Previous studies have revealed autosomal epigenetic differences between males and females in some tissues, including differences in DNA methylation patterns. Here, we report for the first time an analysis of autosomal sex differences in DNAme using the Illumina EPIC array in human whole blood by performing a discovery  with the majority found to be female-biased CpGs (74%). These saDMP’s are enriched in CpG islands and CpG shores and located preferentially at 5’UTRs, 3’UTRs and enhancers. Additionally, we identified 266 significant sex-associated differentially methylated regions overlapping genes, which have previously been shown to exhibit epigenetic sex differences, and novel genes. Transcription factor binding site enrichment revealed enrichment of transcription factors related to critical developmental processes and sex determination such as SRY and ESR1.Our study reports a reliable catalogue of sex-associated CpG sites and elucidates several characteristics of these sites using large-scale discovery and validation data sets. This resource will benefit future studies aiming to investigate sex specific epigenetic signatures and further our understanding of the role of DNA methylation in sex differences in human whole blood.The online version contains supplementary material available at 10.1186/s13148-022-01279-7. Sex is an important covariate in all epigenetic research due to its role in the incidence, progression and outcome of many phenotypic characteristics and human diseases , 2. TherSex differences in disease prevalence are sometimes explained at the molecular level and rooted in genetic differences between males and females. Differences in sex chromosome complement have independently been shown to direct differences in gene expression and chromatin organisation –11. FurtPrevious research has also revealed sex differences in gene expression of autosomal genes as well as sex chromosome linked genes . It is wSex specific gene expression and levels of sex hormones may be mediated by epigenetic mechanisms, including DNA methylation. Several genome wide association methylome studies  have highlighted differences in DNA methylation patterns linked to sex differences in genes on the autosomes –18. PrevHowever, due to X chromosome inactivation in females, large differences in methylation levels of X chromosomes can be observed between males and females . Recent Additionally, it is worth noting that thousands of autosomal CpGs do show very small differences in DNA methylation patterns between males and females. However, a robust and well-annotated catalogue of sites showing the largest differences still needs to be characterised.Here, we use the EPIC BeadChip to assess autosomal sex differences in DNA methylation levels from whole blood at individual sites and genomic regions. All individuals involved in this study were part of Understanding Society: The UK Household longitudinal study . Addition = 747,302 CpGs were analysed . Sites which are known SNP probes, cross hybridising or X/Y linked probes were excluded. Moreover, since whole blood is a bulk tissue, we calculated the estimated cell type proportions for whole blood between our male and female samples to assess whether any differences in cell type proportions would potentially be reflected in our results resulting in false positives. Using Wilcoxon test, we found no significant difference in the proportions of Granulocytes between males and females, but we did find statistically significant differences in proportions of CD4T, CD8T, Natural killer, B cells and monocytes , we identified 54,261 autosomal CpGs associated with sex in our discovery and validation data set  differences between males and females on the autosomes was performed using linear regression for the Illumina EPIC BeadChip for 1171 individuals  for discovery and repeated in a validation data set of 2471 participants . After data processing and cleaning, The lambda value of the Q-Q plots is slightly high  to see how male and female beta values clustered in PC space and to evaluate the effect of DNAme at the saDMPs. As shown in Fig. The saDMPs were found in 174 unique genes with 48 of these genes harbouring several saDMPs , which are known to interact with CDYL. Chromodomain Y-like protein (CDYL) is a chromatin reader binding to heterochromatin  that is crucial for spermatogenesis, male fertility and X chromosome inactivation . In addiNext, to evaluate whether the genes controlled by the saDMPs are part of the same regulatory network, we merged all proximal and distal genes and produced protein–protein interaction networks to visualise the networks of these genes. Following this, we were able to identify the top 30 hub genes by evaluating each gene by its network connectivity. The results for these analyses are produced in Additional file p value < 0.05)  binding site and gene ontology analyses. First, we evaluated whether the saDMPs were enriched in motifs for TFs (100 bp window). For the 293 female-biased CpGs, we found 315 unique enriched TFs (05) Fig. .Fig. 2TrTo analyse whether the TF motifs were enriched for annotation to biological processes or pathways, we performed pathway analyses using the GO and KEGG databases in order to learn more about how potential sex specific regulatory pathways affected different pathways. We identified several enriched KEGG pathways for the TFBS enriched at female-biased CpGs, spanning a wide range of processes such as transcriptional misregulation in cancer, several specific cancer pathways, PI3K-Akt signalling and more Fig. B. In addAs we identified enrichment for some transcription factors encoded on the sex chromosomes (e.g. SRY), we hypothesised that sex chromosome encoded transcription factors may influence CpG methylation at the saDMPs directly or indirectly by acting as hub genes in the enriched TF motif network. To assess this, we firstly produced protein–protein interaction networks to visualise the networks of these TFs  did in fact act as a hub gene in the TF network; however, the other 29 genes were encoded on the autosomes  + (GSE120312). The majority of the differentially expressed genes (DEGs) are located on the sex chromosomes, but we also did observe differential expression between males and females for several autosomal genes  and adjustment for cell type proportions, batch effects and age, we identified many sex-associated differentially methylated regions. We therefore considered a sex associated differentially methylated regions (saDMRs) as significant if it harboured at least 5 CpGs, had an FDR value smaller than 0.05, had a methylation difference within the region greater than 0.05 in either direction and was present in both our discovery and validation data set. Following filtering of the list of saDMRs, we identified 266 significant sex-associated DMRs on the autosomes between males and females located at 231 unique sets of genes . We, thereby, identified 396 sex-associated differentially methylated positions on the autosomes. Previous work has reported contradicting results, some research report that there is higher methylation on autosomes in females , 21, whiTherefore, although the existence of these autosomal sex-associated CpG sites is well established, a robust and consistent catalogue is yet to emerge. When comparing the saDMPs discovered in this study to findings previously reported in blood samples (where a full list of sex-associated sites were available), we do observe some overlap, although it is limited Table . For exaHowever, alternative reasons for this limited overlap exist; firstly, this may be attributed to differences in sample size, as the datasets used within this study are much larger than previously used thus increasing our ability to detect true sex differences in DNA methylation.Secondly, the differing normalisation methods applied to DNAme microarray data. Previous research has shown that the methylation levels of CpG sites on the X chromosome differ largely between males and females  and, thuMoreover, as thousands of autosomal CpG sites show differences in DNA methylation between males and females, differences in the methods for determining the definition of a sex-associated site result in limited reproducibility between studies, a point also raised by Gatev and colleagues in their identification of sex-associated regions . Here, wn = 103) and those that were female-biased CpGs (n = 293). Several saDMPs found to be female-biased CpGs overlapped the transcription start site (TSS) of genes not previously been reported to exhibit sex differences in DNAme including C19orf77, ATP10D and SHANK2. Interestingly, it has previously been shown that sex hormones can regulate SHANK expression leading to a sex differential expression in SHANK2 [We further categorised these 396 saDMPs into two groups, those that were male-biased CpGs , it was also the third most significant saDMR, suggesting these regions could account for important sex biases observed in some diseases. This is further supported by the fact that GABPA has also been heavily associated with early onset of Alzheimer's disease, Parkinson's disease, breast cancer and autism , 73–75.n = 123) is located on chromosome 6, overlaps TUBB, MDC1 and XXbac. MDC1 is thought to play a crucial role in the production of male games, lead to XY body formation and also initiate meiotic sex chromosome inactivation. These functions are achieved through its interaction with DNA damage response (DDR) factors, ultimately leading to transcriptional silencing [The saDMR harbouring the highest number of CpG sites  transcription factor motif, also known as the sex determining factor, to be enriched at female-biased CpGs and further identified this gene to be acting as a hub in the TF network. SRY has been found to bind and repress WNT activation of ovarian genes, and has been shown to bind the promoter regions of many targets of involved in differentiation of the testis , 49, 85.It has previously been reported that 3D genome organisation can impact sex-biased gene expression through direct and indirect effects of cohesion and CTCF looping on enhancer interactions with sex-biased genes . Recentln = 1171 and validation, n = 2471) and improved handling of sex bias introduced by normalising such data with the sex chromosomes. Both factors contribute to our ability to detect true positives and obtain a more robust catalogue of true sex-associated autosomal CpGs.Lastly, we acknowledge limited overlap with previous studies yet conclude that this is due to our extremely large sample size  and also for our validation data set .As whole blood is a heterogenous tissue and contains different cell types, individual samples will have different cell type proportions which may confound analyses. Often, this manifests itself as many false positives being identified. The estimation is based on epigenetic data and expected DNA methylation signatures at specific loci in each cell types are used to estimate cell type composition. To ensure that whole blood cell composition did not differ significantly by sex and would not introduce bias to our results, the relative proportions of Granulocytes, mononuclear, natural killer, CD4T, CD8T and B cells were estimated for all samples using the bigmelon . Furtherbacon for both our discovery and validation data sets [DMRcate function from the R package ChAMP to detect DMRs between males and females on the autosomes [Sex-associated autosomal differentially methylated