Parhippolyte misticia from three marine caves in the Ryukyu Islands and performed population genetic analyses by means of multiplexed inter-simple sequence repeat genotyping by sequencing. Based on 62 single-nucleotide polymorphism markers, no clear population structure or directional gene flow pattern was found among the three sites. These results were unexpected because previous studies of other stygobitic shrimps in this region did find significant population genetic structures and northward directional gene flow patterns. Together, these inconsistent findings imply that marine cave-dwelling species in the region have different mechanisms of larval dispersal. Future studies on larval ecology and the biotic and abiotic factors influencing gene flow patterns are needed to clarify the mechanisms underlying the population dynamics of marine cave-dwelling species.Marine cave habitats in the Ryukyu Islands, Indo-West Pacific, are located at the northern edge of the distribution of many cave-dwelling species. At distribution margins, gene flow is often more restricted than that among core populations due to the smaller effective population size. Here, we used high-throughput sequencing technology to investigate the gene flow pattern among three sampling sites of a marine cave-dwelling species at the margin of its distribution range. We collected individuals of the barbouriid shrimp  To c. To c3])Marine cave environments have restricted exposure to the open sea and are sometimes connected to anchialine environments, which are defined as tidally influenced subterranean estuaries ,7. Such Previous studies using mitochondrial DNA (mtDNA) markers have shown that many cavernicolous species show a population genetic structure that spans only a few kilometres –19. HoweFor many cavernicolous species distributed in the Indo-West Pacific, the northernmost margin of their distribution is the Ryukyu Islands, Japan e.g. ,25), whe, whe25])Parhippolyte misticia, a barbouriid shrimp, inhabits marine and anchialine caves in Palau [P. misticia is a dominant benthic species in marine caves. It is therefore a suitable species with which to investigate the gene flow pattern of cavernicolous species in the Ryukyu Islands. This is the first MIG-seq-based population genetic analysis reported for a cavernicolous species distributed in a northern peripheral population.in Palau , Papua Nin Palau ,31 and tin Palau . In the 2.2.1.P. misticia individuals in each of Hedo and Ie, and 20 individuals in Shimoji for this study. We morphologically identified P. misticia, and all of the samples were fixed and preserved in 99.5% ethanol.We conducted SCUBA sampling at marine caves on Okinawa Island (Hedo Cave) , Ie  and Shimoji  Islands located in the Ryukyu Islands, Japan  in 2016–2.2.P. misticia individuals were included in the MIG-seq analysis. To eliminate low-quality reads and primer sequence reads from the raw data, we used the FASTQ Quality Filter tool in FASTX-Toolkit v. 0.0.14 (http://hannonlab.cshl.edu/fastx_toolkit/index.html) with a setting of –Q 33 –q 30 –p 40. Adapter sequences were removed for the MiSeq run from both the 5′ (GTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC) and 3′ (CAGAGATCGGAAGAGCGTCGTGTAGGGAAAGAC) ends by using Cutadapt v. 1.13 [m)’ = 10, ‘maximum distance allowed between stacks (M)' = 1 and ‘maximum distance allowed to align secondary reads to primary stacks (N)' = 1, and the deleveraging (d) and removal (r) algorithms were enabled; we then used the cstacks program with the option setting of ‘number of mismatches allowed between sample loci when build the catalogue (n)' = 4 followed by sstacks. We used the population software implemented in Stacks v. 1.4 with the minimum percentage of individuals required to process a locus across all data (r) set at 70% and the data analysis restricted to a single SNP per locus. Raw sequences data have been deposited in the DNA Data Bank of Japan (DDBJ) databases with accession no. DRA008872.DNA was extracted from the tissue samples by using a DNeasy Blood & Tissue Kit . Genome-wide single-nucleotide polymorphisms (SNPs) were obtained by using the protocol of . In brie v. 1.13 ,35. Stac2.3.In total, we detected 5454 loci that matched to the catalogue, which resulted in 62 SNP markers from filtering method (i) and (ii), respectively (see DNA extraction and MIG-seq). We used BayeScan v. 2.0  to detecΔK [aN), observed heterozygosity (OH), expected heterozygosity (EH) and fixation index (F) were estimated by using GenAlEx v. 6.5 [FST values were estimated using AMOVA in GenAlEx v. 6.5. Statistical significance levels for all pairwise tests were 0.05 after adjusting for multiple comparisons by using false discovery rate correction [mN as a measure of genetic distance. The significance of asymmetric gene flow among sampling sites was tested by using 1000 bootstrap iterations.A Bayesian individual-based assignment approach as implemented in STRUCTURE 2.3.4  was usedΔK , the mosΔK . CLUMPAKΔK  was usedx v. 6.5 . Pairwisrrection . In addirrection  to obtai3.q-values > 0.05). STRUCTURE indicated no genetic differences among the three sampling sites , observed heterozygosity (OH) and expected heterozygosity (EH) were comparable among the sites (p > 0.05).In total, we detected 5454 loci that matched to the catalogue, which resulted in 62 SNPs that are available for the population genetic analyses. BayeScan indicated that all the loci were neutral , and in the present study, we found a high level of gene flow via larval dispersal among the three sampling sites in the Ryukyu Islands. Thus, the combination of a planktotrophic mode of larval development and repeated reproduction throughout the year [P. misticia.Contrary to the present results that indicated genetic homogeneity among three sites, significant population genetic structures have been detected in various stygobitic species, even at the fine scale –19. It hectively . By cont Islands . Parhippthe year  may contC. rubella and H. trigonophthalma in the present study region [P. misticia. It has been suggested that strong local-scale oceanic currents and the coastal retention of larvae can limit gene flow in C. rubella [Although northward directional gene flow has been suggested in y region , our div rubella . Therefo


Historically, mental imagery has been defined as an experiential state—as something necessarily conscious. But most behavioural or neuroimaging experiments on mental imagery—including the most famous ones—do not actually take the conscious experience of the subject into consideration. Further, recent research highlights that there are very few behavioural or neural differences between conscious and unconscious mental imagery. I argue that treating mental imagery as not necessarily conscious  would bring much needed explanatory unification to mental imagery research. It would also help us to reassess some of the recent aphantasia findings inasmuch as at least some subjects with aphantasia would be best described as having unconscious mental imagery.This article is part of the theme issue ‘Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation’. Imagery is distinct from perception, which is the registration of physically present stimuli., p. 1335What is mental imagery? As a starting point, here is the following famous definition of mental imagery from a paper written by the all-star team of Kosslyn, Behrmann and Jeannerod:It is not entirely clear what those quotation marks mean around the word ‘seeing’ and ‘hearing’. But the point is that, while in the case of perception the relevant mental states are brought about by the ‘the appropriate immediate sensory input’, in the case of mental imagery they are brought about without such ‘appropriate immediate sensory input’.et al. on mental imagery, ‘We use the term ‘mental imagery’ to refer to representations … of sensory information without a direct external stimulus' [According to a more recent definition (without quotation marks) from a review article by Pearson timulus' , p. 590.timulus' ,4.et al. definition, mental imagery—the representation of sensory information—may be conscious or unconscious, given that representations may be conscious or unconscious. The same goes for my own definition, given that perceptual processing may also be conscious or unconscious. And while Kosslyn et al. are not particularly forthcoming about what ‘seeing’ in quotation marks is supposed to mean, given the theoretical commitments of all three authors about the possibility of unconscious vision and unconscious audition, if seeing and hearing can be unconscious, ‘seeing’ and ‘hearing’ (in the absence of the appropriate immediate sensory input) can presumably also be unconscious.On the Pearson This way of thinking about mental imagery as perceptual processing not directly triggered by sensory input lands us with a relatively wide category that would encompass a lot of seemingly very different mental phenomena, most of which could be conscious or unconscious. All the following mental phenomena would count as mental imagery according to this definition : ‘filling in’ the blind spot, peripheral vision, amodal completion . ButIn the psychological and neuroscientific literature, things are a bit more complicated. Often the very same authors refer to mental imagery sometimes as a potentially unconscious representation and sometimes as a (presumably necessarily conscious) experience or even qualia . And soMy aim in this paper is to settle this issue and argue that mental imagery can be unconscious. Further, I argue that treating mental imagery as not necessarily conscious  brings much needed explanatory unification to mental imagery research. Allowing for unconscious mental imagery would also help us to reassess some of the recent aphantasia findings inasmuch as at least some subjects with aphantasia would be best described as having unconscious mental imagery.One helpful metaphor used by neuroscientists is that of an ‘active blackboard’ –22. To uBut there are other processes that can draw on this blackboard. Mental imagery is the umbrella term for these other processes. Some of them are entirely bottom-up—determined by the sensory stimulation that shows up in other parts of the blackboard. Some others are top-down—the drawing is done by mechanisms further up in visual processing ,24. Some2.Close your eyes and visualize an apple. Got it? This is undoubtedly one way of exercising our mental imagery. And one that you may consider the standard and stereotypical way of having mental imagery. But it is not at all representative.Visualizing the apple is something you do voluntarily. But mental imagery does not have to be voluntary. One can have flashbacks of some unpleasant scene—this is also mental imagery, but it is not a voluntary exercise of mental imagery. And some of our mental imagery is of this involuntary kind—this is especially clear in the auditory sense modality, as demonstrated by the phenomenon of earworms: tunes that pop into our heads and that we keep on having auditory imagery of, even though we do not want to.Visualizing an apple is also a conscious imaginative episode. But just as it would be a mistake to generalize from the visualizing example and claim that mental imagery is necessarily voluntary, it is also a mistake to generalize from the visualizing example and claim that mental imagery is necessarily conscious. In both cases, we would be generalizing from something that is true of some instances of mental imagery to all instances of mental imagery.There are three kinds of reasons to think that mental imagery may be conscious or unconscious: conceptual, methodological and empirical reasons. I will spend much of the paper elaborating on the empirical reasons after touching briefly on the conceptual and the methodological reasons in this section.per se can be unconscious, it would be completely ad hoc to postulate that mental imagery cannot be. Remember that mental imagery is a form of perceptual processing: perceptual processing that is not triggered directly by sensory input. If perceptual processing that is triggered directly by sensory input can be unconscious, it is difficult to see why perceptual processing that is not triggered directly by sensory input  would have to be conscious.First, the conceptual reason. Perception can be conscious or unconscious. If the stimulus is masked or presented for a very short period of time, the subject still perceives it, but has no conscious experience of it –28. But The second reason is methodological. Most behavioural or neuroimaging experiments on mental imagery—including the most famous ones—do not actually take the conscious experience of the subject into consideration. Take for example the famous mental rotation tasks, one of the most widely used paradigms in the study of mental imagery. There is a linear correspondence between the time required for deciding whether two three-dimensional shapes are the same and the degree of rotation between these two shapes . Your taWhatever these experiments say about mental imagery (and we can stay away from this question), it must be a claim that is silent about whether mental imagery is conscious, as these experiments are response time experiments and the reasons for inferring the exercise of mental imagery are not introspective ones, but come from the timing of the subjects' responses . The mental