Patterns of modern pollen and plant richness across northern Europe

Sedimentary pollen offers excellent opportunities to reconstruct vegetation changes over past millennia. Number of different pollen taxa or pollen richness is used to characterise past plant richness. To improve the interpretation of sedimentary pollen richness, it is essential to understand the relationship between pollen and plant richness in contemporary landscapes. This study presents a regional-scale comparison of pollen and plant richness from northern Europe and evaluates the importance of environmental variables on pollen and plant richness. We use a pollen dataset of 511 lake-surface pollen samples ranging through temperate, boreal and tundra biomes. To characterise plant diversity, we use a dataset formulated from the two largest plant atlases available in Europe. We compare pollen and plant richness estimates in different groups of taxa (wind-pollinated vs. non-wind-pollinated, trees and shrubs vs. herbs and grasses) and test their relationships with climate and landscape variables. Pollen richness is significantly positively correlated with plant richness (r = 0.53). The pollen plant richness correlation improves (r = 0.63) when high pollen producers are downweighted prior to estimating richness minimising the influence of pollen production on the pollen richness estimate. This suggests that methods accommodating pollen-production differences in richness estimates deserve further attention and should become more widely used in Quaternary pollen diversity studies. The highest correlations are found between pollen and plant richness of trees and shrubs (r = 0.83) and of wind-pollinated taxa (r = 0.75) suggesting that these are the best measures of broad-scale plant richness over several thousands of square kilometres. Mean annual temperature is the strongest predictor of both pollen and plant richness. Landscape openness is positively associated with pollen richness but not with plant richness. Pollen richness values from extremely open and/or cold areas where pollen production is low should be interpreted with caution because low local pollen production increases the proportion of extra-regional pollen. Synthesis. Our results confirm that pollen data can provide insights into past plant richness changes in northern Europe, and with careful consideration of pollen-production differences and spatial scale represented, pollen data make it possible to investigate vegetation diversity trends over long time-scales and under changing climatic and habitat conditions.Peer reviewe

Nothing Lasts Forever: Environmental Discourses on the Collapse of Past Societies

The study of the collapse of past societies raises many questions for the theory and practice of archaeology. Interest in collapse extends as well into the natural sciences and environmental and sustainability policy. Despite a range of approaches to collapse, the predominant paradigm is environmental collapse, which I argue obscures recognition of the dynamic role of social processes that lie at the heart of human communities. These environmental discourses, together with confusion over terminology and the concepts of collapse, have created widespread aporia about collapse and resulted in the creation of mixed messages about complex historical and social processes

An interspecies analysis reveals a key role for unmethylated CpG dinucleotides in vertebrate Polycomb complex recruitment

The role of DNA sequence in determining chromatin state is incompletely understood. We have previously demonstrated that large chromosomal segments from human cells recapitulate their native chromatin state in mouse cells, but the relative contribution of local sequences versus their genomic context remains unknown. In this study, we compare orthologous chromosomal regions for which the human locus establishes prominent sites of Polycomb complex recruitment in pluripotent stem cells, whereas the corresponding mouse locus does not. Using recombination-mediated cassette exchange at the mouse locus, we establish the primacy of local sequences in the encoding of chromatin state. We show that the signal for chromatin bivalency is redundantly encoded across a bivalent domain and that this reflects competition between Polycomb complex recruitment and transcriptional activation. Furthermore, our results suggest that a high density of unmethylated CpG dinucleotides is sufficient for vertebrate Polycomb recruitment. This model is supported by analysis of DNA methyltransferase- deficient embryonic stem cells. © 2012 European Molecular Biology Organization | All Rights Reserved

CpG binding protein (CFP1) occupies open chromatin regions of active genes, including enhancers and non-CpG islands

