Many avian species are considered sexually monomorphic. In monomorphic bird species, especially in young birds, sex is difficult to identify based on an analysis of their external morphology. Accurate sex identification is essential for avian captive breeding and evolutionary studies. Methods with varying degrees of invasiveness such as vent sexing, laparoscopic surgery, steroid sexing, and chromosome inspection (karyotyping) are used for sex identification in monomorphic birds. This study aimed to assess the utility of a non-invasive molecular marker for gender identification in a variety of captive monomorphic birds, as a strategy for conservation.DNA was isolated from feather samples from 52 individuals representing 16 species of 11 families indigenous to both Indonesia and elsewhere. We amplified the chromodomain helicase DNA-binding (CHD) gene using polymerase chain reaction with MP, NP, and PF primers to amplify introns with lengths that differ between the CHD-W and the CHD-Z genes, allowing sex discrimination because the W chromosome is exclusively present in females.Probosciger aterrimus).Molecular bird sexing confirmed 33 females and 19 males with 100% accuracy. We used sequencing followed by alignment on one protected bird species (Sex identification may be accomplished noninvasively in birds, because males only have Z sex chromosomes, whereas females have both Z and W chromosomes. Consequently, the presence of a W-unique DNA sequence identifies an individual as female. Sexing of birds is vital for scientific research, and to increase the success rate of conservation breeding programs. Birds are one of Indonesia’s major biological assets. Of the approximately 10,000 bird species in the world, 1598 are found in Indonesia, of which approximately 372 are endemic . The feaAvian sex chromosomes differ from those of mammals, in which male sex chromosomes are heterozygous (XY) and females are homozygous (XX). Sex chromosomes of birds are the opposite: Females have heterozygous (ZW) and males homozygous (ZZ) sex chromosomes . Two decThe variety of bird sexing methods currently in use have several weaknesses: Karyotyping and steroid hormone analysis are time-consuming and relatively costly, laparotomy and laparoscopy are invasive and challenging to apply in the field, cloacal examination and observations of mating behavior can only be used during particular seasons. Other methods of determining sex include morphometric analysis  and moleMale and female characteristics of monomorphic birds in Indonesia usually involve dimensions of body shape, body weight, head shape, and tail feathers in adult birds . Bird seThis study aimed to differentiate bird sex using molecular sexing, with the ultimate aim of assisting with breeding, conservation, and ecological processes in protected and rare birds.This study met the ethical requirements of both the Ethical Clearance Commission of the Faculty of Veterinary Medicine, Gadjah Mada University , and local laws and regulations.We used a total of 48 bird feather samples from the Gembira Loka Yogyakarta Zoo, and six from the Breeding Facility of the Biology Research Centre, Indonesian Institute of Sciences (Lembaga Ilmu Pengetahuan Indonesia [LIPI])  26-43].-43.26-43A commercial gSYNC DNA extraction kit was used according to manufacturer’s instructions, with some modification. Briefly, a 0.5-1 cm piece from at least three feathers, including the calamus of each sample (n=54) was cut and transferred into 1.5 ml Eppendorf tube. 200 µl of GST Buffer and 20 µl of Proteinase K were then added to each sample tube, mixed by micropestle, and incubated overnight at 60°C while shaken every 5 min. 200 µl GSB Buffer was then added to each sample tube, mixed vigorously, and incubated at 60°C for 20 min while shaken every 5 min. Procedures were then continued according to the gSYNC DNA Extraction Kit instructions.g for 1 min. The solution in the collection tube was discarded. For washing, 400 µl of W1 buffer was added to the GS column of each sample, which was then centrifuged at 15,000× g for 30 s. The solution remaining in the collection tube was then discarded, 600 µl of wash buffer added to each GS column, and the column then centrifuged at 15,000× g for 30 s. The solution remaining in the collection tube was again discarded, and the GS column dried by centrifuging again at 15000× g for 3 min.200 µl of absolute ethanol was added to each tube, mixed immediately for 10 s; then the tube contents were transferred to the GS column in a 2 ml collection tube. All supernatant was transferred into the GS column that had been fitted with a collection tube and then centrifuged at a speed of 15,000× g for 30 s, and 25 µl of elution buffer added before another centrifugation at 15,000× g for 30 s to ensure that all DNA in the GS column was filtered properly.The dried GS column was transferred to a clean, dry 1.5 ml microcentrifuge tube, and 75 µl of elution buffer that had been incubated at 60°C added. The tube was then centrifuged at 15,000× DNA was extracted from bird feather samples collected at the Yogyakarta Gembira Loka Zoo and Captive Facility of the LIPI Biology Research Centre using the gSYNC DNA extraction kit. Extracted DNA was stored at −20°C/−80°C until it was used. DNA extraction product can be directly amplified by PCR: DNA fragments were amplified by targeting the CHD gene on the sex chromosome DNA using P2, NP, and MP primers. The base composition of the primers, annealing temperature (Tm), and melting Tm is presented in ™ DNA Polymerase, forward primer (10 pmol), reverse primer (10 pmol), ddH2O, and isolated DNA, was used as a template -Z gene segment, whereas females will produce two bands of PCR product of approximately 400 bp and 300 bp for the CHD1-Z and CHD1-W gene segments, respectively. The clarity and visibility of the difference between males and females depended on which feather was isolated first. While some samples showed clear DNA band results, others yielded unclear results. This difference was attributable to the number of feather roots  included in the sample. Species for which a greater number of calamuses was obtained showed clearer DNA bands on electrophoresis.Cacatua sanguinea transfreta which occurs in Southern Papua and Papua New Guinea; (2) C. s. sanguinea in North-western Australia; (3) Cacatua sanguinea normantoni in the Western Cape York Peninsula, Australia; (4) Cacatua sanguinea westralensis in Western Australia; and (5) Cacatua sanguinea gymnopis in central and Eastern Australia [L. lory is relatively difficult to do through observation alone because this bird is monomorphic; there is no significant difference in morphology, size and color of the body between the two sexes [Phoenicopteridae that occurs in the western and eastern hemispheres, although it is more common in the eastern hemisphere. On average, males are significantly larger than females in all age groups, although with substantial overlap in all morphological measurements [We used samples from 52 individuals of monomorphic bird species. The little corella has a body length of approximately 380 mm. The plumage and crest are white; the eyelids are rather wide and blue. This species has five subspecies: (1) ustralia ,47. The ustralia . The blawo sexes , so sex wo sexes . It is bwo sexes . Moleculwo sexes ; we adopurements .Psittacus erithacus. Morphological sex identification in birds can be difficult if the animals are young or when there is no appreciable sexual dimorphism. Sex determination can be impossible when few and/or degraded biological material  is available [Psittacidae exhibit one of the highest population declines due to hunting, habitat fragmentation, and degradation, and therefore their conservation is a priority. Sex differentiation in this group is made difficult by sexual monomorphism, and because traditional sexing methods are traumatic or require extensive protocols that do not work on juveniles. Molecular sexing provides a minimally invasive, effective, and rapid technique to determine the sex of individuals [Chest feathers of the macaw are yellow, and the wings are blue. This sex of this bird can be identified with a universal primer . The galvailable . Psittacividuals . Male anividuals . The scaividuals , so we uividuals . TherefoA summary of sex identifications using the CHD gene PCR from 52 monomorphic bird samples and two dimorphic individual roratus parrots as positive controls is presented in Our PCR analyses show that for 16 species of wild birds at both the Gembira Loka Botanical Gardens and Zoo (Gembira Loka KRKB), and LIPI, individuals of the same sex are kept in one cage. For other species in this study, the analyses indicate that male-female pairs are sharing cages, increasing the probability of breeding. These results will be used as a reference for breeding programs in Gembira Loka KRKB and LIPI, to improve the success of breeding programs to protect both Indonesian and exotic birds.Cacatua goffiniana, KT022229.1); ; rose-ringed parakeet ; white-eyed parakeet ; scarlet macaw ; and red-crowned amazon . The analysis was carried out using Mega X software (https://www.megasoftware.net/). The combination sequence of down-stream and up-stream direction products was 332 nt. The alignment results of the CHD-Z gene sequence are presented in Tables-A total of 45 µl of PCR products from palm/great black cockatoo samples were then purified and sequenced using the Sanger method; DNA sequencing of PCR products was carried out both upstream and downstream for each sample. Sequencing results were combined to form the CHD-Z king parrot gene segment. The sequences of CHD-Z gene segments obtained were compared with the CHD-Z segments available at GenBank from the type specimens of the Goffin’s cockatoo/Tinambar corella . MP drafted the manuscript; AH translated and revised the manuscript. All authors contributed to this research and read and approved the final manuscript.


