Core Analysis of these eight molecules identified High Mobility Group Box protein 1 (HMGB1) signaling pathway among the top 5 canonical pathways with most overlap with the 8-molecule dataset. Simulated METH exposure increased APP expression through HMGB1 at a confidence of p < 0.00001 . HMGB1 is a pathogenic hallmark in AD progression. It not only increases the production of inflammatory mediators, but also mediates the disruption of the blood-brain barrier. Our analyses suggest the involvement of HMGB1 signaling pathway in METH-induced modulation of APP as a potential casual factor of AD.The deposition of amyloid-beta (Aβ) through the cleavage of amyloid-beta precursor protein (APP) is a biomarker of Alzheimer’s disease (AD). This study used QIAGEN Ingenuity Pathway Analysis (IPA) to conduct meta-analysis on the molecular mechanisms by which methamphetamine (METH) impacts AD through modulating the expression of APP. All the molecules affected by METH and APP were collected from the QIAGEN Knowledge Base (QKB); 78 overlapping molecules were identified. Upon simulation of METH exposure using the “Molecule Activity Predictor” feature, eight molecules were found to be affected by METH and exhibited activation relationships on APP expression at a confidence of  N-terminal fragment, sAPPα, and a C-terminal fragment, which is then further cleaved by γ-secretase. In contrast, the amyloidogenic pathway involves cleavage of APP by β-secretase to produce a soluble N-terminal fragment, sAPPβ, and a C-terminal fragment, which is then further cleaved by γ-secretase to produce 40-amino-acid peptide Aβ40 and 42-amino-acid peptide Aβ42 [Alzheimer’s disease (AD) is a form of dementia characterized by the accumulation of amyloid beta (Aβ) plaques, neuronal damage and loss, neurofibrillary tangles of tau protein, and neuroinflammation. The deposition of Aβ monomers occurs through the proteolytic cleavage of amyloid precursor protein (APP), a transmembrane protein and one of the most abundant proteins in the central nervous system (CNS) . The proide Aβ42 . The acc25–35-treated hippocampal neuronal cells exhibited increased HMGB1 expression in the AD-related model of neuroinflammation [High Mobility Group Box 1 (HMGB1), a nuclear protein that interacts with DNA and histones and regulates transcription, has been implicated as a critical pathogenic hallmark in AD progression, in addition to Aβ and neurofibrillary tangles . Aβ25–35ammation . HMGB1 mammation . HMGB1 aammation . Inhibitammation , suggestammation . There iammation . Furtherammation  and enhaammation . HMGB1 hammation ,13.Methamphetamine (METH) is a potent psychostimulant drug with potential for psychological and physical dependence, due to its highly addictive nature . METH abThe growing body of literature associates neurodegeneration, neurotoxicity, and cognitive deficits caused by METH exposure with AD, and has raised concerns over significant public health problems caused by METH abuse in various communities across the United States . HoweverQIAGEN Ingenuity Pathway Analysis (IPA), a bioinformatics tool, was used for data mining and functional connectivity analysis of genes found in the QIAGEN Knowledge Base (QKB), a repository of manually curated information collected from over seven million individually modeled relationships between diseases, drugs, biological entities, such as genes, proteins, and metabolites, and processes that include expression, molecular cleavage, and phosphorylation, in addition to published results of omics experiments. In this study, the following tools were used: the “grow” tool expanded the molecules affected by METH, HMGB1, and APP; the “Path Explorer” tool connected the overlapping molecules between METH, HMGB1, and APP based on the expertly curated scientific literature stored in QKB; the “Molecular Activity Predictor” simulated the exposure of METH on the network to determine expression changes in HMGB1 and APP. Together, these tools were used to shed light on each individual connection between METH, HMGB1, and APP, while also providing an integrated and comprehensive network that sums the overall effects of those connections. Additionally, the Core Analysis feature and the canonical pathways were an integral part of this study as they aided in elucidating the role of not only every individual molecule, but how they may cause an impact when taken together—holistically. Finally, an algorithm, part of the “downstream effects analysis” identified by Kramer , was useMolecules affected by METH and APP were obtained from QKB and the influence of METH exposure on APP-related molecules was characterized. Core Analysis of the eight molecules overlapped between METH and APP revealed HMGB1 signaling pathway as a top canonical pathway. Through the various tools of IPA, our network meta-analysis study suggests the involvement of HMGB1 as part of the METH-induced neuroinflammation as a potential casual factor of AD. This sheds light on potential mechanisms by which METH exposure contributes to the development of AD.IPA’s “Build” and “Grow” tools were used to identify molecules affected by METH and APP, respectively, in QKB . A totalThe “Downstream Effect Analysis”, described by Kramer , uses anp-value of 0.000453 of a two tailed distribution; this indicates that the likelihood of finding the same results by chance occurs less than 1% of the time. The weights computed for the 8 molecules compiled in The cumulative z-score of METH impact on APP expression in the network was 3.5 ,30, corr“Expression” analysis was then conducted to compare these 8 intermediate molecules with the molecules that constitute each of the canonical pathways. These 8 molecules that influence APP expression following METH exposure were found to be involved in 296 out of 705 canonical signaling and metabolic pathways in IPA. Moreover, the top five canonical pathways, shown in p-value of 7.74 × 10−11, was chosen as a negative control as it is the highest canonical pathway that has no a-priori associations. A total of 115 molecules were found to overlap between Hypertrophy of Cardiac Muscle and APP. A core analysis of the dataset generated 388 canonical pathways out of the 705 stored in QKB. The neuroinflammation signaling pathway and HMGB1 signaling pathway were not among the top ten canonical pathways and none of the top 5 canonical pathways from the core analysis of the 8-molecule dataset overlapped with the top 5 canonical pathways from the cardiac hypertrophy with APP dataset. Therefore, the results of this negative control increased the confidence that identification of neuroinflammation and HMGB1 as the top of canonical pathways in our analysis was rather unique and not due to broad and non-specific associations.Due to the complexity of the immune system, there is a possibility that any core analysis run on any dataset would implicate the neuroinflammation and HMGB1 signaling pathways in the top canonical pathways. A clear bias favoring the neuroinflammation and HMGB1 signaling pathways as a canonical pathway in any given dataset would weaken the hypothesis of the impact of METH on APP through neuroinflammation and HMGB1. As such, from the top canonical pathways generated from the core analysis run on the dataset of the original 8 molecules, the 14th canonical pathway called, Cardiac Hypertrophy Signaling (Enhanced), with a HMGB1 associated molecules were then identified from QKB using the “Build” and “Grow” tools. METH exposure was then simulated. Among the molecules affected by METH exposure and associated with HMGB1, 16 intermediate molecules were found to influence HMGB1 expression following METH exposure a. SimilaA total of 16 molecules that were found to influence HMGB1 expression following METH exposure were then pooled together with the 19 molecules that were found to influence APP expression upon upregulation of HMGB1. METH exposure was simulated again to produce a comprehensive illustration of the changes in directionality, gene expression, and connections between the molecules within the network. The result was a network in which HMGB1 served as a primary intermediate between METH and APP, while APP maintained its position of being influenced downstream of the network .In order to assign weights to the relationships of the molecules mediating HMGB1 and APP expression following METH exposure, Z-scores were calculated using the “Downstream Effect Analysis”, described by Kramer , based oThe algorithm used 653 references of prior known scientific literature stored in QKB as data points to predict how HMGB1 upregulation may change APP expression. The Z-score for the involvement of each individual molecule on HMGB1 expression was calculated. As shown in p-value of less than 0.00001 in a two tailed hypothesis, suggesting that the likelihood of finding equally strong consistency of the results occurs at a less than 0.001% chance. The results indicated an increase in APP expression with an overall calculation that gave a Z-score of 7.6 ,30, whicTo examine how critical the role of HMGB1 is in the network of METH and APP, the “MAP” tool was used to simulate how inhibition or blocking of HMGB1 may affect METH-induced modulation of APP expression. p-value = 3.36 × 10−31). The pathway illustrated a variety of connections, of which TNF, IL1B, and IL6 are part of, that lead to neuronal damage, as well as disruption of the blood brain barrier, leading to neurodegenerative disease , neuronal damage, formation of amyloid-beta plaques, and an overall increase in APP expression . These dp-value of 0.000453 of a two tailed distribution; this indicates that the likelihood of finding the same results by chance occurs less than 1% of the time. Thus, the 8 molecules presented in This study also sheds light on the controversy of whether METH exposure is harmful ,17,34 orp-value = 7.88 × 10−14)—activates c-JUN [Our studies found that JUN is activated by METH and influences APP expression with a Z-score of 1.411 . Indeed,es c-JUN ,37, and es c-JUN ,39. METHes c-JUN . When mies c-JUN ,41,42. Tes c-JUN . IL1B anes c-JUN , which Mes c-JUN . Our stuMatrix metalloproteinase-9 (MMP9) was another overlapping molecule between those activated by METH and those influenced by APP. In rodents, repeated METH administration induces behavioral sensitization and an increase in MMP-2/9 activity in the brain ,17,45. WFurther analysis of the most significant canonical pathway generated from the dataset from Another mediator between HMGB1 and APP was receptor for advanced glycation end products (RAGE), which has been implicated in various neurodegenerative diseases, such as AD. RAGE has been found in AD brains at a higher concentration than normal brains. Going back to the blood brain barrier damage and its relation to AD, data suggests that RAGE carries Aβ across a leaky BBB . Toxic AAs any methodology, network meta-analysis has its limitations. This study identified 8 intermediate molecules that are affected by METH and influence the activity of APP using IPA and QKB. QKB has been generated using research findings published and curated so far. This study has focused on these 8 molecules. Our findings do not exclude the possible indirect involvement in METH actions or APP activities from other molecules that might be potentially associated with any of the 8 intermediate molecules. Secondly, IPA uses the over representation analysis (ORA), which evaluates the statistical relevance of a given pathway, is based on differentially expressed components within the pathway beyond the molecules that could be randomly expected. Every component in the pathway is assigned an equal weight, regardless of whether the component is inherent to the interactions. However, all of the 8 intermediate molecules between METH and APP consistently exhibited activating effects on APP with a cumulative Z-score of 3.5. These data confirm that these 8 molecules are the key molecules mediating the impact of METH on APP expression. Despite the limitations mentioned above, this study has revealed holistic molecular connections of METH activation leading to enhanced production of APP through HMGB1, shedding light on how METH may increase AD risk.Altogether, this study