Monthly Archives: June 2022

Summary

Host genetics and the gut microbiome can both influence metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear. Here, we compared microbiotas across > 1,000 fecal samples obtained from the TwinsUK population, including 416 twin-pairs. We identified many microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a cooccurrence network with other heritable Bacteria and with methanogenic Archaea. Furthermore, Christensenellaceae and its partners were enriched in individuals with low body mass index (BMI). An obese-associated microbiome was amended with Christensenella minuta, a cultured member of the Christensenellaceae, and transplanted to germfree mice. C. minuta amendment reduced weight gain and altered the microbiome of recipient mice. Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.

The human gut microbiome has been linked to metabolic disease and obesity (Karlsson et al., 2013; Le Chatelier et al., 2013; Ley et al., 2005; Qin et al., 2012; Turnbaugh et al., 2009). Variation in host genetics can also underlie susceptibility to metabolic disease (Frayling et al., 2007; Frazer et al., 2009; Herbert et al., 2006; Yang et al., 2012). Despite these shared effects, the relationship between host genetic variation and the diversity of gut microbiomes is largely unknown.

The gut microbiome is environmentally acquired from birth (Costello et al., 2012; Walter and Ley, 2011), therefore it may function as an environmental factor that interacts with host genetics to shape phenotype, as well as a genetically determined attribute that is shaped by, and interacts with, the host (Bevins and Salzman, 2011; Spor et al., 2011; Tims et al., 2011). Because the microbiome can be modified for therapeutic applications (Borody and Khoruts, 2012; Hamilton et al., 2013; Khoruts et al., 2010; van Nood et al., 2013), it constitutes an attractive target for manipulation. Once the interactions between host genetics and the microbiome are understood, its manipulation could be optimized for a given host genome to reduce disease risk.

Although gut microbiomes can differ markedly in diversity across adults (Consortium, 2012; Qin et al., 2010), family members are often observed to have more similar microbiotas than unrelated individuals (Lee et al., 2011; Tims et al., 2013; Turnbaugh et al., 2009; Yatsunenko et al., 2012). Familial similarities are usually attributed to shared environmental influences, such as dietary preference, a powerful shaper of microbiome composition (Cotillard et al., 2013; David et al., 2013; Wu et al., 2011). Yet related individuals share a larger degree of genetic identity, raising the possibility that shared genetic composition underlies familial microbiome similarities.

Support for a host genetic effect on the microbiome comes mostly from studies taking a targeted approach. For instance, the concordance rate for carriage of the methanogen Methanobrevibacter smithii is higher for monozygotic (MZ) than dizygotic (DZ) twin pairs (Hansen et al., 2011), and studies comparing microbiotas between human subjects differing at specific genetic loci have shown gene-microbiota interactions (Frank et al., 2011; Khachatryan et al., 2008; Rausch et al., 2011; Rehman et al., 2011; Wacklin et al., 2011). A more general approach to this question has linked genetic loci with abundances of gut bacteria in mice (Benson et al., 2010; McKnite et al., 2012), but in humans, a general approach (e.g., using twins) has failed to reveal significant genotype effects on microbiome diversity (Turnbaugh et al., 2009; Yatsunenko et al., 2012). Thus, heritable components of the human gut microbiome remain to be identified using an unbiased approach.

Here, we assessed the heritability of the gut microbiome with a well-powered twin study. Comparisons between MZ and DZ twin pairs allowed us to assess the impact of genotype and early shared environment on their gut microbiota. Our study addressed the following questions: Which specific taxa within the gut microbiome are heritable, and to what extent? Which predicted metagenomic functions are heritable? How do heritable microbes relate to host BMI? Finally, we use fecal transplants into germfree mice to assess the phenotype effects of the most heritable taxon.

We obtained 1,081 fecal samples from 977 individuals: 171 MZ and 245 DZ twin pairs, 2 from twin pairs with unknown zygosity, and 143 samples from just one twin within a twinship (i.e., unrelated). In addition, we collected longitudinal samples from 98 of these individuals (see Supplemental Information). Most subjects were female, ranging in age from 23 to 86 years (average age: 60.6 0.3 years). The average BMI of the subjects was 26.25 ( 0.16) with the following distribution: 433 subjects had a low to normal BMI (<25), 322 had an overweight BMI (25-30), 183 were obese (>30) and 39 individuals in which the current BMI status was unknown. We generated 78,938,079 quality-filtered sequences that mapped to the Bacteria and Archaea in the Greengenes database (average sequences per sample: 73,023 889).

We sorted sequences into 9,646 operational taxonomic units (OTUs, 97% ID). Of these OTUs, 768 were present in at least 50% of the samples. Taxonomic classification revealed a fairly typical Western diversity profile: the dominant bacterial phyla were Firmicutes (53.9% of total sequences), Bacteroidetes (35.3%), Proteobacteria (4.5%), with Verrucomicrobia, Actinobacteria, and Tenericutes each comprising 2% of the sequences, and a tail of rare bacterial phyla that together accounted for the remaining 1% of the sequences.

The most widely shared methanogen was M. smithii (64% of people, using nonrarefied data), followed by vadinCA11, a member of the Thermoplasmata with no cultured representatives (~6%), Methanospheara stadtmanae (~4%), and Methanomassiliicoccus (~4%, a member of the Thermoplasmata). Forty-six of the 61 samples in which we detected vadinCA11 also contained M. smithii, indicating that the two most dominant archaeal taxa are not mutually exclusive. Faith's PD was positively correlated with the relative abundance of the family Methanobacteriaceae (rho = 0.42 rarefied, 0.37 for transformed counts, P < 1 x 1011), which corroborates previous reports of higher richness associating with methanogens.