Crhr2, Fos, Nos1, and Star), and only 4 of the 14 genes were regulated in the same direction . Interestingly, the top 30 regulated genes by binge ethanol drinking for each sex differed markedly in the male and female mice, and this divergent neuroadaptive response in the NAc could result in dysregulation of distinct biological pathways between the sexes. Characterization of the expression differences with Ingenuity Pathway Analysis was used to identify Canonical Pathways, Upstream Regulators, and significant Biological Functions. Expression differences suggested that hormone signaling and immune function were altered by binge drinking in female mice, whereas neurotransmitter metabolism was a central target of binge ethanol drinking in male mice. Thus, these results indicate that the transcriptional response to repeated binge ethanol drinking was strongly influenced by sex, and they emphasize the importance of considering sex in the development of potential pharmacotherapeutic targets for the treatment of alcohol use disorder.We previously determined that repeated binge ethanol drinking produced sex differences in the regulation of signaling downstream of Group 1 metabotropic glutamate receptors in the nucleus accumbens (NAc) of adult C57BL/6J mice. The purpose of the present study was to characterize RNA expression differences in the NAc of adult male and female C57BL/6J mice following 7 binge ethanol drinking sessions, when compared with controls consuming water. This binge drinking procedure produced high intakes (average >2.2 g/kg/30 min) and blood ethanol concentrations (average >1.3 mg/ml). Mice were euthanized at 24 h after the 7th binge session, and focused qPCR array analysis was employed on NAc tissue to quantify expression levels of 384 genes in a customized Mouse Mood Disorder array, with a focus on glutamatergic signaling (3 arrays/group). We identified significant regulation of 50 genes in male mice and 70 genes in female mice after 7 ethanol binges. Notably, 14 genes were regulated in both males and females, representing common targets to binge ethanol drinking. However, expression of 10 of these 14 genes was strongly dimorphic (e.g., opposite regulation for genes such as  Other primary targets include γ-aminobutyric acidA (GABAA), glycine, serotonin-3, and neuronal nicotinic acetylcholine receptors, as well as L-type calcium channels and G protein-activated inwardly rectifying potassium channels , NAc, ventral tegmental area (VTA), and amygdala have found that various models of binge drinking produced changes in expression of genes in male rodents that were involved in some of the following networks: glutamate signaling, BDNF (brain derived neurotrophic factor), synaptic vesicle fusion, synaptic transmission, apoptosis, glucocorticoid receptor (GR) signaling, anti-apoptosis, regulation of G-protein receptor signaling, transcription factors, neurogenesis, and neuroimmune-related pathways e.g., . Binge dNr3c1, the gene encoding the GR, as one of the highest master regulators in the mPFC, NAc, VTA, and central nucleus of the amygdala  than after a period of abstinence (3 weeks) in male and female rodents. Studies conducted in male mice during acute withdrawal found that transcriptionally responsive genes in the PFC were involved in the Ras/MAPK (mitogen-activated protein kinase) pathway, notch signaling, and ubiquitination  and thatamygdala . Importaamygdala . Howeveramygdala . In contamygdala . CollectBased on the above evidence for a strong dichotomy between male and female rodents in the response to ethanol during acute withdrawal, the purpose of the present study was to characterize RNA expression differences from male and female C57BL/6J mice following 7 binge ethanol sessions. Tissue was harvested from the NAc, as this brain region is a central mediator of addiction e.g., . FocusedGuidelines for the Care and Use of Laboratory Animals and were compliant with Institutional Animal Care and Use Committee approved protocols. The specific protocol for these studies was approved by the Institutional Animal Care and Use Committee at the VA Portland Health Care System, where all studies were conducted. All efforts were made to minimize distress and the number of animals used.Adult male and female C57BL/6J mice were purchased from Jackson Laboratories West  at 7 weeks of age. Mice were group housed and separated by sex upon arrival, acclimated to a regular 12 h light/dark cycle (lights on at 0700) in a temperature (22 ± 2°C) and humidity controlled environment, with free access to food  and water. Mice were 8 weeks old at the start of the drinking study, and they were individually housed throughout the experiment. Stage of the estrous cycle was not monitored during this study, based on evidence that binge ethanol consumption was not affected by estrous cycle phase in female C57BL/6J mice and that 6 weeks of binge ethanol drinking did not affect the length or pattern of the estrous cycle . ResultsThe Scheduled High Alcohol Consumption procedure was used to model binge drinking, based on evidence that this procedure produces high ethanol intake in male and female mice (≥2g/kg in 30 min) and blood ethanol concentrations (BECs) ≥ 1.0 mg/mL . Quantitative PCR was performed using customized neuroscience mouse qPCR arrays (Custom 384 Mouse StellARray or Mouse Mood Disorder array) by Bar Harbor BioTechnology . A total of 12 qPCR arrays, 384-well PCR plates with primers targeting genes related to Mood Disorders (2.6 ng/reaction), were run by Bar Harbor BioTechnology, with three biological replicates for each sex  and treatment . PCR plates were run on an ABI 7900 HT Real-Time instrument, and data were analyzed with SDS 2.4 software (ABI), using automatic baseline settings with a manual threshold of 0.096 across all samples. For samples with undetectable expression of any gene, a Ct-value of 40 was assigned to that gene to allow statistical analysis. Quantitative PCR arrays have been documented to provide reliable data that do not require further confirmation, as validation studies in our laboratory have found 100% reproducibility of these data with traditional qPCR methods for testing the expression of individual genes , and genomic DNA was removed with the DNA-Free RNA kit , using routine procedures e.g., . First ses e.g., .Binge and control mice were chosen for the arrays, based on specific criteria. For binge mice, choices were based on the following: (1) Animals with seven binge sessions > 2 g/kg/30 min or with the greatest number of binges ≥ 2 g/kg were chosen; and (2) Mice with the most consistent BECs that exceeded binge BEC (0.80 mg/mL) were chosen. For the water control mice, choices were based on the following: (1) Consistent 30 min water intake across the 7 “binge” sessions and consistency across animals per group with group average. We also ensured that body weights were not significantly different in the control and binge animals that were chosen.The qPCR arrays allow for the identification of changes in the expression of pre-selected gene networks associated with specific signaling cascades and pathways that are altered following repeated bouts of binge drinking. We had two rationales for using the Mouse Mood Disorder array. First, the Mouse Mood Disorder array was used by collaborators in our department to examine genes relevant to selection for high and low methamphetamine consumption, given that many of the 384 genes represented on the array are relevant to findings from other studies of addiction related processes . Second,n = 4/sex/treatment). Real time qRT-PCR was performed with the iCycler IQ Real Time PCR detection system , using a one-step QuantiTect SYBR Green RT-PCR kit (Qiagen) on DNase-treated total RNA  of the current qPCR array data as being affected by binge ethanol drinking . The level of significance was set at post hoc tests. Because we were predicting sex differences, planned comparisons were conducted with or without the presence of a significant interaction.For the drinking data, the dependent variables were BEC, volume (in mLs) of water and ethanol consumed, ethanol dose consumed (in g/kg), and body weight. Analysis of variance (ANOVA) was used to assess day, sex  and treatment  effects or binge day and sex effects when only the ethanol data were examined. Significant interactions were followed up with p-values from the GPR analysis were then used to calculate q-values to control for multiple comparisons using the “qvalue” package in R (version 2.12.01). However, we used an uncorrected p-value to decrease the chance of excluding regulated transcripts  as we (p ≤ 0.05) from either comparison  were then used to create heat maps and hierarchical clustering using the R package “gplots” (version 3.0.1) with complete linkage clustering. Bioinformatic analyses were conducted by uploading the significantly regulated genes to the IPA website2. Proprietary IPA software was used for the analyses, and significance was based on the relative enrichment of the regulated genes to biological function, pathway, or network using the 384 genes present in the qPCR array as the background gene-set and the Fisher’s exact test. Thus, for all the pathway analyses (IPA), using the 384 genes present on the qPCR array as the background gene set controlled for the enrichment of specific gene classes  in our set of regulated genes.For the qPCR array data, Bar Harbor BioTechnology identified significantly changed genes in the data set using their Global Pattern Recognition (GPR) algorithm . GPR goes) as we  and othes) as we  have emppost hoc t-tests