examined the involvement of HMGB1 in METH-modulation on APP expression, where METH exposure was found to upregulate HMGB1 expression, which consequently increased the expression of APP. Blocking HMGB1 in the pathway led to inhibition in APP expression, suggesting that HMGB1 could potentially be a therapeutic target to reduce the accumulation of Aβ plaques in the brain and be effective in mitigating the risks of AD progression from METH use. Our study suggests that METH exposure may increase the expression of APP and that HMGB1 signaling pathway may play a key role in METH-induced expression of APP. Our data shed light on the potential mechanism by which METH exposure causes neurotoxicity and neurological deficits in AD, and that neuroinflammation, commonly found in AD brains, may mediate METH-induced APP expression through HMGB1. https://www.qiagenbioinformatics.com/products). IPA is a bioinformatics tool that was used to analyze data and biological processes using the QKB repository composed of over seven million individually modeled relationships to produce and analyze networks through known metabolic and signaling pathways . This allowed the identification of biological pathways that the molecules in each dataset are involved in.A dataset of the molecules overlapping in each network  was created and uploaded into IPA for “Core Analysis”. Canonical pathway analysis queries the dataset against the 705 canonical pathways stored in QKB. The significance for the association with each of the canonical pathways was calculated using a Benjamini–Hochberg Corrected Fisher’s Exact test to generate a −log, stored in QKB upstream and downstream of it. s(e) identifies the overall sign of a particular direction from the edge, ranging from {−1, 0, 1}; w(e) identifies the weight of the edge, ranging from {0, 1}. Both formulas use the findings, f, plugged in as data points of −1 or 1. SD is the sign of the upstream molecule in a given relationship as {−1, 1}. z(r) is then used to compute the individual change in expression of the target molecule in a relationship by combining the values computed for each direction downstream and upstream from a particular edge.The formula identified by Krämer was utilized to compute a Z-score for each intermediate downstream from METH and upstream of APP by calculating its individual involvement in APP expression. The scale of individual expression change caused by a molecule in a given pathway runs between −2 and 2, where −2 indicates a strong inhibitory relationship and 2 indicates a strong activation relationship. Each relationship is composed of at least one edge, z(r) formula was used to compute an overall Z-score of a network of edges with a slight difference. WR, which corresponds to w(e) in the individual Z-score computation, now corresponds to s(e), which can be {−1, 1}. The same calculations were then performed.The same edge relationship. The formula is outlined by Stouffer and used to compute a Z-score aggregating independently found Z-statistics into a two tailed standard normal distribution [SZ is the overall Z-score of the two-tailed standard normal distribution; iZ is the individual one tailed Z-score; and k is the total number of individual Z-scores in the standard normal distribution.Furthermore, another formula was used to compute an overall Z-score of a particular network combing all the individual Z-scores of each ribution ,64.(4)ZBoth formulas yielded the same Z-scores to the nearest tenth.The neuroinflammation signaling pathway that features METH, HMGB1, APP, intermediates, and other diseases/functions, such as disrupted BBB, neuronal damage, and Aβ accumulation, was generated using the pathway stored in QKB. The neuroinflammation signaling pathway, as part of the 705 canonical pathways stored in QKB, features a large number of molecules, diseases, and functions. The specific portion of the pathway that was overlapped with the majority of the molecules in our dataset was selected for closer investigation. Redundant intermediates were eliminated to focus on the interconnections of the molecules in our dataset and their roles in the neuroinflammation signaling pathway. METH was added to the network and the exposure of METH on the network was simulated using the “MAP” tool.


Huangqi-Honghua herb pair is known for its medicinal value to treat Qi deficiency and blood stasis syndrome with a long history in clinical practice. To understand its possible mechanism in a systematic study, a network pharmacological method was addressed.  Detailed information on the HH compounds was obtained from two public databases, and oral bioavailability (OB) and drug-like (DL) of the compounds were evaluated. A correlation between HH compounds, its potential targets, and known targets was extrapolated, and the herb-compound-target-disease (H-C-T-D) network was established. Next, the pathway enrichment and essential genes were analyzed. Then, three key genes , highly associated with angiogenesis, were screened and verified through western blot assay.  Out of 276 compounds, 21 HH compounds and 78 target genes regulating the major pathways associated with CI in the network are analyzed. The bioactive compounds in HH were active in various signal transduction pathways such as the toll-like receptor signaling pathway, VEGF signaling pathway, TNF signaling pathway, and HIF-1 signaling pathway are important pathways that may regulate anti-inflammatory, antiapoptotic, immune correlation, and antioxidative effects. The core genes are PTGS2, TNF, NOS2, IL6, BCL2, IL1B, SOD2, NOS3, SOD1, MMP9, and VEGFA. The in vitro results suggested that HH treatment could significantly elevate the expression of proangiogenic genes such as VEGFA, VEGFR2, and eNOS compared with OGD groups.  Our results predict that HH may regulate the expression of VEGFA, VEGFR2, and eNOS via the VEGF and HIF-1 signaling pathway to promote angiogenesis and alleviate cerebral ischemia injury. Ischemic stroke  has become a major public health concern with morbidity, mortality, and health care costs . IrrespeAstragalus membranaceus (Fisch.) Bge, which has been commonly used in patients with stroke or chronic weakness in China because it can enhance Qi and dispel blood stasis, with antioxidation, anti-inflammatory, immunomodulation, and anticancer effects in modern medical research [Carthamus tinctorius L. (Honghua), a dried flower, often used to promote blood circulation, dispel blood stasis, and relieve pain [Traditional Chinese medicine (TCM) has been shown to be beneficial in promoting developmental angiogenesis, which highlights “reinforcing qi to enrich the blood, dispelling stasis to promote regeneration” theory by associating with angiogenesis together. Radix Astragali (Huangqi) is the dry root of research , 7. Carteve pain , 9. Huaneve pain –12. In oeve pain .Network pharmacology follows the dogma of “drug-targets-gene-disease,” to predict drug targets and improve the efficiency of drug discovery effort. Network pharmacology maintains integrity and systematic characteristic, similar to the principle of traditional Chinese medicine that involves multiple components and multiple targets . NetworkDespite a few studies that have proved the protective effect of HH in the cerebral hemisphere, pharmacology-based prediction of complete profiling of bioactive components and the network of their target pathways has not fully been elucidated. Thus, in the present study, we have explored the proangiogenesis mechanisms of HH in the etiology of CI. In this study, we have established the herb-compound-target-disease (H-C-T-D) networks by utilizing the network pharmacology approach. Furthermore, to demonstrate angiogenesis, one key pathway of HH in cerebral protection, we have identified three key targets, which is highly associated with angiogenesis, through immunoblot assay. The workflow is shown in http://tcm.cmu.edu.tw/) and the traditional Chinese Medicine system Pharmacology Database . The chemical structures, 2D structure, molecular weight, and chemical number are reported on ChEMBL (http://www.ebi.ac.uk/chembl/) and NCBI PubChem (http://puchem.nvbi.nlm.nih.gov/).Information on the herbs and compounds related to HH were collected from two botanical chemistry databases: TCM Database of @Taiwan  and drug-likeness (DL). OB is one of the most common pharmacokinetic parameters in the drug screening process, which explains the percentage of an oral drug that remains unchanged after entering into the systemic circulation. In addition, OB provides a clue for the fusion of the ADME process. DL measures the extent of the “drug-like” property of a compound and determines whether the compound influences absorption, distribution, metabolism, and excretion (ADME) in the human body like an approved drug. In this study, chemicals only with an OB ≥30 and DL ≥0.18 (recommended by TCMSP database) were considered for further valuation .http://www.uniprot.org/) platform, by setting the filter to “Homo sapiens,” was used to identify the target genes, closely relevant to chemicals. Meanwhile, information on target genes related to the CI was obtained from the therapeutic target database  and DisGeNETv6.0 (http://www.disgenet.org/web/DisGeNET/menu/home). Finally, we assigned the gene names and ID by constructing herbs-ingredients-targets relationship dataset for HH.UniProt (http://david.ncifcrf.gov/).The herb-compound-target-disease (H-C-T-D) networks were established using Cytoscape 3.3.0, JAVA software. Functional pathways annotation related to CI and enrichment evaluation was evaluated using the Kyoto Encyclopedia of Genes (KEGG), Genomes (GO), and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6.8 , and possible protein-protein interactions were predicted. We chose and showed the top 20 target genes with confidence score >0.9 as the core genes for CI.PPI data were collected from the Search Tool for the Retrieval of Interacting Genes (STRING) database . All other reagents are analytical reagents and commercially available.HH granules  were obtained from Guangdong Yifang Pharmaceuticals Co., Ltd. (product batch: 20170606). The rat brain microvascular endothelial cells (BMECs) were purchased from the Cell Biologics Company . Primary antibodies against μg/mL). Oxygen-glucose deprivation (OGD) was established as follows: cells were rinsed once with glucose-free DMEM  and transferred to an anaerobic chamber  containing a gas mixture composed of 7% CO2 and 93% N2 for 6 h at 37°C. Then, the cells were returned to the normal culture condition. Control BMECs were cultured in complete DMEM under normal conditions. HH groups were treated with HH (dissolved in complete DMEM and filtered with a 0.22 μm membrane filter) for 12 h. The control group was treated without HH.Cells were divided into three groups: control group, OGD group, and OGD group treated with HH  for 30 min. The whole cell lysates were separated by 10% sepharose gel, and PVDF membranes  were used to transfer protein. The membranes were incubated with 5% bovine serum albumin (BSA) and overnight with primary antibodies against VEGFA , VEGFR2 , and eNOS  at 4°C. After that, membranes were incubated with secondary antibody at a 1 : 5000 dilution at 37°C for 1 h. After ECL-Plus reagent  treatment, the blots were analyzed with Quantity One System image analysis software .p < 0.05 presented statistical significance.The results were expressed as mean ± standard deviation (S.D.) and analyzed with one-way analysis of variance (ANOVA), followed by a Tukey's post hoc test was used for analyzing differences between groups. HH consists of a total of 276 compounds, 87 in Astragalus (Huangqi) and 189 in Safflower (Honghua). All 276 compounds were screened with OB and DL, out of which, only 42 compounds were recommended for the final screening. The remaining compounds were discarded. The therapeutic effect of a few selected compounds on CI was confirmed through the related literature search. Therefore, preselected active ingredients were added manually, including hydroxysafflor yellow A and astragaloside IV –19. Aftehttps://ctdbase.org/). The repeated targets were deleted, and the unclear targets were eliminated. A total of 459 predicted target genes were obtained related to the 27 identified compounds in HH  were related to more than five genes, and 18 genes  related to more than five compounds. Therefore, we speculate that these compounds and genes might be key nodes in this network. The compound-target gene network will be conducive to interpretation and prediction the “multicompound, multitarget, and multipathway” of HH.