Oryza sativa L. and Zingiber montanum (J. Koenig) Link ex A.Dietr. were the most preferred plants combined in the prescriptions, which had the highest use value (UV = 0.83). The highest relative frequency of citation was represented by Curcuma longa L., Eurycoma longifolia Jack, Knema globularia (Lamk.) Warb, and Senna siamea (Lam.) Irwin & Barneby. (0.55 each). This research suggests the importance of traditional healers in the healing of skin diseases with herbal remedies. A variety of medicinal plants are used in the prescriptions for the treatment of skin disorders in the Songkhla province, in the south of Thailand. Pharmacological and toxicological activities as well as phytochemical constituents of polyherbal remedies should be further investigated to scientifically verify further applications of widely practiced herbal medicines.Skin disorders are a worldwide health problem that normally affect human life. A traditional healer is an important expert in researching notable medicinal plants for skin disease treatment. This study aimed to determine the traditional knowledge and the use of medicinal plants for the treatment of skin diseases among traditional healers in the Songkhla province, Thailand. The ethnobotanical information was collected from experienced traditional healers by semi-structured interviews and participant observations. Plant specimens were also collected and identified using the standard taxonomic method. The data were analyzed by interpretation and descriptive statistics. Twenty-five polyherbal formulations for the treatment of skin diseases were obtained from traditional healers with at least 10 years of experience. A total of 66 plant species in 38 families were documented. Leaves and trees were the most commonly employed plant parts and plant habits included in the herbal remedies, respectively. Fabaceae, Rubiaceae, and Zingiberaceae were the majority of the cited families.  Skin disease is a global public health problem that often has physiological, psychological, and social impacts . The occAloe vera (L.) Burm.f., Azadirachta indica A.Juss., Calendula officinalis L., Cannabis sativa L., Portulaca amilis Speg., and others, have been investigated for the treatment of skin diseases ranging from itching to skin cancer and have been reported to be effective in various skin diseases [Natural products and traditional medicines are valuable for the treatment and prevention of various diseases . They ardiseases . One hundiseases .Phytochemical substances are found in plants that are utilized to treat skin problems. Plant constituents, or phytoconstituents, are divided into primary and secondary metabolites depending on their activity in a basic metabolic process. Secondary metabolites have been demonstrated to have a variety of biological effects, providing a scientific foundation for the use of herbs in traditional medicine in many ancient cultures. Secondary metabolite classes include phenolics, lipids, saponins, carbohydrates, alkaloids, and terpenes according to their chemical structures . Some phTraditional healers are significant for public health in Thai communities, and many individuals have confidence in the healing attributes of herbal medicine . The folTraditional medicines as well as herbal medicines have been used for health care in Southeast Asia and other global regions. They have been established and have developed empirical experience. This wisdom and knowledge are part of the social and cultural heritage of people and countries and can be passed from one generation to another . In thisTwenty-five polyherbal remedies obtained from the folk healers were used to treat skin disorders including ulcers (24%), herpes simplex (20%), abscesses (16%), and tinea (12%). The rest of the treated skin diseases are presented in The information from the prescriptions in The traditional preparations of the remedies were made by poultice (44%), decoction 28%), hot oil extraction (20%), ointment (4%) and powder (4%) as shown in %, hot oiAll plant species in the 25 polyherbal remedies were classified into six habits . Trees , followed by roots (16.4%), rhizomes (9.0%), seeds (9.0%), and stem bark (9%). The rest of the parts used of the plants are shown in .9%, follSepia spp. cuttlebone) were included in the 25 prescriptions of herbal remedies. The plants were classified into 61 genera and 38 families (Acacia catechu (L.f.) Willd., Entada rheedii Spreng., Pterocarpus indicus Willd., Senna alata (L.) Roxb., and Senna siamea (Lam.) Irwin & Barneby. Mitragyna speciosa (Roxb.) Korth., Uncaria gambir (Hunter) Roxb., Prismatomeris tetrandra (Roxb.) K. Schum, Hydnophytum formicarium Jack, and Ceriscoides turgida (Roxb.) Tirveng. were in Rubiaceae, while those in the Zingiberaceae consisted of Zingiber montanum (J.Koenig) Link ex A.Dietr., Curcuma zedoaria (L.) Roscoeex Sm., Curcuma longa L., Zingiber zerumbet (L.) Roscoeex Sm., and Curcuma aromatica Salisb. Fabaceae and Zingiberaceae are well known among plants used in traditional medicine. The study of medicinal plants used in Thai traditional medicine in modern healthcare services reported 89 medicinal plant species. The plants belonged to 37 families, with the highest numbers of medicinal plant species being Zingiberaceae (11 species) and Fabaceae (10 species) [Totally, 66 plant species and one animal material (families . In regaspecies) . Similarberaceae  species species) .Oryza sativa L. and Zingiber montanum (Koenig) Link ex Dietr. show the highest use value with 0.83, followed by Nicotiana tabacum L. with a use value of 0.67. Oryza sativa L. was applied for herpes simplex and abscess treatment, while Zingiber montanum (Koenig) Link ex Dietr. was used for treating tinea, acne, and ulcer leprosy, which is similar to Nicotiana tabacum L. Other important plants were Curcuma longa L., Eurycoma longifolia Jack, Knema globularia (Lamk.) Warb, and Senna siamea (Lam.) Irwin & Barneby. with a use value of 0.50. The species with a use value of 0.33 are listed in Oryza sativa L. was frequently used in skin treatment. Oryza sativa L. contained high levels of anthocyanin polyphenols, which presented beneficial effects on health owing to their antioxidant properties. Anthocyanin from Oryza sativa L. exhibited anti-inflammatory properties and anti-aging activity by modulating type I collagen gene expression and suppressing H2O2-induced NF-κB activation in skin fibroblasts [Oryza sativa L. showed antibacterial effects against multidrug resistant Staphylococcus aureus [Oryza sativa L. against fungi [Oryza sativa L.) extract possessed anti-aging activity on human skin [Oryza sativa L. crude extract reduced the severity of psoriasis [Zingiber montanum (Koenig) Link ex Dietr., another one of the most frequently used plants in this study, has been previously investigated for its phytochemicals and pharmacological activity. Numerous bioactive phytochemicals were discovered in the rhizomes of Zingiber montanum (Koenig) Link ex Dietr. including alkaloids, saponins, tannins, flavonoids, terpenoids, phenolic compounds, phlobatannins, steroids, and glycosides [Zingiber montanum (Koenig) Link ex Dietr. rhizome exhibited antifungal activity against Candida albicans [Zingiber montanum. Among these terpenes, (E)-8(17),12-labdadiene-15,16-dial and zerumbol exhibited antibacterial activity against a number of clinical isolates of multi-drug-resistant (MDR) and methicillin- resistant Staphylococcus aureus (MRSA) [Zingiber montanum. Furthermore, zerumbone-treated wound sections showed greater tissue regeneration and more fibroblasts, possibly through the enhanced expression of VEGF, TGF-β1 and collagen IV [Zingiber montanum rhizome, showed antioxidant activity in biochemical systems and THP-1 cells [Nicotiana tabacum L. was the preferred component in the formulations, and it possessed many biological activities. The different extracts of Nicotiana tabacum L. leaves contain the phytochemical constituents of alkaloids, phenolic compounds, tannins, flavonoids, steroids, terpenoids, cardiac glycosides, essential oils, resins, saponins, quinones, and polypeptides [Nicotiana tabacum L. against Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumoniae, and biofilm-forming Escherichia coli and Klebsiella species. The most common phytochemical components found in the ethyl acetate extract were 3, 4, 5,6-tetrahydro-1, 3-dimethyl-2(1h)-pyrimidinone, pyridine, 3-(1-methyl-2-pyrrolidinyl)-, (S)-, isododecane, n-pentadecane, and tetradecylaldehyde. The antibacterial property demonstrated could be due to pyridine, 3-(1-methyl-2-pyrrolidinyl)-(S), the major compound detected, with a broad spectrum of activity [Nicotiana tabacum L. Tabasesquiterpenes B exhibited high antiviral activity with an inhibition rate of 35.2%. The other compounds also demonstrated antiviral activity with inhibition rates ranging from 20.5–28.6% [Nicotiana tabacum L. leaf cow urine extract was found to have potential anti-dandruff activity against a causative agent, Malassezia furfur [The preferred medicinal plants used in polyherbal remedies for skin diseases and their pharmacological activities are presented in roblasts . The crus aureus . In addist fungi  and virust fungi  has beenman skin . The antsoriasis . Zingibeycosides ,30. The albicans . (E)-8(1s (MRSA) . Zerumbo-1 cells , and ant-1 cells . The leapeptides . Antimicactivity . Six ses.5–28.6% . Nicotiaa furfur .Curcuma longa L., Eurycoma longifolia Jack, Knema globularia (Lamk.) Warb, and Senna siamea (Lam.) Irwin & Barneby. Other high RFC species included Curcuma zedoaria (L.) Roscoeex Sm., Datura metel L., Garcinia mangostana L., Nicotiana tabacum L., Oryza sativa L., Punica granatum L., Quercus infectoria Oliv., and Tiliacora triandra (Colebr.) Diels. The RFC value of 0.17 belonged to the rest of the plants (53 species). Curcumin and derivatives from Curcuma longa L. exhibited biological activities. Curcumin showed anti-bacteria, anti-HIV, antioxidant, anti-inflammatory, and anti-tumor activity. Demethoxy curcumin and bisdemethoxy curcumin had antioxidant activity, while sodium curcuminate showed anti-inflammation [Curcuma longa L. reflected the potency of antioxidant activity [Curcuma longa L. rhizomes was found to have better and faster wound healing activity than the standard drug povidone iodine ointment on the excision wound model [Eurycoma longifolia Jack showed significant antioxidant and anti-inflammatory activity [Eurycoma longifolia Jack. The extracts showed antimicrobial activity against Bacillus cereus, Staphylococcus aureus, and Aspergillus niger [Knema globularia (Lamk.) Warb, exhibited a moderate cytotoxic effect against Hep-G2, MCF-7 and SK-LU-1 cell lines [Knema globularia (Lamk.) Warb, showed strong cytotoxicity against the NCI-H187 and MCF-7 cell lines with IC50 values of 3.08 and 6.68 mg/mL, respectively [Senna siamea (Lam.) Irwin & Barneby leaves had good antifungal activity against Candida albicans and Aspergillus niger. Petroleum ether extracts of leaves were found to be very active against S. aureus [Senna siamea (Lam.) Irwin & Barneby leaf extracts are effective against Klebsiella pneumoneae, Salmonella typhi, Shigella spp., Escherichia coli, and Pseudomonas aeruginosa. The antibacterial activities of the extracts were expected due to the presence