Leishmania parasites can cause zoonotic cutaneous leishmaniasis (CL) by circulating between humans, rodents, and sandflies in Iran. In this study, published data were collected from scientific sources such as Web of Science, Scopus, PubMed, Springer, ResearchGate, Wiley Online, Ovid, Ebsco, Cochrane Library, Google scholar, and SID. Keywords searched in the articles, theses, and abstracts from 1983 to 2021 were cutaneous leishmaniasis, epidemiology, reservoir, vector, climatic factors, identification, and Iran. This review revealed that CL was prevalent in the west of Iran, while the center and south of Iran were also involved in recent years. The lack of facilities in suburban regions was an aggravating factor in the human community. Some parts of southern Iran were prominent foci of CL due the presence of potential rodent hosts in these regions. Rhombomys opimus, Meriones lybicus, and Tatera indica were well-documented species for hosting the Leishmania species in Iran. Moreover, R. opimus has been found with a coinfection of Leishmania major and L. turanica from the northeast and center of Iran. Mashhad, Kerman, Yazd, and sometimes Shiraz and Tehran foci were distinct areas for L. tropica. Molecular identifications using genomic diagnosis of kDNA and ITS1 fragments of the parasite indicated that there is heterogeneity in leishmaniasis in different parts of the country. Although cutaneous leishmaniasis has been a predicament for the health system, it is relatively under control in Iran. Phlebotomus and Lutzomyia sandflies have been shown to transmit Leishmania promastigotes through skin bites in vertebrate hosts in the Old and New World, respectively. Leishmania major, L. infantum, L. tropica, L. aethiopica, and L. donovani have been demonstrated to be mainly responsible for transmission of cutaneous leishmaniasis (CL) in the Old World [ld World , 129.Around 12 countries in Asia, Africa, and Latin America such as Iran, Afghanistan, Pakistan, Saudi Arabia, Syria, Turkey, Algeria, Morocco, Tunisia, Colombia, Peru, and Brazil were reported as engaged states against CL . From MaPhlebotomus papatasi is the main vector reported there [Rhombomys opimus was illustrated as the principal host of zoonotic cutaneous leishmaniasis (ZCL) in the center and northeast of the country, Meriones libycus as the main vector in the south, Meriones hurrianae as the major host in the southeast, and Tatera indica as the most prevalent host in the west and south of Iran [Leishmania spp. among human populations as well as to determine methods used for identifying Leishmania parasites, reservoirs, and vector hosts and the impact of environmental factors in Iran over four decades (1983–2021) to help promote interventions against CL and to decrease the disease transmission rate.The annual incidence of the CL from 1983 to 2013 was 30.9 per 100,000, with spread in areas with dry and desert climates in particular in the central zone of Iran . These aed there . Rhombom of Iran , 83. StrLeishmania parasites in Iran. The authors used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standard guideline to carry out the review process and report findings.This systematic review has 5 sections including epidemiology of CL, reservoirs, vectors, climatic factors, and identification methods of Leishmania major, Leishmania tropica, Iran, rodent, P. papatasi, sandfly, detection, PCR, the kinetoplast, ITS, parasite, reservoir, vector, Gerbillinae, Giemsa stain, and temperature. The authors used these keywords alone or in combination through the Boolean method.In this study, all related published articles were studied from 1980 up to the end of 2021. The authors searched the databases including Web of Science, Scopus, PubMed, Springer, ResearchGate, Wiley Online, Ovid, Ebsco, Cochrane Library, Google Scholar, and SID for medical subject headings (MeSH) and relevant keywords such as cutaneous leishmaniasis, epidemiology, zoonotic cutaneous leishmaniasis (ZCL), anthroponotic cutaneous leishmaniasis (ACL), Leishmania parasite in Iran were included in the study. The authors excluded articles of low quality, studies conducted in other countries of the world, review studies, meta-analyses, case reports, or series of cases.All high-quality English and Persian language articles published in the world on the epidemiology of CL, reservoirs, vectors, climatic factors, and identification methods of We assessed the quality of the articles using the Strobe checklist . This checklist has 22 parts that are scored based on the importance of each section, the minimum score is 15 and the maximum is 33. In this study, the score 20 is considered acceptable .The search results were imported into Endnote software v.x8-1 and duplicate titles were deleted. The authors entered selected studies into an abstract reading phase and checked them against the inclusion criteria. Then, the most relevant studies were selected for independent full-text reading by two researchers. The authors used a checklist to extract data from the selected studies in terms of the study location, study years, reservoir species, parasite species, DNA, and recognized method.Searching databases, the authors extracted 786 studies. First, the articles were entered into Endnote software and after an initial review, 181 articles were removed from the study due to duplication. Then, by reviewing the titles and abstracts of articles, 459 irrelevant articles were removed and after reviewing the full text of articles, 40 articles were excluded since they investigated other pathogens. Finally, 106 articles met the inclusion criteria and entered the process of systematic review .Cutaneous leishmaniasis infection has been found in all 31 provinces of Iran.L. major and L. tropica in Iran. Fars is known as an endemic focus of CL and Nesokia indica or M. libycuserythrourus are confirmed as reservoir of ZCL [The prevalence of ZCL is high in Khorasan, Fars, and Isfahan and the highest frequency of cases has been reported from Ilam, Bushehr, and Yazd, recently. Ahvaz city in Khuzestan province and Kerman and Bam cities in Kerman province are the high endemic foci of ACL in Iran. Overall, the highest and the lowest incidence rates of leishmaniasis are reported from the western and northwest parts of the country, respectively. Two forms of ZCL and ACL occurred due to r of ZCL .Isfahan province has been a focus of CL in recent decades. Moreover, some new foci have been added to the endemic areas listed in this province. In this province, from 2011 to 2016, Isfahan, Ardestan, and Kashan cities had a high burden of CL. Kashan has been reported as the primary focus for CL in Isfahan province, while children were affected a long time ago , 143. InYazd province is another infected region adjacent to Isfahan province in central Iran. In this province, Khatam, Yazd, and Bafgh were reported as the most problematic counties of CL . Qom proIn Semnan province, Damghan is an important city in central Iran concerning CL, while villages in Damghan were reported as a vulnerable area for emergence of CL disease. Most of the cases in this region did not have a previous history of traveling to endemic areas . In TehrMany new CL cases are imported to different provinces of Iran from endemic areas of Pakistan and Afghanistan that are believed to be the primary source of outbreaks in some parts of the country, such as Yazd, Tehran, and Fars provinces . FurtherKhuzestan province is also recognized as a hub for CL in the southwest of Iran and as a newly discovered focus. In 2014, emergence of CL was reported in Ahvaz city . MoreoveIn Kerman province, a comprehensive study conducted by Sharifi et al. showed that Bam, Kerman, and Jiroft cities were the high-risk areas in the southeast of Iran . OrzoiehIn Khorasan Razavi province in northeast Iran, CL is prevalent in Torghabeh–Shandiz, Sarakhs, Daregaz, and Neyshabur cities and children in these areas are affected with the disease , 130. InIn the western part of Iran, different patterns of CL have been observed. Ilam province is adjacent to Khuzestan province and is a major endemic area . Ilam, DIn Kermanshah province, in the west of Iran, a review by Hamzavi and Khademi from 1991 to 2012 showed that the incidence rate had dramatically increased from 1.5 per 100,000 in the 1990s to 7.4 per 100,000 in 2011–2012. Also, half of the cases reported traveling to endemic areas. Qasr-e-Shirin, Eslam Abad, and Sarpol-e-zahab cities in the province are prominent foci of CL . HousewiLeishmania parasites. In the central and northeastern regions of Iran, Rhombomys opimus (the Great gerbil) is more predominant [Rhombomys opimus has two predominant subspecies. In Golestan province, in the northwest of Iran, R. opimus sodalist subspecies has been reported; but in Isfahan, Semnan, Esfarayen, and Shirvan (northeast) cities, R. opimus sargadensis was illustrated as the prevalent subspecies [R. opimus was present