positions (saDMPs) were identified by performing linear modelling using the limma package in R  using seata sets . A probeutosomes . A DMR whttps://genome.ucsc.edu/cgi-bin/hgTables).We annotated the autosomal CpG’s using the manufacturer supplied annotation data (MethylationEPIC_v-1-0_B2 manifest file). Annotation was completed in the R package Minfi . Severalgometh function in the missMethyl package [enrichGO from the clusterProfiler package in R [GO analyses were conducted using the  package  which teage in R , which pp value < 0.05). RNA-seq data for 20 healthy donors  from publicly available data from GEO (GSE120312) were used in our analysis. We used the pre-processed count matrices with DESeq2 [p value of 0.05 and log2 fold change of 1. DESeq2 does apply an automatic filtering step to remove genes with low counts but we did also apply our own independent filtering to this data by removing genes that have counts of at least 10 in all samples.The enrichment analysis of known motifs in sex-associated DMPs was performed using the R package PWMEnrich  using thh DESeq2  to calcufindOverlaps function from the GenomicRanges package using a maxgap of 10,000. Following this, we then annotated the corresponding anchor to the relevant gene ID. These steps then allowed us to perform network analysis in Cytoscape [We examined whether any of the sex-associated DMPs made 3D contacts with distal genes using Hi-C data available from the GEO under accession number GSE124974) for white blood cells and neutrophils. Hi-C library preparation was performed using the Arima-HiC kit and pre-processing of the data was performed using Juicer command line tools . Reads w4 for whiytoscape  and GO aytoscape .We searched all of the genes annotated to our saDMPs using the Search Tool for the Retrieval of Interacting Genes (STRING) (https:://string-db.org) database to generate our networks. We extracted protein–protein interactions with a combined score of > 0.4. Following this, we utilised the cytoscape plugin tool Cytohubba  in orderAdditional file 1. Significant sex-associated autosomal DMPs. Illumina Manifest annotations for all 396 significant CpG sites associated with sex on the autosomes.Additional file 2. Significant sex-associated autosomal DMRs. Test results for all significant DMR’s associated with sex on the autosomes ordered by FDR value.Additional file 3. Enrichment statistics for the TF motifs enriched at female-biased CpGs.Additional file 4. Enrichment statistics for the TF motifs enriched at male-biased CpGs.Additional file 5. Genes annotated to saDMPs via Hi-C analysis.Additional file 6. Figure S1: (A) QQ plot and lambda values (discovery data) distribution of the adjusted p values against the null distribution for EWAS of sex in the understanding society cohort. Genomic inflation lambda score is indicated in the QQ plot to indicate statistical inflation of p values. (B) Boxplots of estimated whole blood cell type proportions for males (orange) and females (blue) in the discovery data set, estimated using the estimateCellCounts function from bigmelon. We performed a Mann-Whitney U test . (C) QQ plot and lambda values  distribution of the adjusted p values against the null distribution for EWAS of sex in the understanding society cohort. Genomic inflation lambda score is indicated in the QQ plot to indicate statistical inflation of p values. (D) Boxplots of estimated whole blood cell type proportions for males (orange) and females (blue) in the validation data set, estimated using the estimateCellCounts function from bigmelon. We performed a Mann–Whitney U test . (E) Venn diagram showing overlap of differentially methylated positions identified in our validation and discovery data set before and after filtering of the list of saDMPs.Additional file 7. Figure S2: (A) Integrated genomics viewer track of chromatin loop on chromosome 6 showing two male-biased CpGs contacting H1/H4/H3/H2V/H2A. (B) Integrated genomics viewer track of chromatin loop on chromosome 1 showing a female-biased CpG contacting the ODF2L gene. Blue lines represent the chromatin loops, with black lines showing the loop anchors. Orange vertical lines represent the male-biased CpGs and blue vertical lines represent the female-biased CpGs. Purple annotations represent genes. (C-D) Subnetworks of the top 30 genes annotated to male-biased CpGs (C) and females (D). Node colour represents the degree of connectivity. The scale from red to yellow represents the top 30 enriched genes rank from 1-30, with red indicating highest degree and yellow indicating lowest degree. Additional file 8. Figure S3: GO terms overrepresented for the significantly enriched TF motifs at male-biased CpGs (A) and female-biased CpGs (B).Additional file 9. Figure S4: (A-B) Network visualisation of protein-protein interactions for all transcription factor motifs found to be enriched at male-biased CpGs (A) and female-biased CpGs (B). Grey coloured boxes represent individual TFs located on autosomes, while purple-coloured boxes represent TFs encoded on the X chromosome and green coloured boxes represent TFs encoded for on the Y chromosome. Grey lines represent edges between transcription factors within the protein-protein network. (C-D) Subnetworks of the top 30 enriched TF motifs at male-biased CpGs (C) and females (D). Node colour represents the degree of connectivity. The scale from red to yellow represents the top 30 enriched TF motif rank from 1-30, with red indicating highest degree and yellow indicating lowest degree.Additional file 10. Figure S5: Volcano plot showing differential gene expression between males and females. We considered the case of: (A) genes annotated to the saDMPs, (B) sex chromosome linked genes and (C) autosomal genes. Points coloured in grey represent non differentially expressed genes. Green points represent genes which had a log2 Fold Change value greater than 1. Blue points represent genes which met the adjusted p value threshold (FDR <0.05). Points coloured in red represent genes which showed differential expression between males and females .


In vitro fermentations with human stool microbiomes show that beneficial taxonomic and metabolic signatures driven by grain from wild type parental lines are lost in fermentations of grain from the waxy derivatives and the beneficial signatures can be restored by addition of resistant starch. These undesirable effects are conserved in fermentations of waxy maize, wheat, rice and millet. We also demonstrate that humanized gnotobiotic mice fed low fiber diets supplemented with 20% grain from isogenic pairs of waxy vs. wild type parental sorghum have significant differences in microbiome composition and show increased weight gain. We conclude that the benefits of waxy starches on food functionality can have unintended tradeoff effects on the gut microbiome and host physiology that could be particularly relevant in human populations consuming large amounts of waxy grains.Waxy starches from cereal grains contain >90% amylopectin due to naturally occurring mutations that block amylose biosynthesis. Waxy starches have unique organoleptic characteristics (e.g. sticky rice) as well as desirable physicochemical properties for food processing. Using isogenic pairs of wild type sorghum lines and their waxy derivatives, we studied the effects of waxy starches in the whole grain context on the human gut microbiome.  These undesirable behaviors of gelling, viscosity, and stability in native starches are typically overcome by modifying the starch through chemical or physical modification, crop breeding, or a combination of the two.2Starches are large polymers of glucose joined by glycosidic bonds and are important components of foods and food products worldwide. In the food industry, starch is introduced into a wide array of food products, but for most applications, the physicochemical properties of starches in their native form are unsuitable3 Waxy starches were discovered in the 1900s in a unique, naturally occurring variant of maize that produced starches containing almost exclusively amylopectin.4 The absence of amylose in waxy starches produces desirable physicochemical properties , and waxy starches are widely used by the food industry as thickeners, gelling agents, and stabilizers.5 Since the discovery of the waxy phenotype in maize, naturally occurring waxy variants have also been identified in many other grain crops, including sorghum, rice, proso millet, and wheat. In the cases that have been studied, causal variants of the waxy phenotypes in cereal grains are due to loss of function mutations in the gene encoding granule bound starch synthase (GBSS), the enzyme responsible for amylose synthesis in starch granules.6–12Starches are made up of two major types of glucose polymers: amylose, an almost entirely linear -α-D-glucan, and amylopectin, a highly branched α glucan containing both  and (16) linkages.13 In humans, the high digestibility amylopectin leads to nearly complete digestion of waxy starches by host amylases in the upper gastrointestinal (GI) tract.15 Unfortunately, this increased digestibility is also associated with a higher glycemic index rating for waxy starches compared to nonwaxy wild type starches.17 The condensed straight chain structure of amylose has fewer sites for degradation by host enzymes, and consequently, a sizable portion of ingested amylose survives transit through the small intestine and enters the colon where it serves as a substrate for fermentation by amylolytic organisms in the colonic microbiota.19In addition to the effects of waxy mutations on physicochemical properties of starch, the reduced amylose in waxy derivatives also has significant effects on starch digestibility. The branched structure of amylopectin increases sites for enzymatic digestion and resists the effects of retrogradation when thermally processed.20–23 Although amylose concentration influences the digestion resistance of RS, other factors such as source, granule composition, and thermal versus chemical modification also contribute and are used to classify RS into different categories.13 RS mediates metabolic improvements through microbiome dependent and independent pathways.24–26 The microbiome dependent pathways appear to be mediated through RS-stimulated growth of beneficial amylolytic bacteria that metabolize RS to short-chain fatty acids (SCFA) such as butyrate.28 The effects of microbially produced butyrate are pleiotropic and afford the host multiple benefits such as providing energy to colonocytes, enhancing anti-inflammatory functions, and protecting against allergic responses.26 Thus, in addition to promoting high glycemic responses, the absence of amylose in waxy starches presumably leads to lower levels of RS, consequently reducing RS-mediated benefits mediated by the colonic microbiota.Resistant starches (RS), which are recalcitrant to hydrolysis and enzymatic digestion, have been shown to provide health benefits to humans, including improved glucose tolerance and cholesterol levels as well as reduced inflammatory markers and toxic biomarkers for chronic kidney disease.30 Therefore, understanding how waxy phenotypes, in the whole grain context, may influence the microbiome and host physiology is of primary interest to domestic and global health.Although resistant starch has beneficial effects on both the gut microbiome and characteristics related to gut health, most of the relationships between RS and human