The initiation of prostate cancer has been long associated with DNA copy-number alterations, the loss of specific chromosomal regions and gene fusions, and driver mutations, especially those of the Androgen Receptor. Non-mutational events, particularly DNA and RNA epigenetic dysregulation, are emerging as key players in tumorigenesis. In this review we summarize the molecular changes linked to epigenetic and epitranscriptomic dysregulation in prostate cancer and the role that alterations to DNA and RNA modifications play in the initiation and progression of prostate cancer. Prostate cancer (PCa) is the second-most diagnosed cancer in men worldwide. In 2019 it accounted for nearly one in five new diagnoses. It is the first cancer in terms of prevalence and is also a leading cause of male cancer-associated deaths . HoweverERG. This fusion was found in 53% of tumours when the complete sequences of seven primary human prostate tumours and their paired normal counterparts were analysed [CDKN1B (p27) and CDKN2A (p16) genes [APC alterations were enriched in these types of cancer, while alterations in ATM and amplifications in AR were specifically enriched in CRPC. Furthermore, other genes such as PTEN were commonly found altered in all stages of PCa [Over recent decades, a number of other genetic alterations have been identified associated with PCa. The malignancy is generally characterised by frequent androgen-regulated promoters’ fusion with members of the E26 transformation-specific (ETS) family, such as analysed . DNA copanalysed , especiaanalysed . Loss of6) genes . Comparas of PCa . DespiteBRCA1, BRCA2 and ATM (DDR pathway), PTEN, PIK3CA, AKT1 (PI3K pathway), BRAF, HRAS and KRAS (MAPK pathway) [With the advent of high-throughput next-generation sequencing, recurrent point mutations in several genes, including Androgen Receptor (AR), SPOP, TP53, FOXA1 and PTEN, are found associated with PCa incidence . In recepathway) ,12, reveThis emerging era of high-throughput sequencing is revealing a complex scenario of the molecular drivers of prostate cancer, some of which are not associated with permanent DNA changes, but with epigenetic changes such as differential DNA methylation patterns . These fAR alterations, DNA copy number and single point mutations or mRNA expression [Until now, profiling studies of primary PCa have been focused on the most studied alterations of this tumour type, such as pression ,30,31. Hpression . In cancpression . Throughpression . Other spression . Using Tpression . More repression . All thiGSTP1 promoter, a fact which was already described 20 years ago. GSTP1 modulates several signalling pathways involved in proliferation, differentiation and apoptosis [PTEN, which causes its silencing [CDKN2A (which encodes p16) that leads to increased proliferation, thus contributing to carcinogenesis [AR expression is regulated in 30% of CRPC by hypermethylation of its promoter [Mechanistically many studies have shed light on the molecular effects underlying epigenetic dysregulation in PCa. One of the most frequent DNA methylation changes occurs at the poptosis . After tilencing , or the ogenesis . Even thpromoter . More inpromoter . writers, proteins that introduce chemical modifications to DNA and/or histones; readers that identify and interpret those modifications with its domains; and erasers, whose function is to remove the modifications added by the writers [Besides DNA methylation, other epigenetic marks regulate chromatin structure and gene expression. Among the plethora of epigenetic master regulators, we can find  writers . UncontrPTEN, CDKN2A, or AR [YAP1 has also been associated with neuroendocrine prostate cancer (NEPC) [RAD9 transcription, which induces tumorigenicity [DNA methylation is mainly carried out by a family of DNA methyltransferases (DNMTs). Five different enzymes exist in humans, DNMT1, DNMT3a, DNMT3b, DNMT3c, and DNMT3L. From those, DNMT3a, DNMT3b and DNMT3c directly carry out the de novo methylation, while DNMT3L performs it indirectly . DNMT1 pA, or AR ,45,46. Hr (NEPC) . Expressr (NEPC) . In addigenicity . DNMT3a genicity . In sum,genicity , and curHistone modifications are also common epigenetic marks. The most critical marks are catalysed by histone methyltransferases (HMT) and histone acetyltransferases (HATs). Lysine methyltransferases (KMT) are an important family involved in gene transcriptional regulation . SpecifiAlterations in some epigenetic writers such as upregulation of PRMT1 and Coactivator-Associated Arginine Methyltransferase 1 (CARM1) are found in early tumours, suggesting that histone modifications and chromatin remodelling may act as epigenetic drivers at the initial stages of the disease . In addiHistone acetyltransferases (HATs) increased activity can also promote PCa by either acetylating histones or transcription factors, thus inducing transcription . For exaAll this evidence indicates epigenetic writers as promising therapeutic targets, and several molecules targeting p300/CBP, PRMT5, EZH2 are being tested under clinical trials, alone or in combination with other therapies such as AR blockade or immunotherapy in advanced prostate tumours , which wThe bromodomain (BRD)-containing proteins, whose function is to induce transcription initiation through chromatin remodelling, are some of the most studied in prostate cancer cells among other epigenetic readers. More than 70% of NEPC and more than 50% of primary and metastatic PCas show some dysregulation in any of the bromodomain containing proteins . Within Apart from the BET family, several BRD-containing proteins have been associated with mCRPC. TRIM24 is a transcription co-regulator overexpressed in CRPC, and is essential for cancer cell proliferation . BAZ2A bThe critical erasers in PCa are DNA demethylases, histone demethylases (HDMT) and histone deacetylases (HDACs), which cause hypomethylation or deacetylation and consequently the upregulation of gene expression, leading to disease progression and higher cell invasion and metastasis . 5C to 5-hydorxymethylation (hm5C) [To date, the only known DNA demethylases are TET enzymes, comprised of three members, namely TET1-3. DNA demethylation is a three-step process, where TETs participate in the first one, oxidizing mn (hm5C) . Decreasn (hm5C) ,96. TET1n (hm5C) .Within HDMTs, lysine demethylases (KDM) play important roles in cancer. For example, lysine-specific demethylase 1 (LSD1/KDM1A) overexpression has been highly studied for its oncogenic potential. Its overexpression is associated with increased tumour progression and promotion of carcinogenesis in several cancer types, including PCa ,99. In PKDM6A deletion inhibits tumour progression [Many other histone demethylases have been associated to PCa, and similarly to LSD1, their gain or loss of activity seem to have a dual role . H3K4 degression . KDM6B  activity is found associated with PCa too. Contrary to HATs resulting in the increased acetylation of histones or AR, the activity of deacetylases such as SIRT1 or SIRT2 can regulate cellular growth and sensitivity to ADT therapy through AR deacetylation , and theIn conclusion, all this evidence highlights that epigenetic alterations are commonly associated with PCa and, most importantly, can be used to stratify patient risk, predict clinical outcomes, and to determine best therapeutic options or to reveal molecular pathway vulnerability to distinct chemotherapeutic agents ,124. In Similarly to DNA, RNA can also be modified. Despite being known for over 50 years, the study of RNA modifications has suffered a delay regarding epigenetics, probably due to the lack of suitable tools for their study . Thus, t6-Methyladenosine (m6A), with around 0.1-0.4% adenines of all mRNAs being modified [These modifications are found in all types of RNA, from messenger RNA (mRNA) to non-coding RNAs such as ribosomal RNA (rRNA), transfer RNA (tRNA), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) among many others . tRNAs amodified .5C) methylation of tRNAs stabilises their structure and protects them from nuclease-mediated cleavage [In contrast with DNA modifications, which are known to mainly regulate gene expression , RNA modcleavage . HoweverDespite the great diversity of modified nucleotides in RNA and the huge expansion of the field in the past years, little is known about the role of RNA modifications in PCa. In this review, we will summarize the most relevant RNA modifications found to date to have a regulatory role in prostate cancer.6 of adenosine is the most studied modification. This modification mainly occurs in mRNA, but also in non-coding RNAs such as tRNAs, rRNA, miRNA, lncRNAs and circular RNAs (circRNAs) [6A deposition in mRNA is, by far, the best characterised. Recent improvements in high-throughput methods have demonstrated that m6A is not randomly distributed, but is specifically enriched near stop codons in 3’-untranslated regions (UTRs), within long exons, in intergenic regions and introns and at 5’-UTRs [Methylation of position NircRNAs) . Despite 5’-UTRs ,139. 6A deposition is modulated by the dynamic crosstalk between methyltransferases or “writers” and demethylases or “erasers”. In nascent pre-mRNA, m6A is deposited by a multimeric methyltransferase complex. The catalytic core of this complex is constituted by methyltransferase-like 3 (METTL3) and methyltransferase-like 14 (METTL14), which catalyses the formation of m6A and recognises RNA substrates, respectively [6A methyltransferase-associated  [6A methyltransferase, METTL16, which acts as an independent RNA methyltransferase and mainly modifies mRNA and small nuclear RNAs (snRNAs) [6A methyltransferase acting as a heterodimer together with TRMT112 [mectively . This coIAA1429) ,140. Rec(snRNAs) . In addi6A demethylase [6A demethylases is still controversial. In 2017, Mauer et al. found that FTO is able to demethylate m6Am at the 5’ cap, rather than m6A, thereby decreasing mRNA stability. The disparity of this discovery initiates a discussion about m6A detection techniques and concerning the nature of its reversibility [6A can be specifically recognised by a group of RNA binding enzymes or “readers” that recruit downstream complexes mediating the function of this modification [Ten years ago, the discovery of two demethylases, fat mass and obesity-associated protein (FTO) and AlkB homolog 5 (ALKBH5), brought to light the reversible nature of this modification  and receethylase . Howeversibility . Moreovefication . Severalfication   represses the proliferation, tumorigenic invasion, and migration capacity of PCa cells in a catalytic-dependent manner in vitro [6A methylation leads to the alteration of several downstream pathways. For instance, Cai et al. showed that METTL3 alters proliferation by regulating apoptosis through the Sonic Hedgehog-GLI pathway in a catalytic-dependent manner [6A-dependent manner, thus altering PCa cell adhesion and motility [CYCLIN-L1 and CDK19 gene promoters through RNA–DNA interactions in a m6A-dependent manner, leading to increased proliferation and migration both in vitro and in xenografts [USP4 mRNA levels, leading to decreased migration and invasiveness in PCa [6A reader IGFBP2 [6A regulator expression and immune infiltration. In Zhao et al., low METTL3 and HNRNPA2B1 expression correlates with increased immune cell infiltration [Recent computational studies have found a complex scenario in the expression of several writers, readers and erasers in PCa ,148,149,position B. For exhylation ,152,153.in vitro ,152,153,in vitro . The dowt manner . MYC prot manner ,152 and motility . Similarmotility . NEAT-1 nografts . Moreover IGFBP2 . These rr IGFBP2 , suggestltration . In Liu ltration , highligYTHDF2 mRNA [6A writers, downregulation of FTO leads to increased cell invasion and migration capacity by increasing m6A levels [Other members of the methyltransferase complex have been found dysregulated in PCa. VIRMA has been found upregulated in PCa , and itsDF2 mRNA . By contDF2 mRNA . SimilarA levels . 