Streptomyces genomes. Here, we report the largest Streptomyces pangenome generated by using 205 complete genomes. Metabolic potentials of the pangenome and individual genomes were analyzed, revealing degrees of conservation of individual metabolic pathways and strains potentially suitable for metabolic engineering. Of them, Streptomyces bingchenggensis was identified as a potent degrader of plant biomass. Polyketide, non-ribosomal peptide, and gamma-butyrolactone biosynthetic enzymes are primarily strain specific while ectoine and some terpene biosynthetic pathways are highly conserved. A large number of transcription factors associated with secondary metabolism are strain-specific while those controlling basic biological processes are highly conserved. Although the majority of genes involved in morphological development are highly conserved, there are strain-specific varieties which may contribute to fine tuning the timing of cellular differentiation. Overall, these results provide insights into the metabolic potential, regulation and physiology of streptomycetes, which will facilitate further exploitation of these important bacteria.Streptomycetes are highly metabolically gifted bacteria with the abilities to produce bioproducts that have profound economic and societal importance. These bioproducts are produced by metabolic pathways including those for the biosynthesis of secondary metabolites and catabolism of plant biomass constituents. Advancements in genome sequencing technologies have revealed a wealth of untapped metabolic potential from  They comprise numerous organisms relevant to human health as antibiotic and therapeutics producers and for biotechnological applications. The soil-dwelling genus Streptomyces is well-known for producing a variety of bioactive compounds as secondary metabolites2, with Streptomyces coelicolor A3(2) serving as a model organism for the genus due to a long history of extensive biological and chemical investigations and availability of its complete genome sequence for over 20 years. Streptomyces bioactive compounds include therapeutics such as streptomycin, the first remedy for tuberculosis, and avermectins, antiparasitic agents4. Other examples of industrial chemicals streptomycetes produce are antibiotics used in animal health, tylosin and monensin6. It is expected that there are thousands more waiting to be identified. In addition, several streptomycetes such as Streptomyces viridosporus are lignocellulolytic bacteria producing extracellular enzymes which modify lignocellulose, and catabolise carbohydrates and aromatic compounds derived from lignocellulose as carbon sources9. Lignocellulose is a constituent of plant cell wall and abundant and renewable feedstocks for chemical production such as biofuels and other bioproducts10. Because of the ability of streptomycetes to catabolise lignocellulose-derived compounds as carbon sources and produce a wide variety of chemical compounds as secondary metabolites, they may be ideal platforms for conversion of plant biomass into valuable chemicals. The other characteristic feature of the biology of streptomycetes is their complex lifecycle, forming branching vegetative hyphae and sporogenic aerial hyphae. This mycelial lifestyle is quite distinct from the majority of bacteria, resembling that of filamentous fungi. When spores encounter favourable conditions, they germinate, and grow to form hyphae, which extend to form vegetative mycelia and take up nutrients from surrounding substrates. In response to specific signals such as nutrient depletion, hydrophobic aerial hyphae extend into air, escaping from aqueous environments. Aerial hyphae synchronously divide by multiple septa and each compartment further develops and maturates forming a reproductive spore11. The regulation of this unique morphological development has been traditionally studied mainly in Streptomyces coelicolor A3(2), Streptomyces griseus NBRC 13350, and Streptomyces venezuelae NRRL B-65442, and a number of regulatory proteins controlling aerial mycelium formation and sporulation have been identified12. Many transcription factors controlling aerial mycelium formation and sporulation are termed Bld and Whi proteins, respectively. Of these, BldD and AdpA/BldH are global transcription factors, controlling genes not only involved in morphogenesis, but also in secondary metabolism15. With many of these regulatory proteins including BldD and AdpA/BldH conserved in these 3 species, morphological development is believed to be controlled by common regulatory cascades, with minor differences as some strain-specific regulatory proteins involved in this process have been recently discovered12. The mycelial lifecycle of streptomycetes results from extension of hyphal tips known as polar growth16. Cell wall materials such as peptidoglycans and other glycans are incorporated at the hyphal tips and their production and incorporation are controlled by multiple enzymes18. Of these enzymes, glucanase and lytic polysaccharide monooxygenase play crucial roles by controlling localised remodeling and degradation of peptidoglycan19.Actinobacteriota, a group of gram-positive bacteria with high G + C content DNA, are one of the largest taxonomic units of bacteria, and are found in a variety of ecosystemsStreptomyces genome, for example, generally encodes 25–50 detectable secondary metabolite biosynthetic pathways20. Because of the biotechnological potential of their metabolism, a number of Streptomyces genomes have been sequenced, which has revealed an ever-increasing number of unique metabolic pathways. This diversity of metabolic pathways is complemented by a large number of transcriptional factors encoded in the Streptomyces genomes, including transcriptional activators and repressors and sigma factors, which enable precise control of expression of specific metabolic pathways21. Acquisition of a number of genes involved in secondary metabolism, regulation, and morphological development resulted in expansion of the Streptomyces genome sizes, which typically range between 6 and 11 Mb22. Comparative genomic analyses of 4 and 17 Streptomyces genomes revealed that large fractions of their genomes were accessory and presumed to be dispensable24. These analyses also revealed that the majority of genes involved in secondary metabolism were parts of the accessory genomes and distributed in sub-telomeric regions of their linear chromosomes. A previous pangenome analysis of 122 Streptomyces genomes revealed that secondary metabolism and xenobiotic metabolism overrepresented the horizontally acquired genes, though gene acquisition through horizontal gene transfer was rare in the genus Streptomyces25. In addition to secondary metabolism, streptomycetes encode a number of enzymes which modify plant biomass, presumably to acquire them as carbon sources. Indeed, an analysis on microbiomes from lignocellulic biomass revealed that streptomycetes dominated these microbiomes and accounted for the largest fractions of glycoside hydrolases, a group of carbohydrate-active enzymes (CAZymes), suggesting that streptomycetes could be the major plant biomass degrading microbes26. Several streptomycetes are also associated with insects such as fungus-farming ants28. Nevertheless, the activity and versatility of streptomycetes to utilise carbohydrates, and their interaction with plants and other organisms are yet to be explored.Unlike morphological development, metabolic pathways involved in the biosynthesis of secondary metabolites and peripheral catabolic pathways are more diverse. A single Streptomyces genomes was performed. This analysis revealed the diversity and commonality of their metabolism and regulation as well as cellular differentiation. This analysis also identified catabolically versatile strains, which may prove to be useful for conversion of biomass into industrial chemicals. Although there exist a few pangenomic analyses of streptomycetes, they focused on particular groups of genes such as lateral gene transfer and auxiliary genes25. Unfortunately, the data used in those studies are not available in public domains for immediate use. Therefore, researchers interested in using previously reported comparative genomics data for the purposes other than those described in those reports need to repeat the entire process. Instead, our data are publicly available with no restriction to use. Additionally, the analysis we conducted is the most comprehensive study of the Streptomyces pangenome using the greatest number of genomes and analysing a variety of metabolic and signalling pathways. Our data expand the understanding of the metabolism and physiology of streptomycetes and are expected to facilitate exploitation of the metabolic capabilities of these industrially useful bacteria.In this study, a comparative genomic analysis of 205 complete Streptomyces were available at NCBI on 11 June 2020 . At least 135 strains were isolated from soil samples  varied between 5361 and 11,170.A total of 213 complete genome sequences of the genus 20 Table . Of thesStreptomyces genomes was conducted. A protein pair with at least 80% amino acid sequence identity of the alignment covering at least 70% of both of the protein sequences were determined orthologues. Although more relaxed thresholds have been used in several studies such as 50% sequence identity and 50% sequence coverage33, such relaxed thresholds ended up clustering proteins known to exhibit distinct functions. For example, multiple sigma factors such as SigB, SigI, SigN and SigL encoded in S. coelicolor A3(2) were considered paralogues when the thresholds of 60% sequence identity and 60% sequence coverage were applied. While these sigma factor sequences are indeed relatively similar