We observed that microbiotas were more similar overall within individuals (resampled) than between unrelated individuals (P < 0.001 for weighted and unweighted UniFrac and Bray-Curtis using a Student's t-test with 1,000 Monte Carlo simulations; Table S1A), and were also more similar within twin pairs compared to unrelated individuals (P < 0.009 for weighted and unweighted UniFrac and Bray-Curtis; , S1 and Table S1). MZ twin pairs had more similar microbiotas than DZ twins for the unweighted UniFrac metric (P = 0.032), but not the weighted UniFrac and Bray-Curtis metrics (, S1). As greater similarities for MZ versus DZ twin pairs are seen in unweighted UniFrac but not abundance-based metrics, MZ similarities are driven by shared community membership rather than structure.

A, C-F: Box plots of beta-diversity distances between microbial communities obtained when comparing individuals within twinships for monozygotic (MZ) twin pairs and dizygotic (DZ) twin pairs, and between unrelated individuals (UN). A: the whole microbiome; C: the bacterial family Ruminococcaceae; D-E: the bacterial family Lachnospiraceae; F: the family Bacteroidaceae. The specific distance metric used in each analysis is indicated on the axes. *P<0.05, **P<0.01, ***P<0.001 for Student's t-tests with 1,000 Monte Carlo simulations. B: The average relative abundances in the whole dataset for the top six most prevalent bacterial families (unrarefied data, see Methods). Relates to Figure S1 and Table S1.

We next constrained the distance metric analyses to the three most dominant bacterial families: the Lachnospiraceae and Ruminococcaceae (Firmicutes) and Bacteroidaceae (). We observed greater similarities for MZ compared to DZ twins using the unweighted UniFrac metric within the Ruminococcaceae family (). Within the Lachnospiraceae family, significantly greater similarity for MZ compared to DZ twins emerged using the weighted UniFrac and Bray-Curtis metrics (). In contrast, when restricted to the Bacteroidaceae family, we found that MZ and DZ twins had similar pair-wise diversity using all three metrics (, S1B and S1E).

We next asked if the abundances of specific taxa were generally more highly correlated within MZ twin pairs compared to DZ twin pairs. For each twin pair we generated intraclass correlation coefficients (ICCs) for the relative abundances of OTUs. Mean ICCs were significantly greater for MZ compared to DZ twin pairs (Wilcoxon signed rank test on ICCs at the OTU level, P = 6 x 1004; ). Since many OTUs are closely phylogenetically related, their abundances may not be independent, which may inflate levels of significance. To account for this effect, we maintained the structure of the phylogenetic tree but permuted the MZ and DZ labels in 10,000 tests to generate randomized ICCs. As an independent validation, we also applied these analyses to two previously published datasets generated originating in a population of twins from Missouri, USA: Turnbaugh (Turnbaugh et al., 2009), which described 54 twin pairs ranging from 21-32 years of age, and Yatsunenko (Yatsunenko et al., 2012), which included 63 twin pairs with an age range of 13-30 years of age. Mean ICCs of OTU abundances were significantly greater for MZ compared to DZ twin pairs in both of these datasets (significance by permutation: P < 0.001 and 0.047 respectively; Figure S2), corroborating our observations.

At left is a phylogeny of taxa in the TwinsUK study (Greengenes tree pruned to include only OTUs shared by 50% of the TwinsUK participants) and at right are corresponding twin-pair intra class correlation coefficients (ICCs). ICCs were calculated for each OTU and the difference in correlation coefficients for MZ twin pairs versus DZ twin pairs. Bars pointing to the right indicate that the difference is positive (i.e., MZ ICCs > DZ ICCs) and bars pointing to the left indicate negative differences (DZ ICCs > MZ ICCs). The scale bar associated with the phylogeny shows substitutions/site. Relates to Figure S2.

We estimated heritability using the twin-based ACE model, which partitions the total variance into three component sources: genetic effects (A), common environment (C), and unique environment (E) (Eaves et al., 1978). The largest proportion of variance in abundances of OTUs could be attributed to the twins unique environments (i.e., E > A; Table S2). However, for the majority of OTUs (63%), the proportion of variance attributed to genetic effects was greater than the proportion of variance attributed to common environment (A > C; Table S2).

From the ACE model we calculated 95% confidence intervals for the heritability estimates, and determined the significance of the heritability values using a permutation method to generate nominal P values (Table S2). We found a high correlation between the tail probability for inclusion of zero in the confidence interval of heritability and the P values obtained from the permutation tests (rho = 0.872, P < 1015), indicating substantial consistency across these tests. Although heritability studies traditionally report confidence intervals and nominal P values only, we also generated FDR-corrected P values (Table S2).

We also applied the ACE model to the abundances of sequences mapping to each node in the phylogeny. Across the three studies, the nodes of the phylogeny with the strongest heritabilities lie within the Ruminococcaceae and Lachnospiraceae families, and the Bacteroidetes are mostly environmentally determined ( and S3). Subsets of the Archaea are also heritable in the TwinsUK and the Yatsunenko studies (the Turnbaugh study did not include data for Archaea).

A: OTU Heritability (A from ACE model) estimates mapped onto a microbial phylogeny and displayed using a rainbow gradient from blue (A = 0) to red (A 0.4). This phylogenetic tree was obtained from the Greengenes database and pruned to include only nodes for which at least 50% of the TwinsUK participants were represented. B: The significance for the heritability values shown in A was determined using a permutation test (n=1,000) and are shown on the same phylogeny as in panel A. P values range from 0 (red) to >0.1 (blue). Relates to Figure S3 and Table S2.

We characterized the longitudinal stability of each OTU by calculating the ICCs of the OTU abundance across repeat samples, which consisted of two samples collected from the same individual at different times. By permuting these repeat sample ICCs, we found that heritable OTUs (A > 0.2) were more stable (ICC > 0.6) than expected by chance (Figure S3E; P < 0.001, P value was determined as the fraction of permutations that had greater than or equal to the observed number of OTUs that are both heritable and stable).