\Out of the 459 matching HH-associated genes and 274 CI-related genes, we streamlined 78 overlapping genes while constructing the gene network , Table 3p < 0.05 signal pathway by KEGG enrichment analysis eliminates signaling pathways that are not associated with CI and get the top 18 signaling pathways , cell component (CC), and molecular function (MF) . Accordipathways . TherefoIn the PPI relationships network, we found 78 nodes and 1429 edges . The top∗p < 0.05, ∗∗p < 0.01 compared with control group, and #p < 0.05 compared with OGD group).To confirm the results from the network and to verify that angiogenesis is indeed one of the key pathways of HH in cerebral protection, we selected three targets related to angiogenesis  for pharmacological validation . WesternMulticompound TCMs, with multiple biological targets, are the prime focus in Chinese clinical practice for more than thousand years. Surprisingly, TCMs are known to target multiple biological pathways to defend against diseases. However, the traditional method of usage of TCMs barely provides insights into the biological complexity of compounds, its biological targets, and associated disease, which limits the development and reformulation of TCMs. Lately, network pharmacology approach of analyzing TCMs gains more popularity that dissects the pharmacological mechanism of action –22.In the current study, a network pharmacology analysis of HH identified 2 herbs, 21 compounds, as well as 78 target gene-regulated major pathways associated with CI. Through the pathway enrichment analysis, we found that the targets of active ingredients in HH against cerebral ischemia injury mainly participate in numerous signal transduction pathways such as TNF signaling pathway, toll-like receptor signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, and VEGF signaling pathway. These key pathways may regulate anti-inflammatory, antiapoptotic, immune correlation, and antioxidative effects. Furthermore, the PPI system analysis indicates these genes played vital roles in CI , such asκB pathway [In vivo study also revealed quercetin to have anti-inflammatory effects by inhibiting oxidative stress and cytokine production [According to the TCM theory, Qi and blood dysfunction is one key pathogenesis of CI. In the clinic, Qi-tonifying drugs, blood-activating drugs, and reinforcing Qi and activating blood drugs team were usually treated for CI with significant efficacy. A fewer side effects , 24 of t pathway . However pathway , has ant pathway . In vivooduction . Formonooduction . These fκB pathway in CI rats [Angiogenesis is similar to “reinforcing qi to enrich blood, Dispelling stasis to promote regeneration” theory and regulated by angiogenesis inducers and inhibitors. Angiogenesis involves basilar membrane degradation, chemotactic migration, and proliferation of EC  and EPC . In the PPI system analysis, top 20 center genes were recognized, and among these genes, VEGFA and eNOS are highly correlated with angiogenesis. Over the few decades, vascular endothelial growth factors (VEGFs) and their receptors (VEGFRs) have been regarded as the principal drivers of angiogenesis and the development and maintenance of vascular system . VEGFA  often trigger angiogenesis , which cIn summary, network pharmacology analysis of HH identified 2 herbs, 21 compounds, and 78 target gene-regulated major pathways associated with CI. The bioactive compounds in HH mainly participate in numerous signal transduction pathways such as TNF signaling pathway, toll-like receptor signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, and VEGF signaling pathway, and these important pathways may regulate anti-inflammatory, antiapoptotic, immune correlation, and antioxidative effects. Furthermore, through the pharmacological experiment, we predict that HH can regulate VEGFA, VEGFR2, and eNOS via the HIF-1 signaling pathway and VEGF signaling pathway to promote angiogenesis and alleviate cerebral ischemia injury.


Focal palatine erosion (FPE) is a misleading term that is used in the literature to describe inflammatory lesions associated with depressions of the palatal mucosa in cheetah. Cheetahs have large cheek teeth and these depressions are formed to accommodate them. Previously FPE was only described as a mandibular molar tooth malocclusion on the hard palate due to suspected rotation and super eruption of the mandibular molar teeth of cheetahs aged 18 months and older. Two hundred and fifty six cheetahs , originating from two independent facilities, had their oral cavities evaluated as part of an annual health visit over a decade. Ninety-nine cheetahs were seen once, 59 cheetahs were seen twice, 33 were seen three times, 43 on four occasions, 16 on five occasions, 5 on six occasions, and 1 cheetah was seen seven times. Apart from these clinical cases a prospective study on 5 cheetah cubs  was conducted to document their skull development and mandibular molar tooth eruption over a period of 25 months. Of the 261 cheetahs observed none developed rotation or super eruption of their mandibular molar teeth. The term FPE is a misnomer as these inflammatory lesions were found in palatal depressions opposing any of the cusps of all of the cheetah mandibular cheek teeth. It consisted mainly of deep ulcerations, inflammation and oedema and also micro abscess formation. In severe cases oro-nasal fistulas were present. Of all the depressions present on the cheetah's palate, the large one palatal to the 4th maxillary premolar tooth was most commonly affected. In the five cubs evaluated prospectively, focal palatitis was evident from the 7 month evaluation, before all the permanent teeth erupted. Conservative treatment of the inflamed depressions by removing the foreign material through curettage and copious flushing reduced the grade of the inflammation when observed on follow-up. Focal palatine erosion is an incorrect term used to describe focal palatitis that occurs randomly in cheetahs. This focal palatitis is often associated with foreign material trapped in the palatal depressions. Conservative management is sufficient to treat these animals without odontoplasties. Focal palatine erosions (FPE) was first described in the cheetah in 1982 by Fitch and Fagan. Initially seen as a disease of captivity, it was later described to occur in both captive and wild cheetahs in Namibia . FPE lesThe malocclusion of the mandibular molar tooth and ensuing FPE was speculated to be the result of eating soft processed diets deficient of adequate structural composition (like bones) for optimal muscle stimulation. Reduced chewing stress was emphasized as an epidemiological factor for occlusal disorders , a view All reports of FPE in cheetahs concur it to be evident only after 10 months of age and to occur most commonly in Namibian cheetahs , 5. A laThe standard anatomy of the cheetah's palate was recently described along with clear evidence of relative macrodontia of the cheetah's mandibular cheek teeth when compared to those of lions and leopards . The staApart from one case described previously  no otherIn the present study a clinical scoring system to quantify the disease in cheetahs is introduced along with more histological analysis of these lesions. Novel hypotheses toward explaining the etiology and pathogenesis of the disease still known as FPE are proposed and a detailed overview of the clinical management of FPE in cheetahs is presented, along with the recommendation of changing the term FPE to focal palatitis (FP). The term FP will be used in this manuscript to describe any lesion, previously known as FPE, with clinical inflammation.′′ E 27° 55′ 25.4′′) and the rescue sanctuary for trapped or injured wild cheetahs at AfriCat Foundation (AF) in Namibia (S 20° 51′ 59″ E 16° 38′ 22′′), were examined during the period 2002–2012. Cheetahs were examined as part of annual health examinations performed at these sanctuaries or when cheetahs had dental fractures. The AfriCat Foundation is housed on a farm in the Okonjima district in Namibia. The camps are large (more than 1 hectare per cheetah) and consist of sandy soil with savannah vegetation. The diet mainly consisted of pieces of meat (mainly donkey) also admixed with a mineral and vitamin supplement. The pieces of meat were presented to the cheetahs in bowls, however, the cheetahs remove the pieces of meat which then come into contact with the sand and often becomes contaminated with the sand before they consume it. The Ann van Dyk Cheetah centre is a sanctuary which houses cheetahs in small camps with very little sand. The area is on a rocky outcrop and has some grass covering as well. They are mainly fed minced chicken combined with commercial cat food together with vitamin and mineral rations fed from bowls. The facilities, cheetahs and specific data recorded by the lead researcher were previously described in detail (th premolar teeth)  center in South Africa (S 25° 40′ 25.1n detail . Molar tr teeth) .Five additional cheetahs originating from the Ann van Dyk Cheetah centre were evaluated clinically and radiographically from 7 months of age, and again on average every 6 months through 25 months of age. This study carried ethics approval from the University of Pretoria's animal ethics committee (Project no. EC062-11).Figures 3, 4, 8 were drawn with Wolfram Mathematica, Version 12 . Results were interpreted as significant when p-value < 0.05, and highly significant when p-value < 0.01.Any depression that presented with inflammation  was manually cleared of any debris (if present) and then flushed with tap water in a 20 ml syringe. No odontoplasty or extraction of the mandibular molar teeth were done and no animal received antibiotics or anti-inflammatory drugs. A detailed histological examination of three inflamed palatine depressions were performed on 10 μm sections stained with haematoxylin and eosin. Statistical analysis, including hypothesis testing, was done using SAS software, Version 9.3 of the SAS System for Windows  and IBM SPSS Statistics for Windows, Version 23.0 . The 3-D column charts in The statistical analyses included logistic regression to estimate the prevalence of FP based on facility, sex and age of the cheetahs. Wald Chi-squared tests based on maximum likelihood estimates obtained from the fitted logistic regression model were used to test the hypotheses that, keeping other variables constant,the prevalence of FP is dependent on the facility,the prevalence of FP is different between male cheetahs and female cheetahs,and the prevalence of FP is affected by the age of the cheetahs.Note that the null hypothesis for each of the abovementioned Wald Chi-square tests is that the response variable  is independent from the explanatory variable . If the null hypothesis of a Wald Chi-squared test was rejected, the particular explanatory variable was kept in the logistic regression model and the estimated odds ratio corresponding to this explanatory variable was then determined to quantify the effect of the explanatory variable on the prevalence of FP.Correlation analyses were done between the height of the mandibular molar teeth and the palatal depression depths, as well as between the mandibular molar teeth heights and FP, and between the depth of the depressions and FP.p-value associated with this correlation measure, it was determined whether there is a positive correlation  between the height of the mandibular molar teeth and the depth of the depressions. A positive correlation between these two variables implies that an increase in height of the mandibular molar teeth corresponds to an increase in depth of the depressions. The null hypothesis for the hypothesis test is that the correlation coefficient is zero so that there is no correlation between these two variables.Firstly, by calculating