imagery involved in this task may or may not be conscious. Therefore, the concept of mental imagery that mental rotation experiments are concerned with should not (and cannot) have consciousness as a built-in feature.The third reason is empirical: positing unconscious mental imagery can explain a number of empirical findings better than not positing unconscious mental imagery. This is what the rest of the paper is about. My general strategy is to take the most influential arguments for unconscious perception and modify them in such a way that they are applicable to unconscious mental imagery.Two of the most important arguments for unconscious perception come from unilateral neglect studies and priming studies. I argue in §4 and §5, respectively, that in the case of both unilateral neglect and priming, we can use the same experimental paradigm to show that not only perception, but also mental imagery, can be unconscious.Some have recently expressed general doubt concerning the standards for when we can be absolutely certain that perception is unconscious  are thought to have two visual subsystems that use different regions of the central nervous system: the ventral and dorsal streams. To put it simply, the ventral stream is responsible for identification and recognition, whereas the function of the dorsal stream is the visual control of our motor actions. In normal circumstances, these two systems work together, but if one of them is removed or malfunctions, the other can still function relatively well . This is called optic ataxia. If the ventral stream is malfunctioning, a condition called visual agnosia, the agent can perform actions with objects in front of her relatively well, but she is incapable of even guessing what these objects are.According to the received view, the dorsal visual subsystem is  unconscious, and is responsible for the perceptual guidance of our actions. The ventral visual subsystem, in contrast, is  conscious, and is responsible for categorization and identification ,32–34. SNow, just as we can distinguish between dorsal vision and ventral vision ,36, we cThe problem with this argument is that the assumption that dorsal vision is through and through unconscious has been criticized both on empirical and on conceptual grounds . In the context of the present paper, this assumption is especially problematic, given the overwhelming evidence that visual agnosia patients’ visual imagery is not in fact unconscious. Two different visual agnosia patients  less fine-grained) . When viCrucially, the contralesional side of the visual cortex is activated during the exercise of mental imagery , in muchIn other words, imaginal neglect patients have early perceptual processing in the contralesional side of the visual cortex that is not triggered directly by sensory input. And this early perceptual processing is entirely unconscious. They have unconscious mental imagery.5.An important set of findings that show that perception can be unconscious involve unconscious priming. The general structure of the argument here is that we can infer that the subject perceived something unconsciously if (i) the subject has no conscious awareness of the stimulus presented perceptually to her and (ii) this unconscious presentation of the stimulus primes her (often in very similar ways to that the conscious presentation of the stimulus does).There is a large number of findings that follow this general pattern when it comes to showing that perception can be unconscious (see §2 for references). But the same general argument could be modified to show that mental imagery can be unconscious. In this case, we could infer that the subject has unconscious mental imagery if (i) the subject has no conscious awareness of her mental imagery and (ii) this unconscious mental imagery primes her in the same way conscious mental imagery does.We have seen the general complications with (i) in §2. But in the case of unconscious mental imagery, (ii) is also more complicated than it looks. In the case of unconscious perceptual priming, as long as the subject's behaviour is altered by the unconsciously presented stimulus the same way as it is altered by consciously seeing the stimulus, we can conclude that it was her unconscious perception that primed her behaviour. But in the case of mental imagery, it is much more difficult to find behaviour that would be primed by mental imagery, let alone unconscious mental imagery.Here is one experiment that could provide all the ingredients for the kind of argument I outlined above . It is aShort-term exposure to stimuli immediately before the binocular rivalry task influences the pattern of these alternating experiences . SupposA relatively new set of findings show that conscious mental imagery influences the patterns of these alternating experiences in much the same way as conscious perception does –69. AgaiThat is for conscious mental imagery. So conscious mental imagery has an impact on the binocular rivalry pattern. The question is whether unconscious mental imagery has a similar impact. Consider the following experiment . SubjectAfter this priming phase, the subjects did the classic binocular rivalry task, with red stimulus presented to one of the eyes and green to the other and the subjects had to report which colour was dominant. A number of control conditions were added, the most important of which was identical to the ‘avoid imagining’ condition, with the exception that during the 7 s when the subject was supposed to avoid imagining, a highly luminous  stimulus was presented in the subject's visual field .Figure The experimenters found that subjects' binocular rivalry pattern was primed just as much in the ‘avoiding imagining’ condition as in the ‘imagining’ condition. When the subjects imagined a red apple for 7 s, the red experience won out systematically in their subsequent binocular rivalry task. No surprise here. What is more surprising is that even when the subjects were avoiding imagining a red apple  for 7 s, the red experience still won out systematically in their subsequent binocular rivalry task.This result in itself does not rule out the possibility that what primed the binocular rivalry pattern was not unconscious mental imagery, but rather some sort of higher-level representation—maybe the linguistic representation presented on the screen (red apple) or some other cognitive strategy  representation) given that sensory presentation of an irrelevant sensory stimulus interfered with the priming effect.The authors of this study did not explicitly draw the conclusion that the experiment demonstrates the presence of unconscious mental imagery, but at least one of the authors of the study would be open to this interpretation  move compared with arguing against unconscious priming or unconscious perception in unilateral neglect. So this line of argument may convince some sceptics who are not convinced by any arguments concerning unconscious perception.There is a recent body of research on subjects who report not having any conscious mental imagery whatsoever. This condition is called aphantasia –74. A suA subject has aphantasia when she reports not having mental imagery—this is a behavioural category. And, as a result, aphantasia may have a number of diverse underlying conditions. Some subjects with aphantasia have difficulties voluntarily conjuring up mental imagery, but they do report mental imagery when dreaming, for example. Some others report no mental imagery at all—either voluntary or involuntary .In other words, aphantasia is not a monolithic category. Some aphantasics dream vivid dreams, clearly involving conscious visual imagery . But theN: 1. This one subject, AI , is a 31-year-old female PhD student, who scored 16 points on the Vividness of Visual Imagery Questionnaire (that is the lowest possible score—the average score of the control group was 61.1 points). She reports not having any mental imagery whatsoever.One experiment  that verThe experimental design was the following : the subAfter 4 s of delay, a random dot was presented, and the subject had to decide whether it was within or without the boundaries of the perceived/imagined geometrical shape. This was followed by a confidence rating of this judgement. The only difference between the first (working memory) condition and the second  condition was that in the latter, the geometrical shape was not seen, but merely imagined within the region indicated by the four placeholders.The striking finding is that the performance of AI, a subject with aphantasia, was not significantly different from controls on either of these tasks. Controls performed with a 90% success rate on the working memory task and with a 89% success rate on the mental imagery task. AI performed just around 3% worse than the controls, which is a statistically insignificant difference. AI's confidence ratings were also very similar to those of the control subjects . The authors' conclusion is that AI's aphantasia did not have a statistically significant effect on the performance of either of these tasks.Let us set the working memory task aside. How could we explain the finding that AI's performance on the mental imagery task was not significantly worse than the controls' performance? The straightforward explanation is that AI does use mental imagery and uses it in a very similar way to the control subjects when performing the mental imagery task. But while the controls use conscious mental imagery, AI uses unconscious mental imagery.So far, evidence seems to support the claim that at least some subjects with aphantasia have unconscious mental imagery. But another piece of experimental finding, on the face of it, seems to go against the existence of unconscious mental imagery among aphantasics. This experiment , like thParticipants were first taught that upon the presentation of the letter G, they are supposed to imagine green vertical lines and upon the presentation of the letter R, they should imagine red horizontal lines. During the experiment, they were shown one of these letters, which cued them to imagine either red horizontal or green vertical lines for 6 s. After this, they rated how vivid their imagery of the lines was. Finally, this was followed by the binocular rivalry task with red horizontal lines presented to one eye and green vertical lines presented to the other .Figure There was no statistically significant effect of the imagining task on the binocular rivalry performance of subjects with aphantasia. While imagining red lines in the case of control subjects led to the dominance of the red lines in the binocular rivalry, in the case of subjects with aphantasia, this effect was missing.There was another difference between aphantasics and control subjects. In control subjects, the priming effect was significantly weakened, when during the imagery phase a luminous  stimulus was shown. But in aphantasics, the luminous stimulus made no difference .On the face of it, these findings may seem to show that aphantasics do not have any imagery, conscious or unconscious. If they had unconscious mental imagery, it would have primed the binocular rivalry performance. But it did not. One could object that, as we have seen, not all aphantasics have unconscious mental imagery, so maybe the subjects in this study do not. However, given that there were 15 subjects, all of whom showed the same response pattern, this is not a very satisfying response.At this point, it is helpful, however, to compare these results with the  results In response to this objection, a crucial difference between the two experimental setups needs to be pointed out. The mental imagery that was supposed to be triggered in the  experimevoluntary mental imagery does not prime their binocular rivalry performance. This says nothing about the possibility that involuntary unconscious mental imagery (in aphantasics or control subjects) would or could prime binocular rivalry performance. And, as the [So the only conclusion we can draw from the  experime, as the  study shA lot more experimental studies could and should be done on subjects with aphantasia that could convince us conclusively that at least some aphantasics do have unconscious mental imagery. Very few neuroimaging studies have been done on aphantasics. And given the non-monolithic nature of aphantasia, we should expect a variety of different results here. But the hypothesis that some subjects with aphantasia have unconscious mental imagery could be very easily confirmed.The studies I have focused on show that unconscious mental imagery in aphantasics and conscious mental imagery in control subjects have the same behavioural profile when the subject perform a certain task. But do they have the same neural profile? What do the visual cortices of the subject AI do in the  experime7.Perception can be conscious or unconscious. Attention can also be conscious or unconscious. So can emotions. And there are more commonalities between conscious and unconscious perception/attention/emotions than dissimilarities. One of the aims of cognitive science  is to use categories that help us find out more about the mind. Perception (conscious or unconscious) is an explanatorily more powerful category than conscious perception. And the same goes for mental imagery. Cognitive science and especially philosophy of mind should stop obsessing over consciousness.