health have been studied in the context of pure RS. We therefore know very little about the relationships of waxy starches in their native, whole-grain context with the microbiome and host health. This gap is particularly relevant in Asia and Africa, where rice and sorghum are consumed daily as whole grain staples. While studies of locally grown and consumed rice and sorghum are limited, recent work on locally grown and consumed rice cultivars in selected areas of Asia has shown that nearly half of the cultivars studied have low amylose content and would be considered waxy.invitro microbiome fermentations and mouse feeding studies, we detected significant differences in the effects of waxy derivatives versus wild type parental lines on the overall composition of the microbiome and the abundances of several microbial taxa, many of which corresponded to significant decreases in abundances of beneficial microbes in treatments with waxy lines. In addition to the strong signatures of effects on the microbiome, our studies with human microbiota-associated mice also demonstrated dramatic effects of waxy starch on host weight gain, with significant increases in weight gain observed among animals fed diets with waxy versus wild type parental sorghum. Collectively, our work highlights the need to understand how traits such as waxy, which have major effects on grain composition, can have strong trade-off effects on the gut microbiome and host health characteristics.To address the knowledge gaps related to effects of waxy grains on the gut microbiome in a whole-grain context, we used near-isogenic lines of wild type and waxy derivatives of sorghum to study the effects of whole-grain starch composition on the human gut microbiome. Using a combination of invitro microbiome fermentation reactions across a set of near isogenic lines (NILs) of sorghum derived from six different elite genetic backgrounds. Amylose and resistant starch content  from each pair of NILs was measured  with distinct baseline microbiome compositional features .In the first set of experiments, we examined the outcomes of measured . Across p < .001). Subsequent analysis of β-diversity in samples from each individual microbiome by canonical analysis of principal coordinates (CAP) based on Bray–Curtis distance illustrated the strength of associations between β-diversity of the microbiomes with wild type or waxy grain lines . When compared to fermentations of the wild type parental lines, our results collectively show that the waxy lines had major effects on the overall α- and β-diversity of the microbiomes, and that microbiomes from each subject were able to differentiate substrates from wild type versus waxy sorghum regardless of the sorghum genetic background in which the waxy mutations were introduced.Compositional features of the microbiomes from multiple subjects showed highly significant treatment effects in fermentations of waxy compared to wild type parental lines. These treatment effects (waxy versus wild type) manifested as differences in ecological metrics (α- and β-diversity) of the microbiomes as well as significant differences in the relative abundances of individual and groups of taxa. With respect to ecological metrics, the Shannon index (α-diversity) was significantly lower in fermentations with waxy sorghum when compared to wild type lines . PERMANOin lines  and S2, Proteobacteria and Bacteroidetes were found in fermentation of waxy lines, whereas significantly higher abundances of Firmicutes and Actinobacteria were found in fermentations from wild type lines  with statistical significance in at least three of the subject microbiomes. Similarly, Sutterella, Escherichia-Shigella, and an unclassified taxon from Enterobacteriaceae were each present at significantly higher abundances in fermentations from waxy lines from microbiomes of five or more donors. Accounting for most of the significant increase in Firmicutes from fermentations of wild type parental lines were members of the family Lachnospiraceae, and genera from this family also showed the most consistent behavior across donor microbiomes. For example, Roseburia was significantly higher in fermentations of wild type sorghum across all 12 donor microbiomes while Blautia and Coprococcus were significantly higher in nine and ten microbiomes  from each donor microbiome. Statistical significance was tested at multiple taxonomic levels. At the phylum level, significantly higher abundances of Roseburia in fermentations of grain from wild type parental versus waxy lines. Using species-specific qPCR reactions, we confirmed the observations from the 16S rRNA sequencing data and found that this behavior was shared by three of the major Roseburia species from human microbiomes  as each of these species were significantly enriched in fermentations from wild type parental sorghum lines across microbiomes from ten or more subjects compared to fermentations from near-isogenic waxy derivative lines  across hosts corresponded to increased abundances of the amylolytic genus ve lines . R. faecsubjects .Figure Roseburia species offers an explanation as to why R. intestinalis, R. hominis, and R. inulinovorans were more responsive to differences in amylose content compared to R. faecis. Genomes of R. intestinalis, R. hominis, and R. inulinivorans had 5–14 different genes encoding GH3-family enzymes associated with starch degradation whereas R. faecis carried only two genes encoding GH3 enzymes  across individual microbiomes.Comparisons of CAZyme glycohydrolase families (GH) found in the genomes of representative strains of four human  enzymes . Thus, eLachnospiraceae  in fermentations of waxy lines across multiple microbiomes would also be expected to be accompanied by decreased production of butyrate, the major end-product of starch fermentation by these organisms. Measurement of the major SCFAs by gas chromatography of supernatants from the fermentations . Concentrations of the other major SCFA  were not significantly affected by treatment (waxy versus wild type lines).The significant decreases in the abundances of amylolytic, butyrate-producing members of the ntations  confirmeLachnospiraceae in butyrate production as the relative abundances of Roseburia (9 out of 12 microbiomes) and Coprococcus (8 out of 12 microbiomes) had some of the strongest correlations with butyrate production  further supports substantial roles for members of the robiomes . Thus, w35 We therefore used “molecular complementation” experiments to confirm that the differential microbiome effects observed in fermentation of whole grain from wild type and waxy lines were due to amylose content alone and not an unknown pleiotropic effect of the GBSS mutations on other seed components. Molecular complementation was achieved by introducing digestion-resistant starch purified from wild type sorghum lines into fermentations with grain from amylose-deficient waxy lines and examining effects on microbiome phenotypes. Microbiome data from the fermentations were analyzed by comparing Bray–Curtis distance of microbiomes from (i) fermentation of waxy sorghum lines alone, (ii) fermentations of waxy lines supplemented with amylose-enriched (digestion-resistant) starch from wild type lines, and (iii) fermentations of wild type lines alone. The addition of the digestion-resistant starch from wild type lines indeed caused significant shifts in β-diversity in the microbiomes across multiple human subjects, with microbiomes from most subjects responding to complementation with profiles that were intermediate to profiles from fermentation of wild type parental or waxy lines  can also have pleiotropic effects on other major components of the seed and further impact microbiome .xy lines  and S3 bxy lines .Figure Roseburia species in the fermentation reactions  and concomitant changes in butyrate production. Consequently, the significant microbiome phenotypes caused by the waxy mutation in our invitro fermentation reactions appear to be primarily dependent on the effects of waxy mutations on amylose content of the grain.We further investigated molecular complementation at higher taxonomic resolution by qPCR quantification of eactions . These rinvitro fecal fermentations on grain derived from waxy and wild type lines of sorghum, maize, millet, rice, and wheat using the same 12 donor microbiomes for all grains tested. While the sorghum and maize grain for this experiment were derived from NILs of wild type parental and waxy derivatives, grain from wheat, rice, and millet were not derived from isogenic pairs.The waxy starch trait has been developed in many small grain plant species due to its unique physicochemical properties. As with our sorghum lines, waxy lines of these other small grain species have a lower concentration of digestion-resistant starch compared to corresponding wild type lines after digestion . To deteRoseburia and elevated abundances of Escherichia based on two-way rANOVA with FDR correction). However, the taxonomic responses were largely unique to each of the crop species  mice enables study of the functional consequences of dietary components on human-adapted gut microbes within a model animal system.37 To that end, we used HMA mice to determine if feeding diets supplemented with 20% whole grain flour from either isogenic wild type or waxy sorghum lines could drive significant microbiome changes in vivo. Four unique HMA mouse lines were created by colonizing germ-free C57BL/6 mice with one of four human microbiomes  demonstrating the most significant differential responses to the sorghum substrates during invitro fermentation studies  in the β-diversity of HMA mouse microbiomes from all four human donors when fed a waxy sorghum diet compared to a wild type sorghum diet  and cecal  bacterial communities of HMA mice by 16S rRNA gene sequencing over time revealed that both wild type and waxy sorghum diets increased the α-diversity  of the bacterial community compared to a low fiber control diet. There were no significant differences in the Shannon index or number of ASVs in the microbiomes of mice fed waxy or wild type sorghum diets, but a lower Pielou’s evenness index was observed in mice fed waxy sorghum , Erysipelotrichiaceae (Allobaculum), Lachnospiraceae (Agathobacter and Anaerostipes), Ruminococcaceae , Clostridia (Christensensellaceae) and Enterococcaceae (Enterococcus) whereas abundances of Erysipeloatoclostridium (Erysipelotrichaceae) and Phascolarctobacterium (Acidaminococcaceae) were higher in mice fed waxy sorghum. Microbiomes from HMA mice harboring stool from subject S778 shared some overlapping taxonomic responses with those carrying the S772 microbiota when receiving waxy sorghum compared to wild type sorghum, including decreased abundances of Christensenellaceae and increased abundances of Erysipeloatoclostridium and Phascolarctobacterium. Another intriguing aspect of the microbiome-dependent responses was the observation that treatment effects on several of the significant taxa were not necessarily in the same direction for each microbiome, implying that the microbiome context is a major determinant of how individual microbial taxa may respond. However, we did note that three taxa, Faecalibacterium, Christensenellaceae, and Enterococcus, exhibited significant diet-driven responses in the same direction  across HMA mouse lines from two or more donor microbiomes.Consistent with the changes in β-diversity, significant differences