Additional file 1. Fig. S1: Analysis of CFP1 binding at individual loci and CpG islands (CGIs). (A-B) Analysis of CFP1 binding at the human α-globin locus in expressing and non-expressing cells. (A) Real-Time PCR analysis of immunoprecipitated chromatin using CFP1 antibody in human erythroblasts (red) and B-lymphocytes (blue). The y-axis represents enrichment over the input DNA, normalised to a control sequence in the human 18S gene. The x-axis represents the positions of Taqman probes used. The coding sequence is represented by the three exons (Promoter/Ex1, Ex2, Ex3) of the α-globin genes. 218 and hBact denote control sequences adjacent to the CpG islands of the human LUC7L (218) and ACTB promoters. Error bars correspond to ± 1 SD from at least two independent ChIPs. (B) Real-Time PCR analysis of immunoprecipitated chromatin using the CFP1 antibody indicated in humanised erythroblasts (normal, +MCS-R2 (left) and mutant, MCS-R2 (right). The y-axis represents enrichment over the input DNA, normalised to a control sequence in the mouse GAPDH gene. CpG Act denotes additional control sequence at the CGI of the mouse ACTB gene. The amplicons highlighted in red represent deleted regions in the humanised mice, for which no PCR signal is observed. Error bars correspond to ± 1 SD from at least two independent ChIPs. (C) CFP1 ChIP signal intensity in the top 200 peaks, by antibody and by cell type. Abcam, ab56035 antibody. Roeder, main antibody used in this study. (D) Analysis of CGI (green) and non-CGI (blue) transcription start sites (1-kb window, centred on TSS). Gene symbols shown with CpG content of individual loci in parentheses. Greek letters represent individual globin genes. Fig. S2: Peak overlaps of CFP1 and marks of active and repressed chromatin in transcription start sites (TSSs). Peaks were detected by MACS2. Venn diagrams show that CFP1 peaks within 1-kb of TSSs are strongly associated with H3K4me3 histone mark and poorly associated with H3K27me3 repressive histone mark. Cell types are (A) ERY and (B) EBV. Public data sets: * NCBI GEO GSE36985, ** NCBI GEO GSE50893. Fig. S3: UCSC tracks showing CFP1 and other ChIP signals in gene loci in erythroblasts (ERY) and EBV-transformed B-lymphoblasts (EBV). Hg38 coordinates for multiple genes, CpG islands (CGI, green boxes), and putative regulatory regions (blue boxes) are shown. CFP1 signals are shown in dark reds, inputs in grey, histone H3 signals in blues and open chromatin marks in greens. All ChIP pileups are scaled to 1x coverage genome-wide and shown in a range 0–50, except CFP1 (Roeder) is shown with extended range and H3K27me3 graphs scaled by 2x. (A) Tissue-specific binding of CFP1 to CGI promoters of tissue-specifically expressed genes. Left (chr16), CGI promoters of active genes in alpha globin locus are CFP1-bound in ERY, and unbound in EBV. Flanking regions are included, with known tissue-specific enhancers. Right (chr6), first seven exons of IRF4 locus, active in EBV and inactive in ERY, with CFP1 binding to CGI promoter in EBV only. (B) CGI promoters of housekeeping genes are CFP1 bound and unmarked by H3K27me3. Left (chr7), ACTB locus. Right (chr16), LUC7L locus. (C) CGI promoter of RHBDF1 locus (chr16) has H3K27me3 mark and the absence of CFP1 binding in both ERY and EBV. Fig. S4: Western blot analysis of CGBP (CFP1) expression in mouse and human erythroid and human lymphoid cell types. Whole cell extracts (20 µg) were loaded in each lane (1) mouse ES, (2) U-MEL, (3) I-MEL, (4) mouse primary erythroblasts and (5) human primary T lymphocytes and (6) human primary erythroblasts and separated on a 10% SDS-polyacrylamide gel. CFP1 antibody was used at a 1:1000 dilution. Fig. S5: Similar cell type-specific CFP1 read depth at CGI TSS of HBA1 gene and non-CGI TSS of HBB gene. Upper two tracks