We used PICRUSt (Langille et al., 2013) to produce predicted metagenomes from the 16S rRNA gene sequence data and applied the ACE model to estimate the heritability of predicted abundances of conserved orthologous groups (COGs). This analysis revealed 6 functions with heritabilities A > 0.2 and nominal P values < 0.05 (P values are generated by permutation testing; Supplementary Methods; Table S2). Correcting for multiple comparisons, one category, secondary metabolites biosynthesis, transport and catabolism (Q), passed a stringent FDR (A = 0.32, 95% CI = 0.16-0.44). We also tested alpha diversity for heritability and found that it was not heritable.

The most heritable taxon overall was the family Christensenellaceae (A = 0.39, 95% confidence interval 0.21-0.49, P = 0.001, and Table S2; this taxon passes a stringent FDR) of the order Clostridiales. Christensenellaceae was also highly heritable in the Yatsunenko dataset (A = 0.62, 95% confidence interval 0.38-0.77; and Table S2). We repeated this analysis for the taxa abundances with the effect of BMI regressed out, and results were highly correlated (Pearson correlation = 0.95, P < 11015).

Scatter plots comparing the abundances of Christensenellaceae in the gut microbiota of MZ and DZ co-twins. Christensenellaceae abundances were transformed and adjusted to control for technical and other covariates (Residuals are plotted, see Supplemental Methods) and the data are separated by zygosity (MZ or DZ twins). A: TwinsUK dataset. B: Yatsunenko dataset.

We observe a module of co-occurring heritable families, and the hub (node connected to most other nodes) of this module is the family Christensenellaceae ( and S4A). The heritable module includes the families Methanobacteriaceae (Archaea) and Dehalobacteriaceae (Firmicutes) and the orders SHA-98 (Firmicutes), RF39 (Tenericutes) and ML615J-28 (Tenericutes). The Christensenellaceae-network is anti-correlated with the Bacteroidaceae and Bifidobacteriaceae families. We validated these results by applying this method to the family-level taxonomic abundances in the Yatsunenko dataset (as this one is most technically similar to the TwinsUK dataset), where we also found the same Christensenellaceae-centered module of heritable families anti-correlated to the Bacteroidaceae/Bifidobacteriaceae module (Figure S4B).

A and B show the same network built from SparCC correlation coefficients between sequence abundances collapsed at the family level. The nodes represent families and the edges represent the correlation coefficients between families. Edges are colored blue for a positive correlation and grey for a negative correlation, and the weight of the edge reflects the strength of the correlation. Nodes are positioned using an edge-weighted force directed layout. In panel A, the nodes are colored by the heritability of the family, and in panel B, the nodes are colored by the significance of the association families and a normal vs. obese BMI. Family names are either indicated on the panel, or nodes are given a letter code. Phylum Actinobacteria: (a) Actinomycetaceae, (b) Coriobacteriaceae; Phylum Bacteroidetes: (c) Barnesiellaceae, (d) Odoribacteraceae, (e) Paraprevotellaceae, (f) Porphyromonadaceae, (g) Prevotellaceae, (h) Rikenellaceae; Phylum Firmicutes: (i) Carnobacteriaceae, (j) Clostridiaceae, (k) Erysipelotrichaceae, (l) Eubacteriaceae, (m) Lachnospiraceae, (n) Lactobacillaceae, (o) Mogibacteriaceae, (p) Peptococcaceae, (q) Peptostreptococcaceae, (r) Ruminococcaceae, (s) Streptococcaceae, (t) Tissierellaceae, (u) Turicibacteraceae, (v) Unclassified Clostridiales, (w) Veillonellaceae; Phylum Proteobacteria: (x) Alcaligenaceae, (y) Enterobacteriaceae, (z) Oxalobacteraceae, (aa) Pasteurellaceae, (ab) Unclassified RF32; Phylum Verrucomicrobia: (ac) Verrucomicrobiaceae. Relates to Figure S4.

The family Christensenellaceae was significantly enriched in subjects with a lean BMI (< 25) compared to those with an obese BMI (> 30; Benjamini-Hochberg corrected P value < 0.05 from t-test on transformed counts; Table S2). Other members of the Christensenellaceae consortium were also enriched in lean-BMI subjects: the Dehalobacteriaceae, SHA-98, RF39, and the Methanobacteriaceae (). Overall, a majority (n=35) of the OTUs with highest heritability scores (A > 0.2, nominal P < 0.05) were enriched in the lean subjects. A subset of OTUs classified as Oscillospira were enriched in lean subjects, and M. smithii, though not significantly heritable, was positively associated with a lean BMI.

Because the names Christensenella and Christensenellaceae were only recently assigned to the bacterial phylogeny, we assessed the abundances of sequences assigned to these taxa in previously published studies. This analysis revealed that members of the Christensenellaceae were enriched in fecal samples of healthy versus pediatric and young adult IBD patients (P < 0.05) (Papa et al., 2012). Christensenellaceae were at greater abundance in lean-BMI compared to obese-BMI twins in the Turnbaugh dataset but the difference was not quite significant (time-point 2 samples, P = 0.07). In a case study of the development of an infant's gut microbiome (Koenig et al., 2011), Christensenellaceae was present at 8.6% in the mother's stool at the time of birth, and at 20% in the infant's meconium. We also noted that Christensenellaceae is enriched in omnivorous compared to herbivorous and carnivorous mammals (Muegge et al., 2011). However, we did not find a relationship between Christensenellaceae and diet information in human studies (Wu et al., 2011; Martinez et al., 2010; Koren et al., 2012).

Methanogens co-occurred with Christensenellaceae in this study and have been linked to low BMI in previous studies. Because of this previous association with a low-BMI, we wanted to ensure that methanogens were present in the Christensenellaceae consortium in an initial experiment exploring its effect on weight phenotypes. Therefore, we selected 21 donors for fecal transfer to germfree mice based on BMI status (low or high) and presence or absence of the methanogen-Christensenellaceae consortium. Donors fell into one of four categories: lean with detectable methanogens (L+), lean without methanogens (L-), obese with methanogens (O+), or obese without methanogens (O-). The abundance of Christensenellaceae positively correlated with the abundance of methanogens in donor stool (rho=0.72, P=0.0002), indicating that methanogen abundance was a good proxy for the methanogen-Christensenellaceae consortium.