in the relative abundances of bacterial genera were also detected in each HMA mouse line fed diets containing waxy versus wild type sorghum  showed that populations of Roseburia species declined rapidly after microbiome introduction in all four HMA lines. Indeed, by day 7, populations of all four Roseburia species declined to levels near the threshold for detection, suggesting that Roseburia did not efficiently engraft and persist in ex-germ-free mice. Further study is needed to determine if poor colonization of the mouse host is a general feature of Roseburia.Although dietary treatments (wild type versus waxy) had significant effects on compositional features of the microbiomes in HMA mouse lines, including members of the robiomes , absenceIn addition to the microbiome phenotypes, we also tested for treatment effects (wild type versus waxy) on feed intake and weight gain throughout the study. Remarkably, we found that animals from all four HMA mouse lines fed diets supplemented with waxy sorghum gained significantly more weight compared to their counterparts receiving either the wild type sorghum or low fiber control diets , and MCP-1 . Mice receiving a wild type parental sorghum flour diet had significantly lower levels of IL-5, KC, and MCP-1 compared to mice fed a low fiber diet. In comparison, feeding a waxy sorghum diet induced intermediate levels of IL-5, KC, and MCP-1 that were not significantly different compared to those observed when feeding either a wild type parental sorghum or low fiber diet (rANOVA followed by FDR correction). Together, these data demonstrate that even within a relatively short feeding period and in the absence of a disease state, there is evidence for low-levels of diet-induced immune modulation in HMA mice.41–43 little is known about how these starches affect the gut microbiome or other characteristics related to human health. To begin filling this knowledge gap, we used a combination of invitro fermentation and preclinical animal model to assess effects of whole grain from parental lines versus their cognate waxy derivatives on compositional and metabolic features of microbiomes from stool samples of multiple human donors. For this study, we capitalized on powerful plant resource populations comprising multiple pairs of NILs in which the waxy trait was bred into different elite parental backgrounds, thus providing biological replication of the waxy trait across multiple genetic backgrounds. Grains from these lines was used for both invitro and invivo studies to examine the influence of starch composition on the human gut microbiome.Although waxy grains and starches are widely consumed,invitro fermentation study revealed significant differences in the effects of waxy versus wild type grain (treatment effects) on ecological metrics (α- and β-diversity), taxonomic features, and metabolite profiles. An unexpected finding from the invitro fermentation studies was the consistency of treatment effects on butyrate production and abundances of specific amylolytic, butyrate-producing taxa that were detected across diverse microbiomes of the different human donors. Different genera of the family Lachnospiraceae were consistently less abundant in fermentations with amylose-deficient waxy sorghum. This conserved microbiome signature effect was particularly strong for Roseburia, which was significantly reduced in fermentations of waxy grain from all 12 of the donor microbiomes tested. This finding is of particular interest because increased abundances of Roseburia have been associated with reduced susceptibility to inflammatory and metabolic diseases.44–46Roseburia species are known to degrade starch47 and produce butyrate as a primary end-product of fermentation.48 Moreover, our molecular complementation studies confirmed that the amylose-enriched RS purified from wild type lines alone could complement the defects of waxy grain in promoting growth of Roseburia and butyrate production. We also highlight a number of different human feeding studies where dietary supplementation with RS was associated with increases in Roseburia in the gut microbiome.50 Thus, despite the differences in ecological environments of invitro fermentations and human feeding studies, the invitro fermentation model may be relevant for mechanistic studies of Roseburia species in humans.Results from our invitro fermentation data was quite compelling, we wanted to further determine if a significant treatment effect of the different starches  in a whole grain context could also be detected in an animal model in which the same human microbiomes we evaluated invitro were present in an intact gastrointestinal environment. We therefore chose to test for these effects in human microbiota-associated (HMA) mice (germ-free mice inoculated with a donor human gut microbiome). Although HMA models have their limitations, including genetic, behavioral, physiological, and anatomical differences from humans,51 they do however allow for examination of the complex interactions between human microbiotas and dietary components and the potential for dietary modulation of the microbiome to influence host phenotypes. Such studies are not possible with conventional mice harboring a mouse microbiome because of the divergence in species composition between mouse and human microbiomes.52 In our studies, feeding diets supplemented with whole-grain sorghum from isogenic parental versus waxy lines revealed significant treatment effects on the gut microbiome. Notably, the mouse diet fabrication process limited the amount of whole grain sorghum that could be introduced to 20% whole grain sorghum from the isogenic pair of wild type/waxy-derivative lines. This amount equates to only a modest difference in amylose content between the two diets of approximately 2% (in the wild type line), whereas studies with purified substrates typically use higher concentrations of RS or other fibers .53 Nonetheless, even with this modest difference in dietary amylose content, we detected significant treatment effects of diet (waxy versus wild type) on overall compositional features of the microbiome as well as changes to several individual taxa. Treatment effects were significant across all four HMA mouse lines, but the differences in taxonomic configurations were unique to a given donor microbiome, a result that is often observed in experiments with HMA mice.39Although our Muribaculaceae, Anaerostipes, Eubacterium, Agathobacter, Blautia, Erysipelatoclostridium, and Phascolarctobacterium showed significant effects of treatment (diet) in two or more of the HMA mouse lines, but the directionality of the effects for these taxa were not necessarily consistent across lines, suggesting their responsiveness is dependent on context of the microbiome. In contrast, the genus Faecalibacterium and members of the taxonomic group Christensenellaceae R-7 group showed consistent effects in the same direction, with significantly lower in abundances in animals fed amylose-depleted waxy sorghum diets in two or more of the HMA mouse lines. Both organisms are well-regarded as beneficial microbes. For example, abundances of Christensenellaceae are associated with longevity,54 reduced susceptibility to inflammatory bowel diseases,55 and positive effects on body mass index56 while the genus Faecalibacterium is associated with human health benefits and negatively correlates to inflammatory bowel disease.57–60 Studies comparing weight gain from consumption of wild type and waxy grains are limited, but one study in broilers demonstrated significant increases in weight gain in animals fed grain from waxy versus non-waxy hybrids of maize.61Perhaps the most intriguing outcomes from the feeding experiments in HMA lines were that (i) some taxonomic differences between waxy and wild type sorghum diets were shared across HMA mouse lines from more than one donor and that (ii) animals fed waxy diets had statistically significant increases in weight gain, regardless of the donor microbiome. With respect to taxonomic effects, genera such as invitro. This result is not surprising since the invitro fermentation conditions are quite different from the mouse gut ecosystem. In particular, Roseburia failed to show significant responses to diet in the HMA lines, despite their strong responsiveness in invitro fermentations. This outcome may be due to challenges with getting Roseburia species from the human microbiomes to efficiently colonize the mouse gut in our HMA model. Attempts to quantify Roseburia species in the HMA lines with our species-specific qPCR assays confirm this hypothesis, with most species colonizing at levels < 105 CFU/g of feces and some declining to even lower levels over time . In contrast to the situation with Roseburia, other families containing prominent amylolytic members such as genera Faecalibacterium from the Ruminococcaceae family showed consistent and significant responses to the dietary treatments in the HMA mouse lines from multiple microbiomes  than non-waxy, largely due to the higher levels of readily digestible amylopectin in waxy lines.17 There could be significant, long-term implications for health outcomes in populations consuming waxy grain on a regular basis. In addition to the potential physiological consequences of diets containing grains that rate high on the glycemic index, our data supports the conclusion of the waxy trait also has undesirable effects on the microbiome in different commodities. The absence or decreased levels of amylose in waxy lines reduces RS content and the beneficial effects of RS on the gut microbiome, thus essentially decreasing a significant fiber benefit of the grain. Recent studies of the glycemic index for a broad panel of rice cultivars, including improved cultivated varieties, showed a strong association of naturally-occurring waxy alleles with elevated glycemic index values and a significant negative correlation between amylose content and glycemic index rating.16 Studies have also showed significant associations between amylose content, predicted glycemic index and glycemic load, and allelic variation linked to waxy variants.30 More detailed studies of health outcomes and microbiome composition in populations consuming large amounts of waxy or high-amylose rice could therefore illuminate the potential for waxy rice consumption to predispose the local populations to type II diabetes.30 More research needs to be done to determine the negative ramifications of consuming high quantities of waxy grain.Waxy starch traits have been identified and/or introduced by conventional breeding in many small grain crop species,63 Given our results and the known beneficial effects of RS on the human gut microbiome and human health, we further posit that high-throughput methods for microbiome phenotyping, such as invitro fermentation, can also be incorporated into improvement programs, permitting improvement of crops for traits that may also be associated with human health.With respect to crop improvement, there is growing recognition that traits such as glycemic index should be included as targets for improvement, and high-throughput methods for phenotyping glycemic index and for microbiome have been reported.invitro fermentations  and the HMA mouse model  are well regarded as beneficial microbes. These undesirable microbiome effects of waxy grain were also accompanied by a weight gain phenotype in HMA mice. Thus, it is reasonable to hypothesize that long-term