were conducted to examine treatment effects in each sex.For the qRT-PCR data, all data were calculated as fold change relative to the female controls after normalizing expression to total RNA measured with RiboGreen. Initial analyses were conducted with ANOVA to assess sex and treatment effects. When there was a significant interaction, F = 7.02, p < 0.001; sex × time: F = 2.61, p < 0.05]. Ethanol intake was significantly lower in female vs. male mice on day 12  and was significantly higher in female vs. male mice on day 21 . The slight decrease in ethanol intake across binge sessions in the male mice likely reflects the increase in fluid access time across sessions, which is a finding that we have observed in some of our prior studies using this binge drinking procedure. BECs were measured after the 3rd (day 9) and 7th (day 21) binge ethanol sessions , and they mirrored the ethanol intake data , with post hoc tests confirming that BEC was significantly higher in female vs. male mice on day 21 (p < 0.01). A similar pattern of results was found for the subgroup of mice that were chosen for the array analysis . Ethanol intake and BECs did not differ in male vs. female mice on day 9, whereas ethanol intake was significantly higher in female vs. male mice on day 21. BEC on day 21 also was higher in females vs. males, but this difference did not reach statistical significance. Importantly, the results confirm that both male and female mice in the binge groups consumed high doses of ethanol in the 30 min binge sessions and achieved BECs that exceeded the NIAAA criteria for binge drinking  or consumed water . Overall binge ethanol intake (g/kg/30 min) did not differ between the sexes, when collapsed across the seven binge sessions. Mean ± SEM intake was 2.34 ± 0.06 g/kg for females and 2.35 ± 0.10 g/kg for males. However, analysis of the seven binge ethanol sessions revealed that the pattern of ethanol intake across time differed in the male vs. female mice . Averaged body weights also were lower in the control vs. binge groups , and this effect was primarily due to the significant difference in the male mice (Table 1). Body weights on day 1 of the study were slightly lower in the control mice when compared to the mice in the binge groups, which likely contributed to the significant difference in average body weight. However, weight gain across the 21 days of the study was similar in the control and binge groups for the male  and female  mice. Overall total fluid intake did not differ in the control vs. binge groups for the male and female mice (Table 1). Thus, treatment (binge vs. control) did not significantly alter body weight gain or total fluid intake in either sex.Body weights, averaged over the 21 days of the study, were lower in female vs. male mice . We also examined Ikbkap, which encodes a protein (IKAP) that was initially thought to be a scaffolding protein for the IκB kinase complex. Expression of Tnfrsf1a was significantly lower in females vs. males , but there was no effect of binge drinking . Expression of Chuk and Ikbkb also was not altered by binge drinking in either sex (not shown). However, Map3k14  and Ikbkap  expression was significantly decreased by binge drinking in both males and females . Expression of Traf2  and Ikbkg  tended to be decreased by binge drinking in both sexes . Additionally, the gene Lta, which encodes the protein lymphotoxin-alpha or TNF-β, was differentially altered by binge drinking , where expression was decreased in females (p = 0.001) and increased in males (p < 0.05). In general, the results in females demonstrate that binge drinking produces a fairly consistent downregulation of signaling through the tumor necrosis factor (TNF) superfamily, which likely influences activation of the transcription factor NF-κB.The third pathway identified was “TNFR2 signaling” . Importantly, because we directly tested males and females following repeated binge drinking, the present results show for the first time that repeated binge drinking experience produces sexually divergent transcriptional responses and activation of distinct networks, similar to what has been reported for males and females tested during acute withdrawal following chronic intoxication , 2015. OIt was not surprising that neurotransmission was significantly affected by binge drinking. The current results are consistent with prior microarray studies that identified networks or biological processes related to glutamate signaling, BDNF and synaptic transmission in the NAc and central nucleus of the amygdala  or in thGrm1 and Grm5 in male C57BL/6J mice, and comparable changes were not found in female C57BL/6J mice , and there was a non-significant increase in Pik3r1 expression in female  and male  mice. As we discuss in Per2 in females  and trends for an opposite effect on Clock in females and males . Regardless, the results add to evidence for sex differences in the effects of binge drinking on glutamatergic signaling.Earlier work with the Scheduled High Alcohol Consumption model of binge drinking found that repeated bouts of binge drinking increased NAc protein levels of Homer2, NMDA receptor 2A and 2B subunits, and PI3K activation in male C57BL/6J mice, without altering protein levels of mGluR1 and mGluR5 at 24 h after the final binge session . Recentl/6J mice . In the Esr1 (which encodes ERα) only in female mice , and evidence indicates that ERα also can localize to the plasma membrane and initiate signal transduction through PI3K (reviewed in It is interesting that Akt (many times associated with PI3K) was identified as a central node in female mice following binge drinking. However, as mentioned above, we recently found that females were insensitive to the ability of intra-NAc rapamycin  to decrease binge drinking, whereas intra-NAc rapamycin significantly decreased binge drinking in males . The redFigure 3). Importantly, the effects of binge drinking on CRH signaling were divergent for all the genes listed in Figure 3D. An examination of the genes responsible for the initiation of CRH signaling indicate that binge drinking reduces activity of the pathway in females (↓ Crhr1 and Crhr2), while it increased activity of the pathway in males (↑ Crh and Crhr2). The decreased activity of the CRH pathway in females is interesting, given evidence for sex differences in the coupling of CRHR1 with the Gs and β-arrestin 2 proteins that render females more responsive to acute stress and less able to adapt to chronic stress as a result of compromised CRHR1 internalization  and the gene encoding the chaperone heat shock protein 90 . Similarly, the identification of GR signaling in the NAc and central nucleus of the amygdala of male rats  subunit of NF-κB . And, one effect of MIF is to prevent glucocorticoids from increasing the expression of IκB, which would offset the glucocorticoid-mediated inhibition of NF-κB . Notably, binge drinking produced a significant and opposite change in the expression of Lta (encodes TNF-β or lymphotoxin-α), with a decrease in females and increase in males , and this protein also is involved in cell survival, proliferation, differentiation, apoptosis, and immune regulation. So, the sex difference in significant expression change in Lta by binge drinking  would be predicted to have opposite effects on cell survival and immune responses. In addition, binge drinking significantly decreased expression of Il6 only in females; an ethanol-induced reduction in levels of these two cytokines in females would be consistent with a decrease in the initiation of signaling at TNFR1 . Overall, the results in females demonstrate that binge drinking produced a fairly consistent decrease in expression of genes in the signaling cascade through the TNF superfamily, with the exception of a trend for an increase in expression of Tnf (encodes TNF-α). These binge ethanol-induced changes in females, including the downregulation of the RelA subunit of NF-κB, likely produce a decrease in the activation of NF-κB. A more complex pattern of changes was identified in males following binge drinking, so the influence on NF-κB activation in males is unclear. Regardless, the results in females in the present study would be consistent with a decrease in cell survival, and an increase in apoptosis and inflammation via a decrease in the activation of the NF-κB. It is interesting that acute withdrawal from chronic intoxication also identified NF-κB as a central node in both male and female networks, but the interacting gene sets were completely distinct between the sexes (Acute withdrawal following chronic intoxication affected pathways related to inflammatory activation and apoptotic/cell death signaling in PFC from females vs. males , 2015. Ahe sexes . An examhe sexes . CollectThe repeated binge drinking sessions produced sexually divergent transcriptional responses and activation of distinct networks. These results add to a body of evidence indicating that binge drinking and chronic ethanol intoxication both produce neuroadaptive changes in neurotransmitter systems and in many cellular pathways that likely alter neuronal function in a manner that can be either adaptive or deleterious. The opposite effects of binge drinking on immune function in the present study, with changes in females consistent with a decrease in cell survival and an increase in inflammation and apoptosis, have important implications, given the evidence for a role of neuroimmune signaling in the acute and chronic effects of ethanol, including neurodegeneration (reviewed in One limitation of the present investigation