6A deposition has an oncogenic effect in prostate cancer cells and targeting METTL3 could have clinical benefits for PCa patients. With the recent emergence of METTL3 as a promising oncogenic target in several haematological malignancies and solid tumours, a great effort is being made to develop small molecule inhibitors to target m6A deposition. Two recent inhibitors have been developed that target and inhibit METTL3, STM2457 and UZH2. These two inhibitors have shown to decrease m6A methylation activity in vitro and in vivo. In addition, they have shown potent inhibitory potential in several cancer cell lines including PCa, and a promising anti-cancer effect in cell lines and pre-clinical models of acute myeloid leukaemia (AML) [In sum, all this evidence suggests that increased mia (AML) ,163. The5C deposition in RNA is conserved in all life domains [5C methyltransferase that has been demonstrated to be associated with PCa. Increased expression of NOP2 has been considered a marker of bad prognosis for years, correlating with Gleason score, PSA serum levels and recurrence after radical prostatectomy [5C RNA methyltransferases are associated with PCa is also still uncovered.m domains  and has  domains ,165. In  domains ,165. To atectomy ,167. Mecatectomy . This meatectomy . In addiatectomy . NSUN4 tatectomy , yet its5C and f5C have been found in mRNA and tRNAs in vivo [5C into f5C in tRNAs in vivo [5C to hm5C in RNA [Similarly to DNA, hm in vivo . ALKBH1  in vivo . In vitrC in RNA . DespiteC in RNA ,174, yet5C as well as other RNA modifications have been linked to the survival, proliferation and differentiation of tumour cells in several cancers [5C as well as other RNA modifications have been associated with drug resistance regulation [NSUN2 and METTL1, which catalyses tRNA 7-methylguanosine, has shown to sensitize tumour cells to the chemotherapeutic agent 5’-fluorouracil (5-FU) [From a therapeutic point of view, m cancers ,176,177.gulation ,177,178.Pseudouridine (Ψ), the C5-glycoside isomer of uridine, is among the most well-known RNA modifications. It comprises about 5% of the total of all RNA nucleotides and is found in almost all types of non-coding and coding RNAs . Ψ biosySHQ1 was the only gene in the 3p region that harboured tumour associated mutations [DKC1 in metastatic tumours compared to primary tumours [Aberrant deposition of Ψ, dysregulated expression of pseudouridylases and somatic mutations have been linked to PCa. For instance, in an integrative genomic profiling study of prostate cancer, somatic mutations in the chromosomal 3p were identified as tumour suppressors; most importantly, utations ,30, and utations . DKC1 isutations . While, utations . Howeverutations , suggest tumours ,188,189. tumours . Other functional studies have highlighted an oncogenic role for increased Ψ deposition. For example, the stand-alone pseudouridine synthase PUS10 has shown to play a key role in TRAIL-induced apoptosis, participating in the release of cytochrome C and SMAC from the mitochondria in PCa cell lines . MoreoveRNA editing is also a very prevalent post-transcriptional event in human transcriptomes. The most common type of RNA editing results from the conversion of adenosine (A) to inosine (I) in double-stranded RNA, a process catalysed by the adenosine deaminase acting on the RNA (ADAR) family of enzymes. A-to-I editing is highly prevalent within Alu elements, as well as introns, untranslated regions (UTRs) and coding transcripts . In a coTaken together, these studies disclose an important regulatory mechanism in cancer for RNA editing; however, a systematic effort to define the putative roles of edited RNAs in PCa tumorigenesis and progression remains to be established.One of the most abundant ribonucleotides in rRNA is 2′-O-methylation , with up to 112 different positions in human ribosomes. Similarly to Ψ, its deposition is carried out by RNA-guided ribonucleoprotein (RNP) complexes formed by the core proteins NOP56, NOP58, SNU13 and fibrillarin (FBL) and the guiding box C/D snoRNAs . AlteredDespite the initial response to hormone-deprivation treatment, one of the main problems in PCa management is the relapse and progression rate to metastatic tumour, which has limited therapeutic options, none of them completely curative ,202. ThiRecent evidence has highlighted that epigenetic alterations are emerging as potential biomarkers to stratify PCa patients and predict clinical outcomes . EpigeneAs in DNA, RNA modifications are also known to regulate responses to environmental signals ,177 suggSimilarly to epigenetics, RNA modifications are emerging as promising therapeutic targets, and great efforts are now being made to develop small molecule inhibitors to rewire the aberrant cancer epitranscriptomes. However, targeting RNA modifications could be fairly complicated since they are linked to most aspects of RNA biology, and their alteration could involve undesirable toxic effects. Moreover, the role of RNA modifications is context-dependent and could differ between cancers or even between different cell populations ,205. Thu


Streptomyces appears as one of the main sources of these enzymes, but complete genome sequences of keratinolytic bacteria are still limited. This article reports the complete genomes of three marine-derived streptomycetes that show different levels of feather keratin degradation, with high (strain G11C), low (strain CHD11), and no (strain Vc74B-19) keratinolytic activity. A multi-step bioinformatics approach is described to explore genes encoding putative keratinases in these genomes. Despite their differential keratinolytic activity, multiplatform annotation reveals similar quantities of ORFs encoding putative proteases in strains G11C, CHD11, and Vc74B-19. Comparative genomics classified these putative proteases into 140 orthologous groups and 17 unassigned orthogroup peptidases belonging to strain G11C. Similarity network analysis revealed three network communities of putative peptidases related to known keratinases of the peptidase families S01, S08, and M04. When combined with the prediction of cellular localization and phylogenetic reconstruction, seven putative keratinases from the highly keratinolytic strain Streptomyces sp. G11C are identified. To our knowledge, this is the first multi-step bioinformatics analysis that complements comparative genomics with phylogeny and cellular localization prediction, for the prediction of genes encoding putative keratinases in streptomycetes.Keratinases present promising biotechnological applications, due to their ability to degrade keratin.  Bacillus [Streptomyces [Keratin is a fibrous and recalcitrant protein belonging to a large family of structural proteins that constitute hair, wool, feathers, nails, bristles, and horns of several animals . This prBacillus ,10,11 anptomyces ,14,15,16Keratinases EC 3.4.21/24/99.11) are a particular class of proteases that possess keratinolytic activity, being able to degrade several insoluble keratin substrates  and intr/24/99.11Streptomyces species are one of the main producers of keratinases [Streptomyces [in silico investigation of putative proteases and peptidases, to obtain those likely to be involved in feather degradation, have been accomplished for the first time. Interestingly, 18 of 24 proteases predicted by this pipeline , and 12 of 17 unique genes of the G11C genome (“unassigned p-orthogroup” peptidases)) and thought to be involved in keratinolytic activity were validated in our previous study by being present in the secretome of strain G11C [Genomic analysis of keratinolytic bacteria is seldom reported ,32,33,34atinases , but so ptomyces . Moreoveptomyces ,38,39 waptomyces , providiain G11C . This stStreptomyces sp. G11C, presented the highest level of keratinolytic activity, exhibiting complete feather degradation in liquid culture; Streptomyces sp. Vc74B-19, evidenced no keratinolytic activity, leaving the feather structure unaltered, and Streptomyces sp. CHD11 presented a low level of degradation, showing the partial dissolution of the fibers of the feathers [Three strains  from ourfeathers . To obtafeathers . Streptomyces genus [Streptomyces emeiensis CGMCC 4.3504T [S. emeiensis type strain—87.5% for strain CHD11 and 87.4% for strain Vc74B-19. On the other hand, strain G11C, with high keratinolytic activity, groups with Streptomyces albidoflavus NRRL B-1271T [albidoflavus species [albidoflavus species has been described in the literature to possess keratinolytic activity, secreting at least six extracellular proteases when cultured on a feather meal-based medium [Previous taxonomic classification using the 16S rRNA gene indicated that these marine strains belong to the es genus . To obtaes genus  using 63 4.3504T . This is B-1271T , consist species . A straid medium . AltogetStreptomyces strains G11C, CHD11, and Vc74B-19, a comparison of the diversity and quantity of peptidases present in all three strains was accomplished using the following approaches: Genome annotation using the Prokka, PANNZER2, and eggNOG servers, classification by MEROPS database and identification of orthologous groups. In addition, to compare these protein sequences with functionally characterized keratinases obtained from literature, a similarity network was developed.To identify potential genes encoding keratinases that could be involved in keratin hydrolysis and the observed differences in Streptomyces strains presented a similar abundance of genes encoding for putative peptidases, corresponding to 3% of their genomic content (Streptomyces sp. G11C presents two putative peptidases belonging to two unique families (C02 and S53) that are not present in strains CHD11 and Vc74B-19. C02 belongs to the cysteine peptidases of the calpain family, although its biological role in bacteria is unclear, and there are not many studies that clarify its properties [Multiplatform annotation revealed that all three  content . Accordi content , where s content . This reoperties . On the operties . In contSubsequently, putative peptidases (584 sequences) of the three strains were classified into 140 protease orthologous groups (p-orthogroups) using Orthofinder . As expeMost of the shared p-orthogroups belong to the serine (n = 37) and metallo- n = 40) super-families, while the cysteine, aspartic, threonine, and mixed super-families are found in a smaller proportion (n = 2–5), which agrees with our previous MEROPS results. On the other hand, some peptidases are not classified into any p-orthogroup. Further exploration of these “unassigned p-orthogroup” peptidases could give an insight into the differences observed in terms of keratinolytic activity between the strains, considering that strain G11C presents comparatively the greatest