, including gene name, the module a gene belonged to, and interconnectivity scores; kTotal (whole network), kWithin (within module), kOut = kTotal-kWithin, and kDiff = kWithin-kOut. Additionally, the table includes information about whether a gene was annotated as an oncogene | tumor suppressor gene | dual role gene | driver gene | fusion gene in the COSMIC census gene set, or in the filtered results of CancerMine text-mining. The columns “DE Gene” and “Gene from Network” denote if a gene was found to be differentially expressed in any comparison and whether it was included in one of the miRNA-mRNA network, respectively. The genes are ranked based on interconnectivity within modules.Additional file 9: Figure S5. Intra-tumor mRNA Co-expression Modules. Results of weighed Gene Co-expression Network Analysis. Upper part of plot shows the clustering of the genes co-expressed in the 31 modules from Weighed Gene Co-expression Network Analysis (WGCNA). Grey denotes that the gene was not assigned to any module. Modules are named by their color. Lower part of plot shows the correlation between patient clinical variables and modules.Additional file 10: Figure S6. Overlap of Differentially Expressed mRNAs with Co-expressed mRNAs. Venn diagrams, depicting the overlap between differentially expressed mRNAs from contrasts, with modules, which were correlated with the patient clinical feature of interest.Additional file 11: Figure S7. Heatmaps of DE intra-tumor mRNAs levels. Heatmaps showing the separation of tissue samples, based on the best DE mRNA candidates from Weighed Gene Co-expression Network Analysis (WGCNA). S7.A: luminal (A + B), Her2-enriched vs TNBC, S7.B: TIL scores, S7.C: tumor grade (plus clusters minus genes from the TILs comparison) and S7.D: estrogen receptor status. Color scale denotes expression levels, purple = high expression, and yellow = low expression.Additional file 12: Figure S8. Results of miRNA Co-abundance Network Analysis. WGCNA resulted in three miRNAs co-abundance modules, denoted Module 1 , Module 2 (Turquoise S7 B) and Module 3 . Shapes indicate which contrast a given miRNA was differentially abundant within. X-axis = name of miRNA, y-axis = log fold change for miRNA in contrast.Additional file 13: Table S5. Interstitial Fluid miRNA and Intra-Tumor mRNA Targets Supported by Databases. Differentially abundant miRNAs with predicted mRNA target(s) from interaction networks and WCGNA, supported by cancer miRNA databases. Databases were: (I) CMEP (Circulating MicroRNA Expression Profiling)http://syslab5.nchu.edu.tw/CMEP/(II) dbDEMC database of http://www.picb.ac.cn/dbDEMC/and (III) miRCancer (microRNA Cancer Association Database)http://mircancer.ecu.edu/download.jsp. Database information on miRNA expression is included, e.g. directionality in comparison, experimental design.Additional file 14: Table S6. Oncogenes and Tumor Suppressors in Co-expression Modules. Co-expressed genes from modules that were annotated as oncogenes, tumor suppressors, dual role genes, driver genes or fusion genes by COSMIC and/or CancerMine. As CancerMine is a prediction tool, we imposed a cut-off of minimum of 10 annotations/citations for a gene (cancer other and pan-cancer) and a minimum of 5 annotations/citations for genes specifically associated with breast cancer .Additional file 15: Table S7. Experimental Support for miRNA and Gene Target Interaction. Prediction Differentially abundant tumor interstitial fluid miRNAs and their experimentally validated intracellular gene targets with support from MiRTarBase , release 7.0.


Arabidopsis thaliana (Arabidopsis). Omics studies using next-generation sequencers have further revealed that transcription factors in the TTFL directly regulate key genes implicated in clock-output pathways. In this review, the target genes of the Arabidopsis clock-associated transcription factors are summarized. The Arabidopsis clock transcriptional network is partly conserved among angiosperms. In addition, the clock-dependent transcriptional network structure is discussed in the context of plant behaviors for adapting to day–night cycles.The circadian clock is the biological timekeeping system that governs the approximately 24-h rhythms of genetic, metabolic, physiological and behavioral processes in most organisms. This oscillation allows organisms to anticipate and adapt to day–night changes in the environment. Molecular studies have indicated that a transcription–translation feedback loop (TTFL), consisting of transcriptional repressors and activators, is essential for clock function in  Arabidopsis thaliana (Arabidopsis) also shows circadian cotyledon and leaf movement, the movement is driven by differential growth of the adaxial and abaxial sides of leaves, not by turgor pressure as in the case of legumes [Circadian regulation of biological processes is thought to be crucial for prediction and adaptation to day–night cycles. Many metabolic, physiological and behavioral processes are under the control of the circadian clock in plants ,2. Leaf  legumes ,4. Arabi legumes . The spe legumes , as is s legumes  and the  legumes ,8. Impor legumes ,10. Thes legumes ,12,13. F legumes ,15.dehydration-responsive element B1/C-repeat-binding factor (DREB1/CBF) genes, encoding key transcription factors for cold-stress responses, are circadian regulated [DREB1/CBF expression is gated by the clock, and induction of these genes is maximal in the morning and minimal in the evening [DREB1/CBF is disrupted in clock mutants [Transcriptome analyses of Arabidopsis grown under diel and circadian conditions revealed that 6 to 26% of all genes are expressed cyclically, depending on the conditions of the experiment ,17,18. Megulated . Cold-st evening ,21. The  mutants ,23. CollEARLY FLOWERING 4 (ELF4), LUXARRHYTHMO (LUX), PSEUDO-RESPONSE REGULATOR 9 (PRR9), PRR7, PRR5, TIMING OF CAB EXPRESSION 1 , COLD-REGULATED GENE 27 (COR27), COR28 and GIGANTEA (GI) [CCA1, LHY, REVEILLE 8 (RVE8), NIGHT LIGHT-INDUCIBLE AND CLOCK-REGULATED 1 (LNK1), LNK2, LNK3, LNK4 and PRR genes expressed during earlier phases [LUX, ELF4 and GI expression [PRR9, PRR7 and LUX [PRR5 and TOC1 [PRR5 and TOC1 expression, although COR27 and COR28 have no DNA-binding activity in vitro. Thus, it is likely that COR27 and COR28 associate with TOC1 and PRR5 promoters by interacting with other transcription factors [CCA1 promoter region and activate CCA1 transcription [CCA1 [Many clock-associated genes encode proteins related to transcription, and these