is that we did not conduct confirmation studies to identify protein changes, neuronal degeneration, or behavioral changes that could possibly account for the sex-specific gene expression profiles that we observed. However, we did examine the expression of additional select genes that were not present on the arrays but that were implicated in the downstream signaling cascades of the IPA-identified pathways to strengthen conclusions about the select pathways that were altered by binge drinking. Future studies will determine whether the current gene expression changes correspond to behavioral and/or physiological differences.Importantly, an increased understanding of sexually dimorphic molecular pathways influenced by binge drinking and chronic intoxication leading to dependence may identify novel treatment options for males and females. The current study is contributing data sets that can be used to generate sex-specific bioinformatics tools, which have the potential to enormously accelerate the discovery of sex-specific changes associated with AUD. Finally, we recently reported that binge drinking produced sex differences in the regulation of PI3K signaling in the NAc and in the ability of intra-NAc rapamycin to decrease binge drinking, with females resistant to these molecular changes . The funThe raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.DF and KW contributed conception and study design. DF, DC, MN, and MK conducted the drinking study. DC dissected accumbens tissue. JH isolated RNA and prepared samples for qPCR array analysis. DF conducted preliminary analysis of drinking data and identified animals for the qPCR analysis. DF, MN, and MK participated in final analysis of drinking data. MH conducted follow-up qRT-PCR analysis, with assistance from JH. JH and KW performed Pathway Analysis. JH, KW, and MG assisted in the interpretation of the array and pathway analyses. DF wrote the first draft of the manuscript, but JH and KW wrote sections of the “Materials and Methods and Results.” All authors contributed to the final version of the manuscript, read, and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Soil bacterial community compositions and temporal changes in organic paddy fields were elucidated using a 16S rRNA gene amplicon sequencing analysis with a high-throughput next generation sequencer. At transplanting, bacterial community compositions in organic and conventional paddy fields were mostly similar despite differences in field management. The bacterial community composition in organic fields differed from that under conventional management during the rice growth period, possibly as a result of the decomposition process of organic fertilizers. However, differences in the frequency of tillage and photosynthetic bacterial inoculations in the organic plots had less of an impact on bacterial communities. Organic agriculture refers to a farming system that markedly reduces the use of chemical fertilizers, pesticides, and genetically modified organisms, and aims to minimize any negative impact in order to improve long-term soil sustainability . Rice iset al.  to elucidate changes in soil bacterial communities in paddy fields under organic management and 2) to clarify the effects of the frequency of tillage and photosynthetic bacterial inoculations on bacterial communities.Experimental plots (30 a each) were established in two adjacent rice fields located in Matsumoto, Nagano, Japan  in 2014. The FAO Soil classification of the rice fields was Gleysol. One of the rice fields had been maintained under conventional management and the other under organic management for 6 years. The soil chemical properties of the experimental fields are shown in −1, 96 kg P2O5 ha−1, and 56 kg K2O ha−1 on 13th May 2014 based on the fertilization standards recommended by the Agricultural Administration Department of the Nagano Prefectural Government  butanoic acid in late June, cyhalofop butyl and bentazon in early July, and etofenprox and tricyclazole on 22nd July. Mid-summer drainage was performed from 3rd July to 16th July.In the conventional field, chemical fertilizers were incorporated at 40 kg N hath May and 300 kg ha−1 rice bran and 400 kg ha−1 rapeseed oil cake were incorporated according to local farming practices. Half of the field was also tilled in the autumn of the previous year (23rd October 2013), which was defined as the double tillage treatment in the present study. After submerging and puddling, rice seedlings were transplanted on 23rd May. Bokashi fertilizer, which was made by a 2-month fermentation of rice bran and rapeseed oil cake as the main materials, was topdressed at 100 kg ha−1 on 26th May. Mechanical weed control was performed four times in June. Four different treatment plots (72 m2 each) were established in the organic field by the combination of different numbers of tillage (single or double tillage) and photosynthetic bacterial inoculations  under a 22 factorial design without replication. In the inoculated plots, R. palustris cultured in Hoshino medium (9 MPN mL−1) were then inoculated into nursery boxes of rice seedlings at 160 L ha−1 just before transplanting. Mid-summer drainage was performed from 21st July to 1st August. The details of these agricultural practices are shown in The organic field was tilled on 9th May), 2–4 weeks before the mid-summer drainage (21st June), and at the rice grain-filling stage (21st August). Soil samples were kept at 4°C during transportation. After arriving at the laboratory, soil samples were homogenized together to produce a single plot sample and a proportion of each sample was stored at −80°C until soil DNA extraction.Fifty grams of soil samples were collected with a hand shovel from a depth of 0–15 cm at five points and mixed well just after transplanting  according to the manufacturer’s instructions. Amplicons were paired-end sequenced on an Illumina MiSeq platform (Illumina) at a read length of 2×300 bp. Sequencing was performed by Fasmac .Soil DNA was obtained from 0.5 g of homogenized soil samples using ISOIL for Beads Beating , according to the manufacturer’s instruction. The partial bacterial 16S rRNA gene (V4 region) was amplified using the bacterial primers 515F/806R with the linker sequences for Nextera primers . PCR products were purified using a High Pure PCR Product Purification Kit , and PCR barcode indexing was then performed with Nextera primers. Amplicons were purified with a QIAquickSequencing data were processed and analyzed in QIIME 2 Core 2018.2 . SequencTo compare bacterial communities among the samples, sequence read numbers were normalized to the minimum number of the sample by random subsampling. A cluster analysis and heat map analysis were performed based on the proportions of the bacterial community at the genus and species levels, respectively.In a total of 745 OTUs were obtained at the genus level in the present study, with 97% having a <1% abundance ratio. An average of approximately 500 OTUs were obtained from each plot.Proteobacteria, 20% Chloroflexi, 15% Acidobacteria, and 10% Actinobacteria , and 4% Nitrospirales (Bradyrhizobiaceae sp.) was approximately 0.3%, and no significant difference was observed between the inoculated and non-inoculated fields at mid-summer drainage or in the grain-filling period . Bacterial diversity indices (Shannon and Faith’s phylogenetic diversity) significantly increased during the cultivation period   , while nThe cluster analysis based on the frequency of bacterial compositions at the genus level revealed differences in bacterial communities between conventional and organic farming . At tranet al. (Firmicutes and Actinobacteria), and Rhizobiales, which aid nutrient cycling, were influenced by the application of compost. In the present study, organic management significantly increased the relative abundance of the phylum Firmicutes , but not the phylum Actinobacteria or order Rhizobiales (Organic amendments have an impact on the soil bacterial community and changes community compositions under upland  and paddet al.  performezobiales .et al. (After mid-summer drainage, bacterial community compositions under organic management slightly differed from the others . Rui et et al.  reportedThe community heat map and clustering of the top 20 dominant species were shown in R. palustris in the present study. Although the bacterial inoculation may have had an impact on soil bacterial community compositions (et al. (R. palustris inoculation did not significantly affect a phototrophic purple bacteria population when rice straw was applied; however, this inoculation increased the rice grain yield. It has also been reported that the inoculation of plant growth-promoting bacteria did not have a significant impact on culturable microbial communities (We examined the inoculation of ositions , the pro (et al.  reportedmunities . Our resAlthough tillage management may affect the bacterial community in cropland , the effIn conclusion, organic management mainly affected bacterial community compositions after transplanting. During the rice growth period, gradual changes were observed in the bacterial community and the bacterial community under organic management differed from that under conventional management, possibly as a result of the decomposition process of organic fertilizers. On the other hand, the frequency of tillage and photosynthetic bacterial inoculations had a weaker effect on bacterial communities. The present results contribute to our understanding of the responses of bacterial communities to organic management in paddy fields.