A 16S rRNA analysis of the fecal microbiomes before and after transfer to germfree mice showed that although members of the Christensenellaceae were present throughout the experiment in recipient mice (), M. smithii was undetectable in the mouse fecal or cecal samples (the first sampling was at 20hrs post-inoculation). At 20 hrs post-inoculation, the microbiota had shifted dramatically in diversity from the inoculation, but by Day 5 had shifted back partially and remained fairly stable through Day 21 (, S5A, and S5B). Abundances of Christensenella were correlated with PC3 (abundances rarefied at 55,000 sequences per sample vs. unweighted UniFrac; Spearman rho = 0.59, P < 2.2 x 1016), and PC3 captured the differences between the 4 donor groups (). We observed a trend for Christensenella abundances as highest in the L+ group and lowest in the O- group (), which mirrored the weight differences between those groups: the percent change in body weights of the recipient mice was significantly lower in the L+ group compared to the O- group (Day 12, P < 0.05, t-test; ). Cecal levels of propionate and butyrate were significantly elevated in mice receiving methanogen-positive compared to methanogen-negative microbiomes controlling for the effect of donor BMI (two-way ANOVA, P < 0.05 for both SCFAs, Figures S5C-E). Stool energy content was significantly higher for the methanogen-positive microbiomes at Day 12, when the percent changes in weight were greatest (two-way ANOVA, P = 0.004, no effect of BMI or interaction; Figure S5F). In a replicated experiment, using 21 new donors, the same weight differences were observed (a significantly lower mean weight gain for the L+ compared to the O- mouse recipients at Day 10 post-inoculation; one-way t-test, P = 0.047; Figure S5G).

A: Median relative abundances for OTUs classified as the genus Christensenella in the four donor treatment groups over time in the recipient mouse microbiotas. B: Principal coordinates analysis of unweighted UniFrac distances for (i) the inoculum prior to transplantation, (ii) fecal samples at 4 time points, and (iii) cecal samples at Day 21 post-transplant; see panel legend for color key. The amount of variance described by the first two PCs is shown on the axes. C: Richness (Faith's PD) for the microbiomes of the transplant mice plotted against time (days post inoculation, with Day 0 = inoculation day). D: The mean values S.E.M. for PC3 derived for the same analysis as shown in panel B are plotted against time (Day 0 = inoculation day) for the four treatment groups. The amount of variance explained by PC3 is in parentheses. E: Percent weight change since inoculation for germfree mouse recipients of 21 donor stools that were obtained from lean or obese donors with or without detectable M. smithii, which was used as a marker for the Christensenellaceae consortium. Means for each treatment group are plotted S. E. M. F: Box plots for percent weight changes for the 4 groups at Day 12 post-transplant, when maximal weight differences were observed. Letters next to boxes indicate significant differences if letters are different (p < 0.05). For all panels, Dark blue = L+, lean donor with methanogens; Light blue = L-, lean donor lacking methanogens; Dark orange = O+, obese donor with methanogens; Light orange = O-, obese donor without methanogens. We repeated this experiment with a set of 21 new mice and unique human donors and recovered the same effect. Relates to Figure S5.

Based on the observation that Christensenella levels in the previous experiment were similar to the weight gain patterns, we performed experiments in which a donor stool lacking detectable Christensenella was amended with C. minuta and weight gain of recipient mice was monitored. One obese human donor was selected from the 21 donors from the first transplant experiment based on its lack of detectable OTUs assigned to the genus Christensenella. At Day 21 post-gavage, mice receiving the C. minuta treatment weighed significantly less than those that received unamended stool (nested ANOVA, P < 0.05, ). Adiposity was significantly lower for mice receiving the C. minuta treatment (nested ANOVA, P = 9.4 x 105, ). Energy content for stool collected at Day 21 was not different between treatments (data not shown).

A: Box plot of percent weight change for germfree mouse recipients of a single donor stool only (lacking detectable Christensenella in unrarefied 16S rRNA data) or the donor stool amended with live C. minuta. B: Box plots showing percent body fat for mice in each group at Day 21 N = 12 mice per treatment. C, D: Principal coordinates analysis of unweighted UniFrac distances for (i) the inoculum prior to transplantation, (ii) fecal samples at 5 time points post-transplant; see panel legend for color key. The amount of variance described by the first two PCs is shown on the axes. The same data projection is shown in panels C and D; sample symbols are colored by time point (C) and by treatment (D). E: Relationship between PCs from the PCoA analysis and levels of Oscillospira at Day 21 (rho = 0.71, P = P < 0.001). Symbols are colored by treatment. Relates to Figure S6.

Analysis of the microbial community by 16S rRNA gene sequencing showed an impact on the overall community diversity that persisted over time (). After an initial acclimation (20 h), the communities within recipient mice began to separate by treatment regardless of the effects of time and co-caging ( and S6). At 5 days post-inoculation, the relative abundance of C. minuta was similar to that observed in the previous transplant experiment and persisted throughout the duration of the study. We identified two genera that discriminated the two treatments at Day 21: Oscillospira and a genus within the Rikenellaceae were enriched in the C. minuta treatment (misclassification error rate of 0.06). Oscillospira abundances were significantly correlated with PC2 in the unweighted UniFrac analysis of the communities (rho = 0.71, P = 0.0009; ), which is the PC that separates the C. minuta-amended and unamended microbiotas.

Our results represent the first strong evidence that the abundances of specific members of the gut microbiota are influenced in part by the genetic makeup of the host. Earlier studies using fingerprinting approaches also reported host genetic effects (Stewart et al., 2005; Zoetendal et al., 2001), but without sequence data it is not possible to know if the taxa shown here to be heritable were also driving those patterns. The Turnbaugh et al. and Yatsunenko et al. studies, which are quite similar in experimental approach, reported a lack of host genetic effect on the gut microbiome, most likely because both studies were underpowered. Nevertheless, re-analysis of the data from both studies validated our observation that the abundances of taxa are more highly correlated within MZ than DZ twin pairs. Thus, host genetic interactions with specific taxa are likely widespread across human populations, with profound implications for human biology.