Pearson's product-moment correlation coefficient and the Secondly, point biserial correlation coefficients  were calbetween the height of the mandibular molar teeth and the presence or absence of FP,and between the palatal depression depths and the presence or absence of FP.The point biserial correlation coefficient is used when one of the variables is dichotomous . Again the null hypothesis for the test associated with this correlation statistic is that the correlation coefficient is zero, indicating no correlation between the two variables under consideration.Finally, the distributions of the probabilities for the presence and level of FP were calculated and graphically presented to determine whether there was continuous deterioration of FP over time. Kolmogorov-Smirnov tests were used to compare the distributions of probabilities for FP across dRPM2 and dLPM2, across dRPM3 and dLPM3, and across dRPM4 and dLPM4 for AF cheetahs and for DW cheetahs, with the null hypothesis that there is no difference between the distributions of probabilities for FP across these pairs of right and left maxillary teeth.A total of 256 cheetahs were seen at least once at AF in Namibia and DW in South Africa. The descriptive statistics of the cheetahs inspected are shown in On the first evaluation, FP was seen in both sexes and at both facilities . The disp-values of the Wald Chi-squared tests confirmed that the prevalence of FP was influenced by facility , sex  and age  of the cheetahs  was 10.91 mm with a standard deviation (SD) of 1.02 mm, while the mean palatal depression depth (n = 1002) was 10.38 mm (SD = 1.71 mm).r = 0.2754  was observed between the heights of the mandibular molar teeth and the palatal depression depths. Since r > 0, the conclusion is that an increase in mandibular molar heights corresponds to an increase in palatal depression depths.A Pearson product-moment correlation coefficient of rpb = 0.0701  for AF cheetahs and rpb = 0.0386  for DW cheetahs. Although the point biserial correlation coefficients were positive for both facilities, these coefficients are close to zero, implying that the height of the mandibular molar teeth are not significantly correlated with the presence or absence of FP in cheetahs in either of these facilities.The point biserial correlation coefficient between the height of the mandibular molar teeth and the presence or absence of FP was rpb = 0.0818 , whereas for cheetahs from DW, rpb = 0.1056 . These results suggest that there is only a significant relation between the palatal depression depth and the presence or absence of FP for cheetahs from DW, but not for cheetahs from AF. This potential relationship between palatal depression depth and FP was further examined with logistic regression modeling. A logistic regression model using facility and depression depth to explain the presence or absence of FP was fitted. As indicated by the p-value from the relevant Wald Chi-squared test, FP was influenced by palatal depression depth . Keeping facility constant, the estimated odds ratio for depression depth was 1.147 with a 95 % Wald confidence interval of . Therefore, although statistically significant, we note the lower limit of the 95% Wald confidence interval to be very close to 1.In order to establish the contribution of palatal depression depth to FP, point biserial correlation coefficients between depression depth and the presence or absence of FP were calculated for both AF and DW cheetahs. For AF cheetahs, A total of 26 cheetahs at AF and 39 cheetahs at DW were seen on four different, yet consecutive occasions . The preCheetahs with FP frequently had foreign material impacted in the palatal depressions . There iConsidering the five additional cheetah cubs, there were again no differences indicated by the Kolmogorov-Smirnov tests between the distributions of probabilities for FP across dRPM2 and dLPM2, across dRPM3 and dLPM3, or across dRPM4 and dLPM4 . The corOf the depressions that had inflammation with content, grit was most commonly seen impacted in these depressions (40%) (6/15) followed by pieces of dietary bone (33.3%) (5/15) and grass (26.7%) (4/15).The three clinically inflamed depressions showed obvious ulceration of the covering epithelium and various degrees of mixed inflammation and oedema, some even showing features of micro-abscess formation with bacterial colonies extending deep into the underlying connective tissue of the depression . In one The bone and soft tissue depressions present in particular areas on adult cheetahs' palates were previously described as a functional structure to accommodate the relatively large mandibular cheek teeth . PreviouThe depth of the depression in the palate is a function of the height of the molar tooth, the latter remaining constant with no super eruption over time (present study). Although molar tooth height did not influence the frequency of FP in cheetahs from either facility, palatal depression depth in the cheetah at DW did. This significant association between palatal depression depth and FP at DW could probably be explained by the fact that bone fragments from the chickens fed to the cheetahs predominated in the palatal depressions at DW. These bony spicules, sharp and irregular compared to the rounded structure of the sand at AF, get trapped easier in a deep or narrow palatal depression. By contrast, AF relies more on feeding donkey meat, cut into 2–3 kg chunks for some animals and further divided into small blocks for those cheetahs that had lost a large number of teeth. Although some of these pieces of meat contain large pieces of bone, it is not consumed by the cheetahs as they are too big. Both facilities use adequate supplementation in order to assure a balanced diet.It is clear that FP can occur from any age when the permanent mandibular cheek teeth have erupted as was seen in one seven month old cheetah cub (this study). This proves FP to occur in association with the cusps of any of the erupted permanent mandibular cheek teeth at any time . FP is tDuring the 18 months we evaluated the five developing cheetah cubs at DW, no change in the molar height was observed once the teeth were fully erupted. This together with our previous study that confirmed no change in angulation of the fully erupted molars  does notHippobosca longipennis) are bloodsucking ectoparasites that live preferentially in the fur of wild carnivores like cheetahs .GS: design of project, acquisition and analysis, interpretation, writing, and editing of manuscript. SB: design of project, acquisition and analysis, histological analyses, writing, and editing of manuscript. PvS: design of project, statistical analyses, interpretation, writing, and editing of manuscript. MB: design of project, writing, and editing of 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.


Tara Ocean project, the functional profiles predicted by DiTing were correlated with environmental condition changes. DiTing can now be confidently applied to wider metagenomic and metatranscriptomic datasets, and it is available at https://github.com/xuechunxu/DiTing.Metagenomics and metatranscriptomics are powerful methods to uncover key micro-organisms and processes driving biogeochemical cycling in natural ecosystems. Databases dedicated to depicting biogeochemical pathways , which is an abundant organosulfur compound) from metagenomic/metatranscriptomic data are rarely seen. Additionally, a recognized normalization model to estimate the relative abundance and environmental importance of pathways from metagenomic and metatranscriptomic data has not been organized to date. These limitations impact the ability to accurately relate key microbial-driven biogeochemical processes to differences in environmental conditions. Thus, an easy-to-use, specialized tool that infers and visually compares the potential for biogeochemical processes, including DMSP cycling, is urgently required. To solve these issues, we developed DiTing, a tool wrapper to infer and compare biogeochemical pathways among a set of given metagenomic or metatranscriptomic reads in one step, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and a manually created DMSP cycling gene database. Accurate and specific formulae for over 100 pathways were developed to calculate their relative abundance. Output reports detail the relative abundance of biogeochemical pathways in both text and graphical format. DiTing was applied to simulated metagenomic data and resulted in consistent genetic features of simulated benchmark genomic data. Subsequently, when applied to natural metagenomic and metatranscriptomic data from hydrothermal vents and the  Biogeochemical cycles mainly refer to the movement of chemical substances  between the biotic and the abiotic compartments, which impact climate change and human health . Microbiphs-A, B, and C, hereafter collectively referred to phsABC)  . Thus, t studies . HoweverHere we developed the software DiTing, which is a pipeline to infer and compare biogeochemical pathways in metagenomic and metatranscriptomic data. DiTing is named after a Chinese mythical creature who knows everything when he put his ears on the Earth’s surface. Similarly, scientists may gain robust knowledge on microbial-driven biogeochemical cycles from environmental ‘omic data after analysis with DiTing. DiTing annotates protein sequences based on the KEGG database  for most1. DiTing can be installed via Conda2. The input source is a set of metagenomic and/or metatranscriptomic clean reads where low-quality reads, primer, and adaptor sequences have been trimmed beforehand , and j is the number of genes in a sample.where threshold> –tblout <output> <hmm database><input protein sequence> when the score type is “full”; hmmsearch –domT <threshold> –domtblout < output> <hmm database><input protein sequence> when the score type is “domain”). This employs methods for detecting remote homologs sensitively and efficiently. KOfam suggested values3 are used as the cutoff threshold values for hmmsearch, in which each KO entry has its unique cutoff threshold values  monooxygenase dmoA, methanethiol oxidase MTO, and DMSO reductase dorA) were collected manually to create the profile HMM using HMMER 3.3.1 (The translated protein sequences are queried against KOfam database [HMM database of KEGG Orthologs (KOs)]  using hmd values . KofamKOd values . For genER 3.3.1 . A table4 :The formula for each pathway is specifically designed to estimate the relative abundance of the pathway according to the definitionsAi is the relative abundance of the i pathway, and am_n is the relative abundance of protein m_n in each sample. m is one of the optional routes for accomplishing the i pathway, and n is the number of proteins in the optional route m. For example, assimilatory sulfite reduction (ASR) that converts sulfite to sulfide has two known possible pathways: (1) Sir protein (K00392) mediated pathway  pathway . Thus, tAASR is the relative abundance of the ASR pathway, aKO is the relative abundance of KO in each sample. Dissimilatory nitrite reduction (DNRA), which converts nitrite to ammonia, may occur via two different enzymatic reactions: (1) NirBD proteins (K00362 + K00363) to convert nitrite to ammonia, or (2) NrfAH protein (K03385 + K15876) to convert nitrite to ammonia. Thus, the relative abundance of DNRA to ammonia is estimated by the following formula:where ADNRA is the relative abundance of DNRA pathway, aKO is the relative abundance of KO in each sample. For other pathways, a customized formula for each pathway was utilized , including DSYB, DsyB, and MmtN are included in DiTing. DSYB and DsyB are methylthiohydroxbutryrate ectively , 2018. Tbacteria . The cutE-value of the DmdA is 1 × 10–130.The first step of DMSP demethylation pathway that results in the production of methylmercaptopropionate (MMPA) is initiated by the DmdA enzyme . The cutE-values of DmdB, DmdC, DmdD, and AcuH are 1 × 10–75, 1 × 10–100, 1 × 10–30, and 1 × 10–56, respectively.Further degradation of MMPA generating gaseous methanethiol (MeSH) catalyzed by the Dmd- B, C, and D (hereafter collectively referred to DmdBCD) or AcuH enzymes . The cutE-values of DddD, DddK, DddL, DddP, DddQ, DddW, DddY, and