To better understand the relationship between soil bacterial communities, soil physicochemical properties, land use and geographical distance, we considered for the first time ever a European transect running from Sweden down to Portugal and from France to Slovenia. We investigated 71 sites based on their range of variation in soil properties , climatic conditions  and land uses . 16S rRNA gene amplicon pyrosequencing revealed that bacterial communities highly varied in diversity, richness, and structure according to environmental factors. At the European scale, taxa area relationship (TAR) was significant, supporting spatial structuration of bacterial communities. Spatial variations in community diversity and structure were mainly driven by soil physicochemical parameters. Within soil clusters (k-means approach) corresponding to similar edaphic and climatic properties, but to multiple land uses, land use was a major driver of the bacterial communities. Our analyses identified specific indicators of land use  or soil conditions . These findings provide unprecedented information on soil bacterial communities at the European scale and on the drivers involved; possible applications for sustainable soil management are discussed. A more thorough knowledge of the geographical distribution of microbial communities across different soil types and the identification of potential biotic indicators of changes are critical to better understand the ways soils and land uses impact microbial diversity under different climates. These indicators will help managers devise strategies for monitoring microbial diversity and soil functioning for end users and policy makers3.Monitoring soil microbial communities represents a major challenge to better valorize soil resources and to implement a more sustainable management of soils in agriculture and forestry6. Local- to continental-scale studies have suggested that soil properties (e.g. the pH) are major drivers of the structure and diversity of soil microbial communities, with plant communities and land use as secondary confounded correlates15. However, more locally focussed experiments revealed that in certain soils, microbial communities are also strongly affected by plants species, developmental stage and phenology18. These observations are consistent with the recent proposal by several authors that a core microbiome associated with plant roots is specific to the host genotype and includes populations beneficial for plant growth and health22. However, the rhizosphere effect is known to vary according to the soil type, even for a given plant species24. Plants indeed recruit their rhizosphere microbiota from the soil reservoir25, whose biodiversity varies among soils as indicated by biogeographical studies12. Moreover, as a given land use can be found in various types of soils, it is essential for sustainable soil management to better understand the effects of land use on microbial communities specific to the soil type. Furthermore, specific indicators of land use vs. soil type are needed. So far, most of the biogeographic surveys have focused on broad-scale patterns and drivers, and few attempts have been made to disentangle the effects of different land use on similar soils, and vice versa. A key issue with broad landscape surveys is that human land management is strongly determined by the prevailing soil and climatic properties of the environment. With the onset of agricultural development, human populations have shaped their environment locally by converting the most fertile and easily manageable soils to agriculture and grassland, leaving infertile and rocky soils to forest, heathland and bog27. At a small scale, this phenomenon confounds gradients of soil properties with land management, masking the primary drivers of soil communities. At a larger spatial scale, these parallel events have led to the distribution of given land uses on very different soil types, although certain soil types are preferentially used for arable land, grassland or forest. For example, grassland tends to occur across a broader range of soil conditions than arable land. Forests are usually developed on nutrient-poor soils, which vary from very acidic to neutral pH values.A large debate on the relative importance of soil properties and plants on belowground biodiversity has been running over the last decadesThis study addresses these shortcomings by specifically determining the relative effects of land use and soil properties on bacterial diversity and structure in a large number of soil samples collected and characterised across the first European transect for soil microbes. This transect ran from Sweden down to Portugal and from France to Slovenia. The distribution of different land uses on various soil conditions across this transect allowed us to test the respective effects of these two categories of drivers on soil bacterial communities. The sampled sites represented three major land uses  across a range of soils chosen for their contrasting pH values, textures, and organic matter contents, and covering a broad range of climatic conditions. We analysed these soil samples for their physicochemical properties, and for their bacterial diversity and community composition by 16S rRNA gene amplicon pyrosequencing. We used a k-mean classification analysis to group the soils on the basis of their physicochemical properties and land use into homogeneous clusters, and then test if a potential land use effect was observable in these clusters. Finally, we performed an indicator species analysis to identify the genera indicative of particular soil conditions and/or land uses.15, but our study characterises bacterial communities at the European scale for the first time28. The sampling design was representative of the variations in soil properties, i.e. organic matter content, pH and texture, across different geographical/climatic zones in Europe, and it covered three land uses  without reaching a plateau. We detected a total of 34,190 OTUs among all the soil samples. At the European scale, the number of OTUs per soil sample ranged from 653 to 1,860 : 244; Fig. 32. Nevertheless, these indices revealed a non-significant trend of lower bacterial richness and diversity from arable to forest soils. This is not in agreement with the literature since other studies demonstrated that microbial indices were affected by land-use at the plot scale33, the landscape scale34, and at more global scales35. The much greater variability of soil physicochemical properties relatively to land use in our study may account for this discrepancy since soil physicochemical characteristics are known to frequently be the most influencing factor of soil microbial indices, followed by land use36.We obtained more than 3*10i.e. a combination of a given soil property and a land use), the most represented bacterial phyla were Proteobacteria (average relative abundance 56.4%), Actinobacteria (15.7%), and Acidobacteria (7.1%)  but their relative abundance greatly varied according to the environmental conditions. We detected a total of 1,033 bacterial genera (data not shown), whose abundance varied according to environmental conditions from 157 genera in a Swedish forest soil, up to 397 in a Dutch arable soil. A total of 7.23% of the sequences remained unclassified at the genus level. The number of detected genera ranged from 310 to 397 in arable land , 157 to 313 in forest , and 264 to 386 in grassland , without any significant difference between land uses. These results are in the range of those reported in the literature36. The absence of a relationship between the number of genera and land use type may be related to the high number of genera represented at very low relative abundances, which we did not take in account. Similarly, Karimi et al.36 identified a total of 1,355 genera out of which only 47 had a relative abundance higher than 0.5%. Similar trends have also been observed at a global scale32.We detected a total of 27 phyla after taxonomic assignment  and to limited dispersal.We performed further analyses to better evaluate the spatial structuration of soil bacterial communities and better specify the environmental and spatial drivers of the variations of bacterial communities across Europe. First, we evaluated TAR for soil bacterial communities at the European scale Fig. . This reP < 0.001, ANOSIM, 1,000 permutations), texture  and organic carbon content  were major drivers of bacterial community structure. Other drivers such as the nitrogen content and CEC were strongly correlated to the organic carbon content; moreover, assimilable phosphorous was non significantly correlated to soil bacterial community variation (P > 0.24). Similarly, we recorded significant differences in the community structures of the three types of land use  . Even if soil properties and land use both had discriminating effects on soil bacterial communities, cross-effects must be considered together with the effect of distance between sites.We applied two complementary approaches to better understand the factors involved in the spatial structuration of soil bacterial communities and supporting environmental selection and dispersal limitation. A descriptive approach based on NMDS applied on Unifrac distance ; that effect was mostly related to pH, texture class and total carbon content, which accounted for 13.8%, 10.8%, and 6.9% of the variance of soil bacterial richness, respectively  except that the texture was very slightly significant. The percentage of variance explained by the soil pH ranged from 17.7% for the Shannon index to 19.0% for evenness, while the total carbon content explained 8.6% for the Shannon index and 20.2% for evenness. Soil texture did not explain any variation of bacterial evenness and was slightly significant for bacterial Shannon index . For each diversity index, interactions between environmental factors represented a large portion of the explained variance . A large portion of the variance still remains unexplained, suggesting that other environmental factors may contribute to structure bacterial communities, and confirming previous findings43. Conversely to diversity indices, soil bacterial community structure based on Unifrac distance was only slightly explained by environmental factors . Among all the environmental factors we identified, the soil pH (3.8%) ranked first, followed by land use (3.3%) and the total carbon content (1.7%). This is in agreement with other studies of soil microbial biogeography47 and is fairly related to the soil reactional conditions and trophic level and to biological interactions, e.g. between plants and soil bacteria. This study also identifies the climatic zone as a driving factor of soil bacterial community structure. Nevertheless, even if its marginal effect appeared to be substantial as compared to other variables, it was only very slightly significant . Its selection may be related to particular sites belonging to boreal and Mediterranean climatic zones located far from the rest of the