Dr. Burk Dehority was an international expert on the classification and monoculture of ruminal ciliated protozoa. We have summarized many of the advancements in knowledge from his work but also in his scientific way of thinking about interactions of ruminal ciliates with the entire rumen microbial community and animal host. As a dedication to his legacy, an electronic library of high-resolution images and video footage catalogs numerous species and techniques involved in taxonomy, isolation, culture, and ecological assessment of ruminal ciliate species and communities. Considerable promise remains to adapt these landmark approaches to harness eukaryotic cell signaling technology with genomics and transcriptomics to assess cellular mechanisms regulating growth and responsiveness to ruminal environmental conditions. These technologies can be adapted to study how protozoa interact  within the entire ruminal microbiota. Thus, advancements and limitations in approaches used are highlighted such that future research questions can be posed to study rumen protozoal contribution to ruminant nutrition and productivity. Rumen ciliated protozoa enhance methanogenesis  and contThe classical ecology of ruminal ciliates was described decades ago . The rumEntodinium caudatum expressed eukaryotic stress response pathways when the inhibitors monensin and wortmannin were introduced ? Indeed, troduced . We questroduced . We alsotroduced . Of courferences . DehoritDr. Dehority’s scientific passion intersected with his artistic side; his drawings of rumen protozoa led to more sophisticated images in his books, which were the basis for the corresponding author’s (JLF) rumen microbiology class (and used in other classes across different universities). For the current perspective, these images seeded the image database as a lasting legacy to our colleague. Readers are referred to the Acknowledgments section to access this image library. In addition to his artistic talents, his scholarship on the study of ruminal ciliates was also a passion few got to witness; even after retirement, stretching out maintenance procedures to avoid tending them over weekends  was not Our current objective is to review his work and connect his findings with current and especially future research objectives to further our understanding of interactions among protozoa and other microbes in the ruminal pool from a microbiology-nutrition interface.Buetschlia parva  in his first PubMed-indexed paper on protozoa in January of 1970. The family Ophryoscolecidae is diverse and has the major role with respect to how ciliates influence rumen function by most ruminants fed most production diets. Ophryoscolecids primarily have cilia only near their oral region, although some have other patches  asked him what prompted this paper, and his reply was “sheer frustration.” This response was from a scientist who described and named 21 new species and had at least two species named after him. This textbook case documents why some morphological characteristics are stronger indicators of taxonomic distinction of species than others. Entodinium caudatum has three main forms of spination (from none to very long), assuming spination is a defense against predation by larger protozoa and is not needed in monocultures . However, as Burk’s health declined, we prioritized our goal of creating a photographic library of a broad range of protozoal species to leave a lasting framework for future researchers (see Acknowledgments for directions to access the image library). Burk agreed to serve as a collaborator in the fall of 2014, when one of the coauthors (JEP) began to collect rumen fluid samples to supplement existing images of confirmed species. Dasytricha ruminantium images and Isotricha intestinalis videos were added. As discussed previously, Burk distinguished Charonina ventriculi, and images are now better represented in the library to help future researchers distinguish this atypical protozoan’s unusual features  or for which morphology types are very similar (and might require a high-resolution microscope). Iodine staining highlights skeletal plates, a morphological property differentiating species in the Ophryoscolecinae subfamily. To avoid the obfuscation from staining both skeletal plates and intracellular starch or glycogen, Burk recommended sampling from donor cows fed an all forage or high fiber diet or withholding feed for 24 h prior to collection. Due to ongoing research projects in our dairy herd (presumably a limitation for numerous researchers), these suggestions could not be followed. Collection before feeding might seem a disadvantage for taxonomic approaches based on morphological characteristics compared with molecular approaches (discussed subsequently), but we also note that large particles also typically are screened even when DNA is extracted from rumen contents containing particulates. Our best option was to sample immediately prior to the morning feeding (cows were fed twice daily but eating at ad libitum). Flocculation and aspiration of the top layer reduced the concentration of particulates that also stained with iodine, but we also noted that large protozoa were sometimes inadvertently removed from the sample. During this process, we captured images that were confirmed by Dr. Dehority to be Ostracodinium gracile and O. obtusum.Collecting a sample and preparing the specimen for proper resolution requires attention to detail, patience, and experience—especially for some genera or species that are very small . Two of the coauthors (JEP and TP) collected fluid from a rumen-cannulated cow in Columbus, Ohio that was previously confirmed to have moderate abundance of Ep. caudatum. Unfortunately, due to the then low concentrations of Epidinium, no monocultures were confirmed. Despite declining health, Burk found a more promising donor in Wooster, OH, United States. He isolated and confirmed a clone culture for our subsequent usage. As was his decades-old practice, he invited two coauthors (JEP and TP) to Wooster to isolate a single cell for clone culture, leading to one of the videos in the visual library.Aware of our challenges to find rare images, Burk generously offered to donate additional materials. Electronic presentations entitled ‘Classification and Morphology,’ ‘Counting Methods,’ ‘Species Identification,’ and ‘Difficulties with th, our last exchange, Burk’s return email expressed the enthusiasm that marked his scientific career; although he had cataloged this rare species, it was the first image in our collection confirmed as Buetschlia parva. Our final efforts also identified images as Entodinium rostratum, Ostracodinium trivesiculatum, Epidinium quadricaudatum, and Ep. parvicaudatum. The authors thank Dr. Svetlana Kisidayova  and Raul Franzolin , who graciously identified more protozoa in the unlabeled photographs of our collection that had not yet been classified because of his swift decline and passing on February 9, 2016.On January 15, 2016, one of the coauthors (JEP) emailed Burk a short video clip and a few still photographs of a specimen previously undocumented by our laboratory. On the 18Dr. Dehority’s technique is captured in a PowerPoint presentation in the image library (see Acknowledgments for directions to access the link), with some of our own examples from his mentoring highlighted below.(1)Pass ruminal samples only through one or two layers of cheesecloth to remove large particles of feed before staining (for good viewing). Larger protozoa can be entrapped and under-represented using multiple  layers . He demo(2)Isotricha, Dasytricha, Epidinium, Ophryoscolex, Entodinium, and subfamily Diplodiniinae. More details and practice are needed to distinguish the subfamilies Entodiniinae (which includes only Entodinium), Diplodiniinae, and Ophryoscolecinae Isotricha  from Dasytricha ; I. prostoma is distinguished from I. intestinalis by the vestibule. If using iodine, visualize as soon as possible, and its concentration might need to vary based on the sample .Either unstained or stained samples, as described in his manual , researc(4)Diplodiniinae can be distinguished using methylene blue or iodine staining only at a higher magnification on separate slides with cover slips; their percentage can subsequently be multiplied by a normalizing count from a matched subsample.Genera can be further distinguished based on cilial zone patterns, the number of contractile vacuoles, the number and shape of skeletal plates, and the shape and location of the macronucleus. However, sometimes smaller (5)Caudal spine location has some value in taxonomy, but spines should not be used in length measurement, and spination can vary with culture conditions of some entodinia (see above discussion).Monocultures made important contributions to ruminal protozoology since the 1950s, but Dr. Dehority established numerous conditions and improvements that standardized approaches used to define the roles of substrate source, how the substrates were processed, and deviations of temperature ; he also3 the counts of protozoa in the ‘protozoal’ monocultures  questioned Dr. Dehority if protozoa overshooting growth in culture can lead to subsequent lysis if substrate or growth factors are subsequently depleted. This discussion shifted our thinking that slowing growth rate “because they can” might maintain a competitive advantage against bacteria if feed intake and ruminal passage rate are low . Based oramecium , we reasProtozoal cultures adapt to monensin after a few transfers . After aEpidinium , although he cited the importance of controlling excess glycogenesis as one of the factors for culturing Dasytricha  in that study would not reflect normal conditions in monoculture, let alone in vivo. The diurnal rhythm of migration and subsequent sedimentation might be heavily entrenched in isotrichid ecology. Hence, After discussion with Dr. Dehority, we questioned if the high expected protozoal autolysis  is inflain vitro  is sporadically observed in ruminal ciliates . Little En. caudatum (Dr. Dehority emphasized that generation time (net of cell growth and lysis) was highly related to transfer interval , 2008. Acaudatum . These scaudatum ; abrupt Faster passage rate in the rumen is typically associated with increasing feed intake, which stimulates protozoa to replenish their numbers but also coincides with increased substrate supply. Passage of ruminal ciliates needs context to connect prior expectations with more current thinking. First, we argue that, unlike many of those publications and based on numerous discussions with Dr. Dehority, the predominant entodiniomorphids do not sequester in the rumen as do the isotrichids. Some of the expectation for sequestration by entodiniomorphids is based on interpretation of ‘attachment.’ In contrast with intimate and long-term attachment by other important ruminal microbes such as by cellulolytic bacteria, 14C-choline almost certainly overestimated protozoal ruminal pool size yet typically was not used to quantify ruminal outflow of protozoal N. Because of the difficulty in collecting a purified sample of choline, specific activity was instead related to radioactivity of 14C per unit of N in samples that were theoretically pure protozoa, whereas those samples were likely significantly contaminated with bacterial N . In addition, removal of protozoa for short-term culture needed to enrich them with 13C-choline followed by washing likely would introduce stress responses that would increase their generation time. This perturbation likely disturbed the assumption that 14C-choline in protozoa turns over the same as unlabeled choline. That is, the 14C-choline specific activity would not be diluted as rapidly as it would have without perturbation. These types of studies should be repeated using better procedures such as a quantitative PCR assay  were at least in part derived from calculations based on protozoal volume and converted to biomass. However, these prior protozoal volumes derived using the geometric formula for cylinders were probably overestimated by 25 to 40% . Based oThe ruminal outflow of protozoa deserves future study because protozoa influence the amino acid profile  of microbial protein . Moreovein vivo, protozoa can theoretically clear the entire ruminal pool of bacteria in just a few hours  and quantitative  bases has been summarized by ew hours . Those aThe findings and limitations of defaunation studies were extensively detailed by Quantitative measures often require collection of a respective standard representing the bacterial and protozoal communities. Washing without filtering does a poor job of removing contaminating bacteria . TypicalEn. caudatum was associated with inhibition by antibiotics  also might be hydrogenosomal, as documented for Dasytricha and Isotricha  influence redox potential, which is associated with and likely influences microbial community and function  reside in both micronuclei and macronuclei. In the latter, its copy number is very large, ranging from 1,010 to 6,210 copies per cell of a mixed protozoal community . In genestimated . As suchll basis . KittelmTrichostomatia, of rumen ciliates have been identified based on 18S rRNA genes . Clearly, further research is needed to improve upon the nearly 50% of the missing Entodinium spp. sequences and to verify specificity with one or even no mismatch allowed. To overcome the limitation of microscopic counting and morphologic identification (described above), specific primers for at least the major genera of rumen protozoa need to be developed and adequately validated; until then, we recommend for cell counting to be continued.Individual protozoal genera and species contribute differently to the overall rumen function both qualitatively and quantitatively. Thus, quantification of individual ciliate genera, if not species, is needed to assess their associations with dietary interventions. Hereto, at least 15 genera, all of which belong to the subclass NA genes . Becausetwo taxa . Based oin silico evaluation. Briefly, using TestPrime 1.0, we compared the sequences of each primer set against the recent SILVA non-redundant SSU reference dataset (SSU r132), which contains over 400 reference 18S rRNA gene sequences representing 15 genera and 29 species of protozoa, including C. ventriculi , thus probably leaving some genera undetected. Among the three primer sets that can generate amplicons with a length suitable for qPCR and NGS , the P-SSU-316F/GIC758R primer pair achieved the highest average coverage . With one mismatch allowed for the primer evaluation, RP841F/Reg1302R achieved 95.9% total coverage with all 15 rumen protozoal genera detected (data not shown). Of the two primer sets that can generate nearly full-length amplicons of protozoal 18S rRNA genes, the primer set P-SSU-54F/P-SSU-1747R achieved a coverage totaling 97.9%. Until new specific primers with improved coverage are developed, we recommend for these three primer sets to be used. Two primer sets, RP841F/Reg1302R and P-SSU-316F/GIC758R, are most suitable for qPCR and preparation of amplicon libraries of 18S rRNA genes of rumen microbiota, whereas P-SSU-54F/P-SSU-1747R is more suitable for generating nearly full-length amplicons for sequencing using the third generation sequencing (TGS) technologies, such as PacBio2.in vivo. The first study that used NGS in sequencing rumen protozoal 18S rRNA genes to analyze the protozoal, prokaryotic, and fungal microbiota was reported by The NGS technologies have enabled comprehensive analysis of diverse and complex microbial communities since 2004 . They arin silico evaluation. None of the NGS platforms in use can sequence the full-length 18S rRNA gene of protozoa. However, the TGS techniques, such as PacBio RS II, produces sequence reads averaging over 10,000 base pairs. TGS can sequence the entire rRNA operon, which is composed of one 18S rRNA gene, one internal transcribed spacer (ITS1), one 5.8S rRNA gene, one ITS2, and one 28S rRNA gene. All but the 5.8S rRNA gene are useful phylogenetic markers. Full-length sequences of the entire operon allow not only the design of universal primers specific for rumen protozoa, but also the design of genera- and species-specific primers. Indeed, ITS1, ITS2, and the 28S rRNA gene are better markers for ciliate identification  rank. Reference sequences need to be full- or nearly full-length to allow proper primer design and fication . SpeciesEn. caudatum are providing starting points for future studies investigating roles of prokaryotes or interactions such as in defaunation studies (discussed previously).Although many researchers have attempted to develop protozoal axenic cultures , no laboratory maintainable axenic cultures have been established . The lacProteobacteria populations in other free-living ciliates in marine or freshwater environments might correlate to the symbiotic relationship between bacteria and host ciliates  and its functions and therefore are key to advancing our understanding of rumen microbial ecosystem. The most obvious and well-documented interaction is predation of bacteria . Althougciliates . Selectis likely ; howeverFirmicutes and Bacteroidetes), some species of Proteobacteria (mostly within α- and γ-Proteobacteria classes) have been exclusively found inside various ciliates  in the study of Live bacteria are required as sources of essential nutrients that cannot be produced or obtained from other sources . In addiciliates . A few ociliates . Therefoin vivo approaches, our colleague Burk Dehority was the consummate gentleman scholar. He always considered the most parsimonious approach to test a hypothesis, yet he always considered the “because they can” interpretation of results (avoiding teleology) within the larger context of advancing our ability to better define roles for ruminal protozoa with respect to positives and negatives both qualitatively and quantitatively. The topics discussed herein often stem from many discussions, particularly those traveling back from meetings that stimulated new questions, techniques, and approaches to science. This review was written in his honor “because we could.”From monocultures to JF initiated the review and wrote a first draft. ZY, TP, and JP made contributions in content and added to the draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The mechanisms by which AHR activation by TCDD leads to these toxicities are not fully understood. Here we studied the effects of TCDD on a major energy pathway, glycolysis, using the chick embryo close to hatching, a well-established model for studying dioxin toxicity. We showed that 24 hr of TCDD treatment causes changes in glycolysis in both thymus and liver. In thymus glands, TCDD decreased mRNAs for glycolytic genes and glucose transporters, glycolytic indices and levels of IL7 mRNA, phosphorylated AKT (pAKT) and HIF1A, stimulators of glycolysis and promoters of survival and proliferation of thymic lymphocytes. In contrast, in liver, TCDD increased mRNA levels for glycolytic genes and glucose transporters, glycolytic endpoints and pAKT levels. Similarly, increases by TCDD in mRNA levels for glycolytic genes and glucose transporters in human primary hepatocytes showed that effects in chick embryo liver pertain also to human cells. Treatment with the glycolytic inhibitor 2-deoxy-d-glucose exacerbated the effects on thymus atrophy by TCDD, supporting a role for decreased glycolysis in thymus atrophy by TCDD, but did not prevent hepatosteatosis. NAD+ precursors abolished TCDD effects on glycolytic endpoints in both thymus and liver. In summary, we report here that dioxin disrupts glycolysis mediated energy metabolism in both thymus and liver, and that it does so in opposite ways, decreasing it in the thymus and increasing it in the liver. Further, the findings support NAD+ boosting as a strategy against metabolic effects of environmental pollutants such as dioxins.Activation of the aryl hydrocarbon receptor (AHR) by the environmental toxin dioxin (2,3,7,8-tetrachlorodibenzo- These include thymus atrophy, a consequence of TCDD exposure in all species thus far examined, and hepatosteatosis [Binding of the environmental toxin 2,3,7,8-tetrachlorodibenzo-teatosis –4, a forteatosis . Thymus teatosis . Notwithin ovo are accompanied by decreased nicotinamide adenine dinucleotide (NAD+) levels in those organs. The decrease in NAD+ was attributable, at least in part, to NAD+ consumption by increased poly-ADP-ribosylase (PARP) activity [+ as a substrate for their catalytic activities [+ boosting with nicotinamide (NAM) or nicotinamide riboside (NR) could prevent thymus atrophy and hepatosteatosis by TCDD while increasing sirtuin activity [We previously reported using chick embryos (CE) close to hatching, a well-established model for studying TCDD toxicity , 8, thatactivity , in partactivity , 10, enetivities . Consistactivity .