Alma1 are 1 × 10–97, 1 × 10–35, 1 × 10–33, 1 × 10–83, 1 × 10–20, 1 × 10–49, 1 × 10–64, and 1 × 10–26, respectively.Eight distinct DMSP lyase enzymes  can cleave DMSP to generate DMS . The cutE-value of the DmoA is 1 × 10–34.Dimethylsulfoniopropionate can be oxidized to generate MeSH via the DMS monooxygenase enzyme DmoA . The cutE-values of both DdhABC, DdhB, and Tmm are 1 × 10–30.Dimethylsulfoniopropionate can be oxidized to generate dimethyl sulfoxide (DMSO) by the DMS dehydrogenase complex (DdhABC)  or trimeS-methylated to generate DMS by the MddA enzyme , which is produced by plants, algae, and cyanobacteria, is an important component of carbon and sulfur cycles . The micYihSVTU were annotated through K18479, K18478, K01671, and K08318 Orthology in KEGG, respectively.Sulfoquinovose is converted to 6-deoxy-6-sulfofructose (SF) through an aldose/ketose isomerase YihS. The SF is phosphorylated to 6-deoxy6-sulfofructosephosphate (SFP) by an ATP-dependent SF kinase YihV. The SFP is then cleaved into 3-sulfolactaldehyde (SLA) and dihydroxyacetone phosphate (DHAP) by an SFP aldolase YihT. Finally, the SLA is reduced via an NADH-dependent SLA reductase (YihU) to DHPS, which is excreted from microorganisms. These four genes +-dependent SQ dehydrogenase (EC:1.1.1.390) oxidizing SQ to 6-sulfogluconolactone (SGL). The SGL is hydrolyzed to 6-deoxy-6-sulfogluconate (SG) by an SGL lactonase (EC:3.1.1.99). The SG is then converted by an SG dehydratase (EC:4.2.1.162) to 2-keto-3,6-deoxy-6-sulfo-gluconate (KDSG). The KDSG is cleaved by a KDSG aldolase (EC:4.1.2.58) into pyruvate and 3-SLA. The SLA can be oxidized by a NAD+-dependent SLA dehydrogenase (EC:1.2.1.97) to SL. The reference sequences of these enzymes were collected manually from Uniprot database5.This pathway starts with an NAD+-dependent SLA dehydrogenase to SL. The SLA dehydrogenase is the same enzyme as the last step of sulfo-ED pathway. The reference sequence of SF transaldolase enzyme was collected from IMG6 according to Three key enzymes take part in this pathway. The SQ is converted to SF by an aldose/ketose isomerase, which is the same enzyme as the first step of sulfo-EMP pathway. SF is cleaved to 3-SLA by SF transaldolase enzyme. Finally, The SLA is oxidized by a NADisoA encoding the α-subunit of IsoMO was selected as a marker gene for distribution, diversity, and abundance of the isoprene-degrading pathway in the environment  is an important volatile organic compound emitted to the atmosphere, and has significant effect on climate . Isoprenironment , 2020. TCyanobacteria genomes . The chemoautotrophic group was made up of five ammonia-oxidizing archaea (AOA) genomes . The heterotrophic group was made up of five SAR11 genomes . The metagenomic samples were simulated according to the relative abundance ratio of Cyanobacteria:SAR11:AOA genomes. Finally, CAMISIM created Illumina 2 × 150 bp paired-end reads with a size of 2 Gb for each simulated sample. These five simulated metagenomic samples were then fed into DiTing (default parameters) to produce the relative abundance of KO families and pathways. Due to the lack of features to specify the relative abundance of genes or pathways in CAMISIM, we manually inferred KO relative abundance profiles as the real result. To this end, all the 15 genomes were annotated by KofamScan software  automated assembly, Open Reading Frame (ORF) prediction, mapping, and gene annotation from reads; (ii) a manually created and curated DMSP cycling-related gene database; (iii) the specific formulae for DMSP and other biogeochemical pathways to calculate the relative abundance of biogeochemically relevant pathways and genes; and (iv) visualization of results comparing biogeochemical cycling potential between different inputted samples. These features make DiTing a flexible and versatile tool wrapper for studying biogeochemical cycles, or just as a platform to tackle metagenomic shotgun sequencing data. Additionally, DiTing has high speed. Five samples  that are ∼500 Gb in total were used to evaluate the speed. The total run time for all analyses from reads to visualization was ∼33 h using 60 CPU threads on a Linux version 4.15.0-20-generic server  Xeon(R) Gold 6140 CPU @ 2.30GHz; RAM, 256 GB).6/fb complex), which were absent in sample 2 and 3 , and V/A-type ATPase was detected in samples simulated by genomes containing AOA genomes .To verify the accuracy of DiTing, we compared DiTing’s result on the simulated data with genetic features of 15 genomes and manually inferred the KO family relative abundance profiles . The ove 2 and 3 . This ised genes . Since o highest . Similar complex . Corresp complex . The nir genomes . Consist genomes  to hydro genomes . As expeSubsequently, the translated gene sequences (amino acid) from 15 genomes for simulation were annotated using KofamScan software . ConsideERR1679394-1679398) generated from hydrothermal vent samples taken at PACManus and North Su fields in the Manus Basin  and methane  levels detected  of essential subunits. The relative abundances of each subunit of thiosulfate reductase were often not equal to each other in the metagenomes (phsA (encoding thiosulfate reductase subunit A) was always far more abundant than phsC (thiosulfate reductase cytochrome B subunit), and phsB (thiosulfate reductase electron transport protein) was not detected in any sample. This may be due to insufficient sequencing depth and/or protein redundancy. Whatever the reason for these discrepancies, it cannot be easily solved by bioinformatics alone and culture-dependent work is necessary. This phenomenon highlighted for the thiosulfate disproportionation genes may occur also in other pathways; thus further analyses at the gene level, not only at the pathway level, are essential in predicting the biogeochemical potential of microbial communities after DiTing analysis.At hydrothermal vents, chemolithoautotrophic microorganisms carry out carbon fixation coupled with oxidation of reduced sulfur compounds . In accoagenomes . For exadddP being the only detected DMSP catabolic gene. In three out of five hydrothermal samples interrogated here, the genetic potential to synthesize DMSP, through prokaryotic dsyB and mmtN genes, is far less than that for DMSP catabolism (DMSP synthesis:DMSP catabolism = 1:16.9) and not so dissimilar to ratios seen in seawater samples  in all hydrothermal samples, although catabolism of MMPA, the initial product of DMSP demethylation by DmdA . Indeed, compared to the other three samples, NSu-F2b and NSu-F5 had significantly more genes encoding bd ubiquinol cytochrome oxidases (p < 0.01) that are associated with low oxygen concentrations (bd oxidase was enriched most in NSu-F2b under the highest sulfide concentration (1.6 mmol l–1) and lowest oxygen concentration. A previous study found that bd oxidase could promote sulfide-resistant O2 consumption and growth in Escherichia coli , nosZ, are significantly enriched. These are alongside the nitrifying genes responsible for aerobic conversion of nitrite to nitrate (nxrAB). Genes encoding the nitrification enzymes involved in ammonia oxidation process, amoABC, hydroxylamine, hao, nitrate, nxrAB, are significantly enriched in The NSu-F2b and NSu-F5 samples. The importance of denitrification and nitrification to the nitrogen cycling in hydrothermal vents has previously been reported , reported . These mreported  in all sa (Chla) maximum layer in Mediterranean Sea from Tara Ocean project. The metagenomic clean reads ranged in size from 1.24 to 52.53 Gbp from each sample. The reads were assembled into 71,183–1,601,956 contigs with the total assembly sizes ranging from 0.045 to 1.38 Gbp. A total of 18,431,131 ORFs within these contigs were then predicted. ∼24%  ORFs were annotated against KEGG databases and affiliated to 8759 KO entries. The 74 pathways related to biogeochemical cycles were found  (DMSP demethylation:DMSP cleavage = 1.69:1). This contrasts with the hydrothermal vent samples. In another nine samples, the potential for DMSP demethylation was comparable to that for DMSP cleavage (DMSP demethylation:DMSP cleavage = 0.82:1). These data support both DMSP demethylation and cleavage being the dominant DMSP catabolic pathways in the Chla maximum layer.DiTing was also applied to analyze 15 metagenomic samples from chlorophyll re found . Compareexpected , 7. Addim layers , indicatcbb3-type cytochrome c oxidase genes/transcripts were found in three samples but were absent in the Marker113 2015 metatranscriptomic samples according to both the DiTing output and the reference result. The gene for the nitrogenase iron protein (nifH) was absent in one metagenomic and two metatranscriptomic samples in both analyses. However, there were also some differences between the reference study and the DiTing results. For example, the nitrate reductase gene (narG) was absent in two samples according to narG gene was present at extremely low levels in the Marker33 2015 and Marker133 2015 metatranscriptome samples, but phylogenetic analysis confirmed that their products cluster with ratified functional sequences instead of those without nitrate reductase activity  used for all gene annotation against the KO database. In contrast, DiTing employed the specific cutoff threshold (—domT 304.50) for narG according to Kofam suggestion  Xeon(R) Gold 6140 CPU @ 2.30GHz; RAM, 256 GB). The overall relative abundance of biogeochemical pathways, after analysis with DiTing, was consistent with the original study . For exaactivity . These nggestion , enablin2S and O2 concentrations). Besides marine environments, DiTing was supposed to be applied easily to other interesting environments . DiTing can be applied readily to metagenomic and/or metatranscriptomic studies with relatively straightforward user intervention. This bioinformatics framework will facilitate our understanding of spatial and temporal changes in microbiome-mediated biogeochemical cycles.In summary, this study developed a pipeline (DiTing) to infer and compare biogeochemical pathways from metagenomic and metatranscriptomic data. DiTing is a portable tool for analyzing metagenomic and metatranscriptomic datasets, providing automatic, multi-threaded bioinformatic workflows for data handling, including read assembly, ORF prediction, annotation, and customized specific formulas for calculating the relative abundance of biogeochemical pathways. The visualization module is designed to more easily compare functions between samples via graphical outputs. Additionally, a verified database was built manually for the annotation of genes involved in the production and cycling of DMSP. As validation of the outputs produced by DiTing, comparisons of the relative abundance of biogeochemical pathways in published metagenomes to those calculated by DiTing were consistent. By applying DiTing to analyze five hydrothermal shotgun metagenomes, we showed that the functional profile could accurately reflect changes in environmental conditions (HGCF_000018105.1, GCF_000020025.1, GCF_000021825.1, GCF_ 000317105.1, GCF_000317615.1, GCF_000299365.1, GCF_000 299395.1, GCF_000875775.1, GCF_000956175.1, GCF_013 407185.1, GCF_000012345.1, GCF_000195085.1, GCF_00029 9095.1, GCF_000299115.1, and GCF_012276695.1. Sequences data for metagenome of five hydrothermal vent datasets can be found in NCBI Sequence Read Archive under accession numbers ERR1679394-1679398. Sequences data for metagenome of 15 Tara Ocean project datasets can be found in NCBI Sequence Read Archive under accession numbers ERR315856, ERR315859, ERR315860, ERR318618, ERR318619, ERR318620, ERR318621, ERR594315, ERR594329, ERR598950, ERR599073, ERR599092, ERR599094, ERR599095, and ERR599153. Additionally, the DiTing software has been deposited in https://github.com/xuechunxu/DiTing.Sequence data for genomes used to simulate benchmark datasets can be found in NCBI under accession numbers: X-HZ conceived the project and designed the study. C-XX and HL implemented the software and drafted the manuscript. X-YZ, JL, YZ, GR, JT, and ML provided great help on data presentation and manuscript writing. All authors read and approved the final manuscript.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.