Results indicated that participants are highly interested in receiving genetic feedback for AUD (79.0%) but there is a lack of understanding of complex genetic concepts in a sizable proportion of the sample (25.4%). Additional research is needed to assess how to address this lack of knowledge before genetic feedback for AUD can be returned in a way that benefits the individual.An exponential growing number of individuals are accessing genetic risk information via direct to consumer companies. Alcohol dependence is the third most accessed genetic risk score on a publicly available direct to consumer website. Better understanding of the degree to which individuals are interested in receiving personalized genetic feedback, the factors that relate to interest, and genetic knowledge will be critical to lay the foundation for precision medicine initiatives, especially for substance use and psychiatric outcomes, where less is known. To assess interest in receiving genetic feedback for alcohol use disorder (AUD) and understanding of genetic concepts related to psychiatric conditions, we conducted a survey with participants recruited from a registry that enrolled incoming cohorts of freshmen at an urban public university; 205 participants (76.5% female; 58.9% self-reported as White; M Genome-wide association studies are rapidly increasing our understanding of the complex genetic architecture of alcohol use disorder (AUD), as well as related substance use and psychiatric conditions ,2,3,4. ON > 5000) of participants with at least a secondary education, from 78 countries, found that 30% of participants assumed that one individual gene was responsible for causing complex disorders and 25% of participants agreed with the statement that their destiny was written in their genes [However, there are concerns that individuals may misinterpret or misunderstand their complex genetic results. There is wide variability in genetic literacy across extant studies, ranging from 42.2–83.6% accuracy ,14,15,16ir genes . AdditioIn order to gauge interest in receiving genetic feedback for AUD and to assess individuals’ understanding of the role of genetic and environmental influences in complex psychiatric conditions, we conducted a survey in a sample of participants recruited from a registry that enrolled incoming cohorts of freshman at a diverse urban public university . Young aThe purpose of this study was to gauge individuals’ interest in receiving genetic feedback for AUD and related psychiatric conditions, examine variables associated with interest in genetic feedback, and assess genetic knowledge as it relates to psychiatric conditions in a sample of young adults. We hypothesized that a significant proportion of the sample (~80%) would be interested in receiving genetic feedback for AUD, based on studies of older individuals ,22. AddiN = 12,365), with 68% participation rate. When students picked up their compensation for completing the survey, they were invited to provide a saliva sample for genotyping; 97% of participants provided a saliva sample. Data for the present study came from the Spit for Science (S4S) Registry, a university-wide research project aimed at understanding how genes and environments impact substance use and mental health outcomes across the college years and beyond . The S4SAnalyses of the present study focused on a subset of participants from the S4S Registry who completed an additional online follow-up survey designed to assess genetic knowledge and interest in receiving genetic feedback for substance use and mental health conditions. In June 2020, invitation emails were sent to 1737 eligible participants who met the inclusion criteria: (1) had enrolled in Spit for Science as part of cohort 4 from 2014–2015 and (2) had GWAS data available such that they provided a DNA sample which passed quality control measures. Two hundred twenty-six individuals consented to participate in the survey by October 2020, 205 individuals completed the survey and were included in the analyses and 21 participants were excluded from the present analyses due to incomplete survey data (0–34.9% completion). All surveys in S4S, including this follow-up survey, were administered using the REDCap software . Informe2 =X 17.566, df = 1, p < 0.001) and White . The subsample was also less likely to have used cannabis as a freshman in college  and more likely to have a positive family history of depression/anxiety ; no differences for family history of alcohol problems or family history of drug problems were detected. Of the 205 participants who completed this follow-up survey, 76.5% of the sample was female; 58.9% self-identified as White, 18.3% Asian, 13.4% African American/Black, 3.5% Hispanic/Latino, 5.4% more than one race, 0.5% Native Hawaiian/Other Pacific Islander; and the mean age of the sample was 24.48 years (SD = 0.36). To determine whether this subset of individuals who completed the follow-up was different from the full sample of invited participants, a series of comparison tests were run on demographic and other relevant variables. No differences between the full sample of invited participants and this subset were detected on age or anxiety and depressive symptoms, alcohol problems, or nicotine use at enrollment in S4S. However, the subsample was more likely to be female , cannabis use disorder (CUD), depression, and anxiety, was assessed with the item “Would you be interested in receiving personalized genetic feedback for any of the following mental health conditions?” in which participants were instructed to select all options that applied. Genetic knowledge was measured using an adapted version of the Public Understanding and Attitudes towards Genetics and Genomics (PUGGS) Questionnaire Sociodemographic variables included sex , self-reported race/ethnicity , and age. Due to the majority of participants self-identifying as White (58.9%), self-report race/ethnicity was coded as a binary variable  in the analyses. Alcohol use and cannabis use were measured using items from the Alcohol Use Disorders Identification Test (AUDIT)  and CannAbbreviated scales from the Symptom Checklist-90 were used to assess anxiety and depressive symptoms occurring within the last 30 days . Four itFamily history was measured using items from a previous S4S survey . Particit-tests and chi-square tests. For quantitative variables, we used t-tests to compare means of variables of interest  between the group interested in receiving genetic feedback and the group not interested in receiving genetic feedback. For qualitative/categorical variables, we conducted chi-square tests to assess whether there was a relationship between variables of interest  and interest in receiving genetic feedback. Tests were run separately for interest in receiving feedback for each of the five outcomes . Additionally, we conducted a series of univariate linear regression analyses to examine the relationship between demographic characteristics, substance use and mental health variables, and family history with overall genetic knowledge. Separate linear regressions were run for each of these variables, with overall genetic knowledge as the dependent variable in the regression models. All analyses were conducted using R 3.6.2 software [Descriptive analyses were used to describe demographic information, interest in receiving genetic feedback (frequencies for each condition) and overall genetic literacy scores (median and interquartile range). To examine the relationship between demographic characteristics, substance use and mental health variables, and family history with individuals’ interest in receiving genetic feedback, we conducted a series of software .t-tests used to compare demographic characteristics, substance use and mental health variables, family history, and overall genetic knowledge between those interested in receiving feedback and those not interested in feedback. Race/ethnicity, cannabis use, and genetic knowledge were associated with interest in receiving genetic feedback for AUD. Individuals who self-identified as White were more likely to indicate interest in receiving genetic feedback for AUD compared to individuals who self-identified as a racial/ethnic minority. Individuals who used cannabis were also more likely to indicate interest in receiving genetic feedback. Finally, overall genetic knowledge was higher in the group of participants interested in receiving genetic feedback for AUD as compared to the group not interested in receiving feedback. None of the mental health or family history variables were significantly associated with interest in receiving feedback for AUD. Results from these analyses are displayed in Both nicotine use and cannabis use were associated with interest in receiving feedback for NUD and CUD, with individuals who reported any nicotine use or cannabis use being more likely to indicate interest in receiving genetic feedback compared to individuals who did not use these substances. Among participants who reported using nicotine, 80% were interested in receiving genetic feedback for NUD and 85% were interested in feedback for CUD. Among participants who reported using cannabis, 74% were interested in feedback for NUD and 87% were interested in receiving feedback for CUD. None of the demographic or family history variables were significantly associated with interest in receiving feedback for either NUD or CUD. Results from these analyses are displayed in Higher depressive symptoms, cannabis use, positive family history of alcohol problems and positive family history of depression/anxiety were associated with interest in receiving genetic feedback for depression, while positive family history of depression/anxiety was the only variable associated with indicating interest in receiving genetic feedback for anxiety. Results from these analyses are displayed in Overall genetic knowledge surrounding the role of genetic and environmental influences in the development of complex psychiatric conditions was high with the median score being 9 out of 11 items correct (mean = 7.47), but with a wide range . Notably, 52 participants (25.4%) incorrectly responded to or did not know the answer to more than half of the items, with 20 of these individuals receiving an overall genetic knowledge score of 0. Fifteen participants (7.3%) responded incorrectly to the statement “A person’s substance use disorder is influenced by many different genes” and 49 participants (23.9%) responded that they did not know the correct response to this statement. None of the variables of interest  were associated with overall genetic knowledge. To our knowledge, this is the first study to assess interest in receiving genetic feedback for AUD in a sample of young adults. Interest in receiving genetic feedback for AUD, as well as other related substance use disorders and mental health conditions, was high in this sample. These findings align with the previous literature that assesses interest in samples with a broader age range ,31,32,33Variables related to interest in receiving genetic feedback for AUD include racial/ethnic background, cannabis use, and genetic knowledge. Individuals who self-identified