keratin degradative capacity under the conditions analyzed [ super-faStreptomyces sp. G11C belonging to the S01 family draw our attention, which are absent in orthogroups shared with the non-keratinolytic strain Vc74B-19: An unassigned p-orthogroup peptidase (G11C_00267) and a peptidase (G11C_00756) belonging to one of the three p-orthogroups shared between the keratin degrading strains G11C and CHD11. Additionally, Streptomyces sp. G11C presents 8, 12, and 3 putative peptidases belonging to the families S08, S01, and M04, respectively, that may be of interest for the search for putative keratinases, despite belonging to orthogroups shared between the three strains. The detail of the promising sequences of Streptomyces sp. G11C  are summarized in Putative proteases belonging to families, S01, S08, and M04, where most known bacterial keratinases are found , can serTo complement the previous analysis, and identify those sequences related to families of known keratinases, a similarity network was constructed using an all-vs-all local alignment. For this analysis, 584 putative proteases of the three strains (hereinafter named “three-strain dataset”), 61 functional keratinases (mainly from Gram-positive bacteria) collected from NCBI , and 50 −40  from other Streptomyces strains, also cluster together. This observation suggests that these two specific peptidases could have keratinolytic activity, although it has not been experimentally tested yet. Finally, in the smaller community N° 41 (Geobacillus strain (AJD77429.1). Scattered in the network, we found three keratinases from Lactobacillus and Bifidobacterium that do not group with any sequence from our three-strain dataset. In general, putative non-keratinase sequences are depicted as unconnected nodes or communities, except for specific cases such as communities N° 4 (mentioned above) and N° 47, both belonging to the peptidase family S01  possess sequences linked with functional keratinases with an E-value threshold of 1 × 10−40 . The lar−40 B, is rel−40 C is compty N° 41 D, sequenmily S01 .Streptomyces strains G11C, CHD11, and Vc74B-19, respectively. Although the number of genes is less for keratinolytic-strain G11C, the percentage of identity when compared with some functional keratinase sequences is higher with this strain , which presented a 96.4% amino acid identity with a predicted protein from strain G11C (G11C_01512), 72.8% identity for strain CHD11 (CHD11_02603) and 72.2% for strain Vc74B-19 (Vc74B-19_03690). The sequence found in G11C, belonging to peptidase family S01, p-orthogroup 8, and community N° 4, can be considered a putative keratinase, considering the high amino acid similarity with the known keratinase from Streptomyces albidoflavus TBG-S13A5, which can also be explained by the phylogenomic closeness between both strains. In summary, there are 11, 13, and 16 putative proteases linked to functional keratinases from s strain . For insStreptomyces sp. G11C, are predicted as cytoplasmic and membrane-associated proteases. Non-keratinases were distributed in sparse coordinates, where seven sequences can be considered putative extracellular proteases.Cellular localization data and phylogeny were employed to narrow the list of candidate keratinases that could potentially make a difference in keratin degradation for strain G11C. Proteases secreted into the extracellular medium are expected to play an important role in keratin degradation . Thus, tSequences were further clustered in the t-SNE bidimensional space using the DBSCAN algorithm , revealiStreptomyces sp. G11C that are phylogenetically related to functional keratinases, multiple sequence alignments (MSA), and phylogenetic analysis were performed on the t-SNE groups 0, 1, and 2 (containing sequences predicted as extracellular). The average occupancy  [To identify extracellular peptidases of ignment)  of the tThe same discrete categories used in the PCA and t-SNE plots  were used for the ancestral state reconstruction analysis. With this tool, a probability distribution is assigned to each ancestor node within the tree, indicating the likeliness of the ancestor to belong to one of these categories. This provides a visual interface to study which branches could potentially be related to proteases with keratinolytic activity, as the ancestral state depends on both branch length  and tree topology ,52. We aActinomadura , Nocardiopsis (AAO06113.1), and Streptomyces (CAH05008.1) strains. Interestingly, a sequence of the keratinolytic strain G11C (G11C_05333) and low-keratinolytic strain CHD11 (CHD11_00976) are phylogenetically close, compared to the non-keratinolytic strain Vc74B-19. It is possible that specific motifs or amino acids within these sequences, not present in strain Vc74B-19, enhance the activity of the codified enzymes, and should be considered as candidates for putative keratinases. In addition, a subclade that presents Vc74B-19 and CHD11 sequences related to the known Streptomyces fradiae K11 keratinase CAH05008.1 was observed. In this case, branch lengths indicate significant sequence divergence, and therefore, no evidence of putative keratinolytic activity can be assigned to the three-strain genes of this subclade.Clade 2 of t-SNE group 0 A, Box 2 Streptomyces sp. OWU 1633 (AAU94350.1) and the other is from Amycolatopsis sp. BJA-103 (QGA70043.1), both belonging to the family S08. In this clade, the sequences of CHD11 and Vc74B-19 strains are phylogenetically closer, compared to strain G11C. In the case of the t-SNE group 1 tree, clade 1 B, Box 1 Streptomyces albidoflavus, strains Fea-10 (AQX39246.1) and TBG-S13A5 (AYM48028.1). In this branch, only one sequence of strain G11C is present (G11C_01512), and given its high similarity with the mentioned keratinases (96.4% amino acid identity), it is probably a potential keratinase, which could explain, to a certain extent, the differences in keratinolytic activity between our strains. All these sequences belong to the peptidase S01 family. Clade 5b B, Box 5bStreptomyces sp. G11C, which evidenced the greater level of keratin degradation, we identified seven putative proteases of interest, that are phylogenetically close to known keratinases, and that could contribute to explaining the observed differential keratinolytic activities. These sequences are the following: G11C_05333, G11C_02264, G11C_03013, G11C_05273, G11C_01510, G11C_01512, and G11C_02546. They belong to p-orthogroups 0, 8, and 17, communities 1 and 4, which are related to peptidase families S01 and S08, therefore, supporting this prediction.Focusing on Streptomyces, a comparative genomic analysis was carried out between three streptomycetes with different keratinolytic activities, strains G11C, CHD11, and Vc74B-19 with high, medium, and null activity, respectively  and the peptidases shared between the keratinolytic strains CHD11 and G11C were predicted to be intracellular. However, given their exclusive relationship with the keratinolytic strains, they are still considered interesting because their presence may possibly explain the differences in keratinolytic activity between the three strains. On the other hand, the peptidases predicted to be extracellular present in the t-SNE groups were subjected to a phylogenetic analysis incorporating the tool: Ancestral state reconstruction, which assigns a probability distribution to each ancestor node within the tree, of belonging to the categories: Functional keratinase, keratinase-linked protein, three-strain category, and non-keratinase. Finally, after applying selection criteria for the analysis of the clades of the phylogenetic trees , seven coding sequences for potential extracellular keratinases were identified in Streptomyces sp. G11C. To determine the genetic determinants potentially involved in the keratinolytic capacity of marine ectively . InitialIn this analysis, sequences codifying for (1) proteases related exclusively to keratinolytic strains and (2) proteases predicted to be extracellular and related to functional keratinases are considered as interesting candidates that could explain the differences in keratinolytic activity between the three strains.Streptomyces strain with keratinolytic activity. To see if genetic features related to these activities could be identified, three strains with differential keratinolytic activity were selected, Streptomyces sp. G11C presented a rather high percentage of feather degradation, reaching approximately 80% and with a relative keratinase activity of 60%, after five days of incubation, whereas Streptomyces sp. CHD11 presented lower relative keratinase activity (less than 10%) [Streptomyces sp. Vc74B-19 presented no keratinolytic activity, even after 10 days of incubation [In this study, a multi-step bioinformatic pipeline, applying several comparative genomics tools, was developed to predict a set of genes that encode putative keratinases in a marine han 10%) . In contcubation . Streptomyces analyzed, the diversity of peptidases could be related to environmental characteristics such as the pH of the ocean, which varies from slightly neutral to alkaline [Genome comparison showed approximately 3% of the total gene count encoded putative peptidases, revealing an unexpected similar abundance and diversity in all three strains. Previous reports described that among bacterial species, the percentage of peptidases encoded in the genomes ranges from 1.5% to 4% . A similalkaline . Serine,alkaline ,56 and malkaline , highligalkaline . TherefoBacillus belong to the peptidase S08 family, and keratinases from Actinobacteria, such as Nocardiopsis, Actinomadura, and Streptomyces, belong mostly to the S01 family. There is scarce information about the metalloprotease family associated with keratinases. In our database, only one of the keratinases, from a Geobacillus strain (AJD77429.1), belongs to the metalloprotease super-family, specifically to the family M04. A protease similarity network, using these functionally characterized keratinases together with our three streptomycete genomes and non-keratinases databases, highlighted three communities of nodes containing keratinase-linked peptidases belonging to the S1, S8 (serine proteases), and M4  families, allowing the identification of 11 promising sequences of the keratinolytic strain G11C. It is worth mentioning that community 41 (family M04) presented the least number of grouped sequences, which is possibly related to the presence of only one functional keratinase, as mentioned above. Furthermore, the three strains show few peptidases belonging to this family, for example, Streptomyces sp. G11C only presents three M04 metallopeptidases that were subsequently discarded because they did not meet the pipeline criteria . On the other hand, the promising sequences, including the unique peptidases of the strain G11C (unassigned p-orthogroup peptidases), and the peptidases belonging to p-orthogroups shared between the keratinolytic strains CHD11 and G11C, did not group in any community of the network, since most belong to other protease families, being divergent from the known keratinase families. In these groups, there are only two serine proteases of the family S01, although they did not group in community 4 (family S01). In addition, it can be mentioned that one of these unique sequences of strain G11C (G11C_00267) presented high abundance in the secretome