consumption of waxy grains may have multiple unintended and undesired effects that are relevant to health. Our work illuminates the need to better understand the potential trade-offs between functional traits and effect on microbiome, and for plant breeders and food scientists to consider these trade-offs when developing hybrids for food ingredients and food product formulations.The findings of this study revealed that the wide use of waxy starches in food products could potentially have significant undesirable effects on the composition and function of the gut microbiome compared to foods with native, amylose-containing starches. Although our study does not permit direct inference of the effects of waxy starch on human health, the organisms most affected by waxy grain in The information of the grains used in this study are provided in 64 Briefly, 5 g of whole grain were ground into fine powder using Geno/Grinder 2025 (SPEX SamplePrep) at 1600 rpm for 10 min. Then, 2.5 g of sample was mixed with 30 mL of water in a 50 mL Falcon tube for 20 min until fully dispersed. Tubes containing the slurries were immersed in boiling water for 20 min with constant shaking. The slurries were then placed on an orbital shaker (200 rpm) and incubated at 37°C for 40 min. The pH was adjusted to 2.5 with 1 M HCl followed by the addition of 1 mL of 10% (wt/vol) pepsin  in 50 mM HCl. The slurry was then incubated on an orbital shaker (200 rpm) at 37°C for 60 min. The pH was adjusted to 6.9 with 0.5 M NaHCO3 and 5 mL of 12.5% (wt/vol) pancreatin  in 0.1 M sodium maleate buffer, and 0.2 mL of amyloglucosidase  was added. The slurry was incubated for 6 h at 37°C with orbital shaking at 200 rpm. Following digestion, the material was transferred to dialysis tubing  and dialyzed against distilled water for 3 days at 4°C with a water change every 12 h. The retentate from the dialysis was collected, freeze-dried and resuspended in 30 mL of sterile distilled water. The resistant starch concentration was determined with a commercial kit using the protocol variation entitled ‘Determination of total starch content of samples containing resistant starch’ , Megazyme).Whole grain samples were digested following established procedures.2, 5% CO2, and 90% N2; Bactron X, Sheldon Manufacturing, Cornelius, OR, USA) within 2 h of collection by adding sterile 10% glycerol in phosphate-buffered saline, pH 7.0  and mixing with a stomacher for 4 min prior to storing at −80°C until fermentation.Fresh fecal samples from 12 healthy adults with no history of gastrointestinal abnormalities and no prebiotic, probiotic, or antibiotic consumption within the past six months were collected using a commode specimen collection kit . All procedures involving human subjects were approved by the Institutional Review Board of the University of Nebraska–Lincoln before initiating the study (20160816311EP). A 1:10 fecal slurry was prepared in an anaerobic chamber (5% HIn vitro batch fermentations were performed inside an anaerobic chamber. Two hundred fifty microliters of resuspended sample was mixed in a 1 mL-deep well in a 96-well plate with 0.25 mL of 2X fermentation medium containing (per liter): 1 g Bacto casitone,1 g yeast extract, 2 g K2HPO4, 3.2 g NaHCO3, 3.5 g NaCl, 1 mL hemin solution , 0.05 g bile salts, 0.5 g/L cysteine HCl, 0.6 mL resazurin (0.1%), 10 mL ATCC trace mineral supplement, 3.6 mL VFA solution , 10 mL ATCC vitamin supplement and 1 mL vitamin K-3 solution (0.14 g vitamin K-3 in 100 mL 95% ethanol).65 The substrate was then reduced at 4°C in the anaerobic box with anerobic gas generator  for 3 days before inoculation with 0.05 mL of fecal slurry. In vitro fermentations were incubated at 37°C for 16 h.67 After fermentation, samples were centrifuged at 4000 g for 10 min. Pellets and supernatants were stored at −80°C until further processing.68 The isolated starch then underwent invitro digestion as described previously.64 The resulting starch concentration was determined as described above. The digested starch was added to digested waxy sorghum substrate  to adjust the starch concentration to that of the corresponding wild type sorghum substrate , thus making the starch concentration 9.31 mg/100 mL for supplemented waxy Wheatland and 6.57 mg/100 mL for supplemented waxy Tx430. Waxy sorghum, waxy sorghum supplemented with starch and wild type sorghum were each inoculated into one of six human fecal microbiomes with the largest and smallest differences between waxy and wild type sorghum observed during invitro fermentations  as described above.Starch from wild type lines ‘Wheatland’ and ‘Tx430’ was extracted using a modified protocol from Xie et al.24 The Institutional Animal Care and Use Committee at the University of Nebraska-Lincoln approved all procedures involving animals (protocol 1700). At 12–13 weeks of age, germ-free mice were transferred from isolators to autoclaved individually ventilated cages mounted on racks with positive airflow as previously described24 and then colonized immediately with human stool microbiomes. Four human fecal microbiomes exhibiting the greatest differences between waxy and wild type sorghum during invitro fermentations  were selected for inoculation into mice. To establish human microbiome-associated (HMA) mice, 100 uL of each human fecal slurry (n = 18 mice for each donor) was orally gavaged into mice once. Mice were then randomly assigned to dietary treatments based on body weight at the time of colonization and housed three per cage by donor microbiome. No significant differences in body weight were observed among treatment groups on the day of randomization (data not shown). Fecal pellets were collected from individual mice twice a week and stored at −80°C until DNA extraction. Cecal contents were collected at necropsy and stored at −80°C until DNA extractions were performed as previously described.69 Serum was also collected at necropsy and later analyzed using a Milliplex Mouse Cytokine/Chemokine Magnetic Bead Premixed 32-Plex Kit  and a MAGPIX instrument  as per the manufacturer’s instructions.Female germ-free C57BL/6 mice were born and reared in flexible film isolators and maintained under gnotobiotic conditions  at the University of Nebraska-Lincoln. Germ-free status of the breeding colony was checked routinely as described.Mice were fed an autoclaved chow diet  ad libitum prior to the introduction of experimental diets at the time of colonization with human stool microbiomes. Experimental diets were prepared and irradiated by Research Diets, Inc. . Mice were fed either a low fiber control diet based on the AIN-93 G rodent diet formulation with dextrose as the sole carbohydrate source or a customized diet based on AIN-93G where the dextrose was replaced with 20% Wheatland sorghum flour from either wild type or waxy isogenic line. Six mice for each donor microbiome and diet combination were fed for four weeks. Diet compositions are listed in Supplementary Table 2.63 The V4 region of the bacterial 16S rRNA gene was amplified from each sample using the dual-indexing sequencing strategy.70DNA was extracted from mouse fecal pellets and cecal contents using the BioSprint 96 workstation  and the BioSprint 96 one-for-all Vet kit with the addition of buffer ASL (Qiagen) and bead beating.71 Sequences were truncated (220 bases for forward reads and 160 bases for reverse reads) and denoised into amplicon sequence variants (ASVs) using DADA2.72 All ASVs were assigned with taxonomic information using pre-fitted sklearn-based taxonomy classifier SILVA database (release 138)73 and were then binned at genus level and transformed to relative abundance by dividing each value in a sample by the total reads in that sample. PICRUSt2 was used to generate predicted metagenomes.74Paired-end sequences were analyzed using Quantitative Insights Into Microbial Ecology (QIIME) program (version 2).Roseburia species responses to waxy sorghum, we used qPCR to identify four Roseburia species. Representative genomes of type strains Roseburia intestinalis DSM 14610, Roseburia inulinivorans DSM 16841, Roseburia hominis DSM 16839 and Roseburia faecis DSM 16840 were downloaded from the NCBI database (http://www.ncbi.nlm.nih.gov/genome) and analyzed using dbCAN275 to investigate the functions of carbohydrate utilization. Species-specific primers were designed using Rapid identification of PCR primers for unique core sequences (RUCS).76 Primer specificity was validated by Primer BLAST against the NCBI RefSeq representative genome database.77 The validated species-specific primer sequences used were: Roseburia intestinalis F-CGAAGCACTTTATTTGATTTCTTCGG, R- TTTTTCACACCAGGTCATGCG; Roseburia hominis F- AAGTCTTGACATCCCACTGACA, R- CACCACTGCTCCGAAGAGAA; Roseburia inulinivorans F- GACATCCTTCTGACCGGACAG, R- GGCTACTGGGGATAAGGGTTG; Roseburia faecis F- CGCAACCCCTGTCCTTAGTAG, R- AGATTTGCTCGGCCTCACG. All primers were synthesized by Integrated DNA Technologies . Real-time PCR reactions were prepared in a 10 µL volume containing 5 µL 2x SYBR Green, 3 µL nuclease free water, 1 µL primer mix (a mixture of forward and reverse primer of 5 µM each), and 1 µL DNA template. Thermocycling conditions included (i) an initial denaturation step of 5 min at 95C; (ii) 40 cycles of 20 s at 95◦C, 25 s at annealing temperature , and 30 s at 72◦C; (iii) one cycle of 15 s at 95◦C; (iv) one cycle of 30 s at 60◦C; and (v) one 20-min interval to generate a melting curve. The cycle threshold of each sample was then compared to a standard curve made by diluting genomic DNA from type strain Roseburia intestinalis DSM 14610, Roseburia inulinivorans DSM 16841, Roseburia hominis DSM 16839, and Roseburia faecis DSM 16840.To quantify individual 69SCFA  and branched chain fatty acids  from fermentation samples were analyzed by gas chromatography as described previously.79 Bacterial community diversity was assessed by α-diversity and β-diversity. α-diversity metrics, including Shannon index, bacterial richness  and Pielou’s Evenness and β-diversity in Bray–Curtis distance, were calculated using rarefied ASV data with the phyloseq and vegan packages.81 Differences in α-diversity were compared by paired Wilcoxon tests. Differences in the microbiome communities were compared by PERMANOVA using the Adonis function in vegan. In the complementation study, differences in Bray–Curtis distance between different groups to waxy sorghum groups were compared by Kruskal–Wallis test followed by post hoc pairwise multiple comparisons using Dunn’s test. Differential abundance analysis was also performed using LEfSe .82 Bacterial genera and Roseburia species were compared by paired-Wilcoxon test for two groups or Kruskal–Wallis test followed by Dunn’s test for more than two groups.All analyses were performed using R and RStudio.invitro SCFA production and weight gain in mice were compared by repeated measures ANOVA with FDR corrections. Spearman’s correlation analysis was performed between fecal SCFA concentrations and the abundances of genera, and between key microbiome features (abundances of genera and Roseburia species and SCFA concentrations) and starch concentration in the complementation experiment. Pearson’s correlation analysis was performed between the difference in average weight gain between mice fed with waxy versus wild type sorghum and average relative abundance difference of bacterial genera. Data was visualized using different programs and R packages.83–86Differences in Click here for additional data file.


Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections . The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors . Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error ( More recently, Ki67 gained clinical utility in the T1-2, N0-1, estrogen receptor-positive (ER) and HER2-negative patient group by allowing to identify those patients that are unlikely to benefit from adjuvant chemotherapy2. However, Ki67 has not been consistently adopted for clinical care due to unacceptable reproducibility across laboratories5.It has been long acknowledged that the immunohistochemical (IHC) detection of Ki67 positive tumor cells provides important clinical information in breast cancer6. According to this consensus, parameters that predominantly influence Ki67 IHC results can be grouped into pre-analytical , analytical (IHC protocol), interpretation and scoring, and data analysis steps6. As the scoring method was the largest contributor to test variability7, the IKWG has undertaken serious efforts to standardize the Ki67 scoring method of pathologists9. Although in multi-institutional studies, standardized Ki67 scoring methods reached pre-defined thresholds for adequate reproducibility10, this was only after completing calibration training and by using tedious counting methods. In this context, recently updated guidelines by the IKWG now recommend Ki67 IHC for clinical adoption in specific situations, including the identification of very low (<5) or very high proliferation (>30) indices, that render more expensive gene expression tests unnecessary2.Therefore, the International Ki67 in Breast Cancer Working Group (IKWG) originally published consensus recommendations in 2011 for best practices in the application of Ki67 IHC in breast cancer2. Indeed, the IKWG recommended use of core biopsies (CB), based on apparent superior results for Ki67 when visual evaluation was compared to that of whole sections (WS).An important additional issue that can cause variability in Ki67 measurements is the type of specimen (core biopsy vs excision) and its effect on Ki67 scoring in a multi-center settingIn this multi-observer and multi-institutional study, we aimed to investigate the comparability of Ki67 measurements across corresponding core biopsy and resection specimens from the same breast cancer cases, when evaluated using a calibrated, automated reading system. Furthermore, we assessed between-(consecutive) section differences in Ki67 scoring as no difference between sections will facilitate the selection of the tumor-block to perform the IHC staining on.9 were employed in this study. No outcome data were collected for this cohort. Patient selection was irrespective of patients’ age at diagnosis, grade, tumor size or lymph node status. The clinicopathological characteristics of these 30 cases can be found in our previous publications10.Thirty cases of ER-positive breast cancer used in phase 3 of IKWG initiatives collecting 15 cases from the UK and 15 cases from Japan designed to cover a range of Ki67 scores9. Briefly, the corresponding core-cut biopsy and surgical resection blocks were centrally cut and stained with Ki67, resulting in 60 Ki67 slides from 30 cases. The IHC was performed using monoclonal antibody MIB-1 at dilution 1:50  using an automated staining system  according to the consensus criteria established by the International Ki67 Working Group6. Sections from the same block were stained in a single immunohistochemistry run, except for four cases where the staining was performed in two different runs. This approach effectively controls for any technical variation in staining.Tissues from UK patients, both core biopsies and surgical resections were collected according to ASCO/CAP guidelines, while patients’ tissues from Japan were collected following ISO  15189 approved by the Japan Accreditation Board. Preparation of the Ki67 slides of the first cohort has been previously describedTwenty volunteer pathologists from 15 countries, most of whom participated in the previous Phase 3A study, were invited to participate. Four adjacent sections from each of the 60 blocks were centrally stained as follows: the first section with haematoxylin and eosin (H&E), the second with p63  and the third to fourth with Ki67 (designated as slide sets 1–2).The Aperio ScanScope XT platform was used at 20× magnification to digitize the slides (pixel size: 0.4987 µm × 0.4987 µm), which were uploaded to a server and distributed as digital images. Seventeen pathologists successfully completed the study Fig. .Fig. 1St11. A detailed guideline for setting up and building an automated Ki67 scoring algorithm was sent to the participating labs. All the participating labs were requested to build their own Ki67 scoring algorithm following the instructions and apply them on these 60 slides. The complete step by step instructions are available in Supplementary File 13. Briefly, after the whole invasive cancer area on a digitized slide was annotated, hematoxylin and DAB stain estimates for each case were refined using the “estimate stain vectors” command. We used watershed cell detection14 to segment the cells in the image with the following settings: Detection image: Optical density sum; requested pixel size: 0.5 µm; background radius: 8 µm; median filter radius: 0 µm; sigma: 1.5 µm; minimum cell area: 10 µm2; maximum cell area: 400 µm2; threshold: 0.1; maximum background intensity: 2. In order to classify detected cells into tumor cells, immune cells, stromal cells, necrosis and others   software was used. Degree of agreement was evaluated by Bland–Altman plot and linear regression. To assess differences between specimen type the Wilcoxon signed-rank test was applied, since the data were not normally distributed. Data were visualized using boxplot, spaghetti plot, and dot-plot.p = 0.08) was found between the two consecutive  sections regarding Ki67 scores. If the Ki67 score is higher for a given case, the difference between the sections tends to be also greater  from subsequent surgical excisions of the same tumor. Systematic error occurred between specimens from the same patient as core biopsy Ki67 scores were greater, with a clinically relevant mean difference of 6.6% (bias p = 0.001). The limits of agreement also have to be considered wide from a clinical perspective (between −13.7 and 27). Furthermore, Ki67 scores on CB were even higher compared to WS on cases with higher Ki67 scores . Moreover, the variability of differences in Ki67 scores between CB and WS showed an increasing trend, proportional to the magnitude of Ki67 score , with still no uniformity between clinicians on how to use this biomarker, let alone which cut-off to use. Although the IKWG set up a guideline in 2011 to improve pre-analytical and analytical performance, inter-laboratory protocols still demonstrated low reproducibility related to different sampling, fixation, antigen retrieval, staining and scoring methods7. As the latter was the largest single contributor to assay variability, the IKWG has undertaken multi-institution efforts that have standardized visual scoring of Ki67 in a manner which requires on-line calibration tools and careful scoring of several hundred cells, which may or may not be ideal for pathologists in daily practice with time-constraints9. This result suggests that digital solutions may still be required to address this issue.Ki67 is one of the most promising yet controversial biomarkers in breast cancer with limited adoption into clinical practice due to its high inter- and intra-laboratory variability17. DIA platforms are able to assess nuclear IHC biomarkers such as Ki67, and numerous studies have been conducted to compare human visual scoring with DIA platforms28. Although the latest guideline of IKWG recommends Ki67 for clinical practice in specific situations, the type of specimen as a potential pre-analytical factor contributing to Ki67 variability was not specifically investigated in a multi-operator/multi-center setting. In this study we aimed to address these biospecimen questions including assessment by specimen type and between serial sections.The rise of digital image analysis (DIA) platforms has improved capacity and automation in biomarker evaluation31.One explanation for our finding would be the presence of tumor heterogeneity, and the broader field of review in a whole section from resection specimen. However, one would expect that this cause of discrepancy would result in random discordance, not the consistent finding that Ki67 scores on core biopsies are higher than that of on resection specimens. Rather, we conclude that lower Ki67 in resection specimens is more easily explained by pre-analytical factors. For example, since longer times to fixation occur with resection specimens compared to CB, persistent cell division will occur even in an unfixed, hypoxic environment. Further, epitope degradation also occurs with prolonged time to fixation10. However, there remains a fundamental issue of exact hot spot definition and where pathologists set its boundaries. Moreover, the International Ki67 Working Group has recommended global scoring over hot spot as it did show a consistent trend towards increased reproducibility in both core biopsy9 and excision10 specimens.In addition, one can expect that hot spot scoring might lead to less discrepancy between CB and WS because it considers only the hottest area of Ki67 positivity (highest percentiles of Ki67 distribution) on both specimen types, while global assessment evaluates the total Ki67 distribution which can be variable32.Additional support for the conclusion that the difference in Ki67 between CB and WS is provided by the observation of clinically relevant differences between specimens in cases from different institutions used in this study, independently scored multiple times by 17 pathologists. Although many studies focused on assessing the level of agreement between CB and resection samples in Ki67 scoring; consensus was not possible due to lack of standardization46. However, some studies showed higher Ki67 scores on resection samples38. This discrepancy among studies may be due to lack of standardization in methodology leading to different scoring methods, which we have previously demonstrated to be highly variable2. Moreover, inter-institutional discrepancies could also be the result of different antibodies and protocols used to detect Ki67, different tissue handling/fixation protocols and at some point tumor Ki67 heterogeneity since Ki67 is heterogeneous in