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. 2022 Aug 22;13(1):4830.
doi: 10.1038/s41467-022-32276-2.

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

Qingbo S Wang  1   2 , Ryuya Edahiro  1   3 , Ho Namkoong  4 , Takanori Hasegawa  5 , Yuya Shirai  1   3 , Kyuto Sonehara  1   6 , Hiromu Tanaka  7 , Ho Lee  7 , Ryunosuke Saiki  8 , Takayoshi Hyugaji  9 , Eigo Shimizu  9 , Kotoe Katayama  9 , Masahiro Kanai  10 , Tatsuhiko Naito  1 , Noah Sasa  1   11 , Kenichi Yamamoto  1   2   12 , Yasuhiro Kato  3   13 , Takayoshi Morita  3   13 , Kazuhisa Takahashi  14 , Norihiro Harada  14 , Toshio Naito  15 , Makoto Hiki  16   17 , Yasushi Matsushita  18 , Haruhi Takagi  14 , Masako Ichikawa  14 , Ai Nakamura  14 , Sonoko Harada  14   19 , Yuuki Sandhu  14 , Hiroki Kabata  7 , Katsunori Masaki  7 , Hirofumi Kamata  7 , Shinnosuke Ikemura  7 , Shotaro Chubachi  7 , Satoshi Okamori  7 , Hideki Terai  7 , Atsuho Morita  7 , Takanori Asakura  7 , Junichi Sasaki  20 , Hiroshi Morisaki  21 , Yoshifumi Uwamino  22 , Kosaku Nanki  23 , Sho Uchida  4 , Shunsuke Uno  4 , Tomoyasu Nishimura  4   24 , Takashri Ishiguro  25 , Taisuke Isono  25 , Shun Shibata  25 , Yuma Matsui  25 , Chiaki Hosoda  25 , Kenji Takano  25 , Takashi Nishida  25 , Yoichi Kobayashi  25 , Yotaro Takaku  25 , Noboru Takayanagi  25 , Soichiro Ueda  26 , Ai Tada  26 , Masayoshi Miyawaki  26 , Masaomi Yamamoto  26 , Eriko Yoshida  26 , Reina Hayashi  26 , Tomoki Nagasaka  26 , Sawako Arai  26 , Yutaro Kaneko  26 , Kana Sasaki  26 , Etsuko Tagaya  27 , Masatoshi Kawana  28 , Ken Arimura  27 , Kunihiko Takahashi  5 , Tatsuhiko Anzai  5 , Satoshi Ito  5 , Akifumi Endo  29 , Yuji Uchimura  30 , Yasunari Miyazaki  31 , Takayuki Honda  31 , Tomoya Tateishi  31 , Shuji Tohda  32 , Naoya Ichimura  32 , Kazunari Sonobe  32 , Chihiro Tani Sassa  32 , Jun Nakajima  32 , Yasushi Nakano  33 , Yukiko Nakajima  33 , Ryusuke Anan  33 , Ryosuke Arai  33 , Yuko Kurihara  33 , Yuko Harada  33 , Kazumi Nishio  33 , Tetsuya Ueda  34 , Masanori Azuma  34 , Ryuichi Saito  34 , Toshikatsu Sado  34 , Yoshimune Miyazaki  34 , Ryuichi Sato  34 , Yuki Haruta  34 , Tadao Nagasaki  34 , Yoshinori Yasui  35 , Yoshinori Hasegawa  34 , Yoshikazu Mutoh  36 , Tomoki Kimura  37 , Tomonori Sato  37 , Reoto Takei  37 , Satoshi Hagimoto  37 , Yoichiro Noguchi  37 , Yasuhiko Yamano  37 , Hajime Sasano  37 , Sho Ota  37 , Yasushi Nakamori  38 , Kazuhisa Yoshiya  38 , Fukuki Saito  38 , Tomoyuki Yoshihara  38 , Daiki Wada  38 , Hiromu Iwamura  38 , Syuji Kanayama  38 , Shuhei Maruyama  38 , Takashi Yoshiyama  39 , Ken Ohta  39 , Hiroyuki Kokuto  39 , Hideo Ogata  39 , Yoshiaki Tanaka  39 , Kenichi Arakawa  39 , Masafumi Shimoda  39 , Takeshi Osawa  39 , Hiroki Tateno  40 , Isano Hase  40 , Shuichi Yoshida  40 , Shoji Suzuki  40 , Miki Kawada  41 , Hirohisa Horinouchi  42 , Fumitake Saito  43 , Keiko Mitamura  44 , Masao Hagihara  45 , Junichi Ochi  43 , Tomoyuki Uchida  45 , Rie Baba  46 , Daisuke Arai  46 , Takayuki Ogura  46 , Hidenori Takahashi  46 , Shigehiro Hagiwara  46 , Genta Nagao  46 , Shunichiro Konishi  46 , Ichiro Nakachi  46 , Koji Murakami  47 , Mitsuhiro Yamada  47 , Hisatoshi Sugiura  47 , Hirohito Sano  47 , Shuichiro Matsumoto  47 , Nozomu Kimura  47 , Yoshinao Ono  47 , Hiroaki Baba  48 , Yusuke Suzuki  49 , Sohei Nakayama  49 , Keita Masuzawa  49 , Shinichi Namba  1 , Takayuki Shiroyama  3 , Yoshimi Noda  3 , Takayuki Niitsu  3 , Yuichi Adachi  3 , Takatoshi Enomoto  3 , Saori Amiya  3 , Reina Hara  3 , Yuta Yamaguchi  3   13 , Teruaki Murakami  3   13 , Tomoki Kuge  3 , Kinnosuke Matsumoto  3 , Yuji Yamamoto  3 , Makoto Yamamoto  3 , Midori Yoneda  3 , Kazunori Tomono  50 , Kazuto Kato  51 , Haruhiko Hirata  3 , Yoshito Takeda  3 , Hidefumi Koh  52 , Tadashi Manabe  52 , Yohei Funatsu  52 , Fumimaro Ito  52 , Takahiro Fukui  52 , Keisuke Shinozuka  52 , Sumiko Kohashi  52 , Masatoshi Miyazaki  52 , Tomohisa Shoko  53 , Mitsuaki Kojima  53 , Tomohiro Adachi  53 , Motonao Ishikawa  54 , Kenichiro Takahashi  55 , Takashi Inoue  56 , Toshiyuki Hirano  56 , Keigo Kobayashi  56 , Hatsuyo Takaoka  56 , Kazuyoshi Watanabe  57 , Naoki Miyazawa  58 , Yasuhiro Kimura  58 , Reiko Sado  58 , Hideyasu Sugimoto  58 , Akane Kamiya  59 , Naota Kuwahara  60 , Akiko Fujiwara  60 , Tomohiro Matsunaga  60 , Yoko Sato  60 , Takenori Okada  60 , Yoshihiro Hirai  61 , Hidetoshi Kawashima  61 , Atsuya Narita  61 , Kazuki Niwa  62 , Yoshiyuki Sekikawa  62 , Koichi Nishi  63 , Masaru Nishitsuji  63 , Mayuko Tani  63 , Junya Suzuki  63 , Hiroki Nakatsumi  63 , Takashi Ogura  64 , Hideya Kitamura  64 , Eri Hagiwara  64 , Kota Murohashi  64 , Hiroko Okabayashi  64 , Takao Mochimaru  65   66 , Shigenari Nukaga  65 , Ryosuke Satomi  65 , Yoshitaka Oyamada  65   66 , Nobuaki Mori  67 , Tomoya Baba  68 , Yasutaka Fukui  68 , Mitsuru Odate  68 , Shuko Mashimo  68 , Yasushi Makino  68 , Kazuma Yagi  69 , Mizuha Hashiguchi  69 , Junko Kagyo  69 , Tetsuya Shiomi  69 , Satoshi Fuke  70 , Hiroshi Saito  70 , Tomoya Tsuchida  71 , Shigeki Fujitani  72 , Mumon Takita  72 , Daiki Morikawa  72 , Toru Yoshida  72 , Takehiro Izumo  73 , Minoru Inomata  73 , Naoyuki Kuse  73 , Nobuyasu Awano  73 , Mari Tone  73 , Akihiro Ito  74 , Yoshihiko Nakamura  75 , Kota Hoshino  75 , Junichi Maruyama  75 , Hiroyasu Ishikura  75 , Tohru Takata  76 , Toshio Odani  77 , Masaru Amishima  78 , Takeshi Hattori  78 , Yasuo Shichinohe  79 , Takashi Kagaya  80 , Toshiyuki Kita  80 , Kazuhide Ohta  80 , Satoru Sakagami  80 , Kiyoshi Koshida  80 , Kentaro Hayashi  81 , Tetsuo Shimizu  81 , Yutaka Kozu  81 , Hisato Hiranuma  81 , Yasuhiro Gon  81 , Namiki Izumi  82 , Kaoru Nagata  82 , Ken Ueda  82 , Reiko Taki  82 , Satoko Hanada  82 , Kodai Kawamura  83 , Kazuya Ichikado  83 , Kenta Nishiyama  83 , Hiroyuki Muranaka  83 , Kazunori Nakamura  83 , Naozumi Hashimoto  84 , Keiko Wakahara  84 , Sakamoto Koji  84 , Norihito Omote  84 , Akira Ando  84 , Nobuhiro Kodama  85 , Yasunari Kaneyama  85 , Shunsuke Maeda  85 , Takashige Kuraki  86 , Takemasa Matsumoto  86 , Koutaro Yokote  87 , Taka-Aki Nakada  88 , Ryuzo Abe  88 , Taku Oshima  88 , Tadanaga Shimada  88 , Masahiro Harada  89 , Takeshi Takahashi  89 , Hiroshi Ono  89 , Toshihiro Sakurai  89 , Takayuki Shibusawa  89 , Yoshifumi Kimizuka  90 , Akihiko Kawana  90 , Tomoya Sano  90 , Chie Watanabe  90 , Ryohei Suematsu  90 , Hisako Sageshima  91 , Ayumi Yoshifuji  92 , Kazuto Ito  92 , Saeko Takahashi  93 , Kota Ishioka  93 , Morio Nakamura  93 , Makoto Masuda  94 , Aya Wakabayashi  94 , Hiroki Watanabe  94 , Suguru Ueda  94 , Masanori Nishikawa  94 , Yusuke Chihara  95 , Mayumi Takeuchi  95 , Keisuke Onoi  95 , Jun Shinozuka  95 , Atsushi Sueyoshi  95 , Yoji Nagasaki  96 , Masaki Okamoto  97   98 , Sayoko Ishihara  99 , Masatoshi Shimo  99 , Yoshihisa Tokunaga  97   98 , Yu Kusaka  100 , Takehiko Ohba  100 , Susumu Isogai  100 , Aki Ogawa  100 , Takuya Inoue  100 , Satoru Fukuyama  101 , Yoshihiro Eriguchi  102 , Akiko Yonekawa  102 , Keiko Kan-O  101 , Koichiro Matsumoto  101 , Kensuke Kanaoka  103 , Shoichi Ihara  103 , Kiyoshi Komuta  103 , Yoshiaki Inoue  104 , Shigeru Chiba  105 , Kunihiro Yamagata  106 , Yuji Hiramatsu  107 , Hirayasu Kai  106 , Koichiro Asano  108 , Tsuyoshi Oguma  108 , Yoko Ito  108 , Satoru Hashimoto  109 , Masaki Yamasaki  109 , Yu Kasamatsu  110 , Yuko Komase  111 , Naoya Hida  111 , Takahiro Tsuburai  111 , Baku Oyama  111 , Minoru Takada  112 , Hidenori Kanda  112 , Yuichiro Kitagawa  113 , Tetsuya Fukuta  113 , Takahito Miyake  113 , Shozo Yoshida  113 , Shinji Ogura  113 , Shinji Abe  114 , Yuta Kono  114 , Yuki Togashi  114 , Hiroyuki Takoi  114 , Ryota Kikuchi  114 , Shinichi Ogawa  115 , Tomouki Ogata  115 , Shoichiro Ishihara  115 , Arihiko Kanehiro  116   117 , Shinji Ozaki  116 , Yasuko Fuchimoto  116 , Sae Wada  116 , Nobukazu Fujimoto  116 , Kei Nishiyama  118 , Mariko Terashima  119 , Satoru Beppu  119 , Kosuke Yoshida  119 , Osamu Narumoto  120 , Hideaki Nagai  120 , Nobuharu Ooshima  120 , Mitsuru Motegi  121 , Akira Umeda  122 , Kazuya Miyagawa  123 , Hisato Shimada  124 , Mayu Endo  125 , Yoshiyuki Ohira  122 , Masafumi Watanabe  126 , Sumito Inoue  126 , Akira Igarashi  126 , Masamichi Sato  126 , Hironori Sagara  127 , Akihiko Tanaka  127 , Shin Ohta  127 , Tomoyuki Kimura  127 , Yoko Shibata  128 , Yoshinori Tanino  128 , Takefumi Nikaido  128 , Hiroyuki Minemura  128 , Yuki Sato  128 , Yuichiro Yamada  129 , Takuya Hashino  129 , Masato Shinoki  129 , Hajime Iwagoe  130 , Hiroshi Takahashi  131 , Kazuhiko Fujii  131 , Hiroto Kishi  131 , Masayuki Kanai  132 , Tomonori Imamura  132 , Tatsuya Yamashita  132 , Masakiyo Yatomi  133 , Toshitaka Maeno  133 , Shinichi Hayashi  134 , Mai Takahashi  134 , Mizuki Kuramochi  134 , Isamu Kamimaki  134 , Yoshiteru Tominaga  134 , Tomoo Ishii  135 , Mitsuyoshi Utsugi  136 , Akihiro Ono  136 , Toru Tanaka  137 , Takeru Kashiwada  137 , Kazue Fujita  137 , Yoshinobu Saito  137 , Masahiro Seike  137 , Hiroko Watanabe  138 , Hiroto Matsuse  139 , Norio Kodaka  139 , Chihiro Nakano  139 , Takeshi Oshio  139 , Takatomo Hirouchi  139 , Shohei Makino  140 , Moritoki Egi  140 , Yosuke Omae  141 , Yasuhito Nannya  8 , Takafumi Ueno  142 , Tomomi Takano  143 , Kazuhiko Katayama  144 , Masumi Ai  145 , Atsushi Kumanogoh  3   6   13   146 , Toshiro Sato  147 , Naoki Hasegawa  4 , Katsushi Tokunaga  141 , Makoto Ishii  7 , Ryuji Koike  148 , Yuko Kitagawa  149 , Akinori Kimura  150 , Seiya Imoto  9 , Satoru Miyano #  5 , Seishi Ogawa #  8   151   152 , Takanori Kanai #  24   153 , Koichi Fukunaga #  7 , Yukinori Okada #  154   155   156   157   158   159
Affiliations

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

Qingbo S Wang et al. Nat Commun. .

Abstract

Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Overview of the study.
Japan COVID-19 Task Force (JCTF) has collected DNA, RNA, and plasma from COVID-19 cases along with detailed clinical information. A subset of n = 500 (n = 465 after QC) harboring RNA-seq data was analyzed in this study. COVID-19 disease severity was used together with RNA-seq data to perform differential gene expression and intron usage analysis (red). Imputed genotyping data with RNA-seq data was used to perform cis-e/sQTL and trans-eQTL analysis, followed by fine-mapping (for cis-QTLs), colocalization and validation with external dataset (dotted line).