analysis carried out previously [Most of the keratinases known to date, have been classified as serine proteases ,19,58,59eviously , indicatStreptomyces sp. G11C could explain the differences observed, in terms of keratinolytic activity, between the three strains. Apparently, the degradation of recalcitrant keratin wastes requires the cooperation of several keratinolytic proteases, as evidenced in some bacterial [Bacillus sp. 8A6 with keratin-rich substrates, belonging to four protease families M12, S01A, S8A, and T3. In fungi, the participation of a set of proteases in keratin degradation has also been reported. Pathogenic fungi mainly secrete endoproteases, including proteases from families A1, S8A, M36, and M35 [Onygena corvina secretes proteases belonging to three protease families: S08, M28, and M03, when cultivated with pig bristles [Streptomyces, the approach has been focused on purifying and characterizing the main keratinolytic enzyme. For example, in work reported by Bressollier et al. [Streptomyces albidoflavus grown on feather meal-based medium, but only the most abundant keratinolytic serine protease was further characterized. Recently, through a transcriptomic analysis performed by Li et al. in 2020, it was possible to elucidate a set of factors involved in the keratin degradation mechanism mediated by Streptomyces sp. SCUT-3 [To narrow the search for potential keratinases, we complemented this information with the subcellular localization data and phylogeny. In the t-SNE clustering analysis, we identified those putative peptidases that are predicted as extracellular (including the keratinase-linked sequences mentioned above). In this analysis, we reasoned that cellular localization prediction is intimately related to a putative keratinolytic function. This is in line with previous reports, that have shown that the macromolecular characteristics of keratin prevent its direct absorption by microbial cells . Thus, tacterial ,65,66,67acterial . Recentl and M35 . The nonr et al. , at leasStreptomyces sp. G11C secretome: Six of seven keratinolytic proteases , and 12 of 17 unique genes of the G11C genome (“unassigned p-orthogroup” peptidases), were detected under the culture conditions with feathers as the sole carbon source, indicating that a set of enzymes may act synergistically during keratin degradation. Interestingly, one of the unique genes of the G11C strain (G11C_00267) presented one of the highest protein abundances in the proteomic analysis [Streptomyces sp. G11C could be potentiating its keratinolytic activity, leading to an advantage over the other Streptomyces analyzed strains CHD11 and Vc74B-19. Genes encoding disulfide reductases and genes related to sulfite export were similar in the three strains (data not shown), suggesting that disulfide bond reduction is not related to the observed functional differences, at least not the common mechanisms described in the literature [To go further on this argument, in our recent work , we confanalysis , suggestterature . AdditioStreptomyces were chosen to perform a genomic comparison according to their keratinolytic activity. Streptomyces sp. G11C isolated from marine sediments derived from Penas Gulf, with high keratinolytic activity, Streptomyces sp. CHD11, isolated from a marine sponge from Chañaral de Aceituno Island, with low keratinolytic activity and Streptomyces sp. Vc74B-19, isolated from marine sediments from Valparaíso Bay, with no keratinolytic activity , resuspended in 50 mM EDTA, and then mechanically separated with a shank. For cell rupture, disaggregated cells were treated with Lysozyme 10 mg/mL, Lysostaphin 10 mg/mL, and Proteinase K 1 mg/mL for 1 h at 37 °C. Then, for DNA purification, the Promega Wizard DNA extraction kit was used, according to the manufacturer’s description. Finally, an alcohol treatment was used for DNA precipitation and cleaning, with 100% isopropanol for DNA precipitation, followed by 70% ethanol to wash the DNA pellet. The sample was rehydrated and stored according to the Promega Wizard DNA Extraction Kit instructions.Strains were grown on ISP2 media for 24–48 h, and cells were collected by centrifugation  and Streptomyces aurantiogriseus NRRL B-5416. S. albogriseoleus genome was not included, since assemblies presented anomalies, according to NCBI Genbank. The genomes of a Catenulispora strain (DSM 44928) and a Kitasatospora strain (KM-6054) were used as outgroups. Selected genomes and the outgroup genomes were retrieved from the Pathosystems Resource Integration Center (PATRIC) database and are described in i et al. , spannini et al.  were alsAnnotations obtained from Prokka, PANNZER2, and eggNOG were consolidated in −20 of the three-strain dataset was performed against MEROPS, the peptidase database, release 11, using the HMMER-based web-server utility [−40 These were named protease orthogroup or p-orthogroup. Venn diagram visualization of common p-orthogroups between strains was done using the package eulerr in R [For the classification into protease families, a reciprocal best hit search . The functional keratinase database was built according to literature and contains 61 sequences from  Thermus . In addi Thermus . This da−5 1 × 10−20, 1 × 10−40, and 1 × 10−80, being 1 × 10−40 the value chosen for the analysis, since it is a typical value (order of magnitude) employed in sequence similarity networks [For the similarity network of the three-strain dataset, a blastp all-vs-all output was processed in Python using the networkX package . Both funetworks , and subnetworks . A depicnetworks  with a cnetworks  and Yifanetworks  layout aPrediction of subcellular localization for proteases was performed using CELLO v2.5  and PSORt-SNE points, i.e., coordinates by sequence, were clustered into groups, called “t-SNE groups”, using the DBSCAN algorithm  implemenStreptomyces strains with varying keratinolytic activities permitted the identification of a set of putative proteases that could potentially be involved in the keratinolytic capacity of Streptomyces sp. G11C. According to peptidase family classification and orthogroup identification, we consider as promising candidates: (1) Unique putative peptidases in the keratinolytic strain G11C (17 unassigned p-orthogroup peptidases), including those belonging to the peptidase families C02 and S53; and (2) three peptidases present in the orthologous groups shared between strains G11C and CHD11, but not present in the non-keratinolytic strain Vc74B-19. Additionally, similarity network analysis identified three communities of keratinases-linked peptidases belonging to families S01, S08, and M04. Complementing this information with sub-cellular localization data and phylogenetic analysis, we identified seven promising genes likely to encode potential keratinases from Streptomyces sp. G11C, belonging to peptidase families S01 and S08. These findings provide genetic information for the proteomic analysis in the keratinolytic strain G11C, described in related work [Our robust comparative genomic analysis between three ted work , which f


MAPK1, MAPK8, TNFAIP3, ATF4, and SLC2A1. Moreover, MAPK1 was one of the key genes related to TNF signaling and oxidative stress, and it may play an important role in ferroptosis after cerebral hemorrhage. The MAPK1-related molecules included hsa-miR-15b-5P, hsa-miR-93-5P, miR-20b-5p, SNHG16, XIST, AC084219.4, RP11-379K17.11, CTC-444N24.11, GS1-358P8.4, CTB-89H12.4, RP4-773N10.5, and FGD5-AS1. We also generated a hemorrhage rat model, which was used to conduct exercise intervention in ICH rats, and qRT-PCR was used to assess the expression levels of our genes of interest. The mRNA levels after cerebral hemorrhage showed that MAPK1, ATF4, SLC2A1, and TNFAIP3 were upregulated, whereas MAPK8 was downregulated. Treadmill training increased the expression of anti-inflammatory molecules TNFAIP3 and SLC2A1 and reduced the expression of MAPK1, ATF4, and MAPK8, indicating that treadmill training may be utilized as antioxidant therapy to decrease neuronal ferroptosis. The results of this study indicated that the MAPK1-related mRNA–miRNA–lncRNA interaction chain could be potentially employed as a biomarker of the inception and progression of ferroptosis after cerebral hemorrhage.Intracerebral hemorrhage (ICH) is a dangerous neurological disease. The mechanism of ferroptosis in ICH remains unclear. Using bioinformatics analysis, we aimed to identify the key molecules involved in ferroptosis and provide treatment targets for ICH to further explore the mechanism of ferroptosis in ICH. GSE24265 was downloaded from the Gene Expression Omnibus (GEO) dataset and intersected with ferroptosis genes. A total of 45 differentially expressed genes (DEGs) were selected, most of which were involved in the TNF signaling pathway and oxidative stress response. Key modules constructed by the protein–protein interaction (PPI) network analysis and screening of genes related to the TNF signaling pathway led to the confirmation of the following genes of interest:  Intracerebral hemorrhage (ICH) is a disease associated with high mortality, disability, and dysfunction . The pat-mitogen-activated protein kinase 1 (p-MAPK1) significantly increases after ICH, and the MAPK/ERK pathway is involved in ferroptosis regulation of ICH  . Recent n of ICH . With re by fe-1 . Activat by fe-1 . HoweverAt present, there have been no bioinformatics-based studies on the mechanism of ferroptosis genes after ICH. Therefore, we used data mining and data analysis techniques to screen differentially expressed genes (DEGs) in perihematomal tissue (PH) and contralateral normal tissue of ICH patients. These DEGs were then intersected with the ferroptosis dataset to obtain ferroptosis DEGs. Moreover, to identify crucial biomarkers and establish the pathogenesis of ICH at the molecular level, we investigated key miRNAs and lncRNAs that may play principal roles in ICH. We also constructed a rat ICH model and an intervention model using treadmill training to test the above-mentioned hypothesis. Our results will help to understand ferroptosis after ICH and provide new thoughts for clinical diagnosis and treatment of ICH.n = 7): rats that underwent surgery without ICH; (2) ICH (n = 7): the ICH group, animals that received autologous blood injection; and (3) ICH+ early exercise (n = 7): rats that received additional treadmill training after ICH. The animal study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Qingdao.Adult male Sprague–Dawley rats (2 months old) were purchased from Qingdao Peng Yue Animal Husbandry Co., Ltd., and kept under a 12-h light/dark cycle in the same colony room, with controlled temperature and humidity. The animals were randomly assigned into the following three groups: (1) sham  the GEO . This da the GEO . This daT test was employed to determine p-values and adjusted p-values in the differential gene expression (DGE) analysis.GEO2R, an online analysis tool, was used to perform the analysis of differential expression . The expP-value < 0.05. We also obtained a dataset that included 265 genes from the Ferroptosis Database  and intersected it with GSE24265 to identify ferroptosis DEGs. The online tool Venny2.1 was employed to generate a Venn diagram of DEGs, and a heat map of DEGs was drawn using Heml Software.Genes retained from diverse tissues were selected using the following criteria: a | log2 (fold-change)| > 1 and adjusted P < 0.05.Functional enrichment analysis of DEGs was performed using DAVID 6.8, Metascape, and WebGestalt. These different enrichment analysis tools have different algorithms, which can play the role of mutual verification. Gene set enrichment analysis (GSEA) is a method to sort genes according to the degree of differential expression of two samples . It is aTo predict protein–protein interactions (PPIs), STRING, an online database which can retrieve the interaction between a group of proteins, was utilized in the PPI network analysis . CytoscaP < 0.05 was considered to show a significant difference.Molecular complex detection (MCODE) was used for clustering analysis of gene networks to identify key PPI network modules . The funIn addition to the abovementioned analytical tools, we also used miRWalk 2.0 to predict targeted pivotal miRNAs and build the gene–miRNA interaction networks that are related to cluster 1 . We intelncRNAs, which are upstream molecules of miRNAs, were identified using StarBase v2.0 . We selen = 7; ICH: n = 7; ICH + early exercise: n = 7), PH was extracted, and qRT-PCR was performed. Briefly, total RNA was collected from the PH of rats using TRIzol. RNA purity was tested using Quantus Fluorometer. First, total RNA was used for reverse transcription reaction. Then, the pre-amplified cDNA samples were mixed with One-Step SYBR PrimeScript PLUS RTPCR Kit. Finally, the reaction was conducted on AriaMx HRM. GAPDH was used as a positive control, and the comparative Ct method was used to calculate each sample . One-way analysis of variance (ANOVA) was used for statistical analysis of sham, ICH, and ICH + early exercise groups, followed by Tukey’s multiple-comparisons GSE24265, the microarray expression profiling dataset, was downloaded from the GEO database, and DEGs were obtained by comparing PH and the corresponding gray and contralateral white matter. We also gained the dataset including 265 genes from the Ferroptosis Database (FerrDb) and intersected them with GSE24265 to identify ferroptosis DEGs. We found altogether 40 upregulated and five downregulated genes . The heaPathway enrichment analyses of DEGs were performed using the online tools Metascape, WebGestalt, and DAVID. First, we uploaded the related information of DEGs in the PH samples and normal samples to the WebGestalt software. The result of the enrichment gene dataset analysis indicated that the genes significantly enriched were involved in the TNF signaling pathway, MAPK signaling pathway, fluid shear stress, atherosclerosis, and Kaposi’s sarcoma-associated herpesvirus infection . DAVID wMAPK8) and 17 upregulated genes were established  in the STRING online database. Genes were represented by nodes, and the interactions between the genes were indicated by edges. The upregulated genes were marked in blue, while the downregulated genes were labeled in red. MCODE, the application from Cytoscape, was used for clustering analysis of gene networks to draw the key modules . Two keyablished . These 1ubstance .P < 0.05, seed sequence lengths > 7, and 3′UTR as the target gene-binding regions  was selected as the criterion. After cross-linking, nine lncRNAs targeting three key miRNAs were revealed, namely, SNHG16, XIST, AC084219.4, RP11-379K17.11, CTC-444N24.11, GS1-358P8.4, CTB-89H12.4, RP4-773N10.5, and FGD5-AS1.Nine miRNAs had higher amounts of cross-linked genes (≥2). Only SLC2A1, TNFAIP3, ATF4, MAPK1, and MAPK8 were identified by MCODE as the genes with high MCODE score in the two clusters. Then, the filtered biomarkers, including MAPK1, MAPK8, TNFAIP3, ATF4, and SLC2A1, were verified in ICH samples using qRT-PCR. The results indicated that the expression levels of MAPK1, ATF4, SLC2A1, and TNFAIP3 were visibly higher and that the expression of MAPK8 was lower in ICH rats than in the sham controls. After the exercise training, MAPK1, MAPK8, and ATF4 showed low expression levels compared with the ICH group. In contrast, TNFAIP3 and SLC2A1 were upregulated by exercise training  and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24265.Publicly available datasets were analyzed in this study. This data can be found here: FerrDb (The animal study was reviewed and approved by The Ethics Committee of Affiliated Hospital of Qingdao University.QW and TL designed the experiments. YW, TL, XL, and YC performed the experiments. TL, XL, PM, and GZ wrote the manuscript and analyzed the data. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background: Esophageal adenocarcinoma (EAC) remains a leading cause of cancer-related deaths worldwide and demonstrates a predominant rising incidence in Western countries. Recently, immunotherapy has dramatically changed the landscape of treatment for many advanced cancers, with the benefit in EAC thus far been limited to a small fraction of patients.Methods: Using somatic mutation data of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium, we delineated the somatic mutation landscape of EAC patients from US and England. Based on the expression data of TCGA cohort, multiple bioinformatics algorithms were utilized to perform function annotation, immune cell infiltration analysis, and immunotherapy response assessment.Results: We found that RYR2 was a common frequently mutated gene in both cohorts, and patients with RYR2 mutation suggested higher tumor mutation burden (TMB), better prognosis, and superior expression of immune checkpoints. Moreover, RYR2 mutation upregulated the signaling pathways implicated in immune response and enhanced antitumor immunity in EAC. Multiple bioinformatics algorithms for assessing immunotherapy response demonstrated that patients with RYR2 mutation might benefit more from immunotherapy. In order to provide additional reference for antitumor therapy of different RYR2 status, we identified nine latent antitumor drugs associated with RYR2 status in EAC.Conclusion: This study reveals a novel gene whose mutation could be served as a potential biomarker for prognosis, TMB, and immunotherapy of EAC patients. Esophageal cancer is the eighth most prevalent malignancy and the sixth leading cause of cancer-related mortality worldwide. The predominant subtype in Western countries is esophageal adenocarcinoma (EAC), which demonstrated a predominant rising incidence in the last 40 years . GastroePD-L1), programmed death 1 (PD-1), and cytotoxic T-lymphocyte-associated protein 4  and British EAC patients (n = 409) were, respectively, derived from TCGASomatic gene mutation data for American EAC patients .Mutation MAF files encompassing somatic alterations for American EAC patients were processed tmb function of “maftools” R package was applied to calculate the TMB of each sample  algorithm was performed to identify dramatically enriched terms related to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and the biological process of gene ontology (GO). Permutations were set to 1,000 to obtain a normalized enrichment score (NES). Gene sets with false discovery rate (FDR) <0.01 were considered to be significantly enriched.To explore the potential molecular mechanisms significantly associated with + T cell, activated dendritic cell, natural killer T cell, macrophage, etc. Besides this, in order to ensure the rationality and robustness of the ssGSEA results, we applied two other different algorithms to further validate. The first one was CIBERSORT, a deconvolution algorithm that took a set of reference gene expression values as a minimum representation of each cell type and, based on these values, used support vector regression to estimate the proportion of 22 immune cell types  was applied to quantify the relative abundance of 28 immune cells in the tumor microenvironment of EAC. The gene set for marking each cell was obtained from the research of ll types . The othll types .PD-1 blockade. The GEP was composed of 18 inflammatory genes associated with antigen presentation, chemokine expression, cytotoxic activity, and adaptive immune resistance. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was employed to predict the immunotherapy response of each patient (RYR2 phenotypes and the patients with a different immunotherapy response (p < 0.05 suggests the significant similarity between two groups.T cell-inflamed gene expression profile (GEP) as proposed by  patient . The TIDresponse . SubMap RYR2 status: (1) because the IC50 value of each drug was not normally distributed , Wilcoxon rank-sum test was utilized; (2) considering the large number of drugs, we adopted an FDR < 0.05 as the screening criteria. FDR was obtained by Benjamini-Hochberg (BH) multiple test correction; and (3) for each drug of interest, if Wilcoxon rank-sum test FDR < 0.05 and the sensitivity of one phenotype was significantly higher than that of another phenotype, it was considered that the drug had specific sensitivity to this phenotype.To evaluate the drug response, we retrieved the imputed response to 138 anticancer drugs in EAC patients from a previous study . Drug sep TMB, IC50, immune cell infiltration abundance, and immune checkpoints (ICP) expression were all less than 0.05. Thus, the comparisons of two groups were conducted by Wilcoxon rank-sum test. Chi-square test or Fisher exact test was used to compare categorical variables. GSEA was performed by “clusterProfiler” R package , TTN (49%), MUC16 (29%), SYNE1 (28%), and HMCN1 , TTN (55%), MUC16 (33%), CSMD3 (22%), and LRP1B , and similar results were demonstrated in the ICGC British cohort . Subsequently, survival analysis was further performed to identify whether these FMGs associated with increased TMB were also related to the OS of patients with EAC. As shown in RYR2 mutation had a significantly longer OS (p < 0.05). Univariate Cox analysis revealed that the HR of RYR2 mutation was 0.645 , suggesting that RYR2 mutation was an independent protective factor of prognosis in EAC (RYR2 (17%), with 15/89 altered samples in TCGA American cohort and 70/409 altered samples in ICGC British cohort.The TMB in the TCGA cohort ranged from 0.04 to 31.70/MB, with a median of 2.1/MB; the TMB in the ICGC cohort ranged from 0.02 to 36.94/MB, with a median of 2.3/MB. Among common FMGs, patients with mutations in  cohorts . Previou cohorts . In EAC,s in EAC . The twoRYR2 mutation phenotype, such as “response to chemokine” , “chemokine-mediated signaling pathway” , “interleukin-2 production” , “lymphocyte-mediated immunity” , and “granulocyte chemotaxis” , “cytokine-cytokine receptor interaction” , “natural killer cell-mediated cytotoxicity” , “T cell receptor signaling pathway” , and “IL-17 signaling pathway” (NES = 2.121, FDR < 0.001; RYR2 mutation was significantly higher than in patients without RYR2 mutation (p < 0.05; RYR2 mutation possessed a stronger level of immune signature compared with patients without RYR2 mutation (RYR2 mutation might promote antitumor immunity in EAC, which had important implications for immunotherapy.According to GSEA analysis, we found plenty of immune-related GO terms that were enriched in mutation . OverallRYR2 mutation had a higher expression level of PD-L1, PD-L2, PD-1, and CTLA-4 than patients without RYR2 mutation (RYR2 mutation phenotype (RYR2 mutation phenotype (RYR2 mutation was higher relative to the patients without RYR2 mutation (mutant type vs. wild type: 43 vs. 16%; RYR2 mutation phenotype and patients with anti-PD-L1 