use the main antibody, and second two tracks use the commercial antibody. Coordinates are from the hg38 human genome build. Read depths are averaged in 50 bp bins and normalised to 1x genome-wide coverage. Blue boxes, known regulatory regions; green box, CGI. Fig. S6: Distribution of TrxG components in erythroid cells. Green indicates CGI and blue indicates other putative regulatory regions. All loci transcribed right to left. Pileups are shown scaled to 1x genome coverage, with full scale 0–50x depth. (A) Housekeeping genes ACTB, left (chr7), and LUC7L, right (chr16). (B) β-globin locus (chr11), (C) Non-expressed RHBDF1 locus (chr16). Fig. S7: Overlap of TrxG subunit ChIP peaks in a high-confidence subset of regions. SET1A complexes are represented by CFP1-SET1A colocalisation. MLL1/2 complexes are represented by Menin, and MLL3/4 complexes are represented by UTX, respectively. HCF1 is found in SET1A/B and MLL1/2 complexes, and RBBP5 is a member of SET1A/B and MLL1/2/3/4 complexes. Red outline (4220 peaks) shows strong colocalisation of Menin and CFP1-SET1A, accounting for the vast majority (99.5%) of 4242 CFP1-SET1A and half (50.0%) of 8432 Menin peak regions. Majority (87.0%, 2089/2400 peaks) of HCF1 (blue region) is accounted for by approximately half (49.5%, 2089/4220) of regions of Menin-SET1A-CFP1 colocalisation. Regions where either SET1A-CFP1 or Menin or both are colocalised with HCF1 (blue dashed line) accounts for nearly all (99.6%, 2390/2400) HCF1 regions, suggesting that HCF1 bound to DNA is primarily present as part of SET1A/B or MLL1/2 complexes. Fig. S8: Chromatin accessibility in TSSs and enhancers in erythroid cells as measured by ATAC-seq and DNase-seq. 1x-normalised, input-subtracted signals from ATAC-seq and DNase were averaged in a 2-kb window about TSSs and putative enhancers. Z-score transformed values for ATAC-seq and DNase-seq at a given locus were averaged. Fig. S9: Relationship of CFP1 signal to three predictive factors in top-decile open chromatin regions. A linear combination of CpG density and SET1A and H3K4me3 ChIP signals explains a substantial fraction of variation in CFP1 ChIP signal. Table S1: Bias of CFP1 for CGI TSSs in cell types and gene classes. Table S2: Bias of CFP1 for housekeeping gene TSSs. Table S3: Motifs associated with CFP1 peaks. Table S4: Dependence of CFP1 ChIP signal in erythroid cells on covariates putatively associated with its binding. Table S5: Analysis of variance of CFP1 signal in top-decile open chromatin regions surrounding TSSs and putative enhancers

Meat and Nicotinamide:A Causal Role in Human Evolution, History, and Demographics

Hunting for meat was a critical step in all animal and human evolution. A key brain-trophic element in meat is vitamin B 3 /nicotinamide. The supply of meat and nicotinamide steadily increased from the Cambrian origin of animal predators ratcheting ever larger brains. This culminated in the 3-million-year evolution of Homo sapiens and our overall demographic success. We view human evolution, recent history, and agricultural and demographic transitions in the light of meat and nicotinamide intake. A biochemical and immunological switch is highlighted that affects fertility in the ‘de novo’ tryptophan-to-kynurenine-nicotinamide ‘immune tolerance’ pathway. Longevity relates to nicotinamide adenine dinucleotide consumer pathways. High meat intake correlates with moderate fertility, high intelligence, good health, and longevity with consequent population stability, whereas low meat/high cereal intake (short of starvation) correlates with high fertility, disease, and population booms and busts. Too high a meat intake and fertility falls below replacement levels. Reducing variances in meat consumption might help stabilise population growth and improve human capital