and they were presumed to recognise similar promoter sequences, their physiological roles are different39. Therefore, we decided to use more stringent thresholds to avoid potentially clustering proteins with relatively similar sequences and distinct functions. However, our stringent threshold may fail to cluster some orthologues when only partial conservation is sufficient such as active site residues for enzymatic activities as discussed below. The data files which include the source organism, predicted function and orthologous group assignment of each gene are available at the Secondary Metabolism Collaboratory (https://smc.jgi.lbl.gov/projects/). This analysis revealed 437,366 clusters of orthologous groups from 1,536,567 proteins. The total number of clusters increased as an additional genome was added  and largest (4691) number of strain-specific proteins, respectively. Nearly half of the proteins  were conserved in less than 5% of the genomes , SCO4505 and SCO0527 are abundantly produced under a non-stress condition40. Nevertheless, there were several orthologous groups conserved in all of the 205 strains that consisted of proteins without definitive functions. For example, the proteins in cluster 5 harboured a roadblock/LC7 domain, which is typically present in dynein proteins in eukaryotic organisms (cluster number assignments are included in the data files at the Secondary Metabolism Collaboratory). Such highly conserved uncharacterised orthologous groups are likely to encode important functions that have been overlooked. Functional categorization using the EggNOG database assigned 1,330,962 proteins in one of the COG categories and revealed the different degrees of conservation in each functional category41 . They included components of transcription and translation machinery, such as RNA polymerase and the ribosome. The largest cluster of orthologous groups consisted of 923 proteins, which were predicted to be cold-shock proteins42 and assigned 634,155 proteins to KEGG pathways  of core proteins followed by the “glycan biosynthesis and metabolism” (27%) and “lipid metabolism” (33%) categories while more than 20% of proteins in these categories were conserved in less than 5% of the strains. This suggests that different strains potentially catabolise different organic compounds as carbon sources and produce structurally diverse peptidoglycans and lipids.Next, we conducted a pathway analysis of the pangenome using the KEGG database43. Several streptomycetes linked to the plant biomass degradation process have been isolated and studied7. They encode a large number of CAZymes, some of which are presumed to be responsible for decomposition of lignocellulose, especially cellulose. The Streptomyces pangenome encoded 44,504 proteins predicted to possess at least one protein domain representative to CAZymes  of CAZymes while Streptomyces spongiicola HNM0071 encoded the smallest number (121 proteins). About 40% of the CAZymes were conserved in 10 strains or less (< 5%) while 2096 proteins (4.7%) were core. Interestingly, the proportions of CAZymes conserved between 5 and 55% of the strains substantially increased and the proportion of CAZymes conserved in less than 5% of the strains was smaller compared to the pangenome  were most abundant  followed by glycosyl transferases  Fig. . No PL wStreptomyces strain encoded between 47 and 170 proteins (97 proteins on average) predicted to be involved in “xenobiotic biodegradation and metabolism”. Many metabolic pathways in this category are involved in degradation or catabolism of aromatic compounds, suggesting the potential of streptomycetes to degrade a variety of aromatic compounds in natural environments. Indeed, many Actinobacteria including streptomycetes are known to catabolise many aromatic compounds including benzoate and cinnamate derivatives as carbon sources47. Aromatic compounds are constituents of lignin, and therefore components of lignocellulose. Many aromatic compounds are converted to catechol or protocatechuate, which are further catabolised via the β-ketoadipate pathway to acetyl-CoA and succinyl-CoA48. As these molecules are precursors of high value chemicals, such as polyketides and triacylglycerols, conversion of the lignin-derived aromatic compounds into acyl-CoA via the β-ketoadipate pathway is an important step for the production of such bioproducts from plant biomass50. The catechol and protocatechuate catabolic pathways consist of 6 biochemical reactions, of which the last 3 reactions are shared by both pathways. Of the 205 strains, 114 strains were predicted to encode the complete protocatechuate catabolic pathway . Using antiSMASH v6.0.152, BGCs present in each genome were predicted (Table Streptomyces sp. CLI2905 and Streptomyces tendae 139) to 53 (Streptomyces hygroscopicus XM201 and Streptomyces sp. YIM 121038) with the mean and median numbers of 31.8 and 31, respectively. Of the 70 BGC types that antiSMASH v6.0.1 supports, the largest BGC type  was non-ribosomal peptide synthases (NRPSs) followed by terpenes  and type 1 polyketide synthetases   wild-type produced hopanoids, the mutants unable to form aerial mycelia did not detectably produce them55. This suggests that the majority of streptomycetes produce structurally similar hopanoids, presumably as a constituent of membranes in aerial mycelia. Interestingly, geosmin cyclase, GeoA, from S. coelicolor A3(2) was conserved in only 79 genomes. Though geosmin cyclase was bioinformatically identified in a number of actinomycetes including streptomycetes, the homology of the overall sequences varies while the active site motifs are more highly conserved56. Indeed, 3 out of 4 active sites of GeoA in S. coelicolor A3(2) and its closest homologue in S. venezuelae NRRL B-65442 were identical and the other active site had a single residue substitution while their overall identity was below the threshold used in this study. For such enzymes, it may be more appropriate to consider conservation of active sites and residues involved in substrate selection rather than an overall sequence alignment.In addition to catabolising a variety of organic compounds as carbon sources and producing essential biomolecules such as nucleotides and lipids as primary metabolites, streptomycetes produce a wide variety of nonessential chemical compounds as secondary metabolitesed Table . The totCs) Fig. . Each BG57. Several GBLs serve as signalling molecules for inter- and intraspecies cell–cell communications58. The antiSMASH analysis revealed that 177 of the 205 strains used in this study (86%) were predicted to encode at least one core enzyme for GBL biosynthesis. Of the 386 core enzymes for GBL biosynthesis, 171 had no orthologues and were, therefore, encoded in single strains  of core enzymes conserved in less than 5% strains. These enzymes are responsible for transfer of the acyl moiety of β-ketoacyl-CoA to dihydroacetone phosphate, which is followed by spontaneous or enzymatical conversion to gamma-butyrolactones (GBLs), such as A-factor, by intramolecular aldol condensation, dephosphorylation, and reductionins Fig. A, which 59. A total of 990 PPTases were identified from the pangenome and each genome encoded 1–12 PPTases with median value of 4 , is a promiscuous enzyme which activates multiple ACPs including a fatty acid synthase ACP and actinorhodin synthase ACP, and is presumed to be a housekeeping PPTase as deletion of acpS was unsuccessful59. The conservation of AcpS in 203 strains suggests that they use AcpS to activate biosynthesis of housekeeping fatty acids and some polyketides and non-ribosomal peptides. Prokaryotic PPTases are classified into 2 types, AcpS- and Sfp-types59. AcpS-type PPTases are small and possess only a single 4’-phosphopantetheinyl transferase domain. Sfp-type PPTases are larger and possess a second 4’-phosphopantetheinyl transferase domain. The majority (901) of the PPTases the Streptomyces pangenome encoded were AcpS-types. S. collinus Tu 365 encoded 12 AcpS-type PPTases, and S. venezuelae strains NRRL B-65442, ATCC 21113, and ATCC 10712 and Streptomyces sp. DSM 40868 encoded 11 AcpS-type PPTases. Importantly, 9 PPTases Streptomyces sp. DSM 40868 encoded were unique. S. venezuelae strains, NRRL B-65442, ATCC 21113 and ATCC 10712 shared 1 PPTase unique to them and 7 PPTases that were conserved in them and S. venezuelae ATCC 10595, which encoded 10 PPTases. Although there were only 89 Sfp-type PPTases that the Streptomyces pangenome encoded, their pattern of distribution was distinct from that of the AcpS-type PPTases f 4 Fig. B. S. colins Fig. C. One ofses Fig. . Strepto60. Many bacteria encode multiple sigma factors, each of which recognises a specific set of its own target promoters and allows expression of condition-specific genes. Sigma factors comprise 2 protein families, σ70 and σ54, although the σ70 family is larger and no σ54-family sigma factors have been reported in streptomycetes to date. Consistent with this, the Pfam domain search only identified σ70-family sigma factors in the 205 Streptomyces genomes. A total of 9741 sigma factors were identified, and the number of sigma factors per genome varied from 27 (Streptomyces luteoverticillatus CGMCC 15060 and Streptomyces sp. NHF165) to 80 (Streptomyces sp. RLB3-17) with a median value of 46. The σ70-family sigma factors are categorised into 4 groups based on domain organisation and function61. All of the 9741 sigma factors were categorised into 3 groups   are involved in expression of essential housekeeping genes66 and some group 2 sigma factors are presumed to be involved in general stress response69, many streptomycetes use these sigma factors to express genes involved in the basic biological processes. The largest number of sigma