genes constitute a transcription–translation feedback loop (TTFL) for clock function ,25. CIRCTEA (GI) ,30. PRR9r phases ,32,33. Tr phases ,35. In a and LUX ,37,38. Land TOC1 . COR27 a factors . LIGHT-Rcription . TCP21, on [CCA1 . This hion [CCA1 ,43,44,45CCA1 and LHY are essential for the clock and influence output biological processes such as hypocotyl elongation, flowering time, cold-stress responses and photosynthesis [Genetic studies have indicated that ynthesis ,47,48,49ynthesis . Anotherynthesis . Gene nuynthesis ,27, a reynthesis ,50. Otheynthesis ,27.cca1 lhy double mutant, expression peaks of CCA1-target genes were advanced to morning phases [Many CCA1-bound genes are expressed in the evening . In the g phases ,48. Thusg phases . The ChIg phases . This reNINE-CIS-EPOXYCAROTENOID DIOXYGENASE 3, encoding a rate-liming enzyme for abscisic acid (ABA) biosynthesis, was strongly reduced in transgenic plants overexpressing LHY. Other genes for ABA-signaling components were altered in lhy loss-of-function and LHY over-expressing constructs. ABA sensitivity is also changed in these lines, showing ABA signaling control by LHY [A study combining ChIPseq of LHY and transcriptome analysis identified the target genes of LHY . Comparil by LHY .lnk1 lnk2 double mutants and rve8 rev6 rev4 triple mutants had long periods and altered clock-output processes, these genes are necessary for clock function [PRR5 and TOC1 promoters recruits the basal transcriptional machinery to induce PRR5 and TOC1 expression [LNK1 and LNK2 activate genes whose expression occurs in the afternoon [FLAVIN-BINDING, KELCH REPEAT and F BOX 1 (FKF1), a key regulator of flowering time [LNK genes control flowering through FKF1 expression  genes that encode transcription factors capable of repressing the florigen gene FT and CONSTANS (CO), a transcriptional activator of FT [PHYTOCHROME INTERACTING FACTOR 4 (PIF4) and PIF5, two genes that encode transcription factors capable of inducing hypocotyl growth in the dark [DREB1/CBF genes that encode transcription factors for cold-stress responses [DREB1 gene, PRR-target genes tend to have expression patterns with peaks at dawn and the early morning. The gate effect of cold stress-dependent DREB1 induction was highly attenuated in the prr9 prr7 prr5 triple mutants [prr9 prr7 prr5 mutants compared to wild type, suggesting that these PRR proteins repress the target genes [CCA1 and LHY [PRR9, PRR7, PRR5 and TOC1 (PRR1) proteins are sequentially expressed from early morning to midnight ,59,60 anrocesses ,67,68,69et genes ,32,33  of the in vitro . A recenin vitro . ChIPseqin vitro ,32,33,35in vitro ,33. Indein vitro ,79,80,81directly ,82, it ielf3, elf4 and lux mutants impair clock output processes such as flowering time regulation and hypocotyl elongation [elf3, elf4 and lux mutants, suggesting a repressive role of the EC [The EC, an essential protein complex in the clock, consists of ELF3, ELF4 and LUX . The elfongation ,87,88,89ongation  / PACLOBUTAZOL1 RESISTANCE1 (PRE1) and BNQ2/PRE2, implicated in cell elongation and flowering time, are directly regulated by the EC [ARABIDOPSIS RESPONSE REGULATOR6 (ARR6), ARR7, CYTOKININ RESPONSE FACTOR (CRF4) and CRF5 genes implicated in cytokinin responses. Collectively, the EC directs key genes involved in photosynthesis, temperature stress and growth [The EC targets two key light signaling genes, and PIF5 . ChIPseqand PIF5 . The EC y the EC . The EC d growth .GI alter the period length and amplitude, GI is required for clock function [gi and ztl mutants have shorter and longer periods, respectively, GI seems to control period length through ZTL-independent pathways. Recently, a chromatin immunoprecipitation assay showed that GI protein associates with the CCA1 promoter [CCA1 promoter. In addition, GI binds to PIF transcription factors and modulates the stability and activity of PIFs [CCA1 promoter in the light–input pathway and affect CCA1 transcription [CCA1 transcription in multiple ways, including controlling the stability of TOC1 and PRR5, two transcriptional repressors of CCA1.Given that null mutants of function . Despitefunction . Since Zfunction ,98,99, Gfunction . Given tpromoter . GI does of PIFs . PIFs bicription . Thus, Ggi null mutants, suggesting that GI inhibits PIF3 binding to DNA. Common targets of PIF3 and GI are enriched in genes related to the circadian clock, the response to water deprivation, the response to chitin, the ethylene-activated signaling pathway and transcription. Although the impact of GI-PIF interaction on the overall physiology of plants still needs to be determined, the interaction is crucial for light input to the clock and for controlling hypocotyl elongation through regulating CCA1 and PIF3-LIKE1 genes [The ChIPseq study indicated that genomic loci bound by GI and PIF3 overlap significantly, showing close interaction between these proteins  Figure . At the E1 genes .GI is known to regulate not only the clock and hypocotyl elongation but also a wide range of physiological processes [GI-controlled developmental process in which GI functions in at least three pathways  and EARLY LIGHT-INDUCABLE PROTEIN 1 (ELIP1) genes are targeted by PRR5 and the EC. DREB2C, DREB2H, RVE7, GI, PRR7, PRR9 and LUX genes are targeted by CCA1 and the EC. RVE7, PRR7 and PRR9 are targets of PRR5 and CCA1. The combinatorial binding of PRR5 and CCA1 may cause shifts in the timing of gene expression of the target genes [There are some common target genes that are regulated by different classes of clock-transcription factors . PIF4, Pet genes . How difCCA1/LHY, GI, LUX, PRR and TOC1, are highly similar among these plants [PRR homologs are expressed, suggesting that the Arabidopsis PRR5 transcriptional network is partly conserved in rice and Populus [Details about the molecular components of the plant clock have been examined thoroughly in Arabidopsis; however, transcriptome analyses of other plants have indicated that the clock transcriptional network is partly conserved among angiosperms . Comparae plants . Ortholoe plants . Furthere plants ,106. Som Populus .LHY- and TOC1-homolog genes are expressed at a specific time-of-day similar to those in Arabidopsis. The expression of these genes is similar in both sides of sunflower stems; however, two homologs of genes involved in phototropism are expressed differently on the opposite sides, suggesting the molecular basis underpinning solar tracking [PIF4 expression to coordinate thermo-responsive growth [Output