as a racial/ethnic minority were less likely to indicate interest in genetic feedback. This may be related to the historical biomedical abuses related to genetics and racial/ethnic discrimination . HoweverCannabis use was associated with interest in receiving genetic feedback for AUD, as well as NUD, CUD, and depression, suggesting it may be a more general predictor of interest in receiving genetic feedback for substance use and related psychiatric conditions rather than specific to one condition. This may be because cannabis use is a more general indicator of concern about one’s health; these individuals may realize they are engaging in less normative, unhealthy behavior and be concerned. Additionally, both nicotine use and cannabis use were associated with interest in receiving genetic feedback for NUD and CUD, again showing that participants may realize these behaviors are unhealthy, and thus, they are interested in their genetic risk related to developing NUD or CUD. It is also important to note that a majority of participants (53–62%) who do not use nicotine or cannabis were still interested in receiving genetic feedback for NUD and CUD, re-emphasizing the idea that participants have a general curiosity about their genetic risk for substance use conditions. Interestingly, our results indicated that frequency of alcohol use was not associated with interest in receiving genetic feedback for any substance use or psychiatric condition. This may be due to the fact that alcohol use is nearly ubiquitous in our sample, with 91.2% reporting some level of alcohol use.Our results also showed high interest in receiving feedback for mental health conditions with 90.2% of the sample interested in receiving genetic feedback for anxiety and 88.8% of the sample interested in receiving genetic feedback for depression. Individuals with a positive family history of depression/anxiety were more likely to indicate interest compared to individuals without a family history of depression/anxiety. High interest in these outcomes may reflect the high levels of internalizing symptoms reported in the sample see  and moreIt was promising to see that genetic knowledge as it relates to psychiatric conditions is relatively high in this sample. Even though individuals in this sample have at least a partial college education, there is still a sizable proportion (25.4%) of the sample that had a lack of understanding of the role of genetic and environmental factors in complex psychiatric conditions. These findings align with previous literature that demonstrates approximately 25–30% of participants have misconceptions regarding genetics of complex disorders ,16. AddiOur findings need to be interpreted in light of several limitations. First, one of the inclusion criteria for participant recruitment was having available GWAS data. This may contribute to the high interest because these participants already consented and provided DNA samples and could be more open to receiving genetic feedback compared to the general population. However, it is important to note that nearly all of the individuals who participated in the overall S4S registry survey component provided a DNA sample (97%), suggesting they are not a highly selected sample. Second, in regards to genetic knowledge, this sample is educated, and have received at least a partial college education, which could inflate the genetic knowledge scores. A more diverse sample is needed to understand genetic knowledge as it relates to psychiatric conditions in the general public. Third, our sample was primarily female (76.5%) and White (58.9%), thus limiting our ability to test sex differences and differences between racial/ethnic background. Our findings may be more representative for females. Despite these limitations, the findings of this study elucidate the importance of additional research related to the return of genetic feedback for AUD and related outcomes. Individuals are highly interested in receiving their genetic feedback for substance use disorders and mental health conditions, but there is a sizable proportion of individuals who might not have the knowledge needed to properly interpret and understand their results. Research is needed to develop and evaluate effective and efficient ways to communicate complex personalized genetic risk information for AUD and related substance use and psychiatric conditions, as well as to understand the impact of receiving genetic risk information for these conditions on the individual. Interest in receiving genetic feedback for AUD, as well as other related substance use disorders and mental health outcomes, is high. However, there is a lack of understanding about the role of genetic influences in the development of substance use disorders and related mental health outcomes in a sizable minority of individuals. Therefore, additional research is needed to design and evaluate the effectiveness of educational materials that address the lack of knowledge before genetic feedback for AUD and related outcomes can be returned in a way that benefits the individuals receiving them.


Public interest in the effects of fermented food on the human gut microbiome is high, but limited studies have explored the association between fermented food consumption and the gut microbiome in large cohorts. Here, we used a combination of omics-based analyses to study the relationship between the microbiome and fermented food consumption in thousands of people using both cross-sectional and longitudinal data. We found that fermented food consumers have subtle differences in their gut microbiota structure, which is enriched in conjugated linoleic acid, thought to be beneficial. The results suggest that further studies of specific kinds of fermented food and their impacts on the microbiome and health will be useful. Lifestyle factors, such as diet, strongly influence the structure, diversity, and composition of the microbiome. While we have witnessed over the last several years a resurgence of interest in fermented foods, no study has specifically explored the effects of their consumption on gut microbiota in large cohorts. To assess whether the consumption of fermented foods is associated with a systematic signal in the gut microbiome and metabolome, we used a multi-omic approach  to analyze stool samples from 6,811 individuals from the American Gut Project, including 115 individuals specifically recruited for their frequency of fermented food consumption for a targeted 4-week longitudinal study. We observed subtle but statistically significant differences between consumers and nonconsumers in beta diversity as well as differential taxa between the two groups. We found that the metabolome of fermented food consumers was enriched with conjugated linoleic acid (CLA), a putatively health-promoting molecule. Cross-omic analyses between metagenomic sequencing and mass spectrometry suggest that CLA may be driven by taxa associated with fermented food consumers. Collectively, we found modest yet persistent signatures associated with fermented food consumption that appear present in multiple -omic types which motivate further investigation of how different types of fermented food impact the gut microbiome and overall health.IMPORTANCE Public interest in the effects of fermented food on the human gut microbiome is high, but limited studies have explored the association between fermented food consumption and the gut microbiome in large cohorts. Here, we used a combination of omics-based analyses to study the relationship between the microbiome and fermented food consumption in thousands of people using both cross-sectional and longitudinal data. We found that fermented food consumers have subtle differences in their gut microbiota structure, which is enriched in conjugated linoleic acid, thought to be beneficial. The results suggest that further studies of specific kinds of fermented food and their impacts on the microbiome and health will be useful. Lactobacillus, Streptococcus, Lactococcus, and Leuconostoc, but other bacteria as well as yeast and fungi are also involved in food fermentations  and is involved in the regulation of immune functions and energy homeostasis under changing metabolic and dietary conditions  . These bntations . In addicells/ml . Recovercells/ml . Moreovenditions .Due to their supposed health benefits , there hWhile we know that both short- and long-term dietary intake affects the structure, function, and activity of the human gut microbiome , 12–16, 12–18–To address the hypothesis that fermented food consumption is associated with compositional or functional changes in the human gut microbiome, we analyzed a subset of the American Gut Project (AGP) cohort based on self-reported consumption of fermented foods, and in particular, fermented plants. We also explored the longitudinal stability and function of the gut microbiota using untargeted high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) and 16S rRNA amplicon sequencing, as well as shotgun sequencing on a subset of subjects at a single time point.P = 0.03) (P = 0.002) and a higher proportion of participants with a normal body mass index (BMI) between 18.5 and 25 , with an average BMI of 23.9 and 24.8, respectively. Consumers also reported eating a greater diversity of plants (>20) . In addition, because alcohol may be an end product of a fermentation process and might be a confounding factor associated with gut microbiota variation, we verified that alcohol consumption was not associated with fermented plant consumption .To explore the differences in the gut microbiome between fermented food consumers and nonconsumers, we analyzed 16S rRNA sequencing data from 28,114 samples from 21,464 individuals in the AGP . After f = 0.03) . SimilarP value = 0.094; Statistically significant differences in mean total carbohydrate and fat intake (grams/day and percentage of energy) and percentage of energy from protein, as estimated by the food frequency questionnaire (FFQ), were observed between fermented plant consumers and nonconsumers, while total energy , dietary fiber (grams/day), and protein (grams/day) intake did not differ (see 10.1128/mSystems.00901-19.1FIG S1n = 483) and nonconsumers (n = 966). (B) Taxonomic differences (16S) between consumers and nonconsumers in the cross-sectional cohort. Unweighted UniFrac principal-coordinate analysis (PCoA) colored by consumers (red) and nonconsumers . Beta diversity (PERMANOVA), unweighted UniFrac, pseudo-F test statistic = 3.677, P = 0.001; weighted UniFrac, pseudo-F test statistic = 2.163, P = 0.058. Alpha diversity , Faith’s PD, H = 4,908,194, P value = 0.9565; Shannon, H = 4947661, P value = 0.6356; richness, H = 4831862, P value = 0.793. (C) Differentially abundant taxa associated with consumers . Download FIG S1, PDF file, 1.9 MB.