Oocyte penetration is an essential step for many biological technologies, such as animal cloning, embryo microinjection, and intracytoplasmic sperm injection (ICSI). Although the success rate of robotic cell penetration is very high now, the development potential of oocytes after penetration has not been significantly improved compared with manual operation. In this paper, we optimized the oocyte penetration speed based on the intracellular strain. We firstly analyzed the intracellular strain at different penetration speeds and performed the penetration experiments on porcine oocytes. Secondly, we studied the cell development potential after penetration at different penetration speeds. The statistical results showed that the percentage of large intracellular strain decreased by 80% and the maximum and average intracellular strain decreased by 25–38% at the penetration speed of 50 μm/s compared to at 10 μm/s. Experiment results showed that the cleavage rates of the oocytes after penetration increased from 65.56% to 86.36%, as the penetration speed increased from 10 to 50 μm/s. Finally, we verified the gene expression of oocytes after penetration at different speeds. The experimental results showed that the totipotency and antiapoptotic genes of oocytes were significantly higher after penetration at the speed of 50 μm/s, which verified the effectiveness of the optimization method at the gene level. The oocyte penetration is an essential step in many biological technologies, such as somatic cell nuclear transfer (SCNT) ,4,5,6,7,During the process of oocyte penetration, the oocytes suffer from large deformation and lead to high intracellular strain, which may cause damage to the oocytes and may eventually lead to the reduction of oocyte development potential. Many studies have shown that large intracellular strain can cause potential damage to cells and reduce the development potential of cells. For example, Scott et al. found that short-term, high-strain mechanical loading may lead to apoptosis ; S. J. GMeanwhile, some researchers found that the penetration speed could affect the cell development potential. For example, Liu et al. used the number of ruptured bonds to describe the damage of the cell during microinjection and found that the larger velocity caused fewer number of ruptured bonds, which might result in better development potential . As the In this paper, we optimized the oocyte penetration speed based on the intracellular strain. Firstly, we analyzed the intracellular strain under the condition of different penetration speed and then performed the penetration experiments on oocytes at different penetration speed. We used the optical flow method to detect the intracellular strain of each oocyte during the penetration process and obtained the penetration speed that can minimize the intracellular strain and ensure the accuracy: 50 μm/s. Secondly, we studied the cell development potential after penetration at different penetration speeds. We penetrated the oocytes with the speed of 10, 20, and 50 μm/s. The results showed that the optimized penetration speed improved the cleavage rate from 65% to 86%. Finally, we verified the gene expression of oocytes after penetration at different speeds. The experimental results showed that the totipotency and antiapoptotic genes of oocytes were significantly higher after penetration at the speed of 50 μm/s, which verified the effectiveness of the optimization speed at the gene level.2, and saturated humidity. After maturation culture, we picked oocytes with clear perivitelline spaces and intact cell membranes and extruded the first polar body (PB1) for use.We collected porcine ovaries from a local slaughterhouse and aspirated ovary follicles with diameter a between 3–6 mm using a sterile 10 mL syringe with an 18-gauge needle to acquire the cumulus–oocyte complexes (COCs). After washing three times in Tyrode’s lactate (TL)-Hepes-PVA , 30 COCs were transferred into 100 μL drops of maturation medium  for 42 h at 38.5 °C, 5% CO2, and 0.1 mM MgCl2). The activation/fusion embryos were selected and transferred to fresh PZM-3 for further culture under 38.5 °C, 5% CO2, and saturated humidity.As the cleavage rate could represent the development potential of the oocytes after cell operation ,21, we nL is the initial distance between two adjacent points and ΔL is the distance variation after deformation. Since the deformation and strain of the oocytes occurred mainly in the horizontal direction, we ignored the vertical deformation and calculated the strain field by Equation (2):L is the horizontal distance between two adjacent points in one frame, while x(i) and x(i + 1) are the horizontal positions of the adjacent points in the next frame. The intracellular structure is stretched when strain is greater than zero, and the intracellular structure is compressed when strain is less than zero.The intracellular strain field of the oocyte was obtained using Equation (1):Primers of Oct4, Rex01, and Bcl-2 genes for Real-Time PCR are shown in t-test for experiments with two treatment groups. For qPCR data, expression levels between treatments were determined using the comparative threshold cycle (CT) method for each gene. Data were reported as means ± SEM. p < 0.05 values were considered significant.Rates of cleavage were firstly subjected to arcsine transformation and then analyzed by one-way ANOVA. Differences were determined by either one-way ANOVA followed by Fisher’s least-significant difference multiple comparison test for experiments with multiple groups, or a Student The oocyte penetration experiments were performed on the NK-MR601 micro-manipulation system ,23,24, aThe holding micropipette (used to hold the oocyte) was made from a borosilicate glass tube with an outer diameter of 1 mm and an inner diameter of 0.6 mm. It was firstly pulled by the puller  and then fractured by the microforge . The holding micropipette was melted with an alcohol lamp by Yaowei Liu to smooth the tip opening. The diameter of the holding micropipette was about 50–80 μm. The injection micropipette was purchased from CooperSurgical  with an outer diameter of 20 μm and a slope angle of 45 degrees.The porcine oocytes were used in this paper. The penetration speed is usually set below 100 μm/s in oocyte penetration, in order to ensure the positioning accuracy of the micropipette in the oocyte. In oocyte penetration experiments, we used 60 oocytes in six groups and penetrated the oocytes by the injection micropipette at the speeds of 10, 20, 30, 40, 50, and 100 μm/s, respectively, after rotating the oocytes to the desired orientation . The oocytes were collected in the same batch and cultured in the same condition.The strain distribution with the maximum deformation in each penetration process was illustrated in In each figure of The statistical results showed that the percentages of the large intracellular strains at the penetration speeds of 50 and 100 μm/s were all below 1% in the penetration process and below 5% on the last frame, respectively, which indicated that the intracellular strain could be effectively reduced at the penetration speeds of 50 and 100 μm/s.n = 10); the average strain values were −0.15 ± 0.01, −0.13 ± 0.02, −0.13 ± 0.02, −0.10 ± 0.01, −0.10 ± 0.01, and −0.05 ± 0.01 (n = 10); the maximum strain values on the last frame before oocyte penetrated were −0.69 ± 0.05, −0.64 ± 0.11, −0.61 ± 0.07, −0.58 ± 0.09, −0.52 ± 0.10, and −0.53 ± 0.10 (n = 10); and the average strain values were −0.29 ± 0.01, −0.24 ± 0.03, −0.23 ± 0.03, −0.17 ± 0.04, −0.18 ± 0.03, and −0.11 ± 0.03 (n = 10). The statistical results indicated that the maximum and average strains at the penetration speeds of 50 and 100 μm/s were similar and decreased greatly compared to those at 10 μm/s, which reduced potential intracellular damage greatly, since the large strains caused potential intracellular damage. Therefore, the penetration speed was set to 50 μm/s to ensure the positioning accuracy and reduce the cell damage at the same time.Furthermore, we calculated the maximum and average of strain values at different penetration speeds, as shown in In order to verify that the development potential of porcine oocytes is actually increased after being penetrated at a speed of 50 μm/s, a total number of 268 oocytes in three groups were penetrated at the speeds of 10, 20, and 50 μm/s. All the oocytes were rotated to the same orientation before penetration (polar body at 4 o’clock in this paper). The oocytes were collected in the same batch and cultured in the same condition. Then, the oocytes were activated by applying one DC electric pulse of 1.2 kv/cm for 80 μs. The cleavage rate was used to evaluate the development potential of the porcine oocytes after penetration . After tAdditional experiments were designed to exclude the influence of the force lasting time. In this experiment, we poked the oocytes at the speed of 50 μm/s under the same condition in the two groups. The oocytes were collected in the same batch and cultured in the same condition. The micropipette poked and left the oocytes immediately in Group One, while the micropipette kept poking for 10 s before leaving in Group Two. A total number of 60 oocytes was used in this experiment, and each group had 30 oocytes. The cleavage rates of embryos in group one and group two were 50% (15/30) and 53.3% (16/30), respectively, which indicated that the lasting time did not affect the development potential of the oocytes. A total of 960 oocytes in four groups were used to assay gene expressions of the embryos. The oocytes were collected in the same batch and cultured in the same condition. The detected genes were developmental totipotency genes (Rex01 and Otc4) and an antiapoptotic gene (Bcl-2). In this experiment, the 240 oocytes in the control group were treated as followed: collected, activated, and gene expressions assayed. The oocytes in other three groups were treated as followed: collected, penetrated at the speeds of 10, or 20, or 50 μm/s, and activated, and then gene expressions were assayed.p < 0.05).p < 0.05); the Oct-4 gene expression at the speed of 50 μm/s (2.61 ± 0.01) was also significantly higher than those of the other groups (1.00 ± 0 vs. 0.95 ± 0.09 vs. 0.82 ± 0.08), (p < 0.05); the Bcl-2 gene expression at the speed of 50 μm/s (1.54 ± 0.09) was significantly higher than those of the other groups (1.00 ± 0 vs. 1.09 ± 0.07 vs. 0.86 ± 0.06), (p < 0.05).To sum up, the developmental totipotency genes of Oct4 and Rex01 and antiapoptotic gene of Bcl-2 had significant higher expressions at the penetration speed of 50 μm/s compared to those of the other groups, which implied oocyte penetration at the speed of 50 μm/s caused higher development potential. The higher totipotency and antiapoptotic gene expressions of the embryos verified the effectiveness of the optimized penetration speed at genetic level.In this paper, the developmental totipotency genes and an antiapoptotic gene were used to imply that the optimized penetration speed could cause higher development potential at genetic level. Additionally, the developmental potential (reflected by the cleavage rate) in this paper was consistent with some previous results: Liu et al. investigated the cell damage during microinjection by counting the number of ruptured bonds . They foHowever, the mechanism of intracellular damage caused by micromanipulation remains unclear. Meanwhile, the damage during micromanipulation may be further reduced if a control algorithm is added. We will work on dynamic modeling of penetration process and explore a better penetration strategy in the future.In this paper, we optimized the oocyte penetration speed based on the intracellular strain. We firstly analyzed the intracellular strain under different penetration speeds and performed the penetration experiments on porcine oocytes. Secondly, we studied the cell development potential after penetration at different penetration speeds. The statistical results showed that the percentage of large intracellular strain values decreased by 80% and the maximum and average intracellular strain values decreased by 25–38% at the penetration speed of 50 μm/s, compared to those at 10 μm/s, which reduced potential intracellular damage greatly. The results showed that the optimal penetration speed improved the cleavage rate from 65% to 86%. Finally, we verified the gene expression of oocytes after penetration at different speeds. The experimental results showed that the totipotency and antiapoptotic of oocytes were significantly higher after penetration at the speed of 50 μm/s, which verified the effectiveness of the optimization method at the gene level. Collectively, the results of the current investigation aimed to devise and optimize intracellular strain-related parameters that turned out to vary depending on different speeds of penetrating the porcine oocytes with the use of robotic  micro-manipulators might contribute to improvement of the efficiency of a variety of modern assisted reproductive technologies (ARTs). Taking the above-mentioned findings into consideration, the augmented efficiencies of microsurgical in vitro fertilization by ICSI and cloning by somatic cell nuclear transfer (SCNT) will be of especially great importance not only in pigs but also in other mammalian species ,29,30,31