factors (75%) belong to group 4 (or extracytoplasmic function subfamily) and they were the least conserved , which is suggested to be involved in clavulanic acid production71, and was conserved only in S. clavuligerus ATCC 27064, S. clavuligerus F613-1 and S. clavuligerus F1D-5. Highly conserved sigma factors, on the other hand, are likely to be involved in fundamental biological processes. Of the 15 core orthologous groups, 13 groups had at least one member which had been characterised previously with the functions of 11 members known  and vnz_RS23885 in S. venezuelae NRRL B-65442, are highly transcribed in later growth phases80, suggesting their possible role in stationary phase and its investigation is warranted.Precise control of gene expression is a key to modulating cellular metabolism and physiology, and adapting to ever-changing environments. Bacteria use sigma factors and other transcription factors such as transcriptional activators and repressors to control gene expression, primarily initiation of transcription. Sigma factors are dissociable components of RNA polymerase and are essential for promoter recognition and transcription initiationwn Table . These g81. Using the domain architectures described by Ortet et a., 201281, 130,380 transcriptional activators and repressors were identified and classified  family was least conserved  is composed of 5 proteins (BldKA-BldKE) and is responsible for uptake of a 655 Da peptide, which is presumed to act as a molecule signalling initiation of aerial mycelium formation93. Its functional orthologue in S. griseus NBRC 13350 exhibits relatively low sequence similarities (20–59%)94. Indeed, we found that BldKA (permease), BldKB (ABC transporter substrate-binding protein) and BldKC (permease) in S. coelicolor A3(2) and S. griseus NBRC 13350 were only conserved in 5–10% of the strains, while BldKD and BldKE (ABC transporter ATPases) in S. coelicolor A3(2) and in S. griseus NBRC 13350 were more highly conserved 95. The low-level conservation of the WhiE proteins suggests that the aromatic polyketides that the whiE genes encode are presumably produced by only limited strains. Indeed, different spore pigments have been observed such as green, blue, and red, in different strains96.The pangenome analysis revealed that several genes involved in aerial mycelium formation and sporulation are highly conserved Fig. A, sugges97. A total of 1500 proteins were predicted to possess this domain from the Streptomyces pangenome  of chaplins and 4 strains encoded a single chaplin. Interestingly, 768 chaplins (51%) belonged to the same orthologous group and were conserved in 193 strains. In S. coelicolor A3(2), 8 chaplins are encoded and ChpE and ChpH are the minimally required chaplins for aerial mycelium formation while ChpC is required for formation of robust, sporulating aerial mycelia98. Of these proteins, ChpE and ChpH belonged to the orthologous group conserved in193 strains while ChpC was conserved in only 14 strains. This suggests that the majority of streptomycetes use ChpE and ChpH homologues to form basal aerial mycelia, and more unique chaplins are involved in formation of robust mycelia, presumably in a strain-specific manner.While the Bld and Whi proteins contribute to determining the timing of aerial mycelium formation and sporulation, several other proteins are involved in formation of aerial mycelia and spore chains. A specific group of amyloid proteins in streptomycetes, known as chaplins, are essential components of the hydrophobic coat of aerial mycelia and sporesome Fig. B. StreptS. bingchenggensis BCW-1 encodes the greatest number of enzymes involved in lignocellulose catabolism. Carbohydrate-active enzymes underwent evolution earlier than the majority of other genes, resulting in a large number of enzymes conserved in taxonomically-related genomes. The exact mechanism of how this evolution occurred is still unknown, which merits further investigation. The Streptomyces pangenome also encodes unique gamma-butyrolactones, presumably as species- or strain-specific signalling molecules, while ectoine and hopanoid products are likely structurally similar. The majority of highly conserved regulatory proteins, including transcriptional activators, transcriptional repressors and sigma factors, appear to play important roles in the Streptomyces physiology. However, the majority of regulatory proteins encoded inside secondary metabolites BGCs are poorly characterised and it is important to elucidate their functions to facilitate the discovery of new secondary metabolites. Although the majority of genes involved in morphological development and the metabolism of second messengers, which control the timing of morphological development, are highly conserved, strain-specific transporters and second messenger metabolic enzymes fine-tune the timing of development, presumably in a strain-specific manner. One limitation of this study is that we used the identity of the sequence alignment and the coverage of the alignment as the criteria when determining orthologues. However, the conservation of the active sites and other residues involved in substrate recognition is more important than homology of the entire sequences for certain enzymes as evident with geosmin cyclase. For such enzymes, taking account for active site motifs if known may be more suitable. Nevertheless, our dataset revealed genetic potential of a number of streptomycetes, which may be further characterised for secondary metabolite discovery and production, and biomass utilisation. In addition to the analyses presented here, we view this dataset to be a community resource to be probed in many different ways and so we have made our dataset freely and openly available in JGI’s Secondary Metabolism Collaboratory (https://smc.jgi.lbl.gov/projects/). Though this study used the 205 complete Streptomyces genomes, which is the largest scale of the comparative genomic analysis of streptomycetes, this number is still less than 1% of the > 20,000-member genus. In order to harness the complete metabolic capability of this biotechnologically important genus, it is warranted to sequence and explore a much larger number of their complete genomes.Actinobacteria, especially streptomycetes, are known for their metabolic prowess with huge potential for biotechnological applications in the production of secondary metabolites and catabolism of lignocellulose constituents. Streptomycetes inhabit diverse environmental conditions and have acquired a large number of metabolic and regulatory genes, presumably as a result of adaptation. Our pangenome analysis shows that many streptomycetes encode a large number of unique enzymes involved in secondary metabolite biosynthesis and catabolism of carbohydrates and aromatics. Of the 205 strains used in this study, Streptomyces genomes were retrieved from the NCBI Reference Sequence Database (RefSeq) on 11 June 202030. Phylogenetic analysis using the 16S rRNA sequences was performed using PhyML 3.099. BLAST + v2.10.1100 was used to cluster proteins using the following criteria: E-value < 10–5, identity ≥ 80%, coverage ≥ 70%. Clusters (or orthologous groups) were identified using the Python package, NetworkX101.A total of 213 complete 102. The independent E-value threshold of 10–10 was used. KofamKOALA v1.3.0 was used to assign KO terms using the independent E-value threshold of 10–10 and the KO term specific score threshold103. KEGG pathways present only in eukaryotes or in the “biosynthesis of secondary metabolites” category were excluded from the analysis. AntiSMASH v6.0.1 was used to predict secondary metabolite biosynthetic gene clusters52. Carbohydrate active enzymes were predicted using dbCAN2 v9104. The Pfam protein families database v33.1 and the SMART protein domain annotation resource v8 were used to assign protein domains using the independent E-value threshold of 10–1106. The Pfam domain, “PF03777”, was used to identify chaplins. Proteins possessing the Pfam domain, “PF01648”, with the E-values of < 10–3 were considered PPTases107. Additional analyses were conducted for the following proteins and metabolic pathway.A COG term was assigned to each protein using the EggNOG database v5.0 and HMMER v3.145. Clusters were identified by using HDBSCAN44.CAZyme families of which at least 70% of the members were conserved in between 5 and 55% of the strains were collected. Orthologous groups conserved in at least 5% of the strains from these families were used to calculate the presence-absence matrix. UMAP was used for dimension reduction of the presence-absence matrix70-family sigma factors. Sigma factors possessing both the Sigma70_r1_2 (PF00140) domain and the Sigma70_r3 (PF04539) domain were assigned to “group 1 or 2” and those possessing the Sigma70_r3 domain but not the Sigma70_r1_2 domain were assigned to “group 3”. All other sigma factors were assigned to “group 4”. Similarly, Pfam domains Sigma54_AID (PF00309), Sigma54_CBD (PF04963) and Sigma54_DBD (PF04552) were used to identify σ54-family sigma factors. A protein was predicted to be a transcription factors if it possessed one of the domain architectures used by P2TF81.Proteins predicted to possess the Sigma70_r2 (PF04542) domain and either Sigma70_r4 (PF04545) or Sigma70_r4_2 (PF08281) domain were considered σ103. Additionally, the proteins that had no KO terms assigned were still assigned highest score KO terms when independent E-value was < 10–80.KofamKOALA v1.3.0 was used to assign KO terms to proteins using the criteria used abovehttps://smc.jgi.lbl.gov/projects/) with no restriction to use.The pangenome data and code to process the data are available at the Secondary Metabolism Collaboratory (Supplementary Information 1.Supplementary Information 2.


