Congratulations Zam! Very well deserved 🥳🍾🎉👏

This work was equally driven by two PhD students, Aleksandra Otwinowska (experiments) and Janusz Koszucki (bioinformatics). Check out the preprint or DM if you want to learn more.
www.biorxiv.org/content/10.1...

Why does this matter?
✔️ Advances our understanding of host-range evolution
✔️ Highlights the importance of capsule modification, not just degradation, in phage infection
✔️ Challenges how we type capsules and assess phage specificity
✔️ Has direct implications for phage therapy and vaccine design
🚨🧬💉

This underscores the complexity of predicting enzyme specificity — even similar proteins may target different capsules.

Yet, the study reveals an enormous functional and structural diversity encoded in temperate phages.

But predictions are just the start.

Together with the group of Zuzanna Drulis-Kawa, we experimentally tested 50 candidate enzymes from prophages on a panel of 119 capsule types.

Only 14 were active — and not always on the predicted target.
🧪🧫

We discovered a widespread class of SGNH hydrolase–containing RBPs — enzymes structurally related to deacetylases.

They likely modify capsules (e.g. by removing acetyl groups) instead of degrading them — a very different phage infection strategy!

We found 16 capsule types (K-loci) with strong genetic predictors, mostly receptor-binding proteins (RBPs).
Some were classical depolymerases… but many were not.

This is one of the first large-scale GWAS efforts focused on phage–host interactions — and shows how prophages carry hidden information about viral host range.

🔥 Here’s what we found!

But...

temperate phages (those that integrate into the bacterial genome) are less understood.

We analysed 3,900 Klebsiella genomes and identified over 8,100 prophages, then applied GWAS to link phage genes to capsule types.

Phages use surface-binding proteins to recognise their bacterial hosts.

In Klebsiella, this often means targeting the capsule — a sugary layer that varies across strains. Some phages bring depolymerases, enzymes that slice through these polysaccharides to enable infection.

Appending the link to the paper again!
journals.asm.org/doi/10.1128/...

PS: MANIAC is offered in three modes: Fragment Mode (inspired by bacterial approaches), CDS (using best-bidirectional hits) and Protein (like CDS but with protein sequences. The latter two also offers the calculation of wGRR proposed by @epcrocha.bsky.social, so is great for prophages.

Finally, we prioritised usability. While no tool is perfect, Maniac is designed to be easy to install & run. If you work on viral comparative genomics or metagenomics, give it a try! 🛠️ Open-source & available here:
github.com/bioinf-mcb/M...

With MANIAC we also obtained some insights into ANI-based taxonomic assignments of dsDNA phages: we found that while it works well for lytic phages, it struggles with temperate phages—likely due to their extensive recombination & mosaicism, as seen in λ-like phages.

To me, this bears resemblance to a recombination threshold hypothesis in bacteria, where above this threshold recombination maintains species cohesiveness (high AF), and below it enhances diversification and evolution (low AF). But it's a hypothesis at this stage. (Figure from PMID 19197054)

Using MANIAC, we noticed that ANI distribution in dsDNA phages shows a distinct gap around 80%. Above this, genomes align well; below it, AF drops. This pattern holds even in large, metagenome-derived datasets like PhageScope, though there we see many more recent recombinants (high ANI and low AF).

MANIAC isn’t just accurate—it’s fast. Its "Fast" setting, which is still reliably accurate for ANI~70%, can process RefSeq on a laptop within minutes and can handle metagenomic datasets on HPCs.

We benchmarked MANIAC's accuracy against both BLAST-based & simulated datasets, showing it estimates ANI reliably over a wider range of sequence divergence than existing tools like CheckV, FastANI or MUMmer, which are better suited for the ANI>80% range.

Most ANI tools handle closely related genomes well (>80% ANI). But for viruses, we often care about ANI around 70%—a range where alignment can be tricky due to genome diversity & rearrangements. MANIAC tackles this using a fragment-based approach inspired by bacterial genome comparisons.