of bioactive compounds, which were dissolved in the solvents [Senna siamea (Lam.) Irwin & Barneby leaves showed interesting activity against inflammation. Alkaloids, polyphenols, terpenoids, steroids, anthraquinones, cardiotonic glycosides, and anthocyanins have been identified in this plant [Curcuma longa L [Curcuma zedoaria (L.) Roscoeex Sm. [Aloe vera (L.) Burm.f. [Garcinia mangostana L. [Punica granatum L. [Quercus infectoria Oliv. [Tinospora crispa (L.) Miers ex Hook.f. [The plants that were commonly used by traditional healers for the treatment of skin diseases were illustrated by relative frequency citation (RFC) . The RFCammation . The preactivity . Curcumiactivity . Ethanolnd model . The hydactivity . Phenolius niger . Two quaus niger . Knecortll lines . Giffithectively . Six comectively . Chlorofsolvents . An aqueis plant . All the longa L , Curcumae plants  species. Burm.f. , Garcinistana L. , Punica natum L. , gall ofia Oliv. , and Tin Hook.f. . A varie2. The north is connected to Nakhon Si Thammarat and Phatthalung provinces, while the east borders on the Gulf of Thailand. The neighboring provinces in the south are Yala and Pattani in Thailand and Kadah and Perlis in Malaysia. Phatthalung and Satun provinces are the neighbors in the west. It had a total population of 1,432,628 in 2018 with 63.71% being Buddhists, 33.16% being Muslims, and 3.19% being Christians and Hindus. The weather conditions in Songkhla are influenced by the southwest and northeast monsoons. The average annual temperature is 27.76 °C. The annual rainfall is about 3,434.9 mm, with a relative average humidity of 79.93% [The Songkhla province is located in the eastern part of the south of Thailand between latitude 6°17′–7°56′ N and longitude 100°01′–101°06′ E. The province is on the Malay Peninsula, on the coast of the Gulf of Thailand. The height above mean sea level is 4 m. It is approximately 950 km from Bangkok, the capital city. It covers 7393.889 kmf 79.93% . The stuIn the present study, all traditional healers were selected from their extensive experience and actively practiced patient treatment. All the informants had experience of at least 10 years. According to the intensive criteria, six traditional healers were chosen. All the professional folk healers were males, 54–74 years old. Based on education, three of the healers had graduated from primary school, while one in each group was a secondary school, vocational diploma, and bachelor’s degree holder. Five of the informants were Buddhist, and one of them was Muslim .Ethnobotanical information about skin diseases was obtained from local herbal healers. Before interviewing, the purposes of the study were distinctly explained to the traditional healers and their family members, and verbal informed consent was obtained.The informants were interviewed using questionnaires and conversations. The interview was performed to investigate the prescriptions for skin disease treatment. Data on the plant’s local names, plant parts used, skin diseases treated with herbal remedies, mode of use, and administration were gathered. Field trips to the sites where the traditional herbal healers normally go to harvest the plants were carried out. Plant materials were collected and processed according to the standard taxonomic method . The sciThe plants were analyzed regarding their habits of plants, and plant parts used in polyherbal remedies for treatment of skin diseases, frequency in families, and quantitative ethnobotany analysis including use value (UV) and relative frequency of citation (RFC).Use value was calculated based on the number of uses and the number of people citing a given plant. It indicated the most significant plant species, recognized by a given population .UV = u/RFC was analyzed to intricate the knowledge of traditional flora about usage of therapeutic flora in the study site.icipants .Oryza sativa L. and Zingiber montanum (Koenig) Link ex Dietr. Based on RFC, the highest was found for Curcuma longa L., Eurycoma longifolia Jack, Knema globularia (Lamk.) Warb, and Senna siamea (Lam.) Irwin & Barneby. Although local treatment of herbal prescriptions and ethnobotanical surveys are underway, more research on phytochemicals and their pharmacological activities is needed to ensure the application of polyherbal prescriptions used by traditional healers, as well as product development in herbal medicine for the treatment of skin diseases, in order to promote the sustainable and safe use of natural resources.Currently, the Thai government has a policy to promote and develop the use of traditional medicines. Thai traditional medicines as well as drugs developed from medicinal plants are included in the national list of essential medicines. The traditional healer is one of the most essential sources for determining the usage of herbal medicine for treating individuals in the community. The results from this study represent the polyherbal remedies used by experienced folk healers for the treatment of skin disorders in the Songkhla province, in the south of Thailand. Ethnopharmacological expertise is abundant among traditional healers. They have significant knowledge of many plant species used for a variety of skin disorder treatment. The utilization of medicinal plants was widespread in the prescriptions, with 66 species in 38 families. The poultice was the most frequent method of administration. The most prominent plant families were Fabaceae, Rubiaceae, and Zingiberaceae. The highest use values were reported for


Plutella xylostella (L.), is one of the most destructive insect pests, affecting cruciferous vegetables all over the world. In this study, two different proteomic methods were used to investigate the ACPs in P. xylostella. In total, 123 putative secreted ACPs were identified. Comparing P. xylostella with other four species of insect ACPs, some new insect ACPs were discovered.Male accessory gland proteins (ACPs) play an important role in insect reproduction. Identification of ACPs is crucial to study female reproduction and physiology in a given insect species. The diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), is a major insect pest of cruciferous vegetables worldwide. Mating has a profound impact on the females’ behavior and physiology in this species. It is still unclear what the ACPs are in this species. In this study, two different proteomic methods were used to identify ACPs in P. xylostella. The proteins of MAGs were compared immediately before and after mating by using a tandem mass tags (TMT) quantitative proteomic analysis. The proteomes of copulatory bursas (CB) in mated females shortly after mating were also analyzed by the shotgun LC-MS/MS technique. In total, we identified 123 putative secreted ACPs. Comparing P. xylostella with other four insect ACPs, trypsins were the only ACPs detected in all insect species. We also identified some new insect ACPs, including proteins with chitin binding Peritrophin-A domain, PMP-22/ EMP/ MP20/ Claudin tight junction domain-containing protein, netrin-1, type II inositol 1,4,5-trisphosphate 5-phosphatase, two spaetzles, allatostatin-CC, and cuticular protein. This is the first time that ACPs have been identified and analyzed in P. xylostella. Our results have provided an important list of putative secreted ACPs, and have set the stage for further exploration of the functions of these putative proteins in P. xylostella reproduction.In insects, male accessory gland proteins (ACPs) are important reproductive proteins secreted by male accessory glands (MAGs) of the internal male reproductive system. During mating, ACPs are transferred along with sperms inside female bodies and have a significant impact on the post-mating physiology changes of the females. Under sexual selection pressures, the ACPs exhibit remarkably rapid and divergent evolution and vary from species to species. The diamondback moth,  Drosophila melanogaster [Helicoverpa zea [Insect seminal fluid, the non-sperm component of ejaculation in male insects, is a complex mixture of biomolecules including proteins/peptides, lipids, prostaglandins , steroidnogaster ,8, but tnogaster . A pheroerpa zea , but it Heliconius butterflies were predicted to be chymotrypsins [Bactrocera dorsalis identified 90 ACPs, including a novel ACP, a juvenile hormone-binding protein (JHBP) which impact reproductive events in the female [Nilaparvata lugens, 94 putative secreted SFPs were identified by proteome studies using high-throughput mass spectrometry. Selenoprotein, epidermal growth factor (EGF) domain-containing proteins, mesencephalic astrocyte-derived neurotrophic factor, and a neuropeptide ion transport-like peptide were discovered as new insect SFPs [Identification of ACPs is challenging especially in non-model organisms, though ACPs are essential for insect reproduction and their functions are largely conserved. So far, it has been found that a large proportion of individual ACPs show unusual, rapid evolution at the primary sequence level . The raptrypsins . Proteome female . In anotect SFPs . Plutella xylostella L. (Lepidoptera: Plutellidae), is a worldwide major insect pest of cruciferous vegetables, such as cabbage, cauliflower, and many other important economic food crops [P. xylostella, making it challenging to control [P. xylostella have been described previously [P. xylostella using tandem mass tag (TMT) technology. Meanwhile, we performed shotgun mass spectrometry-based proteomic analyses to investigate the profiling of mated copulatory bursa (CB). Combined, we had obtained tissue-specific RNA sequencing (RNA-seq) datasets of the MAGs to identify the transferred ACPs of P. xylostella. This work has set the stage for further confirmation of the functions of the putative proteins.The diamondback moth, od crops ,21,22. Tod crops . Due to  control . Studyineviously , there iP. xylostella was collected from a cabbage greenhouse in Yangling , Shaanxi, China, and established a stable laboratory population in our laboratory over 2 months. The laboratory population was reared on cabbage (Brassica oleracea L. var. Qingan 70) in cages . The cages placed in artificial climate chamber at 25 ± 1 °C, 60 ± 5% RH, and 16L: 8D. The pupae were collected and maintained individually in 4 mL microtubes with ventilation holes on the surface until the adults emerged. The newly emerged adults were collected and labeled as one-day-old. The adult moths were fed with 10% sucrose solution . The original strain of 2 and immediately dissected in sterile lepidopteran saline [During the scotophase, a two-day-old virgin male and a two-day-old virgin female were placed together for copulation. They were placed into a 50 mL microtube, the orifice of which was covered with 80 mesh gauze, and a cotton ball soaked with 10% sucrose solution was placed at the bottom. Copulation was examined every 30 min with a faint red light. A male and female were considered mated if they were in copula for more than 30 min and completed the coupling naturally. Once the male and female naturally finished copulation, they were anesthetized with COn saline  on ice ug for 40 min, the supernatant was filtered through 0.22 µm filters. Following that, the BCA Protein Assay Kit  was used to quantify the filtrate. For each