The most highly heritable taxon in our dataset was the family Christensenellaceae, which was also the hub of a co-occurrence network that includes other taxa with high heritability. A notable component of this network was the archaeal family Methanobacteriaceae. Similarly, Hansen et al. had previously identified members of the Christensenellaceae (reported as relatives of Catabacter) as co-occurring with M. smithii (Hansen et al., 2010). These co-occurrence patterns could derive from different scenarios: for instance, multiple taxa may be heritable and co-occur while each taxon is affected by host genetics independently, or alternatively one (or a few) taxa may be heritable and other taxa correlate with host genetics due to their co-occurrence with these key heritable taxa. Further experimental research will be required to elucidate if the co-occurring heritable taxa interact in syntrophic partnerships or simply respond similarly to host-influenced environmental cues in the gut.

Our results suggest that environmental factors mostly shape the Bacteroidetes community, since most were not heritable. These results are consistent with those of a recent study of Finnish MZ twins, in which levels of Bacteroides spp. were more similar between twins when their diets were similar (Simoes et al., 2013). Members of the Bacteroidetes have been shown to respond to diet interventions (Wu et al., 2011; David et al. 2013)

Importantly, the family Christensenellaceae is heritable in the Yatsunenko dataset and its network is also present. This validation did not involve a directed search using the taxa identified in this study but was made by applying the ACE model independently. In the TwinsUK as well as the Missouri twins datasets, the majority of OTUs with the highest heritability estimates fell within the Ruminococcaceae and Lachnospiraceae families. The Missouri and TwinsUK studies differed somewhat in the levels and structure of heritability, which may relate to study size (Kuczynski et al., 2010), participant age (Claesson et al., 2011), population (Yatsunenko et al., 2012), and/or diet (Wu et al., 2011), all of which have been shown to affect microbiome structure.

The high heritability of the Christensenellaceae raises questions about the nature of interactions between the host and members of this family, but to date there is little published work with which to infer their roles. Christensenella minuta is Gram-negative, non-spore forming, non-motile, and produces SCFAs (Morotomi et al., 2012). A review of publicly available data suggests it is present from birth and associates with a healthy state but not with diet. Thus, although diet is a heritable trait in the same population (Menni et al., 2013; Teucher et al., 2007), it does not appear to be driving the heritability of the Christensenellaceae. Obesity is also strongly heritable in the TwinsUK population, raising the question of whether the heritabilities we saw for gut microbes were driven by BMI. To test this, we reran the heritability calculations using residuals after regressing out the effect of BMI and found that results of the two analyses were highly correlated. This suggests that the effect of host genetics on Christensenellaceae abundance is independent of an effect of BMI.

Our transplantation experiments showed a moderating effect of methanogen-presence in the human donor on weight gain of recipient mice, although strikingly, M. smithii did not persist in mice. In contrast, Christensenellaceae levels in mice mirrored their weight gain. Transfer to germfree mice of microbiomes from obese and lean donors generally results in greater adiposity gains for obese compared to lean donors (Ridaura et al., 2013; Turnbaugh et al., 2008; Vijay-Kumar et al., 2010). These studies have not reported the methanogen or Christensenellaceae status of the donors, so whether these microbes affect the host phenotype is unknown. M. smithii has been associated with a lean phenotype in multiple studies (Million et al., 2013; Million et al., 2012; Schwiertz et al., 2010; Armougom et al., 2009; Le Chatelier, 2013), raising the possibility that methanogens are key components of the consortium for regulating host phenotype. The results of our methanogen-Christensenellaceae transfer revealed that although methanogens may be a marker for a low BMI in humans, they are not required to promote a lean phenotype in the germfree mouse experimental model. This result suggests that methanogens may be functionally replaced by another consortium member in the mouse, while the Christensenellaceae are not.

The results of the C. minuta spike-in experiments supported the hypothesis that members of the Christensenellaceae promote a lean host phenotype. Addition of C. minuta also remodeled the diversity of the community. Intriguingly, Oscillospira, which includes heritable OTUs in the TwinsUK dataset and is associated with a lean BMI, was enriched in the C. minuta-amended microbiomes. How C. minuta reshapes the community remains to be explored. The relatively low levels of C. minuta and its profound effects on the community and the host may indicate that it is a keystone taxon. Together these findings indicate that the Christensenellaceae are highly heritable bacteria that can directly contribute to the host phenotype with which they associate.

Fecal samples were obtained from adult twin pair participants of the TwinsUK registry (Moayyeri, 2013). Most participants were women (only 20 men were included). Twins collected fecal samples at home, and the samples were refrigerated for up to 2 days prior to their annual clinical visit at King's College London, at which pointed they were stored at 80C until processing.

We amplified 16S rRNA genes (V4) from bulk DNA by PCR prior to sequencing on the Illumina MiSeq 2x250bp platform at Cornell Biotechnology Resource Center Genomics Facility. We performed quality filtering and analysis of the 16S rRNA gene sequence data with QIIME 1.7.0 (Caporaso et al., 2010).

PICRUSt v1.0.0 was used to predict abundances of COGs from the OTU abundances rarefied at 10,000 sequences per sample.

Heritability estimates were calculated on the OTU abundances, taxon bins, nodes throughout the bacterial phylogenetic tree, -diversity, and PICRUSt-predicted COGs using the structural equation modeling software OpenMx (Boker et al., 2011).