+ is a cofactor for many metabolic reactions, including steps in glycolysis, the cellular energy-generating pathway that breaks down glucose to pyruvate and lactate [In addition to serving as a substrate for PARP and sirtuin activities  NAD+ is  lactate . Glycoly lactate  and is a lactate , 16. T c lactate . There a lactate . A switc lactate . Thus we+ levels normalized the effects of TCDD on glycolysis in both organs.We report here that TCDD decreases glycolysis in the thymus while increasing glycolysis in the liver. Further, boosting NADGallus gallus)  were maintained at 37°C at high humidity. Chick embryos (CE) at 15 or 16 days of development (hatching is at 21 days) were injected in ovo into the fluids surrounding the embryo through a hole in the shell with one or more of the following: TCDD , an index of TCDD mediated CYP1A4 activation [Fertilized eggs from White Leghorn chickens (tivation ), dioxanHepatocytes—Hepatocytes were extracted from CE liver as previously described [Lymphocytes were removed from thymus glands of CE as follows: thymus glands were squeezed between two microscope slides to release lymphocytes into a Petri dish containing 1X phosphate buffered saline (PBS) supplemented with 1% fetal bovine serum (FBS). After passing the cell solution through a 100 μm nylon mesh cell strainer, lymphocytes were pelleted by centrifugation and incubated for 5 min at room temperature with red cell lysis buffer  to remove red blood cells. Lymphocytes were washed twice with 1X PBS before being used to measure glucose uptake, lactate release, ATP levels or ECAR  as described below. escribed , treatedHepatocytes from a 37 year-old female donor with liver focal nodular hyperplasia  were obtained through the Liver Tissue Cell Distribution System  and seeded in collagen coated 12-well plates (Corning) at 0.6 million cells per well in DMEM (MP#1033122) supplemented with 10% FBS, 0.01% dexamethasone (0.4 μg/ml) 0.001% glucagon (7ng/ml), 3.6% iTS mix , selenous acid (12.5 μg), BSA (2.5 g), and linoleic acid (10.7 mg)), 30 ml of HEPES  and 20 ml of Pen-Strep). After 24 hr, the human primary hepatocytes were treated for 24 hr with TCDD (10nM) or the solvent dioxane.6 cells per well) and incubated for 30 min with 2-deoxyglucose added to a final concentration of 1mM. Luminescence was measured using a SpectraMax M2e plate reader  for 1 hr, with readings taken every 2 minutes.Glucose uptake by CE lymphocytes or hepatocytes was measured using Glucose Uptake-Glo Assay kits (Promega J1342) following the manufacturer’s directions. Briefly, lymphocytes or hepatocytes, prepared as described above, were plated in 96-well plates (1 x 106 cells per well) in 1 ml of RPMI1640 medium supplemented with 5% dialyzed FBS  (for lymphocytes) or Std Ham’s [Lactate release from cells was measured using the Lactate-Glo Assay kit (Promega J5021) following the manufacturer’s directions. Briefly, lymphocytes or hepatocytes, prepared as described above, were plated in 24-well plates (1–2 x 10td Ham’s  supplemein vivo with TCDD (0.3 million cells/well in 96 well plate), and in cultured hepatocytes  after 24 hr treatment with TCDD (1nM), using an Agilent Seahorse XF Stress glycolytic kit  and a Seahorse XFe96 Analyzer, following the manufacturer’s directions. Cells were plated in RPMI 1640 (lymphocytes) or DMEM (hepatocytes) supplemented with glutamine (1mM). Oxygen Consumption Rate (OCR) was also monitored in the same conditions.ECAR, an index of glycolysis, was measured in lymphocytes extracted from thymus glands from CE treated ATP was measured using ATPLite luminescence kits , following the manufacturer’s directions, in lymphocytes extracted from thymus glands. Three million cells were used per sample. Luminescence was measured using the SpectraMax M2e plate reader.Tissue samples were homogenized in sample buffer ), boiled for 5 min and centrifuged for 15 min at 14,000 rpm. Proteins were separated by electrophoresis on precast Tris-Glycine gels (Invitrogen/Life Technologies) and transferred to nitrocellulose membranes. The membranes were subjected to Western blotting with primary antibodies against AKT , pAKT (Ser473) , HIF1A  and beta-actin (ACTB) . Goat anti rabbit IgG (Sigma A6154) or mouse IgGk  was used as the secondary antibody. Protein bands were detected using ECL Western Blotting Detection reagents . Band intensities were quantified by densitometry using Syngene Software .ep gradient machine for 40 cycles of amplification. Accession gene numbers and primer sequences are given in S1 File. Fold-changes in mRNA were calculated by the standard 2−ΔΔCt method [Total RNA extracted from tissues was used to prepare cDNA, as previously reported . qPCR ant method  using 186 lymphocytes and incubated for ½ hr in ice in the dark. Preliminary titration experiments were conducted to assess antibody requirements for optimal detection of the cell populations. After incubation, buffer was added to the top of the tubes containing the cell solutions. Cells were centrifuged, buffer was aspirated and 100 μl of fresh buffer added. Cell samples were filtered using BD Falcon Cell-Strainer Cap tubes and analysed in the Flow Cytometry Core Facility at Weill Cornell Medical College using a BD Fortessa FACS machine. Data were analysed using FlowJo software .Lymphocytes were extracted from thymus glands as described above and single cells were resuspended in 1x PBS +1% FBS (buffer) in 3 ml tubes. 0.1 μg of CD4-APC, 0.1 μg of CD8-PE and 0.5 μg CD3-FITC ; anti-CD8A-PE ; anti-CD3-FITC  were added to 100 μl of the lymphocyte solution containing 1 x 10GAPDH activity was measured using a colorimetric GAPDH assay kit  homogenizing thymus or liver (20 mg in 100 μl of lysis buffer) and following manufacturer’s directions.Protein concentrations were measured using Biorad-DC Protein Assay .Differences between group means were evaluated using GraphPad software by unpaired, two tailed t-tests; p values ≤ 0.05 were considered statistically significant. When more than two groups were compared one-way Anova analysis was used with Tukey's honestly significant difference (HSD) test as a post hoc test. All experiments were replicated and the number of independent experiments and of CE used in each experiment are indicated in the Figure legends.Fig 1 (upper panel) shows effects of treatment with TCDD or solvent for 24 hr on mRNA levels for known glucose transporters  in thymus glands of chick embryos (CE). GLUTs 4, 7 and 14 are not shown as they have not been identified in the chicken [GLUT1, the major glucose transporter in thymus [GLUT3 and GLUT11. Although chicken GLUT2 has been identified, it was not detected in the thymus. chicken . TCDD sun thymus , 24, aloFig 1, lower panel) showed that TCDD significantly suppressed mRNAs for four glycolytic genes: aldolase (ALDOB), phosphoglycerate kinase (PGK), enolase (ENO1) and pyruvate kinase (PKM) but did not affect mRNAs for other glycolytic genes except for phosphofructokinase (PFK), which was significantly increased. Lactate dehydrogenase (LDHA) mRNA, which converts pyruvate to lactate, was unaffected by TCDD, while TCDD significantly decreased mRNA for the monocarboxylate transporter 4, MCT4 (SLC16A4), a plasma membrane transporter protein that controls the cellular efflux of lactic acid/H+ and is highly expressed in glycolytic cells [CYP1A4, a positive control to monitor AHR activation by TCDD, was increased by TCDD in the thymus. In summary, these results show that AHR activation by TCDD in the thymus mainly suppressed glucose transporters and genes associated with glycolysis.Parallel assessment of TCDD effects on mRNAs for genes involved in glycolysis in the thymus  and lactate release  by thymic lymphocytes assayed immediately after extraction from thymus glands of CE treated with TCDD in ovo for 24 hr. TCDD suppressed glucose uptake and lactate release by 27%, consistent with decreased glycolysis in the thymus. TCDD suppressed ATP levels in thymic lymphocytes by 17% , indicating that suppression of glycolysis by TCDD in the thymus was accompanied by decreased energy production.To learn whether the suppression by TCDD of mRNA levels for glucose transporters and glycolytic genes in the thymus was accompanied by suppression of glycolysis, we examined effects of TCDD on two hallmarks of glycolysis , glucoseFig 2D and 2E show that in thymus glands TCDD