Feline oral squamous cell carcinoma (FOSCC) is a malignant tumor characterized by an aggressive behavior and poor prognosis, for which no fully effective therapies are available. Studies of comparative oncology suggest that epidermal growth factor receptor (EGFR) may be a therapeutic target in FOSCC, similarly to human head and neck SCC (HNSCC), where the use of anti-EGFR monoclonal antibody Cetuximab has entered the clinical practice. The aim of this study was to assess the efficacy of Cetuximab in three validated preclinical models of FOSCC . Sequencing of tyrosine kinase domain of EGFR in the cell lines revealed a wild-type genotype, excluding the presence of activating mutations. Western blotting experiments demonstrated that Cetuximab inhibited activation of EGFR and its downstream kinase Akt in SCCF1, SCCF2 and SCCF3 along with HNSCC cell line CAL 27 included as control. Importantly, CCK-8 and trypan blue exclusion assays revealed that treatment with Cetuximab caused a decrease in cell proliferation and cell viability in all cell lines, with a general dose- and time-dependent trend. Cell death induced by Cetuximab was associated with cleavage of PARP, indicating occurrence of apoptosis. Taken together, our data suggest that Cetuximab exerts potential anti-cancer activities in FOSCC, paving the way for future translational studies aimed at assessing its employment in the therapy of this lethal cancer of cats. Squamous cell carcinoma (SCC) is the most common oral cancer in cats . It is cEpidermal growth factor receptor (EGFR) is a membrane tyrosine kinase receptor (TKR) that regulates homeostasis and growth of epithelial cells . Once acCetuximab is a recombinant, chimeric monoclonal antibody raised against human EGFR: its Fv  region, of murine origin, is able to bind to the N-terminal region of human EGFR and is fused to the Fc of human IgG1 light and heavy chains . The aff®) has entered the routine therapeutic protocols in association with CT and RT for patients with locally advanced and recurrent metastatic disease , thus it is considered a reliable spontaneous animal model of human counterpart  along with CAL 27. Cells were incubated with increasing doses of Cetuximab  diluted in culture medium for 24, 48, and 72 h. In control plates, Cetuximab was replaced with phosphate buffered saline (PBS). Relative growth inhibition in treated vs. control plates was calculated as previously described , SCCF2  and SCCF3 (tongue SCC), developed in the Rosol laboratory, were a kind gift from Professor T.J. Rosol (The Ohio State University) and have been cultured as described elsewhere –19. Cetuescribed .3 density in 96-well plates and, after 24 h, incubated with Cetuximab at 50 and 100 μg/mL. CCK-8 assay  was performed to assess cell proliferation according to manufacturer protocol and absorbance at 460 nm was measured after 24, 48 and 72 h as reported elsewhere  and protein bands detection as previously described . The folALJ56200.1) to human EGFR protein (AIC61960.1) was performed by using NCBI/Blastp tool.Reverse transcription PCR (RT-PCR) and sequence analysis  of feline EGFR regions spanning the TK domain (TKD) in SCCF2 and SCCF3 were performed by using sequencing strategy, PCR protocol, and the following primers sets A: for 5′GGA GAA GCT CCC AAC CAG GCT3′, rev 5′GAT AGG CAC TTT GCC TCC TTC3′, B: for 5′GAA TAT CAC CTG CAC AGG AC3′, rev 5′GCC ATC ACG TAA GCT TCA TC3′ and C: for 5′TGC GAA GGG CAT GAA CTA C3′, rev 5′ACT CAT CGG CAT CTA CGA C3′ as reported elsewhere for SCCF1 . Alignmevs. control, ANOVA was performed to investigate the differences between the mean of three groups of samples. Statistical tests were performed using SPSS Statistics for Windows, version 17.1 .For statistical analysis, paired Student t-test was run to analyze the differences between each treatment HQ185236) for TKD in a previous study . Most importantly, when aligning the regions encompassing the EGFR domain responsible for Cetuximab binding (domain III)  , the pervia WB  and in SCCF2 at 48 and 72 h see , therefoIn accordance with the calibration experiment, no effect on cell proliferation was detected at 24 h of treatment neither in SCCF2 nor in the other FOSCC cell lines . In SCCFTo further assess whether Cetuximab would cause cell death other than impaired cell growth, trypan blue exclusion assays were conducted. The obtained results revealed that treatment with Cetuximab was associated with a slight and often not statistically significant decrease of cell viability in feline cells , howeverin vivo , accession numbers OP643788 and OP688567.The sequence data presented in the study are deposited in Genbank (GA performed the experiments. GA and GB conceived the study and drafted the manuscript. All authors contributed to the article and approved the submitted version.This work was financially supported by a grant from the Board of Directors, University of Naples Federico II.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.


Lachnospiraceae recovered during the late-acute phase (days 8–10) of hospitalisation. Patients with infections had a lower proportion of Lachnospiraceae in their gut microbiota than controls and patients with primary admitting diagnoses. Keystone species linked to ecological recovery were observed to decline with the length of PICU admission. These species were further suppressed in patients with systemic infection, respiratory failure, and undergoing surgery. Bile acid composition recovers quickly after intervention for critical illness which may be aided by the compositional shift in Lachnospiraceae. Our findings suggest gut microbiota recovery can be readily assessed via measurement of faecal bile acids.Broad-spectrum antimicrobial use during the treatment of critical illness influences gastrointestinal fermentation endpoints, host immune response and metabolic activity including the conversion of primary to secondary bile acids. We previously observed reduced fermentation capacity in the faecal microbiota of critically ill children upon hospital admission. Here, we further explore the timecourse of the relationship between the microbiome and bile acid profile in faecal samples collected from critically ill children. The microbiome was assayed by sequencing of the 16S rRNA gene, and faecal water bile acids were measured by liquid chromatography mass spectrometry. In comparison to admission faecal samples, members of the  Multiple factors, such as delivery method, feeding, diet, illness, age, and antimicrobial use in early life, can impact on the establishment and maintenance a healthy gut microbiota9. The administration of broad-spectrum antimicrobials is often necessary during hospitalisation for critical illness, which reduces the diversity of the gut microbiota3. The effects of broad-spectrum antimicrobial therapy on the microbiota, and the subsequent recovery process, have been well documented12. However, critical illness can have profound effects on feeding and energy intake13, and respiratory complications may lead to low oxygen saturation and subsequent hypoxia, inducing disruption of the gut microbiota14. Furthermore, changes in the structure of the microbiota may exacerbate the disease state or alter the course of recovery15. The body of evidence identifying illness as both a cause and effect of an altered microbiota is becoming more extensive9.The establishment of a healthy gut microbiota in early life is considered to be protective against a range of allergies and communicable and non-communicable diseases16. Notably, the conversion from primary to secondary bile acids requires a cascade of enzymatic modifications incorporating a 7α-dehydratase enzyme expressed by a range of Gram-positive bacteria18. Further metabolism of bile acids by intestinal bacteria enhances passive reabsorption by the intestine; therefore, the microbiota also contribute to the recycling of bile acids19. Secondary bile acids can also inhibit intestinal pathogens and have been shown to restrict the germination and sporulation of Clostridioides21. The relationship between the microbiome and disease is complex; altered bile acid profiles are associated with diseases such as metabolic syndrome and colon cancer, which may also be exacerbated by the microbiota26. The western diet has been associated with higher levels of taurocholic acid, hydrogen sulphide and deoxycholic acid, which have been linked to ulcerative colitis and colon cancer30. Bile acids modulate the farnesoid X receptor and G-protein-coupled bile acid receptor, with both receptors playing active roles in glucose and lipid homeostasis as well as insulin sensitivity26.Bile acids shape the composition of the microbiota and correspondingly the structure of the microbiota contributes to the metabolism of the bile acids10, and observed a shift in the short chain fatty acid and faecal bile acids profiles in critically ill children compared to healthy age-matched controls. Our previous work examined the acute changes in faecal microbiome in children admitted to the Paediatric Intensive Care Unit (PICU) for critical illness. Here we examine differences in the microbiome of critically ill children on ventilators during different stages of hospitalization for critical illness and the associated impact on total faecal bile acid composition.We previously characterised the functional capacity of the intestinal microbiome by exploiting multi-compartmental metabolic profilingp = 0.31). The median weight of the critically ill children was 15.7 kg, ; the healthy controls had a median weight of 20.4 kg  (p = 0.005). The median Weight for Age Z-score for patients was -0.135 . The median Weight for Age Z-score for the healthy controls was 0.420 . Our patient cohort was 55.7% female (39/70), and our control cohort was 55.0% female (28/51). The median number of PICU free days within 30 days of admission in the critically ill children was 22  and the median number of days free of invasive mechanical ventilation within 30 days of admission was 26 days . The survival rate was 96%. Further demographics are shown in Table We examined samples from 70 mechanically ventilated critically ill children and 51 age-matched healthy controls. The critically ill children were aged between 1 and 16.9 years with a median age of 4.1 years (inter-quartile range (IQR); 8.5 years). The healthy controls were aged between 1 and 15.9 years, with a median age of 5.8 years   . The inverse trend