(A) Healthy Eating Index-2010 scores of consumers . Copyright © 2020 Taylor et al.2020Taylor et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the F-statistic = 3.677, P = 0.001). The comparison of nonconsumers with occasional consumers results in a weaker group separation  than with regular or daily consumers , suggesting a dose dependence for the frequency of fermented plant consumption on the gut microbiome. However, there was no dose dependence with frequency of types of plants between consumers and nonconsumers . There was no difference in alpha diversity between the two groups  between different frequencies of fermented plant consumption. Download Copyright © 2020 Taylor et al.2020Taylor et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Q2 value of −5.4249, suggesting possible overfitting related to the subtlety of the differences between fermented plant consumption groups. In order to verify the log ratios chosen by Songbird ranks, we performed a permutation test by taking 1,000 random permutations of log ratios with 20 nonoverlapping features in the numerator and denominator. The rank order, compared to the random permutation, was 16, corresponding to a P value of 0.0159 , suggesting that they are associated with Bacteroides spp., Pseudomonas spp., Dorea spp., Lachnospiraceae, Prevotella spp., Alistipes putredinis, Oscillospira spp., Enterobacteriaceae, Fusobacterium spp., Actinomyces spp., Achromobacter spp., Clostridium clostridioforme, Faecalibacterium prausnitzii, Bacteroides uniformis, Clostridiales, and Delftia spp.Next, we used Songbird  to identf 0.0159 , suggest10.1128/mSystems.00901-19.2FIG S2t test statistical values obtained for the comparison of consumer versus nonconsumer based on the log ratios of each of 1,000 random microbe selections. One caveat to this type of permutation is the dependence caused by the reuse of features in the generation of the null distribution, with only 3,100 and 153 features employed for the 16S and metagenomic data sets, respectively. (C) Relationship between the location of the metagenomic microbial features along the first axis of the mmvec biplot  and microbe-metabolite cooccurrence interpretation (mmvec). (A and B) Distribution of the c biplot  and theiCopyright © 2020 Taylor et al.2020Taylor et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00901-19.9TABLE S3Table S3, PDF file, 0.1 MB.Sets of taxa used in Songbird analyses. Download Copyright © 2020 Taylor et al.2020Taylor et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the One hundred fifteen participants were recruited for a longitudinal study in order to assess the gut microbiome over time and at a finer resolution by using untargeted mass spectrometry in addition to 16S rRNA sequencing . We targ10.1128/mSystems.00901-19.3FIG S3P value = 2.53e−01), age category , number of types of plants consumed , and BMI category . The total number of individuals is displayed on each bar. Download FIG S3, PDF file, 1 MB.Demographic and selected dietary data for longitudinal cohort samples. (A) Fermented plant consumption frequency within the longitudinal cohort subjects. (B) The other types of fermented food that consumers and nonconsumers reported eating, aside from fermented plants. (C to F) Proportion of subjects in the longitudinal cohort between consumers and nonconsumers, described by sex  longitudinal and cross-sectional cohorts. Proportion of subjects within the longitudinal versus the cross-sectional cohorts who consume fermented plants at frequencies of never, rarely, occasionally, regularly, and daily. Download Copyright © 2020 Taylor et al.2020Taylor et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the A separate fermented food questionnaire was provided to these 115 participants to characterize additional types of fermented food consumed and to evaluate the proxy of fermented plant consumption for general fermented food consumption. Briefly, the major fermented foods consumed were beer, kimchi, kombucha, pickled vegetables, sauerkraut, and yogurt. More consumers reported eating fermented foods than did nonconsumers . Only 7.P = 0.20), suggesting that the stability of alpha diversity in the microbiome over 4 weeks is consistent for consumers and nonconsumers.Within the 16S data, we did not observe a difference in alpha diversity (Shannon’s index ) and FaiLactobacillus acidophilus, Lactobacillus brevis, Lactobacillus fermentum, Lactococcus lactis, Leuconostoc mesenteroides, Lactobacillus paracasei, Lactobacillus plantarum, and Lactobacillus rhamnosus , suggesting that consumers were associated with some taxa derived from fermented foods.A subset of 100 samples were sequenced by shotgun metagenomics to provide a finer resolution of the taxonomic differences between the two groups. First, we verified whether the gut microbiota of self-reported fermented food consumers was associated with fermented food-associated species. We computed a log ratio using Qurro  of fermehamnosus . We founQ2 value of −0.12), we further verified the log ratios chosen by Songbird ranks by performing a permutation test of taking 1,000 random permutations of log ratios with 20 nonoverlapping features in the numerator and denominator. The rank order, compared to the random permutation, was 2, corresponding to a P value of 0.0019 .We then used Songbird  to test f 0.0019 , suggestLactobacillus acidophilus, Lactobacillus brevis, Lactobacillus kefiranofaciens, Lactobacillus parabuchneri, Lactobacillus helveticus, and Lactobacillus sakei , because the cohorts were analyzed using different sequencing methods (16S versus metagenomics), or because of a combination of these aspects.Several microbes of relevance to fermented foods were also associated with consumers, including us sakei , 32–35 (sakei 32–. ConsumeP < 0.001) between time points within both consumers and nonconsumers  . We explonsumers . The cor10.1128/mSystems.00901-19.5FIG S5FIG S5, PDF file, 1.1 MB.Longitudinal stability of microbes and metabolites (correlations were assessed by Spearman rho). The first and third columns are based on the Songbird 16S results and metabolomics results, respectively, for all longitudinal samples. The second and fourth columns consider consumers and nonconsumers separately. The first column shows the log ratios of the sets of taxa identified using Songbird on the 16S data between time points. 16S signatures, within participants and within data type, appear consistent over time based on Spearman’s rho. The second column plots these log ratios for consumers and nonconsumers separately. The third column shows the log ratios of the sets of annotated mass spectrometry features identified using Songbird between time points. The fourth column plots these log ratios for consumers and nonconsumers separately. Download Copyright © 2020 Taylor et al.2020Taylor et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P value = 0.04) whereas the unconjugated linoleic acid (LA) was not significantly different between the two groups  . From the food frequency questionnaire, we found that dietary consumption of total LA  and total CLA (g/day) did not differ significantly between consumers and nonconsumers , we found that notable differences exist between consumers and nonconsumers when all time points were taken into account . The maj = 0.52) . As CLA  = 0.52) , it mighonsumers , suggest10.1128/mSystems.00901-19.6FIG S6m/z. The putative annotations from GNPS library search of the top 10 features discriminating consumer and nonconsumer categories are included next to the features. (B) CLA and LA consumption from the Food frequency questionnaire does not differ between consumers and nonconsumers. Comparison of dietary CLA and LA in consumers and nonconsumers (Wilcoxon rank sum test with Bonferroni-Hochberg correction for multiple testing) for both cross-sectional and longitudinal data sets. Sample sizes indicated on the right-hand side. Test P values are indicated under each panel. Download FIG S6, PDF file, 0.8 MB.(A) Variable importance in the projection (VIP) scores of the top 15 features discriminating between consumers and nonconsumers. Abundance across categories is represented by color marking on the right . The values under “feature” refer to the detected Copyright © 2020 Taylor et al.2020Taylor et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Eubacterium rectale, Faecalibacterium prausnitzii, Eubacterium siraeum, Eubacterium hallii, Bifidobacterium adolescentis, and genera Roseburia, Anaerostipes, Eubacterium, Ruminococcus, and Clostridium .–40.Eubactridium –.10.1128/mSystems.00901-19.10TABLE S4Table S4, PDF file, 0.1 MB.mmvec, species and CLA4 cooccurrence. Species (rep82 features) are listed in the column “Feature” and are sorted by “mmvecRank,” the ranked conditional probabilities of cooccurrence between rep82 features and “CLA4.” Species known to produce CLA appear in the column “CLA-producer.” The column “mmvecPC1” contains the associated taxon features and their PC1 axis values from Copyright © 2020 Taylor et al.2020Taylor et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Lactobacillus acidophilus [Lactobacillus brevis [Lactobacillus kefiranofaciens [Lactobacillus parabuchneri [Lactobacillus helveticus [Lactobacillus sakei [Leuconostoc mesenteroides and Lactococcus lactis in subjects who consumed a specific fermented milk product (buttermilk) in the Dutch cohort Lifeline DEEP  and ferus sakei ). This iine DEEP .Analysis of the metabolomics data using PLS-DA found that shifts in lipid metabolism were associated with consumption of fermented plants, since the majority of the top discriminating metabolites appeared to be lipids. Of those that could be identified, CLA was particularly notable. The abundance of the CLA isomer “CLA4”is significantly increased in consumers over nonconsumers. CLA is known to be produced during ruminal bacterial fermentation and impacts the fatty acid composition of meat and dairy products from ruminants that represent the major dietary sources of CLA in humans . Due to Lachnospiraceae and Ruminococcaceae have also been shown to metabolize LA into products that can be precursors of CLA  are CLA-producing Lactobacillus species that we found to be associated with fermented food consumers: L. acidophilus, L. brevis, L. fermentum, L. helveticus, L. paracasei, L. plantarum, and L. sakei , and Faecalibacterium prausnitzii, which are also known to metabolize LA.Indeed, diet-related bacteria, such as LAB, bifidobacteria, and propionibacteria, have been shown previously to produce CLA . Intestis of CLA , and two. faecis ) were foBifidobacterium species previously reported to produce CLA using LA as a precursor  were found to be associated with fermented food consumers, with B. reuteri growth actually inhibited at high concentrations of LA precursor  consumption and the human gut microbiome, with nearly 7,000 individuals at one time point and over 100 individuals across 4 weeks of sampling. We took a multi-omic approach—a combination of 16S rRNA sequencing, shotgun metagenomics, and mass spectrometry—coupled with state-of-the-art tools to evaluate the data. We find that the consumption of fermented plants and, more broadly, fermented foods is associated with quite subtle microbiome variation in healthy individuals. While this explorative study provides the foundation for more-directed research, such as randomized placebo-controlled studies, it has some limitations, particularly that consumers were categorized according to self-reported frequency of fermented plant consumption. First, self-reported