Stool samples from the Twins UK cohort were selected as described in the main text and inoculated into 6-week old germ-free Swiss Webster mice via oral gavage, with one recipient mouse per fecal donor. Mice were single-housed. For the Christensenella minuta addition, three experiments were conducted: In the first experiment, one treatment group received only donor stool inoculum, whereas the other received donor stool amended with 1 108C. minuta cells;for the second experiment, a heat-killed C. minuta control was added; in the third experiment, the heat-killed control was compared to live C. minuta only (no vehicle-only control). Mice were housed 4 per cage, with 3 cages per treatment. In all experiments, mice were provided with autoclaved food and water ad libitum and maintained on a 12-hr light/dark cycle. Body weight and chow consumption were monitored and fecal pellets were collected weekly. At sacrifice, adiposity was analyzed using DEXA. Body, mesenteric adipose tissue, and gonadal fat pad tissue weights were collected at this time. Gross energy content of mouse stool samples was measured by bomb calorimetry using an IKA C2000 calorimeter (Dairy One, Ithaca, NY). Wet cecal contents were weighed and resuspended in 2% (v/v) formic acid by vortexing and measured using a gas chromatograph (HP series 6890) with flame ionization detection.

Values are expressed as the mean SEM unless otherwise indicated. Full methods are described in the Supplemental Information

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Author Contributions: R.E.L. and A.G.C. supervised the study, and J.T.B. and T.D.S. helped design study and provided comments and discussion. J.T.B. and T.D.S. oversaw collection of samples; J.K.G., R.E.L., O.K., J.L.S., A.C.P, J.L.W. oversaw microbial data generation; J.K.G. performed the analysis with contributions from R.E.L, R.B., A.G.C., J.L.W., O.K., A.C.P, M.B., W.V.T. and R.K; J.K.G. and J.L.W. performed microbiota transfer experiments; J.K.G., J. L.W and R.E.L. prepared the manuscript, with comments from A.G.C, T.D.S, J.T.B, R.B., and R.K.

Author Information: The 16S rRNA gene sequences have been deposited in the European Nucleotide Archive (ENA), European Bioinformatics Institute, with accession numbers ERP006339 and ERP006342.

SUPPLEMENTAL INFORMATION

Supplemental Information includes Extended Experimental Procedures and data and can be found with this article online.

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Human genetics shape the gut microbiome - PMC

Data for a new gene-function map are available for other scientists to use. Its a big resource in the way the human genome is a big resource, in that you can go in and do discovery-based research, says Professor Jonathan Weissman.

Scientists used their single-cell sequencing tool Perturb-seq on every expressed gene in the human genome, linking each to its job in the cell.

Genetics research has advanced rapidly over the last few decades. For example, just a few months ago scientists announced the first complete, gap-free human genome sequencing. Now researchers have advanced again, creating the first comprehensive functional map of genes that are expressed in human cells.

The Human Genome Project was an ambitious initiative to sequence every piece of human DNA. The project drew together collaborators from research institutions around the world, including MITs Whitehead Institute for Biomedical Research, and was finally completed in 2003. Now, over two decades later, MIT Professor Jonathan Weissman and colleagues have gone beyond the sequence to present the first comprehensive functional map of genes that are expressed in human cells. The data from this project, published online on June 9, 2022, in the journal Cell, ties each gene to its job in the cell, and is the culmination of years of collaboration on the single-cell sequencing method Perturb-seq.

The data are available for other scientists to use. Its a big resource in the way the human genome is a big resource, in that you can go in and do discovery-based research, says Weissman, who is also a member of the Whitehead Institute and an investigator with the Howard Hughes Medical Institute. Rather than defining ahead of time what biology youre going to be looking at, you have this map of the genotype-phenotype relationships and you can go in and screen the database without having to do any experiments.

CRISPR, which stands for clustered regularly-interspaced short palindromic repeats, a genome editing tool invented in 2009 made it easier than ever to edit DNA. It is easier, faster, less expensive, and more accurate than previous genetic editing methods.

The screen allowed the researchers to delve into diverse biological questions. They used it to explore the cellular effects of genes with unknown functions, to investigate the response of mitochondria to stress, and to screen for genes that cause chromosomes to be lost or gained, a phenotype that has proved difficult to study in the past. I think this dataset is going to enable all sorts of analyses that we havent even thought up yet by people who come from other parts of biology, and suddenly they just have this available to draw on, says former Weissman Lab postdoc Tom Norman, a co-senior author of the paper.

Pioneering Perturb-seq

The project takes advantage of the Perturb-seq approach that makes it possible to follow the impact of turning on or off genes with unprecedented depth. This method was first published in 2016 by a group of researchers including Weissman and fellow MIT professor Aviv Regev, but could only be used on small sets of genes and at great expense.

The massive Perturb-seq map was made possible by foundational work from Joseph Replogle, an MD-PhD student in Weissmans lab and co-first author of the present paper. Replogle, in collaboration with Norman, who now leads a lab at Memorial Sloan Kettering Cancer Center; Britt Adamson, an assistant professor in the Department of Molecular Biology at Princeton University; and a group at 10x Genomics, set out to create a new version of Perturb-seq that could be scaled up. The researchers published a proof-of-concept paper in Nature Biotechnology in 2020.

The Perturb-seq method uses CRISPR-Cas9 genome editing to introduce genetic changes into cells, and then uses single-cell RNA sequencing to capture information about the RNAs that are expressed resulting from a given genetic change. Because RNAs control all aspects of how cells behave, this method can help decode the many cellular effects of genetic changes.

Since their initial proof-of-concept paper, Weissman, Regev, and others have used this sequencing method on smaller scales. For example, the researchers used Perturb-seq in 2021 to explore how human and viral genes interact over the course of an infection with HCMV, a common herpesvirus.

In the new study, Replogle and collaborators including Reuben Saunders, a graduate student in Weissmans lab and co-first author of the paper, scaled up the method to the entire genome. Using human blood cancer cell lines as well noncancerous cells derived from the retina, he performed Perturb-seq across more than 2.5 million cells, and used the data to build a comprehensive map tying genotypes to phenotypes.

Delving into the data

Upon completing the screen, the researchers decided to put their new dataset to use and examine a few biological questions. The advantage of Perturb-seq is it lets you get a big dataset in an unbiased way, says Tom Norman. No one knows entirely what the limits are of what you can get out of that kind of dataset. Now, the question is, what do you actually do with it?