was observed for the secondary bile acids . Conversely, there was a significant reduction in the primary/secondary bile acid ratios in the samples taken on days 8–10 compared to the control  in all patient sampling timepoints relative to the control Fig. c. The prTo assess bile acid differences at different sampling periods, we compared the relative concentration of individual bile acids . Seconda10. No compositional difference was observed between male and females in this study (p = 0.80).Children admitted to a PICU are frequently administered broad spectrum antimicrobials soon after admission. In this study, 42/43 (97.7%) PICU patients whose faecal microbiome was assessed by 16S rRNA gene sequencing, received antimicrobial therapy during the course of PICU admission. Applying a Kruskal–Wallis chi-squared test, no significant difference was observed in the number of antimicrobials given to patients across all sampling groupsp = 0.0001). To identify intergroup significance, we applied Tukey’s Honest Significant Differences (HSD) post hoc test to a principal coordinate analysis of the Bray–Curtis dissimilarity matrix, resulting in adjusted p-values of < 0.0001 for all pairwise comparisons between the control and patient groups (data not shown). Inter-group analysis using Tukey’s HSD identified no difference between patient samples . We observed no difference in microbial populations between male and female patients. The bacterial population profiles of study participants, represented at the Family level, are shown in Fig. To investigate microbial variation between individuals and groups, alpha and beta diversities were examined. Quantifying alpha diversity using Shannon’s Index, we observed greater median diversity with a narrow range in the control group, compared to all patient sampling time points Fig. a. To det12. Bacterial genera Alistipes, Bacteroides, Bifidobacterium, Coprococcus, Desulfovibrio, Faecalibacterium, Parabacteroides, Roseburia, Ruminococcus and Subdoligranulum have been previously described as recovery associated bacteria (RAB). Observing these genera as a group, the general proportion of all RAB significantly decrease from control samples across all patient sampling timepoints . Weak negative correlations were observed between RAB proportion and hospital days before sample collection, but this trend was not significant . RAB were decreased relative to controls for patients admitted with non-respiratory infections, patients with non-infectious respiratory failure, and surgical patients . The amount of Faecalibacterium during all timepoints in PICU stays was dramatic, with an average proportion of 0.25% in patient samples versus 6.7% in control samples. This observation is suggestive of extensive antimicrobial susceptibility among the Faecalibacterium. A significant reduction in abundance of Ruminococcus spp. was observed in all patient groups compared to control microbiome abundances.We observed a decrease in the proportions of RAB as PICU stay lengthened, with most samples collected at days 8–10 containing < 50% RAB by relative proportion Fig. a. RAB proup Fig. . The patmes Fig. . The majoup Fig. . A signiClostridiales23. To determine if there was a link between secondary bile acid recovery and the microbiome, we measured the correlation between secondary bile acids and bacterial proportions for patient samples that were subjected to both analyses (n = 15). Total secondary bile acids correlated weakly (0.20 ≤ ρ < 0.4), with the observed proportion of Clostridium sensus stricto group 1 . The genus Clostridioides was moderately correlated (0.40 ≤ ρ < 0.6) with DCA . Assessing the correlation between the abundances of bacterial genera and major secondary bile acid relative concentrations   Fig. , we obsemes Fig. . The genion Fig.  were froLachnospiraceae identified in this study were Agathobacter, Anaerobutyricum [Eubacterium] hallii group31, Anaerocolumna, Anaerostignum, Anaerostipes, Blautia, Butyrivibrio, Coprococcus, Dorea, Eisenbergiella, Fusicatenibacter, Howardella, Hungatella, Lachnoanaerobaculum, Lachnoclostridium, Lachnospira, Mediterraneibacter [Ruminococcus] gnavus group32, Mediterraneibacter [Ruminococcus] torques group, Oribacterium, Pseudobutyrivibrio, Roseburia, Stomatobaculum and Tyzzerella. There was no significant difference in the proportional representation of the Lachnospiraceae between patient groups  in critically ill children.36, and an essential enzymatic process prior to the 7α/β-dehydratase pathway required to convert CA and CDCA to DCA and LCA/ILCA38. If an over-abundance of BSH expressing bacteria was increasing the concentration of primary BAs, one may expect to observe a decrease in conjugated BAs in faeces; no significant difference was observed in the proportion of conjugated BAs between timepoints. Our data suggests that the opposite may be true in some instances, where treatment for critical illness reduced the number of bacteria capable of de-conjugating bile salts. We observed individual patient samples containing high relative proportions of conjugated BAs where some samples contained proportions of TCA level reaching almost 80% and individual patient GCDCA reaching 50%. It was reported by Van Eldere and colleagues that deconjugation of TCA increased rates of primary to secondary bile acid conversion in mixed cultures39. Extrapolating this theory, one could assume an increased rate of deconjugation drives a conversion from primary to secondary bile acids if taurine conjugates were abundant. Taurine conjugated bile salts account for roughly 50% of the median conjugated primary bile salts in our analysis at each timepoint. BSHs are well represented in the genomes of gut bacteria36, and bacteria capable of a 7α/β-dehydratase reaction are rare40. If the bottleneck in primary to secondary bile acid metabolism is the rare 7α/β-dehydratase pathway, it would follow that a small relative increase in a BSH expressing bacterial population would not drive the difference in primary and secondary BAs a strongly as a decrease in bacteria capable of 7α/β-dehydration.This model would suggest that increased deconjugation of BAs leads to an over-abundance of primary BAs and a reduction in conjugated BAs in patients. BSHs are common among intestinal bacteria11. An expected decrease in microbial richness, quantified by Shannon’s index, can be observed as the length of PICU stay increases. Treatment with broad-spectrum antimicrobials will be the driver for the compositional change and loss of diversity. Beta diversity varies widely in all treatment groups compared to control samples, and as expected there is a difference in faecal microbiome beta diversity between the control and sampling groups, most likely driven by antimicrobial treatment. A study by Chng and colleagues identified bacteria linked to microbiome recovery after antimicrobial therapy12. These RAB may act as keystone species42, providing metabolic substrates for endpoint fermenters such as the Lachnospiraceae. The general trend for RAB was to decrease in proportion with treatment length. The loss of these species in over time in our study is most likely explained by the duration of action of antimicrobial therapy. Decreased motility may account for some protection of RAB bacteria from antimicrobial killing, or the withdrawal of antibiotics but continued hospitalization may have allowed for recovery. Reduced gastric motility during critical illness can hinder sample collection. Patients in this study may not have had a first sample collected until 10 days post-admission.The composition of the gut microbiota was significantly affected during critical illness. Differences in microbial composition and richness between controls and critically ill patients were observed in this study. Decreasing alpha-diversities at varying body sites during hospitalisation in paediatric intensive care facilities has been previously reported by Rogers et al.Lachnospiraceae, with a slight decrease in median abundance. The observed increase in secondary bile acids in patients sampled at day 8–10 could be driven by a change in Lachnospiraceae, specifically, bacteria related to Lachnoclostridium and the A. hallii group bacteria44. Bacteria from Lachnoclostridium and the A. hallii group were present (> 10 counts) in 7 of 11 patient samples at days 8–10. Other bacteria in the family Lachnospiraceae were minor contributors in the microbiota of patients at days 8–10, with the exception of patient 18. Compositional changes of the Lachnospiraceae may be driven by changes in the upstream fermentation capacity of patient microbiota leading to increased production of mid-point fermentation products by the new dominant microbiota of critically ill children10. Bacteria in the A. hallii group and Lachnoclostridium are capable of utilising lactate produced by Escherichia coli, Bifidobacterium, Lactobacillus, and Enterococcus45. Further interpretation of the connection between bile acid production at days 8–10 and the microbiota is limited due to the small overlap in patients with both microbiomes and bile acid profiles (n = 2). An average PICU stay in the United Kingdom lasts 2–3 days, across all ages46. Samples collected at later timepoints are often from the sickest patients. The study is limited by the inability to follow the microbiome and bile acids of patients post-discharge.Treatment for critical illness drives a compositional change in the Lachnospiraceae are known to metabolise primary to secondary bile acids through a 7α-dehydratase process40. The selection of enrichment for A. hallii group bacteria may be due to their ability to use both D- and L-lactate45. Increased total lactate was indicated as a measure of critical illness10. Members of the Clostridiaceae and Ruminococcaceae were not as abundant as the Lachnospiraceae at days 8–10, therefore unlikely to drive bile acid conversion.Members of the Lachnospiraceae are differentially resistant to antibiotics, and strains are expanding to fill empty ecological niches. The interpretation of differential resistance is limited due to the use of 16S rRNA gene profiling and lack of information on the distribution of antimicrobial resistance genes. The shift in Lachnospiraceae composition from control to patient profiles appears as early as the first day of PICU admission. Duration of antimicrobial therapy and classes of antimicrobial can be difficult to quantify in a PICU