sample, trypsin  was used to digest 200 μg of proteins according to the instructions in the filter-aided sample preparation (FASP) [Protein extraction was conducted using the methods described by Wisniewski et al. (2009) with modifications. A total of 200 μg of proteins and 30 μL of SDT  lysis buffer were mixed for each sample, then completely homogenized by using a plastic grinding rod and kept it in 100 °C boiling water for 15 min. After centrifuging at 14,000× n (FASP) . The resTandem mass tags (TMT) reagent was used to label 100 μg of the resulting peptide mixture of each sample  in accordance with the manufacturer’s instructions . The Pierce high pH reverse-phase fractionation kit  was used to fractionate the labeled peptides into 10 fractions by increasing acetonitrile step-gradient elution. Each fraction was injected for liquid chromatography-mass spectrometry/MS (LC-MS/MS) analysis. LC-MS/MS analysis was conducted for 60 min using a Q Exactive mass spectrometer coupled to an Easy nLC . The protocol was carried out according to the methods described in previous studies . Considering that not all ACPs were secreted via a classical pathway and had a well-defined signal peptide at N-terminal, herein, we used qualitative proteomic analysis of mated-CB to identify as many ACPs as possible. After the protein extracts of mated-CB were digested by trypsin, the obtained peptide mixture of the mated-CB sample was separated into 10 separate fractions in accordance with the manufacturer’s instructions using the Pierce high pH reverse-phase fractionation kit . For LC-MS/MS analysis, 5 μL of each fraction was injected. LC-MS/MS analysis was conducted for 60 min using a Q Exactive mass spectrometer coupled to an Easy nLC . The protocol was carried out according to the methods described in previous studies .P. xylostella MAGs transcriptome (accession number SRP306271 in the Sequence Read Archive (SRA) of NCBI) containing 28066 amino acid sequences. The putative coding sequences (CDSs) of transcriptome were predicted using Transdecoder software , and the longest CDS of each transcript was taken as the predicted CDS sequence of that transcript. To improve the confidence of prediction, homology was explored by comparison with Uniprot databases  and Pfam databases , and Evalue cut-off was set at 1 × 10–5. For quantitative proteomic analysis of virgin- versus mated-MAGs, statistical analysis were performed according to the methods described in previous studies [p < 0.05 were used as markers to identify the differentially abundant proteins (DAPs). All DAPs were tested for subcellular localization prediction by the online tool WoLF PSORT . The MASCOT engine  was used to search each sample’s MS raw data and embedded into the software Proteome Discoverer 1.4  for quantitative analysis and identification. The related parameters were as follows: peptide mass tolerance was 20 ppm and fragment mass tolerance was 0.1 Da. The proteolytic enzyme was trypsin, with two maximum missed cleavages. The fix modifications were carbamidomethyl (C) and oxidation (M), as variable modifications. Based on a false discovery rate (FDR) of 1% and 99% confidence, the peptides were extracted. The database used in this study was generated from  studies ,30,31,32https://services.healthtech.dtu.dk/service.php?SignalP-5.0, accessed on 23 January 2023). To improve the predicted signal peptide detection, the proteins without signal peptides were re-predicted CDS sequences from unigene nucleotide sequences by the online tool ESTScan , and signal peptide detection was performed with the new predicted CDS sequences. (2) The DAPs did not possess a signal peptide, but were detected in mated-CB. (3) In addition, the DAPs that did not satisfy the above two criteria were considered as unconfirmed ACPs.The proteins with the following standards were considered to be predicted secreted ACPs: (1) The DAPs contained signal peptides. All coding sequences (CDS) of DAPs were tested for signal peptide prediction by the online tool SignalP 5.0  and SMART , the ACPs were categorized into one of the following groups: “cell and extracellular structure”, “metabolism”, “proteolysis regulators” (including proteases and protease inhibitors), “RNA and protein synthesis”, “signal transduction”, “protein modification machinery”, “transporters and protein export machinery”, and “other” . In this study, proteins having no known function were categorized as “unknown.”We used the annotation and classification methods described by Yu et al. (2015) for identifying ACPs. Briefly, in addition to machine annotation, the ACPs were manually annotated by combining NCBI blast results, GO terms, and conserved domains. Combining the descriptions of conserved domains from NCBI, InterProScan  utilizing the ID number given in the reference [D. melanogaster SFPs sequences were obtained from the Flybase database  utilizing the ID number given in the reference [A. mellifera SFPs sequences were obtained from NCBI  utilizing the ID number given in the reference [A. aegypti SFPs sequences were obtained from the VectorBase database  utilizing the ID number given in the reference [Heliconius butterflies were obtained from NCBI  utilizing the ID number given in the reference [P. xylostella. The conserved domain predictions of these SFPs were performed by the NCBI online tool Batch Web CD-Search . The P. xylostella ACPs that shared a conserved domain with SFPs from the other four insects were marked as “Domain”, and the rest of the proteins were analyzed based on local blastp by TBtools software [−5) with SFPs of the other four insect were marked as “Blast”. A comparison of SFPs among insect species was performed using a similar method.lla ACPs . We chos species . The mosoduction ,33,34. Aeference . D. melaeference . A. melleference . SFPs seeference . The ID software ; blastp P. xylostella with the serine proteases from the other four insect species  conserved domains of the ACPs of  species . The maxP. xylostella  in a reaction mixture of 10 µL, with 900 ng of total RNA. qRT-PCR was carried out in accordance with the method described by Wei et al. (2015) [p < 0.05. The data are displayed as the means ± standard error (SE). The primers that were used for tissue-specific expression of ACP genes using qRT-PCR are given in The testis (TE), MAG, and vas deferen (VD) of the male reproductive system of lostella  were dis. (2015) . Statistt-test detected statistically significant proteins (p < 0.05), and the abundances of proteins that changed by more than 1.5- or less than 0.67-fold were maintained. A total of 197 differentially abundant proteins (DAPs) were detected  was detected in mated-CB, but no signal peptide was found. In total, 123 ACPs were predicted to be secreted ACPs, and 74 out of the 123 secreted ACPs had signal peptides. A total of 49 out of the 123 secreted ACPs were detected in the mated-CB, while SignalP predicted no signal peptides . The remCombined with results of subcellular location analysis, we discovered that there were significant differences between the predicted secreted ACPs and unconfirmed ACPs . “ExtracAccording to the sequence annotation, 168 ACPs were categorized into different functional categories. The number of proteins in each functional category was analyzed . The “prH. sapiens  in P. xylostella ACPs than in other four insects . Interestingly, these trypsins were all predicted to be secreted proteins. Phylogenetic tree analysis showed that 10 of 18 accessory gland protein trypsins were on the same branch in P. xylostella. One trypsin showed a closer relationship with D. melanogaster; the other one was closer to A. mellifera; and the remaining six of eight trypsins were closer to Heliconius butterflies .The 123 secreted ACPs were categorized into 9 groups based on their functions of conserved domains. Most of these proteins, including proteases, protease inhibitors, transporter proteins, and antibacterial proteins, had previously been identified as ACPs in other insects ,16,18,39P. xylostella. This is different from what has been found in D. melanogaster, where 163 out of 173 SFPs had signal peptides. In D. melanogaster, the high percentage of SFPs containing signal peptides could be due to the high quality of protein databases. However, in Heliconius butterflies, 44 out of 51 detected SFPs had signal peptides. The high percentage of SFPs containing signal peptides in Heliconius butterflies may due to the fact that most of the annotated SFPs were predicted to be chymotrypsins [A. aegypti, 71 out of 93 detected SFPs had signal peptides, as did 21 out of 53 detected SFPs in A. mellifera. It is also possible that the lack of signal peptides in secreted ACPs is due to the fact that they are secreted through “apocrine secretion”, whereas cells in the posterior portion of the gland are considered to secrete proteins via granules and/or cell membrane rupture [Typically, a signal peptide is used to determine whether a protein is secreted or not via a classical pathway. In this study, 74 out of 123 secreted ACPs had a signal peptide in  rupture ,19. AlteP. xylostella, 38 (27 proteases and 11 protease inhibitors) out of 123 secreted ACPs identified were predicted regulators of proteolysis. Similar results were found in the other four insects, e.g., 21 proteases and 14 protease inhibitors in D. melanogaster; 20 proteases and 1 protease inhibitor in A. aegypti; 5 proteases in A. mellifera; and 15 proteases and 2 protease inhibitors in Heliconius butterflies. Proteolysis must be strictly regulated to avoid premature activation of pathways or tissue injury; therefore, ACPs are commonly rich in proteases and protease inhibitors [In hibitors ,41,42. Phibitors , mediatihibitors ,45. P. xylostella. Trypsins were detected in the other four insect species besides P. xylostella. The number of trypsins identified in the secreted ACPs of P. xylostella was the highest among the 5 species, including 14 trypsins (40% of proteolysis regulators) in D. melanogaster, 6 trypsins (29% of proteolysis regulators) in A. aegypti, 1 trypsin (20% of proteolysis regulators) in A. mellifera, and 15 trypsins (88% of proteolysis regulators) in Heliconius. A trypsin-like serine protease is necessary for the full induction of egg-laying after mating in crickets [Bombyx mori and P. xylostella resulted in male sterility, which could have been caused by the sperm did not successfully enter the egg [In this study, 18 trypsins (47% of proteolysis regulators) were identified in crickets . DisruptP. xylostella. Apolipophorin-III was not detected in ACPs of other four insect species. Apolipoproteins are proteins that transport hydrophobic substances, such as steroid hormones, lipids, and others, and may be related to immunity [Apriona germari and Spodoptera litura [Two apolipoproteins (a apolipophorin-III and a apolipoprotein D) were identified in the secreted ACPs of immunity . Previoua litura ,50. Apola litura ,52. In aa litura . P. xylostella. OBPs were thought to be present in the olfactory organs and played roles in olfactory recognition [D. melanogaster, B. dorsalis, and N. lugens [D. melanogaster, some OBPs showed MAG-specific expression [Obp10 and Obp22 can significantly impair female and male reproductive capacity, including