treatment for 24 hr decreased two factors known to stimulate thymus growth: IL7 and phosphorylated AKT  [ine 473) , 27, 28,Fig 2F), an effect that would be expected to contribute to decreased glycolysis. Our evidence that TCDD decreased several factors involved in glycolysis in the thymus, IL7 mRNA, pAKT and HIF1A, supports roles for these factors in decreased glycolysis in the thymus by TCDD.HIF1A , like phosphorylated AKT, promotes glycolysis by enhancing expression of glycolytic genes . The low showed that TCDD increased the double negative CD4-CD8- population by 26% while decreasing the CD4+CD8+ double positive population by 24%. TCDD increased the CD8+ single positive population (by 45%). These results show that decreased glycolysis by TCDD in the thymus is accompanied by a decrease of CD4+CD8+ double positive cells, the precursors of mature CD4+ and CD8+ single positive lymphocytes.To learn if the suppression of glycolytic endpoints by TCDD was accompanied by effects on thymocyte precursors, we conducted flow cytometry experiments using lymphocytes extracted from thymus glands of CE treated for 24 hr with TCDD or dioxane. Average changes from four flow cytometry experiments . CE were treated for 72 hr with TCDD (we previously showed this treatment duration lowered thymus weight by TCDD ) with an+ precursors, nicotinamide (NAM) or nicotinamide riboside (NR), increased NAD+ levels in both thymus and liver and could prevent thymus atrophy and hepatosteatosis by TCDD [+ boosting also ameliorated TCDD effects on glycolysis in thymic lymphocytes. Thus, NAM or NR at doses that increased NAD+ levels in CE thymus glands [Fig 3A) and lactate release  by thymic lymphocytes. NR did not affect lactate release by lymphocytes in the absence of TCDD in these conditions . Further, we used Seahorse Technology to measure the extracellular acidification rate , consistent with our other evidence that TCDD decreased glycolysis in the thymus. Administration of NR for the last 4 hr of TCDD treatment restored ECAR to control levels, confirming that NAD+ boosting ameliorated the effects of TCDD on glycolysis in thymic lymphocytes. TCDD decreased also oxygen consumption rate in the conditions in which glycolysis was assayed and NR treatment abrogated this effect . We then measured effects of TCDD with and without NR on the activity of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme that requires NAD+ as a substrate. We found that TCDD decreased GAPDH activity in the thymus and NR treatment corrected this effect . Further, NR normalized ATP levels by TCDD in the thymus, thereby correcting the energy defect caused by TCDD in the thymus .We previously reported that treatment of chick embryos with NAD by TCDD . We shows glands , given 4ng cells ). We fouGLUT2 and GLUT10  and for some glycolytic genes including the hepatic specific hexokinase , phosphoglucoisomerase (PGI), phosphofructokinase, liver type (PFKL), phosphoglycerokinase (PGK), phosphoglyceromutase (PGM) and MCT4, which transports lactate out of the cell . In human primary hepatocytes TCDD treatment for 24 hr increased mRNAs for GLUT1, 3, 4, 7 12, 13, 14  (while decreasing mRNA for GLUT9). TCDD also increased mRNAs for glycolytic genes except for ENO, LDHA and MCT4 , which were unaffected by TCDD. Thus, in contrast to the thymus, TCDD mainly increased mRNAs for genes associated with glycolysis in CE liver and human primary hepatocytes.In liver, in contrast to the thymus, after 24 hr treatment TCDD increased mRNAs for Fig 5A) and increased pAKT (S473) . TCDD did not affect HIF1A levels in liver homogenates . TCDD effects on IL7 mRNA in the liver were variable and there was no significant difference between control and TCDD treated liver samples .Also, in contrast to thymus, TCDD treatment for 24 hr increased glucose uptake by hepatocytes extracted from livers of CE compared to the controls . Cotreatment with TCDD and NR for the last 4 hr corrected lactate release by hepatocytes to control levels . NR did not affect lactate release by CE hepatocytes in the absence of TCDD . GAPDH activity, which was decreased in the thymus by TCDD, was not significantly affected by TCDD in the liver nor was it affected by NR . Seahorse analysis conducted in cultured CE hepatocytes showed that TCDD increased basal glycolysis and maximum glycolytic capacity  and that TCDD and NR cotreatment decreased glycolytic capacity and reserve. Thus increasing NAD+ levels by NR corrected TCDD effects on hepatocyte glycolysis. Oxygen consumption rate (OCR), which was monitored in the conditions in which glycolysis was assessed, was also increased by TCDD and normalized to control levels by cotreatment with TCDD+NR. We then study the effects of the glycolysis inhibitor 2DG on triglyceride (TRG) content in the liver. CE were treated with TCDD (72 hr) with and without 2-DG as described above for thymus. Cotreatment with TCDD+2DG did not prevent increased TRG content in the liver (data not shown), suggesting that increased glycolysis alone is not responsible for increased TRG by TCDD.TCDD increased lactate release by hepatocytes from CE treated with TCDD for 24 hr compared to control (+ boosting using NAD+ precursors (which we previously showed could prevent TCDD toxicities in the thymus and the liver [The major new findings reported here are: 1) AHR activation by TCDD disrupts glycolysis; 2) TCDD has different organ-specific effects on glycolysis in two organs that are sites of TCDD toxicity, decreasing glycolysis in the thymus and increasing it in the liver. Thus, TCDD decreased mRNAs for glucose transporters and glycolytic enzymes in the thymus in association with decreased glucose uptake and lactate release by thymic lymphocytes. In contrast, TCDD increased mRNAs for glucose transporters and glycolytic genes in the liver, effects accompanied by increased glucose uptake and lactate release by hepatocytes; 3) NADhe liver ) normaliIL7, pAKT and HIF1A. The decrease in these factors, alone or together, could contribute to the suppression of glycolysis by TCDD in thymus. Thus, IL7 produced by thymic epithelial cells (TEC) [IL7 KO mice exhibit thymus atrophy [In the thymus TCDD decreases levels of three factors known to stimulate glycolysis: ls (TEC)  binds tols (TEC) , which ils (TEC) , 33 and ls (TEC) . Further atrophy  such as  atrophy , 36.Fig 2G) as well as to decreased glycolysis by TCDD. The changes in thymus subpopulations by TCDD shown here are consistent with findings of others for TCDD effects on thymic subpopulations in mouse models [Thus suppression of IL7 expression by TCDD could contribute to the decrease of pAKT levels by TCDD in the thymus. AKT signalling is an important factor in thymocyte development , 37. Hene models , 38, 39.Future studies should investigate the role of suppressed HIF1A in the effects of TCDD on glycolysis in the thymus and how TCDD decreases HIF1A. Interestingly, Hening Lin’s group  showed tFig 3C). ECAR was not further increased by the addition of oligomycin, an inhibitor of mitochondrial respiration, suggesting that glycolysis is working at full capacity in the lymphocytes, and supporting the importance of this energy pathway in developing lymphocytes. In agreement with these findings, thymocytes removed from thymus glands have been reported to consume 95% of added glucose by glycolysis [Data from the Seahorse experiments showed that addition of glucose to thymic lymphocytes increased ECAR (i.e. glycolysis)  in the conditions in which we measured ECAR. Treatment with NR was able to normalized OCR in both lymphocytes and hepatocytes. Future studies are needed to understand whether the effects of TCDD on mitochondrial respiration are a consequence of those on glycolysis or whether TCDD has independent effects on mitochondrial respiration.et al. [The evidence that TCDD has opposite effects on glycolysis in liver and thymus is also noteworthy. A recent paper by Han et al.  reports et al. , TCDD deet al.  and thymet al. .It is possible that increased glycolysis by TCDD in the liver could reflect decreased SIRT6 activity by TCDD in this organ. Thus, we previously showed that TCDD decreased levels and activity of hepatic SIRT6 in CE liver  showed that TCDD affected mRNAs for different members of the GLUT family in chick embryo liver and human primary hepatocytes. Discrepancies could be due to species differences  as wellTreatment with the glycolysis inhibitor 2-DG exacerbated effects on thymus atrophy by TCDD, supporting a role for decreased glycolysis in TCDD effects in the thymus. However, treatment with 2-DG did not ameliorate hepatosteatosis by TCDD. Future studies are needed to understand the role of increased glycolysis by TCDD in the liver.+ boosting to correct effects of TCDD on glycolysis in both thymus and liver also merits comment. Although NAD+ boosting in vivo has been reported to be beneficial and corrective in different pathologies and tissues, the metabolic changes leading to the corrective effects of NAD+ boosting are not well understood [+ precursors has been shown to increase glycolysis in contexts where glycolysis was suppressed [+ is also a substrate of sirtuins [+ boosting would be expected to decrease glycolysis [+ boosting affects glycolysis in different organs and pathophysiological contexts.The ability of NADderstood , 50. As ppressed , 52 and sirtuins  and incrycolysis , as we s+ levels decline with aging [+ could contribute to age-related effects of thymus involution and hepatic dysfunction of glucose and energy metabolism in the aging population, thus NAD+ boosting might also be a useful strategy against age-related pathologies.Finally, as tissue NADth aging  as with th aging , declineS1 Raw images(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.