setting; patients may have received antimicrobial therapy prior to admission, where information about treatment may held by another hospital and not readily available. After antimicrobial therapy certain bacteria recover more rapidly than others, contributing to the total biomass of faeces. We observed no strong effect due to change in faecal biomass, or biomass recovery when using contamination of 16S rRNA gene sequences derived from DNA extraction kits as a proxy for bacterial biomass.An alternative hypothesis is that members of the 49. As children mature, microbial complexity increases and “healthy” microbiomes are associated with greater bacterial diversity, with Bacteroidetes and Firmicutes making up the majority of the proportional representation50. The general trend in our study was that healthy children had richer microbial diversity and high proportions of Firmicutes and Bacteroidetes. Children requiring hospitalisation in PICUs often had highly disrupted microbial communities when compared to healthy children. Upon arrival to PICU, patients have most often received a course of antimicrobial therapy or will be treated with a broad-spectrum antimicrobial such as ceftriaxone. The effect of these treatments confounds the ability to interpret the impact of critical illness on the microbiome in the absence of internal reference.During early life, the composition of the microbiome changes substantially, from species acquired during delivery, through breastfeeding and the transition to different foods. Studies have linked the method of delivery to an altered microbiome, children born by Caesarean section are at greater risk of autoimmune disease compared to children delivered via normal vaginal delivery10. Faecal metabolic profiling indicated a higher correlation of sugars and lactate in patients, with short chain fatty acids increased in age matched controls. A brief examination of bile acids by Wijeyesekera et al.10 directly linked levels of the secondary bile acids DCA, LCA and ILCA to the proportion of Bacteroides, Ruminococcus, Eubacterium, Lachnospiraceae, and Faecalibacterium in faeces. Our results indicate a positive correlation in patient samples between secondary bile acids and the genera Anaerovorax, Barnesiella, Faecalitalea, Gordonibacter, Megamonas, Odoribacter, Parasutterella, Pseudoflavonifractor, Solobacterium and Turicibacter, however, the limited number of patient samples with both a quantified microbiome and a bile acid profile limits the predictive power of these observations.Previously, we examined paediatric critical illness from a multi-component perspective and demonstrated that critical illness affects the microbiome of paediatric patients, which is consistent with other similar studies52. Clinical diagnostic laboratories may have services using mass spectroscopy as standard analytical techniques54. Measurement of primary and secondary BAs from blood, urine and faeces may therefore be within the capabilities of modestly outfitted laboratories.Currently, profiling of microbiomes by 16S rRNA gene sequencing or shotgun metagenomics is a cost- and labour-intensive process. Even using only nanopore sequencing devices involves multiple steps. Days of data collection and processing are required to obtain a snapshot of the microbiome. The measurement of metabolomes by mass spectroscopy is rapid (under 30 min) and can be performed with minimal preparation from biological samples56. If the microbiome fails to recover quickly, or enters an altered state, there may be differential bile acid metabolism, SCFA production, and overall altered microbial metabolic interactions. The recovery of the microbiota is dependent on the diversification of metabolic interactions after antibiotics57. Current predictive modelling programs attempt to predict host metabolomes from gut microbiomes59 and it may be possible to estimate the recovery of the microbiome based on metabolomics alone.The development of a healthy microbiome early in life is considered essential to systemic healthThe power of this study to predict bacteria contributing to the recovery of bile acid profiles is limited by the overlap of patients with both microbiomes and bile acid profiles. The gastric transit of critically ill children is reduced due to multiple factors. The reduced intestinal motility limits the number and volume of faecal samples collected; therefore, analysis of some samples has been limited to bile acid profiling or 16S rRNA gene profiling.Bacteroides, Ruminococcus, and Fecalibacterium were reduced in microbiomes at day 8–10, but proportional representation of Lachnospiraceae remained close to control levels across all time ranges. Monitoring of faecal metabolites  and bile acids provides a rapid and cost-effective approach to determine intestinal microbiome health and recovery. Further studies are warranted to link markers of microbiome recovery after intervention for critical illness with clinical significance.Our study observed relative concentrations of secondary faecal bile acids to be comparable with those of healthy controls after hospitalization in a PICU for 8–10 days. Compositional proportions of .10.In this study we further examined the data collected by Wijeyesekera et alIn this prospective, multi-centre cohort study, critically ill children were recruited if they were admitted to one of three paediatric intensive care units (PICUs) in the North Thames Region (UK). Following informed consent by caregivers, children aged between 1 and 16 years were eligible if they required mechanical ventilation. Children with chronic illness or prolonged steroid use were excluded. Healthcare data was acquired from hospital medical records. Patients receiving enteral nutrition were given expressed breast milk or formula without probiotic supplementation. Standard antimicrobial treatment upon admission to PICU is the administration of a broad-spectrum agent, often a third-generation cephalosporin unless otherwise clinically indicated. In order to compare data with healthy controls, faecal samples were obtained from age-matched children, who were recruited from the local community. These children were eligible if they were free of intercurrent illness, had a normal healthy diet, and had not received antibiotics in the prior 3 months.Approval for the study was granted by the East Midlands-Nottingham 2 Research Ethics Committee for recruitment from the PICUs at Cambridge University Hospitals NHS Foundation Trust, Great Ormond Street Hospital NHS Foundation Trust, and Imperial College Healthcare NHS Foundation Trust. The City Road and Hampstead Research Ethics committee approved recruitment of healthy children in the Cambridge vicinity. REC reference: 13/LO/0974. All methods were performed in accordance with the relevant guidelines and regulations.Faecal samples were collected at early (within first 3 days of PICU admission), mid (days 4–7 of PICU admission), and late timepoints (days 8–10 of PICU admission) from critically ill children. Serial samples were collected from each child as they became available. Samples were used for 16S rRNA gene sequencing, and or bile acid profiling as biomass allowed. A single faecal sample was collected from healthy children. All samples from critically ill children were taken from nappies, placed in sterile plastic containers, and stored at − 70 °C until use..10. Briefly, total nucleic acid was extracted from faecal samples using the PowerFecal DNA Isolation Kit (MPBio). The V1V2 region of the bacterial 16S rRNA gene was amplified by PCR and sequenced using a MiSeq V3 600 cycle flow cell (Illumina) at the Wellcome Sanger Institute, UK. 16S rRNA gene sequences were quality filtered and analysed using Mothur software60.We performed sequencing of the bacterial 16S rRNA gene in the faecal samples and allocated these data into the various taxonomic groups, as described previously by Wijeyesekera et al.51. Briefly, faecal samples were processed to extract faecal water, and the supernatant was mixed 1:3 with ice-cold methanol as described previously by Gratton et al.61 . Reverse-phase chromatography was performed using two mobile phases and a short alkyl (C8) stationary phase adapted from Shockcor et al.62. Mobile phase A consisted of acetonitrile and ultra-pure water (1:10), with a final concentration of 1 mM ammonium acetate and adjusted to pH 4.18 using acetic acid, and mobile phase B consisted of acetonitrile and 2-propanol (1:1). Bile acid identification was based on the retention times of individual standards. LC–MS settings are published in Sarafian et al.51. Example LC–MS traces are available in Fig. Bile acids were extracted from faeces and assayed using liquid chromatography-mass spectrometry (LC–MS), following the methods described by Sarafian et al63. Non-parametric variance was calculated using the Kruskal–Wallis rank sum test64. Conover-Iman rank sum tests were used to perform multiple comparisons when H0 is rejected by a Kruskal–Wallis test65. Inter-group significance displayed in graphs was calculated using the Conover-Iman test. Conover-Iman tests were performed using the conover.test package for R66. Multiple comparisons were corrected using the method proposed by Benjamini and Hochberg, as implemented in conover.test67. Graphs were created using ggplot268. Data frame melting was performed with reshape269. Weight-for-Age Z-scores were calculated using zscorer70. Values which were not calculated by the R package were calculated with PediTools71. Shannon diversity indices were calculated using the diversity function from vegan 2.5–672. Beta-diversity was calculated using the vegdist function of vegan and plotted with ggplot2. Spearman’s ρ was calculated using cor.test in R63. Correlation interpretations are based on guidelines published by the British Medical Journal73.Data analysis was performed using R v4.0.2Approval for the study was granted by the East Midlands-Nottingham 2 Research Ethics Committee for recruitment from the PICUs at Cambridge University Hospitals NHS Foundation Trust, Great Ormond Street Hospital NHS Foundation Trust, and Imperial College Healthcare NHS Foundation Trust. The City Road and Hampstead Research Ethics committee approved recruitment of healthy children in the Cambridge vicinity. REC reference: 13/LO/0974.Parents of all study subjects provided consent for publication of de-identified data.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.