oviposition, fecundity, fertility, and the development of spermatozoa in A. aegypti [D. melanogaster reproductive tracts after mating [Tribolium castaneum, RNAi of OBPs leads to a decrease in females’ egg production after mating [P. xylostella. It may transport pheromones, odorants, or other small molecules to receptors. The exact function of OBP, which was found in ACPs of P. xylostella, needs to be investigated in the future. A chemosensory protein contains an OS-D domain, also called an odorant binding protein (OBP), which was identified as secreting ACPs in ognition ,55. It h. lugens ,19,39. Ipression . Obp10 a aegypti . Previour mating . In Tribr mating . It is bD. melanogaster [P. xylostella. Galectin  was detected in P. xylostella, but was not found in the SFPs of the other four insects, including D. melanogaster, A. aegypti, A. mellifera, and Heliconius. Galectin is able to recognize the conserved amino acid motif of β-galactoside enzyme from pathogenic microorganism and induce immune responses against foreign microorganisms in insects [D. melanogaster [D. melanogaster, C-type lectins are required in the SP pathway [P. xylostella. It has previously been reported that the CRISPs gene has a high level of expression in MAGs and EDs in B. dorsalis, and may possibly serve an immune-related function in reproductive tissues [Previous studies have demonstrated that males transfer antibacterial proteins to females from MAGs and ED during copulation to improve reproductive success in nogaster ,59. In t insects . Studiesnogaster . In D. m pathway . Two cys tissues . CRISP,  tissues . P. xylostella with the other four insect ACPs, we identified some new insects ACPs in this proteome study, including proteins with chitin binding Peritrophin-A domain, PMP-22/EMP/MP20/Claudin tight junction domain-containing protein, netrin-1, type II inositol 1,4,5-trisphosphate 5-phosphatase, two spaetzles, allatostatin-CC, and cuticular protein. In particular, peritrophic matrix proteins of insects and animal chitinases have the Peritrophin-A domain, which is present in chitin-binding proteins. A protein with chitin binding Peritrophin-A domain is a secreted ACP in P. xylostella, and may have a role in reproduction. The PMP-22/EMP/MP20/Claudin tight junction domain-containing protein is a member of the claudins family, and performs diverse functions at specialized cell–cell contact [D. melanogaster, netrin can affect the fertility of both females and males. Lacking the netrin gene, males significantly reduce the egg-laying capability of wild-type females, and females produce fewer fertilized eggs than wild-types [Manduca sexta, and can significantly inhibit juvenile hormone biosynthesis in vitro by the corpora allata [B. mori, and plays a role in regulation of seminal fluid movement during copulation [N. lugens [P. xylostella as it does in mice.Comparing  contact . In huma contact . The dis contact . Netrin ld-types . In thisld-types ,70. Allaa allata . Allatosa allata ,73. In vpulation . Cuticulpulation . It has . lugens . We spec. lugens . Inpp5b P. xylostella using two different proteomic methods. A total of 20 out of 123 secreted ACPs were functionally unknown proteins. For the other 103 secreted ACPs, we were able to categorize them, according to their functions, into 8 groups. This included regulators of proteolysis, signal transduction, transporters, and protein export machinery, and immunity was the most important. Our results provide a foundation for future functional studies to explore these ACPs in female behavior and reproduction in P. xylostella. In practice, the new knowledge of insect-specific ACPs could aid in the discovery of novel pesticide target sites for pest population control in P. xylostella. Meanwhile, the present study provides an important platform for research regarding ACPs in P. xylostella.In this study, we identified 123 putative secreted ACPs of


Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists.This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes.This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications. Convolutional neural networks and other deep learning methods have been at the heart of recent advances in medicine (see We address these issues using an assisted annotation method that leverages the participation of non-pathologists (NPs), including medical students and graduates. Medical students typically have strong incentives to participate in annotation studies, with increased reliance on research participation in residency selection . We descThere has been growing interest in addressing data scarcity in histopathology by either (i) scaling data generation or (ii) reducing reliance on manually labeled data using data synthesis techniques such as generative adversarial networks . While tWhile the public release of data is only 1 aspect of our study, it is essential to acknowledge related nucleus classification datasets. Some of these datasets can be used in conjunction with ours and include MoNuSAC, CoNSep, PanNuke, and Lizard , 32–38. A few studies are of particular relevance to this article. A study by Irshad et al. showed that non-experts, recruited through the Figure Eight platform, can produce accurate nucleus detections and segmentations in renal clear cell cancer but was limited to 10 whole-slide images . Hou et This work describes a scalable crowdsourcing approach that systematically engaged NPs and produced annotations for localization, segmentation, and classification of nuclei in breast cancer. Our workflow required minimal effort from pathologists and used algorithmic suggestions to scale the annotation process and obtain hybrid annotation datasets containing numerous segmentation boundaries without laborious manual tracing. We show that algorithmic suggestions can improve the accuracy of NP annotations and that NPs are reliable annotators of common cell types. In addition, we discuss a new constrained clustering method that we developed for reliable truth inference in multi-rater datasets. We also show how multi-rater data can ensure the quality of NP annotations or replace expert supervision in some contexts. Finally, we note that downstream deep-learning modeling using the NuCLS dataset is discussed in a related publication and is not the focus of this article .NuCLS is a large-scale multi-class dataset generated by engaging crowds of medical students and pathologists. NuCLS is sourced from the same images as the Breast Cancer Semantic Segmentation (BCSS) dataset . TogethePathologist time is limited and expensive, and relying solely on pathologists for generating annotations can hinder the development of state-of-the-art models based on convolutional neural networks. In this study, we show that NPs can perform most of the time-consuming annotation tasks and that pathologist involvement can be limited to low-effort tasks that include:Training NPs and answering their questions Fig.  44]..44].Qualitative scoring of NP annotations .Low-power annotation of histologic regions  28]..28].We used a web-based annotation platform called HistomicsUI for annotation, feedback, and quality review . Histominot corrected by pathologists and enabled an unbiased assessment of NP performance. Pathologist annotations were also collected for a limited set of multi-rater FOVs to evaluate NP accuracy.Additionally, we obtained 3 multi-rater datasets containing 97,300 annotations, where the same FOV was annotated by multiple participants Figs  and 2. TThe detection accuracy of NPs was moderately high (average precision = 0.68) and was similar to the detection accuracy of pathologists. Classification accuracy of NPs, on the other hand, was only high for common nucleus classes  and was higher when grouping by super-class Fig. . We repoWhen pathologist supervision is not an option, multi-rater datasets need to have annotations from a sufficient number of NPs to infer reliable data. We used the annotations we obtained to perform simulations to estimate the accuracy of inferred NP-labels with fewer numbers of participating NPs Fig. . The infMany nucleus detection and segmentation algorithms were developed using conventional image analysis methods before the widespread adoption of convolutional neural networks. These algorithms have little or no dependence on annotations, and while they may not be as accurate as convolutional neural networks, they can correctly segment a significant fraction of nuclei. We used simple nucleus segmentation heuristics, combined with low-power region annotations from the BCSS dataset, to obtain bootstrapped annotation suggestions for nuclei  28]. Th. Th28]. Bootstrap control: participants were shown unrefined bootstrapped suggestions.Unbiased control: participants were not shown any suggestions. This dataset was the first multi-rater dataset to be annotated.Accurate suggestions can be confirmed during annotation with a single click, reducing effort and providing valuable nucleus boundaries that can aid the development of segmentation models. Participants can annotate nuclei that have poor suggestions using bounding boxes. Bounding box annotation requires more effort than clicking a suggestion but less effort than the manual tracing of nuclear boundaries . We obtaP < 0.001). This observation is consistent with Marzahl et al., who reported similar findings in a crowdsourcing study using bovine cytology slides [There was value in providing the participants with suggestions for nuclear class, which included suggestions directly inherited from BCSS region annotations, as well as high-power refined suggestions produced by Mask R-CNN Fig. . Patholoy slides .Region-based class suggestions for nuclei were, overall, more concordant with the corrected single-rater annotations compared to Mask R-CNN refined (high-power) nucleus suggestions (MCC = 67.6 vs 52.7) . NonetheNaturally, there is some variability in the judgments made by participants about nuclear locations and classes and the accuracy of suggested boundaries. We study the process of inferring a single truth from multi-rater datasets and discuss the effect of various parameters. There is a trade-off between the number of nucleus anchor proposals and interrater agreement Fig. . The cluNucleus detection was a more significant source of discordance among participants than nucleus classification Fig. . Some nuhttps://www.cancer.gov/tcga). They were obtained from 125 patients with breast cancer (1 slide per patient). Specifically, we chose to focus on all carcinoma of unspecified type cases that were triple-negative. The designation of histologic and genomic subtypes was based on public TCGA clinical records [The scanned diagnostic slides we used were generated by the TCGA Research Network , who served as a study coordinator for both the BCSS and NuCLS projects, and approved by a senior pathologist (H.E.). These ROIs were then tiled into non-overlapping potential FOVs, which were automatically selected for inclusion in our study on the basis of predefined stratified sampling criteria. A total of 16.7% of FOVs were sampled such that the majority of refined suggestions were a single class, e.g., almost all suggestions are tumor. In addition, 16.7% were sampled to favor FOVs with 2 almost equally represented classes, e.g., many tumor and fibroblast