transect. Surprisingly, longitude, latitude, or PCNMs (representing the neighbouring relationships between sites) were not selected when we built the model. This suggests that bacterial communities are not dispersal-limited at the scale of Europe. Nevertheless, other studies demonstrated that bacterial communities may be dispersal-limited at smaller spatial scales 44. This discrepancy may be explained by differences in terms of sampling efforts.To disentangle potential cross-effects and take the effect of distance between sites into account, we applied a parsimonious variance partitioning approach on the three diversity indexes  and on soil bacterial community structure to determine the relative importance of soil properties and/or land use and/or climate conditions and/or distance between sites on bacterial communities. This analysis notably showed that soil properties explained a large part of the variance of bacterial richness  also encompassed representative soils from the three land uses but they displayed distinct properties, with medium carbon and nitrogen contents, an average pH of ca. 6 and a medium to fine texture; the third cluster  included mainly forest soils but also one grassland, and the soils exhibited high carbon and nitrogen contents, an acidic pH and an organic texture; finally the fourth cluster  included all three land uses but only one forest site and encompassed soils with a medium to fine texture, a neutral pH, and a low carbon content , phyla, or even genera as environmental indicators51. We recorded a total of 118 genera with an average relative abundance above 0.1% (Table Pseudolabrys (max. relative abundance 4.23%) and Azospira (4.11%). Cluster 2 harboured only three indicator genera, belonging to the Elusimicrobia and Proteobacteria phyla and assigned to the Elusimicrobium (2.61%), Pelobacter (0.41%) and Xanthomonas (0.27%) genera. Cluster 3 was characterised by 59 indicator genera mainly related to Proteobacteria, Actinobacteria, and Bacteroidetes; the most abundant indicator genera were assigned to Blastochloris (max. relative abundance 18.65%), Halophaga (13.2%), and Steroidobacter (8.51%). Nevertheless, this result remains to be confirmed by other studies because of it was obtained from a small number of sites (n = 5). Cluster 4 encompassed 39 indicator genera mostly related to Proteobacteria and Actinobacteria; the most abundant indicator genera were assigned to Gaiella (max. relative abundance 9.65%) and Bacillus (5.22%). Overall, we found higher numbers of indicator genera in the clusters with the lowest variety of land uses (clusters 3 and 4), and conversely lower numbers of indicator genera in the clusters encompassing more different land uses (clusters 1 and 2), suggesting that land use is a major driver of soil biodiversity52.Each sampling site exhibited different soil properties and land uses, so we further explored the relative contribution of these two types of parameters to bacterial diversity. For this purpose, we clustered sites according to the homogeneity of their soil physicochemical characteristics, land use and climate, as assessed by the k-mean method. The k-mean approach allowed us to the delineate four clusters with contrasting soil properties and covering different land uses: the first cluster  included the three land uses  and encompassed soils with a low carbon and nitrogen content, an average pH of i.e., the genus level). We indeed demonstrated that different indicator genera belonging to a same phylum could be indicators of different soil conditions (i.e. cluster type). For example, within the Actinobacteria phylum, the Actinospica genus was an indicator of cluster 3 and the Geodermatophilus genus an indicator of cluster 4  confirming previous works done on this genus25. The Myxococcus genus was more specifically found in cluster 4 . Then we constructed models to refine and predict the contribution of the three environmental parameters - soil properties, land use, and climate - associated with these clusters for each indicator genus (Table Acidothermus in cluster 3 was mostly explained by soil pH (41.08%) and land use (5.79%), while the indicator genus Gaiella in cluster 4 was explained by the total carbon (29.09%) and nitrogen (16.7%) contents. Globally, a broad range of the bioindicators (40 out of 118) identified in this study were determined by the soil pH, confirming the importance of this parameter for soil biodiversity as reported in previous studies14, while C content50, climate zone, land use15 and texture13 explained the relative distribution of 30, 27, 25 and 12 other bioindicators, respectively. Only 12 bioindicator genera were explained specifically by land use exclusive of any other parameter (Table Nitrospira (Betaproteobacteria); in cluster 2, Elusimicrobium (Elusimicrobia); in cluster 3, Skermanella (Alphaproteobacteria), Steroidobacter (Gammaproteobacteria), Novispirillum (Alphaproteobacteria), Herminiimonas (Betaproteobacteria), Holophaga (Acidobacteria), and Aquicella (Gammaproteobacteria); in cluster 4, Methylobacterium (Alphaproteobacteria), Thermoacetogenium (Firmicutes), Dehalococcoides (Chloroflexi), and Thermincola (Firmicutes). Nevertheless, 37 of the bacterial genera identified as indicators of clusters remained unexplained by any of the parameters tested , e.g. Blastochloris (max. relative abundance of 18.65%) or Mycobacterium (max. relative abundance of 2.61%), Myxococcus (0.1%), or Collimonas (0.32%).The bioindicator analysis also revealed that such an analysis must be conducted at a fine taxonomic level . We therefore analysed clusters 1 and 2 to test whether variations of bacterial communities were mostly explained by land use or soil properties. We performed multivariate analyses independently, on the soil physicochemical properties and then on the distribution of the bacterial genera characterising the sites encompassed in clusters 1 and 2. The results showed that the soils were mostly grouped in terms of soil properties according to the land use, with a clear separation between forests and other land uses as confirmed by ANOSIM analysis  ; the major driving effect of the land use in this cluster was also supported by the absence of a significant correlation (P > 0.05) between soil parameters and the distribution of bacterial genera  that encompassed forest sites differing by their constitutive soil properties. Taken together, our data show that, under similar soil properties, land use is a major driver of bacterial community structure. Compared with previous reports54, this conclusion is strongly supported by the large variety of soil properties for each land use in contrast with previous studies that addressed the land use effect only on a given soil type56.We further tested the relative impacts of land use on the bacterial communities under similar soil conditions using the land use distribution found in each cluster as generated by the k-mean classification. In contrast with the very small cluster 3 including only five sites (4 forests and 1 grassland) and cluster 4 that included only one forest site out of 18, clusters 1 and 2 encompassed the three land uses (01) Fig. . Further11, France36 and the Netherlands). Combining soil analyses with 16S rRNA gene amplicon pyrosequencing revealed that the soil bacterial communities are non-randomly distributed across Europe but determined by environmental factors. More specifically, soil properties, notably the pH and texture, appeared to be the main drivers of soil bacterial community composition and distribution at the European scale. However, under given soil properties corresponding to delineated clusters, the influence of land use was apparent. We identified bacterial genus indicators of soil physicochemical properties through a cluster analysis; some genera were land use specific within these clusters.We analysed the effect of soil properties and land use on the soil bacterial communities using an unprecedented extensive European transect allowing us to sample soils representative of a range of soil properties  from different geographical and climatic zones across Europe. Our analysis generated a broad overview of the soil bacterial diversity, richness and composition across Europe, which is strongly complementary to other intensive approaches dedicated to specific countries in Europe  scale the strong impact of soil properties on soil bacterial communities, but we also showed that under similar soil conditions, land use also contributed to their structure. These findings and the identification of indicators open up onto stimulating new prospects in terms of land management for monitoring soil biodiversity and ultimately expected services from soils.et al.28. The sampling sites were identified on the basis of EFSA spatial legacy data (Version 1.0) provided by the Joint Research Centre, of the European Commission57. We chose four parameter maps among the 52 spatial layers available: topsoil organic matter; topsoil pH in water; topsoil texture class; and EFSA Corine land cover data. The sites encompassed three types of land uses   randomly taken within a 1 × 1 m surface. All soil samples were sieved to <2 mm and prepared for soil analyses by Teagasc and DNA extraction was performed in Dijon by the GenoSol platform, UMR Agroécologie, https://www2.dijon.inra.fr/plateforme_genosol/plateforme-genosol. Soil properties were measured as described in Creamer et al.58. Briefly, soil texture was characterised following the particle pipette size method59. Total C and N were measured according to the ISO 10694 standard60; organic carbon (OC) was determined by LECO elemental analysis, conducted on 0.25 mm dry milled soil sub-samples61. The cation exchange capacity (CEC) was analysed using the BaCl2 extraction method62. The pH was measured in a 1:2.5 soil in water suspension using a glass electrode63. N mineralisation was analysed using the Illinois soil nitrogen test for amino sugar-N64. Available phosphorus content was measured using the Mehlich 3 methodology65. The sites were grouped based on their pH values , organic C content  and texture . The distribution and characteristics of the samples are presented in Table Soil samples were collected across 71 European sites Fig.  in 11 coet al.66. Briefly, 1 g of soil of each soil sample was added to a 15 ml Falcon tube containing 2.5 g of 1.4 mm diameter ceramic beads, 2 g of 100 μm diameter silica beads and 4 mm diameter glass beads. The samples were then mixed with a solution of 100 mM Tris-HCl (pH 8), 100 mM EDTA (pH 8), 100 mM NaCl, 2% (w/v) of polyvinylpyrrolidone (40 g mol−1) and 2% (w/v) sodium dodecyl sulphate. The tubes were shaken for 3 × 30 s at 4 m.s−1 in a FastPrep®−24 , before incubation for 10 min at 70 °C and centrifugation at 14,000 x g for 1 min. The supernatant was removed and then proteins were precipitated with 1/10 volume of 3 M sodium acetate prior to centrifugation . Nucleic acids were precipitated by adding 1 volume of ice-cold isopropanol. The DNA pellets obtained after centrifugation  were washed with 70% ethanol. Crude