The first, most obvious application was to look into genes with unknown functions. Because the screen also read out phenotypes of many known genes, the researchers could use the data to compare unknown genes to known ones and look for similar transcriptional outcomes, which could suggest the gene products worked together as part of a larger complex.

The mutation of one gene called C7orf26 in particular stood out. Researchers noticed that genes whose removal led to a similar phenotype were part of a protein complex called Integrator that played a role in creating small nuclear RNAs. The Integrator complex is made up of many smaller subunits previous studies had suggested 14 individual proteins and the researchers were able to confirm that C7orf26 made up a 15th component of the complex.

They also discovered that the 15 subunits worked together in smaller modules to perform specific functions within the Integrator complex. Absent this thousand-foot-high view of the situation, it was not so clear that these different modules were so functionally distinct, says Saunders.

Another perk of Perturb-seq is that because the assay focuses on single cells, the researchers could use the data to look at more complex phenotypes that become muddied when they are studied together with data from other cells. We often take all the cells where gene X is knocked down and average them together to look at how they changed, Weissman says. But sometimes when you knock down a gene, different cells that are losing that same gene behave differently, and that behavior may be missed by the average.

The researchers found that a subset of genes whose removal led to different outcomes from cell to cell were responsible for chromosome segregation. Their removal was causing cells to lose a chromosome or pick up an extra one, a condition known as aneuploidy. You couldnt predict what the transcriptional response to losing this gene was because it depended on the secondary effect of what chromosome you gained or lost, Weissman says. We realized we could then turn this around and create this composite phenotype looking for signatures of chromosomes being gained and lost. In this way, weve done the first genome-wide screen for factors that are required for the correct segregation of DNA.

I think the aneuploidy study is the most interesting application of this data so far, Norman says. It captures a phenotype that you can only get using a single-cell readout. You cant go after it any other way.

The researchers also used their dataset to study how mitochondria responded to stress. Mitochondria, which evolved from free-living bacteria, carry 13 genes in their genomes. Within the nuclear DNA, around 1,000 genes are somehow related to mitochondrial function. People have been interested for a long time in how nuclear and mitochondrial DNA are coordinated and regulated in different cellular conditions, especially when a cell is stressed, Replogle says.

The researchers found that when they perturbed different mitochondria-related genes, the nuclear genome responded similarly to many different genetic changes. However, the mitochondrial genome responses were much more variable.

Theres still an open question of why mitochondria still have their own DNA, said Replogle. A big-picture takeaway from our work is that one benefit of having a separate mitochondrial genome might be having localized or very specific genetic regulation in response to different stressors.

If you have one mitochondria thats broken, and another one that is broken in a different way, those mitochondria could be responding differentially, Weissman says.

In the future, the researchers hope to use Perturb-seq on different types of cells besides the cancer cell line they started in. They also hope to continue to explore their map of gene functions, and hope others will do the same. This really is the culmination of many years of work by the authors and other collaborators, and Im really pleased to see it continue to succeed and expand, says Norman.

Reference: Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq by Joseph M. Replogle, Reuben A. Saunders, Angela N. Pogson, Jeffrey A. Hussmann, Alexander Lenail, Alina Guna, Lauren Mascibroda, Eric J. Wagner, Karen Adelman, Gila Lithwick-Yanai, Nika Iremadze, Florian Oberstrass, Doron Lipson, Jessica L. Bonnar, Marco Jost, Thomas M. Norman and Jonathan S. Weissman, 9 June 2022, Cell.DOI: 10.1016/j.cell.2022.05.013

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New Comprehensive Map Ties Every Human Gene to Its Function - SciTechDaily

June 12, 2022

A conference abstract presented at the European Society of Human Genetics Conference looks at the use of retinal vascular complexityto predict risk of myocardial infarction.

Professor Sir Nilesh Samani, Medical Director at the British Heart Foundation, said:

The earlier and more accurately we can predict someones risk of heart attacks, the better the opportunities for prevention. This study suggests that information from retinal scans can improve such prediction. However, more research is needed to show that this improvement in prediction is robust. Work will also be required to understand the feasibility of this approach and determine how best to incorporate these scans into routine clinical practice.

Dr James Ware, Cardiologist, Reader in Genomic Medicine at Imperial College London and MRC Investigator, MRC London Institute of Medical Sciences, said:

The abstract is very concise and does not contain much methodological detail (as is often the case for a conference abstract). This doesnt mean that there is any problem with the work, but I am not able to judge whether the study is robust from this short summary, and it has not been peer reviewed, so I would await more detail before forming an opinion on the robustness of the findings.

There was a paper that looks very similar earlier this year in Nature Machine Intelligence (https://www.nature.com/articles/s42256-021-00427-7) also using imaging of retinal vessels to predict myocardial infarction (MI). There isnt enough info in the abstract to make clear to me if this approach is importantly different or better.

And there is other prior work predicting coronary artery disease (though not specifically MI) https://www.thelancet.com/journals/landig/article/PIIS2589-7500(21)00043-1/fulltext, also using the UK biobank.

It is well recognised that the retina provides a unique opportunity to directly visualise vessels and assess vascular health. Approaches like this that use computer vision and/or machine learning to detect subtle vascular features predictive of future heart health appear promising.

This study also included genetic information in the prediction model which would not normally be available at the time of a routine eye exam. It will be interesting to see whether the model identifies people at risk of MI without knowing their polygenic risk score, as this would be simpler to implement in practice. Genetic risk scores also promise to be very powerful tools for early identification of at-risk individuals, and indeed genetic risk can be assessed from birth.

The studies also highlight the enormous value of the UK Biobank which is a hugely powerful research resource, and represents a fantastic investment in science in the UK, with far reaching benefits.

The press release is based off an abstract Decreased retinal vascular complexity is an early biomarker of myocardial infarction supported by a shared genetic control being presented at the European Society of Human Genetics Conference and was under embargo until 23:01 UK time Sunday 12 June.