Hypoxia-induced upregulation of lincRNA-p21 in tumor tissue was previously shown by our group to be related to poor prognosis in resected non-small cell lung cancer (NSCLC) patients. In the present study, we have evaluated the presence of lincRNA-p21 in extracellular vesicles (EVs) from NSCLC patients and assessed its potential as a prognostic biomarker. High EV lincRNA-p21 levels in blood from the tumor-draining vein were associated with shorter time to relapse and shorter overall survival. Moreover, the multivariate analysis identified high lincRNA-p21 levels as an independent prognostic marker. In addition, lincRNA-p21 was overexpressed in H23 and HCC44 NSCLC cell lines and their derived EVs under hypoxic conditions. Functional assays using human umbilical vein endothelial cells (HUVECs) showed that tumor-derived EVs enriched in lincRNA-p21 affected endothelial cells by promoting tube formation and enhancing tumor cell adhesion to endothelial cells. Additionally, the analysis of selected EV microRNAs related to angiogenesis and metastasis showed that the microRNAs correlated with EV lincRNA-p21 levels in both patients and cell lines. Finally, EV co-culture with HUVEC cells increased the expression of microRNAs and genes related to endothelial cell activation. In conclusion, EV lincRNA-p21 acts as a novel prognosis marker in resected NSCLC patients, promoting angiogenesis and metastasis. Lung cancer is the most common cancer, accounting for 2.1 million new cases annually, and is the leading cause of death from cancer, with 1.8 million deaths per year . HoweverThe EV cargo includes DNA, proteins, mRNA, and non-coding RNAs, including long non-coding RNAs (lncRNAs). lncRNAs are RNA molecules larger than 200 nt with no protein-coding capability . The speIn a previous study, our group characterized the prognostic role of lincRNA-p21 expression in NSCLC tumor tissue, where higher levels of lincRNA-p21 were related to shorter time to relapse (TTR) and shorter cancer-specific survival . LincRNAConsidering the high tissue specificity of lncRNAs, their analysis in EVs is one of the most interesting sources of biomarkers to be explored . In the This study was conducted in a cohort of 56 resected NSCLC patients with available blood from the tumor-draining pulmonary vein, obtained during surgery. In all samples, EVs were correctly purified. The characterization of isolated EVs had previously been performed by our group in these same samples and in others not included in the present study . None ofTP53 were associated with shorter TTR  and higher disease stage and mutated TP53 were associated with shorter OS .Higher disease stage, adjuvant treatment, and mutated p = 0.00602; p = 0.0225; LincRNA-p21 expression was correctly determined in EVs from all patient samples. No significant associations were observed between lincRNA-p21 EV levels and the main clinic-pathological characteristics. Once the optimal cutoff for lincRNA-p21 expression was determined using X-tile , 13 (23.p = 0.84) or OS (p = 0.885) between the patients with high or low expression of lincRNA-p21 : 6.129; 95% confidence interval (CI): 1.665–22.552; p = 0.006) and shorter OS . Disease stage and TP53 mutational status were independent factors for TTR and OS  of 0.639, 0.737, 0.686, and 0.632, respectively .We cultured two NSCLC cell lines, H23 and HCC44, under hypoxic conditions and evaluated lincRNA-p21 levels in the cells and in their secreted EVs. Correct EV isolation from cell lines was verified using Western blot against EV marker TSG101 and by nanoparticle tracking analysis (NTA) in both normoxic and hypoxic conditions. TSG101 was detected in the purified EVs A and thep = 0.004) and 3.5 times in HCC44  and 7.4 times in HCC44 EVs  with EVs from lincRNA-p21-silenced or control H23 or HCC44 cells and performed a tube formation assay to evaluate their proangiogenic potential. LincRNA-p21 silencing impaired the tube formation capability of HUVECs in both cell lines A–D.p = 0.017; HCC44: p = 0.001, Additionally, in order to study how EVs modulate the interaction of tumor cells with endothelial cells, we performed a tumor cell adhesion assay, in which a monolayer of HUVEC cells was treated with EVs derived from lincRNA-p21-silenced or control cells prior to the addition of tumor cells to evaluate their adhesion capability. LincRNA-p21-silenced EVs reduced the capability of tumor cells to stick to HUVEC cells (H23: p = 0.04); miR-146b: 0.27 (p = 0.01); miR-330: 0.37 (p = 0.01); and miR-494: 0.36 (p = 0.002); and in HCC44: miR-23a: 0.63 (p = 0.02); miR-146b: 0.44 (p = 0.09); miR-330: 0.33 (p = 0.02); and miR-494: 0.53 (p = 0.03).We studied the expression levels of four microRNAs, selected for their bibliographic relevance in EVs or endothelial cells: miR-23a, miR-146b, miR-330, and miR-494. All four microRNAs showed a downregulation of their EV expression after lincRNA-p21 silencing in H23 H and HCCp < 0.05).We evaluated the expression of the previous microRNAs in HUVECs after treatment with EVs from control or silenced lincRNA-p21 cells. After treatment, the expression of these microRNAs in HUVECs increased in comparison with basal levels (non-treated group), with a higher increment in the EV-control group J,K. In bAdditionally, we determined the expression of lincRNA-p21 after treatment with EVs to evaluate if lincRNA-p21 was also transferred to HUVECs. Our results showed that lincRNA-p21 expression increased significantly in HUVECs only after treatment with control-derived EVs, which were enriched in lincRNA-p21, in both cell lines L,M.GLUT1, PFKFB3, and GAPDH [To evaluate whether the EV-mediated transfer of microRNAs and lincRNA-p21 was related to endothelial cell activation, we analyzed the levels of three previously described genes whose expression is upregulated during tumor endothelial cell activation, nd GAPDH  , miR-146b , miR-330 , and miR-494    A. When w= 0.034) B.p = 0.4386). We consider that these results might highlight the relevance of the EV prognostic role of lincRNA-p21. The identification of biomarker combinations is usually derived from profiling studies, where a high number of genes are analyzed together. This is not the case of the present study, where a driven hypothesis based on previously published data led us to speculate on the potential role of exosomal lincRNA-p21 as a relapse biomarker.To our knowledge, this study provides the first evidence of the prognostic role of EV lincRNA-p21 levels in any cancer. Although the EV expression of lincRNA-p21 has previously been identified as a diagnostic tool in prostate cancer, its prognostic relevance was not explored . It has TP53 response and to HIF-1α activation under hypoxic conditions [TP53 mutational status, but found that it was induced under hypoxic conditions in vitro [TP53 mutational status, but overexpression of EV lincRNA-p21 under hypoxic conditions. Interestingly, upregulation of lincRNA-p21 was higher in EVs than in cells in both NSCLC cell lines analyzed. Several studies have examined lincRNA-p21 in response to hypoxic conditions, relating high levels of lincRNA-p21 with a higher resistance to hypoxic stress, migration, and proliferation in vitro [LincRNA-p21 transcriptional activation has been related to nditions ,31. Our in vitro . In the in vitro ,42; howeImportantly, lincRNA-p21 was overrepresented in EVs when compared with their parental cell lines. Gezer et al. had previously described this phenomenon , showingWe have also demonstrated that the silencing of lincRNA-p21 in cell lines impairs its