iiinova — where your digital self keeps evolving

Huge respect for all your hard work on this. Congratulations, Arnon!

010

We are recruiting! Graduate students, postdocs, specially appointed assistant professors, and student research assistants. If our work interests you, please get in touch by email or DM. (10/10)

020

Our lab (Viral Informatics Lab, RIMD, U. Osaka) pursues "AI for Viral Infectious Diseases": viral evolution, viral protein modeling for vaccine optimization, and AI to efficiently search for viruses that could cause the next pandemic. (9/10) sites.google.com/view/virus-i...

120

Arnon Plianchaisuk performed all analyses. The work began at our former affiliation, Division of Systems Virology, IMSUT, University of Tokyo (Kei Sato lab). We thank Spyros Lytras, Hiroyuki Hikida, Yoichiro Nakatani, Emma Harding and Aris Katzourakis for their feedback. (8/10)

Poxvirus gene repertoire evolution thus rests on principles distinct from both small-genome viruses and cellular organisms. By mapping which genome regions vary and which are conserved in these viruses of public health concern, our work also informs vaccine design. (7/10)

Phylogenetics and the origins of host-derived genes suggest that avian poxviruses (Avipoxvirus) may have arisen from a mammal-to-bird host switch. Avipoxvirus also underwent large-scale amplification, so adaptation to a new host may have triggered the expansion. (6/10)

Gene gain and duplication were also strongly constrained by genome architecture. In most lineages, expansion was concentrated at both genome termini; in the most expanded lineages, amplification reached even the normally highly conserved central region. (5/10)

The poxvirus gene repertoire has evolved through a complex process of gain, duplication and loss. Across diverse lineages we found a consistent tendency toward expansion, marking its evolution as expansion-biased birth-and-death dynamics. (4/10)

Poxvirus genome size showed a strong linear relationship with the number of encoded viral genes. In other words, coding capacity increased proportionally with genome size. (3/10)

120

Unlike most viruses, poxviruses carry genomes of several hundred kb with hundreds of genes. Across 59 genomes we integrated gene gain, loss and duplication, horizontal transfer from hosts, and synteny to reconstruct how the repertoire evolved. (2/10)

120

New preprint! We show that poxvirus gene repertoires are shaped by expansion-biased birth-and-death dynamics and genome architectural constraints. Led by Arnon Plianchaisuk (@chainorato) in our lab. Please share! (1/10) www.biorxiv.org/content/10.6...

1189

当研究室では、大学院生、ポスドク、特任助教、特任講師、学生アルバイトを絶賛募集中です！私たちの研究にご興味をお持ちの方は、ぜひメールやDMでお気軽にご連絡ください！（10/10）

000

阪大微研ウイルス情報科学（伊東研）は「AI for Viral Infectious Diseases」を掲げ、ウイルス進化研究、ワクチン最適化に向けたウイルスタンパク質モデリング、次のパンデミックを起こし得るウイルスを効率的に探索するAI技術の開発に取り組んでいます（9/10） sites.google.com/view/virus-i...

解析はすべてArnon Plianchaisuk先生が実施しました。本研究は前職の東大医科研システムウイルス学分野（佐藤佳研究室）で開始したものです。Spyros Lytras先生、疋田弘之先生、中谷洋一郎先生、Emma F. Harding先生、Aris Katzourakis先生から貴重なご助言をいただきました（8/10）

100

以上から、poxvirusの遺伝子レパトア進化は、小型ゲノムのウイルスとも細胞性生物とも異なる原理に支えられていることが明らかになりました。公衆衛生上の懸念が大きいpoxvirusについて、ゲノムのどこが可変でどこが保存されるかを示した本研究は、ワクチン開発にも示唆を与えるものです（7/10）

系統解析と宿主由来遺伝子の起源解析から、鳥類のpoxvirus（Avipoxvirus）が哺乳類から鳥類への宿主転換に由来する可能性が示されました。Avipoxvirusは大規模な遺伝子増幅を経た系統のひとつであり、新たな宿主への適応がレパトア拡大の契機になったのかもしれません（6/10）

100

また、遺伝子の獲得や重複はゲノム構造に強く制約されていました。多くの系統ではレパトアの拡大がゲノム両末端に集中する一方、特に大きく拡大した系統では、通常は高度に保存される中央領域にまで遺伝子増幅が及んでいました（5/10）