suggestions. Finally, 16.7% of FOVs were sampled to favor discordance between the bootstrapped suggestions and Mask R-CNN–refined suggestions, e.g., a stromal region with sTILs. The remaining 50% of FOVs were randomly sampled from the following pool, with the intent of favoring the annotation of difficult nuclei: (i) the bottom 5% of FOVs containing high numbers of nuclei with low Mask R-CNN confidence; (ii) and the top 5% of FOVs containing extreme size detections, presumably clumped nuclei.The annotation protocol used is provided in the digitalslidearchive.github.io) [https://www.youtube.com/watch?v=HTvLMyKYyGs. The process of pathologist review of annotations is illustrated in We used the Digital Slide Archive, a web-based data management tool, to assign slides and annotation tasks (thub.io) . HistomiThe HistomicsTK Restful API was used to manage data, users, and annotations programmatically. This includes uploading algorithmic suggestions, downloading participant annotations, and scalable correction of systematic annotation errors where appropriate.We implemented a constrained agglomerative hierarchical clustering process to obtain anchor proposals Fig. . The algWe used the expectation-maximization framework described by Dawid and Skene , 47, 57.We defined 2 levels of grouping for nuclei classes as illustrated in Fig. Overall interrater agreement was measured using the Krippendorff α-statistic, implemented in Python by Santiago Castro and Thomas Grill . This stWe performed simulations to measure the effect of the number of NPs assigned to each FOV on the accuracy of NP-label inference Fig. . We keptData management, machine learning models, and plotting were all implemented using Python 3+. Pytorch and Tensorflow libraries were used for various deep-learning experiments. Scikit-learn, Scikit-image, OpenCV, HistomicsTK, Scipy, Numpy, and Pandas libraries were used for matrix and image-processing operations. Openslide library and HistomicsTK API were used for interaction with whole-slide images.U test was used for unpaired comparisons. The Wilcoxon signed-rank test was used for paired comparisons. Confidence bounds for the AUROC values were obtained by bootstrap sampling with replacement using 1,000 trials [The Mann-Whitney 0 trials , 64. AURIn summary, we have described a scalable crowdsourcing approach that benefits from the participation of NPs to reduce pathologist effort and enables minimal-effort collection of segmentation boundaries. We systematically examined aspects related to the interrater agreement and truth inference. There are important limitations and opportunities to improve on our work. Our results suggest that the participation of NPs can help address the scarcity of pathologists’ availability, especially for repetitive annotation tasks. This benefit, however, is restricted to annotating predominant and visually distinctive patterns. Naturally, pathologist input—and possibly full-scale annotation effort—would be needed to supplement uncommon and challenging classes that require greater expertise. Some nuclear classes may be challenging to annotate in H&E-stained slides reliably and would be subject to considerable interrater variability even among practicing pathologists. In these settings, and where resources allow, immunohistochemical stains may be used as a more objective form of ground truth .We chose to engage medical students and graduates with the presumption that familiarity with basic histology would help in acquiring higher-quality data. Whether this presumption was warranted or whether it was possible to engage a broader pool of participants was not investigated. On a related note, while we observed differences based on pathologist expertise, this was not our focus. We expect to address related questions such as the value of fellowship specialization in future work. Also, we did not measure the time it took participants to create annotations; we relied on the safe assumption that certain annotation types evidently take less time and effort than others.Another limitation is that the initial bootstrapped nuclear boundaries were generated using classical image-processing methods, which tend to underperform where nuclei are highly clumped/touching or have very faint staining. This theoretically introduces some bias in our dataset, with an overrepresentation of simpler nuclear boundaries. Future work could investigate the use of transfer learning or unsupervised convolutional neural network approaches to generate more accurate algorithmic suggestions. Similarly, we used Mask R-CNN as a function approximator to refine our algorithmic suggestions. Future research can explore other deep-learning architectures that may improve refinement and result in better algorithmic suggestions.We focused our annotation efforts on nucleus detection, as opposed to whole cells. Nuclei have distinct staining (H&E) and boundaries, potentially reducing the interrater variability associated with the detection of cell boundaries. Finally, we would point out that dataset curation is context-dependent and likely differs depending on the problem. Nevertheless, we trust that most of our conclusions have broad implications for other histopathology annotation efforts.Project name: NuCLSgithub.com/PathologyDataScience/NuCLSProject home page: Operating system: Platform independentProgramming language: Pythonhttps://github.com/PathologyDataScience/Mask_RCNN/. We used the Digital Slide Archive for whole-slide image and data management  and its associated annotation user interface HistomicsUI , as well as the annotation and image-processing library HistomicsTK .Other requirements: We used the tensorflow implementation by Matterport Inc. to train the Mask R-CNN tensorflow model used for generating the algorithmic suggestions, along with a set of scripts available on GitHub at: License: The NuCLS codebase is licensed with a CC0 1.0 license (dataset) and the MIT license.Restrictions to use by non-academics: Both the CC0 1.0 license (dataset) and the MIT license (codebase) allow for non-commercial use. License terms can be reviewed for details.RRID:SCR_021888; Biotools ID: nuclsRegistration: https://github.com/PathologyDataScience/BCSS and can be viewed at a demo instance of the Digital Slide Archive at: https://demo.kitware.com/histomicstk/histomicstk#?image=5bbdee62e629140048d01b0d. Both the BCSS and NuCLS datasets are available under a CC0 1.0 license. Snapshots of our code and other data further supporting this work are openly available in the GigaScience repository, GigaDB [The NuCLS dataset is available at the NuCLS website. The BCSS dataset, which helped contribute to the algorithmic suggestions, is available for download from: , GigaDB .Supplementary Table S1: Definitions and abbreviations used. A white paper from the Digital Pathology Association can be consulted for an expanded list of relevant concepts [concepts .Supplementary Table S2: Accuracy of algorithmic suggestions. The accuracy is measured against the corrected single-rater dataset. Mask R-CNN refinement of the bootstrapped algorithmic suggestions results in better detection suggestions. Low-power region-based classification was more accurate than Mask R-CNN–derived classes. Note, however, that this was FOV-dependent, and there were some FOVs in which the Mask R-CNN prediction was better than relying on low-power regions for classificationSupplementary Table S3: Hyperparameters used for Mask R-CNN model training.Supplementary Figure S1: Use of review galleries for scalable review of single-rater annotations. Single-rater annotations were corrected by 2 study coordinators, in consultation with a senior pathologist. The pathologist was provided with a mosaic review gallery showing a bird’s eye view of each FOV, with and without annotations, and at high and low power. The pathologist was asked to assign a per-FOV quality assessment. If the pathologist wanted further context, they could click on the FOV and pan around the full whole-slide image. They were also able to provide brief comments to be addressed by the coordinators, e.g., “change all to tumor.” A demo is provided at the following video: https://youtu.be/Plh39obBg_0.Supplementary Figure S2: Process for obtaining algorithmic suggestions for scalable assisted annotation. Nucleus segmentation boundaries were derived using image-processing heuristics at a high magnification. Low-power region annotations from the BCSS dataset, approved by a practicing pathologist, were then used to assign an initial class to nuclei. This combination of noisy nuclear segmentation boundaries and region-derived classifications are the bootstrapped suggestions. These noisy algorithmic suggestions were the basis for annotating the Bootstrap control multi-rater dataset. A Mask R-CNN model was then used as a function approximator to smooth out some of the noise in the bootstrapped suggestions. Participants were able to view these refined suggestions, along with low-power region annotations, when annotating the single-rater and Evaluation datasets.Supplementary Figure S3: Super-class accuracy of participant annotations and inferred NP-labels . The accuracy is measured against the inferred P-truth.Supplementary Figure S4: Accuracy of algorithmic suggestions (single-rater dataset). The accuracy is measured against the corrected single-rater dataset. A. Per-FOV detection accuracy of algorithmic data at the 2 stages of obtaining algorithmic suggestions; i.e., how well do the suggestions correspond to real nuclei? Mask R-CNN refinement significantly improves suggestion accuracy. B. Number of Mask R-CNN–refined suggestions that correspond to a segmentation  or a bounding box. C. Concordance between suggested classes and classes assigned by participants. Region-based suggestions were, broadly speaking, more concordant with the true classes, but nucleus suggestions had a higher recall for sTILs. D. Comparison of the classification accuracy (MCC) of low-power region class and high-power Mask R-CNN–derived nucleus class. Numbers are normalized column-wise, i.e., represent percentages of true nuclei of a particular class. Note how region-based and nucleus-based suggestions have disparate accuracies for different FOVs and classes. Hence, there was value in providing the participants with both forms of suggestion.Supplementary Figure S5: Abundance and segmentation accuracy of clicked algorithmic suggestions (multi-rater datasets). A. Proportion of nuclei in the FOV that were inferred to have good segmentation. Circle size represents the number of nuclei in that FOV. The proportion is notably higher for the Evaluation dataset than the Bootstrap control. B. Accuracy of algorithmic segmentation boundaries for nuclei that were inferred to have accurate segmentation boundaries in both the Evaluation dataset and Bootstrap control. The comparison is made against manual segmentations obtained for the same nuclei from 1 senior pathologist. Most clicked algorithmic segmentations were very accurate and have a Dice coefficient >0.8. The accuracy was slightly higher for Mask R-CNN–refined suggestions.Supplementary Figure S6: Annotation procedure on HistomicsUI. A, B. Participants were shown suggestions for nucleus segmentation boundaries, as well as 2 types of