biological processes under clock control are divergent among species, organs, tissues and cells. Recently, the expression of an outward anion channel gene was reported to peak around dawn in flexor motor cells, but not in extensor motor cells, in the pulvinus of Samanea saman, a mimosoid tree, suggesting that anion channel-dependent cell shrinkage of flexor cells during the daytime is crucial for leaf movement . Solar ttracking . In Arabe growth ,111. Phoe growth . These se growth ,113. In e growth ,115, thoe growth . In the OsCCA1 and OsTOC1 control tillering and panicle development by regulating genes for strigolactone signaling [OsGI is essential for robust diel transcriptome rhythms in the field [In rice, ignaling . OsGI ishe field . Recent he field ,119,120.CCA1/LHY, RVE and LUX are conserved in Archaeplastida, whereas other clock-associated genes are not. A GI homolog is not found in the bryophyte Physcomitrella patens [GI and FKF1 homologs are crucial for day-length-dependent growth-phase transition in another bryophyte, Marchantia polymorpha [PRR, TOC1, ELF3, ELF4 and GI homologs are not found in Cyanophora paradoxa  or in Porphyridium purpureum (rhodophyte). PRR and TOC1 are found in two species of chlorophyte, Chlamydomonas reinhardtii and Ostreococcus tauri [TOC1 promoter, and this binding is required for evening-phased gene expression [TOC1 in Arabidopsis. Two LUX homologs are required for Chlamydomonas clock function [ELF4 and ELF3 are absent in this alga. A LUX homolog (ROC75) is expressed during the day, and ROC75 protein directly represses a homolog of CCA1/LHY. These results indicate that the TTFL for clock function differs between Chlamydomonas and Arabidopsis [Comparison of genomes and diel transcriptome data for organisms spanning the Archaeplastida provides some evolutionary insight into diel gene expression in the plant lineage ,122. Thea patens , but GI lymorpha . PRR, TOus tauri ,124. Ostfunction , though bidopsis .Thanks to intrinsic genomic approaches such as RNAseq and ChIPseq using Arabidopsis seedlings, the target genes of CCA1/LHY, LNKs, RVE8, PRRs/TOC1, the EC and GI have been characterized. The network architecture provides time-of-day information from the clock TTFL to output pathways. Comparative transcriptome analyses indicate that the clock TTFL is conserved in many land plants; however, clock output gene expression is thought to be organ-, tissue- or cell-specific in order to control the output properly to adjust to environmental fluctuations coming from day–night or seasonal changes. This illustration of the transcriptional network under clock control at the organ or tissue level in diverse plants provides deeper insight into the ability of plants to adapt to 24 h and seasonal cycles.


PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein–protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP. Conclusion: Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear. Methods: In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes. Results: We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs ( Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that limits communication, social interactions, and repetitive behaviors are observed in ASD-affected people. There is no cure for this disease yet. The Centers for Disease Control (CDC) has reported that 1 in 54 children has ASD in the US, according to Autism and Developmental Disabilities Monitoring (ADDM) Network (Data published in 2016) [Changes in gene expression are widely studied to characterize various human diseases and successfully used to predict molecular and cellular processes in complex diseases ,3. RecenWe searched the big transcriptomics database Gene Expression Omnibus (GEO) in March 2020 to collect the RNA-Seq datasets of human ASD brain samples . We focup-values from independent datasets are combined via the following formula [p-value obtained from gth gene for study t. For independent p-values the values of T degrees of freedom. It produces larger values corresponding to smaller values of p-values were adjusted by Benjamini–Hochberg false discovery rate (FDR).The differential expression of individual RNA-Seq dataset was analyzed in R using the package DESeq2 . We used formula ,14:Fg=−2p-value < 0.05 was considered for the selection of the enriched Gene Ontology (GO) terms and pathways.Functional annotation of the identified differentially expressed genes was performed using the clusterprofiler R package . An adjuWe built a brain protein–protein interactome (PPI) network of the proteins encoded by the DEGs using NetworkAnalyst—an online web tool for data analysis and visualization . The zerThe network of interactions between DEGs-TFs was constructed using JASPAR database built in NetworkAnalyst . The topWe cross-checked the identified DEGs found in our meta-analysis with data from SFARI Gene 2.0, a community-driven knowledgebase for the autism spectrum disorders . A gene-p-value < 0.05 and log2FC ≥ 1—we detected 1567 differentially expressed genes (DEGs), with 373 downregulated and 1194 upregulated genes in ASD compared to controls  in the ASD cortex were mainly involved in inflammatory response and immune systems, in particular, regulation of leukocyte activation, leukocyte migration, neutrophil degranulation, and leukocyte migration A. The ceThe pathway analyses revealed that osteoclast differentiation, cytokine–cytokine receptor interaction, TNF signaling pathway, complement and coagulation cascade, pertussis, malaria, legionellosis, Kaposi sarcoma-associated herpesvirus infection were significantly enriched among the DEGs, as shown in MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1). We performed brain protein–protein interaction analysis of the DEGs identified by the meta-analysis using the brain (cortex) protein interactome data. We reconstructed a subnetwork around DEGs encoded protein in dataset or tissue . The firFOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP were detected as TFs regulating DEGs  was identified as one of top DEGs (FDR < 0.05 and logFC = −3.3). Parvalbumin (PV) is a calcium-binding protein expressed in a subset of GABAergic interneurons in the brain. Recently, this population of inhibitory (GABAergic) interneurons, expressing the calcium-binding protein PV , has attracted much attention with regard to ASD . Indeed,PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been reported to be deferentially expressed in ASD. Mutation of PAK1 has been reported to be