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Dogs were present in the Americas prior to the arrival of European colonists, but the origin and fate of these pre-contact dogs are largely unknown. We sequenced 71 mitochondrial and seven nuclear genomes from ancient North American and Siberian dogs spanning ~9,000 years. Our analysis indicates that American dogs were not domesticated from North American wolves. Instead, American dogs form a monophyletic lineage that likely originated in Siberia and dispersed into the Americas alongside people. After the arrival of Europeans, native American dogs almost completely disappeared, leaving a minimal genetic legacy in modern dog populations. Remarkably, the closest detectable extant lineage to pre-contact American dogs is the canine transmissible venereal tumor, a contagious cancer clone derived from an individual dog that lived up to 8,000 years ago. Mitochondrial DNA FASTA fileFASTA file containing 1166 dog mtDNA genomes used in this studyfull_mtDNA_alignment.fastaNEXUS treeMaximum likelihood tree (RAxML) of 1166 dogs mtDNA genomes used in this studyfull_mtDNA_alignment.treExcel sheetPublication source of the 1166 mtDNA genomes used in this studyfull_mtDNA_alignment.xlsxPlink (bed) fileContains genotype for dogs 54 dogsfull_data.bedPlink file (bim)Contains genotype for 54 dogsfull_data.bimPlink file (fam)Contains genotype for 54 dogsfull_data.famNJ tree in Figure 2bNJ tree in Figure 2b (see Table S2 for more info)Figure_b.treNexus fileNexus file used for producing Figure S12 (MKV model in MrBayes)Binary_char_MKV.nexNEXUS treeBayesian tree in Figure S12 (see Table S2 for more info)Figure_S12.tre

chicken.all_samples.galGal3.tpm.refgene.oscData for the analysis of the chicken chromosome Z. FANTOM5 chicken libraries consisted of 25 CAGE libraries including: chicken aortic smooth muscles, hepatocytes, mesenchymal stem cells, leg buds, wing buds, embryo extra-embryonic tissue (day 7 and day 15), and whole body developmental time course (from 5 hours 30 minutes to 20 days). The number of available datapoints to which TPM was normalized was limited by the number of annotated chicken RefSeq transcripts (which was approximately six times smaller than human, N = 4,426 on autosomes, and N = 241 on chromosome Z). Consequently, the cutoff for a gene to be classified as “on” was adjusted six times higher to 60 TPM.human.primary_cell.hCAGE.hg19.tpm.refgene.oscThe FANTOM5 dataset for human primary cells.human.cell_line.hCAGE.hg19.tpm.refgene.oscThe FANTOM5 dataset for human cancer cell-lines.human.tissue.hCAGE.hg19.tpm.refgene.oscThe FANTOM5 dataset for human tissue. CAGE tags were mapped to RefSeq transcripts +/-500 base pairs (bps) from their TSSes and normalized to tags per million (TPM), as previously described [37,45]. The signal of ten TPM was chosen as the cutoff for a gene to be classified as “on” (this cutoff was accepted as the standard for human data throughout the consortium). FANTOM5 is the most comprehensive expression dataset ever generated, including 952 human and 396 mouse tissues, primary cells and cancer cell-lines. FANTOM5 is based on cap analysis of gene expression (CAGE) a unique technology that characterizes TSSes across the entire genome in an unbiased fashion and at a single-base resolution level [21]. CAGE automatically sums expression levels of all transcripts beginning at a given transcription start site.raw_Z_Exp_Anc_LData for Fig 2 "The comparison of change in gene expression (Z) since the human-Chimpanzee common ancestor for five somatic tissues."SUPPLEMENTARY TABLESData in Table S3 underlies Figure 4. Data in Table S7 partially underlies Fig 1. Data in Tables S4 underlies Fig 3. Data in Tables S10-12 underlies Fig S1.data for Fig1R environment containing data underlying Fig1. The environment contains the following variables sorted identically as the gene list in refSeqs: chromosome (chromosomal location), chromosome_short (location on autosomes,chrX, or chrY?), data_matrix (F5 data matrix in TPM for human tissues)‚ MAX (maximal expression for each RefSeq)‚ max (maximal expression for each tissue)‚ strata_classification (strata classification for genes on chromosome X)‚ refSeqs_2entrezIDs (entrez ids mapped to refseqs)‚ boe (the breadth of expression)env_fig1GC-contents data for for Fig S6 and S7This R environment contains GC-contents data for either proximal promoters or isochore around the TSS (marked as big). The data is calculated for either masked or unmasked genome seqeuence.env_gc_contentsdata for Fig S3numbers of ENCODE transcription factor binding sites mapped to TSSes of RefSeq genes in symmetrical windows of different sizes (from 250 to 20000 bps) and depending on ENCODE quality cut-off (strict or all).FigS3_data.txtdata underlying Fig S8Breadth of expression and maximal expression is compared in three groups of observations: (1) autosomal paralogs of X-linked genes, (2) other autosomal paralogs matched by age, (3) X-linked paralogs. Newly formed paralogs are defined as those mapped by phylogenetic timing to taxa Theria or younger. Pre-existing duplications are defined as those descending from duplication notes mapped by phylogenetic timing to taxa Amniota or older.FigS8_data.txtdata underlying Fig7Fig7_data.txtTreeFam data for timing of gene duplications in R environmentsThese files are R environments. Use load() to load them into your R session! You ls() to view contents. You may use attach() syntax to load the namespace or access data members of the environment using the "$" reference operator. There is no warranty for this softwareenv_duplicator_baseAdditional TreeFam gene duplication data with duplication timingenv_duplicator_vectors X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X’s gene content, gene expression, and evolution.

Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement. Results of discrete phylogeographic analysis involving generalized linear models broadly supported these findings, but varied considerably with the level of spatial partitioning. Contrary to common perception, we found no evidence for average temperature having a positive effect on BTV diffusion, though both methodological and biological reasons could be responsible for this result. Our study provides important insights into the drivers of BTV transmission at the landscape scale that could inform predictive models of viral spread and have implications for designing control strategies.

During online speech processing, our brain tracks the acoustic fluctuations in speech at different timescales. Previous research has focused on generic timescales (for example, delta or theta bands) that are assumed to map onto linguistic features such as prosody or syllables. However, given the high intersubject variability in speaking patterns, such a generic association between the timescales of brain activity and speech properties can be ambiguous. Here, we analyse speech tracking in source-localised magnetoencephalographic data by directly focusing on timescales extracted from statistical regularities in our speech material. This revealed widespread significant tracking at the timescales of phrases (0.6–1.3 Hz), words (1.8–3 Hz), syllables (2.8–4.8 Hz), and phonemes (8–12.4 Hz). Importantly, when examining its perceptual relevance, we found stronger tracking for correctly comprehended trials in the left premotor (PM) cortex at the phrasal scale as well as in left middle temporal cortex at the word scale. Control analyses using generic bands confirmed that these effects were specific to the speech regularities in our stimuli. Furthermore, we found that the phase at the phrasal timescale coupled to power at beta frequency (13–30 Hz) in motor areas. This cross-frequency coupling presumably reflects top-down temporal prediction in ongoing speech perception. Together, our results reveal specific functional and perceptually relevant roles of distinct tracking and cross-frequency processes along the auditory–motor pathway. AK_CK_speech_tracking_2018

3D-printing networks of droplets connected by interface bilayers is a powerful platform to build synthetic tissues, in which functionality relies on precisely ordered structures. However, the structural precision and consistency in assembling these structures is currently limited, which restricts intricate designs and the complexity of functions performed by synthetic tissues. Here, we report that the equilibrium contact angle (θDIB) between a pair of droplets is a key parameter that dictates the tessellation and precise positioning of hundreds of picolitre droplets within 3D-printed, multi-layer networks. When θDIB approximates the geometrically-derived critical angle (θc) of 35.3º, the resulting networks of droplets arrange in regular hexagonally close-packed (hcp) lattices with the least fraction of defects. With this improved control over droplet packing, we can 3D-print functional synthetic tissues with single-droplet-wide conductive pathways. Our new insights into 3D droplet packing permit the fabrication of complex synthetic tissues, where precisely positioned compartments perform coordinated tasks. The files are labelled in the format: SILXX_POPCYY_NetNumber_Process.tif Where: "SILXX_POPCYY" indicates the volume fraction of silicone oil (φSIL) and molar fraction of POPC (xPOPC) at which the network was printed (e.g. "SIL55_POPC13" corresponds to φSIL = 0.55 and xPOPC = 0.13) "NetNumber" indicates the repeat number of the specific printed network (e.g. "net3" indicates the 3rd network printed at a specific condition) "Process" indicates the type of processing visualised in the image. This can be: " " : Raw confocal image "Network": Segmented image showing automatic identification of the lipid bilayers in droplet networks "Network_Corrected": Manually corrected image after automatic segmentation "LinkClasses": Segmented image showing classification of lipid bilayers and lipid monolayers "Overlay": Overlay of the confocal image and corresponding packing classification based on Delaunay triangulation.