EV levels. To explore the functional effect of EV lincRNA-p21 downregulation after silencing lincRNA-p21, we used two in vitro procedures: the HUVEC tube formation assay and the cell adhesion assay. We found that EVs derived from lincRNA-p21-silenced cells impaired the capability of HUVECs to form tubular structures. Tube formation assay is a standard method to assess the proangiogenic potential of soluble factors and other molecules . Our resFinally, we explored how EV cargo changes after lincRNA-p21 silencing by determining the expression levels of selected EV microRNAs previously related to angiogenesis or metastasis . We studGLUT1, PFKFB3, and GAPDH in HUVECs after EV co-culture. Our results showed that all four genes significantly increased their expression after EV co-culture. Previous studies have reported that over-expression of GLUT1, PFKFB3, and GAPDH in endothelial cells is a common feature of tumor-activated endothelial cells, and the inhibition of PFKFB3 leads to vessel normalization and reduces metastasis [Additionally, we studied how this EV cargo affects the expression of these microRNAs in HUVECs after co-culture with control or lincRNA-p21-silenced EVs. All four microRNAs increased their expression in HUVECs after EV treatment, in line with the results derived from the functional studies. It has been previously reported that EV transference of miR-23a from lung cancer cells treated with radiotherapy enhances HUVEC proliferation and migration , and thatastasis . Less isWe are aware that the results from the present manuscript are constrained by several limitations. For the patient’s results, the main limitation is the lack of an independent validation study. The challenging access to an independent cohort of samples from tumor-draining pulmonary vein makes the validation of the results difficult as most of the groups are working with peripheral vein and there are no available data on public repositories for in silico validation. Regarding the in vitro results, the inclusion of additional NSCLC cells lines or other endothelial cell types could reinforce conclusions. Moreover, we only explored how lincRNA-p21 affects the EV cargo of a subset of previously described microRNA related to angiogenesis, hypoxia, and endothelial cell activation. Deeper exploration using omic-techniques could improve the comprehension of the role of lincRNA-p21 in this process.In summary, our study sheds light on the prognostic capacity of tumor-draining pulmonary vein EV lincRNA-p21 expression in NSCLC and indicates that it may well be useful as an independent prognostic marker. This study was conducted using blood from the tumor-draining pulmonary vein, which is highly enriched in tumor-derived products compared with a peripheral vein, as previously described by our group . In factWe included 56 stage I–IIIa NSCLC patients who underwent complete surgical resection at Hospital Clinic of Barcelona . Blood samples were collected in a 4 mL EDTA tube during surgery from the tumor-draining pulmonary vein before tumor resection or vessel ligation. Plasma was obtained by centrifugation  and stored at −80 until EV isolation. Approval for the study was obtained from the Clinical Research Ethics Committee of the Hospital Clinic of Barcelona , and written informed consent was obtained from each participant in accordance with the Declaration of Helsinki.EVs from patient samples were isolated as previously described , using 2GLUT1 (Hs00892681_m1), PFKFB3 (Hs00998700_m1), and GAPDH (Hs99999905_m1). Relative quantification was calculated using 2−ΔΔCt. 18S (Hs99999901_s1) was used as an endogenous control for lincRNA-p21 and miR-191 (002299) for microRNAs.Total RNA was isolated from cell lines using TriZol® Reagent  according to the manufacturer’s protocol. For EV RNA isolation, 750 μL of Qiazol  supplemented with MS2 RNA  as carrier were added to the EV pellet. Once resuspended, RNA extraction was completed using miRNAeasy mini kit (Qiagen) according to the manufacturer’s instructions. The High Capacity cDNA Reverse Transcription Kit®  was used to obtain cDNA using 500 ng of total RNA for cell lines and 250 ng of total RNA from EVs. For microRNAs, a total of 10 ng of RNA was used to obtain cDNA using the TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems). Relative expression was determined by real-time PCR using the 7500 Real Time PCR device (Applied Biosystems). LincRNA-p21 levels were quantified using Custom qPCR Probes  using the primers and probes described by Hall et al. . Taqman 2, and 95% relative humidity.Cryopreserved samples of the lung cancer cell lines H23 and HCC44  were received in our laboratory and passaged for less than six months. Both cell lines were cultured in RPMI 1640  supplemented with 10% EV-depleted Fetal Bovine Serum (Invitrogen) and were grown under recommended conditions at 37 °C, 5% CO6 cells were seeded in a 75 cm2 T-flask. On day 1, cells were transfected with 200 nM of a human lincRNA-p21 siRNA, as previously described by Hall et al. [2, 5% CO2, 94% N2). LincRNA-p21 siRNA inhibition was evaluated by real-time PCR at 96 h after transfection.On day 0, 1.5 × 10l et al.  or Smart6 EVs derived from either lincRNA-p21-silenced cells or control cells was added. The tube formation was evaluated at 6 h, 8 h, and 12 h by phase-contrast microscopy observation using cellSense Entry 1.7 software .HUVEC tube formation assay was performed as previously described . In brie5 HUVECs were seeded onto an eight-well culture chamber slide  and allowed to reach confluence. At this point, a suspension containing 800 × 106 EVs derived from either lincRNA-p21-silenced or control cells was added to each well and co-cultured with HUVECs for 12 h. After incubation, a suspension containing 6000 HCC44 NSCLC tumor cells stained with the CellTracker™ Red CMTPX Dye according to manufacturer’s instructions (Life Technologies) was added to each well. Cells were co-cultured for 90 min in a cell incubator at regular conditions, after which the wells were gently washed using DPBS and fixed with paraformaldehyde 2% for 5 min. Finally, the cells were stained using DAPI and observed under an epifluorescence motorize microscope AF6000  and full-well mosaic images were obtained.First, 4 × 104 HUVEC cells were seeded onto a 12-well plate and co-cultured with 4 × 108 EVs derived from either control or lincRNA-p21-silenced H23 and HCC44 cell lines. After 24 h of co-culture, RNA from HUVECs was isolated and the expression of microRNAs and genes was determined.First, 5 × 10p < 0.1 in the univariate analysis were included in the Cox multivariate regression analyses for TTR and OS. Paired or non-paired t-tests were used for comparisons between two groups. Optimal cut-offs of lincRNA-p21 expression data for TTR and OS were obtained using X-Tile program [TTR was defined as the time from surgery to recurrence or last follow-up. OS was defined as the time from surgery to the date of death by any cause or last follow-up. Kaplan–Meier curves for TTR and OS were generated and compared by means of a log-rank test. All factors with  program  and conf program . All staThe quantification of EV lincRNA-p21 levels from the tumor-draining pulmonary vein is a potential biomarker for TTR and OS in resected NSCLC patients. Patients with high EV lincRNA-p21 have worse outcome after surgery than patients with low levels. LincRNA-p21 regulates the microRNA EV cargo and affects endothelial cell behavior, modulating angiogenesis, endothelial cell permeability, microRNA, and gene expression.