with co-infection of L. major and L. turanica [R. opimus [In Iran, several studies were carried out on host species of dominant , 118. Rhturanica . Also, i. opimus .L. major zymodeme MON-26 was detected in Meriones libycus. It is considered an alternative host in this district [L. major in Qomrood city as an endemic area [M. libycus is the main reservoir of ZCL [Meriones lybicus is an absolute host for Leishmania amastigote in the south of Iran and M. persicus is a reservoir in some parts of this region as well. Notably, Leishmania amastigotes were detected in M. persicus for the first time in Iranian Azerbaijan in the northwest of the country [M. libycus, T. indica and Gerbillus spp. were shown to be infected with L. major in Marvdash, Estahban, and Larestan cities [L. major has been isolated and detected by PCR assay in Mus musculus [Rattus norvegicus has been reported as a potential host for L. major using isoenzyme electrophoresis and nested PCR [R. rattus has been shown as a reservoir for L. major [In Isfahan city in central Iran, district , 149. Admic area . Perhapsr of ZCL . Merione country , 115–117n cities , 84. In musculus . Also insted PCR . In JahrL. major .Gerbillus nanus (Muridae) and M. hurrianae have been possible reservoirs of ZCL [M. hurrianae and T. indica were found to be important hosts for L. major [R. opimus was reported as a major reservoir for ZCL [T. indica was documented as a predominant reservoir for ZCL in rural areas [L. major transmission [Nesokia indica was recognized as a probable reservoir of ZCL [R. opimus, M. lybicus, and T. indica are generally well-documented species for the Leishmania parasite [In Hormozgan province, in the south of Iran, s of ZCL . In SistL. major . In Bahr for ZCL . In Mehral areas , 95; butsmission . In Isfar of ZCL , 108. Inparasite , 138  papatasi in all ZCL spots of Iran and was reported as the principal vector [Like studies about reservoirs, there are many published articles on  in 1930 . Phlebotebotomus . Naturall vector .Phlebotomus species are considered probable vectors: P. (Phl.) salehi has been reported in Jask, in Sistan and Baluchestan province in the south of Iran, P. (Syn.) ansarii in Isfahan, and Yazd provinces, P. caucasicus in Isfahan, Damghan, Sarbisheh, Zirkouh, Yazd, and Fasa cities, P. mongoliensis in Isfahan, Ilam, and Fasa cities, P. andrejevi in Isfahan, P. alexandri in Khuzestan province, Zirkouh, and Fasa cities, and P. (Phl.) bergeroti in Fars province. P. (Par.) sergenti was demonstrated to be responsible for ACL in Khorasan-e Razavi, Isfahan, Yazd, Kerman, Fars, and Tehran provinces and in Zirkouh as well [Other  as well , 150  can establish a different pattern of CL occurrence in two nearby cities such as Gilan-e-Gharb and Kermanshah in the west of Iran . TemperaOn the contrary, in the west of Iran, some peaks of CL incidence have been shown in winter, reported in January and February after an incubation period of the disease , 64. TheT. indica and P. papatasi due to agricultural activities [Rhombomys opimus (great gerbil) as a diurnal rodent spends 2/3 of its time outside of the burrow for feeding and this behavior can cause intensive close contact with vectors. Another factor is vegetation, which leads CL to be more prevalent in landscapes with low vegetation cover in the northeast of Iran [Some disasters such as an earthquake can change sleeping patterns and increase CL incidence because people have to stay outside in an open space. This model was presented in Dehloran city, in the west of Iran, after the occurrence of an earthquake in 2015 . Jagin Dtivities . Notably of Iran .Leishmania species are responsible for the spectrum of clinical manifestations or different epidemiologic features; thereby, specific detection of parasites is necessary to understand their complexity. Staining smears by Giemsa under microscope and cultivation were the common diagnostic tests for early detection for a long time. The sensitivity of parasitological diagnostic tests is less than 70%, which makes it necessary to have access to a large number of parasites in biopsy and experienced personnel. In contrast, the culture of promastigotes is a prolonged protocol and contamination is a concerning issue, but both are weak in the identification of Leishmania species [ species .Leishmania [Leishmania and this is a well-founded source for identifying Leishmania species. DNA fragments such as the small subunit rRNA (SSU rRNA) gene, repetitive sequences, microsatellite DNA, splice leader mini-exon (SLME), the beta-tubulin gene region, the gp63 gene locus, mini-exon-derived RNA genes, internal transcribed spacer (ITS) regions of the rRNA genes, and kinetoplast DNA (kDNA), SLME, kDNA, and ITS1 are used in PCR assays for the diagnosis of Leishmania parasites isolated from microscopic and culture samples. As a result, the kDNA PCR is the best diagnostic method in terms of sensitivity for L. major and L. tropica [Leishmania identification methods in human cases carried out in Iran are summarized in Serological tests are not used extensively in CL diagnosis as their specificity and sensitivity fluctuate and have cross-reaction with trypanosomatid parasites . The isoishmania . Molecul tropica , 81, 124Leishmania in Hamedan and Ilam (in the west) and South Khorasan provinces of Iran. In Iran, R. opimus, M. lybicus, and T. indica were well-documented species for hosting the Leishmania parasites. In Khorasan Razavi province, Kerman, Yazd, and sometimes in Shiraz and Tehran cities, L. major is present, but L. tropica is the predominant parasite.Our findings showed that cutaneous leishmaniasis has various patterns in Iran. There is heterogeneity in leishmaniasis in different parts of the country based on molecular identifications. KDNA and ITS1 fragments have mostly been used in genomic diagnostic procedures, while KDNA is a favorite targeted region for amplifying by PCR. Travel has been a hallmark of the distribution of R. opimus extends from Turkmen Sahra in the northeast to Isfahan in center of Iran with co-infection of L. major and L. turanica. More studies are required to clarify the potential role of L. turanica in CL. Furthermore, L. gerbilli has been identified in some vectors, such as P. papatasi, P. sergenti, P. caucasicus, P. mongoliensis, P. andrejevi, and P. ansarii. The relationship schema between vectors and reservoirs is complicated. Some sandflies play the role of common vectors concerning different parasite species. Although cutaneous leishmaniasis has been a predicament for the health system, it is generally controlled in Iran. Spraying, insecticide-treated nets (ITNs), and improvement in social services in the suburban community can reduce the problems associated with CL.The prevalence of


Salmonella enterica (S.) is one of the most common agents of foodborne infections and a risk for children, elder people and immunocompromised patients. S. is the first cause of foodborne outbreaks in the EU, the majority being caused by S. Enteritidis. We report S. serovars prevalence and trends in clinical isolates in central Italy from 2015 to 2021.S. strains of patients from Lazio and Tuscany regions isolated by hospitals and private laboratories were sent to the Regional Reference Centre for Pathogenic Enterobacteria (CREP) at IZSLT (Rome) for serotyping. All metadata and a selection of isolates were shared with ISS according to the National Surveillance Enter-Net Italia program.A total of 2395 strains were collected from 2015 to 2021, with a mean value of 342 strains per year. Notably, reported cases did not decrease during the pandemic in 2020. A total of 116 different serovars were identified. The most common ones were S. Typhimurium var. monophasic, which increased from 2015 to 2021, S. Enteritidis, which peaked in 2018 doubling its average, and S. Typhimurium with a reverse trend compared to the monophasic variant, followed by S. Infantis, S. Napoli and S. Derby. Afterwards, S. Brandenburg showed a constant increase (from 2 cases in 2015 to 18 cases in 2021). S. Strathcona showed a significant peak during 2019 with 23 cases, correlated to a European reported outbreak. The average age of patients was stable , except for an increase in 2021 .The results show a substantial agreement between data collected in central Italy and national trends. The occurrence of cases per year is stable. The serovars prevalence does not agree with the prevalence found in EU, in particular concerning S. Typhimurium var. monophasic frequency. Further investigations are needed to explain the significant increment of patients’ mean age in 2021.This kind of studies provide a detailed picture of the S. serotypes actually relevant to human health and could be used to improve and update food safety regulations and monitoring programs.Surveillance and digital reporting, even retrospective, are pivotal tool for long-term monitoring, outbreaks investigation and research.