In vivo implantation experiments and osteogenic induction showed that differentiation capacity of DFSCs was significantly reduced in the sub-clones. In summary, the findings of the current study show that differentiation potential of DFSCs is correlated with the heterogeneous microenvironment and TGF-β signaling pathway significantly modulates these biological processes.Adult mesenchymal stem cells play an important role in maintaining organ homeostasis owing to their unique ability to generate more specialized cell populations in a coordinated manner. Adult mesenchymal stem cells are heterogeneous, a feature that is essential for their functions. However, studies have not elucidated how heterogeneity of mesenchymal stem cells affects their differentiation capacity. The current study thus explored the heterogeneous Dental Follicle Stem Cells (DFSCs). A previous study by our research group reported that selecting sub-clones can cause artificial damage of the heterogeneous microenvironment of DFSCs. The finds showed a decrease in differentiation capacity of the three subclones, although the underlying mechanism was not elucidated. In this study, cells were harvested and prepared for gene expression microarray analysis. Sequence data was used in gene ontology and pathway enrichment analysis. The results showed that downregulation of the TGF-β signaling pathway was the main cause of changes in differentiation of sub-clones. Additional analyses revealed that the Hippo pathway, WNT pathway and signaling pathways regulating the pluripotency of stem cells were also implicated in these changes, through a cross talk with TGF-β signaling pathway through Bmp2, Bmp4, and Bambi.   Howeverin vivo . In addiin vitro ,7. Severin vitro . HoweverDental stem cells are promising seeding cells for tissue engineering and stem cell therapy because of their multipotent differentiation capacity . In addiin vitro and in vivo to evaluate the differentiation capability, comparing heterogeneous DFSCs. The gene expression array was used to analyze the possible mechanism of differentiation capability change. Finally, we hope to explore the possible effects of heterogeneity on sub-clones’ differentiation and its preliminary mechanism.In this study, we tried to disrupt heterogeneous microenvironments by selecting sub-clones from DFSCs. The sub-clones were induced 2.2.1.DFSCs and three sub-clones were isolated and cultured as previously described ,18. Spec2.2.3 cells were cultivated on 96-well plates . The original cultivation medium was replaced by 120 ml α-MEM with 10% FBS containing 12 ml CCK-8 for each well of 96-well plate at the same time of consecutive 7 d. After incubation at 37°C for 4 h, 100 ml of the above solution was taken from each sample and added to one well of another new 96-well plate. Three parallel replicates were prepared and the absorbance at 450 nm was detected using a spectrophotometer .Cell Counting Kit-8  was utilized to quantitatively evaluate the viability of cells. 2*102.3.Analysis of differentially expressed genes: Raw data were normalized using the Quantile algorithm in the Gene Spring Software 11.0 . Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the Differentially Expressed Genes (DEGs) using the Cluster Profiler package in R . Moreover, GO and KEGG enrichment analyses and annotations were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) and the KEGG database (version: 11.04.2021). Pathway enrichment analysis: Pathway enrichment network analysis and visualization of mesenchymal cell differentiation and odontogenesis were performed using the ClueGO and CluePedia plug-ins in Cytoscape 3.8.3 . Data were categorized into three groups, where DF2 VS DFSCs comprised Group 1, DF8 VS DFSCs comprised Group 2 and DF18 VS DFSCs comprised Group 3.Total RNA was extracted following a standard protocol and Gene Expression Microarray protocol  was used for microarray analysis. 2.4.Total RNA was extracted from cells using 200 μL/mL of Trizol , according to the manufacturer’s instructions. Thereafter, cDNA (cDNA) was synthesized using the TransScript® First-Strand cDNA Synthesis SuperMix . Additionally, quantitative Reverse Transcription-polymerase Chain Reaction (qRT-PCR) was conducted using 1 ml of cDNA and the SYBR Green Supermix , on the FAST 7500 Real-Time PCR System . All procedures were performed according to the manufacturer’s protocol.2.5.Western blot was performed as described previously . Total p2.6.Cells were induced with osteogenic inducing medium containing 10% FBS, 10 mM b-glycerophosphate (Sigma), 100 nM dexamethasone (Sigma), 50 mg/ml ascorbic acid and 0.01 mM 1,25-dihydroxy-vitamin D3 (Sigma)  for 14 d2.7.2.7.1.Treated Dental Matrix (TDM) was prepared following a previously published protocol . The man2.7.2.After anesthesia, 3-month-old SD rats were subjected to anesthesia and the first molar was extracted. The socket was then cleaned, after which the scaffold was implanted. Further, the socket was closed using gingiva. Rats were then sacrificed after 4 weeks, and the implanted parts were extracted for further analysis.2.8.Previous implanted parts were fixed with 4% paraformaldehyde overnight at 4°C, demineralized with 10% EDTA (pH 8.0), and embedded in paraffin. Then HE staining was conducted flowing to standard protocols . Paraffi2.9.Quantitative data were presented as mean SD. Statistical analysis was conducted using Graph-Pad Prism software Version 9.0 . A Student-Newman-Keuls test was performed to determine differences between groups. A value of P < 0.05 was considered statistically significant.3.in vitro and in vivo. We also used gene expression array, GO enrichment and KEGG enrichment to explore the effect of heterogeneity on the differentiation potential of sub-clones.In this part, three sub-clones were selected from DFSCs. The differentiation capability of sub-clones was compared to DFSCs 3.1.At the beginning, there were 18 subclones were selected from rat-DFSCs. However, most of the subclones cannot be passed over 5 passages. Finally, three sub-clones  ) were se3.2.Volcano plots showed that there are more down-regulated genes in the sub-clones, compared with the heterogeneous DFSCs . A totalmesenchymal cell differentiation and odontogenesis, based on the objectives of the study and the origin of DFSCs . GO enrof DFSCs . The finmesenchymal cell differentiation and odontogenesis. In addition, a clustering heatmap was used to illustrate the DEGs in the two biological processes between the sub-clones and DFSCs . Notablogenesis .3.3.Although the two signaling pathways are involved in changes in the properties of sub-clones, GO and KEGG enrichment of previous method  upper paodontogenesis and mesenchymal cell differentiation, in the three groups. The findings showed that 117 genes were involved in the two processes in Group 1 . In Group 2, a total of 118 genes were involved in these two processes ; however, odontogenesis was not enriched in the top 20 BP terms  in Group 3 .3.4.DEGs in the two biological processes were separately analyzed through pathway enrichment analysis using the KEGG database. The findings showed that a total of 15 pathways were enriched in Group 1. Notably, 6 out of 15 pathways were related to cell differentiation, including TGF-β signaling pathway, WNT signaling pathway, signaling pathways regulating the pluripotency of stem cells, Hippo signaling pathway, calcium signaling pathway, and MAPK signaling pathway . In Grou3.5.Interactions of significant DEGs were explored through a network diagram, constructed using Cytoscape . Network3.6.Enrichment results were validated through qRT-PCR ). The fiWestern blot analysis was used to validate genes expression in the enriched pathways . The res3.7.in vitro. The osteogenesis differentiation was conducted to evaluate the differentiation potential of sub-clones. The results show that fewer mineralized nodules can be found in sub-clones . In ord SD rats . In vivo4.In-situ implantation analysis showed that the sub-clones lost their regeneration capacity, owing to differentiation capability changes. Although artificial damage of the heterogeneous microenvironment was an extreme measure and loss of heterogeneity in practice is a very slow process, the findings showed dysregulation of internal homeostasis in stem cells. Therefore, our results indicated that further studies should be conducted on stem cells, selected by surface markers. Moreover, the selection method interfered with homeostasis, which may have led to changes in the properties of stem cells.Multipotential differentiation is one of the main characteristics of stem cells. Therefore, any changes in multipotency of stem cells can cause unpredictable outcomes. Notably, external environment, including induction of differentiation, stress  and radiIn the current study, TGF-β signaling pathway and signaling pathways regulating the pluripotency of stem cells were initially shown to be responsible for changes in differentiation capacity. However, TGF-β was the most dominant signaling pathway. TGF-β signaling pathway is involved in several biological processes, including cell proliferation, migration, and differentiation ,24. ThisFurther analysis  lower paMoreover, the findings of the present study showed that four vital signaling pathways are involved in loss of differentiation capacity in the three sub-clones. Further analysis indicated that cross-talk in signaling pathways was the key regulator rather than activity of an individual signaling pathways. In addition, several DEGs were enriched in the TGF-β signaling pathway, compared with the other three pathways. The findings of the study also showed cross-talk between TGF-β signaling pathway and the other three pathways. Notably, network analysis and validation analysis showed cross-talk between TGF-β signaling pathway and signaling pathways regulating the pluripotency of stem cells, Hippo signaling pathway and WNT signaling pathway, through Bmp2, Bmp4 and Bambi. The three genes were significantly down-regulated in the three clones. In addition, the findings showed that these linker genes were involved in maintaining stemness and regulating differentiation of cells by affecting the corresponding signaling pathways . For ins5.In summary, the findings of the current study showed that loss of heterogeneity can affect the differentiation capacity of the sub-clones, although sub-clones exhibited a self-renewal capability. In addition, TGF-β signaling pathway modulated this decrease in differentiation potential through cross-talk with Hippo signaling pathway, WNT signaling pathway, signaling pathways regulating the pluripotency of stem cells and other pathways . These f