The detection of a feeder aneurysm and an arteriovenous malformation (AVM) is relatively rare for the intracranial AVM. The distal posterior inferior cerebellar artery aneurysm (DPICAAn) is reported to coexist or relate with the cerebellar AVM. In previous reports about the treatment of a DPICAAn and a cerebellar AVM, endovascular embolization with the sacrifice of the posterior inferior cerebellar artery (PICA) has often been selected. However, there have been few reports of simultaneous open surgery for coexistent cases of DPICAAn and cerebellar AVM.A 67-year-old male presented with a headache. We detected a right DPICAAn in the telovelotonsillar segment and a cerebellar AVM primarily fed by the left superior cerebellar artery (SCA). In addition, the nidus was located medially in the left upper cerebellar hemisphere. Magnetic resonance imaging raised suspicions of asymptomatic past hemorrhage in the cerebellar AVM. The left PICA was agenesis, and the right PICA perfused the bilateral inferior cerebellar hemispheres; thus, the right PICA could not be sacrificed. We selected open surgery to prevent any hemorrhagic event from the DPICAAn and the cerebellar AVM. The cerebellar AVM was completely removed, and the DPICAAn was successfully clipped in a single-session open surgery.Open surgery can be considered for DPICAAn and cerebellar AVM. The anatomical location of the DPICAAn and AVM contributed to the success of a single-session open surgery. The arteriovenous malformation (AVM)-related feeder aneurysm is a relatively rare entity that can be detected in 9% of intracranial AVM cases , 2. DistA 67-year-old male presented with complaints of a headache. The patient was a previous smoker and had a medical history of hypertension and dyslipidemia. Although no neurological deficit was apparent, a stroke event was suspected. Magnetic resonance imaging (MRI) revealed a right unruptured DPICAAn. A lesion, which was suspected to be a nidus of an AVM, was also detected in the left upper cerebellar hemisphere. The lesion in the left cerebellar hemisphere revealed hypointensity on T2-star image, indicating past hemorrhage. We performed digital subtraction angiography (DSA) to examine the DPICAAn and the possible nidus in the left cerebellar hemisphere. The DPICAAn was located in the telovelotonsillar segment of the right PICA. While the left PICA was agenesis, the right PICA perfused the bilateral inferior cerebellar hemisphere Fig. a, b. TheThe patient was placed in the prone position. We selected a linear midline skin incision and midline suboccipital approach. The left occipital artery (OA) was harvested when an OA–PICA bypass was required. After craniotomy, the dura of the cerebellar convexity was cut and retracted superiorly. With caudal retraction of the cerebellum, the nidus was easily detected. We coagulated and cut the feeders from the SCA and the PICA. Next, the nidus was removed after the drainers of the tentorial sinus were cut Fig. a. Next, DSA performed 11 days postoperatively revealed the clipping of the DPICAAn and the disappearance of the cerebellar AVM Fig. d. After This report presents a case of a DPICAAn and a cerebellar AVM, both treated with single-session open surgery.A study reported the annual bleeding risk of an aneurysm with an AVM to be 7% . SAH witTo date, the surgical strategy of the AVM with feeder aneurysm remains disputable. A report supported the preceding treatment of the feeder aneurysm because the change of hemodynamic stress could lead to aneurysmal rupture when the AVM was first removed . RegressSurgical approaches for the PICA aneurysms can vary based on the aneurysmal location of the PICA segment, ruptured status, and necessity of posterior fossa decompression. The PICA is anatomically divided into five segments: anterior medullary, lateral medullary, tonsillomedullary, telovelotonsillar, and cortical branches . An aneuA study reported endovascular embolization of the AVM combined with parent artery occlusion or microsurgical clipping of the DPICAAn associated with the AVM . As ischMoreover, the primary feeder of the cerebellar AVM in our case was the left SCA. As interventional embolization of the cerebellar AVM could not be attained only with the sacrifice of the PICA, a single-session open surgery for the cerebellar AVM and DPICAAn was suitable in our case.This report presents a case of a feeder aneurysm in the distal PICA and a cerebellar AVM successfully treated by open surgery. In the event that the dominant PICA feeding to the cerebellar AVM perfuses the bilateral cerebellar hemispheres, as in our case, a single-session open surgery could be suitable option. The anatomical location of the DPICAAn and the nidus of the cerebellar AVM contributed to the decision of opting for single-session open surgery.


Recent advances in experimental biology allow creation of datasets where several genome-wide data types  are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present MONET (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. MONET discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most existent multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures. We tested MONET extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that MONET compares favorably with other multi-omic clustering methods. We demonstrate MONET's biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that MONET is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that MONET is a valuable tool that can provide complementary results to those provided by existent algorithms for multi-omic analysis. PLOS Computational Biology Methods paper.This is a Modern experimental methods can measure a myriad of genome-wide molecular parameters for a biological sample. Each type of such parameters is called "omic" and is measured by a different method. Analysis of omic data improved our understanding of biological processes and human disease, and is now used in therapeutic decisions . While ecommon underlying structure exists across all omics, and use all omic datasets to reveal this structure. Among the algorithms developed under this assumption are SNF and NEMO , and detects in it a heavy subgraph. Denote the nodes of the heavy subgraph by U. We then split M′ into two modules. In one module the nodes are U, and the omics are omics(M′)∪{l}. In the second module the nodes are samples(M′)\U and the omics are omics(M′).- Split M′ with an omic. As in the previous action, a heavy subgraph with nodes U is found in Gl[samples(M′)], but here for every l∈omics(M′). Two modules are constructed. In one the nodes are samples(M′)\U and omics are omics(M′). In the other samples are U and the only omic is l that produced the heavy subgraph.- Split η for the minimum module size. Actions that reduce the number of samples below η are not executed, and module splits are considered under this restriction. Here we used MONET uses a parameter n-node weighted full graph is O(n2).To find a heavy subgraph in a graph, we use a heuristic based on Charikar's 2-approximation to the problem of maximum density subgraph . We iterThe MONET algorithm is guaranteed to converge to a local maximum, because the sum of weights within all modules is increasing in each iteration. The algorithm stops when no action on any module improves the objective.O(Σl(n+|El|)), where El is the number of edges in Gl. The complexity of splitting a module and of creating a new module involves finding a heavy subgraph and is thus O(Σl(n+|El|)+n2). For the last two actions, for the same reason, the same complexity is needed for each omic considered for the split, and the overall complexity is O(L(Σl(n+|El|)+n2)), which is therefore the overall complexity of each iteration. For full graphs, this gives a worst case complexity of O(L2n2). The space complexity is O(Ln2).In each iteration, all actions that do not involve finding heavy subgraphs consider each edge in each of the omic graphs a constant number of times. The complexity of all these actions is therefore η = 10 here), as we never found larger non-significant modules. Samples that do not belong to any module after filtering are marked as lonely.In a post-processing step we perform empirical significance testing to filter modules. Given a module, we sample 500 modules of the same size and omics, and only keep the module if its weight is in the highest 1%. In practice we only performed the testing for modules of minimal size  from adding the new sample u to M: Σv∈samples(M)wl, and classifies the sample to the module with maximal gain. If the gain is always negative, the sample is not classified to any module. This computation takes O(nL) given that the edge weights were already calculated.◼Sample classification after clustering: Once modules were calculated from the data, MONET can naturally classify new samples into modules. For each module We applied MONET and several other algorithms to simulated, image and cancer datasets that are described later. Here we outline the way we evaluated the results.To assess a clustering solution where the true clustering of the data is known, we used the Adjusted Rand Index (ARI) . Note thFor cancer datasets, we sampled 40% of the patients, partitioned them into three equal groups, and removed every group from one of the omics. For the image dataset we removed 20% of the samples in each omic independently. We then applied MONET to the data and calculated ARI with MONET's solution on all data. We repeated this experiment 10 times.Solall. We then partitioned the samples in the dataset into 10 equal folds. For every fold i, we applied MONET to all samples except those in the fold, and denote the solution by Soli. We define the stability of the fold to be ARI where the ARI is computed using only samples that appear in both Solall and Soli. We then classified the held out samples to the modules from Soli, and denote the solution after classification by Rand Index following classification (RFC) of the fold to be ARI, and pre-classification accuracy (preCA) and post-classification accuracy (postCA) respectively.to perform experiments on a dataset we first applied MONET to it. Denote MONET's solution by The simulations are described in the appendix.