therapy (FDR < 0.05; RYR2 mutation suggested better immunotherapy response.Patients with mutation . The T chenotype . We furthenotype . In addiRYR2 mutation were more sensitive to lenalidomide, MG-132, and SB216763, while patients with RYR2 mutation were more sensitive to A-770041, A-443654, CMK, erlotinib, JW-7-52-1, and rapamycin. Drugs were associated with RYR2 wild type if mainly targeting protein stability and degradation and WNT signaling, while drugs were associated with RYR2 mutation if mainly targeting EGFR signaling, kinases, and PI3K/MTOR signaling (RYR2 status.We retrieved the imputed response to 138 antitumor drugs in EAC patients from a previous study to identify potential antitumor drugs with specific sensitivity to each phenotype . As showignaling . These rRYR2 mutated frequently in the two cohorts, and its mutation was dramatically associated with a higher TMB and a favorable prognosis. Meanwhile, patients with RYR2 mutation suggested an “immune-hot” tumor, which enriched abundant immune-related pathways, numerous immune cell infiltrations, and higher expression of ICPs. These results indicated that patients with RYR2 mutation might benefit more from immunotherapy, which was in line with the immunotherapy assessment results of bioinformatics algorithms.In the present study, we, respectively, characterized the somatic mutation landscape of 87 American EAC patients and 409 British patients from TCGA and ICGC datasets. Then, we found that RYR2 is a major component of the intracellular Ca2+ release channels and is associated with the endoplasmic or sarcoplasmic reticulum of several cell types, especially in cardiomyocytes (RYR2 was significantly mutated in multiple cancers, and RYR2 was reported to be a driver gene in cervical cancer, colon cancer, breast cancer, head and neck cancer, and lung adenocarcinoma (RYR2 was a prognosis biomarker of cervical cancer and breast cancer. RYR2 was associated with a favorable outcome in basal-like breast tumors expressing PD-1/PD-L1. RYR2 mutation was a significant biomarker for suggesting high TMB in lung adenocarcinoma. In this study, we found that RYR2 mutation was an independent protective prognostic factor and had a positive relationship with high TMB in EAC. TMB represents the accumulation of somatic mutations in tumors; a high TMB can give rise to mutation-derived neoantigens and improve the immunogenicity of tumor, which is likely to induce a T-cell-dependent immune response (RYR2 mutation might promote antitumor immunity in EAC.myocytes . Recent arcinoma . Femi (2response . Hence, RYR2 mutation phenotype enriched a multitude of immune-related pathways and displayed a higher abundance of immune cell infiltration, suggesting the “immune-hot” subtype. A previous study has demonstrated that the “immune-hot” tumors were more sensitive to immunotherapy (PD-L1, PD-L2, PD-1, and CTLA-4, their expression in patients with RYR2 mutation was higher, which was conducive to obtaining an effective immunotherapy response. Consistent with this, bioinformatics algorithms including T cell-inflamed GEP, TIDE, and SubMap methods further validated this conclusion. These results indicated that patients with RYR2 mutation might be a promising biomarker of immunotherapy. However, the limitation of our study is in evaluating the immunotherapy response using bioinformatics algorithms rather than conducting large-scale immunotherapy clinical trials. In spite of this, the above-mentioned results were highly consistent in terms of functional analysis and predictive results, which indicates that our results are relatively reliable. In addition, we identified latent antitumor drugs associated with RYR2 status in EAC, hoping to provide additional reference for antitumor therapies of different RYR2 status.Actually, the otherapy . Apart fRYR2 was frequently mutated in EAC, and RYR2 mutation was dramatically associated with a higher TMB and suggested a better prognosis. Moreover, RYR2 mutation upregulated the signaling pathways implicated in immune response and enhanced the antitumor immunity in EAC. This study reveals a novel gene whose mutation could serve as a potential biomarker for the prognosis, TMB, and immunotherapy of EAC patients.In conclusion, our study identified that The original contributions presented in the study are included in the article/ZLiu, YZ, and XH designed this work. ZLiu, LL, and CG integrated and analyzed the data. ZLiu, ZS, and YZ wrote the manuscript. ZLiu, DJ, ZLi, LW, and XH edited and revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


IgJ, CD family, and MTMR genes. Furthermore, the progesterone-mediated oocyte maturation and cortisol synthesis and secretion pathway with targeted gene PGR and Pbx1 were significantly enriched between blue- and pink-eggshell chickens. Integrating analysis of lncRNA and mRNA profiles predicted 4 and 25 lncRNA–gene pairs by antisense and cis analysis. They were relative to immune, nerve, and lipids and amino acid metabolisms, porphyrin, and chlorophyll metabolism with targeted gene FECH and oxidative phosphorylation and cardiac muscle contraction pathways with targeted gene COX6A1. Within blue-eggshell chickens, the GO terms hindbrain tangential cell migration and phosphatidylinositol monophosphate phosphatase activity with targeted gene Plxna2 and MTRM1 were identified. Integrating analysis of lncRNA and mRNA profiles predicted 8 and 22 lncRNA–gene pairs. Most pathways were mainly enriched on lipid-related metabolisms as found in mRNA sequencing. The lncRNAs did exert similar functions in color formation by modulating pigment disposition and immune- and lipid-related metabolisms. Our results provide a catalog of chicken uterine lncRNAs and genes worthy of further studies to understand their roles in the selection for blue eggshell color layers.The blue egg is both of biological interest and economic importance for consumers, egg retailers, and scientists. To date, the genetic mechanisms underlying pigment have mainly focused on protein-coding genes. However, the underpinning mechanism of non-coding RNAs on the pigment deposition among different eggshell colors remains unknown. In this study, RNA sequencing was employed to profile the uterine gland transcriptome (lncRNA and mRNA) of 15 Changshun blue eggshell layers, to better understand the genetic mechanisms of deposition of blue eggshell color. Results showed that differentially expressed mRNAs, GO terms, and KEGG pathways among pink-eggshell and blue-eggshell chickens were mainly targeting immune- and transporter-related terms with the SLC family,  The blue egg is both of biological interest and economic importance for consumers, egg retailers, and scientists. A series of studies have been conducted on the nature of the shell pigments, the biochemical and physiological processes in various avian species involved in pigment formation, and its deposition in and on the shell , 2. It hSLCO1B3) is known to be responsible for a causative mutation for the blue eggshell phenotype and is specifically expressed in the uterus, not in the other organs in chickens  also produce brown and pink color eggs. Thus, it is urgent to figure out the underlying genetic mechanism from non-coding RNA and its targeted genes in breeding selection for blue eggshell layers. In the present study, a high-throughput RNA sequencing (RNA-seq) was employed to profile the uterus transcriptome of Changshun blue-eggshell chickens with different eggshell colors (different proportions between protoporphyrin and biliverdin). The aims were to discover and characterize lncRNAs in chicken uterus tissue and identify key genes, lncRNAs, and pathways that are associated with blue eggshell deposition of chickens.The study was approved by the Animal Care Committee of Foshan University . The experiment was carried out at a breeding farm, Changshun blue eggshell layer, Tiannong Corporation, Guizhou province. A total of 331 layers in a house at 210 days old were observed for 30 days. During these days, three hens stably producing dark blue (DB), four hens producing light blue (LB), and dark brown and greenish , respectively, were selected for this study see . BesidesSLCO1C1), solute carrier family 16 member 7 (SLC16A7), CD4, and ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 4 (ST6GALNAC4) was measured. Primer sequence 5.0 was used to design the primer sequence and synthesized by Shanghai Bioengineering limited company (GAPDH) was selected as a reference gene . The 2100 Bioanalyser (Agilent) was used to determine the RNA quality and was further quantified using the ND-2000 (NanoDrop Technologies). Only high-quality RNA sample was used to construct a sequencing library. The relative expression of color deposition-related genes including solute carrier organic anion transporter family member 1C1  enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed mRNAs and lncRNAs were carried out by Goatools (https://github.com/tanghaibao/Goatools) and KOBAS (http://kobas.cbi.pku.edu.cn/home.do).The total RNA of uterine glands from each was further RNA-seq for mRNA and lncRNA. Strand-specific RNA-seq libraries were generated by TruSeq Stranded Total RNA with Ribo-Zero Gold kit  following the manufacturer's recommendations. Sequencing was performed on an Illumina Hiseq 2500 instrument using the TruSeq PE Cluster Kit v3-cBot-HS  to generate 150-bp paired-end reads. Quality control and reads statistics were determined by FastQC (0.11.2) . Reads cAfter quality control, more than 98.2% of the total clean reads with high quality were mapped to Galgal 5.0, and 61,369 assembled transcripts were produced. Detailed information on data quality and mapping statistics is presented in p < 0.05). Between the DB and LB, there were three upregulated and eight downregulated genes in the LB group compared to the DB group (p < 0.05). Between the DP and LB, there were one upregulated and four downregulated genes in the LB group compared to the DP group (p < 0.05). We found that as compared to pink-shell eggs, there were eight upregulated and two downregulated genes in dark green-shell eggs, six upregulated and one downregulated gene in green-blue-shell eggs, and five upregulated and one downregulated gene in light green-shell eggs (p < 0.05).The comparison of differently expressed lncRNAs is presented in There are more known  and novel mRNAs  than known  and novel lncRNAs  in all the four groups. The heatmaps displayed differentially expressed lncRNAs  and mRNAIn relation to the mRNA, 24,102 (79.67%) known transcripts and 199,963 new transcripts were obtained. The differently expressed genes between comparisons are shown in p < 0.05, p < 0.05, GSN), cubilin (CUBN), desmoplakin (DSP), heparan sulfate proteoglycan 2 (HSPG2), fibronectin 1 (FN1), filamin A (FLNA), pre-B-cell leukemia transcription factor 1 (Pbx1), inositol 1,4,5-trisphosphate receptor type 2 (ITPR2), caspase 6 (CASP6), phospholipid phosphatase related 4 (PLPPR4), and ethanolamine kinase 1 (Etnk1) were also differently enriched between groups, of which the relative expression of GSN, CUBN, Pbx1, and PLPPR4 were upregulated, whereas DSP, HSPG2, FN1, FLNA, ITPR2, CASP6, Etnk1, and SLC family genes were downregulated in the former group as compared to the latter group (p < 0.01).There were no significant GO terms between DB and DP. Furthermore, a total of seven significant KEGG pathways including