Background. The redox stress caused by Hodgkin’s lymphoma (HL) also involves the peripheral blood mononucleated cells (PBMCs) even before chemotherapy. Here, we tested whether lymphocytes and monocytes show a different response to the increased mitochondrial generation of reactive oxygen species (ROS). Methods. PBMCs, isolated from the blood of treatment-naïve HL patients and control subjects, underwent assessment of malondialdehyde content and enzymatic activity of both hexose- and glucose-6P dehydrogenase (H6PD and G6PD) as well as flow cytometric analysis of mitochondrial ROS content. These data were complemented by evaluating the uptake of the fluorescent glucose analogue 2-NBDG that is selectively stored within the endoplasmic reticulum (ER). Results. Malondialdehyde content was increased in the whole population of HL PBMCs. The oxidative damage matched an increased activity of G6PD, and even more of H6PD, that trigger the cytosolic and ER pentose phosphate pathways, respectively. At flow cytometry, the number of recovered viable cells was selectively decreased in HL lymphocytes that also showed a more pronounced increase in mitochondrial ROS generation and 2-NBDG uptake, with respect to monocytes. Conclusions. PBMCs of HL patients display a selective mitochondrial and ER redox stress most evident in lymphocytes already before the exposure to chemotherapy toxicity. Among the different host–cancer interactions able to modulate disease aggressiveness, the inflammatory response seems to represent a double-edged sword, mostly because of the heterogeneous composition of inflammatory infiltrates. Indeed, the secretion of trophic factors by tumor-associated macrophages contributes to neovascularization and lesion growth ,2,3,4, aAccording to the wide literature, inflammatory response implies a metabolic switch characterized by a simultaneous increase in glycolytic flux and oxygen consumption rate (OCR) fueling the generation of the large amounts of reactive oxygen species (ROS) needed for the activation of involved immune cells ,13,14. T18F-fluoro-deoxyglucose and the fluorescent probe 2--amino)-2-deoxyglucose (2-NBDG)) [To the best of our knowledge, this hypothesis has not been tested so far. Nevertheless, it agrees with the notion that the ER is enriched with a complete enzymatic asset to activate an autonomous pentose phosphate pathway (PPP) ,22,23. T2-NBDG)) ,25,26,27Accordingly, the present study was designed to verify the hypothesis that the PBMCs of HL patients display the oxidant phenotype typical of inflammatory activation modifying the mitochondria–ER redox balance before the start of chemotherapy, and whether lymphocytes and monocytes display a divergent susceptibility to the redox stress.n = 62) were enrolled from normalcy database collected from our institute in the Preventive Medicine Program. Exclusion criteria were positivity for HBV, HCV and HIV or any other coexistent disease asking for pharmacological therapy. All participants provided their written informed consent to participate in this study that was approved by the Ethical Committee of Regione Liguria (50/20—DB id 10306).The study included 19 patients admitted to our institute for suspected, and subsequently confirmed, HL. Control subjects  and resuspended at 5 × 106 cells/mL. Obtained cells were divided and dedicated to the different experimental evaluations.At the time of blood sampling, further 15 mL of blood was collected and transferred into our laboratory to be analyzed within 24 h. According to standard procedures ,29, PBMC5 PBMCs of either HL patients or controls were stained for 20 min at 37 °C with 5 μM MitoSOX Red, 1 μM ER-Tracker Red or 50 μM 2-NBDG . The cells were then washed two times with PBS, centrifuged for 5 min at 290× g and resuspended in PBS + 1% BSA for flow cytometric analysis. Data were acquired on a FACSCan  and data analysis was performed with FlowJo software. The analysis was restricted to viable PBMCs, after gating procedures based on forward- and side-scatter features [In the first set, 5 × 10features . In a su5 PBMCs/well were seeded in XFp cell plates and centrifuged gently with no brake at 40× g for 3 min, and the plate then rotated 180° before centrifugation again at 80× g for 3 min to encourage adhesion to the plate and the forming of an evenly dispersed monolayer [2 incubator with Agilent Seahorse DMEM, pH 7.4, enriched with glucose (11 mM). OCR and ECAR were monitored according to the manufacturer instructions. Briefly, three measurements of OCR and ECAR were taken under control conditions and after sequential injections of 1.5 μM oligomycin (ATP synthase inhibitor) and 0.5 μM rotenone (Complex I inhibitor) plus 0.5 μM antimycin A (Complex III inhibitor).For this second set of experiments, 10onolayer ,32. OCR g for five minutes and were suspended in PBS supplemented by protease inhibitors to be sonicated twice for 10 s in ice, with a break of 30 s. The activity of H6PD and G6PD were assayed using an absorbance microplate reader  to follow the reduction of NADP at 340 nm [For the third set of experiments, PBMCs were again centrifuged at 300× t 340 nm ,25,26,33Malondialdehyde (MDA) levels were evaluated, by the thio-barbituric acid reactive substances assay, using a UV/visible spectrophotometer  ,34. In aFinally, total antioxidant capacity was evaluated following the manufacturer’s instructions of a dedicated kit  that provides a complete description of the total cell antioxidant power associated with the endogenous scavengers, expressed as Trolox equivalent antioxidant capacity content.p < 0.05 was considered statistically significant. All statistical analyses were carried out by using dedicated software packages, namely SPSS, v20  and GraphPad Prism 9.3.1 .All data were reported as means of ± standard deviation. Unpaired t test was used to compare data in different groups. Univariate linear regression analysis was performed by using the least squares method. A As shown in According to flow cytometric analysis, HL patients showed a lower fraction of recovered viable PBMCs, and a concomitant rise in dead cell fraction, with respect to control subjects A–C. In aThe pronounced vulnerability of HL PBMCs agreed with the occurrence of oxidative damage, documented by the significant enhancement in their MDA content A. By con2−) (p < 0.001) steeper in HL than in the control subjects  C–F. Mito2−) D. Nevertsubjects D, suggessubjects C–F.Therefore, PBMCs harvested from treatment-naïve HL patients showed an evident oxidative damage, as documented by the increased level of the final product of polyunsaturated fatty acid peroxidation, i.e., MDA. This same damage, in turn, largely involved mitochondrial ROS generation and was more pronounced in lymphocytes than in monocytes.Considering that mitochondria represent, at the same time, a main ROS source and a primary target of ROS-induced damage, we aimed to understand the mechanisms underlying their selective redox stress. We thus extended our evaluation to the respiratory function A and glyNevertheless, OCR response to ATP-synthase inhibition by oligomycin was lower in HL than in control PBMCs C, indicaThe non-energetic nature of OCR in HL PBMCs was confirmed by the analysis of glycolytic flux E. In agrSince redox environments in ER and mitochondria are strictly interconnected by the exchange of metabolites and cofactors through the MAMs, we evaluated the activity of pathways dedicated to the oxidative balance in the ER lumen and in the connected MAMs. ER-PPP was activated in HL PBMCs as documented by the fourfold increase in the catalytic function of its triggering enzyme H6PD with respect to the control PBMCs A. The rep < 0.001) steeper in HL than in control subjects  could not be separately investigated for the two PBMC populations because of the limited amount of available blood in these patients. This limitation prevented verifying the presence of possible differences in mitochondrial function between the studied lymphocytes and monocytes. Nevertheless, the divergent MitoSOX Red signal already indicates a greater respiratory uncoupling in the former as a possible contributor to their impairment.A similar consideration also applies to the degree of mitochondrial damage. Indeed, the missing evaluation of OCR during OXPHOS uncoupling does not permit identifying a possible reduction in maximal mitochondrial respiratory capacity. Nevertheless, the significant decrease in ATP-linked mitochondrial OCR, coupled with the increased MitoSOX fluorescence, already confirms a primary role for these organelles in the oxidative damage of HL PBMCs.The redox response to HL might be heterogeneous through the different subtypes of lymphocytes and monocytes populating the peripheral blood. This evaluation would have been feasible by combining the metabolic characterization with a precise definition of involved cell phenotype. Yet, this approach would have necessitated an extra blood sampling for CD staining that was avoided for ethical considerations in all subjects, except for the subset cohort involved in the described pilot test. Nevertheless, the present evidence of a significant metabolic shift induced by HL in PBMCs might represent the basis for future studies aiming to identify the involved cell subtypes.Finally, the present study did not investigate the molecular mechanisms underlying the observed oxidative damage in the PBMCs of untreated HL patients. This task would have implied seeding and culturing these cells in media whose content of nutrients and signals cannot reproduce the peripheral blood environment. Obviously, this difference might per se alter both ROS generation and antioxidant reaction. On the other hand, the observed compartmentalization of redox stress within mitochondria and ER lumen provides a new insight into this metabolic alteration, suggesting the need for new hypotheses about the involved pathways and their potential role in HL aggressiveness.Our results indicate oxidative damage in the PBMCs of HL patients before the beginning of therapy. The redox stress reflects a relative mitochondrial OXPHOS uncoupling that primarily extends to the ER, whose homeostasis is strictly dependent upon the activity of the local PPP. This finding configures the ER–mitochondria connection as a fundamental player in the host response to disease. This direct HL effect should be carefully considered to better understand the mechanisms underlying chemotherapy toxicity in normal tissues.