Fig. 2
Fig. 2. Overview of eQTL call and statistical fine-mapping from 465 samples in COVID-19 Task Force, and their comparison with publicly available eQTL data (GTEx).
a Unique variant-gene pairs (top), variants (middle) and genes (bottom) classified into different marginal p value bins. The lowest p value was taken as a representative for variants and genes. b The number of variant-genes (y, top panel) classified into different marginal p value bins in GTEx v8 (y, bottom panel. 5e-8 = 5.0 × 10−8.), for different marginal p value thresholds (x < −log10(p value) for each x). c Unique variant-gene pairs (top), variants (middle) and genes (bottom) classified into different posterior inclusion probability (PIP) bins assigned by statistical fine-mapping of eGenes. The maximum PIP was taken as a representative for variants and genes. d The number of variant-genes (y, top panel) classified into different PIP bins in GTEx v8 (y, bottom panel), for different PIP thresholds (x < PIP for each x). e Precision-recall curve (PRC) for the task to classify variant-genes with 0.9 < PIP in GTEx and the ones with PIP < 0.001 from GTEx, using marginal p value (purple) or PIP (green). f Probability of presenting the same effect direction (first row), and the Pearson correlation of two (signed, marginal) effect sizes (second row) when comparing the effect sizes in JCTF and GTEx for different PIP bins. x-axis shows PIP bin in JCTF, and within an x-axis window, values are sorted along with PIP in GTEx. Error bar is the standard error of mean estimated via Fisher’s z-transformation, and the large error bar is due to having small data points (n = 4). g Distribution of a regulatory effect prediction score (Expression Modifier Score = EMS) bin for different PIP bins in our study (y-axis) and GTEx (x-axis). The fraction is represented as the area in each bin (the binning is coarser than in f). h Enrichment of variant-genes in specific range of distance to the transcription starting site (dTSS) for each PIP bin (color) and in each dataset condition compared to random. EMS is not available for variants missing in GTEx, and dTSS is of the best individual features predictive for putative causal eQTLs in the absence of EMS.
Fig. 3
Fig. 3. Overview of sQTL call and statistical fine-mapping from 465 samples in COVID-19 Task Force.
a Unique variant-intron pairs (top), variants (middle) and introns (bottom) classified into different PIP bins. The highest PIP was taken as a representative for variants and introns. b Binned distribution of the distance to transcription starting site (TSS) for sQTLs in different PIP bins. c The fraction (top) and the number (bottom) of variant-introns classified as splice regions (left), donors (center) or acceptors (right) variants, for different sQTL PIP bins. d Unique variant-gene pairs (top), variants (middle) and genes (bottom) classified into different bins of colocalization posterior probability (CLPP) with eQTL PIPs in the same study. The highest CLPP was taken as a representative for variants and genes. e Binned distribution of the distance to transcription starting site (TSS) for sQTLs in different PIP bins, for the ones with (top) and without (bottom) suggestive eQTL colocalization. f Locus zoom for eQTL and sQTL effect on TOR1AIP1 gene. rs2274955 (dotted line in the right) is on the canonical splice donor site of intron 9 (9th gray square from the left), whereas rs2249346 (dotted line in the left) is upstream of the transcription start site (TSS) of the gene. g Detailed description of the splice pattern differences. In d–f, maximum PIP was taken for introns in a single gene to derive a PIP for each variant-gene.