Dirofilaria immitis is a vector-borne disease that affects canids and felids, both domestic and wild, throughout the world. It is a chronic disease which causes vascular damage in pulmonary arteries, and in advanced stages, the presence of pulmonary hypertension and right-sided congestive heart failure can be evidenced. Moreover, pulmonary thromboembolism is caused by the death of the worms, which can be lethal for the infected animal. Furthermore, it is the causative agent of human pulmonary dirofilariosis, being a zoonotic disease. The aim of this review was to update the current epidemiological situation of heartworm in Europe in dogs, cats, wild animals, and vectors insects, and to analyse the factors that may have contributed to the continuous spread of the disease in the last decade (2012–2021). In Europe, the disease has extended to eastern countries, being currently endemic in countries where previously only isolated or imported cases were reported. Furthermore, its prevalence has continued to increase in southern countries, traditionally endemic. This distribution trends and changes are influenced by several factors which are discussed in this review, such as the climate changes, presence of vectors in new areas, the appearance of new competent vector species in the continent, increased movement of pets that travelled to or originated from endemic countries, the urbanisation of rural areas leading to the formation of so-called “heat islands”, or the creation of extensive areas of irrigated crops. The continuous expansion of D. immitis must be monitored, and measures adapted to the situation of each country must be carried out for adequate control.Heartworm disease caused by  Dirofilaria immitis is the causal agent of heartworm disease in domestic and wild carnivores, mainly dogs and cats. In addition, it is the causative agent of human pulmonary dirofilariosis, being a zoonotic disease. Its distribution is cosmopolitan, and currently, D. immitis can be found almost anywhere in the world, from tropical to cold climates. It is a vector-borne disease, in which the mosquitoes of genera Culex spp.; Aedes spp.; Anopheles spp.; Coquillettidia spp. act as vectors. When mosquitoes ingest blood with microfilariae, these moult to infective third stage larvae (L3) in approximately 14 days, depending on external temperature conditions: the higher the outside temperature, the shorter the moulting period. When the mosquitoes feed again, L3 are inoculated into the definitive host to continue their biological cycle [al cycle .D. immitis, and it is characterised by the development of pulmonary endarteritis that chronically can lead to the development of pulmonary hypertension and right congestive heart failure, being a severe and potentially mortal condition . In many cases, it is possible to find adult worms in the heart of infected dogs, and cases with >50 adult worms and microfilaremia in the blood in hyperendemic areas are a common finding [Heartworm infection is a vascular and pulmonary disease. The dog is considered the main host of  finding . However finding . For thi finding . Infecte finding . For theD. immitis can cause benign pulmonary nodules that can be mistaken for malignant lung tumours [As heartworm is a zoonotic disease, the presence of this parasite in canid and felid reservoirs poses a health risk to humans through the bite of infected mosquitoes.  tumours ,7,8.In Europe, the epidemiological situation in the two main hosts is markedly different. The number of studies addressing the epidemiological situation in dogs is significantly higher than in cats due to, among other factors, the difficulty in diagnosis and the number of cats compared to the number of dogs that can be accessed . From 20Currently, the epidemiological status has continued to change, as has the number of epidemiological reports. Europe is facing new climatic conditions caused by climate change, or the presence of new competent vectors that increase the time of exposure to the parasite  and that widen the risk zones . This, along with other factors, has contributed to the establishment of vectors in regions where they were not previously found. Therefore, the objective of this manuscript is to review and analyse the current epidemiological situation in Europe during the last decade in dogs, cats, wild animals, and vectors to assess changes in the distribution of animal heartworm through a retrospective analysis of published epidemiological studiesIn the last 10 years, canine heartworm disease has continued to spread to eastern and north-eastern European countries, some of which are considered as new endemic countries. In other non-endemic countries, isolated cases or cases imported from endemic areas have continued to appear .Moreover, in the traditionally endemic countries of southern Europe, prevalence in dogs has continued to increase, except for some territories where the control measures adopted have achieved a decrease in prevalence.Spain and Portugal have traditionally been considered endemic countries, although not all regions of those countries have been studied . In PortD. immitis, respectively [In Spain, the studies have not been carried out uniformly throughout all territories, with several studies conducted in different regions in the past . Recentlectively ,20. Ad hectively . The canectively  and in tectively ,24. FinaD. immitis was detected at 58.9% [In Italy, epidemiological studies throughout the country on dogs have continued to be published. Previously, epidemiological reports indicated that prevalence in the north of the country was decreasing while in the centre and south of the country, it was increasing, and even regions with autochthonous cases were appearing in previously non-endemic areas ,26,27,28at 58.9% . Finallyat 58.9%  between In France, while until 2011 no indication of expansion from the south, traditionally endemic, was published, there are recent data showing presence of canine heartworm in the centre of the country, with prevalence rates between 29.41–35.2% ,43. In aD. immitis in the country [D. immitis and three with co-infections with D. repens [D. immitis from 1998 to 2018, which reported that the number of infections and co-infections with D. repens had increased significantly, especially from 2015 onwards [D. immitis in Austria [In Austria, where previously only isolated cases were reported, there are already several studies reporting the solid presence of  country . On the . repens . On the  onwards , althoug Austria .D. immitis antigens detection by ELISA. Another study reported a prevalence of 0.2% in the federal state of Brandenburg, in the centre of the country [D. immitis which had travelled to endemic countries . Finally, Maerz [Germany faces a similar scenario, since it has been observed an increase in the number of infections in dogs that have travelled or came from endemic countries, but also in dogs that presumably have not travelled outside the country. Vrhovec et al. , after t country . In addi country  reportedy, Maerz  reportedy, Maerz , so the D. immitis as isolated cases. In Ireland, for the first time, a clinical case has been published in a dog imported from the Canary Islands (Spain) [In the United Kingdom and the Netherlands, Genchi et al. , based o (Spain) .D. immitis has been published recently, demonstrating prevalence of 0.8% of microfilaremic dogs in Cahul and Chişinău [In Russia, there is a study reporting a new endemic area, the Voronezh Natural Reserve, with prevalence ranging from 8.2 to 12.2% , which aChişinău .D. immitis infections were seen by veterinarians in the Baltic countries, whereas most cases seen by veterinarians in the Nordic countries appeared to be imported [Based on the answers to a questionnaire, autochthonous cases of imported . Moreoveimported .D. immitis were detected in 14.8% of the dogs tested [D. immitis in dogs in these countries before 2011 [D. immitis is reported for the first time, showing an overall prevalence of 1.89% in the region of the Bosnia-Podriuje canton (southern country), the prevalence being 0.93% for owned dogs and 2.86% for stray dogs [Serbia and Croatia are considered endemic countries. In the latter, there are not reported increases in the prevalence, ranging from 0.46–0.6% in the published studies ,67. Howes tested , being tray dogs .D. immitis and D. repens co-infections. In the western and south-western regions, Giubega et al. [D. immitis circulating antigens were found in 8.2% of the dogs; later, samples were heated and prevalence increased to 26.8% [Romania and Bulgaria are endemic countries as well, showing increased prevalence in the last decade. In Romania, reported prevalence has been increasing in correlation with increased interest and publications on canine heartworm. Mircean et al.  and Ionia et al.  reporteda et al. . The higa et al. . In anotto 26.8% .In Bulgaria, recent studies reported prevalence between 4.8–34.7%, being higher in the central, eastern, and southern regions and lower in the western and south-western regions ,81,82. ID. immitis was present as isolated or imported in cases in the past [In Albania, the past , but recthe past , while rthe past . In Greethe past . The repthe past ,93. Athathe past  carried the past .D. immitis of 6.7 and 2.7%, respectively [D. immitis infections in dogs in the country, reporting prevalence of 0.5 and 4.3% [Turkey showed a similar trend, studies with prevalence of 1.5% being reported in Erzurum, north-eastern Turkey , 0.6% inectively . Finallyectively . In Cyprand 4.3% ,102, resD. repens in Příbram, in the Central Bohemian region, being an imported case of Hungary [D. immitis were based on isolated or imported cases [D. immitis in the country [D. repens [D. repens was reported in the western region, higher than in the eastern region, with no positive cases detected in the centre of the country [D. immitis infection were diagnosed in the Košice region of the southeast [D. repens (3.2%) [The Czech Republic was considered endemic 10 years ago, with reports of autochthonous cases and infected dogs coming from neighbouring endemic countries ,9. Howev Hungary . Until 1ed cases ,4,9. How country . Two stu. repens ,105. In . repens  reported. repens . In anot. repens . Finally country . In addioutheast . In Hungoutheast , the numoutheast ,112,113 s (3.2%) , while as (3.2%) . Széll es (3.2%)  detecteds (3.2%) .D. immitis in cats in traditionally endemic countries, such as Spain, Portugal, Italy, and Greece, while the first reports of feline heartworm have been published in some new endemic countries as well (Germany and Austria).The number of publications in feline heartworm is still substantially lower than in dogs. Some studies have reported D. immitis antibodies) and a prevalence of 0.5% (antigens) [D. immitis had already been evidenced. It is the case of Zaragoza, where the seroprevalence reported was 24.4% and the prevalence was 3.6% [D. immitis antigens [In Spain, a recent nationwide study reported an overall seroprevalence of 9.4% (anti-ntigens) . This stntigens) . This stwas 3.6% . In Madrwas 3.6% . In Barcantigens . Moreoveantigens , so the antigens  and 22.9antigens  demonstrantigens . FurtherIn Portugal, feline prevalence studies are relatively recent, but demonstrate the important presence of this parasite in the cats living in the country. The seroprevalence of samples collected in the centre of Portugal in 2009 and 2010 was 15% , the higD. repens were reported in southern Italy [In Italy, studies are still scarce. If previously, prevalence in cats was heterogeneous being 6.06–27% in the north and centre of the country, in addition to reports of isolated cases ,123,124,rn Italy  and isolrn Italy ,127,128.rn Italy .D. immitis infection is published in 2012 [Ae. abstrusus [In Germany, a retrospective study from 2012 to 2020 identified heartworm in three cats in 2012 . In Aust in 2012 . In Bulgbstrusus ; in Romabstrusus .D. immitis in otters in areas near the Danube River in eastern regions of the country. References [D. immitis infection in a grey wolf and in a red fox. Penezić et al. [Although the studies focused on wild animals have increased substantially, they are still scarce. Between 2001 and 2011, a few studies were carried out in Italy, Spain, Portugal, and Bulgaria in foxes, otters, exotic felids, wolves, and jackals [ferences ,135,136 ć et al.  and Potkć et al.  showed pć et al.  showed pD. immitis infection was also published in a ferret [D. immitis was identified in two of ninety-three examined red foxes [D. immitis was found in foxes, jackals, and raccoon dogs with a global prevalence of 23.4% in Krasnodar Krai, being the highest prevalence on the European continent in the last 20 years [D. immitis antigens between 3–44.4% in red foxes, and 4.4–58.7% in golden jackals; furthermore, isolated cases of infected red foxes and golden jackals were detected by necropsy [In Hungary, a prevalence of 3.7% in red foxes and 7.4% in golden jackals was reported . Moreovea ferret . In souted foxes . In Ported foxes . In Romaed foxes ,141. In 20 years . In Ital20 years . In Bulg20 years  reportednecropsy ,89,144.Cx. theileri in Portugal, Cx. pipiens in peninsular Spain, and Cx. theileri in the Canary Islands (Spain), Cx. pipiens, Ae. albopictus, Ae. caspius, An. maculipennis, and Cq. richiardii in Italy, and Cx. pipiens and Ae. vexans in Turkey [D. immitis have been identified. Moreover, the number of countries that have detected competent mosquitoes has also increased notably , Santarém, and Setúbal (centre-southern) [Cx. pipiens quinquefasciatus was identified as a competent vector in Algarve [Cx. pipiens and Cx. theileri as potential vectors was reported in two endemic regions of the Iberian Peninsula [Dirofilaria in different regions of the country, such as Cx. pipiens in north-eastern Italy [Ae. albopictus, Cq. richiardii, Cs. annulate, Cx. pipiens, and Och. caspius in southern Italy [Ae. albopictus in Pelagie Archipelago [Ae. koreicus, which was identified in the Province of Belluno, north-eastern Italy, and in Chiasso, in the Swiss–Italian border region [In Portugal, the presence of outhern) ,146. Sim Algarve . In Spaieninsula ,149. In rn Italy , Ae. albrn Italy ,151, andhipelago . Moreover region ,153.Cx. pipiens and Och. caspius were identified as vectors for D. immitis [D. immitis larvae were detected in Cx. pipiens/torrentium, [Cx. modestus, Cx. pipiens, and Och. caspius were identified as heartworm vectors; furthermore, Cq. richiardii was also identified in the country [Mosquito species able to transmit the disease were reported for the first time in France, Belarus, Hungary, Germany, Moldova, Romania, Serbia, Slovakia, and Russia: in Corsica (France),  immitis . In Germrentium, . In the  country ,157.D. immitis in three species of mosquitoes; the highest prevalence was detected in An. maculipennis, followed by Cq. richiardii and An. hyrcanus. Other authors have reported the presence of heartworm larvae in the thorax/head of Cx. pipiens as well [An. maculipennis, Cx. pipiens/torrentium, Aedes behningi, and Anopheles pseudopictus were identified as vectors for the transmission of D. immitis [D. immitis in animals, Cx. pipiens/torrentium was identified as a vector of heartworm disease [D. immitis larvae in Ae. vexans mosquitoes in eastern Slovakia [Cq. richiardii, Cx. pipiens pipiens, and Och. sticus in western Slovakia [Cx. pipens, Cq. richiardii, and Och. caspius [In Romania, in the Danube Delta Biosphere Reserve, Tomazatos et al.  reported as well . In Mold immitis . In Bela disease . In SlovSlovakia  and Cq. Slovakia . In Serb caspius .D. immitis larvae were evident in the thorax were: Ae. cantans, Ae. cataphylla, Ae. communis, Ae. intrudens, Ae. geniculatus, Ae. albopictus Cq. richiardii, Cx. modestus, and Cx. pipiens; larvae were found in the abdomen of Ae. cexans, Ae. cinereus, Ae. excrucians, and An. messeae. In addition, in the Tula region near Moscow, Belarus, and Ukraine, the prevalence of D. immitis in infected mosquitoes was 1.5%, and larvae were found in Ae. geniculatus, Ae. vexans, Cx. pipiens, and Och. Cantans [In Russia, Shaikevich et al.  estimate Cantans .The presence and activity of vectors in a given region is directly related to climatic conditions. This is evident in the time needed for the larvae to develop to L3, which is temperature-dependent , while below 14 °C, larvae development stops . In thisThe increase in temperatures favours the transmission of the disease to animals and humans by allowing the annual periods of activity of the mosquitoes to be lengthened and the stages of development of the larvae to be shortened.D. immitis from the south to the north of the country [This has led to the creation of more optimal conditions that allow the spread of mosquitoes to other areas where previously the climatic conditions prevented their presence ,166,167. country . In addi country ,162,163.In relation to some species of culicid mosquitoes that have been reported as vectors of the disease in some countries in the last 10 years or the replacement by other species and/or the absence of other species, this may be due to the absence of studies in some countries in the last decade and the small number of studies that have been carried out on the subject, among other factors. More studies are needed to know whether environmental conditions have influenced or are influencing this change.D. immitis have increased in countries where the presence of this parasite was already known, while heartworm cases have been confirmed in regions and countries that to date remained free of its presence. Therefore, during the last decade, it has been shown that the disease continues to spread to countries in the centre and northeast of Europe.After analysing the epidemiological and sero-epidemiological data of dogs, cats, and wildlife, as well as the presence of culicid vectors, studies show that the number of infected animals and vectors continues to increase. Thus, reports of D. immitis. Several epidemiological reviews carried out in this regard [D. immitis. Moreover, as previously indicated, the introduction of new competent vectors that quickly adapt to the environmental conditions of the European countries  is of great concern. There are other factors of a more markedly anthropogenic nature, such as the urbanisation of rural areas, especially in areas close to irrigated crops or near rivers, marshes, or lakes. Buildings and asphalt retain the heat of the day, leading to the formation of so-called “heat islands”, which favour the creation of microclimates that support the development of D. immitis larvae in mosquitoes even during the colder months [In addition to climate change, mentioned above, there are other factors that could also be directly influencing changes in the distribution and expansion of s regard ,4,9,168 r months . The lacGeospatial analysis, geographical information systems, remote sensing, global positioning systems, and virtual globes have been a breakthrough in the study of vector-borne diseases, since they have made it possible to create maps that show the risk of infection. Currently, the use of these tools has grown in importance in the knowledge of zoonotic diseases because it allows for the evaluation of the risk of transmission to the human host ,171,172.D. immitis generations in mosquitoes and determined that transmission was limited to the summer months, mainly July and August.In Europe, until 2011, some studies on heartworm disease have been carried out by using geospatial analysis and Geographical Information Systems (GIS) in Italy; specifically in Naples, in Campania , and in regions located in the centre of the country ,173,174.D. immitis infection will spread to areas previously considered as free of infection [Moreover, considering that larval development does not occur below the threshold temperature of around 14 °C, the number of heartworm generations was determined and a high risk of infection was estimated in southern Spain and Portugal, some areas of southern France, Corsica, Bosnia and Herzegovina, Turkey, Cyprus, the coast of Albania, several regions of northern and southern Italy, Naples, Sardinia and Sicily, Greece, some areas of Bulgaria, south-eastern Ukraine, and the region of Rostov-na-Donu in Russia. Furthermore, a very high risk of infection was determined in localised areas of southern Spain, two coastal areas of Sicily, southern Calabria, southern Turkey, Cyprus, and in islands of southern Greece. The region with the longest risk of infection was in Murcia (southern Spain), which ranged from March 21 to November 11. It was also suggested that if the current trend of climate change continues, nfection ,175,176.D. immitis transmission in