100

poxvirusの遺伝子レパトアは、獲得・重複・喪失からなる複雑な過程を経て進化してきました。さらに、様々な系統で一貫して拡大する傾向がみられ、その進化は「拡大に偏ったbirth-and-death dynamics」に特徴づけられます（4/10）

100

poxvirusのゲノムサイズは、コードするウイルス遺伝子数と強い線形関係を示しました。すなわち、ゲノムが大きいほどコード可能な遺伝子数（coding capacity）も比例して増加することが明らかになりました（3/10）

100

大部分のウイルスがゲノムの小ささを特徴とするのとは対照的に、poxvirusは数百kbの巨大なゲノムと数百の遺伝子を持ちます。本研究では59種のゲノムを対象に、遺伝子の獲得・喪失・重複、宿主からの水平伝播、シンテニーを統合的に解析し、遺伝子レパトアの進化ダイナミクスを再構築しました（2/10）

100

【拡散希望】プレプリントを公開しました！poxvirusが「拡大に偏ったbirth-and-death dynamics」と「ゲノム構造に制約された遺伝子増幅」により遺伝子レパトアを増大させてきたことを示しました。当研究室のArnon Plianchaisuk先生@chainoratoが主導した研究です（1/10） www.biorxiv.org/content/10.6...

101

感染症制御に資するバイオインフォマティクス技術の創出 Development of bioinformatics technologies for infectious disease control

Bonus: From April, I will start a new lab at Osaka University. Virus discovery and phenotypic prediction based on host gene expression will remain a central topic—stay tuned! (16/n) sites.google.com/view/virus-i...

sites.google.com

ITO Lab

010

Finally, I thank my current supervisor, Kei, for providing an environment that made this work possible. This study was primarily supported by JST PRESTO “Pandemic Social Infrastructure” (15/n).

100

The large-scale analysis of 220k RNA-seq data sets was led by Mai. We also greatly benefited from in-depth discussions with Eddie, Spyros, and Junna (14/n).

100

This work was driven by three outstanding young researchers: Luca led ISG Profiler, Hiroaki led ISG-VIP, and Kyoko conducted large-scale virus discovery and characterization together with me. I sincerely thank them for their exceptional efforts (13/n).

100

In summary, ISG-based virus discovery complements conventional virome approaches and provides a scalable solution for comprehensive virus detection in the rapidly expanding landscape of animal RNA-seq data (12/n).

100

We also uncovered key insights into viral evolution and infection risk, including rat viruses linked to the origins of highly pathogenic parvoviruses in pigs, dogs, and cats, and rodent colonies harboring PRRSV-related viruses at high prevalence (11/n).

100

These chaphamaparvoviruses were frequently detected in the livers of chickens and wild birds, and infected samples showed viral hepatitis-like transcriptomic signatures, suggesting a potential role in avian hepatitis (10/n).

100

Notably, geNomad failed to detect viruses of the recently characterized and highly diverse genus Chaphamaparvovirus, whereas our method identified many, including highly divergent viral species (9/n).

100

Importantly, our approach detected many viral infections missed by the widely used tool geNomad. These were enriched for highly divergent viruses with low sequence similarity to known viruses, highlighting the strength of our strategy for novel virus discovery (8/n).

100

Using this pre-screening strategy, we analyzed ~40k animal RNA-seq data sets released in 2024 and identified ~2,441 infections across diverse species, including 385 infections by novel viruses (7/n).

100

ISG-VIP showed a positive prediction rate of ~8% and a recall of ~0.45. When used as a pre-screening step, it reduces the number of samples subjected to computationally intensive virome analysis to 8%, while still capturing ~45% of viral infections (6/n).

100

In this study, we developed ISG Profiler for rapid ISG quantification from RNA-seq data and ISG-VIP for infection prediction. The framework applies to diverse avian and mammalian species, including those without reference genomes, and processes each sample in ~4 minutes (5/n).

100

Upon viral infection, interferon-stimulated genes (ISGs) are induced as part of the innate immune response. This response is broadly conserved across vertebrates and observed for diverse viruses, making ISG expression a robust indicator of infection (4/n).

However, conventional virus discovery approaches based on homology searches are computationally expensive and have limited sensitivity for highly divergent viruses. As RNA-seq data continue to grow exponentially, more efficient and scalable strategies are needed (3/n).

100

Many human infectious diseases arise through zoonotic transmission of animal viruses. To prepare for future outbreaks, it is essential to comprehensively analyze large-scale RNA-seq data from wildlife and livestock to identify previously unknown pathogenic viruses (2/n).

100