Fig. 4
Fig. 4. Colocalization of eQTLs with possible hematopoietic trait-causal variants suggested in biobank studies.
a Number of variant-gene-trait pairs (y axis) with suggestive colocalization posterior probabilities (0.01<CLPP), for different hematopoietic traits (x axis) in Biobank Japan (BBJ). b–d Association p value (top), eQTL PIP (second row) and BBJ trait PIP (third row) of the SNVs in ±1 Mb (b, c) or ±100 kb (d) window, as well as the location of the genes (bottom row). The putative causal variants and genes are colored with purple. e The alternative allele frequency of rs2902548 in gnomAD. Additional descriptions about these variant-genes are available in Supplementary Note. f Percentage of variant (y axis) with suggestive hematopoietic trait-causal signal (0.01<PIP) only in Biobank Japan (top), or only in UK Biobank (bottom), for variants with possible putative causal eQTL effects (PIP > 0.1) unique to GTEx (left), or our dataset (right).
Fig. 5
Fig. 5. Insights from trans-eQTL analysis.
a Scatter plot showing the trans-eQTL effect sizes (z-score) in our analysis (x-axis) and in eQTLgen (y-axis) for the 37 variant-genes identified as trans-eQTL both in two analyses. The color represents the nominal p value in our analysis. b Percentage of variants presenting trans-eQTL effect in eQTLgen (FDR < 0.05), for variants in our dataset with different conditions (x-axis). c Enrichment of variants presenting trans-eQTL effect in eQTLgen (circle) or assessed in eQTLgen (diamond) relative to all the variants in our dataset, for variants with different maximum cis-eQTL PIP (x-axis). d, e Association p value (top), cis-eQTL PIP (second row) and the location of the genes (third row) for ±1 Mb (d) or 0.5 Mb (e) of the variant with possible trans-eQTL effects mediated by cis-eQTL effects, with schematic overview of the trans-eQTL mechanisms. Blue dotted line represents the decrease of the effect (arrow; positive, non-arrow; negative).
Fig. 6
Fig. 6. Transcriptional interpretation of COVID-19 susceptibility.
a Volcano plot showing the difference of the RNA expression level between severe and non-severe COVID-19 cases (x axis, log2(severe/non-severe)), and the statistical significance (likelihood ratio test p value, y axis). Color shows the log10(count per million + 1). b GO term enrichment of top-enriched genes in severe cases (n = 198), including genes such as CD177 (Human Neutrophil Alloantigen 2a), or FOXC1 as reported in refs. ,. c Cell-type-specific enrichment of the gene sets with different levels of differential expression, for 28 cell types from ImmuNexUT.
Fig. 7
Fig. 7. The effect of COVID-19 phenotype on transcriptional regulation landscape.
a The fraction of genes that are identified as eGenes only in our analysis (orange) or in GTEx (cyan) (y-axis), both (brown) or neither (gray), for a set of genes with different levels of differential expression (x-axis). b Scatter plot presenting the proportion of eGenes (p < 5.0 × 10−8) identified either in whole blood RNA-seq in our study (x-axis) or GTEx (y-axis), that are replicated in each of the 28 cell types from ImmuNexUT. c–e Examples of COVID-19-interaction eQTLs (ieQTLs). y axis is the normalized expression, and the position of each dot is shifted randomly along x-axis direction for visualization purposes. f The fraction of COVID-19-ieQTLs replicated as estimated neutrophil count-ieQTLs, as a function of significance. Error bar in a and f are the standard error of the mean of the bottom bar. Error band in b to e denotes the 95% confidence interval.
Fig. 8
Fig. 8. COVID-19 severity-interaction eQTLs interacts with composition of various immune cell types.
For each of the 13 genes with COVID-19 severity-interaction eGenes (FDR < 0.05) (= row), significance for interaction eQTL effect with inferred cell type compositions (= columns) are plotted in −log10(p) scale. Colors show the significance as well as the direction of the ieQTL effect relative to the COVID-19 severity (red means severe COVID-19 case corresponds to larger cell type composition in terms of interaction effect, and blue is the other way. Bonferroni p = 1.7 × 10−4 = 0.05/13 genes/22 cell types). Row and columns are sorted based on the number of positive and negative significant results, where three cell types with no significant results are removed.
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