Natural selection acts on all organisms, including parasites, to maximise reproductive fitness. Drug resistance traits are often associated with life-history costs in the absence of treatment. Schistosomiasis control programmes rely on mass drug administration to reduce human morbidity and mortality. Although hotspots of reduced drug efficacy have been reported, resistance is not widespread. Using Bayesian State-Space Models (SSMs) fitted to data from an in vivo laboratory system, we tested the hypothesis that the spread of resistant Schistosoma may be limited by life-history costs not present in susceptible counterparts. Schistosoma mansoni parasites from a praziquantel–susceptible (S), a praziquantel–resistant (R) or a mixed line of originally resistant and susceptible parasites (RS) were exposed to a range of praziquantel doses. Parasite numbers at each life stage were quantified in their molluscan intermediate and murine definitive hosts across four generations, and SSMs were used to estimate key life-history parameters for each experimental group over time. Model outputs illustrated that parasite adult survival and fecundity in the murine host decreased across all lines, including R, with increasing drug pressure. Trade-offs between adult survival and fecundity were observed in all untreated lines, and these remained strong in S with praziquantel pressure. In contrast, trade-offs between adult survival and fecundity were lost under praziquantel pressure in R. As expected, parasite life-history traits within the molluscan host were complex, but trade-offs were demonstrated between parasite establishment and cercarial output. The observed trade-offs between generations within hosts, which were modified by praziquantel treatment in the R line, could limit the spread of R parasites under praziquantel pressure. Whilst such complex life-history costs may be difficult to detect using standard empirical methods, we demonstrate that SSMs provide robust estimates of life history parameters, aiding our understanding of costs and trade-offs of resistant parasites within this system and beyond. Mouse_dataCounts of adults worms and miracidea of 3 different lines (susceptible, resistant and mixed resistant-susceptible) inside the mouse host exposed to three different treatments: control, low praziquantel dose and high praziquantel dose; across 4 generations. Mouse health parameters such as body, spleen and liver weight also available.Snail_dataCounts of cercariae of 3 different lines (susceptible, resistant and mixed resistant-susceptible) inside 2 different species of snail host, across 4 generations. The cercariae are from worms that had been exposed to three different treatments: control, low praziquantel dose and high praziquantel dose during passage in the mouse host. Snail health parameters such as egg masses and offspring production also available.