tumors6. Thus, our findings provide further support to the latest IKWG recommendations and provide a consensus that Ki67 should be ideally tested on CB samples because it minimizes many fixation problems as Ki67 IHC is more sensitive than ER or HER2 to variabilities in fixation2. Since pre-analytical factors are critical in diagnostic pathology, the IKWG recommends that breast cancer samples for Ki67 testing should be processed in line with ASCO/CAP guidelines2.Our results are consistent with previous results showing poor/moderate concordance (κ = 0.195–0.814) occurring between CB and resection specimen in Ki67 scoring47 we limited the number of sites to remove the variables associated with the technical aspect of the stain. Finally, while the core cut biopsy and resection are from the same case, only a single core was assessed. Thus, we could be missing heterogeneity seen in larger resection specimens. The effect of heterogeneity could be decreased by taking multiple core cuts when clinical situation allows. However, since examination of a single core cut represents the clinical standard of care in several countries, we did not pursue multiple cores.There are a number of limitations in this study. This study only focused on analytical and preanalytical questions, therefore we cannot demonstrate the clinical validation of the calibrated tool. There are many other studies that address the prognostic or predictive value of this test, and that goal was beyond the scope of this effort. For the same reason, further clinical studies are needed to demonstrate how does this consistent difference in Ki67 between corresponding core-cuts and resection specimen impact on prognostic value or its clinical implication on the assessment of neoadjuvant endocrine therapy benefit. Furthermore, the low correlation suggests a critical difference between a core biopsy score and a whole section excision score, which can undermine the use of data on outcome, derived predominantly from resection samples, to identify patients at high risk using a score derived from a core biopsy. Therefore, this study suggests caution in this approach given that even without intervening therapy a clinically relevant change in Ki67 may occur. Further, the Ki67 assessments were based on biospecimens from only 2 central sites. While the participating pathologists within the IKWG represented 15 countries, specimens were centrally acquired and stained. Whereas other investigators have compared specimens from multiple different sitesIn conclusion, while we find no significant difference in digitally-assessed Ki67 index between serial sections, we do find a systematic discrepancy between core biopsy and corresponding whole sections – core biopsy samples yield higher scores . Therefore, this work suggests that Ki67 IHC tested on core biopsy samples should be preferred to excision specimens in clinical decision-making, because doing so will preclude many pre-analytical factors.Supplementary File 1Supplementary Table 1


The endosomal system orchestrates the transport of lipids, proteins and nutrients across the entire cell. Along their journey, endosomes mature, change shape via fusion and fission, and communicate with other organelles. This intriguing endosomal choreography, which includes bidirectional and stop-and-go motions, is coordinated by the microtubule-based motor proteins dynein and kinesin. These motors bridge various endosomal subtypes to the microtubule tracks thanks to their cargo-binding domain interacting with endosome-associated proteins, and their motor domain interacting with microtubules and associated proteins. Together, these interactions determine the mobility of different endosomal structures. In this Review, we provide a comprehensive overview of the factors regulating the different interactions to tune the fascinating dance of endosomes along microtubules. Summary: Endosomal transport requires dynein and kinesin motors to associate with the endosomal membrane and the microtubule. We summarize the factors regulating these different interactions thereby tuning the fascinating endosomal dance. In mammalian cells, different types of endosomes form a highly organized continuous system, the endosomal system, which is essential for the maintenance of cellular homeostasis . The varThe identity of each endosomal subtype is determined by its membrane composition, in particular the presence of specific small GTPases of the Rab and Arf/Arl family and their interacting effector proteins, along with the membrane lipid composition, which mainly differs in phosphatidylinositol phosphate (PIP) species between endosomal subtypes  Fig. 1AAFig. 1A.glued  supports and stabilizes the interaction between dynein and the microtubule through interaction with DICs (glued CAP-Gly-domain interacts with the microtubule (The kinesin superfamily (or KIFs) is highly diverse, with 14 subfamilies, covering a total of 45 different kinesin heavy chains . These hith DICs , whereasrotubule   such as Niemann–Pick disease, cancer and neurodegenerative diseases . For exaIn this Review, we describe how kinesin and dynein motors coordinate endosomal transport – how they are recruited to specific endosomal membranes and microtubules in a controlled manner, and how endosomal transport is regulated by other cellular compartments, most notably the endoplasmic reticulum (ER).2  and GTPase-activating proteins (GAPs), respectively . In thei2 A. This i+ LEs replace Rab7 with another small GTPase, Arl8b. The switch is regulated by the Arl8b effector SKIP , which forms a bridge between Rab7 and Arl8b. This is followed by HOPS (‘homotypic fusion and protein sorting’) complex recruitment and subsequent binding of TBC1D15, a GAP for Rab7, which clears Rab7 from the membrane to yield an endosome marked by Arl8b only  and the dynein complex (The EE membrane is mostly decorated with Rab4 (Rab4a and Rab4b), Rab5 and PI(3)P, which all recruit different motor proteins that in assembly orchestrate EE movement. Rab5 interacts with the FHF adaptor complex , forming a docking platform at the EE membrane for both dynein and the kinesin-3 motor KIF1C  Fig. 1DDFig. 1D.osophila  P2 at the lysosomal membrane triggers the opening of the lysosomal Ca2+ channel TRPML1  leading to increased cytosolic Ca2+ levels, which activates the dynein binding lysosomal Ca2+-sensor ALG2  and require activation before bringing endosomes into motion. To become active, motor proteins have to switch from an auto-inhibited to an active conformation . Kinesinmovement .2 is generated at the lysosomal membrane and triggers TRPML1–ALG2–dynein transport as discussed above for LE and Lys tubulation  to recycle the autophagosomal contents for cellular needs . Lysosombulation  , which might not reflect the in vivo situation.To allow efficient bidirectional endosomal transport and tubulation, endosomal transport depends on a coordinated balance between kinesin- and dynein-mediated movements. Given that the endosomal membrane contains multiple motor anchors, it is likely that multiple, even oppositely directed, motor proteins bind one individual endosome simultaneously. How do endosomes move with multiple motors attached? Three models have been proposed . In the to beads . It is cTo transport endosomes, motor proteins connect to microtubular highways, which cover the entire cytosol to ensure endosomal transport to many cellular destinations. Motor protein binding and their initiation of movement along the microtubule tracks is controlled by microtubule-associated proteins (MAPs) bound to the microtubule surface and by the characteristics of tubulin subunits, which are the microtubule building blocks.glued, but enhances kinesin-3 motility , which are <30 nm clefts between the ER and endosomal membranes that allow interactions between proteins residing in the opposing membranes. ER–LE contacts are especially prevalent as MCS formation appears to increase during endosome maturation . The ER–At the perinuclear ER, contacts with endosomes are formed by the ER-resident E3 ubiquitin ligase RNF26. Together with its partnering E2 enzyme UBE2J1, RNF26 binds and ubiquitylates p62 , which in turn recruits the ubiquitin-binding endosomal adaptor proteins TOLLIP on LEs and EPS15 on EEs B. This cOne of the proteins present at ER–LE contact sites is protrudin, named after its role in protrusion formation . Protrudglued, allowing dynein-mediated LE transport towards the perinuclear area (glued dissociating from RILP, releasing the dynein motor and resulting in subsequent peripheral LE localization when kinesin motors take over (Another protein present at ER–LE contact sites is the cholesterol sensor ORP1L, which affects dynein binding to the LE membrane. ORP1L associates to Rab7 without affecting Rab7 binding to its effector proteins RILP and FYCO1 . ORP1L cear area  (Fig. 4Dake over  (Fig. 4DAs discussed above, dynein and kinesin motor recruitment to the endosomal membrane is regulated by ER-membrane-embedded molecules, including the proteins protrudin and ORP1L, as well as cholesterol and phospholipids on the LE and Lys membrane . WhetherEndosomal movement was originally considered simple, with a motor protein binding to the endosome resulting in its transport, yet decades of studies on endosomal transport have revealed a far more complex situation with every discovery opening many more layers of control. The diversity in motor anchors at the endosomal membrane as well as MAPs and tubulin-PTMs affecting motor recruitment to the microtubule add to the complexity of endosomal transport regulation. In addition, different endosomal locations have different control systems – the ER protein RNF26 controls perinuclear endosomal location whereas other mechanisms, including ORP1L and protrudin-mediated motor loading, regulate fast moving peripheral endosomes. While many components of the endosomal transport machinery have been identified, we may still miss essential factors, owing to limitations of the current experimental approaches. Another consequence of the wide number of components in control of transport is that it becomes virtually impossible to study all of these simultaneously in isolation. Studying endosomal transport in the context of a living cell, using GFP-knock-in systems, optogenetics and chemical biology combined with time-lapse microscopy, may allow further understanding of the complex and fascinating endosomal dance and the many contributing partners.10.1242/joces.259689_sup1Supplementary informationClick here for additional data file.