the country, based on data of temperature, rainfall, and the location of irrigated crops, concluding that the highest risk of transmission was located in several areas from the centre, east, and south of the Iberian Peninsula, where moderate/high temperatures concurred with the presence of extensive irrigated crops; similarly, high risk of transmission was also found in some places of the Canary Islands and the Balearic Islands. On the other hand, the lowest risk of infection was predicted in the north of the country and in dry non-irrigated regions of the southeast [In the last decade, several randomised studies were carried out in Spain, Romania, and Hungary that helped to understand the risk of infection in these countries. In Spain, a GIS model was developed to predict the risk of outheast . Anotheroutheast .A study of similar characteristics was carried out in Romania , where aD. immitis was evidenced, confirming the suitability of the climate in certain German regions for the establishment of natural cycles of heartworm transmission [In Germany, a study was carried out using data on average daily temperatures recorded by weather stations close to the locations where the presence of vectors infected with smission , which eD. immitis, where the highest prevalence of infected animals was also found [In Hungary, a study carried out between 1995 and 2018 reported a rapid spread of the disease, based on the geo-referencing of infected animals  and the detection of anomalous annual temperature data compared to the four previous years. The authors concluded that two main factors were favouring this spread: the increase in temperatures as well as the increase and expansion in the population of golden jackals, the most important reservoir of heartworm in the fauna of the Mediterranean Balkans. In addition, results showed that the climate of the Great Hungarian Plain was the most suitable for the establishment of so found .D. immitis. Similarly, necessary control measures are necessary to prevent further expansion and/or contribute to the decrease in prevalence in endemic areas, which is especially important given the zoonotic nature of the infection. To this aim, specific guidelines adapted to each country would be necessary, as recommended by the European Society of Dirofilariosis and Angyostrongilosis.In the last 10 years, considering the epidemiological data, the possible factors influencing transmission, and forecasting studies using GIS models in different European territories, it can be stated that animal heartworm has continued to spread to eastern and north-eastern European countries, some of which are currently considered new endemic countries. In other countries, isolated cases or cases imported from endemic areas have continued to appear and have even increased. Moreover, in the traditionally endemic countries of southern Europe, prevalence and seroprevalence in animals have continued to increase, except for some territories where the control measures adopted have achieved a decrease in prevalence. So, at present, most of the European continent could be considered endemic. Therefore, it is necessary to continue to conduct epidemiological and sero-epidemiological studies in animals and vectors, both in endemic and non-endemic countries, in order to monitor distribution trends of


Renanthera (Orchidaceae), comprising about 19 species, has significant ornamental value as a houseplant, in floral design and in landscape gardens. Two species of Renanthera are categorized as endangered and critically endangered in China’s Red List and international trade in these orchids is currently strictly monitored by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). This paper reports on the de novo assembled and annotated plastome of four species of Renanthera; R. citrina, R. coccinea, R. imschootiana, and R. philippinensis. The length of the plastome sequences ranged from 144,673 bp (R. imschootiana) to 149,007 bp (R. coccinea) with GC content of 36.6–36.7%. The plastomes showed a typical quadripartite structure, including a large single-copy , a small single-copy , and a pair of inverted repeats  regions. Of the 120 genes detected, 74 were protein coding, 38 were tRNA, and eight were rRNA genes. The plastome of Renanthera is rather conserved, but nucleotide variations that could distinguish them apart are noticeable—the total number of tandem repeats ranged from 62 (in R. imschootiana) to 74 (in R. citrina); while the number of long repeats ranged from 21 (in R. imschootiana and R. philippinensis) to 43 (in R. citrina). Three hypervariable regions  were identified. Phylogenetic analyses based on the CDS using maximum likelihood (ML) and Bayesian inference (BI) revealed that Renanthera is closely related to Holcoglossum, Neofinetia, Pendulorchis, and Vanda. The relationship between the four species of Renanthera was fully resolved; a monophyletic clade was formed and R. coccinea was recorded as the first to diverge from the rest. The genetic data obtained from this study could serve as a useful resource for species identification in Renanthera as well as contribute to future research on the phylogenomics of Orchidaceae.Owing to its attractive flower shape and color,  Renanthera Lour.  comprises about 19 species of epiphytic and/or lithophytic perennial herbs, mainly distributed in China, India, Indonesia, Malaysia, New Guinea, Philippines Vietnam, and the Solomon Islands  since 1975  and the nuclear ribosomal internal transcribed spacer (ITS) region reportedly resolved the phylogenetic relationship within Renanthera  technology, sequencing of the complete organellar genome has become simpler and affordable. The use of genome-scale data to resolve relationships in complicated plant groups appears promising. Coupled with advanced bioinformatic analytic tools, the phylogenetic relationships of many members of Orchidaceae, which has been uncertain for many years, were resolved . The comR. citrina, R. coccinea, and R. imschootiana, as well as a domesticated species, R. philippinensis, were included in this study. To provide genetic data at genome level, we sequenced and characterized the complete plastomes of four species of Renanthera. At the same time, we analyzed the sequence repeats, the contraction and expansion of the inverted repeat (IR) region, identified the variable sites, and revealed the phylogenetic relationships between the four species of Renanthera to further elucidate evolutionary patterns in Renanthera at the plastome level. The findings obtained through this study yielded useful genomic information on these threatened species that may aid in conservation strategies.As an understudied yet threatened species in China, three species that are native to China, including Renanthera species, including R. citrina, R. coccinea, R. imschootiana, and R. philippinensis, were collected from natural populations, except for R. philippinensis which is a cultivated species from an orchid nursery in Guangdong. The orchids are transferred and planted in the greenhouse of Yunnan Normal University, Kunming, China . Voucher specimens are deposited in the Herbarium of Yunnan Normal University (YNUB) (de novo assembly. Gene annotation was carried out using GeSeq (Four y (YNUB) . Total gy (YNUB)  to removy (YNUB)  pipelineng GeSeq  based onng GeSeq . The cirng GeSeq .Simple sequence repeats (SSRs) were calculated using MISA-web , in whicThe nucleotide sequence of each protein-coding gene in the plastome was extracted using Geneious Prime v2020.0.3 . The relRenanthera were compared on the mVISTA tool (Renanthera coccinea as a reference. To detect the expansion and contraction of the inverted repeat (IR) region, the boundaries between the IR and single-copy regions (IR/SC) of the plastomes were visualized using IRscope , Cattleya crispata , and Cymbidium ensifolium  were included as outgroups. Phylogenetic trees were constructed using two methods: maximum likelihood (ML) and Bayesian inference (BI). For ML, the concatenated dataset was fed into the RAxML v8.2.11 pipeline  to 149,007 bp (R. coccinea) (R. imschootiana) to 25,715 bp (R. coccinea); while the SSC and the LSC regions were 11,468 bp (R. imschootiana) to 12,167 bp (R. philippinensis) and 84,241 bp (R. imschootiana) to 86,404 bp (R. philippinensis), respectively. The overall GC contents for all the plastomes were 36.7%, except for R. citrina, which was 36.6%. All four plastomes were predicted with the same number of genes, which was 120. The genes include 74 CDS, 38 rRNA, and eight tRNA genes. Among these genes, 17 were duplicates in the IR regions, including five CDS , eight tRNA , and four rRNA  genes (The plastomes of the four species of occinea) . The plaoccinea) . For the3) genes .R. citrina, R. coccinea, R. imschootiana and R. philippinensis, respectively , 53 (71.62%), 48 (70.59%), and 47 (75.81%) were recorded in R. citrina, R. coccinea, R. imschootiana and R. philippinensis, respectively.SSRs analysis detected 74, 68, 62, and 68 SSRs in ectively . Among tR. imschootiana and R. philippinensis) to 43 (R. citrina). All the plastomes contained three types of long repeats that were forward, reverse, and palindromic, except for R. citrina. For R. citrina, aside from the three long repeat types, it also contained a complement type. All species, except for R. citrina, had long repeats mostly in the 30–40 bp range; the long repeat of R. citrina was 41–50 bp. For long repeats above 50 bp, R. citrina and R. imschootiana had eight and one, respectively; none was in R. coccinea and R. philippinensis.The number of long repeats detected in the four plastomes ranged from 21 , genes rps19 and psbA were adjacent to the junction. The rpl32 and trnN genes were adjacent to the junction between SSC and IRb (JSB); while the trnN and ycf1 genes were adjacent to the junction between SSC and IRa (JSA). The ycf1 gene of R. citrina crossed over JSA, into the IRa region; while the ycf1 genes for the other three species were still intact within the SSC region.The positions of the IR junctions were well-conserved across the four species of nanthera . At the Renanthera exhibit a high degree of similarity. Three genes, ndhJ, ndhK, and ndhC, were obviously lost in R. citrina and R. imschootiana. Additionally, a distinct large gap was observed from the rps7 gene to trnL-CAA in the IRa and IRb of R. citrina, R. imschootiana