involved in intellectual neurodevelopmental disorder [DNAH17 gene were reported to be associated with ASD [PCDHAC2 gene were associated with ASD [With the present meta-analysis, we were able to detect 235 unique DEGs, not identified by the individual studies , supportdisorder ,39. Genewith ASD . Two sepwith ASD . In anotwith ASD .Among the enriched pathways in ASD, we found the osteoclast differentiation pathway, which has been previously associated with ASD. Indeed ASD children have been found to have low bone density and increased risk of fractures ,43,44,45FOXC1 plays a role in normal development of cerebellar and posterior fossa. It has been demonstrated that deletion or duplication of FOXC1 are associated with cerebellar malformation, suggesting the FOXC1 dysregulation play role in neurodevelopmental disease [GATA2 is involved in maintaining the development of GABAergic neurons, movement and neuron-specific gene expression via direct action or control of other GABAergic genes [YY1 is widely expressed in brain and have role in development and neurodegeneration. Gabriele et al. showed that both deletions and de novo point mutations affecting YY1 activity trigger Intellectual Disability syndrome of haploinsufficiency with a wide variety of development and psychiatric comorbidities [YY1, E2F1 and USF2 enriched in neurodevelopmental and neuro-psychiatric disorder PPI network [NFKB1 to play crucial role in etiology of treatment refractory schizophrenia in the Chinese Han population [TFAP2A and HINFP have not yet been described in other neurodevelopmental disorders.In the present study, we also characterized the potential TFs involved in the regulation of DEGs expression. The  disease . GATA2 iic genes . Howeverbidities . Twelve  network . Many of network . A genompulation . The rolBAG3, CDK2, CDKN1A, EZH2, GABARAPL1, HDAC1, MYC, TP53, TRAF1 and VIM. BAG3 was identified to be significantly differentially expressed in Schizophrenia by gene and pathways-based meta-analysis of microarray data [BAG3 have been reported to be involved in Parkinson’s disease [CDKN1A have been implicated in ASD in dorsolateral prefrontal cortex region of brain [EZH2, is a chromatin remodeler factor and it plays essential functional roles in nervous system biology, including differentiation, neurological and cognitive development. The genetic variation of EZH2-a chromatin remodeling factors is observed in intellectual disabilities and ASD [EZH2 in human embryonic brain suggesting a contributory role of this gene in the etiology of ASD in the Chinese population [HDAC2 in learning and memory behaviors [GABARAPL1 involved in ASD associated pathway [VIM and TRAF1 in ASD pathogenesis is also not known yet. Here, we suggest that further experimental studies are warranted to establish their role as biomarkers in ASD.The topological analysis of the protein–protein interactions is a useful approach to decode the critical signaling molecules in complex disease. The PPI analysis identified hub gene signatures, that included ray data . Genetic disease . A recenof brain . Another and ASD . Li et apulation . Therefoehaviors . Assessm pathway . HoweverDespite the tremendous significance of the present study, several limitations can be noted, in that the two RNA-Seq datasets were obtained at different times and small samples size. Additionally, the racial and ethnic background of the subjects and control did not completely match.Our study differs significantly from other studies performed on circulating blood cells from ASD toddlers . It is lHowever, the study of blood cells cannot provide several pieces of information on dysregulated biological processes that, irrespective of the presence of genetic alterations, occur in ASD brain. One particular example is provided by parvalbumin, which is not a bona fide ASD risk gene, but is significantly altered in a population of inhibitory interneurons, recently recognized to be implicated in ASD manifestations, as previously mentioned ,35,36.MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1) was identified as a key driver in ASD. In general, core signatures of DEGs associated with ASD genes, including already known markers of ASD and novel hub genes, were identified. Potential implicated TFs were also discovered. The identified core gene signatures may represent candidate biomarkers and drug targets. For many of the newly identified DEGs in this study, direct associations with ASD has not previously reported. Therefore, experimental research is needed to validate these new DEGs to decipher their biological roles in ASD in order to better understand the perturbed molecular mechanisms. The present study reported novel genes and pathways in ASD, and further investigation is required for validation of hub genes in the clinical setting.In conclusion, the present study aimed at characterizing the whole-genome transcriptomic profile of the brain cortex from ASD patients. Our meta-analysis and systems biology analyses revealed core gene signatures as candidate biomarkers and etiopathogenetic pathways in ASD. A proteomic signature of hub genes (


Low-protein (LP) diets can be potentially used to reduce the excretion of nitrogenous compounds and feed cost in commercial swine production; however, new strategies are required to be developed to improve the growth performance of pigs receiving these diets. Little is known about the effect of phytogenic additives on the performance of pigs fed with LP diets and the underlying factors involved. The objective of this study was to assess the effect of a phytogenic water additive (PWA) on growth performance, nutrient digestibility, blood metabolites, plasma amino acid (AA) concentration, and gut and skeletal muscle AA transporters in nursery pigs fed with LP diets. Supplemental PWA increased the concentration of circulating essential AA, reduced the transcript of some of the AA transporters in the small intestine and skeletal muscle, improved growth performance when the dietary protein was adequate, and increased muscle lean%, but reduced muscle fat% when the dietary protein was deficient. The used PWA in this study had differential effects on blood calcium and its digestibility depending on the level of dietary protein. This study suggests that PWA can be used for improving the meat composition in protein-restricted pigs, but PWA improves growth performance only when dietary protein is adequate.The objective of this study was to investigate the effect of a phytogenic