The ribosomal protein S21 (bS21) gene has been detected in diverse viruses with a large range of genome sizes, yet its in situ expression and potential significance have not been investigated. Here, we report five closely related clades of bacteriophages (phages) represented by 47 genomes (8 curated to completion and up to 331 kbp in length) that encode a bS21 gene. The bS21 gene is on the reverse strand within a conserved region that encodes the large terminase, major capsid protein, prohead protease, portal vertex proteins, and some hypothetical proteins. Based on CRISPR spacer targeting, the predominance of bacterial taxonomic affiliations of phage genes with those from Bacteroidetes, and the high sequence similarity of the phage bS21 genes and those from Bacteroidetes classes of Flavobacteriia, Cytophagia and Saprospiria, these phages are predicted to infect diverse Bacteroidetes species that inhabit a range of depths in freshwater lakes. Thus, bS21 phages have the potential to impact microbial community composition and carbon turnover in lake ecosystems. The transcriptionally active bS21-encoding phages were likely in the late stage of replication when collected, as core structural genes and bS21 were highly expressed. Thus, our analyses suggest that the phage bS21, which is involved in translation initiation, substitutes into the Bacteroidetes ribosomes and selects preferentially for phage transcripts during the late-stage replication when large-scale phage protein production is required for assembly of phage particles. Escherichia coli [Only recently, ribosomal proteins have been recognized in the genomes of viruses –3, incluhia coli , yet thehia coli . It has hia coli .The bS21 protein is small 8.5 kD), highly basic, and specific for bacterial ribosomes. It comprises two α-helices connected by a coiled region . It loca kD, highIn this study, we report 47 phage genomes that we assigned to five closely related clades of phages whose genomes consistently encode a copy of bS21. Notably, the bS21 gene colocates with genes for structural proteins that are responsible for virion assembly including the large terminase (TerL), portal vertex protein (PVP), prohead protease, and major capsid protein (MCP), but is encoded on the opposite strand. We manually curated all genomes and two outgroup phage genomes  to ensure accurate protein sequence prediction and to determine overall genome structure and genome sizes (when complete genomes were achieved). Nine genomes were completed, the largest of which is 331 kbp in length. CRISPR-Cas spacer targeting, the taxonomic similarity of phage proteins to bacterial proteins, including bS21, all predicted that these phages infect freshwater Bacteroidetes species. We find that phage bS21 gene expression is significant during late-stage phage replication, likely specifically translating genes encoding core structural proteins that are essential to virion assembly and the lytic cycle.Multiple phage-related sequences with a conserved genomic context were detected from several freshwater metagenome-assembled datasets (see Methods). Genes for bS21, TerL, PVP, prohead core scaffolding, and protease protein (hereafter prohead protease for short), and MCP are encoded in the genomic region. BLASTp search of the TerL sequences against the ggKbase sequences (ggkbase.berkeley.edu) obtained a total of 47 unique scaffolds with the conserved genomic region  were curated to completion  . We then predicted the bacterial hosts based on the bacterial taxonomic affiliations of the phage gene inventories as previously described [To predict host-phage relationships we first used CRISPR-Cas spacers targeting. While none of the 16.5k unique spacers from the relevant metagenomes targeted any of the curated phage genomes from the same sampling sites, a single cross-site target was detected. Specifically, MIW1_072018_0_1um_scaffold_78 was targeted by a spacer (24 nt and no mismatch) from a MIW2 We profiled the co-detection of phage clades and Bacteroidetes classes to test for specific connections  and the bacterial bS21 sequences (from the corresponding samples and NCBI RefSeq) clustered separately  is an important residue for blocking the ASD in the 16S rRNA within the ribosome [F. johnsoniae  based on the presence/absence of protein families indicated they shared a total of 16 protein families  and anoxic (3 samples) layers of the water column. The bS21-encoding phages were readily detected in oxic samples, especially in the under-ice samples when the whole water column was oxic Fig. .Fig. 5ThRotsee Lake RNA reads were mapped to the phage genomes curated from this site to reveal the transcriptional activities of bS21-encoding phages Fig. . In geneTo seek evidence of a transcriptional relationship involving bS21 and other genes we focused on the three phages that were most active based on the transcriptional level of their 19 shared single-copy genes Fig. . bS21 haInspection of the RNA reads mapping profiles indicated that the conserved region encoding bS21 and core structural proteins was not transcribed as an operon, whereas bS21 and bS21_CN, MCP and its upstream hypothetical protein gene, and prohead protease and its downstream hypothetical protein gene may each be transcribed together Fig. . Given tn = 310), MCP (n = 154), PVP (n = 120), TerL (n = 78), neck protein (n = 70), and a tail sheath protein (n = 29). A total of 388 genomes contained at least one of these genes within ten genes of bS21, and eight had all of these six core structural proteins in close proximity. Three pVOGs were related to DNA processing, i.e., an exonuclease (n = 37), an endonuclease (n = 32), DNA helicase (n = 30). Other pVOGs included Hsp20 heat shock protein (n = 127), two ATP-dependent CLP proteases , and lysozyme . Interestingly, the prohead protease and the MCP pVOG genes are very close to the bS21 gene  near the C-terminus of bS21  as occurs in Cellulophaga baltica . The reOur genetic context analyses of published bS21-encoding phage genomes showed that many bS21 genes are co-located with genes for core structural proteins, and sometimes with genes for DNA replication and lysis. Thus, we suggest that the acquisition and timing of expression of bS21 may be a more general and consistently evolved phenomenon across diverse phage lineages. This motivates future analyses that could experimentally investigate phage-encoded bS21, especially when the genes co-occur with DNA processing or lysis genes, and test the hypothesis that bS21 may be important for efficient translation of the nearby genes.By carefully manual curating nine huge phage   genomes To detect related phage sequences encoding the detected bS21 gene, a BLASTp search using the TerL protein was performed against all metagenomic datasets in ggKbase (ggkbase.berkeley.edu). All the contigs/scaffolds bearing the top BLASTp top hits were manually checked for bS21. It was confirmed that when the TerL similarity dropped to below ~70% similarity, the bS21 that located near the TerL was no longer observed. Two of the scaffolds with the highest TerL similarity to that of bS21 containing contigs/scaffolds but without bS21 were included in this study as an outgroup for phylogenetic and comparative genomic analyses.The samples with bS21-encoding phages detected were collected from several freshwater lakes, including Lac Pavin  and Lake Fargette  in France, Lake Rotsee  in Switzerland, and three freshwater reservoirs of mining-impacted water in Canada. The freshwater samples were conservation with dry ice before genomic DNA was extracted. See related publications for details of sampling procedures –21.https://github.com/bcthomas/shrinksam). The shrunk sam file was imported into Geneious Prime version 2021.1.1 (https://www.geneious.com). The manual check was performed to identify any assembly errors (regions lacking paired read support), which were subsequently fixed using unplaced paired reads as previously described [To ensure the phage genomes reported in this study were without any assembly errors that could be detected, manual curation was conducted on each of them individually as described previously . Firstlyescribed . Those sTo reveal the distribution of related phages encoding bS21 in public databases, the IMG/VR database (version 2020-10-12_5.1) was downloaded . The IMGTo reveal the phylogenetic relatedness of the phages, phylogenetic trees were built using TerL. The protein sequences were aligned using MUSCLE  with defF. johnsoniae (PDB ID: 7JIL) [To understand the differences between phage and bacteria encoded bS21 we built a sequence alignment of phage bS21 sequences, most closed bacterial bS21 sequences in the corresponding metagenomic samples, and publicly available bacterial bS21 sequences. The alignment was built using MUSCLE v3.8.31 with default parameters . Zebra2 D: 7JIL) .e value <1e–5. Consensus annotations for each family were obtained by computing the HMM with the most above-threshold hits among member sequences of the family (minimum 5% of member sequences). If no HMMs met these thresholds, the protein family was labeled “hypothetical” (hyp). Finally, we plotted the frequency at which each protein family was found as a function of its relative position to the focal bS21 gene  . The pregene_a) in a given sample was determined by calculating as follows: total_basegene_a/lengthgene_a/total_readgenome_a, in which total_basegene_a means the total bases mapped to a given gene, lengthgene_a means the length of the nucleotide sequence of the gene, and total_readgenome_a means the total number of reads mapped to the corresponding genome. Only those protein-coding genes with at least 80% of the bases covered were calculated for normalized transcriptional level. To evaluate competitive mapping to regions of bacterial and phage genomes that are shared due to horizontal gene transfer, we also mapped RNA reads to datasets including both bacterial and phages and found no difference.The seven raw metatranscriptomic RNA datasets from Lake Rotsee were downloaded  and filtTo reveal if the bS21 genes in the published viral genomes are also co-located with these for core structural proteins, we checked all the huge phages genomes reported by Al-Shayeb et al. . The proSupplementary FiguresSupplementary Tables


Behavioral addictions, especially pathological gambling, share many features with substance dependences, namely clinical findings and behavioural patterns, comorbidity with psychiatric disorders, genetic factors and family history, neurobiology, natural history and response to treatment[2].Pathological gambling consists of a persistent and maladaptive pattern of gambling behavior, that often leads to significant adverse psychosocial and financial outcomes. It is currently classified as an “Impulse Disorder” on ICD-10 but the DSM-5 moved this diagnosis from “Impulse-Control Disorders” to “Substance-Related and Addictive Disorders” sectionTo study the impact of substance abuse in patients with pathological gambling.Literary review, using PubMed database search, regarding substance abuse and pathological gambling.[3].There was also a large percentage of patients presenting with nicotine dependence  and a fourfold increase in the risk of developing an alcohol use disorder[3]. Individuals with substance use disorders also show a threefold risk of developing pathological gambling and substance use appears to negatively influence gambling behaviours in this population. Gambling habits in adolescents have been linked to an increased risk of current and lifetime drug use of multiple substances[4]. Other psychiatric comorbidities were also frequent in this population: 37.9% of patients presented with mood disorders and 37.4% with anxiety disorders[3].57,5% of individuals with pathological gambling also present with some form of substance useThere is a significant clinical and neurobiological overlap between substance use disorders and pathological gambling. Individuals with pathological gambling have a high prevalence of substance use disorders and an increased lifetime risk of substance use, which negatively influences gambling behavior.No significant relationships.