and R. philippinensis. The nucleotide diversity (Pi) value for the four plastomes ranged from 0 to 0.04533, with an average of 0.0086. At a cut-off point set at Pi ≥ 0.035, three hypervariable regions, including psbI-trnS-GCU, trnG-GCC, rpl32, were identified , followed by 360 and 203 of them in the SSC and IR regions, respectively.Plastome alignment of the four species of Renanthera was well-resolved ; R. coccinea clustered with R. philippinensis and R. citrina clustered with R. imschootiana. Renanthera is closely-related to the Holcoglossum + Neofinetia + Pendulorchis + Vanda clade. In general, all the branch nodes in the phylogenetic tree were strongly supported in the BI analysis; while seven branch nodes were indicated with moderate bootstrap support values in the ML analysis.Phylogenetic analyses using both ML and BI methods based on the CDS sequences of 30 species of Orchidaceae revealed similar topological structure in the phylogenetic trees; hence, only the ML tree is presented, with the values of the bootstrap support (BS) as well as the posterior probability (PP) of the ML and BI trees indicated on each branch node . In geneRenanthera using next-generation sequencing techniques. Although there is a published report on the plastome sequence of R. imschootiana  and Uncifera acuminata . The species of Renanthera with the smallest plastome size, R. imschootiana, had a relatively smaller plastome size when compared to most of the members of Aeridinae; the three other species of Renanthera had a plastome size similar to most of the members of Aeridinae. The highly conserved number of genes present in the plastome of Renanthera suggested that the difference in plastome size could be due to the presence of indels in the intergenic spacer regions. However, similar to the rest of the members of Aeridinae, the overall GC content of Renanthera was not more than 37%.In this study, we obtained novel plastome sequences of four species of hootiana , informa species . Based ondh genes experienced independent loss in species of Renanthera  region, follow by SSC (19.4%) and IR (7.4%) region. Therefore, the distribution of SSRs in the chloroplast genome of Renanthera is uneven. The SSRs numbers in the LSC region of plastome of R. coccinea and R. philippinensis are 53 and 47, respectively. The distribution of SSR in the four species of Renanthera was consistent with that of most angiosperms  is the most frequently used, cysteine  is the least universal amino acid in the cp genomes of these species. Alternatively, the high leucine frequency in the four plastomes can be attributed to the fact that leucine is largely needed in the chloroplasts due to its important function in photosynthesis-related metabolism (Thuniopsis and Polystachya. (The codon usage among the four plastomes of ancestor . Among ttabolism . Cysteintabolism . This isstachya. .Renanthera. This was true when the 3’ end of the rpl22 gene of all four species of Renanthera that should be intact in the LSC region extended into the IRb region for 31–32 bp. The ycf1 gene in the SSC region of R. citrina was observed crossing over JSA, extending into the IRa region; while ycf1 of the remaining three species of Renanthera were intact in the SSC region. Generally, angiosperms retain the structure and size of the cp genome (Renanthera, although with the limited sampling size reported in this study, the finding suggested that the IR boundary shifts in Renanthera can be considered to be relatively minor, as this can be related to the small difference observed in the genome size across different species.Contraction and expansion of the IR region is usually observed in most angiosperms , includip genome . Howeverp genome  allowingp genome . Yet, inRenanthera. We speculated that this is either due to difficulty in species sampling, or the genus has not received attention. As molecular genetic tools could aid in the identification of the species of orchids and forensic detection at border controls (Renanthera will definitely contribute to safeguarding these orchids by security agencies. Molecular genetic tools such as DNA barcoding are among the promising techniques in the identification of species of land plants (Renanthera. It is noteworthy that an increased sampling size is needed to obtain reliable results (Renanthera.Sequence divergence analysis conducted on the four plastomes revealed that the IR region was highly conserved when compared to the single-copy regions. To our knowledge, there are no published reports on the molecular identification of the species of controls , the ided plants . The gend plants . Three h results . The finRenanthera. According to the phylogenetic analysis using short gene sequences, Renanthera is monophyletic (Aerides clade, where Renanthera is placed, was sister to Vanda clade when using the complete plastome sequence dataset. This finding was incongruent with the results by Aerides clade was represented by only one genus, Renanthera. Despite the fact that the phylogenetic analysis was well-resolved based on the plastome dataset, the finding by Aerides clade in Aeridinae could be more reliable due to greater taxon sampling size. Thus, to ascertain the molecular placement of the Aerides clade in Aeridinae at the plastome level, it is suggested to include plastome data for several taxa of the Aerides clade such as species of Aerides, Arachnis, and Rhynchostylis.Despite being a valuable and threatened orchid, there are no reports on the phylogenetic relationships in phyletic . HoweverRenanthera is well-conserved across four species, R. citrina, R. coccinea, R. imschootiana, and R. philippinensis. Phylogenetic analysis based on the CDS sequences revealed that Renanthera is closely related to Holcoglossum, Neofinetia, Pendulorchis, and Vanda and the relationship of the four species of Renanthera was monophyletic. The genomic data obtained from this study will provide a useful resource for conservation, as well as to contributing in studies on the phylogeny and evolution of Renanthera and Orchidaceae.Our study revealed that the plastome of


Author Contribution section. The correct spelling is AG. A correction has therefore been made to the Author Contribution statement. It previously stated:In the published article, there was an error in the author list, and author Tomasz Frączyk was erroneously excluded. Furthermore, an author’s initials were incorrectly written as AD in the “This project was conceived and designed by EIP, DG-O, MG and WB. GO supervised the work. All of the experimental work was performed by EIP, DG-O, MK, AB, and DP. Computational analyses were performed by SM, AD, and JP. The manuscript was written and prepared by EIP, DG-O and WB”.The corrected statement appears below:“This project was conceived and designed by EIP, DG-O, MG, and WB. GO supervised the work. All of the experimental work was performed by EIP, DG-O, MK, AB, and DP. Selection of some FLG model peptides  at the initial stage of the project was performed by TF. Computational analyses were performed by SM, AG, and JP. The manuscript was written and prepared by EIP, DG-O, and WB”.The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


Epimedium muhuangense S. Z. He & Y. Y. Wang 2017, one of the rare unifoliolate species in the Epimedium genus of Berberidaceae, is distributed in the Guizhou province of China. In present research, we sequenced the complete chloroplast genome of E. muhuangense with Illumina sequencing technology. The whole genome was 157,264 bp in length, which consisted of a large single-copy region , a small single-copy region , and a pair of inverted repeat regions . A total of 112 unique genes were successfully annotated, consisting of 78 protein-encoding genes, 30 rRNA, and four tRNA. Phylogenetic analysis demonstrated that E. muhuangense is closely related to E. elachyphyllum. Epimedium L. is the largest herbaceous genus of Berberidaceae and contains about 62 species. China is the distribution and diversity center of Epimedium, in which there are about 52 species with continued evolution . A specimen was deposited at the Herbarium of the Institute of Wuhan Botanical Garden, Chinese Academy of Medical Science  under the voucher number Yanjun Zhang 568. The genomic DNA was extracted from fresh leaves using the modified CTAB method (Doyle and Doyle E. acuminatum chloroplast genome (GenBank accession number: NC_029941) as a reference. The gene annotation was performed by online programs Geseq .In this study, E. muhuangense was 157,264 bp in length, consisting of a small single copy region , a large single copy region , and two inverted repeat regions . The total GC content of chloroplast genomes was 38.77%, while the content of GC in LSC, IR, and SSC regions was 37.37%, 43.17%, and 32.77%, respectively. The chloroplast genome of E. muhuangense contains 112 unique genes, including 78 protein-coding genes, 30 tRNA genes, and four rRNA genes. Among these genes, 15 genes (six tRNA genes and nine protein-coding genes) contained an intron, and three genes contained a pair of introns. In addition, five protein-coding genes , eight tRNA genes  and four rRNA genes  were duplicated to have double copies in the chloroplast genome.The chloroplast genome of E. muhuangense, the complete chloroplast genome sequences of 15 plant species were downloaded from the NCBI GenBank database. Sequences were aligned using MAFFT v.7  C. Morren & Decne as outgroup (E. muhuangense is closely related to E. elachyphyllum, both of which are distributed in northeastern Guizhou. Furthermore, E. muhuangense and E. elachyphyllum are the only two species only having unifoliolate leaves in Epimedium (Zhang YJ et al. E. muhuangense will contribute to the research of phylogeny and evolution of Berberidaceae.To explore the phylogenetic position of outgroup . The resJing Wang and Ruoqi Huang designed and performed the experiments; Jing Wang also analyzed the data and drafted the manuscript; Qiong Liang and Yanjun Zhang revised critically for intellectual content and approved the final version of the paper; and all authors agree to be accountable for all aspects of the work.The plant materials used in this study were transplanted into Wuhan Botanical Garden for cultivation through legal collection way. And the study was approved by the Wuhan Botanical Garden of the Chinese Academy of Sciences.


