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Supplementary Information (Figures and Tables) for: An evolutionary medicine perspective on Neandertal extinction Alexis P. Sullivan1, Marc de Manuel3, Tomas Marques-Bonet3,4,5, & George H. Perry1,2 Departments of 1Biology and 2Anthropology, Pennsylvania State University, University Park, PA 16802, USA 3Institut de Biologia Evolutiva (CSIC/UPF), Parque de Investigación Biomédica de Barcelona (PRBB), Barcelona, Catalonia 08003, Spain 4CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain 5Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain Corresponding Author: George H. Perry E-mail: [email protected] Supplemental Figure 1: Innate immune system gene permutation analyses – 10,000 sets of 73 randomly selected genes containing nonsynonymous SNPs Supplemental Figure 2: MHC gene permutation analyses – 10,000 sets of 13 randomly selected genes containing nonsynonymous SNPs Supplemental Figure 3: Significantly enriched gene ontology categories (red) among top 1% ape diversity genes Supplemental Table 1: A comparison of genome-wide nonsynonymous SNPs versus total (nonsynonymous + synonymous) SNPs between Neandertal and modern human populations Supplemental Table 2: PolyPhen-2 predictions for genome-wide nonsynonymous SNPs – damaging versus not damaging – for Neandertal and modern human populations Supplemental Table 3: List of Innate immune system genes Supplemental Table 4: List of MHC genes Supplemental Table 5: A comparison of nonsynonymous SNPs (benign + damaging) in innate immune system genes versus genome-wide genes (not including innate immune genes) between Neandertal and modern human populations Supplemental Table 6: A comparison of nonsynonymous SNPs (benign + damaging) in MHC genes versus genome-wide genes (not including MHC genes) between Neandertal and modern human populations Supplemental Table 7: Neandertal MAF comparison of nonsynonymous SNPs in MHC genes Supplemental Table 8: A comparison of nonsynonymous SNP (benign + damaging) MAFs in MHC genes between Neandertal and modern human populations Supplemental Table 9: List of top 1% ape diversity genes Supplemental Table 10: Results of the top 1% ape diversity genes Gene Ontology analysis Supplemental Table 11: A comparison of nonsynonymous SNPs (benign + damaging) in top 1% ape diversity genes between Neandertal and modern human populations Supplemental Figure 1: Innate immune system gene permutation analyses - 10,000 sets of 73 randomly selected genes containing nonsynonymous SNPs Neandertal-European 800 Observed value 600 400 Hypothesis that observed ratio is 200 less than that expected by chance: Frequency of permutations P = 0.1944 0 0 0.5 1.0 1.5 2.0 2.5 Ratio Neandertal:European human nonsynonymous SNPs Neandertal-African Neandertal-Asian 800 800 Observed value Observed value 600 600 400 400 Hypothesis that Hypothesis that observed ratio is observed ratio is 200 200 less than that less than that expected by chance: expected by chance: Frequency of permutations P = 0.2789 Frequency of permutations P = 0.1400 0 0 0 0.5 1.0 1.5 2.0 2.5 0 0.5 1.0 1.5 2.0 2.5 Ratio Neandertal:African human Ratio Neandertal:Asian human nonsynonymous SNPs nonsynonymous SNPs Supplemental Figure 2: MHC gene permutation analyses - 10,000 sets of 13 randomly selected genes containing nonsynonymous SNPs Neandertal-European 2000 Observed value 1500 1000 Hypothesis that observed ratio is 500 greater than that expected by chance: Frequency of permutations P = 0.1556 0 0 2.0 4.0 6.0 8.0 Ratio Neandertal:European human nonsynonymous SNPs Neandertal-African Neandertal-Asian 2000 2000 Observed value Observed value 1500 1500 1000 1000 Hypothesis that Hypothesis that observed ratio is observed ratio is 500 greater than that 500 greater than that expected by chance: expected by chance: Frequency of permutations P = 0.0222 Frequency of permutations P = 0.0759 0 0 0 2.0 4.0 6.0 8.0 0 2.0 4.0 6.0 8.0 Ratio Neandertal:African human Ratio Neandertal:Asian human nonsynonymous SNPs nonsynonymous SNPs Supplemental Figure 3: Significantly enriched gene ontology categories (red) among top 1% ape diversity genes Supplemental Table 1: A comparison of genome-wide nonsynonymous SNPs versus total (nonsynonymous + synonymous) SNPs between Neandertal and modern human populations Population