water additive (PWA) on growth performance and underlying factors involved in pigs fed with low-protein (LP)/high-carbohydrate diets. Forty-eight weaned barrows were allotted to six treatments for 4 weeks: CON-NS, control (CON) diet-no PWA; CON-LS, CON diet-low dose PWA (4 mL/L); CON-HS, CON diet-high dose PWA (8 mL/L); LP-NS, LP diet-no PWA; LP-LS, LP diet-low dose PWA; LP-HS, LP diet-high dose PWA. Relative to CON-NS, pigs fed with CON-HS had increased average daily gain, body weight and serum calcium (Ca) and phosphorous (P) and had decreased mRNA abundance of solute carrier family 7 member 11 and solute carrier family 6 member 19 in jejunum. Compared to LP-NS, pigs fed with LP-HS had increased muscle lean%, decreased muscle fat%, decreased serum Ca and increased serum P. Compared to their NS counterparts, CON-LS, CON-HS, and LP-LS increased the concentration of plasma essential AA and those fed with CON-HS and LP-HS tended to reduce the abundance of the solute carrier family 7 member 1 transcript in skeletal muscle. Thus, PWA improved the performance of weaned pigs fed with protein-adequate diets likely through increased blood essential AA and affected the muscle composition when dietary protein was deficient. The intensive swine production in developed countries has raised environmental and sustainable agriculture concerns associated with the excretion of large amounts of waste including nitrogen (N) or nitrogen-containing compounds ,2. ThereMoringa oleifera leaf extract to drinking water of broilers fed with LP diet (100 mL/L water) improved growth performance parameters such as weight gain and feed conversion ratio , 2 μL of dithiothreitol (DTT) (0.1 M), 1 μL of RNaseOUT (40 U/μL), 1 μL of superscript II reverse transcriptase (200 U/μL), and 3 μL of RNase/DNase-free water  using the following program: 22 °C for 5 min, 42 °C for 30 min, 85 °C for 5 min, and terminated at 4 °C in a thermocycler . The primers’ sequences were obtained from previous publications as listed in −∆∆CT method [Total RNA extraction, reverse transcription, and quantitative PCR were performed as we described previously . ApproxiRNaseOUT  U/μL, 1 T method  was used2+. After the reduction of Cu2+, the solution was chelated with a colorimetric probe, and absorbance was measured at 570 nm using an Epoch microplate reader spectrophotometer . The intra-assay coefficient of variation (CV) was 6.55%.Total antioxidant capacity (TAC) of serum was analyzed using a TAC kit  following the manufacturer’s instructions. Briefly, after preparing all the reagents to work concentrations, 100 μL of standards solution, 5 μL of serum samples, 5 μL of protein masks, and 90 μL of water were added to each well of a 96-well plate in duplicate. The plate was placed on a plate shaker in the dark for 1.5 h at room temperature to reduce the added 100 μL of CuAs we previously described , apparenhttps://www.ai-therapy.com/psychology-statistics/sample-size-calculator (accessed on 3 July 2018)) and data from our previous study [®, IMB SPSS Statistics version 23, Armonk, NY, USA). The difference between the means of treatments for all parameters was separated using pairwise Student’s t-test corrected by the Benjamini–Hochberg procedure [® , with the effect of protein, PWA, time, protein × PWA, protein × time, PWA × time and protein × PWA × time as fixed and the pig as a random variable included in the model. Based on the smallest values of fit statistics for the corrected Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), the covariance structure of the repeated measurements model for daily feed intake, and weekly body weight and CPI were autoregressive and for weekly BWG, CFI, CWI, G:F and G:P were heterogeneous autoregressive. p ≤ 0.05 and 0.05 < p ≤ 0.1 were considered to declare significant difference and trends, respectively.As we previously described  using Alus study , we measrocedure  with 0.1p < 0.05) on final BW and ADG, with LP pigs having lower final BW (24.37 vs. 26.83 kg) and ADG (0.55 vs. 0.63 kg) than CON pigs  BWG than those fed with CON-NS at week 4  on FI, with pigs fed with CON-HS tending to have a 70% higher feed intake compared to those fed with CON-NS on day 5  on G:F and G:P ratios (p < 0.05) on week 1, with LP pigs having a lower G:F ratio compared to CON pigs (0.51 vs. 0.78). Additionally, the effect of PWA on G:F ratio was significant (p < 0.05) in week 1 and 2, with a dose-dependent increase on G:F ratio during the first week  and a decrease in G:F ratio during the second week  for NS, LS, and HS, respectively (p < 0.05) was detected on weekly G:P ratio during weeks 2, 3, and 4, with a higher G:P ratio in pigs fed with LP diets than those fed with CON diets . The effect of PWA on the G:P ratio in weeks 1 and 2 was significant. During week 1, increasing the levels of PWA, increased the G:P ratio . Pigs fed with LP-HS had a 57% increase and a 22% decrease in the G:P ratio compared to those fed with LP-NS, during the first and third week of this study, respectively. Pigs fed with LP-NS had a 43% and 32% greater G:P ratio than those fed with CON-NS during the third and fourth weeks of study, respectively (There was a significant effect of protein (P ratios . Relativectively . A signiectively .p < 0.01) on muscle lean% and fat%. Pigs in the LP group had higher muscle lean% (85.1%) and lower fat% (13.9%) than those in the CON group . Supplementing diets with PWA dose-dependently increased muscle lean%  and decreased muscle fat% . While there was no difference in muscle lean% and fat% of pigs in CON-LS, CON-HS, and CON-NS, pigs fed with LP-HS had 13% more lean and 55% less fat compared to those fed with LP-NS (Muscle lean% and fat% are shown in th LP-NS . p < 0.05) on the majority of plasma AA concentration. Overall, pigs fed LP diets had a lower concentration of plasma leucine, isoleucine, phenylalanine, histidine, arginine, valine, tyrosine, proline, and creatinine, and had higher plasma lysine, methionine, tryptophan, threonine, alanine, and glutamic acids than those fed with CON diets. Pigs fed with LP-NS had lower plasma valine, isoleucine, histidine and 3, methylhistidine, and higher lysine, serine, glycine, sarcosine, and alanine than CON-NS pigs (p < 0.05). Overall, pigs in the HS group had a higher concentration of methionine, tryptophan, leucine, isoleucine, valine, phenylalanine, arginine, serine, glutamine, and tyrosine compared to LS and NS groups. Pigs fed CON-HS had a higher plasma concentration of