classification suggestions: low-power region suggestions and high-power nucleus classification suggestions. The FOV shown here is almost entirely present in a stromal region but contains multiple scattered sTILs that were not dense enough to be captured as a sTILs “region.” C. Participants’ annotations were either points/clicks, for accurate segmentations, or bounding boxes. They picked the color/class of their annotations beforehand and were told to simply ignore any inaccurate suggestions. Participants were able to turn the suggestions off for a clear view of the underlying tissue. D. Participant annotations and algorithmic suggestions were ingested into a database and processed to provide cleaned up data, which were then pushed for viewing on HistomicsUI for correction and review.Supplementary Figure S7: Confusion matrix of participant annotations . A. Confusion of annotations placed by the participants, putting aside detection. Here, we ask the question, if a participant places an annotation that they call tumor, and it matches a true nucleus, what is the class of that nucleus? By definition, there are no “ambiguous” true nuclei. B. For each true nucleus, how many of the participants detected it, and if so, what class did they assign? Note that because truth inference takes participant reliability into account, the inferred P-truth does not have to correspond to the most commonly assigned class. Empty entries are values <1.Supplementary Figure S8: Sample poor annotation data excluded during the single-rater dataset correction process. Despite having received initial training and feedback, the NP who generated these annotations was confused about what is a nucleus and frequently considered chromatin clumps or artifacts to be nuclei (arrows). This underlines the need for quality control.Supplementary Figure S9: Ease of detection of various nucleus classes . If we know for a fact that this is, say, a lymphocyte, how many participants detected it, even if they called it something else?. True class is the inferred P-truth. The color coding used is explained in panel B. A. Nuclei counts, broken down by class and the number of matched participants. B. Ease of detection of nuclei by true class. Interpreting, e.g., the blue curve proceeds as follows: 100% of lymphocytes were detected by ≥3 pathologists, ∼80% were detected by 4 pathologists, and so on.Supplementary Figure S10: Algorithm for obtaining anchor proposals through constrained agglomerative clustering. We cluster bounding boxes from participants to get the anchor proposals corresponding to potential nucleus locations. Note that the threshold we use for maximum linkage, t*, is influential in determining how many anchors we get. We make sure that annotations from the same participant do not end up in the same cluster by creating sets of “do-not-link” bounding boxes. The final anchor proposals are the anchor medoids; using medoids ensures that the box anchor proposals correspond to real nucleus boundaries.API: Application Programming Interface; AUROC: area under receiver-operator characteristic curve; BCSS: Breast Cancer Semantic Segmentation dataset; FOV: field of view; H&E: hematoxylin-eosin; IOU: intersection over union; JP: junior pathologist; MCC: Matthews correlation coefficient; NP: non-pathologist; NP-label: inferred label from multi-rater pathologist data; NuCLS: Nucleus Classification, Localization, and Segmentation; Ps: junior or senior pathologists; P-truth: inferred truth from multi-rater pathologist data; ROI: region of interest; SP: senior pathologist; TCGA: The Cancer Genome Atlas.None to disclose.This work was supported by the U.S. National Institutes of Health National Cancer Institute grants U01CA220401 and U24CA19436201 . The funding body had no role in the design of the study, data collection, data analysis, or data interpretation, or writing the manuscript.M.A. and L.A.D.C. conceived the hypothesis, designed the experiments, performed the analysis, and wrote the manuscript. D.M. and D.A.G. contributed support for the Digital Slide Archive software and database. P.M. provided ideas and support for the computational analysis. B.D. and D.J. provided ideas for the interrater analysis. M.A. and Maha A.T. Elsebaie were the study coordinators and corrected the single-rater dataset. H.E. provided feedback and approved the corrected single-rater dataset. E.H. provided manual nucleus segmentation data. H.E., H.H., and E.H. are senior pathologists and provided multi-rater annotations. L.A.A., K.H.M., P.A.P., and L.E.H. are junior pathologists and provided multi-rater annotations. Maha A.T. Elsebaie, A.M. Alhusseiny, M.A.A., A.M.E., R.A.S., A.R., A.M.S., A.M. Alkashash, I.A.R., A. Alrefai, N.M.E., A. Abdulkarim, A.F., A.E., A.G.E., Y.A., Y.A.A., A.M.R., M.K.N., Mai A.T. Elsebaie, A. Ayad, A.G., and M.E. are non-pathologists and provided single- and multi-rater annotations. All experience designations are based on the time of annotation. All authors reviewed the manuscript draft.giac037_GIGA-D-21-00352_Original_SubmissionClick here for additional data file.giac037_GIGA-D-21-00352_Revision_1Click here for additional data file.giac037_GIGA-D-21-00352_Revision_2Click here for additional data file.giac037_GIGA-D-21-00352_Revision_3Click here for additional data file.giac037_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac037_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giac037_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giac037_Reviewer_1_Report_Original_SubmissionMonjoy Saha, PhD -- 11/21/2021 ReviewedClick here for additional data file.giac037_Reviewer_1_Report_Revision_1Monjoy Saha, PhD -- 1/10/2022 ReviewedClick here for additional data file.giac037_Reviewer_1_Report_Revision_2Monjoy Saha, PhD -- 2/23/2022 ReviewedClick here for additional data file.giac037_Reviewer_2_Report_Original_SubmissionChris Armit -- 11/24/2021 ReviewedClick here for additional data file.giac037_Reviewer_3_Report_Original_SubmissionJin Tae Kwak -- 12/7/2021 ReviewedClick here for additional data file.giac037_Reviewer_3_Report_Revision_1Jin Tae Kwak -- 1/26/2022 ReviewedClick here for additional data file.giac037_Supplemental_FilesClick here for additional data file.


Leishmania (Mundinia)enriettii is a species commonly found in the guineapig, Cavia porcellus. Although it is a dermotropic species,there is still an uncertainty regarding its ability to visceralise duringLeishmania life cycle.L. enriettii (strainL88) to visceralise in lungs, trachea, spleen, and liver of C.porcellus, its natural vertebrate host. Here, we investigated the ability of C. porcellus, animals were experimentallyinfected for viscera removal and PCR typing targeting hsp70gene. Animals were infected sub-cutaneously in the nose and followed for 12 weeksusing histological (hematoxilin-eosin) and molecular tools (polymerase chainreaction-restriction fragment length polymorphism - PCR-RFLP). To isolateparasite from L.enriettii, we performed additional in vivoexperiments. During 6th week PI, the parasite was isolated from the spleenconfirming previous histopathological and PCR observations. Histological analysis revealed intense and diffuse inflammation with thepresence of amastigotes in the trachea, lung, and spleen up to 12 weekspost-infection (PI). Molecular analysis of paraffin-embedded tissuesdetected parasite DNA in the trachea and spleen between the 4th and 8thweeks PI. At the 12th PI, no parasite DNA was detected in any of the organs.To confirm that the spleen could serve as a temporary site for Leishmania enriettii (strain L88) was able to visceralisein the trachea, lung, and spleen of C. porcellus. Leishmania enriettii is a parasite belonging to the subgenusMundinia,,Cavia porcellus. Othermembers of this subgenus include Leishmania (M.)macropodum,Leishmania (M.) martiniquensis,Leishmania (M.) orientalisLeishmania (Mundinia) sp. (Ghanaisolate).Mundinia,Leishmania and Viannia subgenera.,,Culicoidesspp.).,L. enriettii is yet to bedetermined, although Lutzomyia monticula has been suggested.L. enriettii strains in theState of Paraná, Brazil. It did not detect the parasite in the wild reservoirsreinforcing the role of C. porcellus as a the main, if not the only,domestic host.An interesting feature of the members of this subgenus is their ability to infectnon-phlebotomine vectors, such as ceratopogonids  is its ability to specifically infect guinea pigs.C. porcellus, L. enriettii causes a severeulcerated cutaneous lesion (CL) that heals over time. For this reason, C.porcellus was used as a model for CL for many years.,L. enriettii did notuse salivary glands extracts (SGE) until recent biological and immunopathologicalstudies.,Lutzomyia longipalpis SGEs modulated the infection by preventing theattraction of monocytes and CD163 macrophages throughout the course of infection. After12 weeks post-infection (PI) regardless the use of SGEs, the CL lesions healedspontaneously. Early and recent reports,,,,L. enriettii to visceralisein guinea pigs after several inoculation routes. It was, however, unknown if thisphenomenon is permanent or transient requiring fast transmission to vector(s).Several research groups have been studied L. enriettii, there are severalaspects of infection that still need to be better elucidated. In our previous study, wereported the proinflammatory features of L. enriettii infection in theskin.L. enriettii, we provide a more detailed study on theimmunopathological aspects in different organs using molecular and histologicalapproaches in C. porcellus viscera.Over the 75 years since the discovery of Histology evaluations - Trachea, lung, liver, and spleen wererecovered from our previous study.5promastigotes of L. enriettii reference strain (MCAV/BR/1945/L88)in 0.1 mL of phosphate-buffered saline (PBS) supplemented with 1/2 salivary gland ofL. longipalpis. Animals (n = 12) were followed for 12 weeks andeuthanised with an overdose of ketamine and xylazine (500 mg/kg and 100 mg/kg) atweeks four (n = 3), eight (n = 3), and 12 (n = 3). Negative controls included threeanimals euthanised at weeks four, eight, and 12. Tissue fragments were fixed in a10% buffered formalin solution, pH 7.2 for 48 hours. Paraffin blocks were cut in amicrotome (3-4 μm) and mounted on slides prior to routine histological analysis(hematoxylin-eosin - HE). Histological slides were qualitatively analysed underlight microscopy as previously reported.Parasite detection in paraffin tissues - Paraffin-embedded tissuesamples  were subjected to DNA extractionhsp70 gene .In vivo experiments - For visceralisation confirmation, sixexperimental animals were divided in two groups including: (1) two negative controlsinoculated with (PBS+SGE) and (2) four animals infected with L.enriettii. Guinea pigs were infected intradermally with 105L. enriettii promastigotes in PBS + SGE from L.longipalpis as previously reported.Parasite recovery and typing - Tissue fragments from trachea,lungs, liver, and spleen were seeded in NNN-Schneider’s medium and incubated at26ºC. Cultures were followed until appearance of promastigoteforms. Positive cultures were expanded for DNA extraction and polymerase chainreaction (PCR) typing using hsp70 gene as above. The ~1,300-bpfragment was subjected to digestion with HaeIII (New England) andrestriction