dietary information can be flawed with measurement errors . Second,This research was performed in accordance with the University of Colorado Boulder’s Institutional Review Board protocol number 12-0582 and the University of California San Diego’s Human Research Protection Program protocol number 141853. In order to investigate the effect of fermented plant and food consumption on the gut microbiome, a retrospective analysis was performed on the American Gut Project data set . An addiThe entire AGP data set was subset using the metadata version accessed 8 August 2019 for stool samples from adult participants (age >19 and <70 years) who answered the “fermented plant frequency” question from the AGP questionnaire. Participants were excluded if they took antibiotics in the last year or if they had outlier values for their body mass index (<15 or >50), height (<48 cm or >210 cm), or weight (<2.5 kg or >200 kg). If biological replicates were present, the replicate with the lower number of reads was removed . Based on the AGP questionnaire, participants were considered consumers if they reported “daily,” “frequent” and “occasional” fermented plant consumption  and nonconsumers if they reported “rarely” and “never.”Overall diet quality was assessed by the Healthy Eating Index 2010 (HEI-2010) as described elsewhere . BrieflyDaily total consumption of CLA and LA (grams/day) was estimated from the VioScreen FFQ reports. Total CLA consumption was deduced from the following food sources: beef and other meat such as fish and turkey, full-fat dairy products , and eggs. Total LA consumption was obtained from the following reported foods: vegetable oil , salad dressings containing vegetable oils, butter, eggs, meat , potatoes , nuts, nut butters and seeds, mixed Mexican dishes, and meat dishes such as stews and casseroles.http://www.earthmicrobiome.org/protocols-and-standards/16s). DNA was extracted with the Qiagen MagAttract PowerSoil DNA kit as previously described (DNA extraction and 16S rRNA amplicon sequencing were done using Earth Microbiome Project (EMP) standard protocols  and effect size of the log ratios. The stability of the participants’ microbiomes was assessed by comparing sample log ratios in consecutive time points, for both the 16S and metabolomic data sets. The 40 highest- and lowest-ranked features were used in order to compute enough log ratios for Spearman’s rank correlation coefficients across all samples and for ordinary least squares regression  column (50 mm by 2.1 mm) , held at 40°C during analysis. A total of 5 μl of each sample was injected. Mobile phase A was water, and mobile phase B was acetonitrile, both with added 0.1% (vol/vol) formic acid. The solvent gradient table was set as follows: initial mobile phase composition was 5% B for 1 min, increased to 40% B over 1 min and then to 100% B over 6 min, held at 100% B for 1 min, and decreased back to 5% B in 0.1 min, followed by a washout cycle and equilibration for a total analysis time of 13 min. The scanned m/z range was 80 to 2,000, the capillary voltage was 4,500 V, the nebulizer gas pressure was 2 × 105 Pa, the drying gas flow rate was 9 liters/min, and the temperature was 200°C. Each full MS scan was followed by tandem MS (MS/MS) using collision-induced dissociation (CID) fragmentation of the seven most abundant ions in the spectrum. For MS/MS, the collision cell collision energy was set at 3 eV and the collision energy was stepped 50%, 75%, 150%, and 200% to obtain optimal fragmentation for differentially sized ions. The scan rate was 3 Hz. An HP-921 lock mass compound was infused during the analysis to carry out postprocessing mass correction. To determine the specific isomer of the annotations for octadecadienoic acid isomers, authentic standards for linoleic acid  and conjugated linoleic acid   were compared by retention times (RTs) and MS/MS spectra. This brings these annotations to the level 1 identifications .The untargeted metabolomics analysis using high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS) was carried out as described previously . The chrm/z tolerance of 0.1 m/z or 20 ppm; (ii) chromatogram deconvolution—baseline cutoff, minimum peak height of 3,000, peak duration range of 0.01 to 3.00 min, and baseline level of 300; (iii) deisotopization—isotopic peak grouper, m/z tolerance of 0.1 m/z or 20 ppm, RT tolerance of 0.1 min, and maximum charge of 4; (iv) peak alignment—join aligner, m/z tolerance of 0.1 m/z or 20 ppm, weight for m/z 75, weight for RT 25, and RT tolerance of 0.1 min; and (v) peak filtering—peak list raw filter, minimum peak in a row of 3 and minimum peak in an isotope pattern of 2.The collected data were processed as described in reference The metadata were added into the resulting extracted feature table and used as input for the MetaboAnalyst software , 65. Thehttps://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=420a545b5b164d10a20f62c0ec0ce7e7. Feature-based molecular network  assay kit, and 5 ng of input, or a maximum of 3.5 μl, genomic DNA (gDNA) was used in a 1:10 miniaturized Kapa HyperPlus protocol. Per-sample libraries were quantified and pooled at equal nanomolar concentrations. The pooled library was cleaned with the QIAquick PCR purification kit and size selected for fragments between 300 and 700 bp on the Sage Science PippinHT. The pooled library was sequenced as a paired-end 150-cycle run on an Illumina HiSeq2500 v2 in Rapid Run mode at the UCSD IGM Genomics Center, with a target depth of ca. 20 million reads per sample. The sequencing adapter and short reads were first removed using Atropos v1.1.21 (-q 15 –minimum-length 100 –pair-filter any) as well as reads aligning to the human genome using bowtie2 (–very-sensitive). The pass-filter reads were then concatenated per sample, excluding 1 biological duplicate and 8 samples from participants exposed to antibiotics, in order to obtain 91 pairs of fastq files.t tests and Cohen’s D were calculated to assess the significance  and effect size of the log ratios.On each separate sample fastq file, paired-end reads were merged using FLASH v1.2.11  and thenr = −0.651, P = 4.63e−22, n = 249 microbes; In order to identify microbial features associated with fermented food consumption and the metabolites they might be producing, we measured probabilities of cooccurrence between observed species (based on metagenomic data) and either all metabolites, or a set of five linoleic and isomers of conjugated linoleic acids discernible in the data . For this analysis, we used mmvec v1.0.2 , a neuraERP012803. The metabolomics analysis is available at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=420a545b5b164d10a20f62c0ec0ce7e7  and https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=9ce1517e83a94d9a8cd9d79f3e16eea0 (feature-based molecular networking). All of the raw data are publicly available at the UCSD Center for Computational Mass Spectrometry .The data generated in this study are available publicly in Qiita under the study ID 10317. Sequence data associated with this study can be found under EBI accession


Forsythia suspensa using genome sequence and population genomics data obtained from specific-locus amplified fragment sequencing. We assembled a high-quality reference genome of weeping forsythia (Scaffold N50 = 7.3 Mb) using ultralong Nanopore reads. Then, genome-wide comparative analysis was performed for 15 natural populations of weeping forsythia across its current distribution range. Our results revealed that candidate genes associated with local adaptation are functionally correlated with solar radiation, temperature and water variables across heterogeneous environmental scenarios. In particular, solar radiation during the period of fruit development and seed drying after ripening, cold, and drought significantly contributed to the adaptive differentiation of F. suspensa. Natural selection exerted by environmental factors contributed substantially to the population genetic structure of F. suspensa. Our results supported the hypothesis that adaptive differentiation should be highly pronounced in the genes involved in signal crosstalk between different environmental variables. Our population genomics study of F. suspensa provides insights into the fundamental genetic mechanisms of the local adaptation of plant species to climatic gradients.Understanding the genetic basis underlying the local adaptation of nonmodel species is a fundamental goal in evolutionary biology. In this study, we explored the genetic mechanisms of the local adaptation of  For naturally distributed plant species, selection pressure is mainly generated by abiotic factors such as soil chemistry, temperature, precipitation, and solar radiation3. These abiotic factors induce adaptive genetic differentiation on a genome-wide scale via spatially varying selection4. Adaptive divergence may lead to changes in gene expression, metabolism, physiology, phenotype, or phenology that significantly increase the adaptive ability of species across local environmental gradients5. Identifying the regions at the genome involved in local adaptation can aid in the design of appropriate protection and management strategies to reduce the impact of future climate change, in addition to providing insights into the genetic mechanism of local adaptation6. Genome scanning on a population scale enables the identification of genetic variants that control adaptive differences among natural populations8.Spatial heterogeneity of the environment exerts differential selection pressures on natural populations, potentially leading to the local adaptation of a species across its range10. As a result of this genomics revolution, evidence of local adaptation has greatly increased in recent decades. Some notable examples include Apis cerana, Arabidopsis thaliana, Corymbia calophylla, Fundulus heteroclitus, and Peromyscus maniculatus15. These empirical studies identified adaptive genetic variation on a genome-wide scale that is driven by natural selection along environmental gradients.With the rapid development of sequencing technology, it is now possible to generate genomic information to understand the genomic basis underlying local adaptation16. This raises the fundamental question of which environmental factors play key roles in promoting adaptive divergence. Recent studies have suggested that environmental factors related to ecological niche dimensions are the main forces driving adaptive differentiation18.Different environmental factors exert different selection pressures on species, which may be due to the differential sensitivity of species to these environmental factors and how environmental gradients interact with fitness landscapes20. Compared with animals and humans, plants often exhibit low migration abilities, which may increase the strength of local selection pressures in relation to climate change and environmental heterogeneity, resulting in more adaptive genetic variations in the genome to cope with these stresses.However, the results supporting the habitat niche selection hypothesis have been produced using low-density molecular markers, and it is doubtful whether the same conclusion will be reached