valine, isoleucine, leucine, phenylalanine, tryptophan, lysine, histidine, arginine, serine, glycine, alanine, tyrosine, asparagine, proline, and sarcosine and lower glutamic acid, aspartic acid, and taurine than CON-NS. Moreover, the plasma concentration of leucine, serine, glycine, proline, sarcosine, and alanine was higher, and methionine and lysine tended to be higher in pigs fed with CON-LS than those fed with CON-NS. Pigs fed with LP-LS had a higher (p < 0.05) plasma valine, histidine, and alanine and tended to have a higher plasma leucine, isoleucine, phenylalanine, and ornithine compared to those fed with LP-NS. The plasma concentration of histidine, serine, glutamic acid, taurine, and ammonia were lower and lysine and proline tended to be lower in the LP-HS group when compared to LP-NS (p < 0.05).Plasma nitrogen-containing compounds are shown in Pigs fed with CON-LS and CON-HS tended to have a lower AFD of Ca compared to those fed with CON-NS A. Additip < 0.05; p < 0.05; p < 0.05) serum P compared to those fed with CON-NS, but pigs in CON-LS group had lower serum P compared to those in CON-NS group  with a decrease in serum BUN of LP compared to CON pigs   D. Pigs fctively) D.SLC7A11) and solute carrier family 6 member 19 (SLC6A19) in the jejunum  was significant (p < 0.05), with the relative mRNA abundance of 0.86, 0.81, and 0.56 for NS, LS, and HS, respectively (SLC7A11 transcript was decreased (p < 0.05) in the jejunum of CON-LS, CON-HS, and LP-NS (SLC6A19 compared to those fed with CON-NS (SLC6A19 compared to the LP-NS group (SLC15A1), solute carrier family 3 member 1 (SLC3A1), solute carrier family 7 member 1 (SLC7A1), and solute carrier family 7 member 9 (SLC7A9)  A,D–F in SLC7A8, SLC3A1, SLC7A1, and SLC7A11 in skeletal muscle was significant  overall, supplemental PWA increased the ADG and tended to improve BW in CON-HS, but not in LP pigs, suggestive of a positive effect of PWA on growth performance when dietary protein is adequate; (2) regardless of dietary protein content, supplementing PWA increased muscle lean% and decreased muscle fat%, but this effect was significant when PWA was supplemented to LP diets at high dose (LP-HS), but not to CON diets, indicative of a promising effect of PWA on muscle composition when dietary protein is deficient; (3) supplementing CON pigs with a low or high dose of PWA increased the concentration of the majority of plasma essential AA and in LP pigs, a low dose of PWA (LP-LS) recovered the reduced AA levels such as branched-chain AA, phenylalanine, and histidine suggestive of improved digestion and absorption of proteins and AA by PWA; (4) supplementing LP pigs with PWA at high dose (LP-HS) reduced serum Ca concentration while supplementing CON pigs with both low and high doses of PWA increased the serum Ca concentration and decreased the AFD of Ca; (5) CON and LP pigs supplemented with a high dose of PWA had higher serum P concentration; (6) supplementing CON pigs, but not LP pigs, with a high dose of PWA decreased the mRNA abundance of Little is known about whether supplemental phytogenic additives would improve the growth performance and meat composition of pigs fed with LP diets. Supplementation of PWA improved the ADG and body weight of pigs when diets contained recommended amounts of protein in the current study. Similarly, previous studies have shown a beneficial effect of phytogenic additives on growth performance in early weaned  and growThe positive effects of phytogenic additives on the growth performance of pigs have been linked with the beneficial effects of these additives on eating behavior ,21, immuSLC7A11 and SLC6A19 transcripts in the jejunum and supplementing both CON and LP pigs with a high dose of PWA reduced the abundance of the SLC7A1 transcript in skeletal muscle. In contrast, others showed that incubating jejunal cells with the combination of essential oils and saponins at medium and high doses stimulated solute carrier family 15 member 1 (SLC15A1) recruitment to the cytoplasmic membrane in growing broilers [SLC7A11 is a cystine/glutamate exchanger [SLC7A11 in the CON-HS group is likely contributing to the reduced plasma glutamic acid concentration in this group. SLC6A19 is the major neutral AA transporter in the small intestine and kidney [SLC6A19 in the jejunum is suggestive of a feedback regulatory mechanism in this process. A feedback regulatory mechanism has been described between cycling AA pool and expression of intestinal AA transporters [SLC71 is not very well known in skeletal muscle [SLC7A1 in skeletal muscle of CON-HS and LP-HS groups and hence decreased uptake of arginine by skeletal muscle may contribute to the greater concentration of arginine in the circulation for these groups. The role of other basic and acidic transporters in the small intestine that contribute to improved AA profile in pigs supplemented with phytogenic additives requires further investigation. Amino acids are sensed and transported by specific AA transporters ,54. It ibroilers . These cxchanger  and is sd kidney . Given tsporters . Althougl muscle , there il muscle ,60. The Little research has been conducted to explore the interaction of phytogenic additives and dietary protein on growth performance and related underlying factors in young pigs. Overall, supplemental PWA (8 mL/L) increased the concentration of plasma essential AA and reduced the abundance of the transcript for some of the AA transporters in the small intestine and skeletal muscle, improved growth performance when the dietary protein content was adequate and increased muscle lean%, and reduced muscle fat% when the dietary protein was deficient. Depending on the level of dietary protein, supplementation of PWA had differential effects on plasma Ca concentration and its digestibility. Altogether, although the supplemental PWA used in this study failed to improve the growth performance of nursery pigs fed with LP diets, this additive improved the performance of pigs fed with standard protein diets likely through improved blood AA profile and had beneficial effects on muscle composition when dietary protein was deficient. Further research is warranted to elucidate the mechanisms controlling the interactive effects of dietary protein with phytogenic additives on nutrient digestibility and AA transport system. Since phytogenic additives contain a wide variety of bioactive compounds, further investigation is required to understand what active compound(s) of these additives is mediating their beneficial effects.