To the editor,Large variation in response to inhaled corticosteroids (ICS) has been reported in both asthma and chronic obstructive pulmonary disease (COPD), which may partly be explained by genetic factors. The transcriptome of the airways changes following ICS treatment,A strong and consistent response of the airways to ICS in both asthma and COPD patientsp value: 4.21 × 10−06). Of these 76 CpG sites, 24 were associated with 24 gene transcripts  study Table .1 Longts Table . The mosn = 1515) or non‐European descent (n = 1702)  with lung function measurements over 3 years,Subsequently, we investigated whether the identified meQTL were associated with asthma or COPD exacerbations despite ICS use in children with asthma and adult COPD patients, respectively. The asthma analysis was conducted performing a meta‐analysis in eight cohort studies from the Pharmacogenomics in Childhood Asthma (PiCA) consortiumIn conclusion, corticosteroid inducible meQTL analysis enabled us to identify a set of functional SNPs that may be useful for future  studies. Although our results indicate that overlap in genetic response to steroids may exist, comparing the epigenetic responses in adult COPD patients to clinical effects in children with asthma, we do acknowledge that additional, disease and age‐specific effects may be present. However, these SNPs were not significantly associated with exacerbations and OCS courses in children nor with the slope of FEV1 in adults with COPD.Elise M. A. Slob contributed to the statistical analysis and interpretation of the data, design of tables and figures, writing of the original draft and review and editing. Alen Faiz contributed to the conception and design, statistical analysis and interpretation of data, design of tables and figures, writing of the original draft and review and editing. Jos van Nijnatten contributed to the statistical analysis and design of the tables and figures and review and editing. Susanne J. H. Vijverberg contributed to the subject recruitment, data collection and review and editing. Maria Pino‐Yanes contributed to the subject recruitment, data collection and review and editing. Esteban G. Burchard contributed to the subject recruitment, data collection and review and editing. Uroš Potočnik contributed to the subject recruitment, data collection and review and editing. Colin Palmer contributed to the subject recruitment, data collection and review and editing. Steve Turner contributed to the subject recruitment, data collection and review and editing. Katia Verhamme contributed to the subject recruitment, data collection and review and editing. Somnath Mukhopadhyay contributed to the subject recruitment, data collection and review and editing. Leila Karimi contributed to the subject recruitment, statistical analysis and interpretation of the data, data collection and review and editing. Fook Tim Chew contributed to the subject recruitment, data collection and review and editing. Wim Timens contributed to the subject recruitment, data collection and review and editing. PH contributed to the subject recruitment, data collection and review and editing. Mariëlle W. Pijnenburg contributed to the interpretation of the data and review and editing. Maarten van den Berge contributed to the subject recruitment, conception and design, data collection and review and editing. Hananeh Aliee contributed to the subject recruitment, data collection and review and editing. Vojko Berce contributed to the subject recruitment, data collection and review and editing. Corry A. Brandsma contributed to the subject recruitment, data collection and review and editing. Margaret Neighbors contributed to the subject recruitment, data collection and review and editing. Michele A. Grimbaldeston contributed to the subject recruitment, statistical analysis and data interpretation, data collection and review and editing. Gaik W. Tew contributed to the subject recruitment, data collection and review and editing. Antonio Espuela‐Ortiz contributed to the statistical analysis and interpretation of the data and review and editing. Yang Yie Sio contributed to the statistical analysis and interpretation of the data and review and editing. Javier Perez‐Garcia contributed to the statistical analysis and interpretation of the data and review and editing. Merve Kutlu contributed to the statistical analysis and interpretation of the data and review and editing. Don D. Sin contributed to the subject recruitment, data collection and review and editing. Xuan Li contributed to the subject recruitment, data collection and review and editing. Anke H. Maitland contributed to the subject recruitment, data collection and review and editing. Cyrielle Maroteau contributed to the statistical analysis and interpretation of the data and review and editing. Gerard H. Koppelman contributed to the conception and design, statistical analysis and interpretation of data, writing of the original draft, review and editing and is the principle investigator. All authors approved the final version of the manuscript.E.M.A. Slob, A. Faiz, S.J.H. Vijverberg, H. Aliee, J. van Nijnatten, P. Hiemstra, C. Maroteau, C. Palmer, M. Kutlu, S. Turner, S. Mukhopadhyay, M. Gorenjak, C.A. Brandsma, L. Karimi, Y.Y. Sio, C. Longo and M.W. Pijnenburg have nothing to disclose. F.T. Chew reports grants from Singapore Ministry of Education Academic Research Fund, Singapore Immunology Network, National Medical Research Council (NMRC) (Singapore), Biomedical Research Council (BMRC) (Singapore), and the Agency for Science Technology and Research (A*STAR) (Singapore), during the conduct of the study; and consulting fees from Sime Darby Technology Centre; First Resources Ltd; Genting Plantation, and Olam International, outside the submitted work. M. Pino‐Yanes reports grants from the Spanish Ministry of Science, Innovation, and Universities, the State Research Agency, and the European Regional Development Fund from the European Union . E. Herrera‐Luis and J. Perez‐Garcia report a fellowships from MICIU. A. Espuela‐Ortiz declares a fellowship granted by MICIU/ULL. E.G. Burchard reports grants from the National Institutes of Health, the Tobacco‐Related Disease Research Program, the Sandler Family Foundation, the American Asthma Foundation, the Amos Medical Faculty Development Program from the Robert Wood Johnson Foundation, and from the Harry Wm. and Diana V. Hind Distinguished Professorship in Pharmaceutical Sciences II. W. Timens reports fees to the UMCG from Roche Diagnostics/Ventana, personal fees from Merck Sharp Dohme, Bristol‐Myers‐Squibb and AbbVie, outside the submitted work. G.H. Koppelman reports grants from Lung Foundation of the Netherlands, UBBO EMMIUS Foundation, TETRI Foundation, TEVA the Netherlands, Vertex, GSK, European Union (H2020 Prominent Grant), outside the submitted work; and he has acted on advisory board meetings of GSK and Pure IMS, outside the submitted work. A.H. Maitland‐van der Zee has received research grants outside the submitted work from GSK, Boehringer Íngelheim and Vertex, she is the PI of a P4O2 (Precision Medicine for more Oxygen) public private partnership sponsored by Health Holland involving many private partners that contribute in cash and/or in kind , and she has served in advisory boards for AstraZeneca, GSK and Boehringer Ingelheim with money paid to her institution. Dr. van den Berge reports grants to University from GlaxoSmithKline, Novartis, Genentech, outside the submitted work. Dr. Potočnik reports grants from Slovenian Research Agency, grants from Ministry of Education, Science and Sport Slovenia (MIZS), during the conduct of the study. M. Grimbaldeston, G.W. Tew and M. Neighbors are employees of Genentech Inc., a Member of the Roche Group. K. Verhamme works for a research department which receives/received unconditional research grants from Yamanouchi, Pfizer‐Boehringer Ingelheim, Novartis, GSK, Chieisi, Amgen, UCB, none of which are related to the content of this work. F.J. Theis acknowledges support by the BMBF  and by the Helmholtz Association's Initiative and Networking Fund through Helmholtz AI [grant number: ZT‐I‐PF‐5‐01] and sparse2big [grant number ZT‐I‐007]. V. Berce reports grants from Slovenian Research Agency, grants from Ministry of Education, Science and Sport Slovenia (MIZS), during the conduct of the study.The Netherlands Organisation for Scientific Research; the Dutch Asthma Foundation; GlaxoSmithKline, the University Medical Center Groningen; Leiden University Medical Center. Genentech Inc.; Lung Foundation Netherlands, Grant/Award Number: number 5.1.16.094; GlaxoSmithKline and Utrecht Institute for Pharmaceutical Sciences; Slovenian Research Agency, Grant/Award Number: P3‐0067; SysPharmPedia grant; the Ministry of Education, Science and Sport Slovenia (MIZS), Grant/Award Number: C3330‐16‐500106; Sandler Family Foundation; The American Asthma Foundation, the RWJF Amos Medical Faculty Development Program; The National Heart, Lung, and Blood Institute of the National Institutes of Health, Grant/Award Number: R01HL117004, R01HL128439, R01HL135156, X01HL134589, R01HL141992, R01HL141845; National Institute of Health and Environmental Health Sciences, Grant/Award Number: R01ES015794, R21ES24844; The National Institute on Minority Health and Health Disparities, Grant/Award Number: P60MD006902, RL5GM118984, R01MD010443, and R56MD013312; the Tobacco‐Related Disease Research Program, Grant/Award Number: 24RT‐0025, 27IR‐0030; National Human Genome Research Institute, Grant/Award Number: U01HG009080; ISCIII and ERDF; Ramón y Cajal Program by the Spanish Ministry of Science, Innovation, and Universities, Grant/Award Number: RYC‐2015‐17205; MICIU, FPU19/02175; The State Research Agency; The European Union, Grant/Award Number: MICIU/AEI/FEDER, UE, SAF2017‐83417R; MICIU fellowship, Grant/Award Number: PRE2018‐083837; MICIU/ULL (M‐ULL); Slovenian Research Agency, Grant/Award Number: P3‐0067; SysPharmPedia, The Ministry of Education, Science and Sport Slovenia, Award Number: C3330‐16‐500106; Roche Diagnostics GmbH and Cellarity Inc.Supporting Information S1Click here for additional data file.