profiles were analysed in a 4% agarose gel.Ethical considerations - This work was approved by the EthicsCommittee for Animal Use (CEUA), Oswaldo Cruz Foundation (FIOCRUZ) (LW-24/19). Youngmale C. porcellus were bred according to the InternationalStandards for the Breeding and Use of Laboratory Animals,Histopathological evaluation - Histopathological changes in thetrachea, lung, spleen, and liver of infected animals were analysed at the 4th, 8th,and 12th weeks PI using HE. Negative controls with uninfected animals did not showany histological alterations  (L88 strain ofL. enriettii), trachea (Tra) (4th week PI) and spleen (4th and8th weeks PI). At 12 weeks PI, no DNA was detected in the organs  was similar tothose caused by typical viscerotropic species in hamsters’ liver.,L.infantum, where only chronic interstitial pneumonitis with notablethickening of the interalveolar septa were noted.L. enriettii can visceralise andcause immunopathological events in trachea, lungs, and spleen up to 12 weeks PI.While our previous paperConsistent with previous observations, we detected amastigote forms of hsp70 gene has beensuccessfully used as a tool for discriminating L. martiniquensisand L. orientalis.L. enriettii fromtrachea to spleen in the first 8 weeks PI. After two months, it disappears from thetrachea and increases its presence in the spleen. No parasite DNA was found at 12weeks PI suggesting a possible clearance of the parasite in the organs. However,this result is conflicting with the histopathological findings that showedamastigote forms in the tissues during this period. This also does not correlatewith what occurs in the skin of the same animals, whose lesions reached their peakat 6 weeks PI and are completely healed at 12 weeks.,,To confirm this, paraffin-embedded tissues were assayed for the presence of parasiteDNA using molecular tools. Recently, L. enriettii can reach theviscera, cause severe histopathological alterations that decrease, but notcompletely disappear, for example, in the skin 8-12-weeks PI. It is interesting tonotice that under our controlled experimental conditions the animals were apparentlyactive and did not have any clinical signs.,,L.enriettii is yet to be determined.Based on our data, it is likely that Leishmaniainfantum (viscerotropic) can cause cutaneous lesions in Honduras,Central America.Leishmania donovani, another viscerotropic species, can causeunusual dermal lesions after therapeutic failure known as Post-kala-azar dermalLeishmaniasis (PKDL).Leishmania amazonensis(dermotropic). It was isolated from lymph nodes from dogs showing symptoms of caninevisceral leishmaniasis, similar to that caused by L. infantum,L. martiniquensis, another member of theMundinia subgenus, causes not only LV, but also CL inHIV-positive patients.L. enriettii showed adual profile being able to cause dermotropic and viscerotropic lesions under ourexperimental conditions in its main host C. porcellus.Leishmaniasis comprises a wide spectrum of clinical manifestations including diseasescaused by dermotropic and viscerotropic species. Depending on the region,intraspecies tropism variations may occur. For example, in vivo infection targeting the spleen as a possible source forparasite isolation and observed the same pattern of lesion development as reportedelsewhere at four-eight weeks PI.,To confirm our previous histological and molecular findings, next we performed anLeishmania enriettii (strain L88) visceralised in C.porcellus causing severe lesions in lungs, trachea, and spleen. Despitethe clinical cure of the skin lesion, the parasite was not eliminated from thetissues during the 12 weeks PI as judged by the histological studies. In contrast tothe infected field-collected guinea pigs, L. enriettii underlaboratory conditions did not cause any clinical sign to them. This finding is ofepidemiological importance, since those animals may be potential sources for vectorinfection facilitating parasite maintenance and spreading in the environment. Thisphenomenon warrants further investigation to address the role of apparentlycutaneous healed animals in L. enriettii transmission.


Background: CODAS syndrome (MIM 600373) is a multi-system developmental disorder characterized by cerebral, ocular, dental, auricular, and skeletal anomalies. CODAS syndrome is rare in the world and no cases have been reported in Chinese population so far. Mutations in the LONP1 gene can contribute to CODAS syndrome, while the underlying molecular mechanisms requires further investigation.Method: We described a Chinese boy who has suffered from cognition impairment, cataracts, caries, abnormal auricle and skeletal anomalies since birth. The patient’s parents are non-consanguineous and healthy. Whole-exome sequencing (WES) was employed to explore the genetic entity of this family.Results: A compound heterozygous missense mutation (NM_004793: c.2009C>T/p.A670V and c.2014C>T/p.R672C) of LONP1 was identified in the patient. Considering the clinical phenotypes and genetic results, the patient was diagnosed as CODAS syndrome.Conclusion: Here we reported the first case with CODAS syndrome in Chinese population. WES identified a compound heterozygous missense mutation of LONP1 gene in the patients. Our study not only provided data for genetic counseling and clinical diagnosis to this family, but also expanded the clinical spectrum of LONP1-related CODAS syndrome. CODAS syndrome (MIM 600373), described by The phenotypes of CODAS syndrome that have been reported so far including hypotonia and motor delay, intellectual disability, epilepsy, growth retardation, craniofacial deformities, cataracts, ptosis, delayed tooth eruption, enamel dysplasia, dens hypoplasia, scapha and helix dysplasia, scapha and helix dysplasia, conductive or sensorineural hearing loss, short stature, skeletal dysplasia, scoliosis, genu valgus, pes valgus, vertebral coronal clefts and so on . The cliLONP1 (homozygous or compound heterozygous) can cause CODAS syndrome. LONP1 encodes a mitochondrial protease that acts as a key modulator of protein quality control, respiratory complex assembly, gene expression and stress responses in mitochondria  and Sanger sequencing were applied to explore the candidate genes of this family.This three-year-old boy was born at term (38 + 5 weeks) with a birth weight of 3.75 kg to healthy Han-Chinese parents (29 years) of non-consanguineous marriage. Neither parent has a family history of genetic disease. At the age of 2 months, his parents found he did not make eye contact. Subsequently, the boy was diagnosed with cataracts , nystagmhttp://consurf.tau.ac.il/) predicted that two affected amino acids were conserved (https://swissmodel.expasy.org/) was utilized to explore the spatial configuration of two mutations (https://stuart.radboudumc.nl/metadome/dashboard) indicated that two affected residues are located in the intolerant region of LONP1  of LONP1 was identified in the patient . Accordionserved . Moreoveutations . Furtherof LONP1 . ConsideLONP1 was identified in the patient. Sanger sequencing subsequently confirmed that compound heterozygous mutations were parental segregation. The p.A670V heterozygous mutation was maternal and the p.R672C heterozygous mutation was paternal. Thus, the patient was further diagnosed as CODAS syndrome. Our study further confirms that compound heterozygous mutation of LONP1 is associated with CODAS syndrome.Here we reported a Chinese boy who has suffered from cognition impairment, cataracts, caries, abnormal auricle and skeletal anomalies since birth. The patient’s parents are non-consanguineous and healthy. WES was conducted to evaluate the genetic cause of this family. A compound heterozygous missense mutation (NM_004793: c.2009C>T/p.A670V and c.2014C>T/p.R672C) of LONP1 in a patient with CODAS syndrome. We also supplied the typical clinical pictures of this disease, which may contribute to the understanding and diagnosis of the disease combined with genetic analysis. Similarly, different combinations of p.A670V and p.R672C variants have been already reported in CODAS patients by LONP1 variants is showed in in vitro. Lymphoblastoid cells generated from patients showed swollen mitochondria with electron-dense inclusions, abnormal inner-membrane morphology, aggregated mtDNA-encoded subunit II of cytochrome C oxidase and reduced spare respiratory capacity, leading to impaired mitochondrial proteostasis and function. The LONP1 encodes a mitochondrial protein which is a member of the Lon family of adenosine triphosphate (ATP)-dependent proteases. The LONP1 holoenzyme forms a ring-shaped homo-hexamer with each subunit mainly consists of a Lon N-terminal domain, an ATP-binding domain, and a proteolytic domain  of


CD163/pAPN double-gene-knockout (DKO) weaned piglets. A total of 58 samples along the entire digestive tract were analyzed for microbial community using 16S rRNA gene sequencing. The colonic microbiota and their metabolites were determined by metagenomic sequencing and untargeted liquid chromatography-mass spectrometry (LC-MS), respectively. Our results showed that no alterations in microbial community structure and composition were observed between DKO and WT weaned piglets, with the exception of colonic microbiota. Interestingly, the DKO piglets had selectively increased the relative abundance of the Leeia genus belonging to the Neisseriaceae family and decreased the Ruminococcaceae_UCG_014 genus abundance. Functional capacity analysis showed that organic acid metabolism was enriched in the colon in DKO piglets. In addition, the DKO piglets showed increased iron levels in important tissues compared with WT piglets without any pathological changes. Pearson’s correlation coefficient indicated that the specific bacteria such as Leeia and Ruminococcaceae_UCG_014 genus played a key role in host iron absorption. Moreover, the iron levels had significantly (P < 0.05) positive correlation with microbial metabolites, particularly carboxylic acids and their derivatives, which might increase iron absorption by preventing iron precipitation. Overall, this study reveals an interaction between colonic microbiota and host metabolism and has potential significance for alleviating piglet iron deficiency.The gut microbiota is known to play a role in regulating host metabolism, yet the mechanisms underlying this regulation are not well elucidated. Our study aimed to characterize the differences in gut microbiota compositions and their roles in iron absorption between wild-type (WT) and IMPORTANCE Iron deficiency is a major risk factor for iron deficiency anemia, which is among the most common nutritional disorders in piglets. However, it remains unclear how the gut microbiota interacts with host iron absorption. The current report provides the first insight into iron absorption-microbiome connection in CD163/pAPN double knockout piglets. The present results showed that carboxylic acids and their derivatives contributed to the absorption of nonheme iron by preventing ferric iron precipitation. Gut microbiota colonizing the animal digestive tract, is a key factor in regulating host energy harvest and metabolism . ShotgunLactobacillus, Enterococcus, and Enterobacteriaceae family members was associated with iron deficiency  and is resistant