Declared interests

Dr James Ware: I dont have any disclosures that are relevant.!

No others received.

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expert reaction to a conference abstract on retinal screening predicting risk of myocardial infarction - Science Media Centre

Thalidomide is infamous for causing tragic birth defects in the late 1950s and early 1960s, when it was marketed as a sedative and treatment for morning sickness in pregnant women.

The drug inhibits the formation of blood vessel (angiogenesis), but theres now interest in whether thalidomide could be used therapeutically when inhibition of this process could be beneficial.

A small study in 18 patients with severe arteriovenous malformations (AVMs) has now shown that treatment with thalidomide can result in a striking reduction in symptoms and a subsequent improvement of quality of life.

The research has been published in Nature Cardiovascular Research and will be presented on June 12 at the annual conference of the European Society for Human Genetics in Vienna, Austria.

Our group has been studying the causes of vascular abnormalities for 30 years. We have identified several genetic causes and have been able to show that certain mutations activate the signalling inside the blood vessel wall cells, and this promotes the abnormal formation of blood vessels, says Miikka Vikkula, professor of human genetics at the De Duve Institute at the Catholic University of Louvain, Belgium.

This led us to wonder about the possibility of using thalidomide to inhibit abnormal blood vessel formation.

This is because thalidomide inhibits the expression of vascular endothelial growth factor (VEGF), a signalling protein that promotes the growth of new blood vessels.

VEGF levels are high in vascular abnormalities such as AVMs and it is therefore likely that thalidomide reduces signalling via the angiogenesis-promoting pathways.

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AVMs are abnormal tangles of the blood vessels connecting arteries and veins that alter normal blood flow. They are very painful, and cause bleeding and deformation of the affected body part as well as cardiac problems.

Severe cases are usually treated through surgery or embolism (the injection of an agent that destroys the blood vessels locally) but this causes scar tissue to form, is rarely totally effective, and can even make the problem worse.

After having shown that it could work in a mouse model, the researchers recruited 18 patients aged between 19 and 70 years of age with AVMs that could not be treated by conventional approaches to receive doses of either 50, 100 or 200 milligrams of thalidomide per day for between 2 and 52 months.

They all had to agree to use contraception for at least four weeks before and after finishing treatment.

All the patients experienced a rapid reduction of pain, together with cessation of bleeding and the healing of ulcers where these were present, says Vikkula. The three patients with cardiac failure also saw their problems resolved, and one AVM appeared to be completely cured after 19 months of thalidomide and an eight-year follow-up.

Combining the treatment with embolism allowed the dosage of thalidomide to be reduced to 50mg per day, which was important as a higher dose was associated with side-effects like tiredness and peripheral neuropathy damage to the nerves located outside the brain and spinal cord that causes weakness and numbness, particularly in the hands and feet.

We had hypothesised that thalidomide should work in these patients, so our results did not come as a surprise, but it was great to have clinical confirmation that we were right, concludes Vikkula. In our view, this is a breakthrough finding and provides a solid basis for the development of molecular treatments for AVMs.

Professor Alexandre Reymond, chair of the conference, adds: This study shows not only the healthcare and economic benefits of repurposing drugs even the most maligned but also how genetic research can lead to real breakthroughs in therapies for difficult to treat, distressing conditions.

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Thalidomide could be used as a therapeutic for AVMs - Cosmos

The work of Michael Woodley, a 38-year-old British academic, was credited in Payton S. Gendrons proclamation posted online.

The lengthy manifesto posted online by Payton S. Gendron, the 18-year-old mass murderer who killed 10 Black people in Buffalo last month, is exposing some of the racist research taking place at universities around the world.

The New York Times reported Thursday that the work of Michael Woodley, a 38-year-old British academic, was credited in Gendrons proclamation. Woodley claimed that there had been an I.Q. drop in France, which he attributed to migration from North Africa. Further, he has co-written literature declaring a global intelligence decline and supports theories dividing humanity into subspecies, which is a linchpin of white supremacist belief.

The manifesto posted online by Payton S. Gendron (right), the mass murderer who killed 10 Black people in Buffalo, New York, last month, is exposing some of the racist and extremist research taking place at universities around the world. (Photo: Mark Mulville/AP)

Woodley has been affiliated with some the most prestigious universities in the world, and the inclusion of his work in Gendrons manifesto is giving other academics the fuel to publicly denounce Woodleys extremist research.

The Times referenced one population genetics researcher, Alex Mas Sandoval of the University of Bologna, who said that he was appalled to hear that Woodley was credited by Gendron. Sandoval said the killer decontextualized scientific conclusions, noting that Woodleys area of expertise is plant ecology yet his research expanded to human genetics and intelligence.

Woodley has been explicitly racist, Sandoval maintained, per The Times. He has a history of spreading racist, white supremacist theories. He is questioning a consensus based on decades of research.

Sandoval started a petition online to get Woodley suspended and his Ph.D. revoked. Woodleys research is being dissected after Gendron cited it before murdering 10 and wounding three others in a Buffalo, New York, supermarket. The Vrije Universiteit Brussel suspended its relationship with Woodley after a newspaper wrote that it was shocked that an element from a paper by the researcher appeared in Gendrons manifesto.

Story continues

Woodley declined to comment to The Times, but the newspaper contends that his colleagues described him as absolutely devastated by the turn of events. Others note that his far-right politics is a clarion call in academia.

I do think things have changed in recent years, partly because of political discourse, said British journalist Angela Saini, the author of Superior: The Return of Race Science. And with the rise of ethnic nationalism and the far right, we have become more aware of just how risky, how dangerous these people are. They gained a huge following over the years.

Meanwhile, Gendron has been charged with hate-motivated domestic terrorism, first-degree murder, attempted murder and murder as a hate crime in a 25-count indictment after he opened fire on grocery shoppers and working employees at Tops Friendly Market on May 14.

He has pleaded not guilty.

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Buffalo shooters manifesto quoted a university researcher. Thats raising questions about racism in academia - Yahoo News