DNA extracts were then purified using a MinElute gel extraction kit  and quantified using a QuantiFluor staining kit , prior to further investigations.DNA was extracted and purified from all soil samples using the improved ISO 11063 soil DNA extraction procedure as described by Plassart 43. Five ng of DNA were used in 25 μL of PCR mixture. The PCR was conducted under the following conditions: 94 °C for 2 min, 35 cycles of 30 s at 94 °C, 30 s at 52 °C, and 1 min at 72 °C, followed by 7 min at 72 °C. The PCR products were purified using a MinElute gel extraction kit  and quantified using the PicoGreen staining Kit  on an Infinite 200 Pro fluorimeter . A second PCR of nine cycles was then conducted for each sample under similar PCR conditions using purified PCR products as DNA template and modified F479 and R888 primers containing 10 bp multiplex identifiers added at 5′ position to specifically identify each DNA sample. Two 25 µL reactions were performed for each DNA sample and pooled, before purification and quantification as previously described. Pyrosequencing was then carried out on a GS FLX Titanium (Roche 454 Sequencing System) by Genoscreen .The diversity, composition and structure of the bacterial communities were determined on the 71 soil DNA samples by 16S rRNA gene amplicon pyrosequencing. A 450 bp fragment of the 16S rRNA gene was amplified using the primers F479 (5′- AGCMGCYGCNGTAANAC-3′) and R888 (5′-CCGYCAATTCMTTTRAGT-3′)et al.43, using the GnS-PIPE67. Briefly, all the raw reads were sorted according to the multiplex identifier sequences. The raw reads were then quality filtered and trimmed based on (i) their length (370 b), (ii) their number of ambiguities (Ns = 0) and (iii) their primer(s) sequence(s). The reads were dereplicated  and aligned using INFERNAL alignment tool66, then clustered into OTUs using a PERL program that groups rare reads to abundant ones, and does not count differences in homopolymer lengths67. A filtering step was then carried out to check all singletons based on the quality of their taxonomic assignments to keep or discard them from the data sets. Finally, the retained reads were homogenised by random selection to 8,085 sequences per sample (the minimum number of reads per sample after GnS-PIPE processing) to compare the datasets and avoid biaised community comparisons. These sequences were then clustered into OTUs (97% similarity threshold) prior to determining diversity and richness indices. OTUs were further identified taxonomically using the Silva database. The raw data sets are available on the European Bioinformatics Institute database system under BioProject id. 254033, accession numbers from SAMN04384084 to SAMN04384190.We performed the 16S rRNA gene sequence analysis according to Terrat 68, using the packages Ade-469, Vegan70 and Indicspecies71. The physical and chemical heterogeneity of soils was assessed by Hill & Smith Principal Component Analysis for mixed data using the dudi.hillsmith function in ade4 package. This analysis involved the following set of variables: Soil texture according to the FAO classification , soil organic carbon, total nitrogen content, available phosphorous, soil pH, and cation exchange capacity. Land use was used as a categorical variable, and differences among land uses were tested by permutation .All the statistical analyses were performed using R free software version 3.0.2et al.12 following the method described in Harte et al.72 and by applying the equation:d is the observed Sørensen’s similarity between two soil samples that are d meters apart from each other. χd and d are determined based on community structure data and geographical coordinates of sampling sites; respectively. b is the intercept of the linear relationship and z the turnover rate of the community composition. The z estimate and its 95% confidence interval were derived from the slope (−2*z) of the relationship between similarity and distance by weighted linear regression. Only distances greater than 1 km were considered.The turnover rates (z) for bacterial community composition were derived from the slope of the Taxa-Area Relationship (TAR) as described in Ranjard 73. Non metric multidimensional scaling (NMDS) was then used to graphically depict differences between soil microbial communities using the MetaMDS function of the vegan package. The significance of the observed clustering of samples on the ordination plot was assessed by an analysis of similarity .Differences in soil microbial community structure were characterized using UniFrac distances69) on diversity indices  and on soil bacterial community structure. Total nitrogen was not considered as an explanatory variable because it was highly correlated to the total soil carbon content (r2 = 0.96). This approach consisted of two successive steps. First, we investigated the effect of environmental variables. Then, we tested the effect of geographical location and neighbourhood relationships on the residuals derived from the first step using the same methodology. For diversity indices, each step first consisted in evaluating each combination of explanatory variables relatively to its adjusted R2 (to maximize) and its Bayesian Indication Criterion (to minimize) using the regsubsets function in the leaps package. This led to a reduced set of explanatory variables which was submitted to an iterative selection by means of Canonical Redundancy Analysis using the rda and ordiR2Step functions in the vegan package (forward selection) to identify the most parsimonious model. For bacterial community structure, each step was based on a distance-based redundancy analysis on the Unifrac distance matrix and consisted of a forward selection of the most parsimonious model by the capscale and ordiR2step functions in the vegan package. Finally, in each case, the total explained variance and the marginal effect of the selected explanatory variables were determined through an ANOVA-like approach using the anova.cca function in the vegan package.A variance partitioning approach was used to investigate the relative influences of soil physicochemical characteristics , land use, climatic zone, and geographical location  and land use . This classification method identified four clusters . To identify the indicator genera of each cluster obtained by the k-mean method, the multipatt function of the indicspecies package was used71. Based on the microbial groups identified as environmental indicators, a variance partitioning approach was used to model the influence of soil physicochemical properties, land use, climate and geographic location on the relative abundance of each bacterial genus. The land use effect within the clusters was tested by multivariate analysis and analysis of similarity (ANOSIM).In order to evaluate if microbial groups could represent indicators of soil conditions, land use and/or climate conditions, we clustered the sites into different environment types using the k-means methodSupplementary Information


Peptostreptococcaceae were prominent across all ages averaging 21.3% of gene sequence reads and were higher in cats fed canned diets . Age-related effects on body composition and insulin sensitivity may become apparent as the cats grow older; this study will continue to assess these parameters.In humans, aging is associated with changes in the gastrointestinal microbiota; these changes may contribute to the age-related increase in incidence of many chronic diseases, including Type 2 diabetes. The life expectancies of cats are increasing, and they are also exhibiting the same types of diseases. While there are some studies investigating the impacts of diets on gastrointestinal microbiota in young cats, the impacts of aging in older cats has not been explored. We followed a cohort of related kittens, maintained on two commercial diets (kibbled and canned) from weaning (8 weeks) to 5 years of age (260 weeks). We hypothesized that the long-term feeding of specific diet formats would (a) lead to microbial composition changes due to aging, (b) impact body composition, and (c) affect insulin sensitivity in the aging cat. We observed that both diet and age affected fecal microbial composition, and while age correlated with changes in body composition, diet had no effect on body composition. Similarly insulin sensitivity was not affected by age nor diet. 16S rRNA sequencing found unclassified  Companion animals are starting to mirror humans in terms of longer lifespans and acquisition of age-related health problems. Aging in cats is typically defined as: Kitten: < 6 months of age. Young adult: 7 months - 2 years of age Adult: 3–6 years of age. Mature: 7–10 years of age. Senior: 11–14 years of age. Geriatric: >15 years of age  microbiome perspective, with microbiome functions characterized and modeled from this. Recently, our and others' research has shown that dogs do not always respond to dietary ingredients  kittens, maintained on two commercial diets (kibbled and canned) from weaning (8 weeks) to 5 years of age (260 weeks). We hypothesized that the long-term feeding of specific diet formats would (a) lead to microbial composition changes due to aging, (b) impact body composition, and (c) affect insulin sensitivity in the aging cat. To better understand how diet and the microbiome interact in the cat, we undertook a dataset integration approach using the 260 week data. Data pertaining to fecal microbial composition at 8 and 17 weeks have been previously published  Code of Welfare (2007) and approved by the Massey University Animal Ethics Committee . All cats used were owned by Massey University and were housed at the Centre for Feline Nutrition .n = 4 per diet) diet. Each queen was mated with a single unrelated male fed with the same diet. Half of each litter was randomly assigned (within sex) onto the kibbled diet  and half onto a canned diet . The cats were colony-housed according to diet at 8 weeks of age and remained in these groups for the duration of the study. All cats used in the study were neutered at 36 weeks (approximately 9 months) of age.Animals, weaning and housing have been described in detail previously  and canned  diets were utilized in this study ; dy Table . Both didy Table . The dieAt 17 and 260 weeks of age, the apparent digestibility of dietary macronutrients  was determined. Individual total food intake and refusals were recorded daily and total fecal output was collected over a 5 day period  following the manufacturer's instructions. Fecal microbiota profiles were determined by Illumina MiSeq sequencing of the V4–V6 region of the bacterial 16S rRNA gene, as described previously  ready for sampling. This catheter was used to determine body composition, and undertake an intravenous glucose tolerance test (IVGTT) and intravenous insulin tolerance test (IVITT).At 62 and 260 weeks of age, food was withheld from the cats 12–18 h before sedation via intravenous infusion of ketamine hydrochloride (5 mg/kg) and diazepam (0.25 mg/kg). A single port central venous jugular catheter was placed using