Neandertal African European Asian Total Nonsynonymous SNPs 2469 6445 4561 4654 Total Synonymous SNPs 2472 8195 5692 5652 Total Nonsyn/Synon SNPs 4941 14640 10253 10306 Proportion Nonsyn:Total SNPs 0.4997 0.4402 0.4448 0.4516 Fisher's Exact Test P-value - 4.52E-13 2.29E-10 2.59E-08 Supplemental Table 2: PolyPhen-2 predictions for genome-wide nonsynonymous SNPs - damaging versus not damaging - for Neandertal and modern human populations Population Neandertal African European Asian Total Damaging Nonsyn. SNPs 1073 1878 1331 1380 Total Not-Damaging Nonsyn. SNPs 1396 4567 3230 3274 Total Nonsynonymous SNPs 2469 6445 4561 4654 Proportion Damaging SNPs:Total Nonsyn. SNPs 0.4346 0.2914 0.2918 0.2965 Fisher's Exact Test P-value - 2.20E-16 2.20E-16 2.20E-16 Supplemental Table 3: List of innate immune system genes Gene ID Description NLRP1 NOD-like receptors NLRP2 NOD-like receptors NLRP3 NOD-like receptors NLRP4 NOD-like receptors NLRP5 NOD-like receptors NLRP6 NOD-like receptors NLRP7 NOD-like receptors NLRP8 NOD-like receptors NLRP9 NOD-like receptors NLRP10 NOD-like receptors NLRP11 NOD-like receptors NLRP12 NOD-like receptors NLRP13 NOD-like receptors NLRP14 NOD-like receptors NOD1 NOD-like receptors NOD2 NOD-like receptors NLRC3 NOD-like receptors NLRC4 NOD-like receptors NLRC5 NOD-like receptors NLRX1 NOD-like receptors CIITA NOD-like receptors NAIP NOD-like receptors RIG-1 RIG-I-like receptors IFIH1 RIG-I-like receptors LGP2 RIG-I-like receptors TLR1 Toll-Like receptors TLR2 Toll-Like receptors TLR3 Toll-Like receptors TLR4 Toll-Like receptors TLR5 Toll-Like receptors TLR6 Toll-Like receptors TLR7 Toll-Like receptors TLR8 Toll-Like receptors TLR9 Toll-Like receptors TLR10 Toll-Like receptors CLEC7A C-type lectins CD209 C-type lectins CARD9 C-type lectins CLECL1 C-type lectins MR C-type lectins CD206 C-type lectins MRC1 C-type lectins CLEC16A C-type lectins IFI16 Cytosololic DNA sensors MNDA Cytosololic DNA sensors IFIX Cytosololic DNA sensors AIM2 Cytosololic DNA sensors MYD88 adaptors TRIF adaptors MAL adaptors TRAM adaptors IRAK4 adaptors IRAK1 adaptors C3 alternative pathway;classical pathways, Lectin pathway C5 alternative pathway;classical pathways, Lectin pathway C6 alternative pathway;classical pathways, Lectin pathway C7 alternative pathway;classical pathways, Lectin pathway C8A alternative pathway;classical pathways, Lectin pathway C9 alternative pathway;classical pathways, Lectin pathway CFB alternative pathway CFD alternative pathway CFP alternative pathway C2 classical pathways, Lectin pathway C1QA classical pathway C1QB classical pathway C1QC classical pathway C1R classical pathway C1S classical pathway C4A classical pathways, Lectin pathway C4B classical pathways, Lectin pathway MASP1 Lectin pathway MASP2 Lectin pathway MBL2 Lectin pathway Supplemental 4: List of MHC genes Gene ID HLA-A HLA-C HLA-DRA HLA-DRB5 HLA-DQA1 HLA-DQB2 HLA-DOB HLA-DMB HLA-DMA HLA-DOA HLA-DPA1 HLA-DPB1 HLA-DPB2 Supplemental Table 5: A comparison of nonsynonymous SNPs (benign + damaging) in innate immune system genes versus genome-wide genes (not including innate immune system genes) between Neandertal and modern human populations Population Neandertal African European Asian Total Immune Nonsynon. SNPs 16 52 41 45 Total Not-Immune Nonsynon. SNPs 2544 6654 4705 4783 Total Nonsynonymous SNPs 2560 6706 4746 4828 Fisher's Exact Test P-value - 0.4985 0.3294 0.1788 Supplemental Table 6: A comparison of nonsynonymous SNPs (benign + damaging) in MHC genes versus genome-wide genes (not including MHC genes) between Neandertal and modern human populations Population Neandertal African European Asian Total MHC Nonsynon. SNPs 17 9 13 9 Total Not-MHC Nonsynon. SNPs 2527 6645 4692 4774 Total Nonsynonymous SNPs 2560 6706 4746 4828 Fisher's Exact Test P-value - 6.58E-05 1.98E-02 1.59E-03 Supplemental Table 7: Neandertal MAF comparison of nonsynonymous SNPs in MHC genes Population Neandertal (MHC) Neandertal (Not-MHC) Total SNPs with MAF = 3 9 260 Total SNPs with MAF = 1 or 2 8 2283 Total Nonsynonymous SNPs 17 2527 Fisher's Exact Test P-value - 1.56E-05 Supplemental Table 8: A comparison of nonsynonymous SNP (benign + damaging) MAFs in MHC genes between Neandertal and modern human populations Population Neandertal African European Asian Total SNPs with MAF = 3 9 3 1 1 Total SNPs with MAF = 1 or 2 8 