cortisol synthesis and secretion, lipid metabolism, and glycerophospholipid metabolism were obtained , Collagen type XVIII alpha 1 chain (COL18A1), Pbx1, ITPR2, TNF receptor associated factor (TRAF5), progesterone receptor (PGR), cytoplasmic polyadenylation element binding protein 2 (CPEB2), Etnk1, and SLC family genes were targeted genes between the two groups, of which the relative expression of SLC family genes, CUBN, COL18A1, Pbx1, ITPR2, TRAF5, PGR, and CPEB2 was upregulated, while that of DSP, ANXA11, and Etnk1 was downregulated in the DB group as compared to the LB group , inositol polyphosphate multikinase (IPMK), spermidine synthase (SRM), slingshot protein phosphatase 2 (SSH2), and SH3 domain containing ring finger 1 (SH3RF1) were identified, and the relative expression of all these genes was upregulated in the DP group compared to the DL group , adaptor related protein complex 1 gamma 1 subunit (AP1G1), ATP binding cassette subfamily A member 4 (ABCA4), PGR, heat shock protein 90 (Hsp90), UDP glycosyltransferase (UGT), L-gulono-gamma-lactone oxidase (GULO), raf proto-oncogene, serine/threonine kinase (RAF), legumain (LGMN), anaphase promoting complex subunit 1 (ANAPC1), CPEB, CD164, abhydrolase domain containing 2 (ABHD2), polypeptide N-acetylgalactosaminyltransferase 16 , and ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 5 (ST8SIA5), of which the relative expression of gene Ocln, PGR, CPEB, Hsp90, UGT, GULO, Galnt16, and ST8SIA5 was upregulated, while ABCA4, RAF, CD164, LGMN, CPEB, and SLC family genes were downregulated in the former group as compared to the latter group (p < 0.01).There were no significant GO terms between the comparison of PK vs. DB. However, seven pathways including transporters, progesterone-mediated oocyte maturation, ascorbate and aldarate metabolism, lysosome, glycosyltransferases, and butanoate metabolism were found to be significantly enriched , nuclear receptor subfamily 6 group A member 1 (Nr6a1), interferon regulatory factor 4 (IFR4), Pbx1, glycogen synthase kinase 3 alpha (GSK3B), patched 1 (PTCH1), and mannosidase beta (MANBA), were located, of which the relative expression of Nr6a1, GSK3B, Pbx1, and PTCH1 was upregulated, while PGR, FOXP1, IFR4, MANBA, and SLC family genes were downregulated in the former group as compared to the latter group (p < 0.01).There were eight GO terms and one GO term significantly enriched for cellular components and molecular function between PK and DP , Pbx1, and FOXP1 were significantly enriched .Between the PK and LB groups, four and three GO terms for molecular function and biological process , retinol saturas (RETSAT), Catenin beta like 1 (CTNNBL1), ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 4 (ST6GALNAC4), lysocardiolipin acyltransferase 1 (LCLAT1), and diacylglycerol kinase zeta (DGKZ).For the comparison of DB vs. DP, there were 153 differentially expressed lncRNAs. ECM–receptor interaction with candidate gene CD4 binding to lncRNA TCONS_00009117 and RNA transport pathways were differently identified , ST6GALNAC4, LCLAT1, and thioredoxin reductase 1 (TXNRD1).Between the comparison of DB vs. LB, 153 differentially expressed lncRNAs were identified, of which 9 lncRNAs were associated with the binding to sense chain of mRNA by antisense analysis. Cell adhesion molecules with targeted gene VWF) binding to lncRNA TCONS_00002665 was identified between groups . Retinol metabolism, other types of O-glycan biosynthesis, drug metabolism—other enzymes, cell cycle, N-glycan biosynthesis, Wnt signaling pathway, biosynthesis of secondary metabolites, p53 signaling pathway, and neurotrophin signaling pathways were identified , hypoxanthine phosphoribosyltransferase 1 (HPRT1), MDM2 proto-oncogene (MDM2), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon (YWHAE), mannosidase, alpha, class 1C, member 1 (MAN1C1), chitobiosyldiphosphodolichol beta-mannosyltransferase (ALG1), APC, mannose phosphate isomerase (MPI), and caspase 14, apoptosis-related cysteine peptidase (CASP14) as candidate genes were identified to be involved with these pathways.Between the comparison of DP vs. LB, there were 224 differentially expressed lncRNAs, of which 18 lncRNAs were associated with the binding to sense chain of mRNA by antisense analysis. Focal adhesion pathway with a candidate gene von Willebrand factor . Besides, proteasome, TGF-beta signaling, vitamin B6 metabolism, selenocompound metabolism, retinol metabolism, sulfur metabolism, glycosphingolipid biosynthesis-ganglio series, spliceosome, and cell cycle pathways with candidate genes including proteasome subunit beta 1/3 (PSMB1/3), Sp1, 3,-phosphoadenosine 5,-phosphosulfate synthase 1 (PAPSS1), TXNRD1, RETSAT, RNA binding motif protein, X-linked like 1 (RBMXL1), phosphatase, orphan 2 (PHOSPHO2), and retinoblastoma 1 (RB1) were obtained , GH, and 3-hydroxyanthranilate 3,4-dioxygenase (HAAO) were significantly identified . Beta-alanine metabolism, p53 signaling pathway, propanoate metabolism, RNA degradation, porphyrin and chlorophyll metabolism, valine, leucine, and isoleucine degradation, and sulfur metabolism pathways with 3-hydroxyisobutyryl-CoA hydrolas (HIBCH), sestrin 1 (SESN1), CASP14, 5,-3, exoribonuclease 1 (XRN1), ferrochelatase (FECH), and PAPSS1 as candidate genes were significantly identified between groups . ECM–receptor interaction and spliceosome different pathways were located . Retinol metabolism, N-Glycan biosynthesis, cardiac muscle contraction, oxidative phosphorylation, protein processing in endoplasmic reticulum, fatty acid elongation, biosynthesis of unsaturated fatty acids, and SNARE interactions in vesicular transport were significantly identified between groups , ALG1, cytochrome c oxidase subunit 6A1 (COX6A1), and vesicle-associated membrane protein 3 (VAMP3) were identified between groups.Between the comparison of PK vs. LB, 189 differentially expressed lncRNAs were identified, of which 10 lncRNAs were associated with the binding to sense chain of mRNA by antisense analysis. These targeted mRNAs were enriched on 15, 3, and 48 GO for cellular components, molecular function, and biological process, respectively  III beta (Top3b), and MTMR genes. Our results again provide evidence of the candidate gene SLCO1B3 for a causative mutation on pigment deposition of the blue eggshell phenotype  can be found at: NCBI [accession: PRJNA746050].The animal study was reviewed and approved by the Animal Care Committee of Foshan University .HL obtained the funding and designed this project. SC, KC, FL, JD, ZM, JX, GL, and HL performed the experiment. SC, KC, and ZM analyzed and interpreted the data. SC, KC, and HL drafted and revised the manuscript. All authors came to an agreement for publication.This work was supported by the Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding (2019B030301010), the Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes (2019KSYS011), and the Germplasm Improvement Talent Base in Guizhou Changshun Tiannong Green Shell Laying Hen Industrial Co. Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.FL and JD are employed by Guizhou Changshun Tiannong Green Shell Laying Hen Industrial Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Salmonella enterica. Colistin-resistant S. enterica in foods represents a threat to human health. Here, we assessed the prevalence of colistin-resistance in food-borne isolates of S. enterica , and established the genetic basis and transferability of this resistance. Five out of 231 isolates tested (2.2%) were resistant to colistin. Four of them, belonging to the European monophasic ST34 clone of S. Typhimurium, were characterized in the present study. They were collected from pork or pork and beef meat-derived products, either in 2015 (three isolates) or 2019 (one isolate). Molecular typing with XbaI-PFGE and plasmid profiling revealed distinct patterns for each isolate, even though two of the 2015 isolates derived from the same sample. The MICs of colistin ranged from 8 to 16 mg/L. All isolates carried the mcr-1.1 gene located on conjugative plasmids of the incompatibility groups IncX4 (2015 isolates) or IncHI2 (2019 isolate). Apart from colistin resistance, the four isolates carried chromosomal genes conferring resistance to ampicillin, streptomycin, sulfonamides and tetracycline  and heavy metals, including copper and silver (silESRCFBAGP and pcoGE1ABCDRSE2), arsenic (arsRSD2A2BCA1D1) ± mercury (merEDACPTR), which are characteristically associated with the European ST34 monophasic clone. The 2019 isolate was also resistant to other antibiotics, comprising third generation cephalosporins and cephamycins. The latter phenotype was conferred by the blaCMY–2 gene located on an IncI1-I(α)-ST2 plasmid. Results in the present study identified meat-derived products as a reservoir of a highly successful clone harboring transferable plasmids which confer resistance to colistin and other clinically important antibiotics. An important reduction in the number of food-borne S. enterica detected during the period of the study, together with the low frequency of colistin resistance, underlines the success of One Health initiatives, such as those implemented at the UE, to control zoonotic bacteria along the food chain and to halt the spread of antimicrobial resistance.Colistin is a last-resort antibiotic in fighting severe infections caused by multidrug resistant Gram negative pathogens in hospitals. Zoonotic bacteria acquire colistin resistance in animal reservoirs and mediate its spread along the food chain. This is the case of non-typhoid serovars of  Salmonella enterica subsp. enterica are one of the leading causes of bacterial food-borne infections in humans and animals worldwide  34 has emerged in Europe, originally in pigs and later in other domestic animals , located on RR that also contains a mercury resistance locus. This region consists of one or two resistance modules flanked and interspersed with IS26 elements , which includes a copper homeostasis and silver resistance island (CHASRI), and an arsenic resistance locus  with the responsible genes, elements . In mostce locus . It has ce locus .mcr genes, is a cause of serious concern in human medicine, and is actively monitored as part of programs implemented by both national and international organizations  as critically important with highest priority in human medicine . To thisizations . ZoonotiS. Typhimurium, selected on the basis of colistin resistance, and detected in the frame of the Spanish contribution to the monitoring and reporting of antimicrobial resistance in zoonotic and commensal bacteria  detected in retail food samples by the Laboratory of Public Health (LSP) of the Principality of Asturias, Spain, along the 2014 to 2019 period, were tested for colistin susceptibility. MICs with Sensititre were determined for isolates recovered during 2014, 2015, 2016, and 2019, using the EUVSEC panel . The panel consisted of ampicillin, cefotaxime, ceftazidime, meropenem, gentamicin, tetracycline, tigecycline, chloramphenicol, azithromycin, sulfamethoxazole, trimethoprim, nalidixic acid, ciprofloxacin, and colistin. For the 2017 and 2018 isolates, screening of colistin resistance was performed on SuperPolymyxin medium (1. The presence of mcr-1 to mcr-5 genes was screened by PCR using previously reported primers and conditions (S. Typhimurium isolates (n = 84) detected during the