sterile technique and a baseline jugular venous blood sample (0.5 ml) collected. A single cutaneous stay suture was placed and the site was dressed with iodine and sterile gauze. Cats were then given 2O injection.The following day, blood samples were collected (2 ml) immediately prior (T0) to deuterium oxide  being injected into a 20 gauge cephalic venous catheter   coupled online with a Thermo Trace GC via a Thermo Conflow III combustion interface . Acetylene eluting from the GC column was pyrolysed to H2. After drying the gas stream by a nafion membrane, gases were introduced into the IRMS ion source. A capillary column  with helium as carrier gas (1.5 ml/min) was used for the separation of acetylene from air components. Fifteen microliters was injected in the split mode (1:20 spilt ratio) by an autosampler  fitted with a 100 μl headspace syringe. The column head pressure was 150 kPa and injector temperature 110°C. The GC oven temperature was maintained isothermally at 110°C for the duration of the run. Data processing was performed by the vendor provided software, ISODAT. For purposes of IRMS calibration, mixtures of D2O and unlabelled H2O, between 0 and 5 mg of D2O per ml H2O, were prepared and analyzed in analogous fashion to the plasma samples. Body composition was calculated as described previously, using total body mass divided by 0.744 to account for water content of lean mass in cats  was infused into the cephalic venous catheter over 60 s . The next day, 0.01 U/kg recombinant human insulin (Actrapid®) was injected into the cephalic venous catheter. Immediately before injection, and 5, 10, 15, and 20 min after, blood samples (1 ml) were taken from the central venous jugular catheter. The cats were offered ad libitum access to food post-sampling.Immediately after the initial Dg). Plasma levels of glucose (hexokinase) and insulin (radioimmunoassay) were measured. Area under the curve (AUC) and insulin sensitivity indices were assessed as described previously  repeated measures Correlation Models by REML using diet (canned vs. kibbled) and time (age) as factors (Genstat version 17.1). The results from the insulin assay were unreliable and are not reported in this paper. For the glucose parameters, due to catheter failure, two data points are missing for the canned treatment group at 104 weeks; additionally for the 260 week data one data point is missing for each the canned and kibbled diet.R2-value. In order to better understand the relationship between insulin sensitivity index and fecal microbiota insulin sensitivity values were overlaid with microbiota generating Unifrac PCoA plots. For this, only cats where we had data for at least 3 of the time points  were analyzed.Only data relating to the cats at 260 weeks of age were integrated using similar methodology described previously  on bodyweight and lean mass with older cats (260 weeks) being heavier and leaner than when they were 62 weeks of age. There was no significant effect of age on fat mass , 45 (P = 0.02), 60 (P = 0.02), and 90 min (P = 0.08), there were no significant effects due to age on the total AUC for glucose.Plasma glucose concentrations from the IVGTT performed on 62 and 260 weeks old cats are shown Figure . There wInsulin sensitivity index, calculated from the IVITT was unaffected by diet or age Table . InsulinP < 0.05) interaction between diet and age for the apparent digestibility of crude protein, gross energy, and dry matter (Table P < 0.05), however it was unaffected by age of the cat . In contrast, hexanoate was decreased (P < 0.05) in cats fed the canned diets. Age affected the concentration of a number of organic acids: butyrate, valerate, isobutyrate, isovalerate, hexanoate, and succinate all decreased with age whereas lactate concentrations increased with age (P < 0.05). The concentrations of butyrate, propionate, valerate, hexanoate, and succinate all showed significant diet and age interactions  in older cats fed kibbled diets.In young cats (8 and 17 weeks) fed kibbled diets, unclassified 5% Table . HoweverPeptostreptococcaceae were also prominent at all ages, increasing from 16% at 8 weeks to approximately 30% at 17 and 104 weeks and decreasing to 23.4% at 260 weeks of age (Table Prevotella (6.8–16%) and a group of unclassified Prevotellaceae (7.9–10.3%). Clostridium were also prominent in younger cats fed the canned diet , although their abundances declined with age (1.9 and 3.2% in 104 and 260 weeks).In cats fed canned diets, unclassified Peptostreptococcaceae had greater relative abundances in cats fed the canned diet compared to the kibbled diet . Prevotella were more abundant in cats fed the kibbled diet, averaging 20.0% relative abundance, compared to 10.8% in the cats fed the canned diets. Similarly, Megasphaera  and Blautia  were more prevalent in cats fed the kibbled than the canned diet, respectively. Unclassified Prevotellaceae  and Bacteroides  were higher in cats fed canned diets.Overall, the unclassified The key relationships between microbial composition and physiological parameters in five year old week 260) cats are shown in a Clustered Image Map Figure  and a ne60 cats aBacteroidetes, Bacteroidales, and Bacteroidaceae were positively associated with isovalerate and isobutyrate concentrations (>0.64), total intake (>0.76), fecal gross energy concentrations (>0.58), and the Metabolizable energy (ME) content of the diet (>0.58). Unclassified Bacteroidetes and unclassified Bacteroidales were also positively associated with the excretion of crude protein, crude fat and gross energy (>0.52) and with intake (>0.70). Unclassified Bacteroidetes and unclassified Bacteroidales showed weak negative associations with acetate, propionate and hexanoate concentrations (< −0.48).Unclassified members of Eubacteriaceae and Peptostreptococcaceae were positively associated with isovalerate and isobutyrate concentrations (>0.76) and negatively associated with acetate, and propionate (< −0.64). Both were also positively associated with the ME content of the diet (>0.76) and crude protein digestibility (>0.64 and >0.58 respectively). Fusobacteriaceae was also positively associated with isovalerate and isobutyrate, the excretion of crude protein, crude fat and gross energy and total fecal weight (>0.76). This family was also positively associated with intake (>0.76).Members of Veillonellaceae were negatively associated with isovalerate and isobutyrate concentrations and total intake (< −0.76) and fecal gross energy content (< −0.70). Lactobacillaceae were positively associated with acetate, propionate and hexanoate (>0.58) and negatively associated with the apparent digestibility of crude fat and crude protein (< −0.70). Streptococcaceae was negatively associated with crude protein and crude fat apparent digestibility (< −0.64). Unclassified Deltaproteobacteria were also positively associated with acetate (>0.58), propionate and hexanoate (>0.52)  Figure , and negty < −0.6. UnclassGlucose T0, insulin sensitivity index and body composition were poorly associated (< 0.40–0.46) with individual microbial taxa Figure .While previous studies have also shown that age affects the composition of microbiota in the domestic cat , while Bacteroides, Prevotella, and Megasphaera were the dominant genera at 42 weeks of age. These results differ from culture-based studies where the researchers did not detect bifidobacteria in young kittens and showed lactobacilli abundance was similar in cats at various ages  in kittens (8–17 weeks) fed the canned diets, they were not detected from these same cats at 5 years of age. The higher abundances of Lactobacillus and Bifidobacterium in the earlier times points (8–17 weeks) is likely due to the influence of milk-feeding/weaning schedule. The contrasting results between 16S rRNA gene based analyses in the current study, shotgun metagenome sequencing data  and adult (5 years of age). A recent study investigated the fecal microbiome of cats fed a kibbled diet as they grew from 18 to 42 weeks of age  cats , Firmicutes , and Fusobacteria (Fusobacteriaceae).We hypothesized that dietary format would impact microbial composition. We observed large changes in microbial composition in relation to diet. To better understand the changes in microbial composition we integrated them with a number of parameters including diet composition and related physiology parameters such as macronutrient digestibility. This analysis identified nine bacterial families that were associated with parameters relating to gross energy, crude protein and crude fat levels in the diet and their apparent digestibility, fecal organic acid concentrations and fecal DM. These families are mainly members of the phyla Bacteroidaceae was Bacteroides, which were relatively more abundant in the cats fed the canned diets, in agreement with a previous study from our laboratory  were observed. While this suggests that in the domestic cat, Bacteroidaceae are associated with crude protein digestibility, studies that have investigated either high protein diets  and negatively associated with markers of carbohydrate fermentation  suggesting a role in protein fermentation in the cat. This may explain the higher abundance of in Eubacterium observed in cats fed high protein diets , as with Eubacteriaceae and Peptostreptococcaeae, were positively associated with crude protein digestibility and markers of protein fermentation. In the dog Peptococcaceae was also associated with crude protein content of the diet and crude protein digestibility , and identified that neuter status (neutered) was a major risk factor in developing obesity , Lactobacillaceae (Lactobacillus), and Streptococcaceae . However, we did observe greater abundance Prevotella, Acidaminococcus and Faecalibacterium and decreased Blautia and Clostridium in cats fed the kibbled diet, suggesting a similar pattern to obese cats.We hypothesized that when cats were Obesity arises as a mismatch between energy intake and energy expenditure. Certainly, indoor living  has also been identified as a risk factor for obesity in domestic cats . Nonetheless, significant correlations were observed between two related taxa, Streptococcus and Lactobacillus, and insulin sensitivity index , suggesting a possible link between some members of the intestinal community and insulin response. A recent study investigating the fecal microbiota of cats with diabetes and treated with insulin, showed no significant changes in fecal microbial composition although the abundances of Bacterioides and Bifidobacterium trended (P < 0.10) toward decreasing with the disease  cats. This study will continue to assess these parameters as the cats grow older. Microbial families including The experimental design was devised by EB and DT. Animal work was undertaken by EB, DT, CB, and NC. Laboratory work was undertaken by CB and DR. Bioinformatic and statistical analyses were conducted by EB, WY, and CM and dataset integration was undertaken by PM. All authors contributed to the preparation of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.