when analysis is extended to genome-wide markers. An additional critical question is which genes are involved in local adaptation. Recent population genomics studies in animals and humans have shown that only a small number of genes are typically involved in local adaptation22. In some past studies of adaptive evolution, substantial effects of pleiotropy were found23. Moreover, many adaptive differentiation loci are frequently simultaneously associated with more than two environmental variables, which suggests that adaptive differentiation may occur more frequently in the genes showing crosstalk between different environmental modules.The recent growth of plant population genomics has provided key information and perspectives that have revolutionized research on local adaptation18. These limitations make it difficult to ascertain which genes generate adaptive differentiation for local adaptation and whether adaptive differentiation occurs in genes related to signal crosstalk between different environmental factors. Conducting adaptive genomics studies on species with available reference genomic information will better answer these questions and provide a clearer understanding of the genetic mechanisms of local adaptation24.Here, we propose the hypothesis that adaptive differentiation will occur at a high frequency in the genes responsible for signal crosstalk between different environmental variables during the process of local adaptation. However, most of the previous studies in this research area used molecular markers without sequence information or single-nucleotide polymorphism (SNP) markers without a reference genome25. We focused on the effects of gene flow, genetic drift, population demographic history, and geological history27. However, natural selection generated along environmental gradients is also an important factor affecting the genetic structure. Increasing evidence of correlations between population genetic structure and environmental factors indicates a need to examine the role of natural selection in shaping the spatial structure of populations29. In addition, natural selection caused by environmental factors will also lead to the differentiation of loci around these adaptive genes due to the hitchhiking effect, which will also influence the apparent relationships between environmental factors and genetic structure. However, we do not know whether the magnification of this effect will outweigh the effects of other neutral processes, such as gene flow and drift. When molecular markers are used for analyses at the genome level, the role of natural selection in determining population genetic structure will be more readily measurable. Overall, the use of population genomics is helpful for understanding the genetic mechanisms of local species adaptation2.The last question that we investigate in this paper is whether the selection pressures imposed by these environmental gradients are sufficient to affect the population genetic structure of species. In the past, we used neutral molecular markers to investigate the population genetic structure of speciesF. suspensa (Thunb.) Vahl (Oleaceae) (2n = 2x = 28), is a dominant, deciduous shrub that is widely distributed in the warm temperate zone in China25. In previous phylogeographical studies of F. suspensa, it was inferred that this species tracked quaternary climatic changes by expanding to nearby low-elevation plains in glacial periods and retreating to mountaintops during interglacial warm periods25. This suggests that the species has responded to environmental fluctuation through a combination of limited local range shifts, local adaptation, and phenotypic plasticity. Thus, this species may be a good candidate for studies of local adaptation given the long period of environmental selection it has experienced within a relatively fixed range compared to other species that experienced more extensive range shifts following climate fluctuations. Thus, we believe F. suspensa is a good model for inferring local adaptation.Weeping forsythia, F. suspensa indicated that the seasonal variations in precipitation, the extreme lowest temperature, and precipitation in the wettest month play important roles in the process of population adaptive differentiation30. However, whether these environmental gradients are key factors explaining genomic structure still needs to be verified. Furthermore, the identity of the genes associated with the observed adaptive differentiation is still not known because of the anonymous molecular markers used in previous studies30. In addition, Yang et al.30 found that most adaptive loci (22 out of 23) are simultaneously related to multiple environmental variables, indicating substantial pleiotropy. This may indicate that most of the adaptive differentiation occurs in the genes related to crosstalk between different environmental factors.A recent study on F. suspensa using population genomics data from specific-locus amplified fragment sequencing (SLAF)31. To better understand this mechanism, we sequenced the weeping forsythia genome using Nanopore PromethION. To date, the genomes of three species in Oleaceae have been published34. The genome sequencing of F. suspensa will be useful for understanding the structure of Oleaceae genomes in general and the evolution of the Oleaceae family. In this study, our main objective was to address the following questions: (a) what is the genomic basis underlying local adaption? (b) can more adaptive differentiation be seen in the genes related to signal crosstalk between different environmental modules? and (c) is natural selection resulting from these environmental variables is sufficient to explain the observed population genetic structure of this species?In this study, we explored the genetic mechanisms of the local adaptation of F. suspensa is a nonmodel species without a reference genome, we employed both the Illumina and Nanopore platforms to perform de novo genome assembly. To estimate the genome size of F. suspensa, we performed preliminary sequencing using the Illumina HiSeq 2500 platform to survey the genome. A total of 64.39 Gb of data were generated on the HiSeq 2500 platform . The K-mer spectrum analysis suggested an estimated genome size of 701.40 Mb. With this background, we assembled the reference genome of F. suspensa using 113× Nanopore ultralong reads. After correction using Illumina reads, we obtained an integrated assembly of 737.5 Mb in size, which consists of a total of 1214 contigs, with a contig N50 of 7.3 Mb , an enzyme that catalyzes the oxidation of a particular substrate by hydrogen peroxide, was differentially expressed under drought and low temperature stress42. Gibberellin 3-beta-dioxygenase (EVM0027506.1), one of the key enzymes involved in gibberellin synthesis, showed significant differential expression under low temperature and drought conditions43, and its sequence differentiation might suggest that it differentially regulates plant growth through the gibberellin pathway to adapt to different environmental conditions. 3-Ketoacyl-CoA synthase (EVM0017367.1) participates in very-long-chain fatty acid synthesis and plays a role in wax biosynthesis. A previous study of Brassica napus suggested that the overexpression of the genes encoding 3-ketoacyl-CoA synthase promotes cuticular wax production and increases drought tolerance44. Furthermore, Wang et al.44 found that the expression of 3-ketoacyl-CoA synthase genes was significantly downregulated by cold stress, which also indicated that they might be associated with cold adaptation. Cytochrome b6–f complex subunit 4 (EVM0014903.1), a subunit of cytochrome b6-f, plays a role in photosynthesis by transferring electrons between photosystems II and I in plants. Previous results showed the repressed accumulation of the cytochrome b6–f complex under drought stress, which suggests that it is related to drought adaptation45. As most of the identified divergent genes with indirect evidence in other species were simultaneously differentiated in relation to the temperature stress and water stress modules, we speculate that this might indicate that the adaptive differentiation of the genes related to crosstalk between two modules is more efficient than that of other genes. This might also be due to the climatic characteristics of this region. To verify this hypothesis, further functional verification of these divergent genes with unknown functions in temperature stress and water stress modules is still needed.Consistent with our hypothesis, most of these genes (56 out of 87 genes) were adaptively differentiated with respect to both temperature and water stress, suggesting that the crosstalk observed between the two modules might be a common phenomenon during the process of local adaptation. For example, APETALA2-like ethylene-responsive transcription factors  were identified as being under divergent selection in PC1. This plant-specific transcription factor has been shown to play an important role in regulating plant growth and responses to cold, heat, salt, drought and flooding46. Manganese transport protein (EVM0021805.1) is a protein located at the membrane of cells that pumps out manganese. Manganese is an essential plant mineral nutrient and plays a key role in photosynthesis, and manganese deficiency could result in a decrease in chlorophyll content47. Jasmonic acid-amino synthetase (EVM0011590.1) activates the conjugation between jasmonate and various amino acids, and overexpression of this enzyme causes a far-red specific hyperphotomorphogenic response48. The carotenoid cleavage dioxygenases (EVM0011590.1), a family of non-heme iron-containing dioxygenase enzymes, catalyze the oxidative cleavage of carotenoid substrates. Carotenoids participate in various biological processes in plants, such as photosynthesis, photomorphogenesis, photoprotection, and development49.PC2 represents the solar radiation from August to November. We found that 403 out of 640 genes showed adaptive differentiation associated with PC2 based on the LFMM results. A total of 61 of these genes had been previously confirmed to be involved in the light module in other species 51 and gibberellin 3-beta-dioxygenase (EVM0027506.1)52. In addition, we identified a new environmental factor that might play a key role in the adaptive differentiation of F. suspensa: solar radiation during the period of fruit development and seed drying after ripening, which has not been reported in previous studies.In previous population genomics studies of 54. The identification of population genetic structure at the genome level can help provide a comprehensive understanding of the effects of these factors on population genetic structure. Recently, three studies have described the population genetic structure of F. suspensa55. The first study of the phylogeography of F. suspensa was based on chloroplast DNA (cpDNA) and nuclear ribosomal DNA (nrDNA). Importantly, cpDNA-based analyses divide the population of F. suspensa into seven groups, whereas nrDNA cannot separate these populations at all25. The second study of population genetics in F. suspensa used microsatellite markers, in which the populations of