6 12 8 Total Nonsynonymous SNPs 17 9 13 9 Fisher's Exact Test P-value - 0.4291 0.0174 0.08733 Supplemental Table 9: List of top 1% ape diversity genes Gene ID LRRC38 VANGL1 FCRL5 CD1A CD1E SPTA1 OR6N2 C1orf186 CAPN8 ZP4 OR14A16 OR2T1 AP1S3 C3orf30 KLHL6 TMEM207 GP5 PROM1 SLC34A2 SPP1 LOC153328 LYRM4 OR2H1 MAS1L HLA-DRA HLA-DQB2 BET3L PKIB TAAR6 IYD PARK2 PKD1L1 GIMAP5 IDO2 OPRK1 NKAIN3 C9orf150 OR1L8 GLT6D1 OR13A1 ZNF488 SFXN4 C10orf122 OR52R1 OR51G1 OR5B12 GLYAT MS4A2 MMP27 FAM55B OR8D4 GPRC5A OAS1 GLT1D1 OR4K14 SERPINA9 C15orf54 USP50 AGBL1 OR4F6 GSG1L CES7 CLEC3A KIAA0513 OR1A2 SLFN13 C18orf26 ZNF30 RASSF2 KRTAP27-1 UMODL1 PKNOX1 PDXK Supplemental Table 10: Results of top 1% ape diversity genes Gene Ontology analysis GO category subroot: name GO ID Observed # genes Expected # genes Raw P-value Adjusted P-value Gene
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    USOO8703482B2 (12) United States Patent (10) Patent No.: US 8,703,482 B2 Oshimura et al. (45) Date of Patent: Apr. 22, 2014 (54) HUMAN ARTIFICIAL CHROMOSOME (HAC) Rasheed et al. “Characterization of a Newly Derived Human Sar VECTOR coma Cell Line (HT-1080).” Cancer, 1974, vol. 33, pp. 1027-1033. R. Moreadith etal, “Gene targeting in embryonic stem cells: the new physiology and metabolism.” J Mol Med, 1997, vol. 75, pp. 208-216. (75) Inventors: Mitsuo Oshimura, Tottori (JP); L. Mullins et al., “Perspective Series: Molecular Medicine in Geneti Motonobu Katoh, Tottori (JP); Kazuma cally Engineered Animals: Transgenesis in the Rat and Larger Ani Tomizuka, Gunma (JP); Yoshimi mals,” Journal of Clinical Investigation, 1996, vol. 97, No. 7, pp. Kuroiwa, Gunma (JP); Minoru 1557-1560. Kakeda, Gunma (JP) M. Pera et al. “Human embryonic stem cells,” Journal of Cell Sci ence, 2000, vol. 113, pp. 5-10. Hattori et al. “The DNA sequence of human chromosome 21.” (73) Assignee: Kyowa Hakko Kirin Co., Ltd., Tokyo Nature, 2000, vol. 405, No. 6784, pp. 311-319. (JP) European Search Report dated May 18, 2006 for counterpart appli cation PCT/JP0312734 to parent U.S. Appl. No. 10/530.207. (*) Notice: Subject to any disclaimer, the term of this Y. Kuroiwa et al. “Efficient modification of a human chromosome by patent is extended or adjusted under 35 telomere-directed truncation in high homologous recombination U.S.C. 154(b) by 176 days. proficient chicken DT40 cells.” Nucleic Acids. Res., 1998, vol. 26, No. 14, pp. 3447-3448. Y. Kuroiwa et al., “Manipulation of human minichromosomes to carry (21) Appl.
  • Predicting Human Olfactory Perception from Activities of Odorant Receptors

    Predicting Human Olfactory Perception from Activities of Odorant Receptors

    iScience ll OPEN ACCESS Article Predicting Human Olfactory Perception from Activities of Odorant Receptors Joel Kowalewski, Anandasankar Ray [email protected] odor perception HIGHLIGHTS Machine learning predicted activity of 34 human ORs for ~0.5 million chemicals chemical structure Activities of human ORs predicts OR activity could predict odor character using machine learning Few OR activities were needed to optimize r predictions of each odor e t c percept a AI r a odorant activates mul- h Behavior predictions in c Drosophila also need few r tiple ORs o olfactory receptor d o activities ts ic ed pr ity tiv ac OR Kowalewski & Ray, iScience 23, 101361 August 21, 2020 ª 2020 The Author(s). https://doi.org/10.1016/ j.isci.2020.101361 iScience ll OPEN ACCESS Article Predicting Human Olfactory Perception from Activities of Odorant Receptors Joel Kowalewski1 and Anandasankar Ray1,2,3,* SUMMARY Odor perception in humans is initiated by activation of odorant receptors (ORs) in the nose. However, the ORs linked to specific olfactory percepts are unknown, unlike in vision or taste where receptors are linked to perception of different colors and tastes. The large family of ORs (~400) and multiple receptors activated by an odorant present serious challenges. Here, we first use machine learning to screen ~0.5 million compounds for new ligands and identify enriched structural motifs for ligands of 34 human ORs. We next demonstrate that the activity of ORs successfully predicts many of the 146 different perceptual qualities of chem- icals. Although chemical features have been used to model odor percepts, we show that biologically relevant OR activity is often superior.