α = 0.05) comparing the expression level in the module and the rest of the samples  and corrected for multiple hypotheses with Benjamini-Hochberg. Survival analysis was performed as described for the other TCGA datasets. To determine differential survival while controlling for the age and stage, we fitted a Cox multivariate proportional hazard model.To check the clustering solution for enrichment of clinical parameters we used chi-squared test for discrete features (e.g. tumor stage) and Kruskal-Wallis for numeric ones (e.g. age). We also used chi-square to test for enrichment of mutations and used Benjamini-Hochberg to correct for multiple hypotheses. To find genes and miRNAs that are highly expressed in a module, we performed a one sided t-test . We added five outlier samples that do not belong to any module. MONET correctly identified the modules (ARI = 0.92) and their corresponding omics . In another experiment, we simulated 150 samples from five modules in three omics . Module 1 covers all omics. Modules 2–5 cover all omics, are indistinguishable in omic 1, but belong to different clusters in omics 2 and 3. Only a small number of features separate the modules in omic 2, so the signal in omic 2 is weak. When presented with only omics 1 and 2, MONET identified module 1 but chose to treat modules 2–5 as one module that only covers the first omic . When faced with omic 3 as well, the ARI equaled 1, and MONET identified these samples as coming from different modules that cover all omics  . These simulations highlight MONET's approach to multi-omic integration, where sample modules can cover only a subset of the omics, based on the strength of the clustering structure in these omics. Full details on the simulations are in the appendix.We first performed two simulations to test MONET's approach to multi-omics clustering. In the first, we simulated 300 samples from five equal-size modules in two omics. Module 1 covers only the first omic, module 2 only the second omic, and modules 3–5 cover both omics . Methods modeling omic disagreement were much slower than SNF, NEMO and MONET, which required a few seconds or minutes .We applied MONET and seven other methods to the data. We chose BCC, MDI, Clusternomics and TWL, which model disagreement between omics. We also chose SNF and NEMO to represent general multi-omic clustering methods. SNF is widely used, and we recently showed NEMO's high performance . We alsoFig G in S1 Appendix). The performance was largely unchanged, with the ARI decreasing from 0.79 to 0.78.In order to test MONET's scalability to thousands of samples, we also executed MONET on all 2000 images in the dataset. MONET took almost six hours to run, compared to less than ten minutes on 400 images. This was mainly due to increased runtime per iteration, but also because more iterations were required for convergence , Breast Invasive Carcinoma (BIC), Colon Adenocarcinoma, Glioblastoma Multiforme (GBM), Kidney Renal Clear Cell Carcinoma (KRCCC), Liver Hepatocellular Carcinoma, Lung Squamous Cell Carcinoma (LUSC), Skin Cutaneous Melanoma, Ovarian serous cystadenocarcinoma and Sarcoma. Dataset sizes ranged from 170 to 621 patients. Full details on the datasets are available in our recent benchmark . We usedFig 2C, MONET and NEMO had the highest number of cancer types with significantly different survival (at significance level 0.05), with 6 such types. MDI came next with 5, and the other methods had 3–4. Remarkably, in our recent benchmark, eight other multi-omic clustering methods, including the factorization-based methods iClusterBayes and MultiNMF, achieved significance for at most five cancer types. NEMO and MONET were also the best performers in terms of the number of subtypes with enriched clinical parameters . The cancer types for which MONET and NEMO obtained a significant difference in survival were not identical. While both had different survival in AML, GBM, liver hepatocellular carcinoma and Sarcoma, NEMO found differential survival in BIC and melanoma, and MONET in KRCCC and ovarian cancer. Such a difference was also evident in the clinical parameters: NEMO found an enrichment in melanoma, while MONET found in LUSC. These results suggest that NEMO and MONET can be used complementarily. In terms of runtime, SNF and NEMO required seconds per dataset, MONET and MOFA+ a few minutes, and the remaining methods were an order of magnitude slower .As we can see in Fig H in S1 Appendix). SNF had a mean of 2.8, TWL 3.4, NEMO, MONET and BCC 4–5, MOFA+ 5.7, MDI 8.9 and Clusternomics 26.5. The high numbers of MDI and Clusternomics are possibly due to attempting to model clustering in each individual omic. The log-rank p-value, number of enriched clinical labels, running time and number of clusters for each method and dataset are presented in Tables A-D in S1 Appendix.The number of clusters chosen varied considerably among algorithms . Most of the modules were based on only a single omic, and for several cancer types all modules covered only one omic. For some cancer types, this omic was the same for all modules, signifying a strong clustering structure in that omic. In none of the cancer types the solution contained only modules that covered all omics. These results suggest that different omics may have different structures, and that MONET reveals such differences. MONET also reported several (between 0 and 14) lonely samples per cancer .MONET discovered modules that use different combinations of omics .Since MONET is only guaranteed to converge to a local optimum, we experimented with using different numbers of restarts. In addition to the above results, which used 15 restarts, we also executed MONET with 1 and 50 restarts. For both 1 and 50 restarts, 6 cancer datasets were significantly associated with survival. The number of datasets with enriched clinical labels was 8 for one restart, and increased from 8 to 9 for 50 restarts, suggesting that MONET may benefit from more iterations. However, the clustering results of different restarts were generally similar to one another . While SNF and MDI seek to integrate structure across all omics , MONET chooses the omics covered by each module. In its solution all modules cover the gene expression omic, and M1 also covers miRNA expression . To assess the clinical relevance of MONET's modules, we examined the distribution of different clinical parameters across the modules. The modules showed significant differential survival , with M2 showing significantly better survival than the others (p = 4e-3). The modules showed differential survival even after correcting for age at diagnosis and clinical stage . None of the other clustering algorithms found a solution with a significant difference in survival . The modules were not significantly dependent of the clinical stage , and they were enriched for venous invasion status  and for age at initial diagnosis . No module was enriched for any mutation from a list of known driver mutations reported in TCGA's analysis of ovarian cancer  A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. 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PrzytyckaAssociate EditorPLOS Computational BiologyJason PapinEditor-in-ChiefPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The clarity of the exposition has been substantially improved, and the authors have responded to all of my points adequately.There are only a couple of outstanding issues:- Unlike other methods, MOFA+ is not mentioned in the Introduction.- I still believe there is an overstatement of the meaning of the results (pg. 8): "different omics do have different structures". This refers to MONET's solutions on the cancer data, but MONET is designed to capture such signal. In the absence of a ground truth, I do not believe that such a strong statement can be made: it is possible that MONET's increased clustering flexibility is leading it to overfit and identify spurious correlations resulting from the choice of similarity function and optimisation objective.These results suggest that different omics may have different structures.- Regarding the significance of the survival curves of Figure 3C, it seems that M2 is the sole driver of that statistical significance. Without it, the MONET's modules would not show differential survival. Again, it seems that the text is overly optimistic.Reviewer #2: I appreciate the authors' effort to revise and improve this manuscript. The presentation has been strengthened. The authors also added more analysis. Overall, this is a good method contribution with meaningful applications to cancer genomic data and potentially other multi-omics datasets.I only have one remaining question that I hope the authors can clarify with either more comparison or at least more method comparison discussions.It appears that the problem can also be approached by using non-negative tensor factorization. As a special case, this will be non-negative matrix factorization where there are quite a few earlier work in trying to identify the latent variables and also the hidden structures for the clusters, e.g., Zhang et al. (PMID: 22879375) Liu et al. (PMID: 24491042) and Shen et al. (PMID: 19759197). I suggest that the authors put MONET in the context of these related prior work and clarify the advantage.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. 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For instructions see  10 Jul 2020Attachmentitemized_response_to_reviews.pdfSubmitted filename: Click here for additional data file. 22 Jul 2020Dear Prof. Shamir,We are pleased to inform you that your manuscript 'MONET: Multi-omic module discovery by omic selection' has been provisionally accepted for publication in PLOS Computational Biology.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. 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