to the infection with both porcine reproductive and respiratory syndrome virus (PRRSV) and transmissible gastroenteritis virus (TGEV) (The double-gene-knockout (DKO) model has both s (TGEV) . In thisP < 0.05)  and large intestine (cecum and colon) and feces . In the intestinal contents and feces, DKO and WT piglets displayed similar microbial diversity (measured by Chao1 and Richness indexes) . However < 0.05) . Princip < 0.05) . In the  < 0.05) . In the Clostridium_sensu_stricto_1, Actinobacillus, Terrisporobacter, Pasteurella, Ralstonia, Comamonas, Lactobacillus, Pseudomonas, Gemella, Moraxella, Weissella, Streptococcus, and Rothia. However, 37 genera were enriched in the large intestinal region, such as Faecalibacterium, Leeia, Prevotella_9, Lachnospiraceae_XPB1014_group, Megasphaera, Anaerotruncus, Ruminococcus_1, Christensenellaceae_R-7_group, Ruminiclostridium_9, Blautia, Pseudobutyrivibrio, Ruminococcaceae_UCG_014, and Subdoligranulum.Microbial community composition at the phylum, family, and genus levels was analyzed for the DKO and WT piglets. The five most abundant phyla in the small intestine were Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Tenericutes, together constituting up to 97% of the OTUs in each gut segment on average . The domt test was used to identify significant differences in the microbial communities at the phylum, family, and genus levels across the GI tract between DKO and WT piglets. However, the gut bacterial composition showed no obvious (P > 0.05) differences in the feces or the luminal regions of the duodenum, jejunum, ileum, or cecum between DKO and WT piglets; the following results were for the colonic microbiota only. DKO piglets had reduced relative abundance of Firmicutes and Tenericutes and an increase in Proteobacteria compared to WT piglets (P < 0.05) (P < 0.05) (Ruminococcaceae_UCG_014 was significantly lower in DKO piglets than in the WT piglets (P < 0.05) (Leeia genera (belonging to the family Neisseriaceae) compared to the WT piglets (Welch’s  piglets . At the  < 0.05) . In cont < 0.05) . At the  < 0.05) . However piglets .Leeia genera in the Proteobacteria phylum  effect size (LEfSe) method (LDA score ≥ 4) was used to identify significant colonic microbiota. The result showed that five bacterial taxa were enriched in the DKO piglets, including the Betaproteobacteria class, Neisseriales orders, Neisseriaceae families, and a phylum  and C. T_UCG_014  and C. AP < 0.05) between WT and DKO groups   of colonic microbiota included genetic information processing, signaling and cellular processes, metabolism, carbohydrate metabolism, and amino acid metabolism . The principal-component analysis (PCA) showed a clear separation in KO (KEGG Orthology) between WT and DKO piglets . In addiO groups . Among tabolism) . NoteworP < 0.05 in one-way analysis of variance (ANOVA). In total, 402 target metabolites were identified in DKO and WT groups, comprising 163 metabolites (ESI+ mode) and 239 metabolites (ESI-mode) . Biomarkers from carboxylic acids metabolism were elevated in the DKO piglets and included HMDB0126549:2-amino-4-(…) butanoic acid, HMDB0134078:2-amino-4-(…) butanoic acid, CSID30792013:CE-108, HMDB0030402: L-(…)-3-carboxylic acid, CSID1159:1-pyrroline-5-carboxylic acid, HMDB0124983: 3,4,5-trihydroxy-6-(…)carboxylic acid, CSID388915: 3-Carboxy-1-(…)pyridinium, and CSID4445200:-(…)carboxylate -based untargeted metabolomics approach . The ortboxylate . Additioboxylate .P > 0.05; Student's t test; P = 0.0003; colon: P = 0.0251; Student's t test) increases were found in the renal and colonic iron levels in the DKO piglets compared to the WT piglets  increased compared to those of WT pigs. Furthermore, the mRNA expression levels were measured for iron-associated transporters and binding proteins such as DMT1, TFRC, and FTH1 in colonic mucosa and kidney, respectively. The expression of these genes in the colonic mucosa and kidney was significantly (P < 0.05) higher in the DKO piglets compared to WT piglets . As expected, Western blotting showed increased in FTH1 levels of the colonic mucosa and kidney in the DKO piglets, respectively . Expression of proinflammatory genes  in the colon was not significant (P > 0.05) in the DKO piglets compared to WT piglets .We compared iron-related measurements of serum and tissues  between DKO and WT piglets without iron supplementation. The results showed mildly increased iron levels in serum, cardiac, hepatic, and splenic in DKO piglets relative to WT piglets, but the differences were not statistically significant  showed the strongest negative correlation with iron levels in different tissues . In contrast, the Leeia genera belonging to the Neisseriaceae family and Parabacteroides from the family Tannerellaceae were significantly positively associated with iron status . After that, we focused on the association between target metabolites and Ruminococcaceae_UCG_014 and Leeia genera and iron parameters . We then performed Pearson's correlation analysis between iron levels and the relative abundance of gut microbiota. The correlation analysis showed positive correlations between Bacteroidetes and Proteobacteria and iron status . Inversely, the phylum Firmicutes and Actinobacteria were negatively correlated with iron status . Iron status was positively correlated with the family Neisseriaceae and Clostridiales_vadinBB60_group but negatively associated with the abundances of Ruminococcaceae, Coriobacteriaceae, and Erysipelotrichaceae . The abundances of the genera in the Ruminococcaceae family , we discovered that gene editing altered the colonic microbiota composition of weaned piglets, which was most dramatically illustrated by the reduced abundance of Ruminococcaceae_UCG_014 genus and the increased abundance of Leeia genus. Furthermore, metagenomic sequence analysis revealed the enriched organic acid metabolism in the colonic microbiota of DKO piglets. Metabolome profiling further detected increases in the abundances of carboxylic acids and their derivatives in the colon contents of DKO piglets compared to WT piglets.In this study, we investigated changes in microbial community structure across different gut segments in DKO and WT piglets. We found that colonic microbiota of DKO and WT piglets were significantly different based on using alpha- and beta-diversity analyses. At the phylum level, Firmicutes, Proteobacteria, Bacteroidetes, Tenericutes, and Actinobacteria were predominant across different gut segments, which was consistent with previous studies evaluating the microbiota in the piglets , 22, 23.Iron is considered to be one of the most important trace elements for animal growth and health , 24. A lLeeia genus) positively correlated with increasing host iron absorption; in contrast, members of the Erysipelotrichaceae family and Ruminococcaceae_UCG_014 genus in the Firmicutes phylum negatively correlated with improving host iron absorption. A previous study reported that Proteobacteria proliferation was associated with the iron-rich environment in the GIT, which was consistent with our finding  were kept in the same farrowing house . During the lactation period, sows were fed with the basal diet without iron supplementation. Then, all piglets fed breast milk were weaned at 3 weeks and then fed the same diet formula without iron supplementation. To compare the differences in composition and function of intestinal microbiota between DKO and WT piglets, a total of 60 intestinal content specimens  were collected along the entire digestive tract  and feces derived from the weaned piglets without iron supplementation. Due to sample contamination, we excluded one sample of the DKO piglets in the duodenum and ileum, respectively. Besides, tissues including heart, liver, spleen, kidney, and colon and blood were collected. All specimens were quickly frozen in liquid nitrogen, and then stored at −80°C until used.The experimental process for generating escribed . All pigAll experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) at the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences. Animal experiments were performed in strict accordance with the demands of the animal care and management of research projects (IAS2018-12).Genomic DNA of gut bacterial was extracted with a DNA extraction kit (MP bio: 116560200). DNA concentration and integrity were analyzed by measuring ratio of absorbance at 260/280 nm and agarose gel electrophoresis. Sequencing of 16S rRNA was performed as previously described . The V3-http://qiime.org/index.html). Paired-end reads were merged to tags using the mothur pipeline  was carried out according to the standard protocols as our previous description (Metagenomic sequencing of colonic contents (DKO group: cription . Brieflyhttps://hub.nuaa.cf/aquaskyline/SOAPdenovo2), and the remaining reads were then assembled using Megahit v1.1.2 (http://blast.ncbi.nlm.nih.gov/Blast.cgi) to map the clean reads to the microbial reference genomes downloaded from the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov). The taxonomic relative abundance profile was calculated using HUMAnN v2.0 (https://huttenhower.sph.harvard.edu/humann2/).The clean reads were obtaind by filtering adapters and low-quality reads from raw reads using Trimmomatic v0.36 . Then, tt v1.1.2 . Predictt v1.1.2 . The nont v1.1.2 . The funt v1.1.2  and eggNt v1.1.2 , with deg at 4°C for 15 min, the supernatant was analyzed with untargeted LC-MS.The colonic contents for UPLC-QTOF-MS were prepared as described in a previous study . In shorP < 0.05 (t test for filtered data).The clean data were processed with orthogonal partial least-squares-discriminant analysis (OPLS-DA) in SIMCA-P 14.0 software , and the resulting score plots were used to visualize the model. Molecular structures of interest were defined as those with variable importance in the projection (VIP) >1 and Nonheme iron levels in the heart, liver, spleen, kidney, and colon were determined by a wet ashing procedure as previously described . Serum iDuring the necropsy, piglet heart, liver, spleen, and kidney tissues were collected. The tissues were fixed overnight according to the previously described . SubsequThe colonic mucosa and renal tissues were collected from WT and DKO piglets. The qPCR and Western blotting analyses were performed as detailed in a previous study . The 18St test and Mann-Whitney U test were used for assessing the significant difference between groups. For linear discriminant analysis effect size (LEfSe) analysis, a P value < 0.05 for the Kruskal–Wallis test and the linear discriminant analysis (LDA) score ≥ 4 were used to define the significance. Additionally, differential microbiota was defined as those with a corrected P < 0.05 with the Welch’s t test correction. Pearson's correlation coefficient was calculated in SPSS Statistics 22 . Heatmaps were generated using the ggplot2 package (https://www.rdocumentation.org/packages/ggplot2/versions/2.1.0) in R.All data were presented as the means ± standard error of the mean (SEM), with the exception of microbial community data. The Student's PRJNA893407 (16S rRNA sequencing) and PRJNA894240 (metagenomic sequencing).The raw sequencing data generated in this study has been deposited in the NCBI SRA database with the accession numbers