To test associations between DNA methylation levels and gene expression levels in cis (cis eQTMs), we paired each Gene to CpGs closer than 500 kb from its TSS, either upstream or downstream. For each Gene, the TSS was defined based on HTA-2.0 annotation, using the start position for transcripts in the + strand, and the end position for transcripts in the - strand. CpGs position was obtained from Illumina 450K array annotation. Only CpGs in autosomal chromosomes (from chromosome 1 to 22) were tested. In the main analysis, we fitted for each CpG-Gene pair a linear regression model between gene expression and methylation levels adjusted for age, sex, cohort, and blood cell type composition. A second model was run without adjusting for blood cellular composition and it is only reported on the online web catalog, but not discussed in this manuscript. Although some of the unique associations of the unadjusted model might be real, others might be confounded by the large methylation and expression changes among blood cell types. To ensure that CpGs paired to a higher number of Genes do not have higher chances of being part of an eQTM, multiple-testing was controlled at the CpG level, following a procedure previously applied in the Genotype-Tissue Expression (GTEx) project (Gamazon et al., 2018). Briefly, our statistic used to test the hypothesis that a pair CpG-Gene is significantly associated is based on considering the lowest p-value observed for a given CpG and all its paired Gene (e.g., those in the 1 Mb window centered at the TSS). As we do not know the distribution of this statistic under the null, we used a permutation test. We generated 100 permuted gene expression datasets and ran our previous linear regression models obtaining 100 permuted p-values for each CpG-Gene pair. Then, for each CpG, we selected among all CpG-Gene pairs the minimum p-value in each permutation and fitted a beta distribution that is the distribution we obtain when dealing with extreme values (e.g. minimum) (Dudbridge and Gusnanto, 2008). Next, for each CpG, we took the minimum p-value observed in the real data and used the beta distribution to compute the probability of observing a lower p-value. We defined this probability as the empirical p-value of the CpG. Then, we considered as significant those CpGs with empirical p-values to be significant at 5% false discovery rate using Benjamini-Hochberg method. Finally, we applied a last step to identify all significant CpG-Gene pairs for all eCpGs. To do so, we defined a genome-wide empirical p-value threshold as the empirical p-value of the eCpG closest to the 5% false discovery rate threshold. We used this empirical p-value to calculate a nominal p-value threshold for each eCpG, based on the beta distribution obtained from the minimum permuted p-values. This nominal p-value threshold was defined as the value for which the inverse cumulative distribution of the beta distribution was equal to the empirical p-value. Then, for each eCpG, we considered as significant all eCpG-Gene variants with a p-value smaller than nominal p-value. For the meQTLs catalogue, we selected 9.9 M cis and trans meQTLs with a p-value <1e-7 in the ARIES dataset consisting of data from children of 7 years old (Gaunt et al., 2016). Then, we tested whether this subset of 9.9 M SNPs were also meQTLs in HELIX by running meQTL analyses using MatrixEQTL R package (Shabalin, 2012), adjusting for cohort, sex, age, blood cellular composition and the first 20 principal components (PCs) calculated from genome-wide genetic data of the GWAS variability. We confirmed 2.8 M meQTLs in HELIX (p-value <1e-7). Trans meQTLs represented <10% of the 2.8 M meQTLs. Enrichment of eCpGs for meQTLs was computed using a Chi-square test, using non eCpGs as background. Finally, we tested whether meQTLs were also eQTLs for the eGenes linked to the eCpGs. To this end, we run eQTL analyses (gene expression being the outcome and 2.8 M SNPs the predictors) with MatrixEQTL adjusting for cohort, sex, age, blood cellular composition and the first 20 GWAS PCs in HELIX. We considered as significant eQTLs the SNP-Gene pairs with p-value <1e-7 and with the direction of the effect consistent with the direction of the meQTL and the eQTM. Background: The identification of expression quantitative trait methylation (eQTMs), defined as associations between DNA methylation levels and gene expression, might help the biological interpretation of epigenome-wide association studies (EWAS). We aimed to identify autosomal cis eQTMs in children’s blood, using data from 832 children of the Human Early Life Exposome (HELIX) project. Methods: Blood DNA methylation and gene expression were measured with the Illumina 450K and the Affymetrix HTA v2 arrays, respectively. The relationship between methylation levels and expression of nearby genes (1 Mb window centered at the transcription start site, TSS) was assessed by fitting 13.6 M linear regressions adjusting for sex, age, cohort, and blood cell composition. Results: We identified 39,749 blood autosomal cis eQTMs, representing 21,966 unique CpGs (eCpGs, 5.7% of total CpGs) and 8,886 unique transcript clusters (eGenes, 15.3% of total transcript clusters, equivalent to genes). In 87.9% of these cis eQTMs, the eCpG was located at <250 kb from eGene’s TSS; and 58.8% of all eQTMs showed an inverse relationship between the methylation and expression levels. Only around half of the autosomal cis-eQTMs eGenes could be captured through annotation of the eCpG to the closest gene. eCpGs had less measurement error and were enriched for active blood regulatory regions and for CpGs reported to be associated with environmental exposures or phenotypic traits. 40.4% of eQTMs had at least one genetic variant associated with methylation and expression levels. The overlap of autosomal cis eQTMs in children’s blood with those described in adults was small (13.8%), and age-shared cis eQTMs tended to be proximal to the TSS and enriched for genetic variants. See HELIX_Blood_eQTM_READMEfile_20210205.xlsx.

During wounding, tissues are disrupted so that bacteria can easily enter the host and trigger a host response. Both the host response and bacterial communication can occur through quorum sensing (QS) and quorum sensing inhibition (QSI). Here, we characterize the effect of wounding on the host-associated bacterial community of the bed bug. This is a model system where the male is wounding the female during every mating. Whereas several aspects of the microbial involvement during wounding have been previously examined, it is not clear to what extent QS and QSI play a role. We find that the microbiome differs depending on mating and feeding status of female bedbugs and is specific to the location of isolation. Most organs of bedbugs harbor bacteria, which are capable of both QS and QSI signaling. By focusing on the prokaryotic quorum communication system, we provide a baseline for future research in this unique system. We advocate the bedbug system as suitable for studying the effects of bacteria on reproduction and for addressing prokaryote and eukaryote communication during wounding.