F. suspensa that were not separated using nrDNA were divided into two groups, the east and west groups57. The third study of landscape genomics in F. suspensa used start codon-targeted (SCoT) polymorphism markers and further subdivided the western group identified by microsatellite markers into three groups30. The first two studies using nuclear molecular markers suggested that gene flow plays an important role in the population genetic structure of F. suspensa, while the third study using SCoT makers, a kind of nuclear gene-targeted marker, suggested that natural selection also plays an important role in shaping the population genetic structure of F. suspensa.Additional important questions are whether, when and how natural selection introduced by environmental variables will affect population genetic structure. The genetic structure of a species is affected by many factorsF. suspensa. As indicated through population genomic inferences, the 15 natural populations of F. suspensa could be broadly divided into 5 groups corresponding to their geographic locations. Notably, while both the PCA and ADMIXTURE inferences supported the genetic divergence of the five groups, polyphyletic relationships were observed in the NJ tree. There are three possible explanations for this phenomenon: (1) as indicated in the AMOVA, only a small proportion (11.2%) of the identified genetic variants contributed to the observed interpopulation genetic differentiation; (2) the Bayesian-based inference suggested that the F. suspensa populations within the same geographic group differed in their genetic constitution; and (3) historical gene flow occurred frequently among the five geographic groups. Consistent with the results of previous studies, we found that EG was separated from the other populations, and previous studies confirmed that this separation was caused by ecological isolation25. However, the identification of other subgroups in this study differed from the results of previous studies.Here, our SLAF sequencing results provided 0.58 million high-quality SNPs across the genome, which enabled us to identify finer-scale population structures than in previous studies. Using these SNPs, regardless of their neutrality, our analysis provides a more comprehensive understanding of population genetic structure. Compared with phylogenetic tree methods, ADMIXTURE and PCA revealed a more refined population genetic structure of F. suspensa, we conducted a comparison between the clustering results of all SNP loci and the results excluding outlier loci. After excluding outlier loci, the east group was still separated from the other groups, whereas the clustering results of the other groups changed significantly. This further confirmed that the separation of the EG from the other populations is not caused by natural selection, but natural selection plays an important role in shaping the genetic structure of other populations of F. suspensa. These results are consistent with previous landscape genomics studies on F. suspensa30. Although the TREEMIX results showed gene flow between the EG and NWG, NG, and SG groups, the current amount of gene flow is not sufficient to eliminate the genetic differentiation caused by ecological isolation. In previous population genetics studies, we have paid more attention to the effects of genetic drift and gene flow on population genetic structure due to the use of neutral markers54. However, natural selection is also an important force shaping the population genetic structure of species17. It promotes the differentiation of adaptive genes in the genome of species among natural populations, and this genetic differentiation is further amplified due to the hitchhiking effect. This also amplifies the ability of natural selection to shape population genetic structure. Our study provides an empirical example of a species’ genetic structure being significantly influenced by natural selection imposed by multiple environmental factors.To understand the contribution of natural selection imposed by environmental variables to the population genetic structure of F. suspensa, an important medicinal and ornamental plant. The genome information of F. suspensa is an essential resource for biological research and breeding in this species. We used this genome information in combination with a landscape genomics approach to explore the genetic basis of the local adaptation of this species. Our results showed that the genes of F. suspensa in the light, temperature and water modules have undergone adaptive differentiation under the effects of heterogeneous environmental gradients. We also found extensive signal crosstalk involving these adaptive genes between different modules. Among these environmental factors, solar radiation during the period of fruit development and seed drying after ripening, cold, and drought were found to significantly contribute to the adaptive differentiation of F. suspensa. Natural selection driven by multiple environmental factors has significantly contributed to the population genetic structure of F. suspensa. Overall, our study provides insights into the genetic mechanisms of local adaptation in this species.In the present study, we provide the first genome of F. suspensa samples were collected from 15 geographically isolated locations (voucher No. 2018LIFS001–2018LIFS015) covering its current distribution range in China . The 300 ina Fig. ; Table 3F. suspensa was estimated based on the Illumina short insert library (350 bp) using Jellyfish56. The library for Nanopore PromethION sequencing was prepared according the protocol of Jain et al.57. The concentration of the DNA library was assessed with a Qubit fluorometer (Thermo Fisher). After the construction of the library, the sample was added into the flow cell, and the flow cell was transferred to a Nanopore PromethION sequencer at Biomarker Technologies  for real-time single-molecule sequencing to obtain original sequencing data. The read quality values of the original data sequenced on the Nanopore sequencing platform were preliminarily filtered to remove reads with low quality and a length of less than 2 kb. Reads after preliminary filtration were corrected and assembled using the program Canu58. Then, WTDBG2 and SMARTdenovo were used to assemble the error-corrected data from Canu. Quickmerge59 was used to integrate the assembly results yielded by WTDBG2 and SMARTdenovo, and the integration results were corrected with Racon software60. To improve the accuracy of the genome sequences of F. suspensa, we used the Illumina data to further calibrate the sequencing data of Nanopore PromethION. After correction with Pilon software61, the final version of the genome of F. suspensa was obtained. The genome completeness was assessed using a terrestrial plant dataset from BUSCO version 2.062 and a eukaryote dataset from CEGMA version 2.563. The protocol for gene and repeat annotation is provided in Supplementary File Genomic DNA was extracted from fresh young leaves by using plant DNA extraction kits . DNA was quantified using a NanoDrop One microvolume UV–vis spectrophotometer . The genome size of F. suspensa and four outgroup samples was extracted from the silica gel-dried leaf material using a Plant DNA Extraction Kit . DNA was quantified with Nanodrop One UV–vis spectrophotometers . In this experiment, an improved SLAF sequencing strategy was used to construct the sequencing library. Using our own assembled genome as a reference for electron digestion prediction, two enzymes  were selected for the digestion of genomic DNA. The SLAF-seq library was constructed using a customized version of Zhang et al.’s64 protocol. Libraries were sequenced using 125-bp paired end reads on the Illumina HiSeq 2500 platform  at Biomarker Technologies . The low-quality reads  were removed, and the rest of the reads were sorted for each sample according to duplex barcode sequences. Clean reads from the same sample were mapped onto the genome sequence of F. suspensa using BWA software65. Sequences mapping to the same position with over 95% identity were considered to represent a single SLAF locus. Using GATK66 and SAMtools67 to develop SNP markers, the intersection of the SNPs obtained via the two methods was considered to represent the final reliable dataset. After filtering out the allele tags of each SLAF locus with a minor allele frequency under 5% and integrity under 80%, the remaining high-quality SNPs were screened for subsequent population genetic analyses.The genomic DNA of the 300 68. To investigate the maximum likelihood of the ancestry of each individual, population structure was inferred by using ADMIXTURE version 1.234 with a range of 1–10 coancestry clusters. The optimal K value was determined according to the valley value of the error rate. The genetic relationships of all samples of F. suspensa and the four outgroup samples were constructed using the neighbor-joining method in the program MEGA version 10.069. The running parameters were set as follows: Kimura 2-parameter model with 1000 bootstrap replicates. According to the grouping information of ADMIXTURE, Arlequin version 3.570 was used to calculate the variation between individuals within and among all groups. Treemix version 1.171 was used to infer population isolation and mixing patterns, and the direction and degree of gene flow were estimated based on the frequency variation of SNPs between different groups.Population genomic analyses of the collected samples were performed based on PCA, admixture analysis, and phylogenetic inferences. PCA was performed by using EIGENSOFT version 6.072, with the following parameters: sample size of 5000, thinning interval of 10, 20 pilot runs with a 5000 run length, 50,000 burn-in iterations, and 10 prior odds. The cut-off thresholds were a posterior probability over 0.76 and a q value lower than 0.05, which are considered to provide substantial evidence of selection. Then, a total of 43 environmental variables (Supplementary File http://www.diva-gis.org/climate) and extracted using DIVA-GIS 7.573. To eliminate the collinearity of environmental variables, PCA was performed using the FactoMineR package in R. The original environmental data were standardized with the SCALE function prior to PCA. The first method was a latent factor mixed model (LFMM)74, which was run with the R script of LFMM version 1.3. The running parameters were 100,000 sweeps and 50,000 burn-in sweeps. The number of latent factors was the optimal K value from the results of Admixture based on the neutral loci. The loci with |z| values over 4 and P values lower than 0.001 were identified as EAL. The second method was a logistic regression model run in Samβada version 0.876. The cut-off value was set as a P value of the G score lower than 0.01 after Bonferroni correction. To infer the influence of these transformed environmental variables on adaptive genetic differentiation, RDA was performed using the vegan package77 in R. Here, the SNPs of EAL were used as response variables , we identified outlier loci that were assumed under natural selection and detected them using BayeScan version 2.1Supplementary InformationSupplementary Information