Cervical cancer screening is slowly transitioning from Pappanicolaou cytologic screening to primary Visual Inspection with Acetic Acid (VIA) or HPV testing as an effort to enhance early detection and treatment. However, an effective triage tests needed to decide who among the VIA or HPV positive women should receive further diagnostic evaluation to avoid unnecessary colposcopy referrals is still lacking. Evidence from experimental studies have shown potential usefulness of Squamous Cell Carcinoma Antigen (SCC Ag), Macrophage Colony Stimulating Factor (M-CSF), Vascular Endothelial Growth Factor (VEGF), MicroRNA, p16INKa / ki-67, HPV E6/E7/mRNA, and DNA methylation biomarkers in detecting premalignant cervical neoplasia. Given the variation in performance, and scanty review studies in this field, this systematic review described the diagnostic performance of some selected assays to detect high-grade cervical intraepithelial neoplasia (CIN2+) with histology as gold standard.We systematically searched articles published in English between 2012 and 2020 using key words from PubMed/Medline and SCOPUS with two reviewers assessing study eligibility, and risk of bias. We performed a descriptive presentation of the performance of each of the selected assays for the detection of CIN2 + .Out of 298 citations retrieved, 58 articles were included. Participants with cervical histology yielded CIN2+ proportion range of 13.7–88.4%. The diagnostic performance of the assays to detect CIN2+ was; 1) SCC-Ag: range sensitivity of 78.6–81.2%, specificity 74–100%. 2) M-CSF: sensitivity of 68–87.7%, specificity 64.7–94% 3) VEGF: sensitivity of 56–83.5%, specificity 74.6–96%. 4) MicroRNA: sensitivity of 52.9–67.3%, specificity 76.4–94.4%. 5) p16INKa / ki-67: sensitivity of 50–100%, specificity 39–90.4%. 6) HPV E6/E7/mRNA: sensitivity of 65–100%, specificity 42.7–90.2%, and 7) DNA methylation: sensitivity of 59.7–92.9%, specificity 67–98%.Overall, the reported test performance and the receiving operating characteristics curves implies that implementation of p16ink4a/ki-67 assay as a triage for HPV positive women to be used at one visit with subsequent cryotherapy treatment is feasible. For the rest of assays, more robust clinical translation studies with larger consecutive cohorts of women participants is recommended.The online version contains supplementary material available at 10.1186/s13027-020-00335-2. Cervical cancer cases continue to rise despite concerted efforts to provide rapid and effective screening coupled with intensified human papilloma virus (HPV) vaccination to selected age category of females . CurrentRecent discoveries have demonstrated significant milestones in management of cervical cancer based on United States of America Food and Drug Administration (USFDA) approved biomarkers such as Squamous Cell Carcinoma Antigen (SCC-Ag) currently implemented to support physicians with rapid screening of women at high risk of cervical neoplasia, and complemented with Cancer Antigen 125 (CA-125), Serum Fragment of Cytokeratins (CYFRA), Soluble CD44 (sCD44), and Carcinoma Embryonic Antigen (CEA) as prognostic markers for pre-treatment prediction and disease monitoring . The aimIn accordance to Maseno University study guidelines, our systematic review protocol was submitted to the Maseno University Register for study protocols Ref. No.PG/PHD/PH/00086/2017, and to the Open Science Framework (OSF) Register of Systematic Reviews.The study design was based on selected studies: we considered cross-sectional and cohort studies that reported the diagnostic performance of SCC Ag, M-CSF, VEGF, miRNA (miR-9), p16INKa / ki-67, HPV E6/E7 mRNA and DNA methylation  for the detection of CIN2+ ). We included studies of women with different kinds of cervical pathology published in peer-reviewed English journal articles in the period of 2012 to 2020 with the outcome of interest reported in different countries.This review was done following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol (PRISMA) guidelines . ResearcResearch papers that reported the type of miRNA (miR-9), p16INK4a / ki-67, DNA methylation, SCC-Ag, M-CSF, VEGF, and HPV E6/E7 mRNA diagnostic performance for the detection of CIN2+ were included. Searched articles were directly imported and handled using EndNote X5 citation manager . Based on the PRISMA protocol included in Additional file Data such as the name of the first author, year of publication, country where the study was conducted, CIN profile of the study participants, sample type, the proportion of CIN2+, type of diagnostic test for each category of biomarkers, mean turnaround time (TAT), approval status of each assay, the positivity rate of each diagnostic assay, and its diagnostic performance  and Negative Predictive Value (NPV)) were extracted from the included articles.To assess the risk of bias, the Critical Appraisal Skills Programme (CASP) tool , that waThe extracted data were fed into a Microsoft Excel and presented in terms of 1) CIN profile of the study subjects, 2) the proportion of miR-9, p16INK4a / ki-67, DNA methylation, SCC-Ag, M-CSF, VEGF and HPV E6/E7 test result 3) diagnostic performance of each of the seven assays to detect CIN2+ . We performed a descriptive presentation of these elements to compile a best evidence synthesis for the listed assays in the detection of CIN2+. A systematic narrative synthesis was provided in which summary results of recent studies with performance indicators were presented using text, figure, and table. Descriptive statistics, such as percentages were used to describe the findings.From the systematically searched databases and other sources, a total of 298 articles were retrieved and sequentially screened. After removing 83 duplicates, the 215 were further screened by title then 57 were removed. Additionally, 78 were removed by abstract and 22 removed by full text with justifiable reasons described in the PRISMA flow chart   criteriaPark et al [, and Bedkowska et al being the only authors who reported PPVs and NPVs at 77.4, 92 and 82.7%, 75%, respectively, [Owing to the difference in clinical presentation of subjects, pooling the diagnostic performance data was challenging. In its place, we compiled best evidence synthesis for each of the assays to detect high-grade cervical lesions CIN2+) using descriptive statistics. The diagnostic performance of each of the assays as compared with histologically confirmed high-grade cervical intraepithelial neoplasia (CIN2+) as an endpoint was as follows; 1) HPV E6/E7 mRNA test performance varied considerably depending on the diagnostic method used as shown in Table + using drk et al . 