The still-growing field of additive manufacturing (AM), which includes 3D printing, has enabled manufacturing of patient-specific medical devices with high geometrical accuracy in a relatively quick manner. However, the development of materials with specific properties is still ongoing, including those for enhanced bone-repair applications. Such applications seek materials with tailored mechanical properties close to bone tissue and, importantly, that can serve as temporary supports, allowing for new bone ingrowth while the material is resorbed. Thus, controlling the resorption rate of materials for bone applications can support bone healing by balancing new tissue formation and implant resorption. In this regard, this work aimed to study the combination of polylactic acid (PLA), polycaprolactone (PCL) and hydroxyapatite (HA) to develop customized biocompatible and bioresorbable polymer-based composite filaments, through extrusion, for fused filament fabrication (FFF) printing. PLA and PCL were used as supporting polymer matrices while HA was added to enhance the biological activity. The materials were characterized in terms of mechanical properties, thermal stability, chemical composition and morphology. An accelerated degradation study was executed to investigate the impact of degradation on the above-mentioned properties. The results showed that the materials’ chemical compositions were not affected by the extrusion nor the printing process. All materials exhibited higher mechanical properties than human trabecular bone, even after degradation with a mass loss of around 30% for the polymer blends and 60% for the composites. It was also apparent that the mineral accelerated the polymer degradation significantly, which can be advantageous for a faster healing time, where support is required only for a shorter time period. Additive manufacturing (AM) has been applied in the automobile and aeronautical industries since the beginning of the 1980s. However, its application in medical industries is more recent and yet to be fully explored. In the past few decades, the combination of high-resolution imaging with AM has enabled the development of customized implants and surgical devices [Due to the quick processing time as well as its low cost, fused filament fabrication (FFF), or fused deposition modelling (FDM), stands as an attractive 3D-printing technique for fabricating medical devices . FFF manPolylactic acid (PLA) is one thermoplastic polyester applied in clinics as resorbable support materials for bone and ligament fixation ,6, with Additionally, degradation rates and by-products of biomaterials are paramount in regenerative tissue applications. Degradation can occur actively by cellular activity and the physiological environment at the implantation site  or passiRecently, the development of customized composite and blend polymers has been explored , includiIn this study, we aimed at exploring the combination of these two biocompatible polymer matrices with bioactive mineral, such as HA, with the aim of providing customized materials for FFF of potential bone implants. The combination of these materials was investigated with the aim to tune the properties to produce materials suitable for bone-repair applications, with a special focus on their degradation behavior, including mechanical and chemical stability.Two biodegradable polymers were used, polylactic acid  and polycaprolactone  . Hydroxyapatite powder  was used as mineral filler and sieved below 75 µm before use . DichlorPLA-PCL blends were prepared by cutting the PLA filament into small pieces and dissolved into dichloromethane for approximately 1 h. Afterwards, the corresponding amount of PCL pellets was added and further left under agitation until complete homogenization (4 h). The polymer blend was then cast into a crystallizer and left for solvent evaporation at room temperature for at least 20 h. Afterwards, the blend films were collected, cut and stored in desiccator until their extrusion. The composite counterparts were prepared similarly as aforementioned. The mineral HA was dispersed in the dichloromethane for 15 min prior to the addition of the polymers.All films were finely shredded  prior to extrusion. The flakes were fed into a single-screw extruder  to obtain 2.85 mm thick filaments. The setting parameters for extrusion varied depending on the material composition .An FFF printer  was used to print the filaments. Two different print cores were used, 0.4 mm brass  and 0.6 mm ruby , for the blends and the composites blends, respectively. Dense, full infill density (100%), cylinders of 6 mm in diameter and 12 mm in height were printed with a layer height of 0.1 mm. Further, 100% fan speed was used for all materials. Pure PCL also required an external fan, placed in front of the build plate, and 3D lac  was used as adhesion improver on the build plate for all materials except pure PCL which required Kapton tape. The printing parameters are summarized in −1 and between a range of 400 and 4000 cm−1. Phase composition was assessed by X-ray diffraction (XRD) using a D8 Twin-Twin  diffractometer with a copper anode, operating at 40.0 kV and 40.0 mA, from 5 to 70° (2θ), using a step size of 0.018° and 0.50 sec per step. The patterns obtained for HA powders were compared to hydroxyapatite (JCPDS 01-074-0565) and monetite (JCPDS 04-009-3755).Chemical characterization was performed by Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy . A diamond crystal was used and each spectrum was recorded with a total of 64 scans, a resolution of 4 cmMorphological characterization of the cast films and extruded filaments was performed using scanning electron microscopy  with an acceleration voltage of 15 kV. Cross sections of fracture surfaces of the films and the filaments, as well as the film surfaces, were studied on both the polymer blends and the composites. All samples were sputtered with gold/palladium prior to analysis by using a Thermo VG Scientific POLARON SC7640 Sputter Coater .Thermal characterization was carried out by a differential scanning calorimeter  and by thermogravimetric analysis . DSC was performed using a heat–cool–heat procedure, with a heating rate of 10 °C/min until 200 °C and a cooling rate of 5 °C/min until −100 °C, under a nitrogen flow of 50 mL/min. All samples were analysed in Aluminium hermetic pans and sample weight varied between 6 and 10 mg. The second heating scan was used to evaluate the thermal properties for each sample. TGA measurements were performed in Platinum pans at a heat rate of 10 °C/min, from room temperature to 800 °C, under a nitrogen flow of 50 mL/min. TGA sample weight varied between 17 and 23 mg.Mechanical characterization was performed on the 3D-printed cylinders by a uniaxial compression test using a Shimadzu AGS-X universal testing machine . Compliance correction was used by measuring the stiffness of the set-up using a 10kN cell, data which were later used in the calculations. Prior to testing, all specimens were pre-loaded with a force corresponding to 2–4% strain . SamplesLW) of all specimens was determined by using the following equation:W0 represents the initial mass of the cylinders and FW, the final mass of the cylinders after immersion in NaOH solution for each specific time point. Triplicates were used for hour interval measurements  and eight replicas were used for the daytime intervals .An accelerated degradation study was performed on the printed compositions as previously described . BrieflyCompositional changes in the cylinders were analysed by FTIR, XRD, DSC and TGA. Morphological changes on the surfaces were observed by SEM . The mechanical behaviour of degraded samples was investigated at 7-, 14-, 21- and 28-day time points using six specimens per time point. A 5 kN load cell was used, without pre-loading, with a cross-head speed of 1 mm/min. The elastic modulus and yield point were determined from the obtained stress–strain curves as described in p < 0.05. First, Levene’s test was used to assess homogeneity of variances. If Levene’s test was non-significant, an analysis of variance (ANOVA) was performed to analyse the differences among the group means. If the ANOVA was significant, Scheffe’s post hoc test was conducted. If Levene’s test was found significant, Welch’s test was conducted to analyse the differences among group means. Then, Tamhane’s post hoc test was carried out to analyse the differences between pairs of groups.Statistical analysis was conducted using SPSS Statistics . The test results are presented as mean and standard deviation and were considered significant if 2 stretching bands were shown at 2995 cm−1 and 2950 cm−1 for pure PLA and at 2947 cm−1 and 2865 cm−1 for pure PCL, whereas a C=O stretching band appeared at 1750 cm−1 and 1720 cm−1 for PLA and PCL, respectively. Moreover, the CH3 bending band was found at 1453 cm−1 for PLA and C–O–C stretching bands at 1180 cm−1 and 1082 cm−1 for PLA and 1171 cm−1 for PCL [−1, 601 cm−1 and 561 cm−1, corresponding to vibrations in the phosphate group (PO43-) in HA [Spectroscopic analyses of the filaments for each composition are illustrated in  for PCL ,32,33. T-) in HA ,35.In addition to the chemical changes observed by FTIR, the morphology of the materials assessed by SEM varied . Cross-sgT) and melting temperature (mT) of PLA were shifted towards lower temperatures (around 7 °C). Additionally, 90PLA10PCL-15HA and 80PLA20PCL-15HA composites also displayed a double melting peak of PLA, together with a slight decrease in the cold crystallization temperature (ccT) of PLA.DSC measurements were performed to investigate the thermal behaviour of PLA and PCL in the processed blends and composite blends. TGA measurements were performed to further determine the thermal degradation behaviour of the processed materials , to inveStress–strain curves for all materials are presented in The cumulative weight loss of all samples is depicted in No clear changes in chemical composition could be detected when comparing degraded materials to their pristine analogues. However, a small change in the C=O stretching band could be noted after degradation, as a double peak appeared  of the blends and the composite blends (ccT with increasing PCL content might be due to a nucleating effect ascribed to PCL during PLA crystallization, similar to what was reported by Navarro-Baena et al. [Since the thermal properties (e blends  were deta et al. .Along with the improved thermal stability, an increased onset and offset degradation temperature for both the blends and the composite blends was observed compared to pure PLA . On the The overall trend seen for the mechanical properties in the blends and composite blends was a decrease in stiffness as PCL content increased, i.e., increasing amounts of PCL increased the ductile behaviour of the materials . For theThe weight loss  and morp−1, while stretching of the hydroxyl group often is detected around 3000 cm−1 [Despite the significant degradation observed morphologically and with the weight loss in the samples, FTIR spectra did not further evidence degradation by-products . The mai000 cm−1 , hence, ccT and a split in the melting curves for the degraded samples (ccT has been correlated to a reduction in overall crystallinity and molecular weights [gT upon degradation, as a potential result from PLA degradation, leading to an increase in lactic acid oligomer by-products, evidenced also by XRD analyses.The thermal properties analysed through DSC comparing pristine and degraded samples showed, in general, a reduction in the  samples . Lower T weights . Likewis weights . MoreovePCL was found to increase the thermal stability in the non-degraded materials , which cBoth the blends and composite blends showed a decrease in E-modulus after degradation, of almost half for all blends and composites, except 90PLA10PCL-15HA . This coOverall, composition-customized filaments consisting of combinations of PLA, PCL and HA demonstrated good thermal and chemical stability. The materials showed good printability properties to be applied in a cost-effective FFF-printing technique. The mechanical properties illustrated a reinforcement effect on PLA:PCL blends when HA was incorporated, mostly for 90PLA10PCL-15HA. Further characterization of the degraded materials and released by-products during degradation would be necessary to assess the safety of these materials from a biological perspective and further investigate their potential as bone regenerative materials. Importantly, the degradation behaviour could be actively controlled by the incorporation of both PCL and HA into PLA. Through close control of the amount of PCL, degradation rates could increase up to 10%, while further addition of HA fostered degradation 3-fold for pure PLA or 1.5- to 2-fold for PLA:PCL blends. These degradation rates could be further optimized by the incorporation of porosity during FFF printing and the overall geometry of the scaffold.The development of customized materials consisting of biocompatible and biodegradable polymers, alone or in combination with mineral parts, yielded printable materials with chemical stability and mechanical properties suitable for bone regeneration. The mechanical properties were demonstrated to fulfil the mechanical threshold for trabecular bone applications. The customization of degradation rates was highly dependent on the material composition, demonstrating an improvement by the incorporation of mineral phases, such as hydroxyapatite, which further enhanced the degradation of PLA:PCL combinations. This, together with the incorporation of controlled porosity and scaffold architecture, could further offer the possibility of controlling and balancing biomaterial resorption and new bone formation on demand.