period of study. The following commercially available disks (Thermo Scientific), with the amount in μg shown in parenthesis, were used: ampicillin (10), amoxicillin-clavulanic acid (30), cefepime (30), cefotaxime (30), cefoxitin (30), ertapenem (10), chloramphenicol (30), amikacin (30), gentamicin (10), kanamycin (30), streptomycin (10), tobramycin (10), azithromycin (15), nalidixic acid (30), ciprofloxacin (5), sulfonamides (300), tetracycline (30), trimethoprim (5), fosfomycin (300), and nitrofurantoin (300). Results were interpreted according to blaCMY–2 gene was tested by PCR, using the primers reported by All n medium . Colistinditions . Disk diTo place the colistin-resistant isolates within context, information regarding year of recovery, sample of origin, serotype, as well as phage type and antimicrobial resistance properties, when known, is shown in Salmonella with this technique. Typing by Pulsed-Field Gel Electrophoresis (PFGE) was done with the XbaI endonuclease (Thermo Fisher Scientific), following the PulseNet protocol for S. enterica2. XbaI-digested DNA of S. Braenderup H9812 was used as size standard (E. coli strains V517 (E. coli K-12 J53 (resistant to rifampicin) as the recipient. Cultures of donor and recipient strains were incubated overnight at 37°C. Then 100 μl of the donor culture and 200 μl of the recipient culture were added to 1 ml LB. The mixtures were incubated overnight without shaking either at 37°C  or at 28 and 37°C (LSP 38/19). Transconjugants were selected on eosin–methylene blue agar , containing rifampicin (50 mg/L) plus either colistin  or cefotaxime . Eight to twelve transconjugants per mating experiment were tested for antimicrobial susceptibility, plasmid content, PBRT of the IncX4, IncHI2, IncI1 and IncFIB/IncFIC replicons . The phage type of the 2019 isolate is unknown, because since 2017 reference laboratories in Spain have discontinued typing of standard , and a dstandard . Plasmidins V517  and 39R8ins V517  were inceplicons , and PCR3. This tool facilitates full genome analysis, including de novo assembly of all the genome with VelvetOptimizer, followed by the separation of contigs belonging to the chromosome or contigs belonging to Mobile Genetic Elements (MGEs), such as plasmids. At the same time, and in a friendly, easy, and graphical way, PLACNETw allows the exploration of close references to each assembled contig, in order to determine its genetic nature . Besides, this tool can ease MGE typing, both by the analysis of replication initiation proteins and relaxases, which are key elements in plasmid replication and mobilization. Finally, an in silico incompatibility group classification is performed  according to the manufacturer’s instructions. Paired-end reads of 150 nt were generated from ca. 500–400 bp fragment libraries at the CIBIR  or at Eurofins Genomics , using HiSeq 2500 and NovaSeq 6000 S2 PE150 XP platforms, respectively. Genome reconstruction was achieved with PLACNETwerformed .4). Bioinformatic analysis was performed both with PLACNET and with the MLST, ResFinder, PlasmidFinder and pMLST tools of the Center for Genomic Epidemiology5 , and MyDbFinder  using customized databases comprising open reading frames from strains SLK-521 , 105/7/03 and 07-2006 , and LT2 , with STM ORFs following the S. Typhimurium LT2 nomenclature. Reconstruction of the IncX4 plasmids from contigs identified with PLACNET, and of the tetra-R regions (guided by the location and orientation of the multiple IS26 elements carried by them), was achieved by PCR amplifications of the intervening DNA, followed by Sanger sequencing of the obtained amplicons . Once established, the organization of the tetra-R regions, as well as the genetic environments of the mcr-1.1 and blaCMY–2 genes, were represented with EasyFig6.Information regarding the quality of the assemblies, as provided by VelvetOptimiser, is compiled in miology5 . SGI-4, JACXKV0000000 00, JACXKU000000000, JACXKT000000000, and JAGMWH00 0000000, for LSP 237/15, LSP 295/15, LSP 298/15, and LSP 38/19, respectively. The plasmid sequences of pIncX4_LSP 237/15, pIncX4_LSP 295/15, pIncX4_LSP 298/15, pIncHI2_contig 309_LSP 38/19, and pIncI1-I(α)_contig 9_LSP 38/19 were also deposited in GenBank under accession numbers OK642377, OK642378, OK642379, OK642375, and OK642376.The genome sequences of the colistin resistant isolates under study were deposited at the GenBank database with the following accession numbers: S. enterica were recovered from food samples in Asturias. Five of them (2.2%) were resistant to colistin and four of these , pertaining to the monophasic European clone of S. Typhimurium were thoroughly characterized in the present study (S. enterica) detected in food samples during the period of study. The remaining isolate, LSP 136/15, was identified as S. enterica serovar Kedougou and carried the mcr-4.3 gene on a ColE10 plasmid  or 2019 (LSP 38/19). Remarkably, although LSP 295/15 and LSP 298/15 derived from the same sample (pork and beef minced meat), the four isolates were different (see below), and could be then assigned to distinct strains. As shown in in silico” assigned the isolates to ST34, as expected for the European monophasic clone. Experimental typing with XbaI-PFGE revealed a distinct pattern for each isolate, though profiles of the two isolates obtained from the same sample (LSP 295/15 and LSP 298/15) were more closely related , with assembly sizes ranging from 4.94 to 5.42 Mb . MLST ty related . Plasmid related ; Table 1mcr-1.1 gene was identified in all of them. The gene was carried by IncX4/MOBP3 plasmids of 33.3 kb in the 2015 isolates, and by a large IncHI2/MOBH plasmid of 242 kb in the 2019 isolate , characteristically associated with the monophasic ST34 clone and conferred by blaTEM–1, strA-strB, sul2, and tet(B) genes of chromosomal location. Moreover, LSP 38/19 was also resistant to cefotaxime and cefoxitin. This phenotype is consistent with production of an AmpC β-lactamase, and the presence of blaCMY–2 was indeed demonstrated. The gene, which encodes the CMY-2 AmpC β-lactamase, resided on a IncI1-I(α)/mckB/MOBP plasmid, assigned to ST2 by pMLST. The 2019 isolate was additionally resistant to many other antibiotics, including chloramphenicol, aminoglycosides , and trimethoprim, with the responsible genes located on the large IncHI2  and IncFIB-IncFIC/traI/MOBH  plasmids, or carried by a small ColE1 plasmid [aph(3′)-Ia]. The actual location of two additional aminoglycoside resistance genes, aph(4)-1a and aph(3)-IV, which were found together, flanked by two copies of IS26, could not be established.As shown in  isolate ; Table 1mcr-1.1 gene was located on IncX4 plasmids in the 2015 isolates, and carried by a large IncHI2 plasmid in the 2019 isolate , is not detected , is located further downstream. The IncX4 plasmids of LSP 295/15 and LSP 298/15 were identical to each other, and differed from the IncX4 plasmid of LSP 237/15 by a single SNP and a 32 bp insertion present in the latter one but not in the former two. The IncX4 plasmids of these isolates were very closely related (more than 99% identity and coverage) to previously sequenced IncX4 plasmids carrying either mcr-1.1 , or mcr-1.2 .The  isolate . The gendetected . Howevermcr-1.1 gene of the 2019 isolate was located on a large contig of 83 kb . Other IncH12 plasmids contain regions closely related to the 83 kb contig of the LSP 38/19 plasmid, in which the mcr-1.1 gene is either absent  or placed outside the corresponding region  (blaCMY–2 gene of LSP 38/19 resided on an IncI1-I(α)-ST2 plasmid of 105 kb  and a dog .The of 83 kb , which bP020493) . On the f 105 kb . More prepZ gene . This comcr-1.1 or blaCMY–2 genes in the LSP isolates could be transferred into E. coli through conjugation/mobilization experiments . These results indicate that the IncI1-I(α) of LSP 38/19 can be efficiently transferred at 37°C but not at 28°C. All transconjugants analyzed from the 37°C mating were resistant to cefotaxime and colistin, PCR-positive for blaCMY–2 and mcr-1.1, and carried the IncHI2, IncI1-I(α) and IncFIB-IncFIC plasmids. In contrast, when selection was performed with colistin, the conjugation frequencies were of 2.6 × 10–5 and 1.6 × 10–5 at both 28 and 37°C. Transconjugants resulting from matings at the two temperatures were resistant to colistin but susceptible to cefotaxime, PCR positive for mcr-1.1 but not for blaCMY–2, and carried the IncHI2 and IncFIB-IncFIC plasmids. These results are consistent with efficient transfer of the IncHI2 plasmid at 28°C, either by conjugation and/or mobilization, and also at 37°C, although in the latter case it has to be mobilized by the accompanying IncFIB-IncFIC plasmid.The IncX4, IncHI2 and IncI1-I(α) plasmids harboring the eriments . The fre plasmid , both crand 30°C . SelectiiroB and flanked by oppositely oriented copies of IS26, in the case of LSP 295/15 and LSP 298/15, and STM2759 and iroB in strain 07-2006). In contrast, the tetra-R regions of LSP 237/15 and LSP 38/19 comprised two modules embedded between ΔSTM2753 and iroB. The modules of LSP 237/15 resemble those of 105/7/03  are inverted in 38/19, and additional ORFs , are found between the two modules. In all strains except 105/7/03, evolution of the tetra-R region was accompanied by deletions of a number of chromosomal ORFs that always included the fljAB genes, as well as the entire  or part (LSP 237/15) of the hin gene, linking these regions with the monophasic phenotype.As shown in 105/7/03 , except silESRCFBAGP, pcoGE1ABCDRSE2, and arsRSD2A2BCA1D1 gene clusters, accountable for copper, silver and arsenic tolerance. The insertion site of the ICE element, between STM4320 and yjdC , also coincides with previous information.In contrast to the diversity of the tetra-R regions, SGI-4 was identical in the analyzed isolates, and identical or nearly identical to the genomic islands carried by many other ST34 monophasic strains, including SLK-521 used for comparison . It compS. enterica were recovered from food samples in Asturias, a Northern Spanish region. Interestingly, the number of isolates decreased considerably along time, from nearly 96 in 2014 down to 4–10 since 2017, a favorable trend that probably correlates with implementation in Spain of the integrated EU legislation aimed to monitor and control Salmonella along the food chain  or more frequently both copies of ISApl1  of 27 bp, which encompasses the last 26 bp of the pap2 gene, was identified in the IncX4 plasmids of the 2015 isolates, and a shorter version with only 21 bp, overlaps the pap2 gene in the IncHI2 plasmid. In single-ended variants, a unique upstream copy of ISApl1 in conjunction with the IRR of the ancestral downstream ISApl1 may still be able to mobilize the mcr-1 gene. In contrast, variants entirely devoided of ISApl1, like those reported in the present study, have made their way toward stabilization of the resistance gene and maintenance of plasmid integrity  can be found below: JF, VL, RR, and MR conceptualized and designed the study. XV, VG, and MB carried out the experiments. XV, MB, MT, RR, and MR did data curation. XV, VG, RR, and MR prepared tables and figures. XV, MT, VL, RR, and MR performed WGS analyses. XV, RR, and MR drafted the manuscript. All authors contributed to analysis and interpretation of the results, reviewed the manuscript, and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.