Subcutaneous fat is an important factor affecting the meat quality and feed conversion rate of waterfowl. The current study compared the transcriptome data of Muscovy duck subcutaneous fat among three developmental stages, aiming at exploring the key regulatory genes for subcutaneous fat deposition. The results generated abundant candidate genes and pathways involving in subcutaneous fat deposition in Muscovy duck. This study provides an important reference for revealing the developmental mechanisms of subcutaneous fat in duck.HSD17B7 and MSMO1, while it downregulated fatty acid beta-oxidation related genes, including ACOX1 and ACSL1. Notably, most of the DEGs (92.2%) were downregulated in 66 wk compared with 35 wk, consistent with the slower metabolism of aging duck. Protein network interaction and function analyses revealed GC, AHSG, FGG, and FGA were the key genes for duck subcutaneous fat from adult to old age. Additionally, the PPAR signaling pathway, commonly enriched between the two comparisons, might be the key pathway contributing to subcutaneous fat metabolism among differential developmental stages in Muscovy duck. These results provide several candidate genes and pathways potentially involved in duck subcutaneous fat deposition, expanding our understanding of the molecular mechanisms underlying subcutaneous fat deposition during development.Subcutaneous fat is a crucial trait for waterfowl, largely associated with meat quality and feed conversion rate. In this study, RNA-seq was used to identify differentially expressed genes of subcutaneous adipose tissue among three developmental stages  in Muscovy duck. A total of 138 and 129 differentially expressed genes (DEGs) were identified between 35 and 12 weeks (wk), and 66 and 35 wk, respectively. Compared with 12 wk, subcutaneous fat tissue at 35 wk upregulated several genes related to cholesterol biosynthesis and fatty acid biosynthesis, including  Cairina moschata) is an excellent meat-type fowl species, famous for its high growth rate, delicious meat, and relatively high fat content [Poultry is one of the most important meat resources all over the world, providing consumers with diversified animal products such as high-quality meat, eggs, and down feather. Muscovy duck , 35 wk (adult), and 66 wk (agedness)) in Muscovy duck. The result might lead to more knowledge on the molecular mechanism of subcutaneous fat deposition in duck.All experimental procedures and samples’ collection were performed according to the regulations for the Administration of Affairs Concerning Experimental Animals of the State Council of the People’s Republic of China and the Institutional Animal Care and were approved by the Institutional Animal Care and Use Committee of the College of Animal Science and Technology, Anhui Agricultural University, Hefei, China (permit No. AHAU20101025).n = 27) were raised according to standard procedure. Three developmental stages, including 12 wk, 35 wk, and 66 wk, were explored in this study. At each stage, 9 ducks  with similar weights were selected. After slaughter, subcutaneous adipose tissue was collected and stored in liquid nitrogen.Healthy Muscovy ducks were obtained from Anqing Yongqiang Agricultural Science and Technology Co. Ltd. , China. All ducks  according to the manufacturer’s instructions. The RNA concentration and integrity were assessed using a 2100 Bioanalyzer and RNA 6000 Nano Kit  separately. RNA samples with an OD260 nm/OD280 nm ratio between 1.9 and 2.0 were used for RNA sequencing. Before sequencing, 3 samples were quantitatively mixed to form 3 biological replicates at each stage. Based on ultra-high-throughput sequencing , RNA-sequencing was performed by Shanghai Biotechnology Corporation .http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/ (accessed on 2 August 2020)). The paired-end reads were aligned to the duck reference genome using Tophat2 version 2.0.11 (http://ccb.jhu.edu/software/tophat/index.shtml (accessed on 2 August 2020)). To calculate the expression quantity of each transcript, alignment results were analyzed by Cufflinks version 2.1.1. The FPKM (Fragments Per Kilobase of exon model per Million mapped reads) method was used to quantify the gene expression level of all samples. Differentially expressed genes (DEGs) were identified using the DESeq [p value < 0.05 and fold change (FC) > 1.5 or FC < 0.67 was set as the threshold for significantly differential expression.Sequence adapters and low-quality reads were removed using Trimmomatic . The quahe DESeq  (2012) Rhttp://jvenn.toulouse.inra.fr/app/example.html (accessed on 2 August 2020)). Gene ontology (GO) analysis of DEGs was performed using DAVID functional annotation. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed by KOBAS 3.0 (http://kobas.cbi.pku.edu.cn (accessed on 2 August 2020)). The significance level for GO terms and the KEGG pathway was set with the p value < 0.05. The protein–protein interaction (PPI) network was constructed by STRING online database (https://string-db.org/ (accessed on 2 August 2020)) and visualized using Cytoscape _v3.8.0 software [Hierarchical clustering analyses were performed based on all DEGs. A Venn diagram was made by the online software jvenn . The most altered gene between 66 wk and 35 wk was MB (downregulated).The list of the top 20 known up-and down-regulated DEGs between the two comparisons, ranked by FC, are shown in p < 0.05).The function of the known DEGs was examined by GO enrichment analysis. A total of 20 GO terms were assigned between 35 wk and 12 wk subcutaneous adipose tissue, and each term contained more than two DEGs A. Among p < 0.05). Most of the enriched pathways were closely related to lipid metabolism, including steroid biosynthesis, the adipocytokine signaling pathway, the PPAR signaling pathway, metabolic pathways, and fatty acid degradation. Further analysis revealed that seven DEGs  were enriched in the PPAR signaling pathway, steroid biosynthesis, fatty acid degradation, and the adipocytokine signaling pathway.After KEGG pathway analysis, six pathways were significantly enriched between 35 wk and 12 wk subcutaneous adipose tissue B , closely linked to ACOX1, ACSL1, PCK1, and MSMO1, were also the hub gene. In addition, several heat shock protein family members, including HSPA2, HSP90AA1, HSPA5, HSPA4, and HSPA4L, constituted the other network module.To further investigate the interaction of DEGs and screen the hub genes related to lipid metabolism, the PPI network analysis was performed. Based on the STRING online database and Cytoscape software, the network of DEGs between 35 wk and 12 wk was built, consisting of 48 nodes and 63 edges A. To furThe three significant modules, including module 1 (MCODE score = 5.6), module 2 (MCODE score = 3), and module 3 (MCODE score = 3), were constructed from the PPI network of DEGs between 35 and 12 weeks using MCODE . Module p < 0.05). The top 20 GO terms are shown in A total of 26 BP, 11 CC, and 4 MF terms were significantly enriched between subcutaneous adipose tissue of 66 wk and 35 wk  (p < 0.0p < 0.05). As expected, some pathways related to lipid metabolism were enriched, such as the PPAR signaling pathway. Three DEGs  were enriched in the PPAR signaling pathway, which were significantly downregulated in 66 wk subcutaneous adipose tissue, indicating that lipid metabolism of subcutaneous adipose tissue in 66 wk was reduced more than that of 35 wk. Moreover, several metabolism-related pathways were enriched, including Tyrosine metabolism, Fructose and mannose metabolism, phenylalanine metabolism, and pentose phosphate pathway. In addition, the calcium signaling pathway and the tight junction pathway were also enriched.Based on 78 known DEGs, nine KEGG pathways were significantly enriched B , module 2 (MCODE score = 12.7), and module 3 (MCODE score = 5.6), were constructed from the PPI network of DEGs between 66 and 35 weeks using MCODE . Module Duck meat consumption has been on the rise worldwide. Fat deposition is one of the key factors affecting the meat quality of poultry ,19. In tAnas platyrhynchos (the species of the reference genome). Muscovy duck is originated from Cairinamoschata [Anas platyrhynchos [Anas platyrhynchos is not integral in the NCBI [Around 46.2 million raw reads were obtained from each sample and the clean reads ratio was above 92.99%. The mapping rate of this study was 41.23% on average, relatively low compared with chicken, Pekin Duck, and other poultry species ,21. Zengrhynchos . Thus, tthe NCBI , which mTranscriptome analysis reveals that there were significant differences in gene expression among different development stages. As the age increased from 12 wk to 35 wk, Muscovy duck reached maturity, accompanied by increased fat deposition. Several genes encoding key enzymes involved in lipid metabolism were expressed differentially. For example, hydroxysteroid 17-beta dehydrogenase 7 (HSD17B7) and methylsterol monooxygenase 1 (MSMO1) were involved in cholesterol biosynthesis and fatty acid biosynthesis ,27, whicIt is noticeable that most of the DEGs (92.2%) were downregulated in 66 wk compared to 35 wk, indicating that the total metabolism of subcutaneous adipose tissue in 66 wk was slower than that of 35 wk. This could be due to the age of 35 wk to 66 wk representing the stages of Muscovy duck from adult to aging.Apolipoprotein B (APOB) is related to cholesterol uptake and lipids transport ,32. ApolGC, AHSG, and FGA were the hub genes between 66 wk and 35 wk of subcutaneous adipose tissue. Much evidence suggested that low vitamin D status was associated with adiposity [GC gene encodes the vitamin D binding protein, which can affect low-density lipoprotein cholesterol levels [APOB, APOH, ACSL5, GC, and AHSG, etc., may be associated with reduced lipid accumulation in the aging duck.Through PPI network analysis, diposity ,39,40. Tl levels . Additiol levels . A previl levels . Moreovel levels . Hence, ACSL1, PCK1, and ACOX1 genes. Whereas in the comparison of 66 wk and 35 wk duck subcutaneous fat, the PPAR signaling pathway was the only enriched pathway directly related to fat metabolism. It is well known that PPAR signaling is the most important pathway to regulate lipid metabolism [Based on the identified DEGs, KEGG pathway enrichment was performed to explore the regulatory network of lipid deposition underlying differential developmental stages in duck subcutaneous fat tissue. Comparing 35 wk with 12 wk of duck subcutaneous adipose tissue, six pathways were significantly enriched. In particular, five out of six pathways were directly related to lipid metabolism, including the PPAR signaling pathway, steroid biosynthesis, the adipocytokine signaling pathway, the metabolic pathways, and fatty acid degradation. Moreover, PPAR signaling, adipocytokine signaling, and fatty acid degradation pathways were linked through tabolism ,46,47. FIn summary, this study compared the transcriptome data of subcutaneous fat in Muscovy duck at different developmental stages. The results showed that subcutaneous fat tissue of 35 wk upregulated several genes related to cholesterol biosynthesis and fatty acid biosynthesis, while it downregulated fatty acid beta-oxidation-related genes. Notably, most of the DEGs were downregulated in 66 wk compared to 35 wk, consistent with the slower metabolism of aging duck. In addition, we identified several regulatory hub genes that potentially can be used in duck breeding. These findings provide new clues on exposing the molecular mechanisms underlying the regulating of Muscovy duck adipose tissue at different stages.