H. sapiens, M. musculus, and A. thaliana. First, the powerful SHAP method was used to select the optimal feature subset from seven different kinds of sequence-based features. Second, different machine learning algorithms were used to train the models. The results of five-fold cross-validation indicate that the model based on XGBoost achieved the highest prediction accuracy. Finally, our model was compared with other state-of-the-art models, which indicates that m5Cpred-XS is superior to other methods. Moreover, we deployed the model on a web server that can be accessed through http://m5cpred-xs.zhulab.org.cn/, and m5Cpred-XS is expected to be a useful tool for studying m5C sites.As one of the most important post-transcriptional modifications of RNA, 5-cytosine-methylation (m5C) is reported to closely relate to many chemical reactions and biological functions in cells. Recently, several computational methods have been proposed for identifying m5C sites. However, the accuracy and efficiency are still not satisfactory. In this study, we proposed a new method, m5Cpred-XS, for predicting m5C sites of  RNA modification plays pivotal roles in many biological processes . Until nSeveral experimental methods have been developed to identify m5C sites, including miCLIP-seq , Aza-IP-The computational methods are mainly classified into two categories: random forest (RF)-based models and support vector machine (SVM)-based models. Based on RF, As mentioned above, different kinds of features have been generated for predicting m5C sites, and the dimension of these features can be very high; however, not all the features are relevant for building machine learning models. Moreover, the features with ultrahigh dimensions also pose a great challenge to computer performance . SelectiRecently, different feature selection methods have been used in developing models for predicting the RNA modification sites. http://m5cpred-xs.zhulab.org.cn/) was deployed for the users.In this study, we established a new method to predict m5C sites by using XGBoost based on features selected by SHAP. We named this method m5Cpred_XS, which can be used to predict m5C sites in multiple species. Extensive experiments demonstrated that the proposed predictor, m5Cpred_XS, outperformed other existing prediction methods. Finally, a web server  A benchmark data set was collected. The benchmark data set was divided into the training set and the independent test set. II) The features were extracted from RNA sequences. III) The SHAP-based feature selection was carried out to select the optimal feature subset. IV) The XGBoost was used to train the model. V) The comparison and analysis of different models was conducted. VI) A web server for predicting m5C sites was developed for the community. The overall flow chart of our study is shown in H. sapiens, M. musculus, and A. thaliana. As shown in H. sapiens, M. musculus, and A. thaliana were collected from the work of For fair comparison, we used the same data sets as in A. thaliana, 1000 positive and 1000 negative samples were randomly selected from the data set as the independent test data set, and the remaining 5298 positive and 5298 negative samples were selected as the training data set. Similarly, 1000 positive and 1000 negative samples from M. musculus’ benchmark data set were selected as the independent test set, and the remaining 4563 positive and 4563 negative samples were selected as the training data set. For H. sapiens, 69 positive and 69 negative samples were randomly selected as the independent test set, and the remaining 200 positive and 200 negative samples were selected as the training data set. The specific partitioning of the data sets is shown in To build and evaluate the models, the benchmark data sets were divided into two parts: the training data sets and the independent test sets. The training data sets were used for the model construction, cross-validation, and the determination of the hyperparameters of machine learning algorithms, whereas the independent test sets were used for testing the prediction performance and generalization ability of the models. For For each RNA segment, it can be expressed in the following form:In this formula,  studies  show thaENAC encoding  is used In this formula, The CKSNAP feature encoding scheme  is basedANF, also known as nucleotide density (ND), fully considers the distribution and nucleotide frequency information of each nucleotide in the RNA sequence . The denAdenine (A), guanine (G), cytosine (C), and uracil (U) are the four types of nucleotides in RNA, each of which has unique chemical properties and physical structure. According to different chemical properties, these four nucleotides can be divided into three categories . The detBased on the three types of chemical properties, A, C, U, and G can be expressed as , , , and , respectively. The feature dimension generated by NCP is 123.The method of using a four-dimensional binary vector to encode the nucleotide is called the binary encoding scheme  by whichThe expression of SCPseDNC  coding iHere, FastText is a natural language model released by Facebook . By consFeature selection is an important step in building effective machine learning models when high-dimensional features were generated. In this study, three different feature selection methods were employed to select the optimal feature subsets. As one of the frameworks for explaining the prediction model, the SHAP algorithm was proposed to characterize feature importance and assess feature behavior . The conThe F-score  is anothThe third feature selection method used in this study is maximum relevance minimum redundancy (mRMR), which was developed by Peng et al. . In thisThe XGBoost was a distributed gradient enhancement library that was widely used in classification scenarios . It has Cross-validation is often used to evaluate the performance and generalization ability of machine learning models. In this paper, five-fold cross-validation was used to evaluate the models, and the random sampling method was used to divide the training data set into five subsets with very close data volume . In eachDifferent evaluation metrics are used in bioinformatics classification. In this study, we selected the accuracy (Acc), sensitivity (Sen), specificity (Spe), precision (Pre), Matthews correlation coefficient (Mcc), and F1-score as the main evaluation metrics . Counts In addition to the above evaluation indicators, the precision recall curve (PRC curve)  and recen features. The highest AUROCs were obtained when the top 48, 228, and 208 features were used for H. sapiens, M. musculus, and A. thaliana, respectively. The corresponding AUROC values are 0.935, 0.834, and 0.787, for the three species, respectively.Seven kinds of features were generated from the RNA segments of the three species of which the dimension is 808 in total. Considering the redundancy between the features, SHAP was used to select the optimal feature subsets by which the scores of importance of the 808-dimensional features were calculated based on XGBoost ensemble algorithm. In addition, n features selected by these two methods are also plotted in Besides this, another two kinds of feature-selection methods, F-score  and mRMRTo verify the effectiveness of the XGBoost algorithm in m5C site prediction, two other learning algorithms, random forests  and suppA. thaliana, the AUROC value of the model based on XGBoost is 0.787, which is higher than the models based on RF (0.780) and SVM (0.768). For M. musculus, the AUROC value of the model based on XGBoost is 0.834, which is also higher than the models based on RF (0.795) and SVM (0.824). For H. sapiens, the AUROC value of the model based on XGBoost is 0.935, which is also higher than the models based on RF (0.911) and SVM (0.903). The ROC and PRC curves for three species are shown in H. sapiens, the AUPRC of the model based on XGBoost is 0.942, which is higher than the models based on RF (0.910) and SVM (0.897). Similarly, for A. thaliana, the AUPRC of the model based on XGBoost is 0.794, which is higher than that based on RF (0.784) and SVM (0.771). In addition, for M. musculus, the AUPRC of the model based on XGBoost is 0.827, which is higher than the models based on SVM (0.812) and RF (0.791). Thus, the models built by using XGBoost were selected as our final models.H. sapiens and M. musculus but not for A. thaliana. iRNAm5C-PseDNC . For A. thaliana, our model achieved the highest values for all seven evaluation metrics. Thus, we prove the superiority of our m5Cpred_XS model for predicting the m5C sites for three species. By using other decision thresholds as shown in To further evaluate the generalization of our models, the predictive results of our models on the independent test sets were compared with other existing methods, iRNA-m5C , m5CPredC-PseDNC  and RNAmC-PseDNC  can only PEA-m5C  can onlyIt is noted that the predictive accuracies of iRNA-5mC and PEA-m5C on the independent test sets are even less than 0.50. The possible reason is that the corresponding training data sets for building these models are small. For example, the model of iRNA-m5C for homo sapiens is based on a data set that only contains 120 positive samples, and PEA-m5C is based on a data set that contains 1196 positive samples. Both data sets were smaller than the data sets used in this study. The small size of the data set limits the generalization of the model on the independent test set. In addition, the model was not evaluated on an independent test set in the original paper of iRNA-m5C and the redundancy of the data set used for PEA-m5C was not removed.http://m5cpred-xs.zhulab.org.cn/. The server was implemented using flask, docker, and nginx. The users can easily carry out the prediction by the following procedures: First, users can type the query RNA sequences into the input box or upload a FASTA format file.  After that, one of the three species, H. sapiens, M. musculus, and A. thaliana, should be chosen. Users can provide their email address as a way to obtain the query results. Then, by clicking the “submit” button, the server generates a unique task ID and do the calculation until the final result is reached. During this process, you can query the task status by task ID. When the task was done, the results would be sent back to the users as an email attachment.To facilitate the use of our model, we built a web server that is freely available at To further analyze the features selected by SHAP, the most important top 20 features for the three species are shown in H. sapiens, features belonging to ENAC account for the largest proportion of the top 20 most important features, including a total of seven features. The three types of features, binary, ANF, and word2vec, are not included in the top 20 most important features, which indicates that these features contribute little to the prediction m5C sites of H. sapiens. For M. musculus, five features from NCP and SCPseDNC appeared in the top 20 features, and ANF and CKSNAP did not appear. For A. thaliana, five features of SCPseDNC and FastText appeared in top 20 features, and NCP was not included. These results indicate that the relevant features are related to the data sets, and feature selection is helpful for building high-performance models.Moreover, the principal component analysis was used to visualize the effectiveness of the selected features. H. sapiens. However, the model of H. sapiens does not performs well on the independent test sets of the other two species. M. musculus performs on the independent set of H. sapiens even better than that of M. musculus. In addition, the model of A. thaliana performs worse on the independent test set of M. musculus. We thought the small size of the benchmark data set of H. sapiens was one of the possible reasons for the results. The other reason is that both M. musculus and H. sapiens are mammals.To further evaluate the generalization of our models, we conducted the cross-species validation to analyze the species-specificity and transferability of the models that were tested on the three independent test sets of the three species. In this study, we proposed a new computational model, m5Cpred_XS, for predicting m5C sites. Three different feature-selection methods were used to select the optimal subset from 808 dimensional data of seven kinds of features. It turns out that the features selected by SHAP are more relevant compared with the features selected by the other two methods. The selected feature subsets were used to build our models. Our results show that the models based on XGBoost are superior to the models trained with RF and SVM. The m5Cpred_XS was further compared with other existing methods on the independent test sets, which demonstrates that our model outperforms the other methods according to AUROC values.