3) For rk et al –30. 4) Erk et al , 38–44.5rk et al –47. 6). ctively, –49.7). Octively, –52, withOverall, Table , Shah et al., and American Society for Clinical Pathology [. Our review is therefore an update of the latest knowledge on the test performance of these assays compiled from articles published since 2012, some of which included a number of studies with varying methodological quality, but our finding is in line with this review. Accordingly, together with the previous reviews [Similar reviews on the performance characteristics of some of the listed assays had been reported earlier by Tornesello et alathology –8. Our r reviews –8, our f reviews , 54, 55., [From experimental studies, it has been established that woman exposed to HPV E6/E7 mRNA following infection with HPV have higher risk of progressing to high-grade cervical neoplasia due to the integration of viral DNA sequence into host genome causing loss of E2 tumor suppressor gene that regulates expression of E6 and E7 oncogenes , 54, 56., , compare, ; and ext, , compare, . Similar, , compare, , of whicMeanwhile, our review of the performance characteristic of microRNAs (miRNA) assays, particularly miR-9 in detection of CIN2+ recorded sensitivity, specificity and area under the ROC curve ranges of 52.9–67.3%%, 76.4–94.4%, and 0.71–0.85, respectively,  with 77., and extreme PPV of 11.1% reported by Ren et al. [Moreover, in our review of p16INK4a / ki-67 assays, twenty studies recorded sensitivity, specificity, PPV, NPV, and AUC ranges of 50–100%, 39–90.4%, 11.1–92.3%, 86.7–100%, and 0.76–0.90, respectively, for the detection of CIN2+ , 28–30, n et al.  comparedn et al. , with arn et al. , 29. Altn et al. . Studiesn et al. , and cann et al. , 63. Recn et al. .Accordingly, this review established that p16/Ki-67 assay is preferable for triaging HPV-DNA or VIA positives cases given the robust clinical translation studies with larger consecutive cohorts of women participants recorded in the recent past . MoreoveDong et al [; and extreme PPV of 15% reported by van Leeuwen et al [DNA methylation assays on the other hand recorded sensitivity, specificity, PPV, NPV, and AUC ranges of 59.7–92.9%, 67–98%, 15–95.4%, 65.5–98.3%, and 0.81–0.86, respectively, in detection of CIN2+ , 38–44, ng et al ; and exten et al , compareen et al . Studiesen et al . Given ten et al . From ouFour studies evaluating Squamous Cell carcinoma Antigen (SCC-Ag) assays recorded sensitivity, specificity, PPV, NPV and AUC ranges of 78.6–81.2%, 74–100%, 66.7–100%, 82.6–84.1%, and 0.79–0.89, respectively, in detection of CIN2+ , 45–47, , and Zajkowska et al recommended the interpretation of elevated levels in conjunction with other complementary markers such as VEGF or SCC-Ag. to improve specificity for optimal treatment benefit, given that their levels are equally raised in other types of cancer as well [Macrophage Colony Stimulating Factor (M-CSF) assays included in our review reported sensitivity, specificity, PPV, NPV, and AUC ranges of 68–87.7%, 64.7–94%,77.4–92%, 75–82.7%, and 0.75–0.86, respectively, –49, with as well ; a sugge, of the diagnostic usefulness, and clinical applicability of VEGF assay in cervical, breast or endometrial cancer, particularly with regards to consistency in specificity and AUC across all studies; and the diagnostic correlation with other complementary assays such as M-CSF and SCC-Ag [Finally, four of the studies evaluating Vascular Endothelial Growth Factor (VEGF) assays included in our review recorded sensitivity, specificity, and AUC ranges of 56–83.5%, 74.6–96%, and 0.83–0.86, respectively, –52, withd SCC-Ag . Membersd SCC-Ag . Althougd SCC-Ag , 75. Cecd SCC-Ag . AccordiThis systematic review presents the latest developments in the field of SCC Ag, M-CSF, VEGF, miRNA (miR-9), p16INKa / ki-67, HPV E6/E7 mRNA and DNA methylation tests accuracy. We have included relatively adequate number of articles published in different countries employing large number of study participants. However, our review result should be interpreted in light of a few shortcomings. Our main setback was lack of studies that employed similar and well-defined population with same cervical pathology characteristics. Thus, our review suffered from heterogeneity of studies which made it difficult to pool the performance characteristics of each of the tested assays. Additionally, use of histologically confirmed CIN2+ endpoint when evaluating the test accuracy represents a challenge because of the regression  or progression  of many confirmed lesions. Moreover, confining our inclusion criteria to include only articles published in English languages would also mean missing some of the relevant studies; thus reducing the accuracy of our results.The larger AUC and higher NPV correspond to a better diagnostic tool. Consequently, the reported test performance and the receiving operating characteristics curves implies that implementation of p16ink4a / ki-67 assay as a point of care test to be used specifically to triage HPV-DNA positive women at one visit with subsequent cryotherapy treatment is feasible, especially in regions with inadequate histology infrastructure such as Kenya , 77. ThiAdditional file 1.Additional file 2.